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ENDORSE
Environmental determinants of overweight in Rotterdam schoolchildren
Klazine van der Horst - Nachtegaal
Colofon
: ----
Copyright © Klazine van der Horst - Nachtegaal
All rights reserved. No part of this publication may be reproduced, stored in a retrieval
system, or transmitted, in any form or by any means, electronic, mechanical, photocopying,
recording or otherwise, without the prior permission of the author or the copyright-owning
journals for previously published chapters.
Lay-out and Print: Optima Grasche Communicatie
Cover-illustration: Optima Grasche Communicatie
is thesis was printed with nancial support of the Department of Public Health,
Erasmus MC, Rotterdam.
ENDORSE
Environmental Determinants of Overweight in Rotterdam Schoolchildren
ENDORSE
Omgevingsdeterminanten van overgewicht bij Rotterdamse scholieren
Proefschri
ter verkrijging van de graad van doctor aan de
Erasmus Universiteit Rotterdam
op gezag van de rector magnicus
Prof.dr. H.G. Schmidt
en volgens besluit van het College voor Promoties.
De openbare verdediging zal plaatsvinden op
woensdag oktober om . uur
door
Klazine-Anja van der Horst - Nachtegaal
geboren te Zwolle
PROMOTIECOMMISSIE
Promotor: Prof. dr. ir. J. Brug
Overige leden: Prof. dr. J.P. Mackenbach
Prof. dr. M.C.H. Donker
Prof. dr. W. van Mechelen
Co-promotor: Dr. A. Oenema
CONTENTS
Part I - Introduction to the ENDORSE project
. General Introduction
. e ENDORSE study: research into environmental determinants of obesity
related behaviors in Rotterdam schoolchildren
Part II - Environmental correlates of energy balance-related behaviors:
reviews of the literature
. A review of environmental correlates of obesity-related dietary behaviors in
youth
. Environmental correlates of physical activity in youth – A review and
update
Part III – Socio-demographic correlates of energy balance-related behaviors
. Gender, ethnic and educational dierences in overweight and energy balance-
related behaviors among Dutch adolescents
. Socio-demographic factors as correlates of active commuting to school in
Rotterdam, the Netherlands
Part IV - Individual and environmental correlates of energy balance-related
behaviors
. e school food environment: associations with adolescent so drink and
snack consumption
. Do individual cognitions mediate the association of socio-cultural and
physical environmental factors with adolescent sports participation?
. Perceived parenting style and practices and the consumption of sugar-
sweetened beverages by adolescents
. General discussion
Summary
Samenvatting
Dankwoord
Curriculum Vitae
List of publications
PhD Portfolio
Part I Introduction to the
ENDORSE project
1 General Introduction
General Introduction 11
Chapter 1
General Introduction 11
1.1 INTRODUCTION
Overweight and obesity are a major threat to public health as the prevalence’s of overweight
and obesity are rising worldwide in all age groups. Obesity in children and adolescents is
of particular interest since it persists into adulthood and is associated with severe health
consequences. erefore, the prevention of overweight and obesity is important for public
health. To be able to develop theory and evidence-based interventions aimed at the preven-
tion of excess weight gain, it is essential to identify which specic energy intake and energy
expenditure behaviors contribute most to excess weight gain, and which determinants are
associated with engagement in such behaviors.
is thesis reports on a number of studies on the identication of individual and en-
vironmental correlates of behaviors related to the energy balance (i.e. energy intake and
expenditure behaviors). ese studies were part of the ENDORSE project (ENvironmental
Determinants of Obesity in Rotterdam SchoolchildrEn) which was initiated to contribute
to systematic, evidence based research on individual and environmental determinants of
overweight and obesity. e ENDORSE project was initially a cross-sectional study and
aer the rst data collection a follow-up data collection was funded. Data were collected
at baseline (/) and two years later (/) in a cohort of adolescents aged
- years. e studies in this thesis were based on the baseline data collection, as the
longitudinal data was not available in time to use in this thesis. is introductory chapter
describes the background, aims and theoretical framework used in the ENDORSE project
and presents an overview of the individual studies that are part of this thesis.
1.2 A MODEL FOR PLANNED HEALTH PROMOTION
Overweight and obesity prevention has to target the most important risk factors and the
underlying determinants. Prevention of overweight and obesity through the promotion of
healthy dietary habits and a physically active lifestyle by means of health education and
health promotion should therefore be carefully planned. To increase the likelihood of
intervention success, careful evidence-based planning of obesity prevention interventions
should be a standard procedure. e use of health promotion planning models helps to
improve the quality of interventions and in these models ve important steps can be dis-
tinguished (Figure .) []. e rst two steps in this model for planned health education
and promotion cover the epidemiological analysis. ese rst two steps should (I) identify
important threats to public health and (II) the risk factors including risk behaviors for these
public health threats. e result is a set of priorities for preventive interventions, health
change goals and specic target groups for interventions. e third step identies important
and changeable determinants of risk behaviors. In step of the model, intervention strate-
12 Chapter 112 Chapter 1
gies, methods and materials need to be selected and/or developed that address the most
important and changeable determinants identied in the third step. In the nal step the
interventions should be implemented and disseminated in such a way that the target popu-
lation is reached and exposed to the intervention. Each step should be based on established
theory and sucient scientic evidence.
e ENDORSE project was initiated to contribute to systematic, evidence based research
on individual and environmental determinants of overweight and obesity and focused
mainly on step and of the model for planned health education and promotion (Figure
.).
1.3 ANALYSIS OF HEALTH AND QUALITY OF LIFE: OBESITY
Obesity is a vast and growing public health problem as the prevalence of overweight and
obesity is rising worldwide in all age groups [, ]. Obesity may become the most important
determinant of preventable diseases within the foreseeable future []. Overweight and
obesity oen manifest early in life [, ] and are associated with an increased risk of seri-
ous diseases during childhood and adolescence [, ]. Obesity during childhood causes a
clustering of cardiovascular disease risk factors such as hypertension and dyslipidaemia.
Other important complications of childhood obesity are type diabetes, musculoskeletal
and pulmonary disorders and obesity is further associated with psychosocial problems such
as a low self esteem, depression and eating disorders (Table .) [, ]. Furthermore, obese
Subjective
norm
Behavioral
attitude
Perceived
behavioral
control
Intention
Behavior
Figure 1.1 A model for planned health education and promotion [1]
Figure 1.2 Theory of Planned Behavior [48]
Figure 1.3 Analysis Grid for Environments Linked to Obesity [58]
Levels
Types
Micro-
environment
Macro-environment
Physical environment
Economic environment
Political environment
Socio-cultural
environment
Step 1: Analysis of health and quality of life
Step 3: Analysis of determinants of risk behaviors
Step 4: Intervention mapping
Step 5: Intervention implementation
E
V
A
L
U
A
T
I
O
N
Step 2: Analysis of behavior and environmental risk factors
Figure 1.1 A model for planned health education and promotion [1]
General Introduction 13
Chapter 1
General Introduction 13
children and adolescents are likely to become obese adults, who have an increased risk for
various chronic diseases and premature death [, ].
In the Netherlands, an increasing proportion of adolescents are classied as overweight
and obese (Table . & .). Between and , in to year old adolescents the
prevalence of overweight ranged between . and . and between . and . for
obesity [-]. Youth from Moroccan and Turkish backgrounds have the highest preva-
lence rates of overweight [, ]. In Rotterdam, overweight and obesity prevalence is higher
compared to the general Dutch adolescent population. Especially youth in Rotterdam from
a Turkish background show very high prevalence rates of overweight and obesity with per-
centages ranging between and []. e complications that obese adolescents may
develop, the tracking of obesity into adulthood, and the vulnerability of obese adolescents
make a strong case for the prevention and treatment of overweight and obesity in youth.
However, treatment of overweight and obesity in adolescents is dicult as adolescents have
less autonomy over food and physical activity behaviors compared to adults and they are
more susceptible to peer pressure. e overall success of existing family and school-based
interventions has been disappointing [, ]. Only few adolescents succeed to maintain
their lower body weight and most of the weight loss is oen regained within a few years [].
Adolescents are therefore an important target group for intervention activities that prevent
them from gaining excess weight and becoming overweight or obese.
Table 1.1 Complications of childhood obesity [9, 10]
Complications of childhood obesity Examples
Psychosocial problems Poor self esteem
Depression
Eating disorders
Pulmonary problems Sleep apnoea
Asthma
Exercise intolerance
Gastrointestinal problems Gall stones
Endocrine problems Type 2 diabetes
Cardiovascular problems Dyslipidaemia
Hypertension
Neurological problems Pseudo tumor cerebri: headache, vision
abnormalities
Renal problems Glomerulosclerosis
Musculoskeletal problems Flat feet
Low back pain
14 Chapter 114 Chapter 1
1.4 ANALYSIS OF BEHAVIORAL RISK FACTORS FOR OBESITY
A long-term positive energy balance in which energy input through food intake exceeds
energy expenditure through physical activity eventually causes obesity. erefore, high
energy intake and low physical activity can be identied as important risk behaviors for
overweight and obesity. Prevention of overweight and obesity can be achieved by lower-
ing energy intake and/or increasing energy expenditure. e increase in overweight and
obesity is therefore largely related to behavioral factors that can be referred to as energy
balance-related behaviors. However, it is not yet very clear which specic risk behaviors are
related to overweight and obesity in children and adolescents []. Recent overviews have
suggested a range of energy balance-related behaviors that may contribute substantially to a
higher risk for unnecessary weight gain, such as high intake of energy-dense, micronutrient
poor foods and a sedentary lifestyle, and behaviors that may contribute to a lower risk for
weight gain such as physical activity and high ber intake [-]. However, studies and
reviews have also reported inconsistent results on the role of specic dietary and physical
activity sub-behaviors []. An overview of the available evidence is given in Table .. Most
evidence in this overview is based on systematic reviews of observational and intervention
studies.
Table 1.2 Prevalence of overweight in the Netherlands (adolescents 12-15 years old) [13, 14, 19]
Overweight (%)
Boys Girls
Age 1980 1997 2002-2004 1980 1997 2002-2004
12 3.4 7.1 16.2 6.1 9.0 17.1
13 3.6 7.1 15.3 6.0 9.1 15.2
14 3.9 7.3 15.6 6.1 9.1 16.2
15 4.2 7.7 16.8 6.2 9.4 20.1
Table 1.3 Prevalence of obesity in the Netherlands (adolescents 12-15 years old) [13, 14, 19]
Obesity (%)
Boys Girls
Age 1980 1997 2002-2004 1980 1997 2002-2004
12 0.2 0.7 2.8 0.4 1.1 3.1
13 0.2 0.7 2.8 0.4 1.0 2.7
14 0.2 0.7 3.4 0.4 1.0 2.8
15 0.2 0.7 3.9 0.4 1.1 4.7
General Introduction 15
Chapter 1
General Introduction 15
With respect to food intake, convincing evidence for an association with overweight and
obesity exists for sugar-sweetened beverage consumption and ber intake. e evidence for
associations with overweight and obesity are less clear for other behaviors such as breakfast
consumption, portion sizes and consumption of dairy products and fast food or snack
consumption (Table .). Snacking, fast food intake and large portion sizes have been found
to be associated with energy and fat intake, but none of these factors have been found to be
consistently related to obesity [].
Furthermore, the relative importance of dierent aspects of physical activity is poorly
understood. It is unclear whether obesity is similarly related to a reduction in physical
activity behaviors and/or an increase in sedentary behaviors [, ]. A small but signicant
association has been found between television viewing and body fatness among children
and adolescents [, ], while evidence for specic physical activity sub-behaviors such as
active transport and leisure time sports, such as walking and bicycling is lacking.
In summary, the available evidence on behavioral risk factors for overweight shows that
for many of the dietary and physical activity sub-behaviors the evidence is insucient.
erefore, more and stronger studies are needed to examine the specic risk behaviors for
Table 1.4 Overview of the available evidence on risk behaviors for overweight and obesity
Behaviors Evidence References
Dietary behaviors
Snacks / fast food intake Probable / insucient for children [26, 27]
Sugar-sweetened beverage intake Convincing [23, 28]
Breakfast consumption Possible [29]
Fruit / vegetable Insucient [30, 31]
Fiber intake / Non-starch po-
lysaccharide
Convincing [23, 32-34]
Intake of dairy products Possible [23, 35, 36]
Portion sizes Possible [23, 37]
Physical activity behaviors
Overall physical activity Probable [38, 39]
Leisure time physical activity (sports,
walking, cycling)
Insucient [38]
Physical education Insucient [40-42]
Active transport to school Insucient / no relationship [43-45]
Sedentary behaviors
Television viewing Convincing (small eect) [21, 25]
Computer use No relationship [25]
16 Chapter 116 Chapter 1
overweight and obesity in the dietary and physical activity domain, such as leisure time
walking and bicycling, sports and snack consumption. It has, however, been established that
a combination of changes in both energy balance behaviors, i.e. diet and physical activity
seems the most promising strategy for successful obesity prevention.
To curb the obesity epidemic, it is important to identify specic target groups of ado-
lescents who are more at risk of becoming obese by engaging in more (or specic) energy
balance-related behaviors. Being able to distinguish specic target groups provides the
opportunity to better tailor interventions to the needs and perceptions of those most at risk
[]. Currently, there is insucient insight into the occurrence of a number of overweight
related risk behaviors among adolescents and also whether it is possible to distinguish
specic subgroups that are more likely to engage in such specic risk behaviors.
1.5 ANALYSIS OF DETERMINANTS OF ENERGY BALANCERELATED
BEHAVIORS
Individual determinants
In determinant research the emphasis has been primarily on individual (intrapersonal)
cognitive determinants of behavior such as attitudes, perceived behavioral control, subjec-
tive norms and intentions, informed by social cognition models such as the Social Cogni-
tive eory [] and the eory of Planned Behavior (TPB) []. According to the TPB,
if people evaluate the behavior as positive (attitude), if they think signicant others want
them to perform the behavior (subjective norm), and if people are convinced that they can
successfully execute the behavior required to produce the desired outcomes (perceived be-
havioral control), they will be more likely to have a high intention (motivation) and a higher
likelihood of engaging in the behavior (Figure .). e TPB has been found to be useful for
the identication of potential determinants of energy balance-related behaviors such as so
drink consumption [-], snack consumption [] total physical activity and physical ex-
ercise [, ]. On average of the variance in health behaviors can be explained with the
TPB variables [, ]. e TPB is useful in examining intrapersonal determinants of obesity
related behaviors. However, individuals interact with people in their environment such as
parents and peers and the individual behavior takes place in environmental settings such
as the home, the neighborhood or school. erefore, next to intrapersonal factors, other
factors might also be important for examining determinants of obesity related behaviors.
General Introduction 17
Chapter 1
General Introduction 17
Environmental determinants
An important development in overweight and obesity prevention research has been the
recognition of the role of “the environment” in inuencing health and health behavior
and there has been a shi in focus from individual inuences on health behaviors to en-
vironmental inuences []. Socio-ecological models include environmental factors as an
important element. e environment can be dened as everything and anything outside
the person []. e environment is the context in which human beings act and interact.
Socio-ecological models focus on how individual, intrapersonal and environmental factors
interact and shape health behavior. Socio-ecological models have a complex and multidi-
mensional nature as they describe the environment not only in terms of physical and social
components, but also in terms of the objective (actual) or subjective (perceived) attributes
of the environment [].
A useful framework for the classication of environmental determinants is the Analysis
Grid for Environments Linked to Obesity (ANGELO) (gure .) []. is framework was
specically developed to conceptualize obesogenic environments, and enables the identi-
cation of potential intervention settings and strategies.
According to the ANGELO framework, environmental determinants can be classied
according to two environmental levels (micro and macro) and four environmental types
(physical, socio-cultural, economic, and political). Individuals interact with the environment
in various micro or local environments such as schools, homes, workplaces and neighbor-
hoods. Broader macro environments such as health and education systems, food industry,
media and the government inuence these micro environmental settings. Dierent types
of the environment can be distinguished, such as the physical environment, which refers to
Subjective
norm
Behavioral
attitude
Perceived
behavioral
control
Intention
Behavior
Figure 1.1 A model for planned health education and promotion [1]
Figure 1.2 Theory of Planned Behavior [48]
Figure 1.3 Analysis Grid for Environments Linked to Obesity [58]
Levels
Types
Micro-
environment
Macro-environment
Physical environment
Economic environment
Political environment
Socio-cultural
environment
Step 1: Analysis of health and quality of life
Step 3: Analysis of determinants of risk behaviors
Step 4: Intervention mapping
Step 5: Intervention implementation
E
V
A
L
U
A
T
I
O
N
Step 2: Analysis of behavior and environmental risk factors
Figure 1.2 eory of Planned Behavior [28]
18 Chapter 118 Chapter 1
which foods and physical activity opportunities are available, the economic environment,
which refers to the costs of food and activity, the political environment, which refers to the
rules related to food and activity (e.g. laws and regulations), and the socio-cultural environ-
ment which refers to social interactions, norms, beliefs and values in a community related
to food and activity []. ese types of environments have representations at the micro-
level and on the macro-level. An example of a physical environmental factor at a micro
level is the availability of physical activity equipment at home, while an urban or suburban
neighborhood setting and infrastructure is an example of a physical environmental factor
at the macro level.
As more and more studies focus on environmental determinants of health behaviors it is
important to get a clear overview of the evidence these studies have provided so far, the gaps
that exist in the available literature and the possibilities to improve the research in this eld.
Other determinants of energy balance-related behaviors
Next to cognitive and environmental determinants there are also other possible deter-
minants that may inuence energy balance-related behaviors such as habit strength and
demographic factors. Research is needed to explore the importance of these determinants
for various energy balance-related behaviors and the working mechanisms of these possible
determinants.
Most energy balance-related behaviors occur regularly in daily life, such as the consump-
tion of breakfast and walking for transportation. erefore, such behaviors might be more
or less automatic behaviors not requiring much or any cognitive eorts. If a behavior is
oen repeated, it might become a habit, i.e. an automatic response to a certain environ-
mental cue []. Recent studies indicated that habit strength seems to be a useful variable to
incorporate in studies on correlates of energy balance-related behaviors [-].
Demographic factors such as gender, age, educational level and ethnicity are also associ-
ated with energy balance-related behaviors [, , -] and can be considered as more
distal or upstream determinants. ese variables can act also as moderators of the envi-
ronment – behavior relationship as the determinants of energy balance-related behaviors
Subjective
norm
Behavioral
attitude
Perceived
behavioral
control
Intention
Behavior
Figure 1.1 A model for planned health education and promotion [1]
Figure 1.2 Theory of Planned Behavior [48]
Figure 1.3 Analysis Grid for Environments Linked to Obesity [58]
Levels
Types
Micro-
environment
Macro-environment
Physical environment
Economic environment
Political environment
Socio-cultural
environment
Step 1: Analysis of health and quality of life
Step 3: Analysis of determinants of risk behaviors
Step 4: Intervention mapping
Step 5: Intervention implementation
E
V
A
L
U
A
T
I
O
N
Step 2: Analysis of behavior and environmental risk factors
Figure 1.3 Analysis Grid for Environments Linked to Obesity [38]
General Introduction 19
Chapter 1
General Introduction 19
might vary by population sub-groups. Studies are needed that explore whether potential
determinants have a dierent impact on energy balance-related behaviors with respect to
gender, ethnicity, age and educational level or socio-economic status [].
Mediating and moderating eects of individual and environmental determinants
Well-developed ecological models specify not only that dierent types of variables (indi-
vidual and environmental) interact, but also the working mechanisms of these variables,
thus how they interact. Kremers and colleagues proposed in their Environmental Research
framework for weight Gain prevention (EnRG framework) that environmental factors can
have a direct and an indirect inuence on behavior [, ] (Figure .).
e direct inuence reects an automatic, “mindless” process of the environment on
behaviors. For instance, dietary behaviors and the amount of foods eaten are strongly in-
uenced by factors such as portion size, food visibility and the ease of obtaining foods [].
e indirect inuence of environments on energy balance-related behaviors is through a
more cognitive process in which the individual factors, such as attitudes, subjective norms
and perceived behavior control play a role. For example, environments that oer appealing
opportunities to eat unhealthy foods may result in positive attitudes regarding the con-
sumption of these unhealthy foods, resulting in higher intakes of these foods. In the EnRG
framework it is also proposed that the direct and indirect pathways can be inuenced by
moderating factors or eect modiers; the level of cognitive mediation or direct environ-
mental inuence is expected to dier according to personal and behavioral attributes such
as habit strength, demographic factors and personality [, , ].
Only few studies have examined the relative importance of environmental determinants and
individual (cognitive) determinants and there is also lack of empirical evidence regarding
the inuence of environmental determinants on energy balance-related behaviors among
adolescents [, -]. More research is needed on environmental determinants of en-
ergy balance-related behaviors in school, neighborhood and home settings with preferably
stronger study designs in which mediating and moderating eects can also be examined.
In the ENDORSE study, the TPB and the ANGELO framework which are both incorpo-
rated in the EnRG framework were used as a theoretical framework. To inform obesity
prevention interventions for adolescents, the ENDORSE study focused mainly on physical
and socio-cultural environmental factors in micro settings such as the school, home and
neighborhood.
20 Chapter 120 Chapter 1
1.6 OUTLINE OF THE THESIS
As stated before, the ENDORSE project was initiated to contribute to systematic, evidence
based research on individual and environmental correlates of overweight and obesity. e
ENDORSE project focused on the analysis of risk behaviors for overweight and the analysis
of determinants of these risk behaviors, step and of the model for planned health edu-
cation and promotion (Figure .). is resulted in specic recommendations for obesity
prevention interventions among adolescents. e specic aims of the ENDORSE project
were:
. to identify which presumed energy balance-related behaviors are associated with over-
weight and obesity;
. to examine important individual and environmental correlates of presumed energy
balance-related behaviors;
. to investigate the associations with and the interactions between these correlates and
energy balance-related behaviors;
. to formulate objectives to be targeted in interventions aimed at the prevention of over-
weight in adolescents aged - years.
Figure 1.4 Environmental Research framework for weight Gain prevention [67]
Figure 4.1 Distribution of the 150 publications retrieved, by year of publication (1980 to 2004)
0
5
10
15
20
25
30
35
40
45
50
1980-84 1985-89 1990-94 1995-99 2000-04
Year of publication
# of studies
Children Adolescents
Figure 1.4 Environmental Research framework for weight Gain prevention [47]
General Introduction 21
Chapter 1
General Introduction 21
e studies presented in this thesis focus on aim and of the ENDORSE project, and the
central research questions that will be addressed are:
I. What are important individual and environmental correlates of energy balance-related
behaviors?
II. To what extent is the association between environmental factors and energy balance-
related behaviors mediated by individual cognitions?
e thesis is divided in three parts. e rst part of this thesis gives an introduction to the
ENDORSE study, with this general introduction as the rst chapter. Chapter presents the
study protocol of the ENDORSE study in which the design and methods are described, as
well as the results of the pilot work on the identication of risk behaviors for overweight
and obesity.
In the second part of this thesis environmental correlates of energy balance-related behav-
iors are identied by studying the existing literature. Two systematic reviews of the litera-
ture were conducted, one for dietary behaviors (Chapter ) and one for physical activity
(Chapter ). e review for dietary behaviors is an original review, whereas the review for
physical activity is an update of an existing review conducted by Sallis and colleagues [].
In the third part of this thesis, demographic factors as correlates of energy balance-related
behaviors are studied. Chapter presents gender, ethnic and educational dierences in
overweight and energy balance-related behaviors. Chapter describes the results of a study
on socio-demographic correlates of active commuting to school. Although such socio-
demographic factors are not easily modiable and are therefore not easy access points for
intervention development, these factors can be important to identify specic target groups
for obesity prevention interventions.
In the fourth part of the thesis, the associations of individual and environmental corre-
lates with energy balance-related behaviors, and possible mediation through individual
correlates from the eory of Planned Behavior are examined, with the EnRG framework
as the theoretical framework. In Chapters - the potentially important mediation role of
cognitions in the association between environmental factors and energy balance-related
behaviors is investigated. e study in Chapter is based on another dataset to examine the
mediating role of cognitions in further detail with other variables. is study used data from
the Dutch Obesity Intervention in Teenagers Study [].
In the general discussion a summary of the main ndings of this thesis and recommenda-
tions for further research and practice are provided.
22 Chapter 122 Chapter 1
REFERENCES
. Brug J, Oenema A, Ferreira I: eory, evidence and Intervention Mapping to improve behavior nutri-
tion and physical activity interventions. Int J Behav Nutr Phys Act , ():.
. Lobstein T, Frelut ML: Prevalence of overweight among children in Europe. Obes Rev , ():-
.
. Hedley AA, Ogden CL, Johnson CL, Carroll MD, Curtin LR, Flegal KM: Prevalence of overweight
and obesity among US children, adolescents, and adults, -. JAMA , ():-.
. Peeters A, Barendregt JJ, Willekens F, Mackenbach JP, Al Mamun A, Bonneux L, Nedcom tNEaD-
CoMRG: Obesity in adulthood and its consequences for life expectancy: a life-table analysis. Ann
Intern Med , ():-.
. Power C, Lake JK, Cole TJ: Measurement and long-term health risks of child and adolescent fatness.
Int J Obes Relat Metab Disord , ():-.
. Must A, Jacques PF, Dallal GE, Bajema CJ, Dietz WH: Long-term morbidity and mortality of over-
weight adolescents. A follow-up of the Harvard Growth Study of to . N Engl J Med ,
():-.
. Must A, Strauss RS: Risks and consequences of childhood and adolescent obesity. Int J Obes Relat
Metab Disord , Suppl :S-.
. Wabitsch M: Overweight and obesity in European children: denition and diagnostic procedures,
risk factors and consequences for later health outcome. Eur J Pediatr , Suppl :S-.
. Ebbeling CB, Pawlak DB, Ludwig DS: Childhood obesity: public-health crisis, common sense cure.
Lancet , ():-.
. Speiser PW, Rudolf MC, Anhalt H, Camacho-Hubner C, Chiarelli F, Eliakim A, Freemark M, Gruters
A, Hershkovitz E, Iughetti L et al: Childhood obesity. J Clin Endocrinol Metab , ():-.
. Singh AS, Mulder C, Twisk JW, van Mechelen W, Chinapaw MJ: Tracking of childhood overweight
into adulthood: a systematic review of the literature. Obes Rev .
. Schokker DF, Visscher TL, Nooyens AC, van Baak MA, Seidell JC: Prevalence of over weight and
obesity in the Netherlands. Obes Rev , ():-.
. van den Hurk K, van Dommelen P, de Wilde JA, Verkerk PH, van Buuren S, Hirasing RA: Prevalentie
van overgewicht en obesitas bij jeugdigen - jaar in de periode -. TNO Kwaliteit van
Leven; .
. van den Hurk K, van Dommelen P, van Buuren S, Verkerk PH, Hirasing RA: Prevalence of overweight
and obesity in the Netherlands in compared to and . Arch Dis Child , ():-
.
. Fredriks AM, Van Buuren S, Sing RA, Wit JM, Verloove-Vanhorick SP: Alarming prevalences of
overweight and obesity for children of Turkish, Moroccan and Dutch origin in e Netherlands
according to international standards. Acta Paediatr , ():-.
. Rapportage gemeente Rotterdam [http://www.jeugdmonitorrotterdam.nl/Rotterdam/Internet/
Overig/JMR/pdf/GemeenterapportJMRmaart.pdf]
. Shaya FT, Flores D, Gbarayor CM, Wang J: School-based obesity interventions: a literature review. J
School health , ():-.
. Livingstone MB, McCarey TA, Rennie KL: Childhood obesity prevention studies: lessons learned
and to be learned. Public Health Nutr , (A):-.
. Hirasing RA, Fredriks AM, van Buuren S, Verloove-Vanhorick SP, Wit JM: [Increased prevalence
of overweight and obesity in Dutch children, and the detection of overweight and obesity using
international criteria and new reference diagrams]. Ned Tijdschr Geneeskd , ():-.
General Introduction 23
Chapter 1
General Introduction 23
. Moreno LA, Rodriguez G: Dietary risk factors for development of childhood obesity. Curr Opin Clin
Nutr Metab Care , ():-.
. Gorely T, Marshall SJ, Biddle SJ: Couch kids: correlates of television viewing among youth. Int J Behav
Med , ():-.
. Rennie KL, Johnson L, Jebb SA: Behavioural determinants of obesity. Best Pract Res Clin Endocrinol
Metab , ():-.
. Swinburn BA, Caterson I, Seidell JC, James WP: Diet, nutrition and the prevention of excess weight
gain and obesity. Public Health Nutr , (A):-.
. Van Der Horst K, Paw MJ, Twisk JW, Van Mechelen W: A brief review on correlates of physical
activity and sedentariness in youth. Med Sci Sports Exerc , ():-.
. Marshall SJ, Biddle SJ, Gorely T, Cameron N, Murdey I: Relationships between media use, body
fatness and physical activity in children and youth: a meta-analysis. Int J Obes Relat Metab Disord
.
. Rosenheck R: Fast food consumption and increased caloric intake: a systematic review of a trajectory
towards weight gain and obesity risk. Obes Rev .
. Pereira MA, Kartashov AI, Ebbeling CB, Van Horn L, Slattery ML, Jacobs DR, Jr., Ludwig DS: Fast-
food habits, weight gain, and insulin resistance (the CARDIA study): -year prospective analysis.
Lancet , ():-.
. Malik VS, Schulze MB, Hu FB: Intake of sugar-sweetened beverages and weight gain: a systematic
review. Am J Clin Nutr , ():-.
. Rampersaud GC, Pereira MA, Girard BL, Adams J, Metzl JD: Breakfast habits, nutritional status, body
weight, and academic performance in children and adolescents. J Am Diet Assoc , ():-;
quiz -.
. te Velde SJ, Twisk JW, Brug J: Tracking of fruit and vegetable consumption from adolescence into
adulthood and its longitudinal association with overweight. Br J Nutr , ():-.
. Tohill BC, Seymour J, Serdula M, Kettel-Khan L, Rolls BJ: What epidemiologic studies tell us
about the relationship between fruit and vegetable consumption and body weight. Nutr Rev ,
():-.
. Pereira MA, Ludwig DS: Dietary ber and body-weight regulation. Observations and mechanisms.
Pediatr Clin North Am , ():-.
. Ludwig DS, Pereira MA, Kroenke CH, Hilner JE, Van Horn L, Slattery ML, Jacobs DR, Jr.: Dietary
ber, weight gain, and cardiovascular disease risk factors in young adults. JAMA , ():-
.
. Howarth NC, Saltzman E, Roberts SB: Dietary ber and weight regulation. Nutr Rev , ():-
.
. Barba G, Russo P: Dairy foods, dietary calcium and obesity: a short review of the evidence. Nutr
Metab Cardiovasc Dis , ():-.
. Huang TT, McCrory MA: Dairy intake, obesity, and metabolic health in children and adolescents:
knowledge and gaps. Nutr Rev , ():-.
. Fisher JO, Kral TV: Super-size me: Portion size eects on young children’s eating. Physiol Behav ,
():-.
. Wareham NJ, van Sluijs EM, Ekelund U: Physical activity and obesity prevention: a review of the
current evidence. Proc Nutr Soc , ():-.
. Connelly JB, Duaso MJ, Butler G: A systematic review of controlled trials of interventions to prevent
childhood obesity and overweight: a realistic synthesis of the evidence. Public health , ():-
.
24 Chapter 124 Chapter 1
. Kahn EB, Ramsey LT, Brownson RC, Heath GW, Howze EH, Powell KE, Stone EJ, Rajab MW, Corso
P: e eectiveness of interventions to increase physical activity. A systematic review. Am J Prev Med
, ( Suppl):-.
. Cleland V, Dwyer T, Blizzard L, Venn A: e provision of compulsory school physical activity: as-
sociations with physical activity, tness and overweight in childhood and twenty years later. Int J
Behav Nutr Phys Act , ():.
. Doak CM, Visscher TL, Renders CM, Seidell JC: e prevention of overweight and obesity in chil-
dren and adolescents: a review of interventions and programmes. Obes Rev , ():-.
. Heelan KA, Donnelly JE, Jacobsen DJ, Mayo MS, Washburn R, Greene L: Active commuting to and
from school and BMI in elementary school children-preliminary data. Child Care Health Dev ,
():-.
. Landsberg B, Plachta-Danielzik S, Much D, Johannsen M, Lange D, Muller MJ: Associations between
active commuting to school, fat mass and lifestyle factors in adolescents: the Kiel Obesity Prevention
Study (KOPS). Eur J Clin Nutr , ():-.
. Rosenberg DE, Sallis JF, Conway TL, Cain KL, McKenzie TL: Active transportation to school over
years in relation to weight status and physical activity. Obesity , ():-.
. Kreuter MW, Lukwago SN, Bucholtz RD, Clark EM, Sanders-ompson V: Achieving cultural ap-
propriateness in health promotion programs: targeted and tailored approaches. Health Educ Behav
, ():-.
. Bandura A: Social foundations of thought and action: a social cognitive theory.: Englewood Clis, NJ:
Prentice-Hall; .
. Ajzen I: Attitudes, personality, and behavior: Homewood, IL, US: Dorsey Press; .
. Grimm GC, Harnack L, Stor y M: Factors associated with so drink consumption in school-aged
children. J Am Diet Assoc , ():-.
. Kassem NO, Lee JW: Understanding so drink consumption among male adolescents using the
theory of planned behavior. J Behav Med , ():-.
. Kassem NO, Lee JW, Modeste NN, Johnston PK: Understanding so drink consumption among
female adolescents using the eory of Planned Behavior. Health Educ Res , ():-.
. de Bruijn GJ, Kremers SP, Schaalma H, van Mechelen W, Brug J: Determinants of adolescent bicycle
use for transportation and snacking behavior. Prev Med , ():-.
. Godin G, Kok G: e theory of planned behavior: a review of its applications to health-related
behaviors. Am J Health Promot , ():-.
. Hagger MS, Chatzisarantis NL, Biddle SJ: e inuence of autonomous and controlling motives on
physical activity intentions within the eory of Planned Behaviour. Br J Health Psychol , (Part
):-.
. Booth SL, Sallis JF, Ritenbaugh C, Hill JO, Birch LL, Frank LD, Glanz K, Himmelgreen DA, Mudd
M, Popkin BM et al: Environmental and societal factors aect food choice and physical activity:
rationale, inuences, and leverage points. Nutr Rev , ( Pt ):S-; discussion S-.
. Sallis JF, Owen N: Ecological models of health behavior. In Health behavior and health education.
edition. Edited by Glanz K, Rimer BK, Lewis FM. San Fransisco: Jossey-Bass.
. Stokols D: Establishing and maintaining healthy environments. Toward a social ecology of health
promotion. Am Psychol , ():-.
. Swinburn B, Egger G, Raza F: Dissecting obesogenic environments: the development and application
of a framework for identifying and prioritizing environmental interventions for obesity. Prev Med
, ( Pt ):-.
General Introduction 25
Chapter 1
General Introduction 25
. Verplanken B, Orbell S: Reections on past behavior: A self-report index of habit strength. J Appl Soc
Psychol , ():-.
. Brug J, de Vet E, de Nooijer J, Verplanken B: Predicting fruit consumption: cognitions, intention, and
habits. J Nutr Educ Behav , ():-.
. Verplanken B, Aarts H, van Knippenberg A, Moonen A: Habit versus planned behaviour: a eld
experiment. Br J Soc Psychol , ( Pt ):-.
. De Bruijn G-J, Kremers SPJ, De Vet E, De Nooijer J, Van Mechelen W, Brug J: Does habit strength
moderate the intention-behaviour relationship in the eory of Planned Behaviour? e case of fruit
consumption. Psychol Health , (): - .
. Aarts H, verplanken B, van Knippenberg A: Predicting behavior from actions in the past: Repeated
decision making or a matter of habit? J Appl Soc Psychol , :-.
. Sallis JF, Prochaska JJ, Taylor WC: A review of correlates of physical activity of children and adoles-
cents. Med Sci Sports Exerc , ():-.
. Delva J, O’Malley PM, Johnston LD: Racial/ethnic and socioeconomic status dierences in over-
weight and health-related behaviors among American students: national trends -. J Adolesc
Health , ():-.
. te Velde SJ, Wind M, van Lenthe FJ, Klepp KI, Brug J: Dierences in fruit and vegetable intake and
determinants of intakes between children of Dutch origin and non-Western ethnic minority children
in the Netherlands - a cross sectional study. Int J Behav Nutr Phys Act , :.
. Kremers SP, De Bruijn GJ, Visscher TL, Van Mechelen W, De Vries NK, Brug J: Environmental inu-
ences on energy balance-related behaviors: A dual-process view. Int J Behav Nutr Phys Act ,
():.
. Cohen D, Farley TA: Eating as an automatic behavior. Preventing chronic disease , ():A.
. Kremers SP, de Bruijn GJ, Droomers M, van Lenthe F, Brug J: Moderators of environmental interven-
tion eects on diet and activity in youth. Am J Prev Med , ():-.
. de Bruijn GJ, Kremers SP, de Vries H, van Mechelen W, Brug J: Associations of social-environmental
and individual-level factors with adolescent so drink consumption: results from the SMILE study.
Health Educ Res .
. de Bruijn GJ, Kremers SP, Lensvelt-Mulders G, de Vries H, van Mechelen W, Brug J: Modeling in-
dividual and physical environmental factors with adolescent physical activity. Am J Prev Med ,
():-.
. Brug J, van Lenthe F: Environmental determinants and interventions for physical activity, nutrition and
smoking: A review. Zoetermeer: Speed-Print; .
. Singh AS, Chin APMJ, Kremers SP, Visscher TL, Brug J, van Mechelen W: Design of the Dutch Obesity
Intervention in Teenagers (NRG-DOiT): systematic development, implementation and evaluation of
a school-based intervention aimed at the prevention of excessive weight gain in adolescents. BMC
Public Health , :.
2 e ENDORSE study: research
into environmental determinants
of obesity related behaviors in
Rotterdam schoolchildren
van der Horst K, Oenema A, van de Looij-Jansen P, Brug J. e
ENDORSE study: research into environmental determinants of obesity
related behaviors in Rotterdam schoolchildren.
BMC Public Health , : .
28 Chapter 228 Chapter 2
ABSTRACT
Background: Children and adolescents are important target groups for prevention of
overweight and obesity as overweight is oen developed early in life and tracks into adult-
hood. Research into behaviors related to overweight (energy balance-related behaviors) and
the personal and environmental determinants of these behaviors is fundamental to inform
prevention interventions. In the Netherlands and in other countries systematic research
into environmental determinants of energy balance related behaviors in younger adoles-
cents is largely lacking. is protocol paper describes the design, the components and the
methods of the ENDORSE study (Environmental Determinants of Obesity in Rotterdam
SchoolchildrEn), that aims to identify important individual and environmental determi-
nants of behaviors related to overweight and obesity and the interactions between these
determinants among adolescents.
Methods: e ENDORSE study is a longitudinal study with a two-year follow-up of a
cohort of adolescents aged - years. Data will be collected at baseline (/) and
at two years follow-up (/). Outcome measures are body mass index (BMI), waist
circumference, time spent in physical activity and sedentary behaviors, and so drink,
snack and breakfast consumption. e ENDORSE study consists of two phases, rst em-
ploying qualitative research methods to inform the development of a theoretical framework
to examine important energy balance related behaviors and their determinants, and to
inform questionnaire development. Subsequently, the hypothetical relationships between
behavioral determinants, energy balance related behaviors and BMI will be tested in a
quantitative study combining school-based surveys and measurements of anthropometrical
characteristics at baseline and two-year follow-up.
Discussion: e ENDORSE project is a comprehensive longitudinal study that enables
investigation of specic environmental and individual determinants of overweight and
obesity among younger adolescents. e project will result in specic recommendations for
obesity prevention interventions among younger adolescents.
ENDORSE Study Protocol 29
Chapter 2
ENDORSE Study Protocol 29
Chapter 2
BACKGROUND
Adolescent overweight and obesity are important public health concerns in the Netherlands
as well as in other western countries, due to the increasing proportion of adolescents classi-
ed as overweight or obese []. Children and adolescents are an important target group for
intervention activities aimed at the prevention of overweight. Overweight and obesity oen
manifest early in life [, ] and is associated with an increased the risk of serious diseases
during childhood and adolescence []. Furthermore, obese children and adolescents are
likely to become obese adults, who have an increased risk for various chronic diseases
and premature death []. erefore, it is important to develop interventions that prevent
children and adolescents from gaining excess weight. Prevention of weight gain can best
be achieved by focusing on both sides of the energy balance equation; energy intake (diet)
and energy expenditure (physical activity). To be able to develop theory and evidence-based
interventions aimed at the prevention of excess weight gain, it is essential to identify which
specic energy intake and energy expenditure behaviors contribute most to excess weight
gain, and which determinants mediate or predict engagement in such behaviors.
An important development in overweight and obesity prevention research has been the
recognition of the environment as a potentially important determining factor for energy
balance related behaviors []. Currently, there is only limited scientic evidence regarding
the inuence of environmental determinants on energy balance related behaviors among
adolescents [, ]. Few studies have examined the relative importance of environmental
determinants and individual (cognitive) determinants that have been the more traditional
focus of behavior change interventions, and there is also lack of empirical evidence regard-
ing the interactions between these determinants [-]. Research into environmental deter-
minants is now emerging and more rigorous and well-designed studies are needed to draw
stronger inferences for relationships between environmental determinants, energy balance
related behaviors and BMI. Such studies are also needed to identify the interactions between
determinants of various energy balance related behaviors and the mechanisms underlying
the associations between individual and environmental determinants of these behaviors
[, ]. erefore, a comprehensive study was designed that examines key energy balance
related behaviors, the individual and an environmental determinants of these behaviors
and that contains objective measures of height, weight and waist circumference. e target
population of the study is adolescents aged - years. e specic aims of the study are:
(i) to identify important behaviors related to overweight (energy balance related behaviors),
(ii) to examine important individual (cognitive) and environmental determinants for the
energy balance related behaviors identied, (iii) to investigate the associations with and the
interactions between these determinants and BMI in cross-sectional and prospective analy-
ses with a two year follow-up, and (iv) to formulate objectives to be targeted in interventions
aimed at the prevention of overweight in adolescents aged - years.
30 Chapter 230 Chapter 2
e ENDORSE study (Environmental Determinants of Obesity in Rotterdam’s Schoolchil-
drEn) is conducted within the Center for Eective Public Health In the larger Rotterdam
area (CEPHIR), an established collaboration between a university research center (Erasmus
MC) and the Municipal Health Service Organization in the Rotterdam region. e data
collection process takes place in close cooperation with the Municipal Health Service Rot-
terdam area, using an existing research infrastructure. In this article we describe the design
and protocol of the ENDORSE study.
e study comprises of two parts. e rst part focused on the identication of the key
energy balance related behaviors and the important individual and environmental determi-
nants to examine. Based on this identication, measurement instruments were developed.
e second part consists of a combined cross-sectional and longitudinal study utilizing
these instruments.
PILOT WORK: IDENTIFICATION OF RISK BEHAVIORS AND IMPORTANT
ENVIRONMENTAL DETERMINANTS
is phase of the ENDORSE study involved the development of questionnaires for ado-
lescents and parents, interview forms for school representatives and canteen managers, an
audit instrument used to observe the school and the neighborhood around schools, and a
list of important census data on neighborhood level. To develop these instruments system-
atically, important behaviors and determinants were identied in the following steps.
Identication of important energy balance related behaviors in youth
e most important energy balance related behaviors were identied to gain insight in the
contribution of these behaviors to overweight and obesity in adolescents. Based on a review
of relevant reviews of the literature, a preliminary list of specic relevant energy balance
related behaviors was compiled. is list contained: watching television, computer use,
sports, physical education, transport to school, leisure time activities, so drink consump-
tion, skipping breakfast, consumption of foods high in fat, fruit and vegetable consumption,
portion sizes and dining out. Subsequently national experts on energy balance related
behaviors were asked to review this list and suggest additional important behaviors and
score the behaviors on the importance and changeability of each behavior. is procedure
resulted in the following identication of behaviors to be examined in the present study:
active transport to school, leisure time activities, sports, watching television, computer use,
so drink consumption, sweets/cookies/cake/chocolate bar consumption, savory snack
consumption and breakfast consumption.
ENDORSE Study Protocol 31
Chapter 2
ENDORSE Study Protocol 31
Chapter 2
Identication of environmental and individual determinants of energy balance related
behaviors among youth
In the ENDORSE study the environment was dened as ‘anything outside the individual’.
e environment can be subdivided by means of distinguishing various environmental
factors. A suitable framework for the classication of environmental determinants is the
Analysis Grid for Environments Linked to Obesity (ANGELO) []. is framework was
specically developed to conceptualize health behavior environments, and enables the
identication of potential intervention settings and strategies. According to the ANGELO
grid, environmental determinants can be grouped in four environmental types (physical,
socio-cultural, economic, and political) and specic environmental levels (micro and
macro). To integrate important environmental types and levels in the ENDORSE study;
physical, socio-cultural, economic and policy determinants were examined at the micro
level (home, school and neighborhood level). Combinations of perceived and objectively
measured environmental determinants were used to investigate the interactions between
environmental and individual determinants.
Environmental determinants previously shown to be important were identied by
conducting two systematic reviews, one with physical activity as the outcome behavior []
and the other with specic obesity related dietary behaviors as outcome []. e results
from the reviews were categorized using the ANGELO grid. Convincing evidence of an
important role for physical environmental determinants was not found. However, only a
limited number of studies assessing physical environmental determinants of energy bal-
ance related behaviors were retrieved. Most consistent determinants of physical activity in
adolescents were support from signicant others, mother’s education level, family income
and non-vocational school attendance and low neighborhood crime incidence []. e
most consistent determinants of obesity related dietary behaviors among adolescents were
parental and family inuences, e.g. parental and sibling intakes, parenting style, family con-
nectedness and parental education []. e results of the reviews were used to guide the
design of questionnaires and observation forms. Since the evidence from the reviews itself
was not sucient, potential determinants of physical activity and dietary behaviors were
also included in the measurement instruments.
e theory of planned behavior (TPB) was used for the selection of potential individual
determinants to be included in the study []. e TPB postulates that intention to perform
a behavior, the determinant most proximal to behavior, is determined by three conceptu-
ally independent constructs: attitude, subjective norms and perceived behavioral control.
To further explore what specic concepts, beliefs or perceptions would be important for
adolescents; focus group interviews with adolescents were held. A focus group interview
is conducted among a small group of people who, led by a moderator and following a pre-
determined interview scheme, discuss several topics related to a specic subject. e aim
32 Chapter 232 Chapter 2
of the focus groups was to gain insight in the individual and environmental determinants
of snacking, so drink consumption, eating breakfast and physical activity. ree schools
participated and teachers were asked to select adolescents who would be able to function
in a group discussion (i.e. who were not too shy or too dominant). Five focus groups were
conducted with seven to nine adolescents aged - years old, and a total of adolescents
participated. Two of these groups consisted of boys only; two of girls only; and one was a
mixed group with boys and girls. ree of the groups were composed of adolescents from
cultural and ethnic minorities reecting the cultural diversity of the residents of Rotterdam.
Each interview was tape-recorded and lasted about minutes. e focus groups were tran-
scribed verbatim and from these transcripts, quotes were categorized into the determinants
or concepts that they reected. Many adolescents identied that seeing other people eating
or drinking and smelling fast food were factors that inuenced their eating and drinking
patterns. ese factors can be translated as the concept ‘external cues’. Assessment of a ten-
dency to respond to external cues was therefore included in the adolescent questionnaire as
an individual determinant. Rules at home (e.g. not allowed leaving the house without eating
breakfast) or the lack of rules (e.g. allowed to drink as much so drinks the adolescent
wants) were also mentioned by participants. Parental inuences already were identied as
potential important determinants from the systematic reviews, and based on the results
of the focus group interviews, items examining parents’ rules or ‘parenting practices’ were
included in the adolescent and parent questionnaires.
METHODS
Design
e ENDORSE study has a cross-sectional and a prospective two year follow-up compo-
nent. Data will be collected at baseline (/) when adolescents aged - years,
and two years later (/). Outcome measures are body mass index (BMI), waist
circumference, physical activity, sedentary behaviors, and so drink, snack and breakfast
consumption. e study is an integral part of the ongoing health surveillance system of
the Municipal Health Service in the Rotterdam area (Youth Monitor Rotterdam), in which
general health, well being and related factors of youth aged - years are monitored. e
Medical Ethics Committee of Erasmus University Medical Center reviewed the proposal
and issued a “declaration of no objection” for the ENDORSE project.
ENDORSE Study Protocol 33
Chapter 2
ENDORSE Study Protocol 33
Chapter 2
Recruitment of schools
Schools located in the Rotterdam area that participate in the Youth Monitor Rotterdam
(YMR) (N=) were invited for participation in the ENDORSE study. A letter and an
information sheet explaining the goals and the logistics of the study were sent to school
principals. e schools Principals were contacted by a researcher, upon which they could
express their interest in participating in the study. If necessary, additional information to
make a more informed decision was provided. Subsequently, a random sample of school
locations was drawn from the pool of schools that were willing to participate, aer stratica-
tion of the schools according to the area in the city in which they are located. Stratication
was done, to ensure a range of physical and cultural environments. Rotterdam is the second
largest city of the Netherlands. It has approximately , inhabitants of which are
of non-Dutch origin [].
Recruitment of participants within schools
Five classes in each participating school were randomly selected for participation in the cross-
sectional study, which took place in /. All adolescents in one class participated in
the study, unless they or their parents indicated that they were not willing to participate. e
adolescents in the rst year classes were also asked to complete the questionnaires at two
years follow up. To have sucient power, we assumed (conservatively) that obesity inducing
risk behaviors will be present among at least of adolescents, and we assumed moderate
eect sizes of determinants on behaviors. With a signicance level of . and power, a
sample size of approximately students would be sucient. Since we plan to do separate
analyses for girls and boys we aimed at including students. First year adolescents were
over-sampled to as these adolescents will be followed up two years later.
Procedure
e ENDORSE study follows the logistics of the YMR. e YMR routinely collects data
among adolescents in the rst and third year of secondary school. e school levels vary
from lower vocational training to high school. According to the usual procedure of the
YMR, the ENDORSE study was announced through a letter to the parents. is letter
explained that the YMR was extended with an extra part, aimed to gain insight in the preva-
lence and causes of overweight. Parents could keep their child from participating in the
study by sending the attached form to the adolescent’s teacher (passive consent procedure).
Approximately two weeks aer the usual YMR questionnaire, the adolescents completed the
ENDORSE questionnaire condentially during a school hour with a teacher and a research
assistant present. Within a month aer completion of the ENDORSE questionnaire, two
34 Chapter 234 Chapter 2
trained research assistants measured height, weight, waist circumference and pubertal de-
velopment according to standardized procedures described in a measurement protocol. e
adolescents were asked to come in succession to a private room where they were measured
without shoes. Aer the anthropometrical measurements, the adolescents completed a Tan-
ner scale to assess pubertal development []. To guarantee condentiality the adolescents
could put the Tanner scale form in an envelope before handing it over to the research as-
sistant. Aer these measurements, the adolescents received a Frisbee as a compensation
for their participation and were requested to give an envelope with a questionnaire to their
parents. e envelope contained a letter explaining the purpose of the study and the reason
why the parents were asked to complete the questionnaire, a pre-addressed and stamped
envelope and a card which they could complete to participate in a rae to win one of ve
I-pods. Parents were reminded twice to complete and return the questionnaires by means of
reminder cards delivered to parents via the adolescents. Parents were not addressed directly,
since the YMR procedure did not allow us to have any personal or address details.
Two observers independently conducted audits of the schools, school canteens, schoolyards,
and an area of meter radius surrounding the schools. e observations were conducted
within three months from the completion of the adolescent questionnaire. A brief interview
with canteen managers and school representatives was part of the audit. One of the observ-
ers conducted the interviews with school representatives, and the other observer conducted
the interviews with the school canteen managers.
Census data (year /) from the Center for Research and Statistics (COS), the
research center of the municipality of Rotterdam was collected on all neighborhoods of
Rotterdam.
In the follow-up data collection the same procedures are used. All measurements (ques-
tionnaire, anthropometrics, audits and interviews) are conducted within one week per
school. As an incentive for their participation, the adolescents received a key holder.
Measurements
e ENDORSE questionnaires were developed by using existing validated Dutch ques-
tionnaires where possible. If no validated questionnaires were available the ENDORSE
questionnaires were informed by questionnaires on related topics that were used in on-
going projects in the Netherlands, and questionnaires used in other countries. Relevant
parts of these questionnaires were adapted to tailor the specic behaviors identied. If no
relevant and validated questionnaires were available, new questions were developed for the
ENDORSE study. e ENDORSE study contained the following measurements: adolescent
questionnaire, parent questionnaire, interviews with school representatives and canteen
managers, observations of the school environment, census data collection and adolescent
ENDORSE Study Protocol 35
Chapter 2
ENDORSE Study Protocol 35
Chapter 2
body measurements. ese are described in detail in the following paragraphs. All determi-
nants measured in the ENDORSE study are listed in Table ..
Table 2.1 Individual and environmental correlates measured in the ENDORSE study
Perceived / self-reported variables Objectively measured variables
Adolescent
questionnaire
Parent
questionnaire
Interview Observation Census data
Individual
variables
attitude; parental
norms; modeling;
perceived behavior
control; intention;
habit; external
eating behavior
Physical
environment
Home*:
availability of
sports facilities,
bicycle; so
drinks, breakfast
products, snacks/
sweets, television
set in bedroom.
accessibility
of so drinks,
snacks/sweets,
breakfast products,
television set
School:
amount of trac;
safety for cycling;
availability of
sidewalks and
cycle lanes;
availability of a
bicycle shed
Neighborhood:
amount of trac;
safety for cycling;
availability of
sidewalks and
cycle lanes;
safety and
attractiveness of
neighborhood;
availability of
playgrounds,
parks, squares,
sports clubs
Home:
availability of
bicycles, cars, so
drinks, breakfast
products, snacks/
sweets
Accessibility
of so drinks,
snacks/sweets,
breakfast
products,
television set
Neighborhood:
amount of trac;
safety for cycling;
availability of
sidewalks and
cycle lanes; safety
in neighborhood;
attractiveness of
neighborhood
School :
Availability of
bicycle shed, food
products in the
school canteen and
vending machines,
PA facilities on the
school playground;
Shops, fast food
restaurants &
PA facilities
in the school
neighborhood.
Trac amount and
safety
Facilities and
frequency of public
transport
School:
availability of
shops, sports
facilities and
playgrounds for
children > 12
years old; areas of
sidewalks, bicycle
lanes, roads, grass,
plants, water;
trac accidents;
criminality, crime
reports
Neighborhood:
availability of
shops, sports
facilities and
playgrounds for
children > 12
years old; areas of
sidewalks, bicycle
lanes, roads, grass,
plants, water;
trac accidents;
criminality, crime
reports
table continued on next page
36 Chapter 236 Chapter 2
Adolescent questionnaire
Physical activity and sedentary behaviors were assessed with an adapted version of the Ac-
tivity Questionnaire for Adolescents & Adults (AQuAA) [Chin A Paw MJ, Slootmaker SM,
Schuit AJ, van Zuidam M, Van Mechelen W, unpublished] which is a short questionnaire
to assess physical activity at school and during leisure time, active transportation to school
and sedentary behaviors in leisure time. e structure of the AQuAA was obtained from the
SQUASH-questionnaire []. e AQuAA refers to activities in the past week (-day recall).
e test-retest reproducibility was fair to moderate for this questionnaire, with intra-class
correlations ranging from . to ..
Dietary intake was assessed with food frequency questions referring to a general week,
and a -hour recall question. e questionnaire included TPB items for all behaviors. All
the questions on TPB variables were measured on a ve-point bipolar scale. Attitude was
assessed with two items by asking if the adolescent considered the behavior as good or bad,
and as pleasant or unpleasant (‘e.g. Regular physical activity is very good (+) – very bad
(-)’). Subjective norm was assessed with one item, for example ‘my parents consider eating
breakfast as very good (+) – very bad (-)’. Modeling was assessed with two items by asking
if the parents and friends perform the behavior (‘ My friends eat snacks…a lot (+) – very
little (-)’). Perceived behavioral control was assessed with two items by asking how easy or
Perceived / self-reported variables Objectively measured variables
Adolescent
questionnaire
Parent
questionnaire
Interview Observation Census data
Economic
environment
Home:
income; amount of
money that can be
spent in 1 week
Home:
having a paid job;
educational level
School:
pricing of school
canteen products;
pricing of products
in shops around the
school
Neighborhood:
residential types;
household income;
educational level;
% unemployment,
% living on
social security: %
rented houses /
owner-occupied
properties; mean
value of houses;
% various ethnic
groups
Political
environment
School:
Food &
physical
activity
policy
ENDORSE Study Protocol 37
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ENDORSE Study Protocol 37
Chapter 2
dicult the behavior is to perform (How easy/dicult is it for you to eat breakfast? Very easy
(+) – very dicult (-)), and by asking if the decision to perform a behavior is completely
under the control of the adolescent (Do you decide by yourself if you eat breakfast? Yes, that
is completely my own decision (+) – no, that is not fully my own decision (-)). Intention
to perform the behavior was assessed with one item asking how certain the adolescent is to
perform the behavior in the coming six months (Do you intend to eat breakfast the next six
months? Yes, certainly do (+) – no certainly do not (-)).
Habit strength of dietary and physical activity behaviors was measured by means of the
Self Report Habit Index []. is questionnaire assesses three features of habitual behavior:
the extent to which a behavior is automatic, the repeated character of the behavior and
the sense of identity the behavior reects. ree items assessed these features, namely:
the behavior ‘x’ is something…. ‘I do frequently’, ‘is something I do automatically’ and ‘is
something that’s typically ‘me’’. ese items were measured on a ve-point scale, ranging
from ‘I completely agree’ (+) to ‘I completely disagree’ (-).
External cues that can inuence eating and drinking patterns were questioned with nine
items on a four point Likert scale (always (+) – never (-)), for example ‘I get hungry
when I see snacks or candy’ or ‘When I walk past a fast-food restaurant, I feel like buying
something. ese questions were based on the external eating behavior questions from the
Dutch Eating Behavior Questionnaire [] and adapted to address the topics adolescents
mentioned in the focus group interviews.
In the adolescent questionnaire the following perceived environmental determinants were
assessed: availability and accessibility of facilities for physical activity and food, school fac-
tors, neighborhood factors, parenting factors and economic factors. Demographic factors
(gender, age, ethnicity) were available for each adolescent through the YMR questionnaire.
e adolescent questionnaire was pre-tested among ten adolescents by means of cognitive
interviewing. Subsequently, the questionnaire was completed twice by schoolchildren
(aged -) ten-days apart to assess the test-re-test reliability and other psychometrics of
the questionnaire. Items with low reliability were adjusted or deleted from the questionnaire.
Parent questionnaire
Parental behavior, family and household environmental determinants were assessed in the
parent questionnaire. Parental physical activity and sedentary behaviors were assessed with
the adapted version of the AQuAA [Chin A Paw MJ, Slootmaker SM, Schuit AJ, van Zuidam
M, Van Mechelen W, unpublished], referring to activities in the past week (-day recall).
Dietary behaviors were assessed with food frequency questions referring to a general week.
Neighborhood factors as perceived by the parents, such as safety in neighborhood and
attractiveness of neighborhood, parenting practices, parental allowance, availability and
accessibility of so drinks, breakfast products, snacks/sweets, television set and parental
38 Chapter 238 Chapter 2
self-reported body weight and height, and demographics (gender, educational level, having
a paid job) were assessed. One parent completed this questionnaire.
Interview questionnaires
To assess school food and physical activity factors, pre-structured interview forms were
developed for interviews with the school canteen managers and with a school representa-
tive. e interview form for canteen managers contained questions on the availability of
food products, opening hours of the canteen, pricing policy, who will receive prots from
the foods sold, and canteen policies, for example if there are agreements on the assortment
with the catering organization. e form also included information about the so drink and
snack vending machines, e.g. how oen the vending machines are lled. e interviews with
a school representative were aimed at gaining insight in school policies regarding diet and
physical activity. As a basis for the interview form, the -item school-wide food practices
scale was used []. is scale assesses food practices allowed at school with the following
items: ‘Are students allowed to have food in the classroom’, ‘Are students allowed to have
beverages in the classroom?’, ‘Are students allowed to have snacks in the hallways?’, ‘Are
students allowed to have beverages in the hallways?’, ‘Are food or food coupons used as
reward or incentive for students?’, ‘Do you have classroom fundraising that includes food
sales?’, and ‘Do you have school wide fundraising that includes food sales?’. e question-
naire furthermore contained questions about whether or not the school has a formal food or
physical activity policy and if yes, to indicate what this policy is. Questions on what health
education programs they use in schools and possibilities and promotion activities for the
adolescents to be active before, during and aer school time, were also included.
Audit instrument for area observations
An audit instrument was developed to assess the availability and accessibility of foods and
physical activity facilities in the schools, in the schoolyards and in the neighborhood around
schools. e audit instrument consists of a pre-structured form with ve parts: school infor-
mation, school building, nutrition, physical activity and school environment. As much as
possible, the instrument had a ‘tick box’ answering format and included observation of ‘ob-
jective’ characteristics. When more subjective characteristics such as ‘state of maintenance
of the school yard’, or ‘trac situation around the school’ were reported, photographs were
taken from pre-dened angles. e audit instrument included also a description of the item
to be observed. e neighborhoods around schools that were observed were dened as a
radius of meters from the school. is denition was based on the basic assumption that
the facilities in the neighborhood around schools should be accessible in a general school
lunch break of approximately minutes, and that adolescents use facilities that are close by
the school. e audit instrument included maps of the -meter radius around the schools,
on which the route walked to observe the area could be drawn and the location of green
ENDORSE Study Protocol 39
Chapter 2
ENDORSE Study Protocol 39
Chapter 2
spaces could be indicated. e rst part (A) of the audit instrument involved some general
school information e.g. the address and number of pupils. Part B involved items on the
school building e.g. number of oors, entrance for schoolchildren, availability of an elevator
and location and visibility of stairs. Part C involved observation of the school canteens e.g.
counting the number of so-drink and snack vending machines in the canteen, checking
the items that are available in these machines, how well they were lled and advertising
in the canteen. Part D involved observation of the school physical activity facilities e.g.
the bicycle shed, facilities for activity and aesthetics of the schoolyards. Part E involved
observation of the neighborhood around schools e.g. the facilities for physical activity (e.g.
parks, elds, playing and sports elds) that were visible from the schoolyards and that were
present in the neighborhood ( meters). e component of the neighborhood observa-
tion related to dietary intake involved observation of the food retail outlets (baker’s shops,
snack bars, fast-food chains, supermarkets, kiosks, gas stations, tobacco shops, chemist’s
shops) that were visible from the schoolyards and that were present in the neighborhood
around schools.
e audit instrument rst was reviewed by experts on accuracy and completeness of the
instrument for its intended purpose. Secondly, the instrument was pilot tested by conduct-
ing the observations at two schools and in the corresponding neighborhoods with two ob-
servers. Important aspects of the pilot test were the completeness of the forms and feasibility
and suitability of using the denition of -meter radius for school environment. Aer the
pilot tests at the two schools, the audit forms were adapted if needed. e adapted forms
were tested at a third school, by three observers.
Census data
Census data was utilized to gather additional environmental data regarding the neighbor-
hoods around schools and the neighborhoods in which the children live. e data included
area-level household income, educational level, residential types, percentage of residents
aged -, percentage unemployment, percentage living on social security, percentage of
rented houses and owner-occupied properties, mean value of houses, percentages of various
ethnic groups, number of stores, fast food restaurants and the amount and type of green
spaces, water, bicycle tracks and foot paths. e census data could be linked to the home
environment of the adolescents with information on the ZIP code, which was asked in the
adolescent and parent questionnaires. Neighborhoods were dened based on a formal clas-
sication from Statistics Netherlands.
Body measurements
Body height was measured without shoes with a Seca mobile height rod with an ac-
curacy of . cm. A calibrated electronic digital oor scale (SECA class III) was used to
determine body weight of the participant in street clothes, without shoes, with an accuracy
40 Chapter 240 Chapter 2
of . kg. Waist circumference was measured using a spring loaded measuring tape (SECA
) to the nearest . cm. e waist circumference was measured twice. In case of a dier-
ence of more than . cm between these two measurements, the waist circumference was
measured twice again. Adolescents self-reported on their stage of pubertal development
using drawings of Tanner stages [] in the baseline data collection only.
DISCUSSION
e ENDORSE study is a comprehensive, longitudinal study in which both individual
and environmental determinants of selected obesity related behaviors in adolescents are
examined. e study has several strengths. It examines both sides of the energy balance
equation. It was designed to examine the inuence of environmental factors on obesity
related behaviors and BMI and objective measures for mapping the environment were in-
cluded. Moreover, the study includes environmental factors in various settings, including
the home, school and neighborhood. e study involves assessments of both individual and
environmental determinants, as opposed to many previous studies, that focused on one or
the other. e study has a longitudinal design, allowing analyses of prediction rather than
cross-sectional associations only. To date, there are very few studies that examine environ-
mental factors of energy balance related behaviors longitudinally. ere is an urgent need
for such studies, in order to be able to draw stronger inferences for relationships between
environmental factors and BMI. However, there were also some limitations in the study
protocol. For instance previously validated instruments were not available for all neces-
sary measures. Another limitation is that assessments of adolescents’ and parental physical
activity and dietary behaviors are self-reported. e denition of environment and neigh-
borhood is also somewhat arbitrary. e scale of environment to be studied needs further
conceptual development []. A clear denition of ‘the neighborhood’ is needed in terms
of measurement of respondent perceptions and objective measures of the environment.
However there is to date little evidence or consensus as to what constitutes a neighborhood.
ere is poor agreement about which boundary or scale to use, and how this might impact
on the association between predictor and outcome variables is unknown. Moreover the
boundary to be used might dier for dierent target groups and dierent settings (school
or home environments) [].
e ENDORSE study contains rich data examining individual and environmental de-
terminants of energy balance related behaviors among adolescents. With this information
the inuence of risk behaviors for overweight and relationships with socio-economic status
and ethnicity can be investigated. Individual and environmental determinants of obesity-
related behaviors among adolescents can be examined as well as the interactions between
individual and environmental determinants of obesity inducing behaviors. erefore,
ENDORSE Study Protocol 41
Chapter 2
ENDORSE Study Protocol 41
Chapter 2
data will be analyzed by means of multi-level regression analyses and structural equation
modeling. Eventually, the ENDORSE study will provide objectives and entry points for
prevention of overweight interventions in younger adolescents. In the questionnaires
for adolescents and parents, the school policy interview forms and the audit instrument will
be made available on the Internet [].
ACKNOWLEDGEMENTS
is study was nancially supported by a grant from ZonMw, e Netherlands Organiza-
tion for Health Research and Development (grant ID no .).
42 Chapter 242 Chapter 2
REFERENCES
. Lobstein T, Frelut ML: Prevalence of overweight among children in Europe. Obes Rev , ():-
.
. Power C, Lake JK, Cole TJ: Measurement and long-term health risks of child and adolescent fatness.
Int J Obes Relat Metab Disord , ():-.
. Must A, Jacques PF, Dallal GE, Bajema CJ, Dietz WH: Long-term morbidity and mortality of over-
weight adolescents. A follow-up of the Harvard Growth Study of to . N Engl J Med ,
():-.
. Must A, Strauss RS: Risks and consequences of childhood and adolescent obesity. Int J Obes Relat
Metab Disord , Suppl :S-.
. Ebbeling CB, Pawlak DB, Ludwig DS: Childhood obesity: public-health crisis, common sense cure.
Lancet , ():-.
. Booth SL, Sallis JF, Ritenbaugh C, Hill JO, Birch LL, Frank LD, Glanz K, Himmelgreen DA, Mudd
M, Popkin BM et al: Environmental and societal factors aect food choice and physical activity:
rationale, inuences, and leverage points. Nutr Rev , ( Pt ):S-; discussion S-.
. Ferreira I, van der Horst K, Wendel-Vos W, Kremers S, van Lenthe F, Brug J: Environmental cor-
relates of physical activity in youth - A review and update. Obesity Reviews , ():-.
. van der Horst K, Oenema A, Ferreira I, Wendel-Vos W, Giskes K, van Lenthe F, Brug J: A systematic
review of environmental correlates of obesity-related dietary behaviors in youth. Health Educ Res
, :-.
. de Bruijn GJ, Kremers SP, de Vries H, van Mechelen W, Brug J: Associations of social-environmental
and individual-level factors with adolescent so drink consumption: results from the SMILE study.
Health Educ Res .
. de Bruijn GJ, Kremers SP, Lensvelt-Mulders G, de Vries H, van Mechelen W, Brug J: Modeling in-
dividual and physical environmental factors with adolescent physical activity. Am J Prev Med ,
():-.
. Kremers SP, De Bruijn GJ, Visscher TL, Van Mechelen W, De Vries NK, Brug J: Environmental inu-
ences on energy balance-related behaviors: A dual-process view. Int J Behav Nutr Phys Act ,
():.
. Swinburn B, Egger G, Raza F: Dissecting obesogenic environments: the development and application
of a framework for identifying and prioritizing environmental interventions for obesity. Prev Med
, ( Pt ):-.
. Ajzen I: Attitudes, personality, and behavior: Homewood, IL, US: Dorsey Press; .
. e Centre for Research and Statistics [http://cos.rotterdam.nl/Rotterdam/Openbaar/Diensten/
COS/Publicaties/PDF/KCUK.pdf]
. Coleman L, Coleman J: e measurement of puberty: a review. J Adolesc , ():-.
. Wendel-Vos GC, Schuit AJ, Saris WH, Kromhout D: Reproducibility and relative validity of the short
questionnaire to assess health-enhancing physical activity. J Clin Epidemiol , ():-.
. Verplanken B, Orbell S: Reections on past behavior: A self-report index of habit strength. Journal of
Applied Social Psychology , ():-.
. Van Strien T, Frijters JER, Bergers GPA, Defares PB: e Dutch Eating Behaviour Questionnaire
(DEBQ) for assessment of restrained, emotional and external eating behaviors among Dutch and
American college students. International Journal of Eating Disorders , :-.
. Kubik MY, Lytle LA, Story M: Schoolwide food practices are associated with body mass index in
middle school students. Arch Pediatr Adolesc Med , ():-.
ENDORSE Study Protocol 43
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ENDORSE Study Protocol 43
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. Giles-Corti B, Timperio A, Bull F, Pikora T: Understanding physical activity environmental cor-
relates: increased specicity for ecological models. Exerc Sport Sci Rev , ():-.
. Department of Public Health, Erasmus MC [http://survey.erasmusmc.nl/intern/actreport/phpwcms/
index.php?index]
Part II Environmental
correlates of energy balance-
related behaviors: reviews of
the literature
3 A systematic review of
environmental correlates of obesity-
related dietary behaviors in youth
van der Horst K, Oenema A, Ferreira I, Wendel-Vos W, Giskes K, van
Lenthe F, Brug J. A systematic review of environmental correlates of
obesity-related dietary behaviors in youth.
Health Education Research , (): -.
48 Chapter 3 48 Chapter 3
ABSTRACT
Background: ere is increasing interest in the role the environment plays in shaping the
dietary behavior of youth, particularly in the context of obesity prevention. An overview
of environmental factors associated with obesity-related dietary behaviors among youth is
needed to inform the development of interventions.
Methods: A systematic review of observational studies on environmental correlates of
energy, fat, fruit/vegetable, snack/fast food, and so drink intakes in children (-) and
adolescents (-) was conducted. e results were summarized using the Analysis Grid for
Environments Linked to Obesity (ANGELO).
Results: e papers reviewed mostly focused on socio-cultural and economical environ-
mental factors at the household level. e most consistent associations were found between
parental intake and children’s fat, fruit/vegetable intakes, parent and sibling intake with
adolescent’s energy and fat intakes, and parental education with adolescent’s fruit/vegetable
intake. A less consistent but positive association was found for availability and accessibility
on children’s fruit/vegetable intake.
Conclusion: Environmental factors are predominantly studied at the household level and
focus on socio-cultural and economic aspects. Most consistent associations were found for
parental inuences (parental intake and education). More studies examining environmental
factors using longitudinal study designs and validated measures are needed for solid evi-
dence to inform interventions.
Environmental correlates of dietary behaviors 49
Chapter 3
Environmental correlates of dietary behaviors 49
Chapter 3
INTRODUCTION
e promotion of healthful eating in children and adolescents has become an increasingly
important public health and research priority as the prevalence of overweight and obesity
among children and adolescents continues to rise [, ]. Preventing the onset of obesity
inducing dietary behaviors or modifying these behaviors at an early age is likely to contrib-
ute to the prevention of overweight and obesity. A detailed understanding of factors that
determine these behaviors is essential, to be able to eectively prevent or modify obesity
inducing eating patterns. e research of determinants of dietary intake in children and
adolescents has predominantly focused on individual level determinants of these behaviors,
such as attitudes, taste preferences, social inuences and perceived behavioral control.
However, more recently a shi in attention to environmental determinants of behavior has
occurred as it has been acknowledged that a major driving force for the increasing obesity
prevalence may be the environment that encourages eating and discourages physical activ-
ity [, ]. ese environmental factors are highlighted in so-called ecological models, and
are conceptualized as being interrelated with factors at the individual level []. As stated
by Rothschild [], the likelihood that an individual will engage in a healthy behavior is
largest when someone is motivated to act healthily, has the abilities to engage in the healthy
behavior, and the environment oers the right opportunities to engage in the healthy behav-
ior. Motivation and abilities can be regarded as individual determinants of health behavior,
whereas opportunities depend on environmental factors.
Child and adolescent dietary behavior is likely to be strongly inuenced by environmental
factors, since children may have less autonomy in food choice. From the age of about three
years, children’s eating behavior is inuenced by their responsiveness to environmental
cues, and a variety of family and social factors start to inuence children’s eating behaviors
[]. e role of parents is considered to be of particular importance, since parents directly
determine the child’s physical and social environment, and indirectly inuence behavior
and habits through socialization processes and modeling []. When children grow older and
move into adolescence they become more autonomous, and lifestyle, developmental, social
and environmental changes take place. During this transition to adolescence, dietary intake
patterns change and decline in quality compared to childhood. Intakes of fruit, vegetables,
milk and fruit juice decrease, whereas intake of so drink increases during this time [].
e expected importance of the environment for obesity related behaviors in children
and adolescents is well documented in position papers and narrative reviews [, -]. e
number of studies examining the inuence of environmental factors on behavior is expand-
ing, but there is no systematic overview of which environmental factors have been studied
extensively, and what aspects of the environment are more inuential than others. Such an
50 Chapter 3 50 Chapter 3
overview is needed to identify a research agenda for further investigation and to inform
interventions that take environmental factors into account.
We conducted a systematic review of environmental factors that may potentially inuence
obesity related dietary behaviors of children and adolescents. We focused our review on
energy, fat (total and percent energy), fruit/vegetable, snack/fast food, and so drink intake.
ese behaviors have been identied as factors most strongly associated with obesity in
adults [], and are considered to be important obesity inducing behaviors in children and
adolescents as well [, ]. e environment was dened as ‘anything outside the individual’.
Many classications have been proposed to order the complexity of potential environmental
factors. We chose to use the ANGELO framework (Analysis Grid for Environments Linked
to Obesity) [] as a tool to classify the various environmental determinants. e ANGELO
framework dissects the environment by two dimensions: the size (micro and macro) and
the type of environment. Micro-environments are environmental settings where groups of
people meet and gather (e.g. homes, schools, restaurants, neighborhoods). Macro-envi-
ronments include the broader infrastructure that may support or hinder health behaviors
(e.g. town planning, transport infrastructure, the health system, the media). e ‘types’ of
environments distinguished in the ANGELO framework are the physical, socio-cultural,
economic, and political environment. e physical environment refers to the availability of
opportunities for healthy and unhealthy choices, for instance the availability and accessibil-
ity of healthy and unhealthy foods. e socio-cultural environment refers to the social and
cultural subjective and descriptive norms and other social inuences such as parental inu-
ences and peer pressure. e economic environment refers to the costs related to healthy
and unhealthy behaviors for instance costs of fruit and vegetables and household income.
e political environment refers to the rules and regulations that may inuence food choice
or availability, for example bans on snack vending machines in schools.
e review aimed to address the following specic research questions:
Which environmental correlates have been studied in relation to child and adolescent en-
ergy, fat (total and energy percent), fruit, vegetable, snack, fast food, and so drink intake?
Which environmental factors are consistently associated with these obesity-related dietary
behaviors?
Environmental correlates of dietary behaviors 51
Chapter 3
Environmental correlates of dietary behaviors 51
Chapter 3
METHODS
Data sources and search strategy
Studies eligible for inclusion in the review were located from the Medline (PubMed),
PsychInfo, Web of Science, and Human Nutrition databases, from January to December
. Our search strategy involved using a combination of the broad indexing terms of each
database and searching for terms in article titles and abstracts. We used the combination of
dietary intake keywords with environmental factor keywords to locate suitable articles. For
dietary intake the following keywords were used: energy intake, caloric intake, fat intake,
fat consumption, so drink, so drink consumption, so drink intake, sweetened beverage,
fruit, fruit consumption, fruit intake, vegetable, vegetable consumption, vegetable intake,
eating, diet, nutrition, food habits, food preferences. For environmental factors the follow-
ing keywords were used: physical environment, social environment, cultural environment,
socio-cultural environment, socio-economic environment, social inuences, neighborhood,
political environment, built environment, urban environment, rural environment, local
environment, school environment, home environment, availability, accessibility, residence
characteristics, environment design, parental inuence, parenting. Key terms were matched
to database specic indexing terms. e sensitivity of the search strategy was veried by
checking whether key articles from our personal databases that should be selected through
the search strategy, were actually retrieved. In addition to database searches, reference lists
of review studies and of articles included in the review were screened for titles that included
key terms.
Inclusion / exclusion criteria
A study had to meet the following criteria to be eligible for inclusion: healthy young people
in the age range of - years (or mean age within this range) as subjects of study; a measure
of energy and/or fat intake (total or percent energy), fruit, vegetable, snack, fast food or so
drink consumptions as the dependent variable(s); an outcome measure that was assessed for
at least one complete day (for example, studies assessing fruit intakes at just one meal were
not eligible). e study samples had to be drawn from countries with established market
economies as dened by the World Bank, and the paper had to be published in international
peer-reviewed journals in English. Intervention studies and studies that included only over-
weight/obese children were excluded.
52 Chapter 3 52 Chapter 3
Identication of relevant studies
Potentially relevant papers were selected by screening the titles (rst step), abstracts
(second step) and the entire article (third step) retrieved through the database searches.
Two researchers (KVDH, IF) independently conducted this screening. Disagreement about
eligibility between the reviewers was solved through discussion with a third co-author (JB).
Data extraction
Two authors (KVDH and AO) extracted the data from the included studies. Each study’s
ndings and methodological details, such as study design, sample size, dietary outcome(s),
environmental determinant(s) assessed, assessment methodology (child and/or parent-
report, objectively measured), and statistical analysis methods were listed in tables.
Summarizing study ndings
Associations between environmental factors and dietary outcomes were coded as ‘+’ for a
positive association, ‘-’ for an inverse association and ‘’ for no association. Associations
were regarded signicant when the p-value reported in the study was smaller than .. In
studies that reported results from univariate and multivariate analysis, only the multivariate
results were included. To reduce the number of specic environmental correlates studied,
conceptually similar environmental factors were combined (e.g. intakes from father and
mother to parental intake). An independent sample was used as the unit of analysis and was
dened as the smallest independent sub-sample for which relevant data were reported (e.g.
boys/girls) [].
Categorization of variables
Study ndings were tabulated by categorizing the distinct dietary outcomes in a grid dis-
secting dierent environmental settings, i.e. home/household, educational institutions,
neighborhoods, city/municipality, and the various types of environmental factors: physical,
socio-cultural, economic and political, following the ANGELO framework [] (Tables .
and .).
Environmental correlates of dietary behaviors 53
Chapter 3
Environmental correlates of dietary behaviors 53
Chapter 3
RESULTS
Search and selection of studies
e databases search located titles (Pubmed ; PsychInfo ; Web of Science ;
Human Nutrition ), resulting in unique titles of potentially relevant articles. Refer-
ence sections of earlier reviews and primary studies added titles. Screening the titles and
abstracts resulted in a selection of articles, for full text review. Twenty-three of these articles
did not meet the inclusion criteria, resulting in a nal inclusion of articles with samples.
Characteristics of included studies
Most of the studies were cross-sectional (n=) (Table .). Twenty-nine studies ( samples)
had children as the study population [-], and ( samples) included adolescents
[-]. One study included a child and adolescent sample []. In one paper the age of
the population was unclear, and this study was reviewed under an adolescent sample [].
Environmental determinants of fruit/vegetable intake were examined in studies, deter-
minants of fat intake in studies, determinants of fast food/snack intake in studies,
determinants of energy intake in studies and determinants of so drink intake in
studies. Only ve studies reported the validity, and seven studies reported the reliability of
the dietary intake measurements used.
Potential environmental correlates of children’s dietary behaviors
e ndings from the studies are summarized in Tables . and .. Table . provides
a summary of the number of studies and the consistent associations in each cell of the
ANGELO framework. In the following sections a summarized description of the results is
provided for the various behaviors. e factors examined on each environmental level and
the environmental factors that showed consistent associations with dietary behaviors in at
least two replicated studies are described.
Environmental correlates of energy intake
At the household environmental level, physical factors (one study/sample), socio-cultural
factors (ve studies, six samples), and economic factors (ve studies/samples) were ex-
amined in relation to energy intake (Table .). One study examined factors in the school
environment, no studies examined factors in the neighborhood environment, and two stud-
ies examined factors at the city/municipality level. At the household socio-cultural level, an
inverse association with energy intake was found for encouragement, oering assistance and
giving prompts to increase food intake during meals in two out of three samples [, , ].
54 Chapter 3 54 Chapter 3
Table 3.1 Characteristics of studies included in the review: sample size; sex; study design; assessment of dietary intake; data analysis; country
Children (3-12 years) Adolescents (13-18 years)
References Samples References Samples
Number % Number %
Sample size 37 100 40 100
<100 [25M/I, 29, 35, 37, 40, 43M/F] 8 21.6 [61M] 1 2.5
100-199 [19, 25F, 26F, 27F, 32, 33F] 6 16.2 [45M/F, 60] 3 7.5
200-299 [28] 1 2.7 [46] 1 2.5
300-499 [16, 24, 36, 38, 44] 5 13.5 [56, 68M/F, 71M/F] 5 12.5
500-999 [17, 18] 2 5.4 [47M/F, 48, 49I, 50, 51M/F, 54M/F, 62-64,
73M/F]
14 35.0
1,000-2,999 [21I/II, 23, 30, 34M/F, 39F, 41, 42] 9 24.3 [55, 72M/F] 3 7.5
3,000-4,999 [22M/F, 72M/F] 4 10.8 [49II, 59, 65-67, 70] 6 15.0
≥5000 [20, 31] 2 5.4 [52M/F, 53, 57M/F, 58, 69] 7 17.5
Sex
Girls only [26, 27, 33, 39] 4 10.8 - -
Boys only - - [61] 1 2.5
Boys and girls combined [16-20, 21I/II, 23, 24, 25I, 28-32, 35-38, 40-42,
44]
23 62.2 [46, 48, 49I/II, 50, 53, 55, 56, 58-60, 62-67,
69, 70]
19 47.5
Boys and girls, separately [22M/F, 25I/M/F, 34, 43M/F, 72M/F] 10 27.0 [45M/F, 47, 51, 52, 54, 57, 68, 71, 72M/F,
73]
20 50.0
Study design
Cross-sectional [16-20, 21I/II, 22M/F, 23, 24, 25M/F/I, 26F,
27F, 28-32, 33F, 34M/F, 35, 37, 38, 39F, 40-42,
43M/F, 44, 72M/F]
36 97.3 [46, 47M/F, 48, 49I/II, 50, 51M/F, 52M/F,
53, 54M/F, 55, 56, 57M/F, 58-60, 61M, 63-
67, 68M/F, 69, 70, 71M/F, 72M/F, 73M/F]
37 92.5
Longitudinal (length of study) [36] (2.5 years) 1 2.7 [62](6 years) 1 2.5
Case-control - - [45M/F] 2 5.0
Dietary outcome
Energy intake [23, 33F, 35, 36, 38, 39F, 40, 41, 43M/F] 10 27.0 [45M/F, 46, 50, 54M/F, 56, 67, 70, 73M/F] 11 27.5
Energy from fat (%) [23, 27F, 36, 38, 39F, 41, 44] 7 18.9 [47M/F, 50, 51M/F, 54M/F, 56, 60, 64, 67,
70, 71M/F, 73M/F]
16 40.0
Total fat intake (g) [22M/Fa, 43M/F] 4 10.8 [45M/F, 47M/F, 57M/F, 68M/F, 71M/F,
73M/F]
12 30.0
Fruit intake [16, 18, 24, 28, 32, 37, 72M/F] 8 21.6 [48, 50, 53b, 57M/F, 58, 60, 61M, 63, 64,
67, 69, 70, 72M/F]
15 37.5
Environmental correlates of dietary behaviors 55
Chapter 3
Environmental correlates of dietary behaviors 55
Chapter 3
Children (3-12 years) Adolescents (13-18 years)
References Samples References Samples
Number % Number %
Vegetable intake [16, 18, 24, 28, 32, 37, 42, 72M/F] 9 24.3 [50, 53b, 57M/F, 58, 60, 61M, 64, 67, 69,
70, 72M/F]
13 32.5
Juice intake [28, 32] 2 5.4 [48, 61M] 2 5.0
Composite measure FV intake [21M/F, 22M/F, 26F, 30, 31, 34M/F, 42] 10 27.0 [52M/F, 62, 64-67] 7 17.5
Composite measure of FJV intake [25I/M/F, 28, 29c, 32] 6 16.2 [61M] 1 2.5
Fast food consumption [20, 24] 2 5.4 [57M/F, 59d] 3 7.5
Snack food intake [19, 31, 42, 72M/F] 5 13.5 [49I/IIe, 52M/Fe, 60, 67, 72M/F] 8 20.0
Pizza & snack [29f ] 1 2.7 [56] 1 2.5
So drink consumption [16, 17, 24, 29f, 72M/F] 6 16.2 [48, 55, 57M/F, 60, 67, 72M/F] 8 20.0
Assessment of dietary outcome
Self-report [17, 19, 21I, 22M/F, 25M/F, 28-32, 34M/F, 36,
38, 39F, 42]
18 48.6 [46, 47M/F, 48, 49I/II, 52M/F, 53, 54M/F,
55, 56, 57M/F, 58-60, 61M, 62-67, 68M/F,
69, 70, 71M/F, 72M/F, 73M/F]
35 87.5
Parent-report [16, 18, 21II, 25I, 26F, 33F, 35, 40, 41, 44,
72M/F]
12 32.4 - -
Self- or parent-report [20, 23] 2 5.4 [45M/F, 50] 3 7.5
Parent- and self-report (together) [24, 27F, 37, 43M/F] 5 13.5 [51M/F] 2 5.0
Measurement instrument dietary
outcome
24-hour recall [20, 23, 26F, 27F, 29, 30, 33F, 34M/F, 38, 42] 11 29.7 [46, 49I, 61M, 64] 4 10.0
48-hour recall - - [50] 1 2.5
Food frequency questionnaire [16, 18, 19, 21M/F, 22M/F, 24, 31] 9 24.3 [51M/F, 53, 56, 57M/F, 58, 60, 63, 65-67,
68M/F, 70, 71M/F]
17 42.5
2-day food record [28, 32] 2 5.4 - -
3-day food record [37, 39F, 43M/F] 4 10.8 [54M/F, 73M/F] 4 10.0
7-day food record [25I/M/F, 40] 4 10.8 [45M/F] 2 5.0
Questionnaireg [17, 35, 36, 72M/F] 5 13.5 [48, 49II, 52M/F, 55, 59, 62, 69, 72M/F] 10 25.0
24-hour recall & 2-day food record [41, 44] 2 5.4 [47M/F] 2 5.0
56 Chapter 3 56 Chapter 3
Children (3-12 years) Adolescents (13-18 years)
References Samples References Samples
Number % Number %
Reliability of dietary intake measurement
Unknown / not reported [16-20, 22M/F, 23, 24, 25I/M/F, 26F, 30, 31, 33F,
35-38, 39F, 40-42, 43M/F, 44, 72M/F]
29 78.4 [45M/F, 46, 47M/F, 48, 49I/II, 50, 51M/F,
52M/F, 53, 54M/F, 57M/F, 58, 60, 63-65,
69, 72M/F]
26 65.0
Reported elsewhere [27F, 28, 29, 32, 34M/F] 6 16.2 [56, 59, 67, 71M/F, 73M/F] 7 17.5
< 0.7 - - [61M, 66, 68M/F, 70] 5 12.5
≥ 0.7 [21M/F] 2 5.4 [55, 62] 2 5.0
Validity of dietary intake measurement
Unknown / not reported [17-20, 23, 24, 26F, 30, 33F, 35-38, 39F, 40-42,
43M/F, 44, 72M/F]
22 59.5 [45M/F, 46, 47M/F, 48, 49I/II, 50, 52M/F,
53, 54M/F, 55, 57M/F, 58, 61M, 62, 69,
72M/F, 73M/F]
25 62.5
Reported elsewhere [22M/F, 25I/M/F, 27F, 28, 29, 32, 34M/F] 11 29.7 [51M/F, 56, 59, 60, 63-65, 67, 68M/F,
71M/F]
13 32.5
< 0.6 [16fruit, veg., 21M/F, 31] 4 10.8 [66, 70] 2 5.0
≥ 0.6 [16so drinks] 1 2.7 -
Data analysis
Univariate [19, 24, 28, 32, 35, 38, 39F, 72M/F] 9 24.3 [45M/F, 48, 50, 51M/F, 54M/F, 56, 57M/F,
68M/F, 71M/F, 72M/F, 73M/F]
19 47.5
Multiple [20, 23, 31, 36, 40, 41] 6 16.2 [47M/F, 49I/II, 52M/F, 55, 58-60, 63-65,
67, 69, 70]
16 40.0
Univariate & multiple [16-18, 21M/F, 22M/F, 27F, 29, 37, 42, 43M/F,
44]
14 37.8 [46, 53, 61M, 62] 4 10.0
Model testing (Structural equation
modeling)
[25I/M/F, 26F, 30, 33F, 34M/F] 8 21.6 [66] 1 2.5
Country
North America [17, 20, 23, 25M/F/I, 26F, 27F, 28-32, 33F,
34M/F, 35, 36, 38, 39F, 41, 42, 43M/F, 44]
25 67.6 [46, 47M/F, 49I/II, 52M/F, 53, 58, 61M,
64-67, 68M/F, 69, 70, 73M/F]
20 50.0
Europe [16, 18, 19, 21I/II, 22M/F, 24, 37, 40] 10 27.0 [45M/F, 48, 50, 51M/F, 54M/F, 56, 57M/F,
60, 62, 63, 71M/F]
16 40.0
Oceania [55, 59] 2 5.0
Environmental correlates of dietary behaviors 57
Chapter 3
Environmental correlates of dietary behaviors 57
Chapter 3
Children (3-12 years) Adolescents (13-18 years)
References Samples References Samples
Number % Number %
Asia [72M/F] 2 5.4 [72M/F] 2 5.0
F = girls only; M = boys only; M/F = boys and girls analyzed separately; I/II = two independent samples based
on dierent age groups
a Frequency of high fat food consumption
b Inadequate fruit / vegetable consumption (less than once a day)
c Percent of total daily energy intake contributed to fruit, vegetable and juice intake
d Frequency of take-away food consumption
e Low nutrient dense / high fat snacks consumption
f Percent of total daily energy intake contributed by that food group & consumption frequency per day
g Unclear whether dietary intake was measured with a food-frequency questionnaire or with another
questionnaire
58 Chapter 3 58 Chapter 3
Table 3.2 Summary of micro and macro environmental correlates of dietary intakes among children (3 to 12 year olds)
Correlate Related to dietary
behaviors
Assoc
(+ or -)†
Unrelated to dietary behaviors #
Samples
Summary (n)
References References + - 0
Physical
Energy
Accessibility to food [36] - 1 0 1 0
Minutes foods present at home [36] 1 0 0 1
Fat (total fat, en% fat)
Accessibility to food [36] 1 0 0 1
Minutes foods present at home [36] 1 0 0 1
Fruit, Juice, Vegetables
Availability [25F, 28, 30, 34F] + [25M/I, 34M] 7 4 0 3
Accessibility [21I/II, 25F/I] + [25M, 28] 6 4 0 2
Home FJV barriers [28] - 1 0 1 0
Television on during meals [29] - 1 0 1 0
Snacks, Fast food
Television on during meals [29] + 1 1 0 0
So drink 0
Television on during meals [29] + 1 1 0 0
Availability [17] + 1 1 0 0
Socio-Cultural
Energy
Parental intake [43M/F] 2 0 0 2
Parenting practices
Control/restriction/discouragement [33F] - [35, 36] 3 1 0 2
Encouragement/assistance/prompts to
increase food intake
[35, 40] - [36] 3 0 2 1
Food as reward [36] 1 0 0 1
Parents negative statements about foods [40] - 1 0 1 0
Family support [36] 1 0 0 1
Minutes spent eating at home [36] 1 0 0 1
No. meals eaten out [36] 1 0 0 1
Marital status parents [36] 1 0 0 1
Food presentations/ food oers [35] + 1 1 0 0
Home/household
Environmental correlates of dietary behaviors 59
Chapter 3
Environmental correlates of dietary behaviors 59
Chapter 3
Correlate Related to dietary
behaviors
Assoc
(+ or -)†
Unrelated to dietary behaviors #
Samples
Summary (n)
References References + - 0
Fat (total fat, en% fat)
Parental intake [27F, 43M/Fmother] + [43M/Ffather] 3 3* 0 2*
Parenting practices
Control/restriction [36**]
[27F, 36$]
-
+
2 2* 1* 0
Prompts to increase/decrease food intake [36increase] - [36decrease] 1 0 1* 1*
Food as reward [36] 1 0 0 1
Pressure to eat [27F] + 1 1 0 -
Mothers monitoring [27F] 1 0 0 1
Family support [36] 1 0 0 1
Minutes spent eating at home [36] 1 0 0 1
No. meals eaten out [36] 1 0 0 1
Marital status parents [36] 1 0 0 1
Single parent family [38] + 1 1 0 0
Fruit, Juice, Vegetables
Modeling (parents, important others) [21I, 28] + 2 2 0 0
Mothers intake of so drinks and sweets [16] 1 0 0 1
Avoidance of negative modeling [16] 1 0 0 1
Parental intake [16, 18, 21II, 26F,
37fruit]
+ [37veg] 5 5* 0 1*
Parental intake if FV are highly available [30] + 1 1 0 0
Parent FV intake if FV are low available [30] 1 0 0 1
Parenting style [32negative] - [32authoritative] 1 0 1* 1*
Parenting practices [18] 1 0 0 1
Food as reward [16] 1 0 0 0
Encouragement / verbal praise [16veg] + [16fruit, 32] 2 1* 0 2*
Discouragement to eat sweets, so drinks [16] 1 0 0 1
Control / restriction [28, 32] 2 0 0 2
Permissiveness [16] 1 0 0 1
Negotiation [16] 1 0 0 1
Pressure to eat [26F] - [16] 2 0 1 1
Catering on children’s demands [16] 1 0 0 1
60 Chapter 3 60 Chapter 3
Correlate Related to dietary
behaviors
Assoc
(+ or -)†
Unrelated to dietary behaviors #
Samples
Summary (n)
References References + - 0
Parental preparation practices [32] 1 0 0 1
Age of introduction of FJV (late) [18fruit] - [18Veg] 1 0 1* 1*
Breastfeeding [18fruit] + [18Veg] 1 1* 0 1*
Parent preparation of quick & easy food [29] 1 0 0 1
Two parent family [29] 1 0 0 1
One parent family [42] - 1 0 1 0
Family dinner [31]
[72Fveg]
+
-
[72Ffruit] [72Mfruit/veg] 3 1* 1* 3*
Family breakfast [72Ffruit] [72Mfruit/
veg]
+ [72Fveg] 2 3* 0 1*
Snacks, Fast food
Parental intake [19] + 1 1 0 0
Parenting practices (general) [19] + 1 1 0 0
Control / reward [19] 1 0 0 1
Parent preparation of quick & easy food [29] 1 0 0 1
Two parent family [29] 1 0 0 1
One parent family [42] 1 0 0 1
Family dinner [31 fried snack foods] - [31 snack foods, 72M/F] 3 0 1* 3*
Family breakfast [72F] - [72M] 2 0 1 1
So drink
Parental intake [16, 17] + 2 2 0 0
Refraining from negative modeling [16] 1 0 0 1
Parenting practices
Food as reward [16] 1 0 0 1
Discouragement to drink so drinks [16] 1 0 0 1
Encouragement to eat fruit, vegetables [16] 1 0 0 1
Control / pressure [16] 1 0 0 1
Verbal praise [16] 1 0 0 1
Permissiveness [16] + 1 1 0 0
Negotiation [16] 1 0 0 1
Catering on children’s demands [16] 1 0 0 1
Parent preparation of quick & easy food [29] 1 0 0 1
Environmental correlates of dietary behaviors 61
Chapter 3
Environmental correlates of dietary behaviors 61
Chapter 3
Correlate Related to dietary
behaviors
Assoc
(+ or -)†
Unrelated to dietary behaviors #
Samples
Summary (n)
References References + - 0
Two parent family [29] 1 0 0 1
Family dinner [31]
[72F]
-
+
[72M] 3 1 1 1
Family breakfast [72M/F] - 2 0 2 0
Economic
Energy
Income suciency [38] 1 0 0 1
Household income (high) [23] - [39F, 41] 3 0 1 2
Parents educational level (high) [23] + [39F] 2 1 0 1
# persons/household [36, 38, 41] 3 0 0 3
Occupation [23] 1 0 0 1
Parents live less than 10y in present house [23] + 1 1 0 0
Fat (total fat, en% fat)
Income suciency [38] 1 0 0 1
Household income (high) [39F] - [27F, 41] 3 0 1 2
Parents educational level (high) [23, 39F] - 2 0 2 0
Maternal employment [44] 1 0 0 1
# persons/household [38] - [36, 41] 3 0 1 2
Parents live less than 10y in present house [23] - 1 0 1 0
Fruit, Juice, Vegetables
Household income (high) [29] 1 0 0 1
Deprivation index (high) [37fruit] - [18, 37veg] 2 0 1* 2*
Parents educational level (high) [18veg, 37fruit] + [16, 18fruit, 29, 37veg] 4 2* 0 4*
Number of hours/week worked by mother [29, 42] 2 0 0 2
SES / occupational class [24veg] + [24fruit, 42] 1 1* 0 2*
Snacks, Fast food
Household income (high) [20] + [23, 29] 3 1 0 2
Parents educational level [29] 1 0 0 1
Number of hours/week worked by mother [42white children]
[42black children]
-
+
[29] 2 1* 1* 1*
SES / occupational class [42] + [23, 24] 3 1 0 2
So drink
62 Chapter 3 62 Chapter 3
Correlate Related to dietary
behaviors
Assoc
(+ or -)†
Unrelated to dietary behaviors #
Samples
Summary (n)
References References + - 0
Household income [29] 1 0 0 1
Parents educational level [16] 1 0 0 1
Occupational class [24] 1 0 0 1
Number of hours/week worked by mother [29] 1 0 0 1
Physical
Energy
Minutes food present at school [36] 1 0 0 1
Fat (total fat, en% fat)
Minutes food present at school [36] 1 0 0 1
So drink
Availability [17] + 1 1 0 0
Socio-Cultural
Energy
Prompts to increase food at school lunch [36] 1 0 0 1
Fat (total fat, en% fat)
Prompts to increase food at school lunch [36] - 1 0 1 0
Fruit, Juice, Vegetables
Modeling peers FJV [28] 1 0 0 1
So drink
Friends intake [17] + 1 1 0 0
Economic
Fruit, Juice, Vegetables
Area deprivation index (most deprived) [22F] - [22M]
Snack, Fast food
Area deprivation index (most deprived) [22M/F] + 2 2 0 0
Physical
Energy
Non-metropolitan residence [41] + [23] 2 1 0 1
Fat (total fat, en% fat)
Non-metropolitan residence [41] + [23] 2 1 0 1
Snack, Fast food
Region (southern USA vs. other) [20] + 1 1 0 0
Urbanization [20] 1 0 0 1
Educational
Institutions
NeighbourhoodCity/municipality
Environmental correlates of dietary behaviors 63
Chapter 3
Environmental correlates of dietary behaviors 63
Chapter 3
† Associations between environmental factors and dietary outcomes were coded ‘+’ for a positive association, ‘-‘ for an inverse association
* If in one study, a determinant is examined in relation to two outcomes (e.g. fruit intake and vegetable intake), and the results dier for the two outcomes (e.g. a positive
association was found for fruit intake, and no association was found for vegetable intake), the study is counted once in the column ‘# of samples’, and twice in the summary
column.
** Parental control over child’s fat intake
$ Parental control over child’s food intake
Italic printed text, indicate factors with consistent associations.
64 Chapter 3 64 Chapter 3
Environmental correlates of fat intake
At the household environmental level, physical factors (one study/sample), socio-cultural
factors (four studies, ve samples), and economic factors (seven studies/samples) were
examined in relation to fat intake. One study examined factors in the school environment,
no studies examined factors in the neighborhood environment, and two studies examined
factors at the city/municipality level. At the household socio-cultural level a positive as-
sociation was found for parental fat intake (three out of three samples) [, ] , and parental
control over food intake (two out of two samples) [, ]. Parental education was inversely
associated with fat intake in two out of two samples [, ].
Environmental correlates of fruit and vegetable intake
At the household environmental level, physical factors (six studies, eleven samples), socio-
cultural factors (twelve studies, samples), and economic factors (six studies/samples)
were examined in relation to fruit/vegetable intake. One study examined factors in the
school environment, one study (two samples) examined factors in the neighborhood envi-
ronment, and no studies examined factors at the city/municipality level. At the household
physical level, availability of fruit/vegetables was associated with higher fruit/vegetable
intake in four out of seven samples [, , , ]. Accessibility of fruit/vegetables was
positively associated with fruit/vegetable intake in four out of six samples [, , ]. At the
household socio-cultural level positive associations were found for modeling (two out of
two samples/studies) [, ] and parental intake of fruit/vegetables (six out of six samples)
[, , , , , ].
Environmental correlates of snack/fast food intake
At the household environmental level, physical factors (one study/sample), socio-cultural
factors (ve studies, six samples), and economic factors (ve studies/samples) were ex-
amined in relation to snack/fast food intake. No studies examined factors in the school
environment, one study (two samples) examined factors in the neighborhood, and one
study examined factors at the city/municipality level. None of the factors examined showed
consistent associations with snack/fast food intake.
Environmental correlates of so drink intake
At the household environmental level, physical factors (two studies/samples), household
socio-cultural factors (ve studies, six samples), and household economic factors (three
studies/samples) were examined in relation to so drink intake. One study examined fac-
tors in the school environment, no studies examined factors in the neighborhood or city/
municipality environment. At the household socio-cultural level, parental so drink intake
was positively associated with children’s so drink intake in two out of two samples [, ].
Environmental correlates of dietary behaviors 65
Chapter 3
Environmental correlates of dietary behaviors 65
Chapter 3
Potential environmental correlates of adolescent’s dietary behaviors
Environmental correlates of energy intake
At the household environmental level, socio-cultural factors (six studies, nine samples) and
economic factors (two studies/samples) were examined as potential correlates of energy
intake (Table .). No studies examined factors in the school, neighborhood or city/mu-
nicipality environment. At the household socio-cultural level parental energy intake was
positively associated with adolescent’s energy intake (six out of six samples) [, , , ].
A positive association was also found for sibling intake (four out of four samples) [, ].
Environmental correlates of fat intake
At the household environmental level, nine studies ( samples) examined socio-cultural
factors and ve studies (eight samples) examined economic factors as potential correlates
of fat intake. One study examined factors in the school environment, no studies examined
factors in the neighborhood. One study (two samples) examined factors at the city/mu-
nicipality level. At the household socio-cultural level parental fat intake was found to be
positively associated with adolescent’s fat intake (eight out of nine samples) [, , , ,
]. A positive association was also found for sibling intake (four out of four samples) [,
].
Environmental correlates of fruit and vegetable intake
At the household environmental level, physical factors (two studies/samples), socio-cultural
factors (ten studies, eleven samples) and economic factors (eight studies, ten samples) were
examined as potential correlates of fruit/vegetable intake. One study examined factors in
the school environment, one study examined factors in the neighborhood environment, and
one study (two samples) examined factors at the city/municipality level. At the household
socio-cultural level an authoritative parenting style was positively associated with fruit/
vegetable intake (two out of two samples) [, ]. Family connectedness was positively
associated with adolescent fruit/vegetable intake (two out of two samples) [, ]. At the
household economic level parent educational level was found to be positively associated
with fruit/vegetable intake (six out of six samples) [, , , , ].
Environmental correlates of snacks/fast food intake
At the household environmental level, socio-cultural factors (four studies, six samples) and
economic factors (three studies, ve samples) were studied in relation to snack and fast food
intake. One study examined factors in the school environment, no studies examined factors
in the neighborhood environment, and one study (two samples) examined factors at the
city/municipality level. None of the factors examined showed consistent associations with
snack/fast food intake.
66 Chapter 3 66 Chapter 3
Table 3.3 Summary of micro and macro environmental correlates of dietary intakes among adolescents (>12 to 18 year olds)
Correlate Related to dietary behaviors Assoc
(+ or -)†
Unrelated to dietary behaviors # Samples Summary (n)
References References + - 0
Physical
Fruit, Juice, Vegetables
Availability [66] + [61M] 2 1 0 1
Socio-Cultural
Energy
Parental intake [46, 54M/F, 56, 73M/F] + 6 6 0 0
Siblings intake [54M/F, 73M/F] + 4 4 0 0
Single mother family [45M] + [45F] 2 1 0 1
Frequency of family meals [67] + 1 1 0 0
Fat (total fat, en% fat)
Parental intake [51M, 54M/F, 56, 68F, 73M/F]
[51Fmother]
+ [51Ffather, 68M] 9 8* 0 2*
Siblings intake [54M/F, 73M/F] + 4 4 0 0
Friends intake [56] 1 0 0 1
Communication strategies [60] 1 0 0 1
Family food routines [60] 1 0 0 1
Food rules [60] 1 0 0 1
Frequency of family meals [67] 1 0 0 1
Shopping:
Healthy food is asked for
Food asked for is bought
Shopping alone / family
[60] -
[60]
[60]
1
1
1
0
0
0
1
0
0
0
1
1
Single mother family [45M/F] 2 0 0 2
Head of household status (male vs. other) [47M]
[47F]
+
-
2 1 1 0
Fruit, Juice, Vegetables
# of evening meals eaten with parent present [69] + 1 1 0 0
Frequency of family meals [67] + 1 1 0 0
Breakfast with family [72M/F] 2 - 0 2
Dinner with family [72Ffruit] + [72M/Fveg] [72Mfruit] 2 1* 0 3*
Home/household
Environmental correlates of dietary behaviors 67
Chapter 3
Environmental correlates of dietary behaviors 67
Chapter 3
Correlate Related to dietary behaviors Assoc
(+ or -)†
Unrelated to dietary behaviors # Samples Summary (n)
References References + - 0
Shopping:
Healthy food is asked for
Food asked for is bought
Shopping alone/ family
[60fruit]
[60fruit/veg]
+
-
[60veg]
[60fruit/veg]
1
1
1
1*
0
0
0
2*
0
1*
0
2*
Negative communication strategies [60veg] - [60fruit] 1 0 1* 1*
Family food routines [60] 1 0 0 1
Food rules [60] 1 0 0 1
Parent present during leave/return from school [69] 1 0 0 1
Parental control on food choice [69] 1 0 0 1
Parenting style (authoritative, indulgent vs.
authoritative, neglective)
[63, 65authoritative] + 2 2 0 0
Residence other than with family:
J, F in summer
F in winter
[48] +
[48]
1
1
1
0
0
0
0
1
Family connectedness (high vs. mod/low) [53, 58] + 2 2 0 0
Positive relation with parents [62] + 1 1 0 0
Positive relation with peers
F& V intake at 15 years
F& V intake at 21 years
[62] +
[62]
1
1
1
0
0
0
0
1
Snacks, Fast food
Parental and friends’ intake of foods [56] + 1 1 0 0
Frequency of family meals, breakfast/dinner with
family
[67, 72M/F] 3 0 0 3
Breakfast, lunch, dinner at home vs.
school
[49I/II] 2 0 0 2
Breakfast at other site than home or
school
[49I] + 1 1 0 0
Lunch at other site than home or school [49I/II] + 2 2 0 0
Breakfast/dinner at other site than home
or school
[49II] 1 0 0 1
Dinner at other site than home [49I] 1 0 0 1
So drink
Parental intake [55] + 1 1 0 0
68 Chapter 3 68 Chapter 3
Correlate Related to dietary behaviors Assoc
(+ or -)†
Unrelated to dietary behaviors # Samples Summary (n)
References References + - 0
Friends intake [55] + 1 1 0 0
Frequency of family meals [67] - 1 0 1 0
Breakfast with family [72M] - [72F] 2 0 1 1
Dinner with family [72M/F] 2 0 0 2
Residence other than with family [48] - 1 0 1 0
Shopping:
Healthy food is asked for
Food asked for is bought
Shopping alone / family
[60]
[60]
[60]
1
1
1
0
0
0
0
0
0
1
1
1
Communication strategies [60] 1 0 0 1
Family food routines [60] 1 0 0 1
Food rules [60] - 1 0 1 0
Economic
Energy
Family income [70] 1 0 0 1
Fathers occupation [50] 1 0 0 1
Parental education [50, 70] 2 0 0 2
Fat (total fat, en% fat)
Family income / household income [47M/F, 70] 3 0 0 3
Fathers occupation [50] - 1 1 0 0
Parental education [70] - [50, 71M/F] 4 0 1 3
Socio-economic index [57M/F] 2 0 0 2
Household size [47M/F] 2 0 0 2
Fruit, Juice, Vegetables
Family income [52M/F, 61M, 70] 4 0 0 4
Fathers occupation [50] 1 0 0 1
Parental education [50fruit, 52M/F, 65, 69, 70] + [50veg] 6 6* 0 1*
Socio-economic index / SES [53, 57Mveg]
[57Ffruit]
+
-
[57Mfruit/Fveg] 3 2* 1* 2*
Snacks, Fast food
Household income [52M/F] 2 0 0 2
Education of responsible parent [52F] - [52M] 2 0 1 1
Environmental correlates of dietary behaviors 69
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Environmental correlates of dietary behaviors 69
Chapter 3
Educational Institutions
Neighbour-
hood
City/municipality
Correlate Related to dietary behaviors Assoc
(+ or -)†
Unrelated to dietary behaviors # Samples Summary (n)
References References + - 0
Socio-economic index [57M/F] 2 0 0 2
Pocket money > $20/week [59] + 1 1 0 0
Physical
Fat (total fat, en% fat)
A la carte program [64] + 1 1 0 0
Snack vending [64] 1 0 0 1
Fruit, Juice, Vegetables
A la carte program [64fruit] [64fruit&veg] - [64veg] 1 0 2* 1*
Snack vending [64fruit] - [64veg] [64fruit&veg] 1 0 1* 2*
Beverage vending [64veg] [64fruit] [64fruit&veg] 1 0 0 3*
Socio-Cultural
Snacks, Fast food
School social environment [59] 1 0 0 1
Teacher support
Highly supportive – fairly unsupportive
Highly unsupportive [59] +
[59] 1
1
0
1
0
0
1
0
Physical
Fruit, Juice, Vegetables
Availability in restaurant menu [61Mveg/fruit juice/FJV] + [61Mfruit] 1 3* 0 1*
Availability in grocery stores [61M] 1 0 0 1
Physical
Fat (total fat, en% fat)
Geographic region (southern USA vs. other) [47F] - [47M] 2 0 1 1
Degree of urbanization [47M/F] 2 0 0 2
Fruit, Juice, Vegetables
City vs. county [57M/F] + 2 2 0 0
Snacks, Fast food
Geographic region (west, Midwest, south) [49II] - [49I] 2 0 1 1
So drink
City vs. County [57M/F] 2 0 0 2
† Associations between environmental factors and dietary outcomes were coded ‘+’ for a positive association, ‘-‘ for an inverse association
* If in one study, a determinant is examined in relation to two outcomes (e.g. fruit intake and vegetable intake), and the results dier for the two outcomes (e.g. a positive
association was found for fruit intake, and no association was found for vegetable intake), the study is counted once in the column ‘# of samples’, and twice in the summary
column.
Italic printed text, indicate factors with consistent associations.
70 Chapter 3 70 Chapter 3
Table 3.4 ANGELO framework with the number of studies and the associations found in each cell.
Physical Socio-cultural Economic Political
Home/
household
#
studies
Assoc. # studies Assoc. #
studies
Assoc. #
studies
Assoc.
Energy 1 11 Encouragement/
assistance (-;c)
Parental intake
(+;a)**
Sibling intake (+;a)
7
Fat 1 13 Control over intake
(+;c)
Parental intake (+;c)
Parental intake (+;a)
Sibling intake (+;a)
12 Parental
education
(+;c)
Fruit/
vegetables
8 Availability
(+;c)*
Accessibility
(+;c)
16 Modeling parents
(+;c)
Parental intake (+;c)
Parenting style (+;a)
Family
connectedness (+;a)
12 Parental
education
(+;a)
Snack/fast food 1 9 8
So drink 2 10 Parental intake (+;c) 3
Educational
institutions
Energy 1 1
Fat 2 1
Fruit/
vegetables
1 1
Snack/fast food 1
So drink 1 1
Neighborhood
Energy
Fat
Fruit/
vegetables
1 1
Snack/fast food 1
So drink
City/
municipality
Energy 2
Fat 3
Fruit/
vegetables
1
Snack/fast food 2
So drink 1
c Positive association found for children
a Positive association found for adolescents
Macro Micro
Environmental correlates of dietary behaviors 71
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Environmental correlates of dietary behaviors 71
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Environmental correlates of so-drink intake
At the household environment level, socio-cultural factors (ve studies, six samples) were
examined as potential environmental correlates of so drink intake. No studies examined
factors in the school and neighborhood environment. One study (two samples) examined
factors at the city/municipality level. None of the factors examined showed consistent as-
sociations with so drink intake.
Summary in ANGELO framework
Socio-cultural factors on the household level are the most studied environmental factors
for all dietary behaviors, followed by economic factors on the household level (Table .).
Factors studied on the school environmental level (physical and socio-cultural) were mostly
single studies. At the city/municipality level only physical factors were studied.
DISCUSSION
e present systematic review of the literature on environmental correlates of energy, fat,
fruit, vegetable, snack/fast food, and so drink intake in children and adolescents showed
that household socio-cultural factors (e.g. parental and sibling intake, parenting practices)
and household economic factors (e.g. household income, parent educational level) were
studied most extensively as potential environmental determinants. Few studies examined
the inuence of physical environmental factors, few looked at environmental factors in
schools, neighborhoods and city/municipality, and none looked at political factors. is
review showed consistent evidence (ndings replicated in multiple studies), for the relation-
ship between parental intake and children’s fat, fruit and vegetable intake, for parent and
sibling intakes with adolescent’s energy and fat intake, and for parent educational level with
adolescent’s fruit and vegetable intake. A positive association was found for the relationship
between availability and accessibility with children’s fruit and vegetable intake, even though
the samples that found a positive association only slightly outnumbered the samples that
found no association. Further positive associations were found for controlling/restrictive
practices (fat), parent educational level (fat), modeling (fruit/vegetable), parental intake
(so drink) parenting style (fruit/vegetable), family connectedness (fruit/vegetable) and
encouragement to increase food intake (fruit/vegetable). A negative association was found
for encouragement/assistance/prompts (energy). ese factors were examined in only two
studies, which limits the possibility to draw rm conclusions regarding consistency of as-
sociations. e direction of the association for encouragement/assistance/prompts seems
unexpected. However, since these studies were cross-sectional, it could also be that a low
child food intake provokes parental encouragement, assistance and prompts to increase in-
72 Chapter 3 72 Chapter 3
takes. All other associations studied between dietary behaviors and potential environmental
factors were inconsistent, appeared non-existent, or were not replicated.
A major limitation of the currently available published research papers is that many po-
tential environmental determinants have been examined for a variety of dietary behaviors,
but that only few studies have been conducted on the same specic environmental factor
– dietary behavior combination. Replication of studies on such combinations is necessary,
to generate more compelling evidence for associations between environmental factors
and dietary intake. With regard to the strength of the study designs, most of the included
studies were cross-sectional, making conclusions about direction and possible causality of
associations impossible. Furthermore, most studies relied on self-reported data, of which
the validity and reliability of the instruments used was hard to judge, since this information
was not reported in the majority of the studies. We retrieved few studies that used objec-
tive observation instruments to assess factors in the physical environment or to measure
the behavioral outcome. e behavioral outcome measures in the studies included, may be
somewhat biased because the studies mostly relied on self-reports.
ere are some issues and limitations that have to be taken into account in interpreting
the results of the review. In order to summarize the ndings of the studies we collapsed
conceptually similar environmental determinants into one category, although potential
determinants in the same category were oen dissimilar or measured in dierent ways.
Our search strategy only included studies that were published in English in peer-reviewed
journals and referenced in electronic databases; therefore our ndings may be inuenced by
a publication bias. However, the high number of non-associations reported in the included
studies may indicate that a bias towards publication of signicant results only, was not very
strong. e studies included were heterogeneous in the conceptualization, measurement
of the environmental determinant and/or dietary intakes, samples and analyses used, and
therefore it was not possible to assess the overall strength of associations. Finally, we in-
cluded multiple environmental factors examined in one study in the review, and it must be
kept in mind that these associations are not independent.
Previously published reviews on the associations of environmental factors regarding eat-
ing behaviors in children and adolescents were narrative as opposed to systematic reviews
[-, , , ]. e main conclusions from those reviews were that the role of parents is
particularly important, that parents should create supportive food environments for their
children [-, ], and that school food environments may have a large impact on food
choices [, , ]. In the present review we indeed found that parental intake and to a lesser
extent availability and accessibility were associated with intakes in adolescents and children.
Furthermore, some evidence (examined in two studies) was found for a positive association
Environmental correlates of dietary behaviors 73
Chapter 3
Environmental correlates of dietary behaviors 73
Chapter 3
between an authoritative parenting style and adolescent’s fruit and vegetable intake, and for
specic parenting practices and children’s energy and fat intakes. Only very few studies on
peer inuences were retrieved in this systematic review. e importance of the school food
environment can also not be substantiated with the evidence from the studies included in
the present review.
We identied several gaps in the currently available evidence of relationships between en-
vironmental factors and child and adolescent dietary intakes. First, we were able to identify
very few studies examining associations between micro environmental factors in school and
neighborhood settings, and on macro environmental factors in city/municipality settings:
the broader, more anonymous infrastructure that may support or hinder health behaviors.
It must, however, be noted that there are studies available that examine the eects of adver-
tising and marketing on eating behaviors of youngsters [, ], but since these studies are
mostly intervention studies, these were not included in the review. Secondly, the studies
mainly depended on perceived and self-reported environmental information, as opposed
to more objective observations of environments. Objectively assessing characteristics of the
environment (observations or Geographic Information System), is a topic of recent interest
[, ]. Furthermore, we retrieved only a limited number of studies assessing environ-
mental determinants of snack and so drink intakes, while these two behaviors may be of
specic importance in obesity prevention [, ]. An important reason for some of the gaps
may be that attention to the role of the physical environment is of recent interest, and many
studies that examine possible inuences of the physical environment may be underway.
e current evidence of associations between environmental determinants and dietary
intakes among children and adolescents suggests that parental intakes, sibling intakes and
educational level of parents are environmental determinants most consistently associated
with intakes. A less consistent repeated but positive association was found for availability
and accessibility on child fruit and vegetable intake. e nding that parental behavior
is associated with child and adolescent intakes implies that interventions should take the
behavior of parents into account, or desensitize adolescents for the (unfavorable) behavior
of their parents. Parents should be more strongly encouraged to give the right example,
especially where fat and energy intakes are concerned. Fruit and vegetable promotion
should focus especially on adolescents from parents with lower levels of education. To get
a broad understanding of the inuence of environmental factors associated with obesity
inducing behaviors in children and adolescents at the various levels distinguished in the
ANGELO framework, studies are needed that target the environmental levels and factors
that have found to be (nearly) empty in the ANGELO grid (Table .), such as physical,
socio-cultural, economic and political factors in the school (e.g. school food policy and
food prices), neighborhood (e.g. availability and accessibility of foods in shops) and city/
74 Chapter 3 74 Chapter 3
municipality environment (e.g. food policy, food prices, marketing). Furthermore factors
such as availability and accessibility at home, school and neighborhood should be studied in
relation to energy, fat, so drink, snacks and fast food intake. For all environmental factors,
including the factors that have already been studied, there is a need for longitudinal studies
in which valid or objective measurement instruments are used.
ACKNOWLEDGEMENTS
e authors wish to thank Carlijn Kamphuis and Gert Jan de Bruijn for their assistance in
the review process.
is study was nancially supported by a grant from ZonMw e Netherlands Organiza-
tion for Health Research and Development. Dr. Katrina Giskes is supported by an Australian
National Health and Medical Research Council Sidney Sax Fellowship (grant ID number:
).
Environmental correlates of dietary behaviors 75
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Environmental correlates of dietary behaviors 75
Chapter 3
REFERENCES
. Lobstein T, Frelut ML: Prevalence of overweight among children in Europe. Obes Rev , ():-
.
. Hedley AA, Ogden CL, Johnson CL, Carroll MD, Curtin LR, Flegal KM: Prevalence of overweight
and obesity among US children, adolescents, and adults, -. JAMA , ():-.
. French SA, Story M, Jeery RW: Environmental inuences on eating and physical activity. Annu Rev
Public Health , :-.
. Swinburn BA, Caterson I, Seidell JC, James WP: Diet, nutrition and the prevention of excess weight
gain and obesity. Public Health Nutr , (A):-.
. Sallis JF, Owen N: Ecological models of health behavior. In Health behavior and health education
eory, research, and practice. edition. Edited by Glanz K, Reimer BK, Lewis FM. San Fransisco: CA:
Jossey-Bass; :-.
. Rothschild ML: Carrots, sticks, and promises: a conceptual framework for the management of public
health and social issue behaviors. Journal of Marketing , :-.
. Patrick H, Nicklas TA: A review of family and social determinants of children’s eating patterns and
diet quality. J Am Coll Nutr , ():-.
. Ritchie LD, Welk G, Styne D, Gerstein DE, Crawford PB: Family environment and pediatric over-
weight: what is a parent to do? J Am Diet Assoc , ( Suppl ):S-.
. Story M, Neumark-Sztainer D, French S: Individual and environmental inuences on adolescent
eating behaviors. J Am Diet Assoc , ( Suppl):S-.
. Story M, French S: Food Advertising and Marketing Directed at Children and Adolescents in the US.
Int J Behav Nutr Phys Act , ():.
. Crockett SJ, Sims LS: Environmental-Inuences on Childrens Eating. Journal of Nutrition Education
, ():-.
. Rennie KL, Johnson L, Jebb SA: Behavioural determinants of obesity. Best Pract Res Clin Endocrinol
Metab , ():-.
. Pereira MA, Ludwig DS: Dietary ber and body-weight regulation. Observations and mechanisms.
Pediatr Clin North Am , ():-.
. Swinburn B, Egger G, Raza F: Dissecting obesogenic environments: the development and application
of a framework for identifying and prioritizing environmental interventions for obesity. Prev Med
, ( Pt ):-.
. Cooper H: Synthesizing research: a guide for literature reviews. rd ed edition. London: Sage; .
. Vereecken CA, Keukelier E, Maes L: Inuence of mother’s educational level on food parenting prac-
tices and food habits of young children. Appetite , ():-.
. Grimm GC, Harnack L, Story M: Factors associated with so drink consumption in school-aged
children. J Am Diet Assoc , ():-.
. Cooke LJ, Wardle J, Gibson EL, Sapochnik M, Sheiham A, Lawson M: Demographic, familial and
trait predictors of fruit and vegetable consumption by pre-school children. Public Health Nutr ,
():-.
. Brown R, Ogden J: Children’s eating attitudes and behaviour: a study of the modelling and control
theories of parental inuence. Health Educ Res , ():-.
. Bowman SA, Gortmaker S, Ebbeling CB, Pereira MA, Ludwig DS: Eects of fast-food consumtion
on energy intake and diet quality among children in a national household survey. Pediatrics ,
:-.
76 Chapter 3 76 Chapter 3
. Bere E, Klepp KI: Correlates of fruit and vegetable intake among Norwegian schoolchildren: parental
and self-reports. Public Health Nutr , ():-.
. Wardle J, Jarvis MJ, Steggles N, Sutton S, Williamson S, Farrimond H, Cartwright M, Simon AE:
Socioeconomic disparities in cancer-risk behaviors in adolescence: baseline results from the Health
and Behaviour in Teenagers Study (HABITS). Prev Med , ():-.
. Mazur RE, Marquis GS, Jensen HH: Diet and food insuciency among Hispanic youths: accultura-
tion and socioeconomic factors in the third National Health and Nutrition Examination Survey. Am
J Clin Nutr , ():-.
. Haapalahti M, Mykkanen H, Tikkanen S, Kokkonen J: Meal patterns and food use in -to -year-
old Finnish children. Public Health Nutr , ():-.
. Cullen KW, Baranowski T, Owens E, Marsh T, Rittenberry L, de Moor C: Availability, accessibility,
and preferences for fruit, fruit juice, and vegetables inuence children’s dietary behavior. Health
Educ Behav , ():-.
. Fisher JO, Mitchell DC, Smiciklas-Wright H, Birch LL: Parental inuences on young girls’ fruit and
vegetable, micronutrient, and fat intakes. J Am Diet Assoc , ():-.
. Lee Y, Mitchell DC, Smiciklas-Wright H, Birch LL: Diet quality, nutrient intake, weight status, and
feeding environments of girls meeting or exceeding recommendations for total dietary fat of the
American Academy of Pediatrics. Pediatrics , ():E.
. Cullen KW, Baranowski T, Rittenberry L, Cosart C, Hebert D, de Moor C: Child-reported family and
peer inuences on fruit, juice and vegetable consumption: reliability and validity of measures. Health
Educ Res , ():-.
. Coon KA, Goldberg J, Rogers BL, Tucker KL: Relationships between use of television during meals
and children’s food consumption patterns. Pediatrics , ():E.
. Kratt P, Reynolds K, Shewchuk R: e role of availability as a moderator of family fruit and vegetable
consumption. Health Educ Behav , ():-.
. Gillman MW, Rifas-Shiman SL, Frazier AL, Rockett HR, Camargo CA, Jr., Field AE, Berkey CS,
Colditz GA: Family dinner and diet quality among older children and adolescents. Arch Fam Med
, ():-.
. Cullen KW, Baranowski T, Rittenberry L, Cosart C, Owens E, Hebert D, de Moor C: Socioenvi-
ronmental inuences on children’s fruit, juice and vegetable consumption as reported by parents:
reliability and validity of measures. Public Health Nutr , ():-.
. Birch LL, Fisher JO: Mothers’ child-feeding practices inuence daughters’ eating and weight. Am J
Clin Nutr , ():-.
. Reynolds KD, Hinton AW, Shewchuk RM, Hickey CA: Social cognitive model of fruit and vegetable
consumption in elementary school children. J Nutr Educ , ():-.
. Drucker RR, Hammer LD, Agras WS, Bryson S: Can mothers inuence their child’s eating behavior?
J Dev Behav Pediatr , ():-.
. Zive MM, Frank-Spohrer GC, Sallis JF, McKenzie TL, Elder JP, Berry CC, Broyles SL, Nader PR:
Determinants of dietary intake in a sample of white and Mexican-American children. J Am Diet Assoc
, ():-.
. Gibson EL, Wardle J, Watts CJ: Fruit and vegetable consumption, nutritional knowledge and beliefs
in mothers and children. Appetite , ():-.
. Johnson-Down L, O’Loughlin J, Koski KG, Gray-Donald K: High prevalence of obesity in low income
and multiethnic schoolchildren: a diet and physical activity assessment. J Nutr , ():-.
Environmental correlates of dietary behaviors 77
Chapter 3
Environmental correlates of dietary behaviors 77
Chapter 3
. Crawford PB, Obarzanek E, Schreiber GB, Barrier P, Goldman S, Frederick MM, Sabry ZI: e eects
of race, household income, and parental education on nutrient intakes of - and -year-old girls.
NHLBI Growth and Health Study. Ann Epidemiol , ():-.
. Koivisto UK, Fellenius J, Sjoden PO: Relations between parental mealtime practices and children’s
food intake. Appetite , ():-.
. Johnson RK, Guthrie H, Smiciklas-Wright H, Wang MQ: Characterizing nutrient intakes of children
by sociodemographic factors. Public Health Rep , ():-.
. Wolfe WS, Campbell CC: Food pattern, diet quality, and related characteristics of schoolchildren in
New York State. J Am Diet Assoc , ():-.
. Oliveria SA, Ellison RC, Moore LL, Gillman MW, Garrahie EJ, Singer MR: Parent-child relationships
in nutrient intake: the Framingham Children’s Study. Am J Clin Nutr , ():-.
. Johnson RK, Smiciklas-Wright H, Crouter AC, Willits FK: Maternal employment and the quality of
young children’s diets: empirical evidence based on the - Nationwide Food Consumption
Survey. Pediatrics , ( Pt ):-.
. Darke SJ, Disseldu MM, Try GP: A nutrition survey of children from one-parent families in New-
castle upon Tyne in . Br J Nutr , ():-.
. Laskarzewski P, Morrison JA, Khoury P, Kelly K, Glatfelter L, Larsen R, Glueck CJ: Parent-child
nutrient intake interrelationships in school children ages to : the Princeton School District Study.
Am J Clin Nutr , ():-.
. Johnson RK, Johnson DG, Wang MQ, Smiciklas-Wright H, Guthrie HA: Characterizing nutrient
intakes of adolescents by sociodemographic factors. J Adolesc Health , ():-.
. Sweeting H, Anderson A, West P: Socio-demographic correlates of dietary habits in mid to late
adolescence. Eur J Clin Nutr , ():-.
. Dausch JG, Story M, Dresser C, Gilbert GG, Portnoy B, Kahle LL: Correlates of high-fat/low-
nutrient-dense snack consumption among adolescents: results from two national health surveys. Am
J Health Promot , ():-.
. Laitinen S, Rasanen L, Viikari J, Akerblom HK: Diet of Finnish children in relation to the family’s
socio-economic status. Scand J Soc Med , ():-.
. De Bourdeaudhuij I: Resemblance in health behaviours between family members. Arch Public Health
, :-.
. Lowry R, Kann L, Collins JL, Kolbe LJ: e eect of socioeconomic status on chronic disease risk
behaviors among US adolescents. JAMA , ():-.
. Neumark-Sztainer D, Stor y M, Resnick MD, Blum RW: Correlates of inadequate fruit and vegetable
consumption among adolescents. Prev Med , ():-.
. Vauthier JM, Lluch A, Lecomte E, Artur Y, Herbeth B: Family resemblance in energy and macronutri-
ent intakes: the Stanislas Family Study. Int J Epidemiol , ():-.
. Woodward DR, Boon JA, Cumming FJ, Ball PJ, Williams HM, Hornsby H: Adolescents’ reported
usage of selected foods in relation to their perceptions and social norms for those foods. Appetite
, ():-.
. Feunekes GI, de Graaf C, Meyboom S, van Staveren WA: Food choice and fat intake of adolescents
and adults: associations of intakes within social networks. Prev Med , ( Pt ):-.
. Hoglund D, Samuelson G, Mark A: Food habits in Swedish adolescents in relation to socioeconomic
conditions. Eur J Clin Nutr , ():-.
. Story M, Neumark Sztainer D, Resnick MD, Blum RW: Psychosocial factors and health behaviors
associated with inadequate fruit and vegetable intake among Amarican-Indian and Alaska-Native
adolescents. J Nutr Educ , :-.
. McLellan L, Rissel C, Donnelly N, Bauman A: Health behaviour and the school environment in New
South Wales, Australia. Soc Sci Med , ():-.
. De Bourdeaudhuij I, Van Oost P: Personal and family determinants of dietary behaviour in adoles-
cents and their parents. Psychology and Health , :-.
. Edmonds J, Baranowski T, Baranowski J, Cullen KW, Myres D: Ecological and socioeconomic cor-
relates of fruit, juice, and vegetable consumption among African-American boys. Prev Med ,
():-.
. Lien N, Jacobs DR, Jr., Klepp KI: Exploring predictors of eating behaviour among adolescents by
gender and socio-economic status. Public Health Nutr , ():-.
. Kremers SP, Brug J, de Vries H, Engels RC: Parenting style and adolescent fruit consumption. Appetite
, ():-.
. Kubik MY, Lytle LA, Hannan PJ, Perry CL, Story M: e Association of the School Food Environ-
ment With Dietary Behaviors of Young Adolescents. Am J Public Health , ():-.
. Lytle LA, Varnell S, Murray DM, Story M, Perry C, Birnbaum AS, Kubik MY: Predicting adolescents’
intake of fruits and vegetables. J Nutr Educ Behav , ():-.
. Neumark Sztainer D, Wall M, Perry C, Story M: Correlates of fruit and vegetable intake among
adolescents: Findings from Project EAT. Prev Med , ():-.
. Neumark-Sztainer D, Hannan PJ, Story M, Croll J, Perry C: Family meal patterns: associations with
sociodemographic characteristics and improved dietary intake among adolescents. J Am Diet Assoc
, ():-.
. Stanton CA, Fries EA, Danish SJ: Racial and gender dierences in the diets of rural youth and their
mothers. Am J Health Behav , ():-.
. Videon TM, Manning CK: Inuences on adolescent eating patterns: e importance of family meals.
J Adolesc Health , ():-.
. Xie B, Gilliland FD, Li YF, Rockett HRH: Eects of ethnicity, family income, and education on dietary
intake among adolescents. Prev Med , ():-.
. Tur JA, Puig MS, Benito E, Pons A: Associations between sociodemographic and lifestyle factors and
dietary quality among adolescents in Palma de Mallorca. Nutrition , ():-.
. Kusano-Tsunoh A, Nakatsuka H, Satoh H, Shimizu H, Sato S, Ito I, Fukao A, Hisamichi S: Eects
of family-togetherness on the food selection by primary and junior high school students: family-
togetherness means better food. Tohoku J Exp Med , ():-.
. Perusse L, Tremblay A, Leblanc C, Cloninger CR, Reich T, Rice J, Bouchard C: Familial resemblance
in energy intake: contribution of genetic and environmental factors. Am J Clin Nutr , ():-
.
. Jenkins S, Horner SD: Barriers that inuence eating behaviors in adolescents. J Pediatr Nurs ,
():-.
. Wardle J: Parental inuences on children’s diets. Proc Nutr Soc , ():-.
. Young B, Hetherington M: e literature on advertising and children’s food choice. Nutrition & Food
Science , ():-.
. Richter KP, Harris KJ, Paine-Andrews A, Fawcett SB, Schmitd TL, Lankenau BH, Johnston J: Measur-
ing the health environment for physical activity and nutrition among youth: a review of the literature
and applications for community initiatives. Prev Med , :S-S.
. Glanz K, Sallis JF, Saelens BE, Frank LD: Healthy nutrition environments: concepts and measures.
Am J Health Promot , ():-, ii.
4 Environmental correlates
of physical activity in youth
– a review and update
Ferreira I, van der Horst K, Wendel-Vos W, Kremers S, van Lenthe FJ,
Brug J. Environmental correlates of physical activity in youth – a review
and update.
Obesity Reviews , (): -.
80 Chapter 4 80 Chapter 4
ABSTRACT
Background: Obesogenic environments are thought to underlie the increased obesity
prevalence observed in youth during the past decades. Understanding the environmental
factors that are associated with physical activity (PA) in youth is needed to better inform
the development of eective intervention strategies attempting to halt the obesity epidemic.
Methods: We conducted a systematic semi-quantitative review of studies on environ-
mental correlates of youth PA published in the past years. e ANalysis Grid for Environ-
ments Linked to Obesity (ANGELO) framework was used to classify the environmental
correlates studied.
Results: Most studies retrieved used cross-sectional designs and subjective measures of
environmental factors and PA. Especially variables of the home and school environments
were associated with PA in youth. Most consistent positive correlates of PA were father’s PA,
school PA-related policies (in children), and support from signicant others, mother’s edu-
cation level, family income, and non-vocational school attendance (in adolescents). Time
spent outdoors (in children) and low crime incidence (in adolescents) were characteristics
of the neighborhood environment associated with higher PA. Convincing evidence of an
important role for many other environmental factors was however not found.
Discussion: Further research should aim at longitudinal study designs and use more objec-
tive measures of PA and its potential (environmental) determinants.
Environmental correlates of physical activity 81
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Environmental correlates of physical activity 81
Chapter 4
INTRODUCTION
Physical activity (PA) is a health enhancing behavior: when practiced regularly, PA reduces
the risk for a range of chronic disease [-]. Also among the young current and future health
benets can be obtained through engaging in physically active lifestyles []: it helps building
strong bones, healthy joints, a strong heart, a good mental health and prevents today’s major
public health concern – obesity[-]. Despite these health benets, many young people are
not engaging in recommended levels of PA [-]. In addition, longitudinal studies have
shown that a steep decrease in PA levels occurs during adolescence [-] and that PA levels
established in youth tend to track into adulthood[-]. PA promotion in youth is thought
to facilitate a carryover of healthful habits into adulthood and a lifelong protection from
other risk factors, and is therefore a priority in current public health policies [, ].
Given the short time frame in which the obesity prevalence has increased to epidemic
scales many scientists postulate that this is more likely due to changes in environments
than in biology [-]. In this vein, recent studies have indeed demonstrated associations
between childhood obesity and environmental features, namely at the home and neighbor-
hood [-]. Consequently, it is important to understand, measure and alter environments
that promote or hinder obesity-inducing behaviors, such as low physical activity [, -].
Environmental inuences can be especially relevant to children and adolescents since they
have less autonomy in their behavioral choices []. Specic recommendations for research
on the determinants of PA in youth have emphasized the need to examine environmental
inuences on youth PA at dierent levels (e.g. home, neighborhood, school)[-] to bet-
ter inform the development of interventions attempting to improve PA levels [, ].
Now that more and more studies focus on potential environmental inuences on
children’s and adolescents’ PA behavior, it is important to get a detailed overview of the
evidence these studies have provided so far, to dene a research agenda in this area. In
the year , a comprehensive review of personal and environmental correlates of PA in
children and adolescents[] identied several variables which were consistently associated
with children/adolescent’s PA levels, including social and physical environmental factors
such as direct help and support from parents and signicant others, access to programs/
facilities, opportunities to be active and time spent outdoors. We now update the review
of evidence provided by Sallis et al. focusing specically on, and characterizing into more
detail, the environmental correlates of PA in children and adolescents.
82 Chapter 4 82 Chapter 4
METHODS
Search strategies and procedures
Relevant studies were located from main sources. Firstly, the computerized literature
databases MedLine (PubMed), PsychInfo, Web of Science, EMBASE and SportDiscuss were
searched. e following keyword combinations were used: physical activity, physical active
lifestyle, vigorous activity, leisure activities, recreation, exercise, sport(s), motor activity,
physical education, walking, running, (bi)cycling, commuting, determinants, correlates,
inuences, associations, environment, physical environment, built environment, psychoso-
cial determinants, social environment, social norms, socio-economic status, socio-cultural
environment, parents, peers, neighborhood, school, facilities, recreation, equipment, safety.
ese searches were restricted to studies performed in humans aged up to years, and
published between January and December . Aer excluding duplicate studies,
over , articles were hereby identied. Two independent reviewers (IF, KvdH) screened
and selected the articles retrieved whenever it could be ascertained rst, from the title (
articles), second from the abstract ( articles), and nally from the full text ( articles),
that the selection criteria (see below) were met. ese stepwise analyses were performed
separately by each reviewer, and at each step an article was kept whenever selected by at
least one of the reviewers.
Secondly, manual searches using the reference of the previous systematic review from
Sallis et al,[] primary studies located from the previous source and our personal databases
were performed and cross-checked with the articles found through the previous source.
is led to the inclusion of additional articles. Together, these search strategies resulted
in a total of articles, which are reviewed herein.
Inclusion/exclusion criteria
Types of studies
e present review was concerned with PA levels occurring ‘naturally’ in populations of
children and adolescents. erefore, only observational studies (either cross-sectional or
longitudinal) were included, whereas studies investigating samples of PA-related interven-
tions or with a quasi-experimental design were excluded (with exception of studies report-
ing on baseline data from intervention studies). Qualitative studies or studies that were
solely descriptive in nature (i.e., reporting only frequency data), abstracts, case reports,
expert opinions, dissertations and unpublished data were also excluded.
Environmental correlates of physical activity 83
Chapter 4
Environmental correlates of physical activity 83
Chapter 4
Participants and country
Subjects (or the majority of the participants) had to be in the age range of - years old;
similarly to the review by Sallis et al., we have divided studies among children (i.e., - years
old) and adolescents (>- years old). Studies on children and adolescents with chronic
diseases (that may aect PA levels) or children participating in top-level competitive sports
were not included. Only studies from samples drawn in countries with established market
economies (as dened by the World Bank) and published in English as papers in interna-
tional peer-reviewed journals were included.
e dependent variable(s) - PA
e dependent variable was any measure of (overall) PA of various types (i.e., play, games,
sports, work, transportation, recreation, physical education, or planned exercise) performed
in the context of home/family, school and community, and expressed in terms of duration
(e.g. in minutes), or frequency (e.g. times per week), or intensity (e.g. vigorous) or a combi-
nation of these, i.e. in terms of volume (e.g. METs or Kcal) []. When studies had multiple
dependent measures of PA; the correlates of mutually exclusive outcomes (e.g. habitual
levels of moderate- and vigorous-intensity PA) were investigated and reported separately.
Studies in which the dependent variable was aerobic tness, intention, self-ecacy, or
other intermediate (non-behavioral) measures were not included; physical inactivity/
sedentary behavior was not considered as outcome because PA and inactivity are distinct
behaviors, oen unrelated and with distinct determinants [-]. In addition, although
we acknowledge physical inactivity as an important heath-impairing behavior, a recent
systematic review of its determinants among youth has been published recently [].
e predictor variable(s) - environmental characteristics
Environmental variables were broadly dened as ‘anything outside the individual that can
aect its PA behavior’. To structure our review we were in need of a conceptual framework
to categorize the various environmental factors studied. Dierent classications of possible
environmental determinants of health behaviors have been proposed [, , , , ], all
of them showing great overlap and similarities. In the present review we have adopted e
ANalyses Grid for Environments Linked to Obesity (ANGELO) conceptual framework []
to classify potential environmental determinants of PA in children and adolescents. is
framework was specically developed to conceptualize ‘obesogenic’ environments (i.e. those
that promote excessive energy intake and low PA), enabling the identication of specic
areas and settings to be targeted by intervention programs. Specically, environmental vari-
ables can be distinguished within two ‘sizes’ (micro and macro) and four types (physical,
socio-cultural, economic and political) of environment. Micro-environments are dened
as environmental settings where groups of people meet and gather. Such settings are oen
geographically distinct and allow direct mutual inuences between individuals and the
84 Chapter 4 84 Chapter 4
environment. Examples of micro-environmental settings are homes, schools, and neighbor-
hoods. Macro-environments, on the other hand, include the broader, more anonymous in-
frastructure that may support or hinder health behaviors. Examples of macro-environments
are the town planning, the transport infrastructure, the media and the health-care system.
All studies reviewed herein were required to examine at least one environmental variable
(the independent variable), and this variable needed to be tested for its association with a
measure of PA (the dependent variable), obtained at the individual level.
Data analyses
Due to the great variety of variables and methods drawn from diverse samples, a meta-
analytical review was not possible to conduct. We have therefore adopted the same semi-
quantitative approach outlined by Sallis et al.,[] recently also used by Gorely et al.[], in
a review of the correlates television viewing among youth. An independent sample, i.e. the
smallest independent sub-sample (based on age and gender) for which relevant data was
reported (e.g. studies reporting ndings for boys (M) and girls (F) separately, provide
independent samples) was used as the unit of analyses [].
Study characteristics
e relevant characteristics from all the selected publications listed in the Bibliography sec-
tion were retrieved and registered in detailed tables (which are available upon request from
the corresponding author), according to current review guidelines [, ]. is extensive
information was then summarized in one background table (Table .).
Categorization of variables
Correlates of PA investigated in the studies reviewed were categorized in the ANGELO grid,
i.e. were grouped in environment types (physical, socio-cultural, economic, and political)
for each environmental setting (Micro and Macro) with a further distinction in specic
levels (home, educational institution, neighborhood, city/municipality, region). ese data
was then summarized in two tables providing an overview of the potential determinants of
PA of children and adolescents separately (Tables . and ., respectively).
Coding and summarizing associations with PA
A variety of statistical techniques (e.g. correlations, t-tests, linear or logistic regression
analyses, ANOVA and structured equation models) were used to evaluate the associa-
tions. Most studies not only reported univariate but also multivariate analyses (e.g., with
adjustment for demographic and/or other potential correlates investigated); whenever
possible ndings reported here were those from the fully adjusted models. As with regard
to prospective studies, the associations found within the shortest follow-up period were
Environmental correlates of physical activity 85
Chapter 4
Environmental correlates of physical activity 85
Chapter 4
the ones considered, and the cross-sectional ndings embedded within these studies were
disregarded.
Studies reporting positive (coded as ‘+’) or inverse (coded as ‘-’) association(s) between
the independent variable and PA were registered under the column ‘related to PA’; non-sig-
nicant associations were coded under the column ‘unrelated to PA’ (coded as ‘’). Findings
for each independent variable were summarized by adding the number of associations in a
given direction (+, -, ); a nal summary association code for each correlate examined was
derived as follows: ≥ of the associations in any direction was considered evidence for
a positive (summary code ‘+’), negative (summary code ‘-‘) or non-association (summary
code ‘’); a mixed pattern of associations < (but above ) was considered evidence
for probable but inconsistent association (summary code ‘+?’ or ‘-?’ or ‘?’); a variable that
has been frequently studied (i.e., in ≥ independent samples) but with considerable lack
of consistence was attributed a summary code of two questions marks (??); where ndings
were consistent, the codes ‘++’, ‘- -’ or ‘’ were attributed. Final summary codes were only
computed for variables that have been studied in at least independent samples; otherwise
a ‘non-applicable’ (N/A) summary code was attributed
RESULTS
General characteristics of the studies reviewed (Table 4.1)
We have identied a total of publications that presented an empirical association be-
tween PA and at least one environmental correlate. e vast majority of studies (.)
were published in the last decade (Fig..) and a steep, almost -fold increase in adolescent
studies was noticed in the last years. e overall studies reported data on independent
samples. Sixty-six studies ( independent samples) of children were reviewed, represent-
ing . of the total independent samples; only (.) of those independent samples
included more than , subjects. Eighty-four studies of adolescents ( independent
samples; .) were reviewed ( of which provided also data on children); about one third
included more than , subjects. In both children and adolescents, the vast majority of
the studies used a cross-sectional design reported results for boys and girls separately, relied
on child and/or parental self-reports as method of PA data collection (about half of which
with acceptable reliability/validity), and were mostly conducted in North America. Studies
that used objective methods of PA assessment were in the great majority restricted to stud-
ies among children; direct observation and doubly labeled water assessment were never
used in studies of adolescents.
86 Chapter 4 86 Chapter 4
Potential environmental determinants of children’s PA (Table 4.2)
Potential determinants at the home level
We have identied a total of independent samples investigating the associations between
variables of the home physical environment, namely the amount of cars in the family and
the availability and access of exercise equipment (e.g. PA promoting toys), and PA levels
of children. Both variables were unrelated to children’s PA. Socio-cultural environmental
correlates of children’s PA at the home/family level were the most frequently investigated.
Family structure variables such as single-parent family, household size or number of chil-
dren in the family, dog ownership and level of acculturation to the country of residence,
were unrelated to children’s PA. Modeling of PA from parents, siblings and friends were
extensively examined ( independent samples in total). Studies that have examined the
relationship between children’s PA levels and those of their parents, not disentangling those
of the father from those of the mother, as well as those from other signicant others (e.g.
parents, siblings or friends), found no relevant associations. However, in studies where fa-
ther’s and mother’s PA levels were disentangled from each other, father’s PA levels emerged
as a probable positive correlate (in of the cases), whereas mother’s PA levels were mostly
unrelated to children’s PA. Studies investigating potential familial inuences other that
modeling, namely support, encouragement and PA-related social norms of parents, friends
and signicant others, have also been numerous (a total of independent samples). ese
variables were generally unrelated to children’s PA. e economic environment of children’s
Figure 1.4 Environmental Research framework for weight Gain prevention [67]
Figure 4.1 Distribution of the 150 publications retrieved, by year of publication (1980 to 2004)
0
5
10
15
20
25
30
35
40
45
50
1980-84 1985-89 1990-94 1995-99 2000-04
Year of publication
# of studies
Children Adolescents
Figure 4.1 Distribution of the 150 publications by year of publication (1980-2004).
Environmental correlates of physical activity 87
Chapter 4
Environmental correlates of physical activity 87
Chapter 4
Table 4.1 Child and adolescents studies categorized by sample size, sex, study design, physical activity measurement issues, and country
CHILDREN (3-12 years) ADOLESCENTS (>12-18 years)
Bibliography no. N Samples % Bibliography no. N Samples %
SAMPLE SIZE 91 100 134 100
<100 5F, 15M/F, 25, 32, 39, 66, 71, 82,
89M/F, 111, 113MI,V/FI,II,V, 129,
134M
19 20.9 20I, 23M/F, 24M/F, 26M/F, 30F, 50M, 68, 69, 90M/F,
106F, 113MIV,VI/FVI, 123, 126M, 133M, 138I,II
22 16.4
100-199 10*, 21, 22F, 31*, 42, 46, 51,
58, 75M, 76, 103M/F, 108M/F,
113MII, 124M/F, 131MI/FI, 134F,
150
20 22.0 14, 44M/F, 45M/F, 48II, 49, 50F, 101, 102F, 110II,
113MIII/FIII,IV, 126F, 128, 133F
17 12.7
200-299 13F, 28, 37, 38, 41, 63M/F, 70, 75F,
96, 109, 110I, 112MI/FI, 118
15 16.5 12, 16M/F, 20II, 93F, 102M, 112MII,III/FII,III, 125,
148MIII
12 8.9
300-499 27M/F, 56F, 81, 97, 107M/F, 115,
131MII/FII, 132, 148MI/FI
13 14.3 8M/F, 34M/F, 40F, 43, 67M, 79M/F, 105, 116F, 135, 147,
148MII/FII,III
16 11.9
500-999 56M, 64, 84M/F, 100, 104, 119, 144 8 8.8 4M/F, 7M/F, 17F, 18F, 29, 33, 36, 40M, 47, 48I, 61M/F,
62, 67F, 87F, 99, 145, 149M/F
21 15.7
1,000-2,999 11M/F, 57, 73, 85, 86M/F, 88,
95M/F, 120, 122, 137, 143M/F
15 16.5 1M/F, 3M/F, 6, 35F, 54, 65M/F, 80M/F, 87M, 91F, 94I,II,
114F, 117M/F, 136, 139, 141, 142M/F, 146M/F
25 18.7
3,000-4,999 2M/F, 72, 78, 83, 121, 140 7 5.2
≥5,000 19 1 1.0 9, 52, 53, 55, 59M/F, 60, 74, 77, 92, 98, 127M/F, 130 14 10.5
SEX
Girls only 5F, 13F, 22 3 3.3 17, 18, 30, 35, 91, 93, 106, 114, 116 9 6.7
Boys and girls
combined
10/31, 19, 21, 25, 28, 32, 37, 38, 39,
41, 42, 46, 51, 57, 58, 64, 66, 70, 71,
73, 76, 81, 82, 85, 88, 96, 97, 100,
104, 109, 110I, 111, 115, 118, 119,
120, 122, 129, 132, 137, 144, 150
42 46.2 6, 9, 12, 14, 20I,II, 29, 33, 36, 43, 47, 48I/II, 49, 52, 53,
54, 55, 60, 62, 68, 69, 72, 74, 77, 78, 83, 92, 94I,II, 98,
99, 101, 105, 110II, 121, 123, 125, 128, 130, 135, 136,
138, 139, 140, 141, 145, 147
49 36.6
88 Chapter 4 88 Chapter 4
CHILDREN (3-12 years) ADOLESCENTS (>12-18 years)
Bibliography no. N Samples % Bibliography no. N Samples %
Boys and girls,
separately
11, 15, 27, 56, 63, 75, 84, 86, 89, 95,
103, 107, 108, 112I, 113I,II,V, 124,
131I,II, 134, 143, 148I
46 50.5 1, 2, 3, 4, 7, 8, 16, 23, 24, 26, 34, 40, 44, 45, 50, 59, 61,
65, 67, 79, 80, 87, 90, 102, 112II,III, 113III,IV,VI, 117,
126, 127, 133, 142, 146, 148II,III, 149
76 56.7
STUDY DESIGN
Cross-sectional 5F, 10/31, 13F, 15M/F, 19, 21,
22F, 25, 27M/F, 28, 32, 37, 38, 39,
41, 46, 51, 56M/F, 57, 58, 63M/F,
64, 66, 70, 71, 73, 75M/F, 76, 81,
84M/F, 85, 86M/F, 88, 89M/F,
95M/F, 96, 97, 100, 103M/F, 104,
108M/F, 109, 110I, 111, 112MI/FI,
113MI,II,V/FI,II,V, 115, 119, 120,
122, 124M/F, 129, 131MI,II/FI,II,
132, 134M/F, 143M/F, 144, 150
81 89.0 1M/F, 3M/F, 4M/F, 6, 7M/F, 8M/F, 9, 12, 14, 16M/F,
17F, 18F, 23M/F, 24M/F, 29, 30F, 33, 34M/F, 35F, 36, 43,
44M/F, 47, 48I/II, 49, 50M/F, 52, 53, 54, 55, 59M/F, 60,
61M/F, 62, 65M/F, 67M/F, 68, 69, 72, 74, 77, 78, 79M/F,
80M/F, 83, 87M/F, 90M/F, 91F, 92, 94I,II, 98, 99, 101,
105, 106F, 110II, 112MII,III/FII,III, 113MIII,IV,VI/
FIII,IV,VI, 114F, 116F, 117M/F, 121, 123, 125, 126M/F,
127M/F, 128, 130, 135, 136, 138I,II, 139, 140, 141,
142M/F, 145, 146M/F, 147, 149M/F
115 85.8
Longitudinal (length
of study)
11M/F (1 year), 42(1 year), 82(8
weeks), 107M/F (2 years), 118(1
year), 137(1 year), 148MI/FI (3
years)
10 11.0 2M/F (2,5 years), 20I (1 week), II (9 months), 26M/F
(3 years), 40M/F (1 year), 45M/F (3 years), 93F (8
months), 102M/F (4 months), 133M/F (1 year),
148MII,III/FII,III (3 years)
19 14.2
ASSESSMENT OF
PHYSICAL ACTIVITY
Collection method
Self-report 11M/F, 25, 27M/F, 38, 41, 42, 46,
56M/F, 57, 58, 64, 75M/F, 84M/F,
85, 86M/F, 95M/F, 97, 103M/F,
104, 110I, 115, 118, 119, 120,
124M/F, 132, 137, 143M/F, 144,
148MI/FI
41 45.1 1M/F, 2M/F, 3M/F, 4M/F, 6, 7M/F, 8M/F, 9, 14, 16M/F,
17F, 18F, 20I,II, 26M/F, 30F, 33, 34M/F, 35F, 36, 40M/F,
43, 44M/F, 45M/F, 47, 48I/II, 49, 50M/F, 52, 53, 54, 55,
59M/F, 60, 61M/F, 62, 65M/F, 67M/F, 68, 69, 72, 74, 77,
78, 79M/F, 80M/F, 83, 87M/F, 91F, 92, 93F, 94I,II, 98,
99, 102M/F, 105, 106F, 110II, 114F, 116F, 117M/F, 121,
125, 126M/F, 127M/F, 128, 130, 133M/F, 135, 136, 139,
140, 141, 142M/F, 145, 146M/F, 147, 148MII,III/FII,III,
149M/F
112 83.6
Environmental correlates of physical activity 89
Chapter 4
Environmental correlates of physical activity 89
Chapter 4
CHILDREN (3-12 years) ADOLESCENTS (>12-18 years)
Bibliography no. N Samples % Bibliography no. N Samples %
Parent-report 15M/F, 21, 66, 73, 88, 100,
113MI,II/FI,II, 122, 129, 131MI,II/
FI,II, 150
18 19.8
Composite: self- &
parent-report
19, 76, 112MI/FI 4 4.4 12, 29, 112MII,III/FII,III, 113MIII,IV/FIII,IV 10 7.5
Accelerometer 5F, 13F, 37, 63M/F, 89M/F, 96,
113MV/FV, 134M/F
12 13.2 113MVI,FVI, 123 3 2.2
Direct observation 10/31, 28, 70, 71, 81, 82, 109, 111 8 9.0
Doubly labeled water 51 1 1.1
Self-report &
accelerometer/hear rate
monitor
32, 108M/F 3 3.4 23M/F, 24M/F, 90M/F, 101, 138I,II 9 6.7
Parent-report &
accelerometer
39 1 1.1
Composite: self- &
parent-report &
accelerometer
107M/F 2 2.3
Composite: 2 self-
reports + tness test
22F 1 1.1
Reliability/validity of self- and parent reported methods
Poor or unknown 19, 21, 22F, 27M/F, 32, 38, 46,
56M/F, 57, 58, 66, 73, 88, 100,
103M/F, 118, 122, 129, 131MI,II/
FI,II, 143M/F, 148MI/FI, 150
30 42.9 1M/F, 2M/F, 3M/F, 6, 7M/F, 8M/F, 9, 12, 14, 18F, 23M/F,
24M/F, 29, 34M/F, 36, 40M/F, 43, 44M/F, 45M/F, 54, 55,
60, 62, 65M/F, 67M/F, 68, 69, 72, 74, 77, 78, 87M/F, 92,
94I,II, 98, 105, 106F, 116F, 121, 125, 126M/F, 128, 130,
136, 139, 140, 141, 145, 148MII,III/FII,III
68 51.9
90 Chapter 4 90 Chapter 4
CHILDREN (3-12 years) ADOLESCENTS (>12-18 years)
Bibliography no. N Samples % Bibliography no. N Samples %
Acceptable 11M/F, 15M/F, 25, 39, 41, 42, 64,
75M/F, 76, 84M/F, 85, 86M/F,
95M/F, 97, 104, 107M/F, 108M/F,
110I, 112MI/FI, 113MI,II/FI,II,
115, 119, 120, 124M/F, 132, 137,
144
40 57.1 4M/F, 16M/F, 17F, 20I,II, 26M/F, 30F, 33, 35F, 47, 48I,II,
49, 50M/F, 52, 53, 59M/F, 61M/F, 79M/F, 80M/F, 83,
90M/F, 91F, 93F, 99, 101, 102M/F, 110II, 112MII,III/
FII,III, 113MIII,IV/FIII,IV, 114F, 117M/F, 127M/F,
133M/F, 135, 138I,II, 142M/F, 146M/F, 147, 149M/F
63 48.1
COUNTRY
North America 5F, 10/31, 11M/F, 13F, 21, 22F,
25, 28, 32, 37, 39, 41, 42, 46, 51,
57, 63M/F, 70, 71, 75M/F, 76, 81,
82, 84M/F, 85, 86M/F, 88, 89M/F,
95M/F, 96, 97, 100, 103M/F, 104,
107M/F, 108M/F, 109, 110I, 111,
112MI/FI, 113MI,II,V/FI,II,V,
115, 118, 120, 122, 124M/F, 132,
134M/F, 137, 144
68 74.7 4M/F, 6, 9, 16M/F, 17F, 18F, 20I,II, 26M/F, 29, 30F, 33,
34M/F, 35F, 36, 47, 48I/II, 49, 50M/F, 52, 53, 54, 55,
59M/F, 60, 61M/F, 62, 68, 69, 74, 77, 78, 79M/F, 80M/F,
87M/F, 90M/F, 91F, 92, 93F, 98, 99, 101, 102M/F, 106F,
110II, 112MII,III/FII,III, 113MIII,IV,VI/FIII,IV,VI,
114F, 116F, 117M/F, 121, 123, 125, 126M/F, 127M/F,
128, 133M/F, 135, 149M/F
85 63.4
Europe 38, 56M/F, 64, 66, 73, 119, 129,
143M/F, 148MI/FI
12 13.5 1M/F, 2M/F, 3M/F, 7M/F, 8M/F, 12, 14, 23M/F, 24M/F,
40M/F, 44M/F, 45M/F, 65M/F, 67M/F, 72, 94I,II, 105,
130, 136, 138I,II, 139, 140, 141, 142M/F, 145, 146M/F,
147, 148MII,III/FII,III
47 35.1
Oceania 15M/F, 19, 27M/F, 58, 131MI,II/
FI,II, 150
11 12.4 43, 83 2 1.5
* ese two studies report on the exact same dataset and were therefore considered as one individual sample only (hereaer coded as 10/31);
F, girls only; M/F, boys and girls analysed separately; I,II,III, IV, V, VI, data reported for dierent age sub-groups, separately.
Environmental correlates of physical activity 91
Chapter 4
Environmental correlates of physical activity 91
Chapter 4
home/family in relation to their PA levels was studied in independent samples. Dif-
ferent estimates of family/parental SES were generally unrelated to children’s PA. Finally,
and within the household’s ‘political’ environment, parenting styles were also unrelated to
children’ PA.
Potential determinants at the school level
Aspects of the school environment were studied seldom (most of them only once or twice,
which has not enabled us to calculate a summary association). Only one aspect of the school
political environment – PA policies (i.e., time allowed from free play, time spent outdoors,
and number of eld trips) - was investigated in three or more independent samples, with
of the cases showing a positive association with children’s PA levels.
Potential determinants at the neighborhood level
A total of independent samples have examined associations between environmental
characteristics at the neighborhood levels and PA levels of young children. We have iden-
tied a total of potential correlates of PA at the neighborhood physical environment,
of which studied more than times. Among these, time spent outdoors was consistently
associated with higher PA levels of children, whereas availability and accessibility of PA
programs or facilities, neighborhood safety and neighborhood hazards (e.g., many roads,
no lights crossings, heavy trac, physical disorder and pollution - estimated as perceived
by parents in almost all studies) were consistently unrelated to children’s PA. Aspects of the
social and economic environments were unrelated to children’s PA.
Potential determinants at the city/municipality and region/country level
Only few studies have investigated dierences in PA levels between children living in urban
vs. suburban (only examined twice) and coastal vs. mountainous locations (only examined
once). Whether residence in urban vs. rural regions is associated with children’s PA levels
was undetermined by the available studies. Seasonal ‘eects’ on children’s PA were also
undetermined by the available literature.
Potential environmental determinants of adolescents’ PA (Table 4.3)
Potential determinants at the home level
We have identied a total of independent samples investigating the associations between
variables of the home physical environment, namely the availability and accessibility of
exercise equipment, and PA levels of adolescents; these variables were mostly unrelated
to adolescents’ PA. Socio-cultural environmental correlates of adolescents’ PA at the home/
family level were the most frequently investigated. Family structure variables such as single-
parent family and household size or number of children in the family were unrelated to
92 Chapter 4 92 Chapter 4
Table 4.2 Summary of correlates of physical activity among children (3 to 12 year olds)
Correlate Related to PA Assoc.
(+ or -)
Unrelated to PA # Samples Summary (n)
Biblio. no. Biblio. no. + - 0 Assoc
MICRO ENVIROMENT
Physical
# cars in household 19, 131FI - 131MI,II/FII 5 - 2 3 0
Access/availability of exercise equipment 30F, 124F, 134F + 30F, 81, 97, 109, 124M, 132, 134F, 134M/F, 12 3 - 9 00
Socio-Cultural
Single-parent family 103F, 108M, 113MI + 95M/F, 95M/F, 103M, 107M/F, 108F,
108M/F, 112MI/FI, 113MII,V/FI,II,V
20 3 - 17 00
# household residents/children - 38, 95M/F, 95M/F, 113MI,II,V/FI,II,V 11 - - 11 00
Acculturation (language spoken at home;
lifetime in the county; index)
11M, 95M/F
19, 137
+
-
11F, 11M/F, 13F, 95M/F, 137 12 3 2 7 0
Dog ownership - 131MI,II/FI,II 4 - - 4 0
Parents’ PA 32, 63M, 89M/F, 100, 107M, 111,
112MI, 144, 150
124F
+
.
-
11M/F, 25, 32, 63F, 107F, 108M/F,
108M/F, 112FI, 113MI,II,V/FI,II,V, 124M
29 10 1 18 00
Father’s PA 22F, 38, 39, 39, 46, 89M/F, 95M,
119M/F, 134M, 148MI/FI, 148MI/FI
+ 15M/F, 15M/F, 84M/F, 95M/F, 95F, 97,
110I, 134M/F, 134F
29 15 - 14 +?
Mother’s PA 15F, 38, 39, 39, 95F, 110I, 124F,
134M, 148FI, 148FI
+ 15M/F, 15M, 22F, 84M/F, 89M/F, 95M,
95M/F, 97, 109, 119M/F, 124M, 134M/F,
134F, 148MI, 148MI
31 10 - 21 00
Sibling’s PA 110I + 1 1 - - N/A
Friend’s PA 46 + 97, 134M/F, 134M/F 6 1 - 5 0
PA from signicant others
(parents, siblings, friends)
41 1 - - 1 N/A
Encouragement from parents 71, 82, 95F, 95F, 107M, 144 + 11M/F, 63M/F, 70, 95M, 95M, 95M/F,
95M/F, 107F, 108M/F, 108M/F
22 6 - 16 00
Home/household
Environmental correlates of physical activity 93
Chapter 4
Environmental correlates of physical activity 93
Chapter 4
Correlate Related to PA Assoc.
(+ or -)
Unrelated to PA # Samples Summary (n)
Biblio. no. Biblio. no. + - 0 Assoc
Support (logistic) from parents
(transports child to play, plays with child,
pays fees
5F, 22F, 107M, 107M, 107M,
108M/F, 108M, 144, 144
+ 5F, 22F, 63M/F, 63M/F, 70, 107F, 107F
107F, 108F, 108M/F, 108M/F, 112MI/FI
28 10 1 17 00
Support/encouragement from signicant
others (family, peers, teachers)
112MI/FI, 113MI/FI,V, 115, 115,
120, 124M
+ 41, 97, 97, 109, 113MI,II,V/FI,II,V,
113MII,V/FII, 124F
24 9 - 15 0?
Social norms
(value/enjoyment of PA of signicant
others - parents, siblings, peers)
25, 75M/F, 75M/F, 134M,134M, 150 + 25, 25, 41, 84M/F, 97, 100, 112MI/FI,
112MI/FI, 124M/F, 129, 129, 134F, 134F
25 8 - 17 00
Economic
Parental SES 27F, 27M/F, 32, 72, 88, 95F, 122
58
+
-
27M, 27M/F, 32, 71, 72, 95M, 95M/F, 103,
109, 119, 137
22 8 1 13 0?
Parental occupational status 148FI
19
+
-
123, 148MI/FI, 148MI 6 1 1 4 0
Father occupational status 11M/F, 95M/F, 95M/F 6 - - 6 0
Mother occupational status 11F, 56M/F + 11M, 95M/F, 95M/F 7 2 - 5 0
Parental education 63M, 112MI
108F
+
-
37, 37, 37, 63F, 96, 103, 107M/F, 108M,
38, 46, 108M/F, 112FI, 113MI,II,V/FI,II,V,
137
24 2 1 21 00
Father’s educational level 56M/F, 95F, 148MI, 148MI
57
+
-
95M, 95M/F, 148FI, 148FI 11 5 1 5 ??
Mother’s education level 148MI + 19, 46, 95M/F, 19, 46, 95M/F, 131MI,II/
FI,II, 148FI
12 1 - 11 00
# hours parents work + 95M/F, 108M/F, 108M/F, 150 7 - - 7 0
House owned 19 1 - - 1 N/A
Political
Parenting styles (PA rules, control) 109, 109 - 43, 109, 112MI/FI 6 2 - 4 0
94 Chapter 4 94 Chapter 4
Correlate Related to PA Assoc.
(+ or -)
Unrelated to PA # Samples Summary (n)
Biblio. no. Biblio. no. + - 0 Assoc
Physical
Distance (from home) 150 - 1 - 1 - N/A
Availability of PA equipment 81 1 - - 1 N/A
Socio-Cultural
Teacher’s PA 100 1 - - 1 N/A
Teacher’s attitudes toward PA 100 1 - - 1 N/A
Teachers specic education level 28, 100 + 2 2 - - N/A
Economic
School type attended
(public vs. private; nursery vs. day care)
19, 100 + 2 2 - - N/A
Political
Support from community PA
organizations
28 1 - - 1 N/A
PA related policies
(e.g. time allowed for free play/spent
outside, # eld trips)
28, 81, 96 + 28, 28 5 3 - 2 +
Class size 28 + 1 1 - - N/A
School quality 28 1 - - 1 N/A
Physical
Distance to destinations 58 - 1 - 1 - N/A
Access/availability to PA facilities/
programs
41, 109, 131FII + 5F, 5F, 30F, 30F, 131MI,II/FI,II, 131MI,II/
FI, 113MI,II,V/FI,II,V
20 3 - 17 00
Available shelters/foot path conditions 150, 150 2 - - 2 N/A
Time spent outdoors 10, 70, 81, 109, 109 + 5 5 - - +
Educational Institutions (Schools,…)
Neighbourhood
Environmental correlates of physical activity 95
Chapter 4
Environmental correlates of physical activity 95
Chapter 4
Correlate Related to PA Assoc.
(+ or -)
Unrelated to PA # Samples Summary (n)
Biblio. no. Biblio. no. + - 0 Assoc
Neighbourhood hazards (e.g. many
roads/no lights crossings; heavy trac;
physical disorder; pollution)
123
58, 131MII, 131MII, 131MI
+
-
58, 88, 113MI,II,V/FI,II,V, 150, 131MI/
FI,II, 131MI/FI,II 131MII/FI,II, 150
24 1 4 19 00
Neighbourhood physical disorder 88 1 - - 1 N/A
Limited public transport 131FI,II - 131MI,II 4 2 - 2 ?
Social
Neighbourhood social disorder 88 - 1 1 - - N/A
Involvement in community PA
organizations
132, 134M, 134M + 11M/F, 132, 134F, 134F 8 3 - 5 0
Length of residence in community 30F + 30F 2 1 - 1 N/A
Safety 88 - 5F, 5F, 107M/F, 113MI,II,V/FI,II,V,
131MI,II/FI,II, 150
16 - 1 15 00
Economic
Neighbourhood SES/education level 143F
64, 64
+
-
30F, 30F, 88, 143M, 6 1 2 3 0
MACRO ENVIRONMENT
Physical
Urban vs. suburban 21 - 66 2 1 1 - N/A
Urban vs. rural 27M/F, 56M/F, 66, 72, 72, 118
27M/F, 27M/F
+
-
46, 57, 85, 86M/F 17 8 4 5 ??
Coastal vs. mountains 46 - 1 1 - - N/A
Season (spring, summer) 42, 51, 100, 118
10&31, 118
+
-
21, 37, 37, 37 10 4 2 4 ??
Biblio. no., reference number under the Bibliography section; Assoc., association; +, positive; -, negative; 0, no relation; ?, indeterminate; N/A , summary code not applicable
because the number of independent samples investigating the relationship is below 3; PA, physical activity; M, boys only; F, girls only; SES, social-economic status; studies
with prospective study designs are highlighted in bold.
City/ municipality /Regions
96 Chapter 4 96 Chapter 4
adolescents’ PA as were indicators of acculturation. Modeling of PA from parents, siblings
and friends were extensively examined (in independent samples). Overall, all these
studies found no relevant associations. However, this lack of associations was somewhat
undetermined with regard to father’s PA levels and those from signicant others, since
they were observed in less than of the cases. Studies investigating potential familial
inuences other than modeling were also numerous (a total of independent samples)
but mostly unrelated to adolescents’ PA. However, a trend toward a positive association was
found with regard to general support from signicant others. e relationship between the
economic environment of adolescents’ home/family and their PA levels was examined in
independent samples. Studies in which parental SES was dened as a composite of parent’s
education and income levels/occupational status were generally unrelated to children’s PA.
However, studies in which the specic association between parent’s education levels was
analyzed separately from parent’s occupational status or income level revealed that higher
mother’s education levels and family (per capita) income were positively associated with
PA; occupational status of the household’s head emerged as an undetermined correlate of
PA. With regard to the political environment, parenting styles were unrelated to adolescents’
PA.
Potential determinants at the school level
Similarly to what we have described in children, aspects of the school physical, socio-
cultural, economic or political environment were studied relatively seldom in adolescents.
Regarding the socio-cultural environment, role modeling and support from teachers were
generally unrelated to adolescents’ PA, whereas the existence of problems with (or teasing
from) classmates was undetermined. Finally, the type of school attended, namely high- vs.
vocational school, was positively, whereas the provision of instruction on PA or sport-
related health benets and special Physical Education programs and/or school sports, were
unrelated to adolescents’ PA.
Potential determinants at the neighborhood level
A total of independent samples have examined associations between environmental
characteristics at the neighborhood level and PA levels of adolescents. Although we have
identied a wide range of potential correlates at the physical, socio-cultural and economical
level, only few were examined in more than independent samples. Among these, and
within the physical environment, the availability and/or accessibility of PA equipment or
facilities, was unrelated to PA. Within the socio-cultural environment, crime incidence
(measured objectively) was inversely associated with adolescents’ PA in out of the studies
available, a nding that was at odds with the lack of association between adolescents’ PA
and neighborhood safety estimates perceived by them.
Environmental correlates of physical activity 97
Chapter 4
Environmental correlates of physical activity 97
Chapter 4
Potential determinants at the city/municipality and region/country level
Only few studies have investigated dierences in PA levels between adolescents’ residence
location. Residence in urban vs. rural regions was not associated with adolescents’ PA levels;
seasonal ‘eects’ on adolescents’ PA were undetermined; and exposure to or interest in
sports media was not associated with adolescents’ PA.
DISCUSSION
Overall, the current review of the literature on environmental correlates of PA in children
and adolescents provided us with a broader and more detailed overview of the specic
research performed through the course of the past -years. In the past years in particular
an increased attention to this eld was observed, that may reect a paradigm shi from
intra-personal to ecological conceptual models in the study of health-related behaviors such
as PA.
Updating the previous review: current vs. previous ndings
We have updated the review of Sallis et al. by merging of its original studies (those report-
ing on environmental potential determinants of PA, as dened in the present study) with
additional publications; twenty-three of the additional studies had not been included
in the previous review although they were published within the same period covered by it
(-); interestingly half of those studies ( out of ) were performed in Europe, a
region that may have thus been under-represented in that review. With regard to the main
ndings, a comparative summary between the two reviews is presented on Table .; in
children, time spent outdoors remained a main correlate of children’s PA, although this was
due to the fact that no additional studies in this regard were included in the present review.
e correlates of children and adolescents’ PA that have emerged in the present review dier
considerably from those in the previous review. Overall, we can argue that the additional
publications of which were published in the last years), have thus contributed signi-
cantly to a better understanding of factors associated with the PA behaviors of children and
adolescents, and have led to the identication and addition of new potential determinants
to the body of knowledge in the eld. However, the fact that the associations coded and
summarized in our review were those derived, whenever possible, from multivariate rather
than from univariate analyses may also have contributed to the dierences between the two
reviews. e previous review, which drew exclusively from univariate models may have
thus been somewhat inated (since signicant correlates are generally more abundant in
univariate analyses).
98 Chapter 4 98 Chapter 4
Table 4.3 Summary of correlates of physical activity among adolescents (13 to 18 year olds)
Correlate Related to PA Assoc + or - Unrelated to PA N Samples Summary (%)
Bibliography no. Bibliography no. + - 0 Assoc.
MICRO ENVIRONMENT
Physical
Access/availability of PA
equipment
18F, 18F, 33
24F
+
-
18F, 23M/F, 23M/F, 24M, 24M/F, 26M/F,
93F, 93F 133M/F, 133M/F
20 3 1 16 00
Socio-Cultural
Single-parent family 29, 76, 113MIV
76, 130
+
-
45M/F, 61M/F, 67M/F, 76, 112MII,III/
FII,III, 113MIII,VI/FIII,IV,VI, 128,
142M/F
24 3 2 19 00
# household residents/children 61M/F, 113MIII,IV,VI/FIII,IV,VI,
142M/F, 149M/F
12 - - 12 00
Acculturation (adolescent/
parent born abroad; generation
of residence in country)
45F, 52 + 45M, 52, 53, 116F 6 2 - 4 0
Parents’ PA 33, 54, 98, 99, 142M/F + 17F, 26M/F, 68, 79M/F, 79M/F,
90M/F, 90M/F, 112MII,III/FII,III,
113MIII,IV,VI/FIII,IV,VI, 135, 149M/F
31 6 - 25 00
Father’s PA 23M, 24F, 48I, 49, 98,
105, 110II, 140, 140, 141,
142M/F, 148MII,III
+ 3M/F, 23F, 23M/F, 24M, 24M/F, 48II,
48II, 49, 133M/F, 133M/F, 148FII,III
31 14 - 17 0?
Mother’s PA 3F, 23F, 48I, 49, 98, 106F,
110II, 133F, 142M/F,
148FII,III
+ 3M, 23M, 23M/F, 24M/F, 24M/F, 26M/F,
48II, 48II, 49, 105, 133M, 133M/F, 140,
141, 148MII,III
33 12 - 21 00
Sibling’s activity 3M/F, 98, 99, 110II, 141 23M/F, 23M/F, 24M/F, 24M/F, 110II,
140, 140, 141
18 6 - 12 00
Friend’s PA 24M, 33, 116F, 140, 140 + 17F, 23M/F, 23M/F, 24F, 24M/F, 133M/F,
133M/F, 141, 149M/F
20 5 - 15 00
Home/household
Environmental correlates of physical activity 99
Chapter 4
Environmental correlates of physical activity 99
Chapter 4
Correlate Related to PA Assoc + or - Unrelated to PA N Samples Summary (%)
Bibliography no. Bibliography no. + - 0 Assoc.
PA from signicant others
(parents, friends, other adults)
8M/F, 9, 18F, 24M, 102F,
126M
+ 14, 18F, 18F, 24M/F, 24F, 102M, 126F,
141
16 7 - 9 0?
Support/encouragement from
parents
8M/F, 18F, 18F, 29,
44M, 61M/F, 68, 79M/F,
79M/F, 90F, 112MII,III/
FII,III, 112MII,III,
113FIII, 114F, 114F, 135,
139, 149F
+ 17F, 18F, 18F, 18F, 18F, 44F, 45M/F, 90M,
90M/F, 90M/F, 90M/F, 101, 101, 101,
113MIII,IV,VI/FIV,VI, 112FII,III, 149M
52 26 - 26 ??
Support/encouragement from
friends
44F, 83, 101, 113MIII,VI,
149M
+ 17F, 44M, 101, 113MIV/FIII,IV,VI, 139,
149F
15 6 - 9 00
Support/encouragement from
signicant others
8M/F, 12, 14, 18F, 18F,
24F, 24F 44M/F, 93F,
114F, 114F
+ 18F, 24M, 24M, 60, 93F, 133M/F,
133M/F, 139
18 12 - 10 +?
Social norms (value/enjoyment
of PA of signicant others -
parents, siblings, peers)
9, 26F, 47, 47, 48I/II,
80M/F, 80M/F, 87M,
91F, 91F, 112FIII, 123,
127M/F
+ 8M/F, 16M/F, 16M/F, 17F, 26M, 47,
68, 68, 69, 79M/F, 87F, 112MII,III/FII,
112MII,III/FII,III, 114F, 114F, 123
42 17 - 25 0?
Economic
Parental SES 9, 12, 18F,18F, 121, 145,
147
+ 4M/F, 7M/F, 7M/F, 17F, 18F, 48II, 76, 76,
76, 128
19 7 - 12 00
Occupational status of
household head
45F, 65M/F, 73, 140 + 45M, 67M/F, 140, 141 10 5 - 5 ??
Father’s occupational status 54, 94I, 136 + 2M/F, 94II, 148MII,III/FII,III, 148MII/
FII
12 3 - 9 00
Mother’s occupational status 2M, 94I + 2F, 94II, 136 5 2 - 3 0
Parents’ educational level 74, 77, 112MIII, 117F,
142M/F
+ 61M/F 112MII/FI,II,III, 113MIII,IV,VI/
FIII,IV,VI, 117M
19 6 - 13 00
Father’s educational level 136 + 48II, 148MII,III/FII,III, 148MII/FII 8 1 - 7 0
100 Chapter 4 100 Chapter 4
Correlate Related to PA Assoc + or - Unrelated to PA N Samples Summary (%)
Bibliography no. Bibliography no. + - 0 Assoc.
Mother’s educational level 53, 92, 136 + 48II, 116F 5 3 - 2 +
Family (per capita) income 29, 53, 74, 77, 142M/F + 50M/F, 60, 73 10 6 - 4 ++
# parents working full time 117M + 117F 2 1 - 1 N/A
Adolescent’s paid work/pocket
money
34M/F, 83, 141 + 53, 116F, 125, 149M/F 9 4 - 5 0?
Political
Parenting styles (authoritative;
PA rules)
87M, 117M, 90M - 87F, 90F, 117F, 90M/F, 112MII,III/FII,III 12 3 - 9 00
Physical
School facilities/resources 33 + 1 1 - - N/A
Socio-Cultural
Main teacher’s/coach PA 90M + 90M/F, 90F, 140, 140, 141, 149M/F 9 1 - 8 0
Support from teacher/coach 45M, 83, 149F + 45F, 149M/F, 149M 7 3 - 4 0?
Classmates problems/teasing 45F, 45F -45M, 45M 4 2 - 2 ?
School support 44M/F, 83 3 - - 3 0
Relationship with PE teacher 33 1 - - 1 N/A
Economic
Public vs. private school 34M/F - 2 - 2 - N/A
Political
School type (high school vs.
vocational/alternative)
1M/F, 2M/F, 7M/F, 55,
55, 55, 55
40M
+
-
7M/F, 40F, 55 15 10 1 4 ++
School provide (special) PE
program/sport teams
53 + 36, 133M/F, 133M/F 6 1 - 5 0
Education Institutions (childcare, schools)
Environmental correlates of physical activity 101
Chapter 4
Environmental correlates of physical activity 101
Chapter 4
Neighbourhood
Correlate Related to PA Assoc + or - Unrelated to PA N Samples Summary (%)
Bibliography no. Bibliography no. + - 0 Assoc.
Instruction on sport/health
benets
140 + 140, 141, 141 4 1 - 3 0
Physical
Distance to PA facilities 50M - 50F 2 1 - 1 N/A
Access/availability to PA
equipment/facilities/programs
33, 44M/F, 61M/F,
61M/F, 61F, 61M/F,
113FVI
29, 29
+
-
17F, 23M/F, 23M/F, 24M/F,
24M/F, 61M/F, 90M/F, 112MII,III/
FII,III, 113MIII,IV,VI/FIII,IV,VI,
113MIII,IV,VI/FIII,IV, 125, 149M/F,
61M
45 11 2 32 00
Level of urbanization 74 1 - - 1 0
Dogs unattended 90M/F, 90M/F 4 - - 4 0
Socio-cultural
% married couples 67M/F 2 - - 2 N/A
% youth 67M/F 2 - - 2 N/A
Neighbourhood exercisers 90M/F, 90M/F, 149M/F 6 - - 6 0
Social disorganization 74 1 - - 1 N/A
Ethnic minority concentration 74 1 - - 1 N/A
Crime incidence 50F, 53 - 50M 3 - 2 1 -
Safety 50F - 50M, 90M/F, 90M/F, 113MIII,IV,VI/
FIII,IV,VI, 149M/F
14 - 1 13 00
Economical
SES 74 1 - - 1 N/A
% upper occupational status 67M/F 2 - - 2 N/A
% owner occupied housing 67F + 67M 2 1 - 1 N/A
102 Chapter 4 102 Chapter 4
Correlate Related to PA Assoc + or - Unrelated to PA N Samples Summary (%)
Bibliography no. Bibliography no. + - 0 Assoc.
% dwellings provided by
employer
67M/F 2 - - 2 N/A
% unemployment among
residents
67F - 67M 2 - 1 1 N/A
Length of unemployment 67M/F 2 - - 2 N/A
MACRO ENVIRONMENT
Physical
Urban vs. suburban 67F - 67M 2 1 - 1 N/A
Town size 73 + 1 1 - - N/A
Urban vs. rural 140 + 35F, 53, 140, 141 5 1 - 4 0
Season 20II, 138I 53, 138II 4 2 - 2 ?
Unsuitable weather 20I 125 2 1 - 1 N/A
Socio-cultural
Exposure to/interest in sports
media
62, 62 + 17F, 26M/F 5 2 - 3 0
Wanting to look like media
gures
127M/F + 2 2 - - N/A
Biblio. no., reference number under the Bibliography section; Assoc., association; +, positive; -, negative; 0, no relation; ?, indeterminate; N/A , summary code not applicable
because the number of independent samples investigating the relationship is below 3; PA, physical activity; M, boys only; F, girls only; SES, social-economic status; PE,
physical education; studies with prospective study designs are highlighted in bold.
City/Municipality/Region
Environmental correlates of physical activity 103
Chapter 4
Environmental correlates of physical activity 103
Chapter 4
Home/family correlates of children’s and adolescents PA levels
Characteristics of the home environment, particularly those related to parental inuences,
were by far the most explored in the literature, in both children and adolescents.
Parents as roles models
Research ndings regarding the relationship between PA levels of parents and those of their
children have been mixed. Most of the studies have in fact failed to nd any association.
Nevertheless, fathers appear to be more important role models as compared to mothers,
especially in childhood; fathers’ PA may be related to their child’s PA regardless of their
gender, whereas mothers’ PA appears to be more oen associated with girls’ rather than
boys PA; however, parents’ PA has been generally unrelated to children’s future PA levels (as
could be ascertained by the few prospective studies examining this issue).
In samples of children, parental PA levels were almost always assessed by the parents
themselves (self-reports) whereas in the adolescent samples they were assessed by both
adolescents’ reports (’perceived’ parental PA levels) and parents’ self-reports. It is thus pos-
sible that dierences in the agent reporting on parental PA levels (parent vs. ospring) may
explain some of the lack of associations found. Indeed, there is some evidence that a low
agreement exist between parents and children reports with regard to the levels of parental
PA [] and we have noticed that associations between PA of mothers or fathers and those of
their ospring (adolescents) tended to be more oen positive when the mothers or fathers
reported their own level of PA (Table .).
Parental support, encouragement and beliefs
It has been hypothesized that the support and encouragement parents provide, rather than
their own PA behavior, may inuence the PA behavior of their ospring. In the present
review, these potential inuences could not be clearly found, particularly among children;
however, as many studies have shown parental support to be positively or not to be associ-
ated with PA levels of adolescents. Taken together, these ndings lend some support to the
view that parents may need to be more than just active role models if their child is to lead a
physical active lifestyle [, ]. is is supported by several (school-based) risk-reduction
programs that have included and evaluated (generally positively) parental involvement as a
means to enhance program eectiveness (e.g. e San Diego Family Heart Project []; the
Children and Adolescent Trial for Cardiovascular Health (CATCH I and II) [, ]; e
Minnesota Home Team [, ]).
Parental Socioeconomic status (SES)
Parental/family SES is associated with a wide array of health, cognitive and socio-emotional
outcomes in children, throughout their development from (even before) birth to adulthood
104 Chapter 4 104 Chapter 4
[, ]. In the studies reviewed herein, several measures of SES have been used, most includ-
ing some quantication of family income, parental education and occupational status (or a
combination of these). Mother’s education level and family income emerged as independent
correlates of adolescents’ (but not children’s) PA levels. ese ndings not only emphasize
the need to disentangle such aspects as education, occupational status and income levels,
but suggest also that on reaching adolescence and young adulthood, those who have lower
income may be more restricted in their PA choices and opportunities. In younger children,
PA is mostly of informal nature, and may therefore not involve much extra nancial cost.
Possibly, with increasing age participation in physical activities becomes more elaborate and
nancial costly (e.g. sport clubs fees), which may reduce the likelihood of PA in adolescents
from lower income families []. is needs further investigation.
School inuences on children and adolescents’ PA
Schools oer many opportunities for young people to engage in physical activities, such as
Physical Education classes, recess periods, extracurricular sports or PA programs, leisure
time free use of its playing elds and playgrounds. Schools have also the personnel who,
with sucient training and commitment can dene and deliver PA programs and policies
that support the adoption of healthy lifestyles. e literature showing that well-designed and
well implemented school-based programs can improve PA of young people is paramount
[-] and guidelines for school programs to promote lifelong PA actually exist [-].
Despite this, little research has investigated specic features of the school environment
that impact on youth PA. Indeed, although most studies reviewed herein, have recruited
their target populations from school settings, aspects of the school physical, socio-cultural,
economic or political environment, remained however relatively unexplored. Most of the
Table 4.4 Comparative summary of the main environmental correlates of physical activity in children and
adolescents: earlier vs. current review
Children Adolescents
Previous Review Current review Previous Review Current review
Program /facility
access (+)
Father’s PA (+?) Support from signicant
others (++)
Support from signicant
others (+?)
Time spent outdoors
(+)
School PA-related
policies (+)
Parent support (++) Mother’s education level (+)
Time spent outdoors (+) Sibling PA (++) Family income (++)
Direct help from parents (+) Non-vocational school (++)
Opportunities to exercise
(+)
Neighbourhood crime
incidence (-)
PA, physical activity; +, positive association; -, inverse association; ?, indeterminate.
Environmental correlates of physical activity 105
Chapter 4
Environmental correlates of physical activity 105
Chapter 4
Table 4.5 Analyses of the review ndings regarding the association between physical activity (PA) levels of
parents and their ospring (adolescents) according to the agent reporting on parental PA levels
Association Chi-
squared
(P value)
+ 0
(a) studies examining parental associations (total
of 31 independent samples)
Assessment of parents’ PA
Parent self-report 4
(98, 99, 142M/F)
16
(68, 79 M/F, 79M/F, 112
MII,III/FII,III,
113MIII,IV,VI/FII,III)
0.02
(0.90)
Perceived by the child 2
(33, 54)
9
(17F, 26M/F, 90M/F,
90M/F, 149M/F)
(b) studies examining paternal associations (total
of 31 independent samples)
Assessment of father’s PA
Father’s self-report 7
(98, 105, 110II,
142M/F, 148 MII,III)
4
(3M/F, 148 FII,III)
2.35
(0.13)
Perceived by the child 7
(23M, 24F, 48I, 49,
140, 140, 141)
13
(23F, 23M/F, 24M,
24M/F, 48II, 48II, 49,
133M/F, 133M/F)
(c) studies examining maternal associations
(total of 33 independent samples)
Assessment of mothers’ PA
Mother’s self-report 7
(3F, 98, 110II,
142M/F, 148FII,III)
6
(3M, 26M/F, 105,
148MII,III)
2.83
(0.09)
Perceived by the child 5
(23F, 48I, 49, 106F,
133F)
15
(23M, 23M/F, 24M/F,
24M/F, 48II, 48II, 49,
133M, 133M/F, 140,
141)
Data are number of independent samples (bibligraphy #).
characteristics of the school environment identied were almost never tested in more
than and oen in less than independent samples. Despite this, the present review has
identied ‘school policies related to PA’ to be positively associated with children’s PA and
‘school type’ (i.e. attending high- rather than vocational-schools) to be a positive correlate
of adolescent’s PA.
Additionally, we have identied an interesting set of studies that have investigated PA
levels of classes/groups of youngsters in the context of PE lessons or recess time. One study
106 Chapter 4 106 Chapter 4
found that: classes of children taught by PE specialists (as compared to generalists) received
longer as well as more very active lessons, leading to higher energy expenditure rates;
outdoor lessons generated more time spend in vigorous activities and higher total energy
expenditures than indoor classes []. In another study, school size, length of recess and
the availability of balls in the playground were identied as additional correlates of higher
engagement in physical activities by children []. In adolescents, teacher’s specialty and
gender were not associated with classes PA levels, neither was the location where the lesson
were taught; the only signicant correlates were class size and lesson specic context (tness
activities; free play, game play and skill drills; management time, and knowledge) (inversely
associated with class PA) []. Another study found that, despite the availability of the PA
facilities, they were used by very few students during their leisure time at school (i.e., before
and aer school classes, and lunch break) []. ese ndings were then further explored
and followed by the observation that not only the availability of PA facilities, but its size
and state of conservation, and particularly the existence of supervision/organized activities,
were decisive of adolescents’ engagement in physical activities during their leisure time at
school []. ese ndings and those of the present review, together with the observation
that many schools are not providing enough time for physical activities [, ], emphasize
the important role school’s environments may play in children and adolescents PA levels
[, ]. Further, school-based PA may represent an important equalizing factor for op-
portunities for PA in children and adolescents of dierent SES backgrounds [].
Neighborhood inuences on children and adolescents’ PA
Recently, the importance of neighborhood physical and socio-cultural characteristics in
shaping PA of individuals has been increasingly investigated, but relatively few studies in
the current review had already addressed these possible associations. Among these studies,
features of the physical environment (also commonly referred in the literature as the ‘built
environment’), in particular the availability and accessibility to PA equipment, facilities or
programs were investigated more oen, but were generally unrelated to youth PA. e pres-
ent review identied time spent outdoors to be positively associated with children’s activity
levels; in adolescents, crime incidence, as measured through objective police reports, was
inversely associated with adolescents PA levels, a nding that apparently contrasted with
the lack of association between perceived neighborhood safety levels and adolescents PA.
is contradiction suggests that the dierential associations with youth PA may depend on
the method assessment (perceived vs. objective) of environmental characteristics. Which
features are more important remains unknown, an issue that therefore deserves further
investigation within the same population (see methodological considerations below).
e importance of understanding neighborhood eects on health-related behaviors rely
on their potential to inuence large populations [, ]. Although researchers are start-
Environmental correlates of physical activity 107
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Environmental correlates of physical activity 107
Chapter 4
ing to address the potential eects of communities and neighborhoods in individuals’ PA
behavior, few empirical studies have determined, using appropriate multilevel statistical
techniques, whether relations between the environment and PA actually exist at the neigh-
borhood rather than the individual level [, ].
Methodological considerations
Measurement of PA and environmental characteristics
e selection of the an appropriate instrument depends on the specic research question(s)
to be addressed and on an ‘accuracy-practicality’ trade o ’ [-]. e majority of the
research on the potential determinants of PA reviewed herein relied on (parental or child/
adolescent) self-reports, which included diaries and recall instruments; these methods may
pose serious limitations since they provide less accurate estimates of PA levels than those
obtained by more objective methods such as direct observation, motion sensors, heart rate
monitors, and doubly-labeled water []. In addition, because the degree of the relation-
ship between objectively and self-report measures of PA is only moderate, notably among
self-report methods with ‘acceptable’ validity [], there may be a substantial amount of
variance not shared by the two methods; in other words, dierent instruments (objective
vs. self-report) may have measured dierent aspects of the PA behavior, and therefore those
measures are not interchangeable. As such, the correlates of PA may also dier as a function
of the method used to measure the behavior, thereby impairing the generalization of the
ndings obtained with the use of one or the other method []. In the present review we
were able to identify seven publications ( independent samples – in children and in
adolescents; all with a cross-sectional design) which enable a more close examination of this
issue, by providing self-report and objective data in the same samples (Table .). In these
studies, the magnitude of the associations between the two measures of PA was at the most
moderate. Furthermore, clear discrepancies between correlates of objectively measured and
self-reported PA levels were found. Several factors may explain these discrepancies: the
proposed correlates investigated in each study may have more explanatory power for self-
reported measures (e.g. of vigorous activities) than for total PA levels (mostly computed
by the objective measures); in addition, accelerometers, the most frequently used objective
measure, are unable to access common activities such as bicycling riding and swimming that
could have been (self-) reported, but pick-up incidental physical activities throughout the
day, which in turn could have been forgotten on self-reports that usually refer specically
to intentional physical activities; nally, there may be a shared method variance between
self-reported PA and self-reported potential determinants, which then leads to an inated
association between the two.
Furthermore, self-reports of environmental factors represent perceived rather than ‘real’
features of the physical, socio-cultural, economic and political environments. Little is know
108 Chapter 4 108 Chapter 4
about the accuracy of such perceived features []. In adults, some studies have shown
objective environmental measures to be associated with PA whereas the same features
measured through self-reports where not [, ].
Limitations of study design and data analyses methodologies
e studies incorporated in the present review had mostly a cross-sectional design and
therefore their ndings were limited in that only association could be established and not
prediction or causation. Nevertheless, all those cross-sectional studies have interpreted the
results as if ‘causality’ existed and to be uni-directional (e.g., parents may inuence their
children). It is of course possible that reverse or reciprocal inuences are operative as well
(e.g., children inuence their parents), an issue that needs to be further explored.
In an attempt to disentangle the information provided by prospective from cross-sectional
studies we have highlighted those studies in tables . and .. However, their low number
does not enable solid conclusion with regard to the potential environmental predictors of
PA change.
e main question of how such environmental features inuence youth PA remained
further largely unanswered due to the data analytical methods used. Conceptually, envi-
ronmental inuences can play a direct role in shaping PA behavior or can be mediated by
cognitive processes [-]. In order to understand these mechanistic processes better data
analytical methods (and study designs) are needed (for details see Bauman et al. []).
e majority of the ndings reviewed herein were those that resulted from adjusted models
(most oen, for potential confounders such as age, sex, and ethnicity, but in many studies
for potential mediators such as self-ecacy and attitudes), and thus concern the indepen-
dent contribution of environmental characteristics in the explanation of PA behavior.
Further, although most of the data included in the present review have an intrinsic mul-
tilevel structure, they were most frequently analyzed as obtained in simple random samples
of a single population. As such, the potential inter-dependence within clusters (e.g. schools
and/or neighborhoods) has been ignored, which can have led to inated estimation of the
associations, and multilevel or hierarchical analytic approaches are thus needed.
Limitations of the present review
We acknowledge several limitations of our current review. First, the search terms used to re-
trieve studies from existing databases may have not been sensitive enough. is is sustained
by the fact that almost half of the studies included in this review were found through the
literature sections of articles primarily retrieved in those databases. is may have been due
to the fact that some articles included are simply not registered within those databases, and/
or in many articles retrieved, environmental correlates of children/adolescents’ PA were not
the primarily research goal but were embedded within a broader (i.e. health-enhancing be-
Environmental correlates of physical activity 109
Chapter 4
Environmental correlates of physical activity 109
Chapter 4
haviors in general) or related research question. Nevertheless, better search terms may still
need to be dened. However, the vast amount of studies included suggests we have covered
the existing literature in a quite satisfactory way. Second, the use of only English published
data may have discarded some studies that could have added relevant information into the
eld. ird, the main outcome was any form of PA. In most studies this was measured
across several settings (e.g. the total amount of moderate-to-vigorous PA, performed at
school and during leisure time – either at home or in the neighborhood, or in sport clubs,
accumulated throughout the day or the past week), not enabling us to determine the specic
environmental correlates of specic physical activities. Fourth, the conceptual framework
we have used may have led to disputable categorizations of the correlates of PA investigated.
Conclusions, implications and recommendations
Clearly, many factors inuence the complex behavior of youth PA. We have identied
father’s PA habits, school PA-related policies and time spent outdoors as potential deter-
minants of PA in children; in adolescents, such potential determinants were support from
signicant others, mother’s education levels, family income, attendance of a non-vocational
school and low neighborhood crime incidence have emerged as potential determinants of
adolescents’ PA. ese variables need to be target by multi-level interventions aiming at the
increase of youth PA. e other variables, however, should not be discarded without further
investigation, namely those whose associations with PA were undetermined or not possible
to infer from the limited number of existing studies (particularly those at the neighborhood
and school settings as well as at the macro-environment level).
Future studies that use prospective or intervention designs enabling the analyses of
whether the environment-PA behaviors of children and adolescents associations are casual
and which (if any) cognitive processes may mediate or contextual variables may moderate
such associations, are in great need. In addition, it is important to conduct future research
with clear, possibly standardized denitions and objective methods of environmental at-
tributes and PA behavior assessment, within the strongest study design possible.
110 Chapter 4 110 Chapter 4
Table 4.6 Determinants of objective vs. self-report measured physical activity - summary of ndings
Bibliography
no.
Method of Physical Activity (PA) Assessment Environmental correlates of PA*
Objective Self-report Correlation
between PA
assessed
by the 2
methods
Objective Self-report
23 Heart-rate
monitoring (1
week – time spent
in moderate-to-
vigorous PA, >140
beats min-1)
Recall of PA and
sport participations
(1 week; hours)
‘not
associated’
(estimate size
not reported)
Father’s PA (M) Mother’s PA (F)
24 Heart-rate
monitoring (1
week – time spent
in moderate-to-
vigorous PA, >140
beats min-1)
Recall of PA and
sport participations
(1 week; hours)
‘not
associated’
(estimate size
not reported)
- Father’s PA (F)
Friends’ PA (M)
Parental
encouragement
(F)
Parental support
(F)
Home
equipment (F)
32 Accelerometer (2
week days + 1
weekend day); METs
Frequency, duration
and types of PA
(2 week days +
1 weekend day;
(METs)
r=0.46 Parental PA
Parental SES
-
39 Accelerometer
(2 weekdays + 1
weekend day; counts
d-1)
Frequency, duration
and types of PA
(2 week days +
1 weekend day;
(METs)
r=0.39 (Light
PA)
r=0.35
(moderate-
to-high
intensity PA)
Father’s PA
Mother’s PA
Father’s PA
Mother’s PA
90 Accelerometer (up
to 8 d; counts h-1)
PA Record of hard
and very hard
intensity PA (7 d; h
week-1)
‘Not
associated’
(estimate not
reported)
Teacher’s PA
(M)
PA rules (M)
Parent
transports child
to PA location
(F)
101 Accelerometer (5-
day period; min d-1)
Participation in PA
for ≥60 min (PACE+)
(past week; d week-1)
r=0.46 - Parent support
Peer support
110 Accelerometer
(1 week day + 2
weekend days);
score
Recall checklist of
PA performed for at
least 15 min (1 week
day + 1 weekend day;
score)
?
(Not
reported)
Parental
education (F)
Single-parent
status (M)
Parent
transports child
to PA location
(F)
Parent plays
with child (M)
* Only the environmental variables that were correlated with physical activity levels measured either by one or
the other method are reported (i.e., listed variables do not cover all the variables investigated in each study).
PA, physical activity; M, boys only; F, girls only; SES, socioeconomic status.
Environmental correlates of physical activity 111
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Environmental correlates of physical activity 111
Chapter 4
REFERENCES
. Erlichman J, Kerbey AL, James WP: Physical activity and its impact on health outcomes. Paper : e
impact of physical activity on cardiovascular disease and all-cause mortality: an historical perspec-
tive. Obes Rev , ():-.
. Erlichman J, Kerbey AL, James WP: Physical activity and its impact on health outcomes. Paper :
Prevention of unhealthy weight gain and obesity by physical activity: an analysis of the evidence.
Obes Rev , ():-.
. Strong WB, Malina RM, Blimkie CJ, Daniels SR, Dishman RK, Gutin B, Hergenroeder AC, Must A,
Nixon PA, Pivarnik JM et al: Evidence based physical ativity for school-age youth. J Pediatr ,
:-.
. WHO: Global Strategy on Diet, Physical Activity and Health. Geneva: World Health Organization;
.
. Children and young people - the importance of physical activity [http://www.ehnheart.org]
. Goran MI, Reynolds KD, Lindquist CH: Role of physical activity in the prevention of obesity in
children. Int J Obes Relat Metab Disord , Suppl :S-.
. Kohn M, Booth M: e worldwide epidemic of obesity in adolescents. Adolesc Med , ():-.
. Lobstein T, Baur L, Uauy R: Obesity in children and young people: a crisis in public health. Obes Rev
, Suppl :-.
. Steinbeck KS: e importance of physical activity in the prevention of overweight and obesity in
childhood: a review and an opinion. Obes Rev , ():-.
. Johnson RK: Changing eating and physical activity patterns of US children. Proc Nutr Soc ,
():-.
. Services USDoHaH: Physical Activity and Health: a report of the Surgeon General. Atlanta, GA:
Centers for Disease Control and Prevention; .
. WHO: Health behaviour in school-aged children: A WHO cross-national study (HBSC). Geneva:
International Report World Health Organization Regional Oce for Europe; .
. Sallis JF: Age-related decline in physical activity: a synthesis of human and animal studies. Med Sci
Sports Exerc , ():-.
. Caspersen CJ, Pereira MA, Curran KM: Changes in physical activity patterns in the United States, by
sex and cross-sectional age. Med Sci Sports Exerc , ():-.
. Aaron DJ, Storti KL, Robertson RJ, Kriska AM, LaPorte RE: Longitudinal study of the number and
choice of leisure time physical activities from mid to late adolescence: implications for school cur-
ricula and community recreation programs. Arch Pediatr Adolesc Med , ():-.
. Malina RM: Tracking of physical activity and physical tness across the lifespan. Res Q Exerc Sport
, ( Suppl):S-.
. Gordon-Larsen P, Nelson MC, Popkin BM: Longitudinal physical activity and sedentary behavior
trends: adolescence to adulthood. Am J Prev Med , ():-.
. Telama R, Leskinen E, Yang X: Stability of habitual physical activity and sport participation: a longi-
tudinal tracking study. Scand J Med Sci Sports , :-.
. Twisk JW, Kemper HC, van Mechelen W: Tracking of activity and tness and the relationship with
cardiovascular disease risk factors. Med Sci Sports Exerc , ():-.
. Raitakari OT, Porkka KVK, Taimela S, Telama R, Rasanen L, Viikari JSA: Eects of persistent physi-
cal activity and inactivity on coronary risk factors in children an young adults. e Cardiovascular
Risk in Young Finns Study. Am J Epidemiol , ():-.
112 Chapter 4 112 Chapter 4
. WHO: Obesity: Preventing and Managing the Global Epidemic - Report of a World Health Organiza-
tion Consultation on Obesity. Geneva; .
. Booth KM, Pinkston MM, Poston WS: Obesity and the built environment. J Am Diet Assoc ,
( Suppl ):S-.
. Hill JO, Wyatt HR, Reed GW, Peters JC: Obesity and the environment: where do we go from here?
Science , ():-.
. Hill JO, Peters JC: Environmental contributions to the obesity epidemic. Science , ():-
.
. Jeery RW, Utter J: e changing environment and population obesity in the United States. Obes Res
, Suppl:S-S.
. Peters JC, Wyatt HR, Donahoo WT, Hill JO: From instinct to intellect: the challenge of maintaining
healthy weight in the modern world. Obes Rev , ():-.
. Booth ML, Macaskill P, Lazarus R, Baur LA: Sociodemographic distribution of measures of body
fatness among children and adolescents in New South Wales, Australia. Int J Obes Relat Metab Disord
, ():-.
. Dowda M, Ainsworth BE, Addy CL, Saunders R, Riner W: Environmental inuences, physical activ-
ity, and weight status in - to -year-olds. Arch Pediatr Adolesc Med , ():-.
. Strauss RS, Knight J: Inuence of the home environment on the development of obesity in children.
Pediatrics , ():e.
. Timperio A, Crawford D, Telford A, Salmon J: Perceptions about the local neighborhood and walk-
ing and cycling among children. Prev Med , ():-.
. Trost SG, Kerr LM, Ward DS, Pate RR: Physical activity and determinants of physical activity in obese
and non-obese children. Int J Obes Relat Metab Disord , ():-.
. Ritchie LD, Welk G, Styne D, Gerstein DE, Crawford PB: Family environment and pediatric over-
weight: what is a parent to do? J Am Diet Assoc , ( Suppl ):S-.
. Ball K, Crawford D: e obesity epidemic: Contextual inuences on physical activity and body
weight. , ():-.
. Baranowski T, Mendlein J, Resnicow K, Frank E, Cullen KW, Baranowski J: Physical activity and
nutrition in children and youth: an overview of obesity prevention. Prev Med , :S-S.
. Egger G, Swinburn B: An “ecological” approach to the obesity pandemic. Bmj , ():-
.
. French SA, Story M, Jeery RW: Environmental Inuences on eating and physical activity. Annu Rev
Public Health , :-.
. Nestle M, Jacobson MF: Halting the obesity epidemic: a public health policy approach. Public Health
Rep , ():-.
. Story M, Neumark-Sztainer D, French S: Individual and environmental inuences on adolescent
eating behaviors. J Am Diet Assoc , ( Suppl):S-.
. Nutbeam D, Aar L, Catford J: Understanding childrens’ health behaviour: the implications for health
promotion for young people. Soc Sci Med , ():-.
. Kohl III HW, Hobbs KE: Development of physical activity behaviors among children and adolescents.
Pediatrics , ( Pt ):-.
. Richter KP, Harris JO, Paine-Andrews A, Fawcett SB, Schmid TL, Lankenau BH, HJohnston J:
Measuring the health environment for physical activity and nutritions among youth: a review of the
literature and applications for community initiatives. Prev Med , :S-S.
Environmental correlates of physical activity 113
Chapter 4
Environmental correlates of physical activity 113
Chapter 4
. Sallis JF, Simons-Morton BG, Stone EJ, Corbin CB, Epstein LH, Faucette N, Iannotti RJ, Killen JD,
Klesges RC, Petray CK et al: Determinants of physical activity and interventions in youth. Med Sci
Sports Exerc , ( Suppl):S-.
. Brug J, Oenema A, Ferreira I: eory, evidence and Intervention Mapping to improve behavioral
nutrition and physical activity interventions. Int J Behav Nutr Phys Act , :.
. Baranowski T, Cullen KW, Nicklas T, ompson D, Baranowski J: Are current health behavioral
change models helpful in guiding prevention of weight gain eorts? Obes Res , Suppl:S-S.
. Sallis JF, Prochaska JJ, Taylor WC: A review of correlates of physical activity of children and adoles-
cents. Med Sci Sports Exerc , ():-.
. Howley ET: Type of activity: resistance, aerobic and leisure versus occupational physical activity. Med
Sci Sports Exerc , ( Suppl):S-; discussion S-.
. Biddle SJ, Gorely T, Marshall SJ, Murdey I, Cameron N: Physical activity and sedentary behaviours in
youth: issues and controversies. J R Soc Health , ():-.
. Gordon-Larsen P, McMurray RG, Popkin BM: Determinants of adolescent physical activity and
inactivity patterns. Pediatrics , ():E.
. Lindquist CH, Reynolds KD, Goran MI: Sociocultural determinants of physical activity among
children. Prev Med , ():-.
. Owen N, Leslie E, Salmon J, Fotheringham MJ: Environmental determinants of physical activity and
sedentary behavior. Exerc Sport Sci Rev , ():-.
. Schmitz KH, Lytle LA, Phillips GA, Murray DM, Birnbaum AS, Kubik MY: Psychosocial correlates of
physical activity and sedentary leisure habits in young adolescents: the Teens Eating for Energy and
Nutrition at School study. Prev Med , ():-.
. Gorely T, Marshall SJ, Biddle SJ: Couch kids: correlates of television viewing among youth. Int J Behav
Med , ():-.
. Flay BR, Petraitis J: e theory of triadic inuence: a new theory of health behavior with implications
for preventive inter ventions. Advances in Medical Sociology , :-.
. Kumanyika S, Jeery RW, Morabia A, Ritenbaugh C, Antipatis VJ: Obesity prevention: the case for
action. Int J Obes Relat Metab Disord , ():-.
. Swinburn B, Egger G, Raza F: Dissecting obesogenic environments: the development and application
of a framework for identifying and prioritizing environmental interventions for obesity. Prev Med
, ( Pt ):-.
. Cooper H: Synthesizing research: a guide for literature reviews. rd ed edition. London: Sage; .
. Pocock SJ, Collier TJ, Dandreo KJ, de Stavola BL, Goldman MB, Kalish LA, Kasten LE, McCor-
mack VA: Issues in the reporting of epidemiological studies: a survey of recent practice. Bmj ,
():.
. Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, Moher D, Becker BJ, Sipe TA,
acker SB: Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-
analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA , ():-.
. Anderssen N, Jacobs DR, Jr., Aas H, Jakobsen R: Do adolescents and parents report each other’s
physical activity accurately? Scand J Med Sci Sports , ():-.
. Norton DE, Froelicher ES, Waters CM, Carrieri-Kohlman V: Parental inuence on models of primary
prevention of cardiovascular disease in children. Eur J Cardiovasc Nurs , ():-.
. Nader PR, Sallis JF, Patterson TL, Abramson IS, Rupp JW, Senn KL, Atkins CJ, Roppe BE, Morris
JA, Wallace JP et al: A family approach to cardiovascular risk reduction: results from the San Diego
Family Health Project. Health Educ Q , ():-.
114 Chapter 4 114 Chapter 4
. Luepker RV, Perry CL, McKinlay SM, Nader PR, Parcel GS, Stone EJ, Webber LS, Elder JP, Feldman
HA, Johnson CC et al: Outcomes of a eld trial to improve children’s dietary patterns and physical
activity. e Child and Adolescent Trial for Cardiovascular Health. CATCH collaborative group.
JAMA , ():-.
. Nader PR, Sellers DE, Johnson CC, Perry CL, Stone EJ, Cook KC, Bebchuk J, Luepker RV: e eect
of adult participation in a school-based family inter vention to improve Children’s diet and physical
activity: the Child and Adolescent Trial for Cardiovascular Health. Prev Med , ():-.
. Perry CL, Luepker RV, Murray DM, Kurth C, Mullis R, Crockett S, Jacobs DR, Jr.: Parent involvement
with children’s health promotion: the Minnesota Home Team. Am J Public Health , ():-
.
. Perry CL, Luepker RV, Murray DM, Hearn MD, Halper A, Dudovitz B, Maile MC, Smyth M: Parent
involvement with children’s health promotion: a one-year follow-up of the Minnesota home team.
Health Educ Q , ():-.
. Bradley RH, Corwyn RF: Socioeconomic status and child development. Annu Rev Psychol ,
:-.
. Evans GW: e environment of childhood poverty. Am Psychol , ():-.
. Shropshire J, Carrol B: Family variables and children’s physical activity: inuence of parental exercise
and socio-economic status. Sport Educ Soc , ():-.
. Timperio A, Salmon J, Ball K: Evidence-based strategies to promote physical activity among children,
adolescents and young adults: review and update. J Sci Med Sport , ( Suppl):-.
. Matson-Koman DM, Brownstein JN, Neiner JA, Greaney ML: A site-specic literature review of
policy and environmental interventions that promote physical activity and nutrition for cardiovascu-
lar health: what works? Am J Health Promot , ():-.
. Stone EJ, McKenzie TL, Welk GJ, Booth ML: Eects of physical activity interventions in youth.
Review and synthesis. Am J Prev Med , ():-.
. Prevention. CfDCa: Guidelines for school and community programs to promote lifelong physical
activity among young people. MMWR , :(N. RR-).
. Story M: School-based approaches for preventing and treating obesity. Int J Obes Relat Metab Disord
, Suppl :S-.
. Wechsler H, Devereaux RS, Davis M, Collins J: Using the school environment to promote physical
activity and healthy eating. Prev Med , :S-S.
. McKenzie TL, Feldman H, Woods SE, Romero KA, Dahlstrom V, Stone EJ, Strikmiller PK, Williston
JM, Harsha DW: Children’s activity levels and lesson context during third-grade physical education.
Res Q Exerc Sport , ():-.
. Zask A, van Beurden E, Barnett L, Brooks LO, Dietricht UC: Active school playgrounds - myth or
reality? Results of the “Move it groove it” project. Prev Med , :-.
. McKenzie TL, Marshall SJ, Sallis JF, Conway TL: Student activity levels, lesson context, and teacher
behavior during middle school physical education. Res Q Exerc Sport , ():-.
. McKenzie TL, Marshall SJ, Sallis JF, Conway TL: Leisure-time physical activity in school environ-
ments: an observational study using SOPLAY. Prev Med , ():-.
. Sallis JF, Conway TL, Prochaska JJ, McKenzie TL, Marshall SJ, Brown M: e association of school
environments with youth physical activity. Am J Public Health , ():-.
. Simons-Morton BG, Taylor WC, Snider SA, Huang IW: e physical activity of h-grade students
during physical education classes. Am J Public Health , ():-.
Environmental correlates of physical activity 115
Chapter 4
Environmental correlates of physical activity 115
Chapter 4
. Simons-Morton BG, Taylor WC, Snider SA, Huang IW, Fulton JE: Observed levels of elementary and
middle school children’s physical activity during physical education classes. Prev Med , ():-
.
. McKenzie TL: Promoting physical activty in youth: focus on middle school environments. Quest
, :-.
. Kristjansdottir G, Vilhjalmsson R: Sociodemographic dierences in patterns of sedentary and physi-
cally active behavior in older children and adolescents. Acta Paediatr , ():-.
. King AC, Jeery RW, Fridinger F, Dusenbury L, Provence S, Hedlund SA, Spangler K: Environmental
and policy approaches to cardiovascular disease prevention through physical activity: issues and
opportunities. Health Educ Q , ():-.
. Sallis JF, Johnson MF, Calfas KJ, Caparosa S, Nichols JF: Assessing perceived physical environmental
variables that may inuence physical activity. Res Q Exerc Sport , ():-.
. Braza M, Shoemaker W, Seeley A: Neighborhood design and rates of walking and biking to elemen-
tary school in California communities. Am J Health Promot , ():-.
. Duncan SC, Duncan TE, Strycker LA, Chaumeton NR: Neighborhood physical activity opportunity:
a multilevel contextual model. Res Q Exerc Sport , ():-.
. Pate RR: Physical activity assessment in children and adolescents. Crit Rev Food Sci Nutr , (-
):-.
. Welk GJ, Corbin CB, Dale D: Measurement issues in the assessment of physical activity in children.
Res Q Exerc Sport , ( Suppl):S-.
. Schutz Y, Weinsier RL, Hunter GR: Assessment of free-living physical activity in humans: an over-
view of currently available and proposed new measures. Obes Res , ():-.
. Janz KF, Witt J, Mahoney LT: e stability of children’s physical activity as measured by accelerometry
and self-report. Med Sci Sports Exerc , ():-.
. Kohl III HW, Fulton JE, Caspersen CJ: Assessment of physical activity among children and adoles-
cents: a review and synthesis. Prev Med , ():S-S.
. Dishman RK, Darracott CR, Lambert LT: Failure to generalize determinants of self-reported physical
activity to a motion sensor. Med Sci Sports Exerc , ():-.
. Kirtland KA, Porter DE, Addy CL, Neet MJ, Williams JE, Sharpe PA, Ne LJ, Kimsey CD, Jr., Ain-
sworth BE: Environmental measures of physical activity supports: perception versus reality. Am J
Prev Med , ():-.
. Sallis JF, Hovell MF, Hofstetter CR, Elder JP, Hackley M, Caspersen CJ, Powell KE: Distance between
homes and exercise facilities related to frequency of exercise among San Diego residents. Public
Health Rep , ():-.
. Hoehner CM, Brennan Ramirez LK, Elliott MB, Handy SL, Brownson RC: Perceived and objective
environmental measures and physical activity among urban adults. Am J Prev Med , ( Suppl
):-.
. Owen N, Humpel N, Leslie E, Bauman A, Sallis JF: Understanding environmental inuences on
walking; Review and research agenda. Am J Prev Med , ():-.
. Lewis BA, Marcus BH, Pate RR, Dunn AL: Psychosocial mediators of physical activity behavior
among adults and children. Am J Prev Med , ( Suppl):-.
. Bargh J, Chartrand T: e unbearable automaticity of being. Am Psychol , :-.
. Bauman AE, Sallis JF, Dzewaltowski DA, Owen N: Toward a better understanding of the inuences
on physical activity: the role of determinants, correlates, causal variables, mediators, moderators, and
confounders. Am J Prev Med , ( Suppl):-.
116 Chapter 4 116 Chapter 4
BIBLIOGRAPHY
* indicates studies included in the previous review of Sallis et al. (2000).
.* Aarnio M, Kujala UM, Kaprio J. Associations of health-related behaviors, school type and health
status to physical activity patterns in year old boys and girls. Scand J Soc Med ; :-.
. Aarnio M, Winter T, Kujala U, Kaprio J. Associations of health related behaviour, social relationships,
and health status with persistent physical activity and inactivity: a study of Finnish adolescent twins.
Br J Sports Med ; :-.
. Aarnio M, Winter T, Kujala UM, Kaprio J. Familial aggregation of leisure-time physical activity -- a
three generation study. Int J Sports Med ; :-.
.* Aaron DJ, Kriska AM, Dearwater SR, Anderson RL, Olsen TL, Cauley JA, Laporte RE. e epidemi-
ology of leisure physical activity in an adolescent population. Med Sci Sports Exerc ; :-.
. Adkins S, Sherwood NE, Story M, Davis M. Physical activity among African-American girls: the role
of parents and the home environment. Obes Res ; :S-S.
. Allison KR, Dwyer JJ, Makin S. Perceived barriers to physical activity among high school students.
Prev Med ; :-.
. Andersen LB, Schelin B. Physical activity and performance in a random sample of adolescents at-
tending school in Denmark. Scand J Med Sci Sports ; :-.
.* Anderssen N, Wold B. Parental and peer inuences on leisure-time physical activity in young adoles-
cents. Res Q Exerc Sport ; :-.
. Anthsel KM, Anderman EM. Social inuences on sports participation during adolescence. J Res Dev
Educ ; :-.
.* Baranowski T, ompson WO, DuRant RH, Baranowski J, Puhl J. Observations on physical activity
in physical locations: age, gender, ethnicity, and month eects. Res Q Exerc Sport ; :-.
. Barnett TA, O’Loughlin J, Paradis G. One- and two-year predictors of decline in physical activity
among inner-city schoolchildren. Am J Prev Med ; :-.
. Baxter-Jones AD, Maulli N. Parental inuence on sport participation in elite young athletes. J Sports
Med Phys Fitness ; :-.
. Beech BM, Kumanyika SK, Baranowski T, Davis M, Robinson TN, Sherwood NE, Taylor WC, Relyea
G, Zhou A, Pratt C, Owens A, ompson NS. Parental cultural perspectives in relation to weight-
related behaviors and concerns of African-American girls. Obes Res ; Suppl:S-S.
.* Biddle S, Goudas M. Analysis of children’s physical activity and its association with adult encourage-
ment and social cognitive variables. J Sch Health ; :-.
. Bogaert N, Steinbeck KS, Baur LA, Brock K, Bermingham MA. Food, activity and family - environ-
mental vs. biochemical predictors of weight gain in children. Eur J Clin Nutr ; :-.
. Bungum T, Dowda M, Weston A, Trost SG, Pate RR. Correlates of physical activity in male and
female youth. Pediatr Exerc Sci ; :-.
.* Bungum TJ, Vincent ML. Determinants of physical activity among female adolescents. Am J Prev
Med ; :-.
.* Butcher J. Socialization of adolescent girls into physical activity. Adolescence ; :-.
. Carlin JB, Stevenson MR, Roberts I, Bennett CM, Gelman A, Nolan T. Walking to school and trac
exposure in Australian children. Aust N Z J Public Health ; :-.
.* Crocker PR, Bailey DA, Faulkner RA, Kowalski KC, McGrath R. Measuring general levels of physical
activity: preliminary evidence for the Physical Activity Questionnaire for Older Children. Med Sci
Sports Exerc ; :-.
Environmental correlates of physical activity 117
Chapter 4
Environmental correlates of physical activity 117
Chapter 4
. Damore DT. Preschool and school age activities: comparison of urban and suburban populations. J
Community Health ; :-.
. Davison KK, Cutting TM, Birch LL. Parents’ activity-related parenting practices predict girls’ physi-
cal activity. Med Sci Sports Exerc ; :-.
. Deandre A, Lorant J, Gavarry O, Falgairette G. Determinants of physical activity and physical and
sports activities in French school children. Percept Mot Skills ; :-.
. Deandre A, Lorant J, Gavarry O, Falgairette G. Physical activity and sport involvement in French
high school students. Percept Mot Skills ; :-.
.* Dempsey JM, Kimiecik JC, Horn TS. Parental inuence on children’s moderate to vigorous physical
activity participation: an expectancy-value approach. Pediatr Exerc Sci ; :-.
.* DiLorenzo TM, Stucky-Ropp RC, Vander Wal JS, Gotham HJ. Determinants of exercise among
children. II. A longitudinal analysis. Prev Med ; :-.
. Dollman J, Norton K, Tucker G. Anthropometry, tness and physical activity of urban and rural
south Australian children. Pediatr Exerc Sci ; :-.
. Dowda M, Pate RR, Trost SG, Almeida M, Sirard JR. Inuences of preschool policies and practices on
children’s physical activity. J Community Health ; :-.
. Duncan SC, Duncan TE, Strycker LA, Chaumeton NR. A multilevel analysis of sibling physical activ-
ity. J Sport Exerc Psychol ; :-.
. Dunton GF, Jamner MS, Cooper DM. Assessing the perceived environment among minimally active
adolescent girls: validity and relations to physical activity outcomes. Am J Health Promot ; :-
.
.* DuRant RH, Baranowski T, Johnson M, ompson WO. e relationship among television watching,
physical activity, and body composition of young children. Pediatrics ; :-.
.* Epstein LH, Paluch RA, Coleman KJ, Vito D, Anderson K. Determinants of physical activity in obese
children assessed by accelerometer and self-report. Med Sci Sports Exerc ; :-.
. Fein AJ, Plotniko RC, Wild T, Spence JC. Perceived environment and physical activity in youth. Int
J Behav Med ; :-.
. Feldman DE, Barnett T, Shrier I, Rossignol M, Abenhaim L. Is physical activity dierentially associ-
ated with dierent types of sedentary pursuits? Arch Pediatr Adolesc Med ; :-.
. Felton GM, Dowda M, Ward DS, Dishman RK, Trost SG, Saunders R, Pate RR. Dierences in physical
activity between black and white girls living in rural and urban areas. J Sch Health ; :-.
.* Ferguson KJ, Yesalis CE, Pomrehn PR, Kirkpatrick MB. Attitudes, knowledge, and beliefs as predic-
tors of exercise intent and behavior in schoolchildren. J Sch Health ; :-.
. Finn K, Johannsen N, Specker B. Factors associated with physical activity in preschool children. J
Pediatr ; :-.
. Fogelholm M, Nuutinen O, Pasanen M, Myohanen E, Saatela T. Parent-child relationship of physical
activity patterns and obesity. Int J Obes Relat Metab Disord ; :-.
.* Freedson PS, Evenson S. Familial aggregation in physical activity. Res Q Exerc Sport ; :-.
.* Fuchs R, Powell KE, Semmer NK, Dwyer JH, Lippert P, Homeister H. Patterns of physical activity
among German adolescents: the Berlin-Bremen Study. Prev Med ; :-.
.* Garcia AW, Broda MA, Frenn M, Coviak C, Pender NJ, Ronis DL. Gender and developmental dif-
ferences in exercise beliefs among youth and prediction of their exercise behavior. J Sch Health ;
:-.
.* Garcia AW, Pender NJ, Antonakos CL, Ronis DL. Changes in physical activity beliefs and behaviors
of boys and girls across the transition to junior high school. J Adolesc Health ; :-.
118 Chapter 4 118 Chapter 4
. Garton AF, Harvey R, Price C. Inuence of perceived family environment on adolescent leisure
participation. Aust J Psychol ; :-.
.* Gentle P, Caves R, Armstrong N, Balding J, Kirby B. High and low exercisers among - and -year-
old children. J Public Health Med ; :-.
. Gillander Gadin K, Hammarstrom A. Can school-related factors predict future health behavior
among young adolescents? Public Health ; :-.
. Gilmer MJ, Harrell JS, Miles MS, Hepworth JT. Youth characteristics and contextual variables in-
uencing physical activity in young adolescents of parents with premature coronary heart disease. J
Pediatr Nurs ; :-.
.* Godin G, Shephard RJ. Normative beliefs of school children concerning regular exercise. J Sch Health
; :-.
.* Godin G, Shephard RJ. Psychosocial factors inuencing intentions to exercise of young students from
grades to . Res Q Exerc Sport ; :-.
.* Godin G, Shephard RJ, Colantonio A. Children’s perception of parental exercise: inuence of sex and
age. Percept Mot Skills ; :-.
. Gomez JE, Johnson BA, Selva M, Sallis JF. Violent crime and outdoor physical activity among inner-
city youth. Prev Med ; :-.
. Goran MI, Nagy TR, Gower BA, Mazariegos M, Solomons N, Hood V, Johnson R. Inuence of sex,
seasonality, ethnicity, and geographic location on the components of total energy expenditure in
young children: implications for energy requirements. Am J Clin Nutr ; :-.
. Gordon-Larsen P, Harris KM, Ward DS, Popkin BM. Acculturation and overweight-related behaviors
among Hispanic immigrants to the US: the National Longitudinal Study of Adolescent Health. Soc Sci
Med ; :-.
. Gordon-Larsen P, McMurray RG, Popkin BM. Determinants of adolescent physical activity and
inactivity patterns. Pediatrics ; :E.
.* Gottlieb NH, Chen MS. Sociocultural correlates of childhood sporting activities: their implications
for heart health. Soc Sci Med ; :-.
. Grunbaum JA, Lowry R, Kann L. Prevalence of health-related behaviors among alternative high
school students as compared with students attending regular high schools. J Adolesc Health ;
:-.
.* Guillaume M, Lapidus L, Bjorntorp P, Lambert A. Physical activity, obesity, and cardiovascular risk
factors in children. e Belgian Luxembourg Child Study II. Obes Res ; :-.
. Harrell JS, Gansky SA, Bradley CB, McMurray RG. Leisure time activities of elementary school
children. Nurs Res ; :-.
. Harten N, Olds T. Patterns of active transport in - year old Australian children. Aust N Z J Public
Health ; :-.
. Heath GW, Pratt M, Warren CW, Kann L. Physical activity patterns in American high school students.
Results from the Youth Risk Behavior Survey. Arch Pediatr Adolesc Med ; :-.
. Higgins JW, Gaul C, Gibbons S, Van Gyn G. Factors inuencing physical activity levels among Cana-
dian youth. Can J Public Health ; :-.
. Hoefer WR, McKenzie TL, Sallis JF, Marshall SJ, Conway TL. Parental provision of transportation for
adolescent physical activity. Am J Prev Med ; :-.
. Hofstetter RC, Hovell MF, Sallis JF, Zakarian J, Beirich H, Mulvihill M, Emerson J. Exposure to sports
mass media and physical activity characteristics among ethnically diverse adolescents. Med Exerc
Nutr Health ; :-.
Environmental correlates of physical activity 119
Chapter 4
Environmental correlates of physical activity 119
Chapter 4
.* Hovell MF, Kolody B, Sallis JF, Black DR. Parent support, physical activity, and correlates of adiposity
in nine year olds: an exploratory study. J Health Educ ; :-.
. Hussey J, Gormley J, Bell C. Physical activity in Dublin children aged - years. Br J Sports Med ;
:-; discussion .
. Huurre T, Aro H, Rahkonen O. Well-being and health behaviour by parental socioeconomic status:
a follow-up study of adolescents aged until age years. Soc Psychiatry Psychiatr Epidemiol ;
:-.
. Johansson B, Drott P. Informal parental trac education and children’s bicycling behaviour. Ups J
Med Sci ; :-.
. Karvonen S, Rimpela AH. Urban small area variation in adolescents’ health behaviour. Soc Sci Med
; :-.
. Kimiecik JC, Horn TS. Parental beliefs and children’s moderate-to-vigorous physical activity. Res Q
Exerc Sport ; :-.
.* Kimiecik JC, Horn TS, Shurin CS. Relationships among children’s beliefs, perceptions of their par-
ents’ beliefs, and their moderate-to-vigorous physical activity. Res Q Exerc Sport ; :-.
.* Klesges RC, Eck LH, Hanson CL, Haddock C, et al. Eects of obesity, social interactions, and physical
environment on physical activity in preschoolers. Health Psychol ; :-.
.* Klesges RC, Malott JM, Boschee PF, Weber JM. e eects of parental inuences on children’s food
intake, physical activity, and relative weight. Int J Eat Disord ; :-.
. Kristjansdottir G, Vilhjalmsson R. Sociodemographic dierences in patterns of sedentary and physi-
cally active behavior in older children and adolescents. Acta Paediatr ; :-.
. Lasheras L, Aznar S, Merino B, Lopez EG. Factors associated with physical activity among Spanish
youth through the National Health Survey. Prev Med ; :-.
. Lee RE, Cubbin C. Neighborhood context and youth cardiovascular health behaviors. Am J Public
Health ; :-.
. Lewko JH, Ewing ME. Sex dierences and parental inuence in sport involvement of children. J Sport
Psychol ; :-.
. Lindquist CH, Reynolds KD, Goran MI. Sociocultural determinants of physical activity among
children. Prev Med ; :-.
.* Lowry R, Kann L, Collins JL, Kolbe LJ. e eect of socioeconomic status on chronic disease risk
behaviors among US adolescents. JAMA ; :-.
. Macintosh D. Socio-economic, educational and status characteristics of Ontario interschool athletes.
Can J Appl Sport Sci ; :-.
. McGuire MT, Hannan PJ, Neumark-Sztainer D, Cossrow NH, Story M. Parental correlates of physical
activity in a racially/ethnically diverse adolescent sample. J Adolesc Health ; :-.
. McGuire MT, Neumark-Sztainer DR, Story M. Correlates of time spent in physical activity and
television viewing in a multi-racial sample of adolescents. Pediatr Exerc Sci ; :-.
.* McKenzie TL, Sallis JF, Nader PR, Broyles SL, Nelson JA. Anglo- and Mexican-American preschool-
ers at home and at recess: activity patterns and environmental inuences. J Dev Behav Pediatr ;
:-.
.* McKenzie TL, Sallis JF, Nader PR, Patterson TL, Elder JP, Berry CC, Rupp JW, Atkins CJ, Buono MJ,
Nelson JA. BEACHES: an observational system for assessing children’s eating and physical activity
behaviors and associated events. J Appl Behav Anal ; :-.
. McLellan L, Rissel C, Donnelly N, Bauman A. Health behaviour and the school environment in New
South Wales, Australia. Soc Sci Med ; :-.
120 Chapter 4 120 Chapter 4
.* McMurray RG, Bradley CB, Harrell JS, Bernthal PR, Frauman AC, Bangdiwala SI. Parental inuences
on childhood tness and activity patterns. Res Q Exerc Sport ; :-.
. McMurray RG, Harrell JS, Bangdiwala SI, Deng S. Cardiovascular disease risk factors and obesity of
rural and urban elementary school children. J Rural Health ; :-.
. McMurray RG, Harrell JS, Bangdiwala SI, Gansky SA. Biologic and environmental factors inuenc-
ing the aerobic power of children. Med Exerc Nutr Health ; :-.
. Mellin AE, Neumark-Sztainer D, Story M, Ireland M, Resnick MD. Unhealthy behaviors and psycho-
social diculties among overweight adolescents: the potential impact of familial factors. J Adolesc
Health ; :-.
. Molnar BE, Gortmaker SL, Bull FC, Buka SL. Unsafe to play? Neighborhood disorder and lack of
safety predict reduced physical activity among urban children and adolescents. Am J Health Promot
; :-.
.* Moore LL, Lombardi DA, White MJ, Campbell JL, Oliveria SA, Ellison RC. Inuence of parents’
physical activity levels on activity levels of young children. J Pediatr ; :-.
. Morgan CF, McKenzie TL, Sallis JF, Broyles SL, Zive MM, Nader PR. Personal, social, and environ-
mental correlates of physical activity in a bi-ethnic sample of adolescents. Pediatr Exerc Sci ;
:-.
. Motl RW, Dishman RK, Ward DS, Saunders RP, Dowda M, Felton G, Pate RR. Examining social-
cognitive determinants of intention and physical activity among black and white adolescent girls
using structural equation modeling. Health Psychol ; :-.
. Murphey DA, Lamonda KH, Carney JK, Duncan P. Relationships of a brief measure of youth assets
to health-promoting and risk behaviors. J Adolesc Health ; :-.
. Neumark-Sztainer D, Story M, Hannan PJ, arp T, Rex J. Factors associated with changes in physical
activity: a cohort study of inactive adolescent girls. Arch Pediatr Adolesc Med ; :-.
. Nutbeam D, Aar L, Catford J. Understanding childrens’ health behaviour: the implications for health
promotion for young people. Soc Sci Med ; :-.
. O’Loughlin J, Paradis G, Kishchuk N, Barnett T, Renaud L. Prevalence and correlates of physical
activity behaviors among elementary schoolchildren in multiethnic, low income, inner-city neigh-
borhoods in Montreal, Canada. Ann Epidemiol ; :-.
. Pate RR, Pfeier KA, Trost SG, Ziegler P, Dowda M. Physical activity among children attending
preschools. Pediatrics ; :-.
.* Pate RR, Trost SG, Felton GM, Ward DS, Dowda M, Saunders R. Correlates of physical activity
behavior in rural youth. Res Q Exerc Sport ; :-.
.* Pérusse L, Leblanc C, Bouchard C. Familial resemblance in lifestyle components: results from the
Canada Fitness Survey. Can J Public Health ; :-.
.* Pérusse L, Tremblay A, Leblanc C, Bouchard C. Genetic and environmental inuences on level of
habitual physical activity and exercise participation. Am J Epidemiol ; :-.
.* Poest CA, Williams JR, Witt DD, Atwood ME. Physical activity patterns of preschool children. Early
Child Res Q ; :-.
. Prochaska JJ, Rodgers MW, Sallis JF. Association of parent and peer support with adolescent physical
activity. Res Q Exerc Sport ; :-.
.* Reynolds KD, Killen JD, Bryson SW, Maron DJ, Taylor CB, Maccoby N, Farquhar JW. Psychosocial
predictors of physical activity in adolescents. Prev Med ; :-.
. Robinson CH, omas SP. e Interaction Model of Client Health Behavior as a conceptual guide in
the explanation of children’s health behaviors. Public Health Nurs ; :-.
Environmental correlates of physical activity 121
Chapter 4
Environmental correlates of physical activity 121
Chapter 4
. Romero AJ, Robinson TN, Kraemer HC, Erickson SJ, Haydel KF, Mendoza F, Killen JD. Are perceived
neighborhood hazards a barrier to physical activity in children? Arch Pediatr Adolesc Med ;
:-.
. Rossow I, Rise J. Concordance of parental and adolescent health behaviors. Soc Sci Med ; :-
.
. Runyan SM, Stadler DD, Bainbridge CN, Miller SC, Moyer-Mileur LJ. Familial resemblance of bone
mineralization, calcium intake, and physical activity in early-adolescent daughters, their mothers,
and maternal grandmothers. J Am Diet Assoc ; :-.
.* Sallis JF, Alcaraz JE, McKenzie TL, Hovell MF. Predictors of change in children’s physical activity over
months: Variations by gender and level of adiposity. Am J Prev Med ; :-.
.* Sallis JF, Alcaraz JE, McKenzie TL, Hovell MF, Kolody B, Nader PR. Parental behavior in relation to
physical activity and tness in -year-old children. Am J Dis Child ; :-.
.* Sallis JF, Nader PR, Broyles SL, Berry CC, Elder JP, McKenzie TL, Nelson JA. Correlates of physical
activity at home in Mexican-American and Anglo-American preschool children. Health Psychol ;
:-.
.* Sallis JF, Patterson TL, Buono MJ, Atkins CJ, Nader PR. Aggregation of physical activity habits in
Mexican-American and Anglo families. J Behav Med ; :-.
.* Sallis JF, Patterson TL, McKenzie TL, Nader PR. Family variables and physical activity in preschool
children. J Dev Behav Pediatr ; :-.
. Sallis JF, Prochaska JJ, Taylor WC, Hill JO, Geraci JC. Correlates of physical activity in a national
sample of girls and boys in grades through . Health Psychol ; :-.
. Sallis JF, Taylor WC, Dowda M, Freedson PS, Pate RR. Correlates of vigorous physical activity for
children in grades through : Comparing parent-reported and objectively measured physical activ-
ity. Pediatr Exerc Sci ; :-.
. Saunders RP, Motl RW, Dowda M, Dishman RK, Pate RR. Comparison of social variables for under-
standing physical activity in adolescent girls. Am J Health Behav ; :-.
. Saunders RP, Pate RR, Felton G, Dowda M, Weinrich MC, Ward DS, Parsons MA, Baranowski T.
Development of questionnaires to measure psychosocial inuences on children’s physical activity.
Prev Med ; :-.
. Saxena R, Borzekowski DL, Rickert VI. Physical activity levels among urban adolescent females. J
Pediatr Adolesc Gynecol ; :-.
. Schmitz KH, Lytle LA, Phillips GA, Murray DM, Birnbaum AS, Kubik MY. Psychosocial correlates of
physical activity and sedentary leisure habits in young adolescents: the Teens Eating for Energy and
Nutrition at School study. Prev Med ; :-.
.* Shephard RJ, Jequier JC, Lavallee H, La Barre R, Rajic M. Habitual physical activity: eects of sex,
milieu, season and required activity. J Sports Med Phys Fitness ; :-.
. Shropshire J, Carrol B. Family variables and children’s physical activity : inuence of parental exercise
and socio-economic status. Sport Educ Soc ; :-.
. Simons-Morton BG, McKenzie TJ, Stone E, Mitchell P, Osganian V, Strikmiller PK, Ehlinger S, Cribb
P, Nader PR. Physical activity in a multiethnic population of third graders in four states. Am J Public
Health ; :-.
. Stareld B, Riley AW, Witt WP, Robertson J. Social class gradients in health during adolescence. J
Epidemiol Community Health ; :-.
. Stareld B, Robertson J, Riley AW. Social class gradients and health in childhood. Ambul Pediatr
; :-.
122 Chapter 4 122 Chapter 4
. Strauss RS, Rodzilsky D, Burack G, Colin M. Psychosocial correlates of physical activity in healthy
children. Arch Pediatr Adolesc Med ; :-.
.* Stucky-Ropp RC, DiLorenzo TM. Determinants of exercise in children. Prev Med ; :-.
.* Tappe MK, Duda JL, Ehrnwald PM. Perceived barriers to exercise among adolescents. J Sch Health
; :-.
.* Tappe MK, Duda JL, Menges-Ehrnwald P. Personal investment predictors of adolescent motivational
orientation toward exercise. Can J Sport Sci ; :-.
. Taveras EM, Rifas-Shiman SL, Field AE, Frazier AL, Colditz GA, Gillman MW. e inuence of
wanting to look like media gures on adolescent physical activity. J Adolesc Health ; :-.
.* Terre L, Ghiselli W, Taloney L, DeSouza E. Demographics, aect, and adolescents’ health behaviors.
Adolescence ; :-.
.* eodorakis Y, Doganis G, Bagiatis K, Gouthas M. Preliminary study of the ability of reasoned action
model in predicting exercise behaviour of young children. Percept Mot Skills ; :-.
. eodorakis Y, Papaioannou A, Karastogianidou K. Relations between family structure and students’
health-related attitudes and behaviors. Psychol Rep ; :-.
. Timperio A, Crawford D, Telford A, Salmon J. Perceptions about the local neighborhood and walking
and cycling among children. Prev Med ; :-.
.* Trost SG, Pate RR, Dowda M, Saunders R, Ward DS, Felton G. Gender dierences in physical activity
and determinants of physical activity in rural h grade children. J Sch Health ; :-.
.* Trost SG, Pate RR, Saunders R, Ward DS, Dowda M, Felton G. A prospective study of the determi-
nants of physical activity in rural h-grade children. Prev Med ; :-.
. Trost SG, Pate RR, Ward DS, Saunders R, Riner W. Correlates of objectively measured physical activ-
ity in preadolescent youth. Am J Prev Med ; :-.
. Trost SG, Sallis JF, Pate RR, Freedson PS, Taylor WC, Dowda M. Evaluating a model of parental
inuence on youth physical activity. Am J Prev Med ; :-.
. Tuinstra J, Grootho JW, van den Heuvel WJ, Post D. Socio-economic dierences in health risk
behavior in adolescence: do they exist? Soc Sci Med ; :-.
. Unger JB, Reynolds K, Shakib S, Spruijt-Metz D, Sun P, Johnson CA. Acculturation, physical activity,
and fast-food consumption among Asian-American and Hispanic adolescents. J Community Health
; :-.
. Vermorel M, Vernet J, Bitar A, Fellmann N, Coudert J. Daily energy expenditure, activity patterns,
and energy costs of the various activities in French --y-old adolescents in free living conditions.
Eur J Clin Nutr ; :-.
. Vilhjalmsson R. Eects of social support on self-assessed health in adolescence. J Adolesc Health ;
:-.
. Vilhjalmsson R, Kristjansdottir G. Gender dierences in physical activity in older children and
adolescents: the central role of organized sport. Soc Sci Med ; :-.
. Vilhjalmsson R, orlindsson T. Factors related to physical activity: a study of adolescents. Soc Sci
Med ; :-.
. Wagner A, Klein-Platat C, Arveiler D, Haan MC, Schlienger JL, Simon C. Parent-child physical
activity relationships in -year old French students do not depend on family socioeconomic status.
Diabetes Metab ; :-.
. Wardle J, Jarvis M, Steggles N, Sutton S, Williamson S, Farrimond H, Cartwright M, Simon AE.
Socioeconomic disparities in cancer-risk behaviors in adolescence: baseline results from the Health
and Behaviour in Teenagers Study (HABITS). Prev Med ; :-.
Environmental correlates of physical activity 123
Chapter 4
Environmental correlates of physical activity 123
Chapter 4
. Welk GJ, Wood K, Morss G. Parental inuences on physical activity in children: An exploration of
potential mechanisms. Pediatr Exerc Sci ; :-.
. Williams EA, Jenkins C, Nevill AM. Social area inuences on leisure activity - an exploration of the
ACORN classication with reference to sport. Leisure Studies ; :-.
. Wold B, Oygard L, Eder A, Smith C. Social reproduction of physical activity, Implications for health
promotion in young people. Eur J Public Health ; :-.
. Woodeld L, Duncan M, Al-Nakeeb Y, Nevill A, Jenkins C. Sex, ethnic and socio-economic dier-
ences in children’s physical activity. Pediatr Exerc Sci ; :-.
. Yang X, Telama R, Laakso L. Parent’s physical activity, socioeconomic status and education as predic-
tors of physical activity and sport among children and youths - a -year follow-up study. Int Rev Soc
Sports ; :-.
.* Zakarian JM, Hovell MF, Hofstetter CR, Sallis JF, Keating KJ. Correlates of vigorous exercise in a
predominantly low SES and minority high school population. Prev Med ; :-.
. Ziviani J, Scott J, Wadley D. Walking to school: incidental physical activity in the daily occupations of
Australian children. Occup er Int ; :-.
Part III Socio-demographic
correlates of energy balance-
related behaviors
5 Gender, ethnic and school type
dierences in overweight and
energy balance-related behaviors
among Dutch adolescents
van der Horst K, Oenema A, te Velde SJ, Brug J. Gender, ethnic and
school type dierences in overweight and energy balance-related
behaviors among Dutch adolescents.
International Journal of Pediatric Obesity , May :-. [Epub ahead of
print].
128 Chapter 5 128 Chapter 5
ABSTRACT
Objective: e aim of this study was to investigate gender, ethnic and school type dier-
ences in overweight and energy balance-related behaviors: snack, so drink and breakfast
consumption, walking, bicycling, and playing sports during leisure time, active commuting
to school, television viewing and computer use among - - year-old adolescents.
Methods: Cross-sectional data on weight status and energy balance-related behaviors were
obtained from adolescents (-). Energy balance-related behaviors were self-
reported and body mass index was calculated from measured height and weight. Gender,
ethnic and school type dierences in weight status and behaviors were examined with
multi-level logistic regression analyses.
Results: Overweight and unfavorable energy balance-related behaviors were more
likely among youth from non-Western ethnic backgrounds and those attending vocational
schools. Analyses stratied by ethnicity showed that girls from non-Western ethnic back-
grounds were more likely to be overweight (OR=.) and to report not bicycling (OR=.)
and watching more than two hours of television (OR=.) compared to boys from non-
Western ethnic backgrounds. Vocational students from Western ethnic backgrounds were
more likely to report high levels of so drink consumption (OR=.), watching television
(OR=.) and computer use (OR=.) compared to higher-level education students from
Western ethnic backgrounds.
Conclusions: e study ndings indicate important ethnic and educational dierences in
overweight and energy balance-related behaviors. Future research should focus on what
kind of interventions work and for which target groups they work, taking demographic
variables such as gender, ethnicity, school type into account.
Socio-demographic dierences in overweight and EBRB 129
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Socio-demographic dierences in overweight and EBRB 129
Chapter 5
INTRODUCTION
To curb the obesity epidemic, it is important to identify and target adolescents at risk
for overweight and obesity. Because obesity persists into adulthood [] and is associated
with severe health consequences [], a detailed understanding of risk behaviors related to
the development of obesity is essential to developing preventive interventions. It is also
important to identify specic target groups of adolescents who are more at risk of becom-
ing obese by engaging in more (or specic) obesity-related risk behaviors. Being able to
distinguish specic target groups provides the opportunity to better tailor interventions
to the needs and perceptions of those most at risk []. Recent overviews have suggested a
range of specic energy balance-related behaviors (EBRB, i.e. behaviors that contribute to
energy intake or expenditure) that may contribute substantially to a higher or lower risk for
unnecessary weight gain [-]. Currently, there is insucient insight into the occurrence
of a number of overweight risk behaviors among adolescents and whether it is possible to
distinguish specic subgroups that are more likely to engage in specic risk behaviors for
overweight and obesity.
Earlier evidence points out that the prevalence of overweight is considerably greater among
youth from racial or ethnic minority backgrounds [-]. In addition to genetic, economic
and environmental factors, ethnic disparities in overweight and obesity are likely due to
dierences in EBRB [, ]. Studies conducted in dierent countries indicate that ethnic
minority groups participate less in physical activity, spend more time watching television [,
-], are less likely to eat breakfast regularly [], consume so drinks and savory snacks
and visit fast-food restaurants more oen [], but also have higher fruit intakes []. e
higher rates of overweight among ethnic minority groups might be explained in part by
their lower educational levels. Educational level has also been found to be an independent
determinant of overweight [] and to be associated with adolescent health behavior such
as physical activity []. Furthermore, dierences have been found between boys and girls
in overweight and obesity and related risk behaviors, with girls being more likely to be
overweight [, , ] and to engage in less physical activity [, ].
Although most of the previous studies examined single behaviors, it is most likely that a
number of risk behaviors contribute to an increased risk for overweight. erefore, we ex-
amined how overweight and specic unfavorable EBRB (high so drink intake, high snack
consumption, not eating breakfast on a daily basis, high amounts of television viewing and
computer use, little participation in sports, little walking and bicycling during leisure time,
and an absence of active commuting to school) vary by gender, ethnicity and school type. In
addition, we investigated possible interaction eects between gender, ethnicity and school
type, and performed stratied analyses when interaction eects were signicant. Based on
130 Chapter 5 130 Chapter 5
the ndings of previous studies, we hypothesized that girls, adolescents from non-Western
ethnic backgrounds, and adolescents attending vocational schools have a higher risk of
overweight and obesity and unfavorable EBRB compared to boys, adolescents from Western
ethnic backgrounds, and adolescents participating in higher-level education.
METHODS
Study design and sample selection
Data from the ENvironmental Determinants of Obesity in Rotterdam SchoolchildrEn
(ENDORSE) study were used. e ENDORSE study is a prospective two-year study among
adolescents aged to , with assessments at baseline and a two-year follow-up. More de-
tails on this project are described elsewhere []. e Medical Ethics Committee of Erasmus
University Medical Center reviewed the proposal and issued a “declaration of no objection”
for the ENDORSE project.
Aer stratication according to the area in the city in which the schools are located,
seventeen school locations were randomly selected from out of a total of schools
that were willing to participate in the ENDORSE study. Stratication was done, to ensure a
range of physical and cultural environments. An average of ve classes per school location
was randomly selected to participate in the study, and adolescents from these classes
were eligible for participation. In the baseline survey, adolescents were absent during the
questionnaire assessment. Due to printing mistakes, it was necessary to delete adoles-
cents, including one entire school. Respondents with missing data on ethnicity were deleted
from the sample (n=). is meant that the study sample included participants ()
from classes and schools.
Procedure
e ENDORSE study collects data among adolescents in the rst (- to -year-olds) and
third (- to -year-olds) years of secondary school. e school types varied from lower
vocational schools to higher-level secondary education. All data were gathered within the
ongoing health surveillance system of the local Municipal Health Service and as a part of the
government approved routine health examinations of the preventive youth health care [].
Separate informed consent therefore was not requested. ENDORSE classroom question-
naires and anthropometrics were completed on a voluntary basis. Parents received written
information on these measurements and were free to object to participation of their child.
From October to May , the students completed the ENDORSE questionnaire
in the classroom in the presence of a teacher and a trained research assistant during one
Socio-demographic dierences in overweight and EBRB 131
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Socio-demographic dierences in overweight and EBRB 131
Chapter 5
class period of approximately minutes. Within a month aer completing the ENDORSE
questionnaire, two trained research assistants measured height and weight. e adolescents
were asked to come into a private room one by one, where they were measured in street
clothes without shoes and heavy clothes.
Measures
Weight, height and body mass index
Body height was measured using a Seca mobile height rod with an accuracy of . cm. A
calibrated electronic digital oor scale (Seca class III with accuracy of . kg) was used to
determine the body weight of the participants. Body mass index (BMI) was calculated from
the measured height and weight (kg/m²). BMI cut points for children/adolescents from the
International Obesity TaskForce (IOTF) were used to dene overweight and obesity [].
Energy balance-related behaviors
e following EBRB were assessed: snack, so drink and breakfast consumption, walking,
bicycling and playing sports during leisure time, active commuting to school (walking and
bicycling), television viewing and computer use.
Snacks were dened as sweet (candy, candy bars, chocolate, cake, cookies) and savory
(fast food, pizza, fries, chips, nuts) snacks. Sweet snacks were assessed by two questions:
“How many days a week do you usually eat sweet snacks or cookies?” and “On average, how
many times a day do you eat sweet snacks or cookies?” e same two questions were asked
for savory snacks. ese questions were combined to compute a single score for the mean
snack intake in times per day.
So drinks were dened as carbonated so drinks, other non-carbonated sugar-sweetened
drinks (water-based beverages that contain sugar) and sport drinks. e consumption of
so drinks was assessed by two questions: “How many days a week do you usually drink
sugar-sweetened (not ‘light’ or ‘diet’) beverages?” and “If you drink sugar-sweetened bever-
ages, how many glasses, cans, and/or bottles do you drink on average per day?” Total so
drink consumption was expressed in milliliters per day and therefore, serving sizes were
transferred to a quantication in milliliters using the Dutch standard serving sizes ( glass =
ml, can = ml, bottle = ml).
Breakfast consumption was assessed with two questions: “How oen do you eat breakfast
on school days?” and “How oen do you eat breakfast on weekend days?” ese questions
were combined to compute a single score for breakfast consumption in days per week.
Ten-day test-retest reliability for snack consumption, so drink consumption and break-
fast consumption was r=., r=. and r=. respectively.
An adapted version of the Activity QUestionnaire for Adolescents & Adults (AQUAA)
was used to assess physical activity (transportation, activities and sports during leisure time)
132 Chapter 5 132 Chapter 5
and sedentary behaviors (television viewing, computer use). e structure of the AQUAA
is obtained from the validated Short QUestionnaire to ASses Health-enhancing physical
activity (SQUASH) []. e frequency (days per week) and duration (hours and minutes)
of the activities were multiplied, and then divided by the total number of days to provide the
average minutes per day of the physical activity and sedentary sub-behaviors.
As most behaviors were not normally distributed the EBRB were dichotomized to express
these behaviors in “favorable” and “unfavorable” categories. If possible, categories were
distinguished based on participating in a behavior (favorable group) and not participating
in a behavior (unfavorable group) (bicycling during leisure time, playing sports and com-
muting to school). Variables that could not be dichotomized by engaging or not engaging
in behavior were dichotomized based on recommendations (television viewing, so drink
consumption) or the median value in the data set (snacking, breakfast consumption, walk-
ing, computer use).
e questions, response options and the cut-o points for all EBRB are provided in the
appendix to this paper.
Demographics
Ethnicity was dened according to the denition used by Statistics Netherlands []. Ado-
lescents were considered to be from a Western ethnic background if both parents had been
born in an European country, North America, Oceania, Indonesia or Japan. Adolescents
were considered to be from a non-Western ethnic background if one or both parents had
been born in a non-Western country. School type was categorized into two levels: vocational
schools and higher-level secondary education. e schools provided this information. Age
was determined based on the date of the anthropometrical measurements and the date of
birth (provided by the schools).
Data analyses
Chi-square tests were used to test dierences in gender, school type, overweight status and
EBRB between the participants who were included and not included in the analyses (i.e.
those with and without data on ethnicity). Chi-square tests were also used to test dierences
in gender, ethnicity and school type between adolescents with missing values on the EBRB
and weight status and adolescents with reported EBRB and weight status. Respondents with
missing data on the behavior variables were not deleted from the sample, but were deleted
from the analysis. Because of this, the numbers of students in the analyses are dierent for
various outcome variables.
Categorical data were described using frequencies and percentages. To examine if gender,
ethnicity and school type are signicant correlates of the EBRB and weight status, multi-level
logistic regression analyses were performed with the demographic factors as independent
variables.
Socio-demographic dierences in overweight and EBRB 133
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Socio-demographic dierences in overweight and EBRB 133
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Interaction eects between gender and ethnicity and school type and ethnicity were
examined by adding interaction terms into the regression models. If the interactions had
P values <., stratied analyses were conducted for ethnicity. Multi-level models with a
three-level structure were used (child, class and school) in order to take into account that
children were nested within classes and schools. e univariate analyses were conducted
in SPSS version and the multi-level analyses were performed using MlwiN version ..
RESULTS
Adolescents from Western ethnic backgrounds and adolescents attending vocational
schools had signicantly more missing values on several EBRB compared to adolescents
from Western ethnic backgrounds and adolescents attending higher-level education (data
not shown).
Table . presents the frequency of demographics, weight status and EBRB in the study
population. Among the respondents, . was female, the mean age was . years, .
was attending vocational schools and . was from non-Western ethnic background.
Overweight or obesity was present in . of the participating adolescents. Unfavorable
sedentary and dietary behaviors such as consuming more than two glasses of so drink per
day (.) were more oen reported compared to unfavorable physical activity behaviors
such as not playing sports (.).
Gender dierences
In the multivariate analyses (Table .), we found girls to be more likely to report low
breakfast consumption (OR=.), not bicycling during leisure time (OR=.), no sports
participation (OR=.) and high television viewing (OR=.). Girls were less likely to report
high so drink consumption (OR=.). ere were no dierences between boys and girls
in weight status.
Ethnic dierences
e multivariate analyses (Table .) demonstrated that adolescents from non-Western eth-
nic backgrounds were more likely to be overweight or obese (OR=.), to not eat breakfast
everyday (OR=.), to do no bicycling during leisure time (OR=.), to not participate in
sports (OR=.), to use non-active modes of transportation to school (OR=.) and to spend
more than two hours watching television (OR=.). A signicant inverse association was
found for walking during leisure time, indicating that adolescents from non-Western ethnic
backgrounds were less likely to report low levels of walking (OR=.).
134 Chapter 5 134 Chapter 5
Table 5.1 Frequency of demographics, weight status and energy balance-related behaviors.
Frequency in study population (%)
(unless otherwise specied)
Demographic variables
Mean age of respondents in years 14.1 (SD=1.2; range 10-17)
Girls 544 (45.1)
Non-Western ethnic background 609 (50.5)
Vocational education 684 (56.7)
Weight status
Overweight (according to IOTF) 189 (15.7)
Obesity (according to IOTF) 54 (4.5)
Mean BMI
Boys
Girls
Western ethnic background
Non Western ethnic background
Vocational education
Higher-level education
20.7 (SD=3.6; range 14.2-36.1)
20.2 (SD=3.6)
21.3 (SD=3.6)
20.0 (SD=3.1)
21.4 (SD=4.0)
21.1 (SD=3.8)
20.2 (SD=3.3)
Dietary behaviours
So drinks > 2 glasses/day 811 (67.2)
Snacks > 2 times/day 603 (50.0)
Breakfast 0-6 days/week 525 (43.5)
Physical activity
Walking during leisure time < 60 min/day 531 (44.0)
Not bicycling during leisure time 344 (28.5)
Not playing sports 239 (19.8)
Non-active commuting to school 398 (33.0)
Sedentary behaviours
Television viewing > 120 min/day 490 (40.6)
Computer use > 90 min/day 568 (47.1)
School type dierences
e multivariate analyses (Table .) showed that vocational students were more likely to be
overweight or obese (OR=.) and to report high so drink consumption (OR=.), high
snack consumption (OR=.), no sports participation (OR=.) and to spend more than two
hours watching television (OR=.). A signicant inverse association was found for walking
during leisure time, indicating that vocational students were less likely to report low levels
of walking (OR=.).
Socio-demographic dierences in overweight and EBRB 135
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Socio-demographic dierences in overweight and EBRB 135
Chapter 5
Table 5.2 Results of multiple logistic regression analyses (odds ratios (OR) and 95% condence intervals (95%
CI)) with gender, ethnicity and school type as independent variables and energy-balance related behaviours as
dependent variables
Gender (girls)*
OR (95% CI)
Ethnicity (non-
Western)*
OR (95% CI)
School type
(vocational)*
OR (95% CI)
Weight status
Normal weight 1.00 1.00 1.00
Overweight and obesity 1.2 (0.91-1.65) 1.8 (1.29-2.39) 1.7 (1.19-2.33)
Dietary behaviours
So drink consumption
≤ 2 glasses 1.00 1.00 1.00
> 2 glasses 0.6 (0.43-0.76) 1.2 (0.88-1.64) 2.0 (1.19-3.22)
Breakfast consumption
Every day 1.00 1.00 1.00
0-6 days/week 1.8 (1.30-2.36) 1.9 (1.35-2.58) 1.5 (0.88-2.39)
Snack consumption
≤ 2 times/day 1.00 1.00 1.00
> 2 times/day 1.0 (0.80-1.33) 1.2 (0.91-1.52) 1.5 (1.16-2.00)
Physical activity
Walking during leisure time
≥60 min/day 1.00 1.00 1.00
<60 min/day 1.1 (0.79-1.39) 0.6 (0.46-0.85) 0.5 (0.32-0.79)
Bicycling
Bicycling during leisure time 1.00 1.00 1.00
Not bicycling during leisure time 1.6 (1.14-2.12) 3.2 (2.29-4.58) 0.9 (0.55-1.34)
Sports
Playing sports during leisure time 1.00 1.00 1.00
Not playing sports during leisure time 3.0 (1.98-4.48) 1.7 (1.11-2.62) 2.5 (1.37-4.43)
Commuting to school (walking/bicycling)
Active commuting 1.00 1.00 1.00
Non-active commuting 0.9 (0.66-1.25) 1.6 (1.13-2.32) 1.4 (0.62-3.20)
Sedentary behaviours
Television viewing
≤ 2 hours per day 1.00 1.00 1.00
> 2 hours per day 1.8 (1.33-2.38) 2.4 (1.74-3.25) 1.7 (1.08-2.71)
Computer use
≤ 90 minutes per day 1.00 1.00 1.00
> 90 minutes per day 0.8 (0.59-1.04) 1.3 (0.98-1.80) 1.4 (0.90-2.14)
Odds ratios in bold indicate a signicant association. All analyses were adjusted for age.
* Reference groups were boys (gender), Western ethnic background (ethnicity) and higher-level education
students (school type).
136 Chapter 5 136 Chapter 5
Interaction eects between ethnicity and gender
Interaction eects between ethnicity and gender were signicant for weight status, so
drink consumption, bicycling during leisure time, playing sports, television viewing and
computer use. e stratied analyses (Table .) showed that compared to boys from non-
Western ethnic backgrounds, girls from non-Western ethnic backgrounds were more likely
to be overweight (OR=.), to do no bicycling during leisure time (OR=.) and to spend
more than two hours watching television (OR=.). ese gender dierences were not
signicant for adolescents from Western ethnic backgrounds. Compared to boys, girls from
both non-Western (OR=.) and Western ethnic backgrounds (OR=.) were more likely to
report no sports participation. Compared to boys from Western ethnic backgrounds, girls
from Western ethnic backgrounds were less likely to report high so drink consumption
(OR=.) and using the computer for more than minutes (OR=.).
Interaction eects between ethnicity and school type
Interaction eects between ethnicity and school type were signicant for so drink
consumption, television viewing and computer use. e stratied analyses showed that
compared to high-level education students from Western ethnic backgrounds, vocational
students from Western ethnic backgrounds were more likely to report high so drink con-
sumption (OR=.), more than two hours of television viewing (OR=.) and more than
minutes of computer use (OR=.). ere were no signicant school type dierences for
adolescents from non-Western ethnic backgrounds.
DISCUSSION
is study examined gender, ethnic and school type dierences in weight status and EBRB
among Dutch adolescents aged to . As expected, we found girls to be more likely than
boys to engage in unfavorable behaviors (i.e. low breakfast consumption, not bicycling
during leisure time, not playing sports and spending more than two hours a day watching
television). Adolescents from non-Western ethnic backgrounds and vocational schools were
more likely to be overweight or obese and to engage in unfavorable EBRB. ese results are
in accordance with studies from other countries in which girls were also found to be less
physically active [, , ] and minority groups were more likely to be overweight and
showed more unhealthy EBRB [, -, -, ] such as watching television [, -], low
breakfast consumption [], so drink and snack consumption []. at ethnic minority
groups have higher rates of overweight and unhealthy behaviors might partly be due to
their lower educational levels. However the examination of potential ethnicity by school
Socio-demographic dierences in overweight and EBRB 137
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Socio-demographic dierences in overweight and EBRB 137
Chapter 5
Table 5.3 Results of multiple logistic regression analyses with energy balance-related behaviours as dependent
variables (odds ratios (OR) and 95% condence intervals (95% CI)) stratied by ethnicity with gender and
school type as independent variables
Gender (girls)* School type (vocational)*
Western
OR (95% CI)
Non-Western
OR (95% CI)
Western
OR (95% CI)
Non-Western
OR (95% CI)
Weight status
Normal weight 1.00 1.00 1.00 1.00
Overweight 0.9 (0.55-1.51) 1.5 (1.05-2.26) NA NA
Dietary behaviours
So drink consumption
≤ 2 glasses 1.00 1.00 1.00 1.00
> 2 glasses 0.4 (0.28-0.64) 0.7 (0.50-1.08) 3.2 (1.63-6.34) 1.5 (0.92-2.30)
Breakfast consumption
Every day 1.00 1.00 1.00 1.00
0-6 days/week NA NA NA NA
Snack consumption
≤ 2 times/day 1.00 1.00 1.00 1.00
> 2 times/day NA NA NA NA
Physical activity
Walking during leisure time
≥60 min/day 1.00 1.00 1.00 1.00
<60 min/day NA NA NA NA
Bicycling
Bicycling during leisure time 1.00 1.00 1.00 1.00
Not bicycling during leisure time 0.8 (0.45-1.25) 2.4 (1.64-3.45) NA NA
Sports
Playing sports during leisure time 1.00 1.00 1.00 1.00
Not playing sports during leisure time 2.0 (1.07-3.77) 3.5 (2.20-5.67) NA NA
Commuting to school (walking/bicycling)
Active commuting 1.00 1.00 1.00 1.00
Non-active commuting NA NA NA NA
Sedentary behaviours
Television viewing
≤ 2 hours per day 1.00 1.00 1.00 1.00
> 2 hours per day 1.1 (0.69-1.63) 2.3 (1.60-3.30) 2.9 (1.55-5.46) 1.3 (0.82-1.96)
Computer use
≤ 90 minutes per day 1.00 1.00 1.00 1.00
> 90 minutes per day 0.6 (0.38-0.85) 0.9 (0.66-1.35) 2.1 (1.25-3.59) 1.2 (0.70-1.97)
NA = not applicable, no signicant interaction by ethnicity. Odds ratios in bold indicate a signicant
association. All analyses were adjusted for age. * Reference groups were boys (gender) and higher-level
education students (school type).
138 Chapter 5 138 Chapter 5
Appendix Items on the energy balance-related behaviours questionnaire
Items Response categories
Sweet and savoury snacks (≤ 2 times/day; > 2 times/day)
How many days a week do you usually eat sweet snacks or
cookies?
8-point scale: from 0 = never to 7 = 7 days per week
On average, how many times a day do you eat sweet snacks
or cookies?
10-point scale: from 1 = 1x to 10 = 10x or more
How many days a week do you usually eat savoury snacks? 8-point scale: from 0 = never to 7 = 7 days per week
On average, how many times a day do you eat savoury
snacks?
10-point scale: from 1 = 1x to 10 = 10x or more
So drinks (≤ 2 glasses/day (400 ml); > 2 glasses/day)
How many days a week do you usually drink sugar-
sweetened (not “light” or “diet”) beverages?
8-point scale: from 0 = never to 7 = every day
If you drink sugar-sweetened beverages, how many glasses,
cans, and/or bottles do you drink on average per day?
8-point scale: from 0 = none to 7 glasses
8- point scale: from 0 = none to 7 cans
8-point scale: from 0 = none to 7 bottles
Breakfast consumption (every day; 0-6 days/week)
How oen do you eat breakfast on school days? 5-point scale: from 0 = I never eat breakfast on school days
to 5 = 5 days
How oen do you eat breakfast on weekend days? 3-point scale:
0 = I never eat breakfast on weekend days
1 = I eat breakfast on one weekend day (Saturday or
Sunday)
2 = I eat breakfast on both weekend days (Saturday and
Sunday)
Bicycling during leisure time (bicycling; not bicycling)
How many days a week do you bicycle during leisure time?
(include things like bicycling to the supermarket, sports club
and movie theatre)
8-point scale: from 0 = never to 7 = 7 days per week
On a day that you bicycle, how long do you bicycle on
average?
Open question (hours and minutes could be reported).
Walking during leisure time (≥60 min/day; <60 min/day)
How many days a week do you walk during leisure time?
(include things like walking to the supermarket and sports
club and walking the dog)
8-point scale: from 0 = never to 7 = 7 days per week
On a day that you walk, how long do you walk on average? Open question (hours and minutes could be reported).
Active commuting to school (active commuting; non-active commuting)
How many days a week do you walk from home to school? 6-point scale: from 0 = never to 5 = 5 days per week
How long does it take you to walk from home to school (one
way only)?
Open question (minutes could be reported).
How many days a week do you bicycle from home to school? 6-point scale: from 0 = never to 5 = 5 days per week
How long does it take you to bicycle from home to school
(one way only)?
Open question (minutes could be reported).
Sports during leisure time (playing sports; not playing any sports)
Which sports did you play last week (at a sports club or with
friends)?
Open question (three sports could be reported).
How many days did you play this sport last week? 7-point scale: from 1 = 1 day per week to 7 = every day.
is scale could be lled in for the three sports listed in the
preceding question.
On a day that you participate in this kind of sport, how long
do you do this on average?
is open question could be lled in for the three sports
listed in the rst question (hours and minutes could be
reported).
Watching television (≤ 120 min/day; > 120 min/day)
How many days a week do you watch television? 8-point scale: from 0 = never to 7 = 7 days per week
On a day that you watch television, how long do you watch
television on average?
Open question (hours and minutes could be reported).
Computer use (≤ 90 min/day; > 90 min/day)
How many days per week do you use the computer (include
things like the internet, games, X-box and PlayStation)?
8-point scale: from 0 = never to 7 = 7 days per week
On a day that you use the computer, how long do you use
the computer on average?
Open question (hours and minutes could be reported).
Socio-demographic dierences in overweight and EBRB 139
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Socio-demographic dierences in overweight and EBRB 139
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type interaction eects and subsequent stratied analyses showed that dierences according
to school type were not found among adolescents from non-Western ethnic backgrounds.
Cultural dierences or level of acculturation [, ] might account for individual dier-
ences in unhealthy behaviors in non-Western ethnic groups. ese ndings indicate that
especially adolescents attending vocational schools and all adolescents from non-Western
ethnic backgrounds have to be targeted with interventions, since they are most likely to
engage in risk behaviors. Developing and implementing school based healthful diet and
physical activity promotion interventions that were specically designed for lower voca-
tional schools is a promising strategy to prevent overweight and obesity [, ].
Examining gender by “ethnicity interaction eect” and subsequent stratied analyses
showed that girls from non-Western ethnic backgrounds were more likely to be overweight
or obese compared to boys from non-Western ethnic backgrounds and that they were more
likely to engage in risk behaviors. ese gender dierences were not found for adolescents
from Western ethnic backgrounds. is indicates that girls from non-Western ethnic back-
grounds in particular are an important target group. e same pattern of higher overweight
prevalence among non-Western female groups has also been found in the United States.
Ethnicity-overweight dierences were greater among females, showing a higher overweight
prevalence among African-American girls compared to boys [, ]. We also found that
girls from non-Western ethnic backgrounds had a signicantly higher risk of not bicycling,
not participating in sports and watching television for more than two hours compared to
boys from non-Western ethnic backgrounds. ese similarities in patterns are interesting,
because the non-Western ethnic groups in the US are dierent from those in the Neth-
erlands. Whereas in the US ethnic minority groups are African American and Hispanic,
in the Netherlands the most important ethnic minority groups are Turkish, Moroccan,
Surinamese and Cape Verdean. Our ndings are consistent with other studies that found
especially non-Western migrant women (Turkish and Moroccan) to be less physically active
[, ]. Hosper et al. () also found that the prevalence’s for physical inactivity and
overweight of second-generation Turkish and Moroccan women seem to converge towards
the prevalence rates in the Dutch population []. Because it is unclear whether this accul-
turation process will occur for all risk behaviors and for all non-Western ethnic groups[],
preventive interventions should still target these high-risk groups.
e following limitations should be taken into account when interpreting the results of
this study. e cross-sectional design of the study did not allow us to determine causal
eects. Longitudinal data are needed to see whether changes in weight status are associated
with changes in the dierences in EBRB between boys and girls, Western and non-Western
adolescent and higher-level and lower-level education students. No test-retest data exists
for the physical activity measures and validation data is lacking for both dietary intake and
140 Chapter 5 140 Chapter 5
physical activity measures. e use of self-reported measures of EBRB could have caused
an overestimation of intakes and activities. e categorization of the data could also have
inuenced the outcomes. In this study we made a distinction between adolescents from
Western and non-Western ethnic backgrounds. However, the group of adolescents from
non-Western ethic backgrounds was quite diverse, including adolescents with parents born
in Turkey, Morocco, Cape Verde and Surinam. We were not able to examine the dierences
between these groups.
Examining gender, ethnic and educational dierences in overweight and risk behaviors for
overweight is important for target group segmentation and intervention development. In
this study, we observed the most dierences in overweight and EBRB for ethnicity and
school type. erefore, adolescents from non-Western ethnic backgrounds (girls in particu-
lar) and adolescents attending vocational schools (particularly those with Western ethnic
backgrounds) are important target groups for preventive interventions aimed at preventing
overweight and obesity. Interventions should focus on behaviors that are of specic impor-
tance to these high-risk groups. e eectiveness of interventions will probably increase
when they also take dierences in individual cognitions, cultural inuences and environ-
mental determinants of EBRB into account.
Conclusion
e results of this study showed that adolescents from non-Western ethnic backgrounds
and those attending vocational schools are important target groups for obesity preven-
tion. Given the relatively few published obesity-prevention and treatment studies that are
designed for specic educational or ethnic groups, it is important to promote the develop-
ment of culturally appropriate intervention strategies that are shown to be eective among
youth of diverse backgrounds. Future research should consider subgroups in the adolescent
population and focus on what kind of interventions work and for which target groups they
work taking demographic variables such as gender, ethnicity and school type into account.
Further qualitative and longitudinal research is needed to examine determinants of the
EBRB to better tailor interventions to the needs and perceptions of these specic target
groups.
ACKNOWLEDGEMENTS
Financial support for this study was provided by a grant from ZonMw, the Netherlands
Organization for Health Research and Development (grant ID no. .). is study
was part of CEPHIR: the Center for Eective Public Health in the larger Rotterdam Area.
Socio-demographic dierences in overweight and EBRB 141
Chapter 5
Socio-demographic dierences in overweight and EBRB 141
Chapter 5
REFERENCES
. Serdula MK, Ivery D, Coates RJ, Freedman DS, Williamson DF, Byers T: Do obese children become
obese adults? A review of the literature. Prev Med , ():-.
. Must A, Jacques PF, Dallal GE, Bajema CJ, Dietz WH: Long-term morbidity and mortality of over-
weight adolescents. A follow-up of the Harvard Growth Study of to . N Engl J Med ,
():-.
. Kreuter MW, Lukwago SN, Bucholtz RD, Clark EM, Sanders-ompson V: Achieving cultural ap-
propriateness in health promotion programs: targeted and tailored approaches. Health Educ Behav
, ():-.
. Swinburn BA, Caterson I, Seidell JC, James WP: Diet, nutrition and the prevention of excess weight
gain and obesity. Public health nutrition , (A):-.
. Gorely T, Marshall SJ, Biddle SJ: Couch kids: correlates of television viewing among youth. Int J Behav
Med , ():-.
. Rennie KL, Johnson L, Jebb SA: Behavioural determinants of obesity. Best Pract Res Clin Endocrinol
Metab , ():-.
. Fredriks AM, Van Buuren S, Sing RA, Wit JM, Verloove-Vanhorick SP: Alarming prevalences of
overweight and obesity for children of Turkish, Moroccan and Dutch origin in e Netherlands
according to international standards. Acta Paediatr , ():-.
. Lobstein T, Frelut ML: Prevalence of overweight among children in Europe. Obes Rev , ():-
.
. Gordon-Larsen P, Adair LS, Popkin BM: e relationship of ethnicity, socioeconomic factors, and
overweight in US adolescents. Obes Res , ():-.
. Maeis C: Aetiology of overweight and obesity in children and adolescents. Eur J Pediatr ,
Suppl :S-.
. Olden K, White SL: Health-related disparities: inuence of environmental factors. Med Clin North
Am , ():-.
. Gordon-Larsen P, McMurray RG, Popkin BM: Adolescent physical activity and inactivity vary by
ethnicity: e National Longitudinal Study of Adolescent Health. J Pediatr , ():-.
. Delva J, O’Malley PM, Johnston LD: Racial/ethnic and socioeconomic status dierences in over-
weight and health-related behaviors among American students: national trends -. J Adolesc
Health , ():-.
. Sallis JF, Prochaska JJ, Taylor WC: A review of correlates of physical activity of children and adoles-
cents. Med Sci Sports Exerc , ():-.
. Kuepper-Nybelen J, Lamerz A, Bruning N, Hebebrand J, Herpertz-Dahlmann B, Brenner H: Major
dierences in prevalence of overweight according to nationality in preschool children living in
Germany: determinants and public health implications. Arch Dis Child , ():-.
. te Velde SJ, Wind M, van Lenthe FJ, Klepp KI, Brug J: Dierences in fruit and vegetable intake and
determinants of intakes between children of Dutch origin and non-Western ethnic minority children
in the Netherlands - a cross sectional study. Int J Behav Nutr Phys Act , :.
. McMurray RG, Harrell JS, Deng S, Bradley CB, Cox LM, Bangdiwala SI: e inuence of physical
activity, socioeconomic status, and ethnicity on the weight status of adolescents. Obes Res ,
():-.
. Westerstahl M, Barnekow-Bergkvist M, Jansson E: Low physical activity among adolescents in practi-
cal education. Scand J Med Sci Sports , ():-.
142 Chapter 5 142 Chapter 5
. Wang Y, Liang H, Tussing L, Braunschweig C, Caballero B, Flay B: Obesity and related risk fac-
tors among low socio-economic status minority students in Chicago. Public health nutrition ,
():-.
. Shaw NJ, Crabtree NJ, Kibirige MS, Fordham JN: Ethnic and gender dierences in body fat in British
schoolchildren as measured by DXA. Arch Dis Child , ():-.
. van der Horst K, Chin a Paw MJM, Twisk JWR, Van Mechelen W: A brief review on correlates of
physical activity and sedentary behavior. Med Sci Sports Exerc , ():-.
. van der Horst K, Oenema A, van de Looij-Jansen P, Brug J: e ENDORSE study: research into
environmental determinants of obesity related behaviors in Rotterdam schoolchildren. BMC public
health , :.
. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH: Establishing a standard denition for child over weight
and obesity worldwide: international sur vey. Bmj , ():-.
. Wendel-Vos GC, Schuit AJ, Saris WH, Kromhout D: Reproducibility and relative validity of the short
questionnaire to assess health-enhancing physical activity. J Clin Epidemiol , ():-.
. Statistics Netherlands [ http://statline.cbs.nl]
. Wang Y, Beydoun MA: e obesity epidemic in the United States--gender, age, socioeconomic,
racial/ethnic, and geographic characteristics: a systematic review and meta-regression analysis.
Epidemiologic reviews , :-.
. Hosper K, Klazinga NS, Stronks K: Acculturation does not necessarily lead to increased physical
activity during leisure time: a cross-sectional study among Turkish young people in the Netherlands.
BMC public health , :.
. Hosper K, Nierkens V, Nicolaou M, Stronks K: Behavioural risk factors in two generations of non-
Western migrants: do trends converge towards the host population? Eur J Epidemiol , ():-
.
. Martens MK, Van Assema P, Paulussen TG, Van Breukelen G, Brug J: Krachtvoer: eect evaluation
of a Dutch healthful diet promotion curriculum for lower vocational schools. Public health nutrition
, ():-.
. Singh AS, Chin APMJ, Brug J, van Mechelen W: Short-term eects of school-based weight gain
prevention among adolescents. Archives of pediatrics & adolescent medicine , ():-.
. Crespo CJ, Smit E, Andersen RE, Carter-Pokras O, Ainsworth BE: Race/ethnicity, social class and
their relation to physical inactivity during leisure time: results from the ird National Health and
Nutrition Examination Survey, -. Am J Prev Med , ():-.
6 Socio-demographic
factors as correlates of active
commuting to school in
Rotterdam, the Netherlands
Bere E, van der Horst K, Oenema A, Prins R, Brug J. Socio-demographic
factors as correlates of active commuting to school in Rotterdam, the
Netherlands.
Preventive Medicine , : -.
144 Chapter 6 144 Chapter 6
ABSTRACT
Objective: Report frequencies of adolescents’ active commuting to school in an inner city
environment in the Netherlands, and to explore potential socio-demographic correlates of
active commuting to school.
Methods: Cross-sectional data were obtained from the ENDORSE-study (–)
including adolescents (response=), aged - from schools in Rotterdam. Socio-
demographic variables were assessed by questionnaire, height and weight were measured
and distance to school was calculated based on route planner information. Multilevel
logistic regressions were performed to analyze the data.
Results: e proportions of participants categorized as walkers, cyclists, non-active
commuters were , and respectively. With cyclists as the reference category,
adolescents of non-Western ethnic background were more likely to be walkers (OR=.;
CI=.-.) and non-active commuters (OR=.; CI=.-.), compared to native
Dutch adolescents. A further distance from home to school was inversely associated with
being a walker (OR=.; CI=.-.) and being a cyclist (OR=.; CI=.-.)
and positively associated with being a non-active commuter (OR=.; CI=.-.).
Conclusion: Almost of the adolescents reported to actively commute to school, and
mode of commuting was associated with ethnicity and distance. Further research is needed
to examine main barriers to active commuting among adolescents from non-Western ethnic
background.
Socio-demographic correlates of commuting to school 145
Chapter 6
Socio-demographic correlates of commuting to school 145
Chapter 6
INTRODUCTION
e prevalence of overweight and obesity among adolescents is increasing worldwide, as is
also the case in the Netherlands []. Evidence indicates that engaging in at least minutes
of moderate intensity physical activity on preferably all days of the week, contributes to pre-
vention of overweight and obesity and to better health [, ]. Active commuting to school is
one of the daily activities that could be an important component of the daily-recommended
level of physical activity for adolescents. A meta-analytic review stated recently that active
commuting (among adults) was associated with an reduction in cardiovascular risk [].
ere is a lack of research on trends in mode of transport to school over the past years.
In the US and Australia low and decreasing frequencies of active commuting to school have
been reported [-]. Dierent reasons have been suggested for these low and decreasing
levels of active commuting such as safety concerns, trac, road-crossing, crime, conve-
nience to drop children o on way to work and environmental factors such as walkability
and distance to school [, -].
In the Netherlands between and no clear decreases in the total number of cy-
cling and walking trips and distances have been seen in the Dutch population []. Between
and , - year old adolescents reported to cycle approximately km/per day and
to walk approximately . km/day []. e built environment in cities in the Netherlands
appears to be good for cycling compared to cities in other countries. e Netherlands have
a long tradition of cycling, which has resulted in a cycling-friendly infrastructure mak-
ing it more convenient and safer to cycle than in other countries. However, no study has
reported frequencies of active commuting to school in the Netherlands and few studies
in general have reported socio-demographic determinants of active commuting to school.
In the Netherlands, a large number of adolescents from non-Dutch ethnic backgrounds
live in the larger cities and we expect that dierences between cultures exist for the mode
of commuting to school. Better insight in socio-demographic factors associated with ac-
tive commuting to school will enable tailoring interventions aimed at the prevention of
overweight to the needs of specic risk groups. erefore, the aim of the present study was
to report frequencies of adolescents’ active commuting to school in Rotterdam, the second-
largest city in the Netherlands, and to explore potential socio-demographic correlates of
active commuting to school.
METHODS
e present study is part of the ENDORSE (Environmental Determinants of Obesity among
Rotterdam SchoolchildrEn) project on identication of important individual and environ-
mental determinants of adolescent behaviors related to overweight and obesity. e EN-
146 Chapter 6 146 Chapter 6
DORSE study is an integral part of the ongoing health surveillance system of the Municipal
Health Service in the Rotterdam area (Youth Monitor Rotterdam, YMR). e Medical Ethics
Committee of the Erasmus University Medical Center approved the ENDORSE project.
Procedure and sample
e YMR and ENDORSE studies collected data in school year / among adoles-
cents in the rst (- year olds) and third year of secondary school (- year olds). A
total of schools from the schools participating in the YMR were willing to participate
also in the ENDORSE study. Aer stratication according to location in the city, seventeen
school locations were randomly selected. On average ve classes per school location were
selected at random to participate and a total of adolescents were eligible to participate
in the ENDORSE study. Between October and May , the adolescents completed
the YMR and the ENDORSE questionnaires. Within a month aer completion of the EN-
DORSE questionnaire, two trained research assistants measured height and weight.
During administration of the ENDORSE survey adolescents were absent. Due
to printing mistakes, it was necessary to delete adolescents, including one complete
school from the sample. erefore, the study sample includes participants (), from
classes and schools; boys, . non-Dutch Western ethnic background and
non-Western ethnic background. Mean age was . years (SD=.; range .-.).
Measures
Commuting to school was measured by three questions: How many days a week do you
travel to school; () walking, () cycling, () by public transport or car. Response categories
were never, one day/week, two days/week, three days/week, four days/week, and ve days/
week. e three items were combined to one variable with four categories: () walking
days/week or more (WALKERS), () cycling three days/week or more (CYCLISTS), () non-
active commuting three days/week or more (NON-ACTIVE COMMUTERS), () pupils
where the sum of the three answers counted up to less than or more than ve days/week
(PUPILS NOT CATEGORIZED INTO MODE OF COMMUTING).
Sex, school level (vocational or university preparatory high school) and date of birth were
provided by the schools. Age was determined on the date of the anthropometrical measure-
ments. Employment of parents was assessed in the YMR questionnaire by two questions
asking whether their mother and father had paid work or not (=mother or/and father
have NOT paid work, = both mother and father have paid work). Ethnicity was assessed in
the YMR questionnaire by two questions asking in which country their mother and father
had been born. Ethnicity was dened upon the denition used by Statistics Netherlands
[]. e pupils were considered to be native Dutch if both parents had been born in the
Socio-demographic correlates of commuting to school 147
Chapter 6
Socio-demographic correlates of commuting to school 147
Chapter 6
Netherlands, the pupils were considered to be from Western ethnic background if one
or both parents had been born in another European country, North America, Oceania,
Indonesia or Japan. Adolescents with one or both parents born in a non-Western country
were considered as from non-Western ethnic background. Body Mass Index was calculated
from the measurements of height and weight, carried out by research assistants. Age and
sex specic cut o points were used to categorize adolescents in categories of normal weight
and overweight or obese []. Distance from home to school was calculated from pupil’s
reports of home address postal codes. e home address postal codes and the exact school
addresses were entered into the route planner www.routenet.nl (in March ). e length
of the optimal route for cars was derived from this service and entered into the data set
for each pupil. Distances over km were regarded as outliers, and therefore, pupils living
further than km from school were not included in the analyses ( pupils).
Statistics
Descriptive analyses of commuting to school in relation to the potential determinants
were conducted using SPSS version . Multilevel logistic regression analyses, taking the
clustering of pupils within schools into account, were preformed with walking, cycling or
non-active commuting to school as dependent variables, using MLwiN version .. Walk-
ing, cycling and non-active commuters were rst compared to the rest of the sample (e.g.
walkers, were compared to non-walkers (i.e. cyclists, non-active commuters and pupils not
categorized into mode of commuting)), and then walkers and non-active commuters were
compared to cyclists. All regression models included sex, work status of parents, ethnicity,
weight status, age, distance from home to school and school level (high school or voca-
tional). ree dummy variables were created and included in the analyses in order to keep
adolescents with missing values on one or more of the following variables in the models: ()
Work status: quite a few adolescents (n=) reported not to know whether their parents
had paid work or not, or reported to have no parent or no contact with their mother and/
or father. () Weight status: due to absence, anthropometrical measurements were lacking
for adolescents. () Ethnicity and work status: due to absence, adolescents did not
participate in the YMR survey. Odds ratios (OR) with condence interval () are given
for each independent variable.
RESULTS
Table . shows descriptive characteristics of the sample and how the dierent potential
correlates were bivariately related to commuting to school. e proportions of participants
categorized as walkers, cyclists, non-active commuters and ‘pupils not categorized into mode
148 Chapter 6 148 Chapter 6
Table 6.1 Description of the proposed determinants and the unadjusted relationship between these factors and commuting to school among adolescents in Rotterdam, the
Netherlands, school year 2005/2006 (proportions of total sample, or mean, with 95% CI)
WALKERS
(n=168)
CYCLIST
(n=471)
NON-ACTIVE COMMUTERS
(n=465)
PUPILS NOT CATEGORISED INTO
COMMUTING MODE (n= 257)
N Proportion (95% CI) Proportion (95% CI) Proportion (95% CI) Proportion (95% CI)
All 1361 12% (11, 14) 35% (32, 37) 34% (32, 37) 19% (17, 21)
Sex
Boys 752 10% (8, 12) 37% (33, 40) 36% (32, 39) 18% (15, 20)
Girls 609 16% (13, 18) 32% (28, 36) 32% (28, 36) 21% (17, 24)
Work status parents
Both parents have work 611 9% (6, 11) 44% (40, 48) 31% (27, 35) 16% (13, 19)
Not two working parents 338 18% (14, 22) 24% (20, 29) 37% (32, 42) 21% (16, 25)
Ethnicity
Native Dutch 512 4% (2, 6) 54% (49, 58) 28% (24, 32) 14% (11, 17)
Western ethnicity 75 5% (0, 11) 40% (29, 51) 36% (25, 47) 19% (10, 28)
Non-Western ethnicity 614 20% (17, 23) 18% (15, 21) 39% (35, 43) 23% (19, 26)
Weight status
Normal weight 919 11% (9, 13) 39% (36, 42) 32% (29, 35) 17% (15, 20)
Overweight or obese 299 16% (12, 20) 26% (21, 31) 37% (31, 42) 21% (17, 26)
Age (years, mean) 1361 14.5
(14.3,
14.7) 14.0
(13.9,
14.1) 14.1
(14.0,
14.2) 14.1 (14.1, 14,2)
Socio-demographic correlates of commuting to school 149
Chapter 6
Socio-demographic correlates of commuting to school 149
Chapter 6
WALKERS
(n=168)
CYCLIST
(n=471)
NON-ACTIVE COMMUTERS
(n=465)
PUPILS NOT CATEGORISED INTO
COMMUTING MODE (n= 257)
Distance to school 1308
Mean, km 1.4 (1.3, 1.6) 4.8 (4.5, 5.0) 9.7 (8.9, 10.4) 6.9 (6.0, 7.8)
Type of school
High school 553 11% (9, 14) 42% (38, 46) 33% (29, 37) 14% (11, 17)
Vocational school 808 13% (11, 15) 30% (27, 33) 35% (32, 38) 22% (19, 25)
150 Chapter 6 150 Chapter 6
of commuting’ were , , and respectively. e majority within the walking,
cycling and non-actively commuting categories, respectively, reported to walk (), cycle
() or non-actively commute () all ve school days/week (data not shown). Mean
distances from home to school were . km, . km, . km, and . km respectively for the
walkers, the cyclists, the non-active commuters and the pupils not categorized into mode of
commuting. e proportion of walkers, cyclists and non-active commuters living less than
three km away from school were , and respectively. Of the cyclists and the non-
active commuters and lived within ten km from school. Fewer adolescents from
non-Western () and Western (non-Dutch) ethnic background () reported to have
bikes at home than native Dutch adolescents (). Adolescents with two working parents
() and with at least one parent not working () reported to have bikes at home.
Comparing walkers, cyclists and non-active commuters respectively to the remaining
sample (including also the pupils not categorized into mode of commuting) (Table .);
adolescents from non-Western ethnic background (OR=.; CI=.-.) and older ado-
lescents (OR=.; CI=.-.) were more likely to be walkers, while adolescents living
further away from school (OR=.; CI=.-.) were less likely to be walkers. ose
having a parent without paid work (OR=.; CI=.-.), from Western (OR=.;
CI=.-.) and non-Western ethnic background (OR=.; CI=.-.), as well
as those living further away from school (OR=.; CI=.-.) were less likely to
be cyclists. Adolescents from Western (OR=.; CI=.-.) and non-Western ethnic
background (OR=.; CI=.-.), as well as those living further away from school
(OR=.; CI=.-.) were more likely to be non-active commuters.
Comparing walkers to cyclists (Table .); adolescents from non-Western ethnic background
(OR=.; CI=.-.) were more likely to be a walker than a cyclist, while adolescents
living further away from school (OR = .; CI=.-.) were less likely to be a walker
than a cyclist. Comparing non-active commuters to cyclists; those having at least one parent
without paid work (OR=.; CI=.-.), being from Western (OR=.; CI=.-.)
and non-Western ethnic background (OR=.; CI=.-.) and those living further away
from school (OR=.; CI=.-.) were all more likely to be non-active commuters than
cyclists.
DISCUSSION
Almost half () of the sample do actively commute to school most school days. In the
present study, dierences in mode of commuting to school were found between adolescents
from Dutch, non-Western and Western ethnic backgrounds. Cycling was the dominant
Socio-demographic correlates of commuting to school 151
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Socio-demographic correlates of commuting to school 151
Chapter 6
Table 6.2 Odds ratios for being a walker, cyclist or non-active commuter among adolescents in Rotterdam, the
Netherlands, school year 2005/2006 (reference is “all other adolescents”)
WALKER CYCLIST
NON-ACTIVE
COMMUTER
OR (95%CI) OR (95%CI) OR (95%CI)
Girls vs. boys 0.9 (0.5, 1.4) 0.8 (0.6, 1.0) 1.0 (0.7, 1.3)
Not two working parents vs. both
working 0.8 (0.4, 1.5) 0.6 (0.4, 0.9) 1.2 (0.9, 1.7)
Western ethnicity vs. native Dutch 0.7 (0.2, 2.7) 0.5 (0.3, 0.8) 2.3 (1.3, 4.2)
Non-western ethnicity vs. native
Dutch 2.0 (1.0, 4.0) 0.3 (0.2, 0.4) 3.0 (2.1, 4.4)
Overweight vs. non-overweight 1.1 (0.6, 1.9) 0.8 (0.6, 1.1) 1.1 (0.8, 1.6)
Age (years) 1.3 (1.0, 1.7) 0.9 (0.8, 1.0) 1.1 (0.9, 1.2)
Distance (km) 0.22 (0.17,
0.29) 0.83 (0.79, 0.86) 1.20 (1.16,
1.23)
Vocational vs. high school 1.2 (0.6, 2.7) 0.6 (0.3, 1.2) 0.9 (0.5, 1.7)
Table 6.3 Odds ratios for being a non-active commuter (compared to cyclists) and for being a walker
(compared to cyclist) among adolescents in Rotterdam, the Netherlands, school year 2005/2006
WALKER
vs. CYCLIST
NON-ACTIVE COMMUTER vs.
CYCLIST
OR (95% CI) OR (95% CI)
Girls vs. boys 0.9 (0.6, 1.6) 1.2 (0.9, 1.7)
Not two working parents vs. both working 1.1 (0.5, 2.1) 1.7 (1.1, 2.6)
Western ethnicity vs. native Dutch 1.0 (0.3, 4.1) 2.6 (1.3, 5.2)
Non-western ethnicity vs. native Dutch 4.1 (2.1, 8.2) 5.1 (3.3, 7.9)
Overweight vs. non-overweight 1.2 (0.6, 2.2) 1.3 (0.9, 2.0)
Age (years) 1.3 (1.0, 1.7) 1.1 (0.9, 1.3)
Distance (km) 0.32 (0.25, 0.43) 1.36 (1.28, 1.44)
Vocational vs. high school 1.7 (0.7, 4.4) 1.3 (0.5, 3.4)
mode of transport among the native Dutch adolescents, and travel to school by bike
at least three days per week. Non-active commuting was the dominant mode of transport
among adolescents from non-Western ethnicity (), followed by walking (). e
ndings are in line with a study from de Bruijn et al. [], which found native Dutch
adolescents (mean age . years) to be nearly three times as likely to use a bicycle for
general transportation as adolescents from other ethnic backgrounds. is dierence may
be explained by a dierence in culture. In the Netherlands the bicycle is traditionally an
important mode of transport and most Dutch families do have bikes []. In the present
152 Chapter 6 152 Chapter 6
study, lower proportions of immigrants ( of non-Western and of Western ethnicity)
reported to have bikes at home than native Dutch ().
Cycling seems to be a more prominent transportation mode among adolescents of higher
socio-economic position in Rotterdam. Adolescents with at least one parent without a paid
job were less likely to be cyclists, and more likely to be non-active commuters. ere was
also a disparity in the parental work status measure on having bikes at home; vs.
respectively among the “two working parents” and the “at least one parent not working”
groups. Higher SES groups have previously been reported to cycle more oen to school in
Australia; living in a high SES area increased the odds for walking and cycling to school [].
However, in the USA and Portugal opposite results have been reported; adolescents from
lower socio-economic positions were more likely to walk or cycle to school [, ].
Bivariately an association between weight status and cycling to school was observed
(Table ). However, no signicant associations between mode of commuting to school and
weight status were seen in the multivariate analyses. Similarly, other studies did not nd
clear associations between active commuting to school and overweight or BMI [-].
However, it has been reported that increased walking or cycling distance was signicantly
associated with lower fat mass []. Although some studies failed to nd an association with
overweight, active commuting to school is still an opportunity to increase physical activity
levels, and therefore contribute to a healthy lifestyle [].
Few evidence-based indications have been reported in the international literature about
how far we can expect adolescents to walk and/or cycle to school. Studies have reported that
most adolescent ‘walkers’ live within a distance of . km from school [, ]. Colabianchi
et al. [] found that girls think of an ‘easy’ walking distance as minutes (approximately
. miles/. km). In Australia, parents reported . km as an appropriate walking distance
for - year old children [], which is rather similar to the median walking distance
for the walkers in the present study (.km). However, walking to and from school, a total
of - km probably does not lead to signicant increases in energy expenditure and will
probably not have an impact on weights status. e current study indicates that adolescents
can commute rather long distances in the Netherlands, at least up to km of walking and
km of cycling (one way), and actively commuting such distances might make a dierence
for obesity prevention. Further research is needed to examine individual and environmental
barriers to active commuting especially for adolescents from non-Western ethnicities who
live within cycling and walking distance to school to tailor interventions better to the needs
and perceptions of this target group. e cultural dierences in transportation to school
might also indicate that other strategies for increasing physical activity are needed for
adolescents from non-Western ethnic background.
Socio-demographic correlates of commuting to school 153
Chapter 6
Socio-demographic correlates of commuting to school 153
Chapter 6
Study limitations
ere are some limitations of the present study. Only one Dutch city was included in the
study and since cycling in the Netherlands is so typical for the native-Dutch population,
generalization of the ndings to other countries might be dicult. No test/retest or valida-
tion data exist for the commuting to school measure. e measure can neither dierentiate
public transportation commuters from car commuters, which would be an interesting
comparison since public transportation commuters do more physical activity than car
commuters []. Using public transportation to work has also been negatively associated
with overweight and obesity among both Swedish [] and Australian [] men. e SES
measures used in this paper are rather simple and including more proper measures for
SES (family educational level, type of work, and income) might explain more of the large
ethnicity disparities observed. Ethnicity is clearly not as homogenous as the three classied
groups. Stratifying the results on more specic ethnicities would be interesting, and this
clearly is an issue for future research. Distance to school was calculated as the optimal route
for cars, and not necessarily reecting the true walking or cycling distance. e strength of
the study is that the analyses were adjusted for an objective measure of distance to school,
and that it reports walking and cycling rates from a cycling country, which might serve as a
good example for other countries.
Conclusion
Almost half of the adolescents living in an inner city environment in the Netherlands
actively commuted to school on most school days, and mode of commuting was strongly as-
sociated with ethnicity. Adolescents from non-Dutch ethnicities and from lower SES groups
are important target groups for the promotion of active commuting to school. However,
further research is needed to examine determinants of active and inactive commuting to
school to better tailor interventions to the needs and perceptions of these target groups.
ACKNOWLEDGMENTS
is study was nancially supported by grants from ZonMw, e Netherlands Organization
for Health Research and Development (grant ID no .). Elling Bere had a post doc
grant from the Norwegian Research Council, and spent one year (/) at Department
of Public Health, Erasmus MC Rotterdam. is study was part of CEPHIR: the Center for
Evidence-based Public Health In the Rotterdam area.
154 Chapter 6 154 Chapter 6
REFERENCES
. Schokker DF, Visscher TL, Nooyens AC, van Baak MA, Seidell JC: Prevalence of overweight and
obesity in the Netherlands. Obes Rev , ():-.
. Diet, nutrition and the prevention of chronic diseases. World Health Organ Tech Rep Ser , :i-
viii, -, backcover.
. Strong WB, Malina RM, Blimkie CJ, Daniels SR, Dishman RK, Gutin B, Hergenroeder AC, Must A,
Nixon PA, Pivarnik JM et al: Evidence based physical activity for school-age youth. J Pediatr ,
():-.
. Hamer M, Chida Y: Active commuting and cardiovascular risk: a meta-analytic review. Prev Med
, ():-.
. McDonald NC: Active transportation to school: trends among U.S. schoolchildren, -. Am J
Prev Med , ():-.
. Salmon J, Timperio A: Prevalence, trends and environmental inuences on child and youth physical
activity. Med Sport Sci , :-.
. Salmon J, Timperio A, Cleland V, Venn A: Trends in children’s physical activity and weight status in
high and low socio-economic status areas of Melbourne, Victoria, -. Aust N Z J Public Health
, ():-.
. van der Ploeg HP, Merom D, Corpuz G, Bauman AE: Trends in Australian children traveling to
school -: burning petrol or carbohydrates? Prev Med , ():-.
. Bringolf-Isler B, Grize L, Mader U, Ruch N, Sennhauser FH, Braun-Fahrlander C: Personal and
environmental factors associated with active commuting to school in Switzerland. Prev Med ,
():-.
. Carver A, Timperio A, Crawford D: Playing it safe: the inuence of neighbourhood safety on chil-
dren’s physical activity. A review. Health Place , ():-.
. Kerr J, Rosenberg D, Sallis JF, Saelens BE, Frank LD, Conway TL: Active commuting to school: As-
sociations with environment and parental concerns. Med Sci Sports Exerc , ():-.
. Nelson NM, Foley E, O’Gorman DJ, Moyna NM, Woods CB: Active commuting to school: How far is
too far? Int J Behav Nutr Phys Act , :.
. Sjolie AN, uen F: School journeys and leisure activities in rural and urban adolescents in Norway.
Health Promot Int , ():-.
. Timperio A, Ball K, Salmon J, Roberts R, Giles-Corti B, Simmons D, Baur LA, Crawford D: Personal,
family, social, and environmental correlates of active commuting to school. Am J Prev Med ,
():-.
. [http://www.statline.cbs.nl]
. e Ministry of Transport PWaWM, Public Works and Water Management (Rijkswaterstaat): Mobil-
ity Survey Netherlands. Published online at http://www.mobiliteitsonderzoeknederland.nl.
. Netherlands S: Hoe doet het CBS dat nou? Standaard denitie allochtonen. Voorburg; .
. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH: Establishing a standard denition for child overweight
and obesity worldwide: international sur vey. BMJ , ():-.
. de Bruijn GJ, Kremers SP, Schaalma H, van Mechelen W, Brug J: Determinants of adolescent bicycle
use for transportation and snacking behavior. Prev Med , ():-.
. Mota J, Gomes H, Almeida M, Ribeiro JC, Car valho J, Santos MP: Active versus passive transporta-
tion to school-dierences in screen time, socio-economic position and perceived environmental
characteristics in adolescent girls. Ann Hum Biol , ():-.
Socio-demographic correlates of commuting to school 155
Chapter 6
Socio-demographic correlates of commuting to school 155
Chapter 6
. Heelan KA, Donnelly JE, Jacobsen DJ, Mayo MS, Washburn R, Greene L: Active commuting to and
from school and BMI in elementary school children-preliminary data. Child Care Health Dev ,
():-.
. Landsberg B, Plachta-Danielzik S, Much D, Johannsen M, Lange D, Muller MJ: Associations between
active commuting to school, fat mass and lifestyle factors in adolescents: the Kiel Obesity Prevention
Study (KOPS). Eur J Clin Nutr , ():-.
. Rosenberg DE, Sallis JF, Conway TL, Cain KL, McKenzie TL: Active transportation to school over
years in relation to weight status and physical activity. Obesity (Silver Spring) , ():-.
. Alexander LM, Inchley J, Todd J, Currie D, Cooper AR, Currie C: e broader impact of walking to
school among adolescents: seven day accelerometry based study. BMJ , ():-.
. Colabianchi N, Dowda M, Pfeier KA, Porter DE, Almeida MJ, Pate RR: Towards an understanding
of salient neighborhood boundaries: adolescent reports of an easy walking distance and convenient
driving distance. Int J Behav Nutr Phys Act , :.
. Timperio A, Crawford D, Telford A, Salmon J: Perceptions about the local neighborhood and walk-
ing and cycling among children. Prev Med , ():-.
. Edwards RD: Public transit, obesity, and medical costs: assessing the magnitudes. Prev Med ,
():-.
. Lindstrom M: Means of transportation to work and overweight and obesity: a population-based
study in southern Sweden. Prev Med , ():-.
. Wen LM, Rissel C: Inverse associations between cycling to work, public transport, and over weight
and obesity: ndings from a population based study in Australia. Prev Med , ():-.
Part IV Individual and
environmental correlates
of energy balance-related
behaviors
7 e school food environment:
associations with adolescent so
drink and snack consumption
Van der Horst K, Timperio A, Crawford D, Roberts R, Brug J, Oenema A.
e school food environment: associations with adolescent so drink and
snack consumption.
American Journal of Preventive Medicine , (): -.
160 Chapter 7160 Chapter 7
ABSTRACT
Background: Because students may purchase food and drinks in and around their schools,
the school food environment may be important for obesity-related eating behaviors such as
so drink and snack consumption. However, research exploring the associations between
school environments and specic eating behaviors is sparse.
Methods: Associations of the availability of canteen food and drinks, the presence of food
stores around schools, and individual cognitions (attitudes, norms, modeling, perceived
behavioral control, and intentions) with so drink and snack consumption were examined
in a cross-sectional study (–) among adolescents aged – years. So
drink and snack consumption and related cognitions were assessed with self-administered
questionnaires. e presence of food stores and the distance to the nearest food store were
calculated within a -meter buer around each school. Data on the availability of so
drinks and snacks in school canteens were gathered by observation. In , multilevel
regression models were run to analyze associations and mediation pathways between cogni-
tions, environmental factors, and behaviors.
Results: Adolescents’ attitudes, subjective norms, parental and peer modeling, and inten-
tions were positively associated with so drink and snack consumption. ere was an
inverse association between the distance to the nearest store and the number of small food
stores with so drink consumption. ese eects were mediated partly by cognitions.
Conclusions: is study provided little evidence for associations of environmental factors
in the school environment with so drink and snack consumption. Individual cognitions
appeared to be stronger correlates of intake than physical school-environmental factors.
Longitudinal research is needed to conrm these ndings.
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e school food environment 161
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INTRODUCTION
Obesity is a major problem in many countries, and its prevalence is increasing [, ]. Dietary
patterns such as the consumption of fast food, snacks, and so drinks may contribute to the
development of overweight and obesity through the foods’ high energy density and large
portion sizes [-].
e theory of planned behavior (TPB) has proven to be useful in understanding cor-
relates or determinants of so drink and snack consumption [-]. According to the TPB,
behavior can be predicted from the intention to perform behavior that is determined by
attitudes, subjective norm, modeling, and perceived behavioral control [-]. However,
obesogenic dietary behaviors may also be inuenced by the environmental opportunities to
eat food [-]. In social-ecologic models, (e.g., the Environmental Research framework for
weight Gain prevention [the EnRG framework]), it is proposed that environmental factors
indirectly inuence behavior via the individual’s cognitions []. Environments that oer
appealing opportunities for unhealthy foods may result in positive cognitions regarding the
consumption of these unhealthy foods, resulting in higher intake of them.
Schools may be an important setting for obesity-prevention interventions, as many schools
provide extensive facilities for selling food and drinks [, ]. During breaks, adolescents
may also purchase food items in the immediate area around the school. Relatively few stud-
ies [, , ] are available that examine environmental factors in a school setting. ese
studies found that the number of snack vending machines was associated with student snack
purchases and lower fruit intake, and that in schools where so drink machines were turned
o during lunch time, adolescents purchased fewer so drinks [, ]. Many fast-food
restaurants are located within walking distance of a school, and an open-campus policy
during lunchtime was found to be associated with a higher likelihood of students’ eating
lunch at a fast-food restaurant [-].
e present study expands on the limited literature that explores the role of school food
environments in inuencing the dietary behaviors of youth. e overall hypothesis for this
study was that a greater availability of so drinks and snacks at school and in the school
neighborhood as well as positive cognitions would lead to a higher intake of so drinks and
snacks. A second hypothesis was that environmental factors inuence behavior via cogni-
tions, that is, that cognitive factors mediate the association between environmental factors
and behavior. is study specically aimed to () examine the associations between school
food availability and food stores in the school neighborhood with so drink and snack
consumption, () examine the associations between cognitions and so drink and snack
consumption, and () examine whether the eect of environmental factors on so drink and
snack consumption is partly mediated by cognitions (mediation eect).
162 Chapter 7162 Chapter 7
METHODS
Study Design and Sample Selection
e Environmental Determinants of Obesity in Rotterdam SchoolchildrEn (ENDORSE)
study is a prospective -year study among adolescents (aged – years) in the rst year and
third year (aged – years) of secondary school []. In –, of schools in
Rotterdam participated in the ENDORSE study. Aer stratication according to city region,
schools were selected. From them, a total of classes ( adolescents) were randomly
selected to participate in the ENDORSE study. Because of absence, printing mistakes, and a
school location outside the municipal border, it was necessary to omit respondents from
the sample. e study sample therefore included participants ( of those eligible)
from classes and schools. e Medical Ethics Committee of the Erasmus University
Medical Center reviewed the proposal and gave a declaration of no objection for the EN-
DORSE project.
Procedure
e ENDORSE study was announced through a letter to parents. Parents could refuse to
allow the participation of their child(ren). From October to May , the adoles-
cents completed a questionnaire in a lesson of approximately minutes in the presence
of a teacher and a trained research assistant. Observations of the school canteens were
performed in the same time period.
Measures
Sugar-sweetened so drink and snack consumption
So drinks were dened as carbonated drinks, other noncarbonated sugar-sweetened
drinks (water-based beverages that contain sugar), and sport drinks. e consumption of
so drinks was assessed by two questions: How many days a week do you usually drink
sugar-sweetened (not “light” or “diet”) beverages? and If you drink sugar-sweetened beverages,
how many glasses, cans, and/or bottles do you drink on average per day? Total so drink
consumption was expressed in liters per day, and was calculated from the two questions
according to Dutch standard serving sizes ( glass= ml, can= ml, bottle= ml).
e so drink consumption variable was normally distributed, allowing linear regression
analysis.
Snacks were classied as sweet (candy, candy bars, chocolate, cake, biscuits) and savory
(fast-food, pizza, fries, chips, nuts). Sweet-snack consumption was assessed by two ques-
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tions: How many days a week do you usually eat sweet snacks or cookies? and On average, how
many times a day do you eat sweet snacks or cookies? Two similar questions were asked for
savory snacks. ese questions were combined so that a single score could be computed for
the mean snack intake in times per day. As this variable was not normally distributed, snack
consumption was dichotomized by means of a median split, distinguishing adolescents
consuming two or fewer times per day (reference group) from those consuming more than
two times per day.
Personal factors
Cognitions specic to so drink and snack consumption (attitude, subjective [parental]
norm, modeling, perceived behavioral control, and intention) were assessed according to
the TRB, using a -point bipolar scale []. Attitude was assessed with two items that asked
if the adolescent considered the behavior as good or bad and as pleasant or unpleasant.
e two items were collapsed in a single attitude variable by calculating the mean item
score (Cronbach’s α=. so drink, . snack consumption). Parental norm was assessed
with one item: My parents consider consuming so drinks/snacks as good/bad. Modeling was
assessed with two items that asked if parents and friends consume a lot of or very little/very
few so drinks/snacks. Perceived behavior control was assessed with two items that asked
how easy or dicult it is to consume so drinks/snacks, and then asking if the decision
to consume so drinks/snacks is completely or not completely under the control of the
adolescent. e two items were collapsed into one variable by calculating the mean item
score (Cronbach’s α=. so drink, . snack consumption). e intention to consume
so drinks, snacks, or both was assessed with a single item that asked about the adolescent’s
intention to consume so drinks/snacks in the next months. Because of a skewed distribu-
tion, responses to all variables were dichotomized to indicate agreement with the statement
(i.e., very good or good=) or otherwise (i.e., neither good/bad, bad, very bad=).
School food environment
Two observers audited each school. An audit instrument was developed to assess the avail-
ability of food in the schools (see Appendix). e instrument was reviewed by experts and
pilot-tested. It included observations of the dierent types of so drinks/snacks that were
available from vending machines and at the canteen counter. Eight availability variables
were created: the availability of sugar-sweetened so drinks in vending machines () and
at the canteen counter (); the total availability of low-calorie drinks (); the availability of
energy-dense snacks in vending machines () and at the canteen counter (); the availability
of low-energy snacks in vending machines () and at the canteen counter (); and the total
availability of fruit and vegetables at school ().
164 Chapter 7164 Chapter 7
Products from one brand with dierent avors (e.g. Coke and Coke with vanilla avor) were
counted as two distinct products. e availability variables were re-coded into variables
with two or three categories. Where possible, a group with no products available was cre-
ated; otherwise, the variables were categorized into tertiles or dichotomized, depending on
the variation in the data.
Local neighborhood environment around school
e neighborhood around a school was dened as a crow-y buer of meters (stores
that could be accessed within a lunch break of minutes). Each school address was geo-
coded using ArcView Version ., and -meter buers were created around each school.
e municipality of Rotterdam supplied cadastral data as well as road and road-attribute
information. Records from Locatus, a company that provides information on stores in e
Netherlands, were used to identify the locations of the food establishments surrounding
each school. ese locations were geocoded, and the availability of stores (the total number
within meters) was computed for ve types of food establishments: () fast-food outlets;
() large supermarkets; () small food stores (small supermarkets, ethnic-food stores, news
agencies, stores at petrol stations); () bakeries; and () fruit/vegetable stores. e distance to
the nearest food store was calculated using the street network (walking route). All variables
were re-coded to categories based on tertiles (small food stores, bakeries, fruit/vegetable
stores, distance); the median value (fast-food outlets); and the possibility of distinguishing
a no-availability category (large supermarkets).
Demographics
Age was derived from date of birth and date of measurement. Ethnicity was dened accord-
ing to the denition used by the Netherlands Statistics. Adolescents were considered to be
from a Western ethnic background if both parents had been born in the Netherlands; in
another European country; or in North America, Oceania, Indonesia, or Japan. Adolescents
with one or both parents born in a non-Western country were considered to be from a non-
Western ethnic background. Schools provided the school-level information (higher-level
secondary education or vocational training).
Analyses
Respondents with missing data on relevant cognitive variables and so drink or snack
consumption were deleted from the sample, resulting in study samples of and
adolescents for so drink and snack consumption, respectively. In , multilevel linear
(so drinks) and logistic (snack) regression analyses were performed, using MLwiN version
e school food environment 165
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.. A three-level structure was used to take into account that adolescents were nested
within classes and classes were nested within schools [].
First, a -level, random-intercept model was tted without any explanatory variables to
examine the signicance of the between-school and between-class variance (Model ). A
signicant variance would indicate that the individual behavior clustered within schools
and/or classes []. Second, demographic factors were included in Model . To control
for possible confounding, these factors were included in all other models that were tted.
ird, school-canteen factors were added (Model ) as well as school-neighborhood factors
(Model ). Fourth, a model was tted with individual cognitions (Model ). To examine
individual cognitions as mediators of the associations between environmental factors and
behavior, the four-step procedure indicated by Baron and Kenny [] was used. Mediation
can be established if () environmental factors are associated with the outcome behavior
(Models and ); () the individual cognitions are associated with the outcome behavior
(Model ); () the environmental variables are associated with the individual cognitions
(model not presented); and () the association between the environmental factor and the
outcome behavior decreases when controlling for the mediators (Mediation Model).
RESULTS
School Environment and Participant Characteristics
Four of the schools sold fruit/vegetables, and two schools had low-energy snacks avail-
able in their vending machines. A small food store was the closest store for ve schools,
while only one school had a fruit/vegetable store as the closest store. e mean number
of food establishments within meters of schools was . (range=–), and consisted
mostly of small food stores (M=., range=–), followed by fast food outlets (M=.,
range=–); bakeries (M=., range=–); fruit/vegetable stores (M=., range=–); and
large supermarkets (M=., range=–). e mean street-network distance from a school to
the nearest food establishment was meters (range=–) (data not presented).
e demographic, cognitive, and behavioral characteristics of the sample are shown in Table
.. Adolescents reported drinking an average of liter (SD=.) of so drink per day, and
. reported consuming more than two portions of snacks per day. e adolescents had
particularly positive cognitions regarding so drink consumption.
166 Chapter 7166 Chapter 7
Table 7.1 Demographic, cognitive, and behavioral characteristics of the study population
So drink
(n=1174)a
Snack
(n=1139)
Demographics
Age (mean, SD), years 14.1 (1.2) 14.1 (1.2)
Gender (boys), % 52.9 53.6
Ethnicity (Dutch + Western immigrants),
%
49.2 50.2
School level (high school), % 46.1 46.8
Individual cognitions
Attitude (I think consuming … is good and
pleasant), %
Disagree/unsure 26.7 43.3
Agree 73.3 56.7
Parental subjective norm (If I consume…, my parents think it’s good), %
Disagree/unsure 54.2 78.4
Agree 45.8 21.6
Parental modeling (My parents consume a
lot of…), %
Disagree/unsure 79.5 92.0
Agree 20.5 8.0
Friends modeling (My friends consume a
lot of…), %
Disagree/unsure 32.8 35.2
Agree 67.2 64.8
Perceived behavioral control (I am able to determine my own consumption, and I think it is easy for me to consume
…), %
Disagree/unsure 9.3 22.1
Agree 90.7 77.9
Intention (I intend to consume … in the coming 6 months), %
Disagree/unsure 20.0 35.3
Agree 80.0 64.7
Behavior
So drink consumption (mean, SD), liters 1.05 (0.97) —
Snack consumption, %
≤2 pieces/day — 48.5
>2 pieces/day — 51.5
ae number of respondents is dierent for so drink and snack consumption as the respondents with missing
data on a behavior were excluded from the sample.
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So Drink Consumption
Table . shows the multivariate associations of the potential correlates with so drink
consumption. Signicant between-school variance was found in the null model, which
was explained by the individual-level demographics of the students (when controlling for
demographics, there remained no between-school variance).
Gender, ethnicity, and school level were signicantly associated with so drink consump-
tion. School canteen–availability factors were not associated with so drink intake. An
intermediate distance to the nearest store of – meters and the number of small food
stores were inversely associated with so drink consumption. Attitude, parental norm,
modeling from friends and parents, and intention were positively associated with so drink
consumption. No signicant association was found for perceived behavioral control.
In the mediation analyses, signicant inverse associations were found between cognitions
and the distance to the nearest store and the number of small food stores, with ORs ranging
between . and . (results not presented). e association between environmental factors
and so drink consumption decreased, with percentages ranging from to aer
controlling for the signicant cognitive variables, indicating that more small food stores and
a – meter distance to the nearest shop decreased the positive cognitions toward so
drink consumption, resulting in lower intake.
Snack Consumption
No signicant between-school and between-class variance was found for snack consump-
tion. Vocational-training students were more likely to have high snack intake compared to
those attending higher-level education (OR=.; CI=., .). No signicant associations
were found for school-canteen and school-neighborhood factors. Adolescents with positive
scores on attitude (OR=.; CI=., .); modeling parents (OR=.; CI=., .);
modeling friends (OR=.; CI=., .); parental norm (OR=.; CI=., .); and
intention (OR=.; CI=., .) were more likely to report high snack intake compared
to adolescents with more negative cognitions toward snack consumption.
DISCUSSION
is is one of the rst studies to systematically examine the association of self-reported
cognitive factors and objectively measured school-environment factors with so drink
and snack consumption among adolescents. As in other studies [-], signicant positive
168 Chapter 7168 Chapter 7
Table 7.2 School and class dierences in so drink consumption,a and the eect of individual cognitions,
school-canteen availability, and school-neighborhood factors on so drink consumption in liters per day
(unstandardized regression coecients)b
Model 0cModel 1 Model 2 Model 3 Model 4 Mediation
Random eects
Between-school variance 0.074* 0.029 0.006 0.000 0.015 0.000
Between-class variance 0.029 0.013 0.017 0.008 0.009 0.007
Demographics
Gender (girls) –0.231** –0.247** –0.245** –0.177*** –0.180**
Age 0.008 0.022 –0.005 –0.017 –0.026
Ethnicity (non-Western) 0.162*0.141*0.157*0.168*** 0.179***
School level (vocational) 0.282*** 0.561** 0.355** 0.270*** 0.258**
School canteen
Sugar-sweetened so drink counter (0=ref)
Medium –0.373
High –0.168
Sugar-sweetened so-drink vending (0=ref)
Medium –0.258
High –0.066
Low-calorie drinks (low=ref)
Medium –0.086
High 0.010
School neighborhood
Supermarket (0=ref) 0.077
Fast food (low=ref) –0.055
Small food stores (low=ref)
Medium –0.322*** –0.167*
High –0.259*–0.211*
Distance to nearest store (<200m=ref)
200–300 meters –0.376** –0.246***
>300 meters –0.098 –0.015
Individual cognitions
Attitude 0.324** 0.352**
Modeling parents 0.294** 0.320**
Modeling friends 0.190**
Parental norm 0.203** 0.214**
Perceived behavioral control 0.038
Intention 0.424** 0.426**
Note: Beta’s in bold indicate a signicant association.
aSchool and class dierences are indicated by the between-school and between-class variance. Signicance
was calculated with the Wald statistic following a chi-square distribution with 1 df. A signicant variance
would indicate that the individual behavior clusters within schools and/or classes. bUnstandardized regression
coecients express the likelihood of so drink consumption in liters per day. cAll models are adjusted for
between-school and -class variance and all variables that were included in the specic model.
*p<0.05; **p<0.001; ***p<0.01
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associations were found for individual cognitions for both behaviors. is may indicate
that cognitions are important factors to target in interventions, that is, by means of health
education techniques.
Only small associations between environmental factors and intake were found. No associa-
tions were found for the availability of products in school canteens, and associations with
school-neighborhood factors were found only for so drink consumption. e associations
found did not clearly conrm the hypothesis that a higher availability of snack food and so
drink in the school environment would be associated with higher intake of such items. e
association between the distance to the nearest food store and so drink consumption was
inconsistent, as no signicant inverse association was found for food stores located farther
than meters away from a school. is might indicate that adolescents consider
meters too far to walk and do not visit these stores to buy drinks. Another study found that
residing closer to a fast-food restaurant was associated with increased high-fat vegetable
intake (e.g., fried potatoes) by adolescents []. However, the current study did not nd
this association for snack intake, indicating that the distance to food stores may not be
important for all dietary behaviors. e inverse association between the number of small
food stores and so drink consumption was unexpected, and the opposite of the hypothesis
that the presence of more food stores would have a positive eect on intake. e inverse
association that was found might indicate that the presence of a greater range of food stores
close to schools provides a larger variety of food and drinks from which student can choose,
including more healthful options; this may account for the inverse association, but more
research is necessary.
e mediation eect that was found provides some evidence for the hypothesis that environ-
mental factors inuence so drink consumption via the cognitions, as proposed by Kremers
and colleagues [] in their EnRG framework. A possible reason why associations between
school-environment factors and intake were not detected might be that only crude measures
of the complex constructs of proximity and availability were used, without taking into ac-
count trac safety, food prices, policy, and social factors [, , ]. e use of a broad
crow-y buer instead of a network buer (a boundary based on potential walking routes)
may also be a reason for the lack of ndings. In addition, all adolescents may have enough
access to so drinks and snacks, so that the minor dierences in the availability of products
in the school environment are not a limiting factor. is is expressed in the nonsignicant
between-school variance for snack consumption and in the between-school dierence for
so drink consumption that was, in large part, accounted for by dierences in demographic
factors. Furthermore, intake was assessed based on the average intake per day instead of
the intake at school. Other studies found that adolescents consume so drinks and snacks
mainly in other settings—for instance, at home and in fast-food restaurants [, ].
170 Chapter 7170 Chapter 7
e results of the present study must be interpreted in the light of several limitations. e
cross-sectional design of the study did not allow the determination of causal eects. e use
of self-reported measures of intake is a well-known source of potential bias. e categoriza-
tion of the in-school availability measures could have led to nonsensitive categorization.
Testing the validity of the audit instrument was not possible, because a gold standard does
not exist for assessing environmental factors in the school environment. e strength of this
study is the combination of individual and objective environmental measures. However,
there are also limitations concerning GIS data, as the number of food stores in the com-
mercial database might be an under- or over-representation of the actual number of stores.
Conclusion
is study provided little evidence for associations of environmental factors in the school
environment with so drink and snack consumption, while nding clear positive associa-
tions between cognitions and so drink and snack intake. is indicates that such cogni-
tions, rather than environmental factors, should be the primary target for interventions.
However, the inverse associations between environmental factors and so drink intake
might indicate that the environment can also exert a positive inuence on dietary behaviors
and cognitions. As this is one of the rst studies to examine these factors in the school
environment, longitudinal and experimental studies are needed to draw rmer conclusions.
ACKNOWLEDGEMENTS
is study was nancially supported by a grant from ZonMw, e Netherlands Organization
for Health Research and Development (.). AT and DC are supported by fellowships
from the Victorian Health Promotion Foundation, Australia.
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Appendix. Audit instrument for school canteen availability of drinks and snacks
Yes # of
products
Category
Availability of drinks in vending machines
Sugar-containing carbonated so drinks Sugar-sweetened so drinks
Sugar-containing noncarbonated so drinks Sugar-sweetened so drinks
Carbonated diet drinks Low-calorie drinks
Noncarbonated diet drinks Low-calorie drinks
Sport drinks Sugar-sweetened so drinks
Water products Low-calorie drinks
Availability of drinks at canteen counter
Sugar containing carbonated so drinks Sugar-sweetened so drinks
Sugar containing noncarbonated so drinks Sugar-sweetened so drinks
Carbonated diet drinks Low-calorie drinks
Noncarbonated diet drinks Low-calorie drinks
Sport drinks Sugar-sweetened sof drinks
Water products Low calorie drinks
Availability of snacks in vending machines
Candy bars/chocolate products Energy-dense snacks
Peppermints/candy in a roll Energy-dense snacks
Candy in a bag Energy-dense snacks
Chips Energy-dense snacks
Cake Energy-dense snacks
Biscuit Energy-dense snacks
Low-calorie biscuits Low-energy snacks
Gingerbread Low-energy snacks
Russian salad Energy-dense snacks
Raw vegetables / salads Low-energy snacks
Fruits Low-energy snacks
Availability of snacks at canteen counters
Candy bars/chocolate products Energy-dense snacks
Peppermints/candy in a roll Energy-dense snacks
Candy (small bag) Energy-dense snacks
Candy (a piece) Energy-dense snacks
Chips Energy-dense snacks
Cake Energy-dense snacks
Biscuit Energy-dense snacks
Low-calorie biscuits Low-energy snacks
Gingerbread Low-energy snacks
Warm savory snacks Energy-dense snacks
Ice cream (cream-based) Energy-dense snacks
Water ice Energy-dense snacks
Russian salad Energy-dense snacks
Raw vegetables/salads Low-energy snacks
Fruits Low-energy snacks
172 Chapter 7172 Chapter 7
REFERENCES
. Hedley AA, Ogden CL, Johnson CL, Carroll MD, Curtin LR, Flegal KM: Prevalence of overweight
and obesity among US children, adolescents, and adults, -. JAMA , ():-.
. Lobstein T, Frelut ML: Prevalence of overweight among children in Europe. Obes Rev , ():-
.
. Swinburn BA, Caterson I, Seidell JC, James WP: Diet, nutrition and the prevention of excess weight
gain and obesity. Public Health Nutr , (A):-.
. Moreno LA, Rodriguez G: Dietary risk factors for development of childhood obesity. Curr Opin Clin
Nutr Metab Care , ():-.
. Bowman SA, Gortmaker SL, Ebbeling CB, Pereira MA, Ludwig DS: Eects of fast-food consumption
on energy intake and diet quality among children in a national household survey. Pediatrics ,
( Pt ):-.
. Paeratakul S, Ferdinand DP, Champagne CM, Ryan DH, Bray GA: Fast-food consumption among US
adults and children: dietary and nutrient intake prole. J Am Diet Assoc , ():-.
. Frary CD, Johnson RK, Wang MQ: Children and adolescents’ choices of foods and beverages high
in added sugars are associated with intakes of key nutrients and food groups. J Adolesc Health ,
():-.
. Harnack L, Stang J, Story M: So drink consumption among US children and adolescents: nutritional
consequences. J Am Diet Assoc , ():-.
. Sanigorski AM, Bell AC, Swinburn BA: Association of key foods and beverages with obesity in
Australian schoolchildren. Public Health Nutr , ():-.
. Grimm GC, Harnack L, Story M: Factors associated with so drink consumption in school-aged
children. J Am Diet Assoc , ():-.
. de Bruijn GJ, Kremers SP, Schaalma H, van Mechelen W, Brug J: Determinants of adolescent bicycle
use for transportation and snacking behavior. Prev Med , ():-.
. Kassem NO, Lee JW: Understanding so drink consumption among male adolescents using the
theory of planned behavior. J Behav Med , ():-.
. Kassem NO, Lee JW, Modeste NN, Johnston PK: Understanding so drink consumption among
female adolescents using the eory of Planned Behavior. Health Educ Res , ():-.
. Ajzen I: Attitudes, personality, and behavior: Homewood, IL, US: Dorsey Press; .
. Brug J, Lechner L, De Vries H: Psychosocial determinants of fruit and vegetable consumption. Ap-
petite , ():-.
. Vries HD, Backbier E, Kok G, Dijkstra M: e impact of social inuences in the context of attitude,
self-ecacy, intention, and previous behavior as predictors of smoking onset. Journal of applied social
psychology , ():-.
. Swinburn B, Egger G, Raza F: Dissecting obesogenic environments: the development and application
of a framework for identifying and prioritizing environmental interventions for obesity. Prev Med
, ( Pt ):-.
. van der Horst K, Oenema A, Ferreira I, Wendel-Vos W, Giskes K, van Lenthe F, Brug J: A systematic
review of environmental correlates of obesity-related dietary behaviors in youth. Health Educ Res
, ():-.
. Ball K, Timperio AF, Crawford DA: Understanding environmental inuences on nutrition and physi-
cal activity behaviors: where should we look and what should we count? Int J Behav Nutr Phys Act
, :.
e school food environment 173
Chapter 7
e school food environment 173
Chapter 7
. Kremers SP, de Bruijn GJ, Visscher TL, van Mechelen W, de Vries NK, Brug J: Environmental inu-
ences on energy balance-related behaviors: a dual-process view. Int J Behav Nutr Phys Act , :.
. Johnston LD, Delva J, O’Malley PM: So drink availability, contracts, and revenues in American
secondary schools. American journal of preventive medicine , ( Suppl):S-.
. O’Toole TP, Anderson S, Miller C, Guthrie J: Nutrition ser vices and foods and beverages available at
school: results from the School Health Policies and Programs Study . e Journal of school health
, ():-.
. Kubik MY, Lytle LA, Hannan PJ, Perry CL, Story M: e association of the school food environment
with dietary behaviors of young adolescents. Am J Public Health , ():-.
. Neumark-Sztainer D, French SA, Hannan PJ, Story M, Fulkerson JA: School lunch and snacking
patterns among high school students: associations with school food environment and policies. Int J
Behav Nutr Phys Act , ():.
. Kipke MD, Iverson E, Moore D, Booker C, Ruelas V, Peters AL, Kaufman F: Food and park environ-
ments: neighborhood-level risks for childhood obesity in east Los Angeles. J Adolesc Health ,
():-.
. Austin SB, Melly SJ, Sanchez BN, Patel A, Buka S, Gortmaker SL: Clustering of fast-food restaurants
around schools: a novel application of spatial statistics to the study of food environments. Am J Public
Health , ():-.
. Zenk SN, Powell LM: US secondary schools and food outlets. Health Place .
. van der Horst K, A O, van de Looij PM, H B: e ENDORSE study: Research into environmental
determinants of obesity related behaviors in Rotterdam Schoolchildren. BMC Public Health to be
resubmitted.
. Twisk JWR: Applied Multilevel Analysis: A Practical Guide for Medical Researchers. edition: Cam-
bridge University Press; .
. Merlo J, Chaix B, Yang M, Lynch J, Rastam L: A brief conceptual tutorial of multilevel analysis in so-
cial epidemiology: linking the statistical concept of clustering to the idea of contextual phenomenon.
J Epidemiol Community Health , ():-.
. Baron RM, Kenny DA: e moderator-mediator variable distinction in social psychological research:
conceptual, strategic, and statistical considerations. J Pers Soc Psychol , ():-.
. Jago R, Baranowski T, Baranowski JC, Cullen KW, ompson D: Distance to food stores & adolescent
male fruit and vegetable consumption: mediation eects. Int J Behav Nutr Phys Act , ():.
. Hilbert A, Rief W, Braehler E: What determines public support of obesity prevention? J Epidemiol
Community Health , ():-.
. Nollen NL, Befort CA, Snow P, Daley CM, Ellerbeck EF, Ahluwalia JS: e school food environment
and adolescent obesity: qualitative insights from high school principals and food service personnel.
Int J Behav Nutr Phys Act , :.
. Savige G, Macfarlane A, Ball K, Worsley A, Crawford D: Snacking behaviours of adolescents and their
association with skipping meals. Int J Behav Nutr Phys Act , ():.
. French SA, Lin BH, Guthrie JF: National trends in so drink consumption among children and
adolescents age to years: prevalence, amounts, and sources, / to /. J Am Diet
Assoc , ():-.
8 Do individual cognitions
mediate the association of
socio-cultural and physical
environmental factors with
adolescent sports participation?
Van der Horst K, Oenema A, te Velde SJ, Brug J. Do individual cognitions
mediate the association of socio-cultural and physical environmental
factors with adolescent sports participation?
Public Health Nutrition (submitted).
176 Chapter 8176 Chapter 8
ABSTRACT
Objective: To examine the associations of perceived physical environmental factors (avail-
ability of physical activity attributes at home, physical activity facilities in the neighbour-
hood, neighbourhood pleasantness and safety) and social environmental factors (parental
sports behaviour and parental rule regarding sports participation) with adolescent leisure
time sports participation, and to explore whether the associations found were mediated by
individual cognitions as derived from the eory of Planned Behaviour (TPB).
Design: Cross-sectional
Setting: Adolescents from schools in Rotterdam, the Netherlands, completed a ques-
tionnaire during school hours that included self-report measures of leisure time sports
participation, the perceived physical environmental factors and TPB variables. Information
about parental sports behaviour and parental rule was obtained from a questionnaire that
was completed by one parent of the adolescents.
Subjects: Data was collected from adolescent – parent combinations.
Results: Data was analyzed with multilevel logistic regression analyses. Availability of
physical activity attributes at home (OR = .), parents’ sports behaviour (OR = .) and
parental rule (OR = .) were associated with a higher likelihood of adolescents’ leisure
time sports participation. ese associations were partly mediated by attitude and intention.
Conclusions: Adolescents were more likely to engage in leisure time sports when PA at-
tributes were available at home, when parents participated in sports activities and had a
rule about their ospring participation in sport activities. ese associations were partly
mediated by attitude and intention. ese results suggest that parents can importantly
promote sports participation among their ospring by making sports activities accessible
and a family routine.
Environmental factors and sports participation 177
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Environmental factors and sports participation 177
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INTRODUCTION
Insucient physical activity (PA) is a risk factor for a range of chronic conditions including
obesity, among adults as well as adolescents [, ]. Most adolescents do not meet the recom-
mended minimum levels of engaging in at least minutes of moderate to vigorous inten-
sity PA each day [-]. Adolescents are a particularly important target group to improve PA
levels since physically active adolescents are more likely to become active adults []. To be
able to increase PA levels among adolescents, it is important to develop interventions that
target the most important determinants of PA.
In addition to individual cognitions such as attitude, subjective norm, perceived behavioural
control and intention, as derived from the eory of Planned Behaviour (TPB) [] physical
and social environmental factors may be important determinants of PA behaviour. Kremers
and colleagues, in their Environmental Research framework for weight Gain prevention
(EnRG) [] suggest that environmental factors may have a direct and an indirect association
with behaviour. e direct association reects a more automatic and unconscious eect of
the environment on behaviours. e indirect inuence suggests that environmental factors
inuence PA via the individual cognitions, e.g. environments that oer appealing and easily
accessible opportunities for PA may result in more positive attitudes, perceived behavioural
control and intentions toward leisure time PA, which may result in higher PA levels. e
TPB also assumes that the impact of various external variables such as physical and social
environmental factors on behaviour is mediated by attitude, subjective norm, perceived
behavioural control and intention.
Physical environmental factors such as the availability and accessibility of PA opportuni-
ties have received most attention in exploring environmental determinants of PA [-].
However, a recent review indicated that the evidence for the role of social environmental
factors is stronger [-].
Earlier studies have found that among adults the association of perceived neighbourhood
with walking was mediated by attitude [] and that associations of perceived neighbour-
hood aesthetics with walking were mediated by attitude and intention []. De Bruijn
and colleagues found that the association of environmental aesthetics and distance to PA
facilities on PA among adolescents was mediated by intention to be physically active [].
Motl and colleagues found that the association of equipment accessibility with adolescent
girls PA was mediated by self-ecacy []. ese previous studies indicate that some TPB
variables may be more likely to serve as a mediators in environment – behaviour relation-
ships than others [, ], with the strongest evidence for attitudes as a potential mediating
variable [-, , ]. Most previous studies have investigated mediation pathways for
physical environmental factors.
178 Chapter 8178 Chapter 8
e aim of the present study was to examine the associations between physical environ-
mental factors (availability of PA attributes at home, PA facilities in the neighbourhood,
neighbourhood pleasantness and safety) and social environmental factors (parents own
sports behaviour and parental rule about sport participation) with adolescents’ leisure
time sports participation and to explore whether these associations are mediated by TPB
variables (gure ).
METHODS
Study design and sample selection
Baseline data from the ENvironmental Determinants of Obesity in Rotterdam Schoolchil-
drEn (ENDORSE) study were used [] for which data were collected among adolescents in
the rst (- to -year-olds) and third (- to -year-olds) years of secondary school. e
Medical Ethics Committee of Erasmus University Medical Center declared no objection to
the project. Schools located in the Rotterdam area that participate in the Youth Monitor
Rotterdam (YMR) (N = ) were invited for participation in the ENDORSE study. Sub-
sequently, a random sample of school locations was drawn from the pool of schools
that were willing to participate. On average, ve classes per school location were randomly
selected to participate in the study and adolescents and their parents were eligible for
participation. In the baseline survey, adolescents were absent during the questionnaire
assessment. Due to questionnaire printing mistakes, records, including those from one
entire school had to be removed. Response rate for the parent questionnaire was , result-
ing in adolescent – parent combinations. ere was no data available on the parents and
adolescents that did not participate in the study and examining response bias was therefore
not possible. Compared to the data from the total adolescent sample, in the sample used for
this study adolescents with a non-Western ethnic background (. compared to .)
and attending vocational schools (. compared to .) were underrepresented.
Procedure
Parents received a letter announcing and explaining the ENDORSE study and could refuse
participation of their child(ren) by sending a note to the adolescent’s teacher. Between October
and May , the students completed the ENDORSE questionnaire in the classroom in
the presence of a teacher and a trained research assistant within one school hour ( minutes).
e adolescents were handed a questionnaire with a pre-addressed and stamped envelope for
completion by one of their parents. To increase the participation rates, ve I-pods were raed
amongst the parent respondents and two reminders were send to the parents.
Environmental factors and sports participation 179
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Environmental factors and sports participation 179
Chapter 8
Measures
Leisure time sports participation
e relevant questions from the Activity QUestionnaire for Adolescents and Adults
(AQUAA) were used to assess leisure time sports activities []. e test-retest reliability
for this questionnaire was moderate (intraclass correlations .-.) and validity with
accelerometer data was low (Spearman correlation coecient = . for vigorous activities)
[]. No validity data are available on sports behavior. First, adolescents were asked to write
in text boxes in a pre-structured format up to three sports activities that they had engaged
in, in the past week. Adolescents were asked to write down organized and unorganized
sports they engaged in. Second, they had to tick on how many days in the past week ( to
) they had engaged in this activity. ird, they had to indicate how long on average they
participated in this activity per occasion, in an open answering format with text boxes to
indicate hours and minutes. e frequency and duration of the activities were multiplied,
and then divided by the total number of days to provide the average minutes per day spent
doing leisure time sports activities. As this variable was highly skewed and the distribution
could not be improved through transformation, two categories were created: engaging in
leisure time sports activities for less than minutes per day (coded as ), or minutes or
more per day (coded as ). is cut-o point for sports behaviour was based on the Dutch
physical activity recommendation that children and adolescents should engage in moderate
to vigorous physical activity at least minutes per day and practice sports at least days
a week.
TPB variables
Attitude, subjective parental norm, perceived behaviour control and intention were spe-
cically assessed in relation to participation in leisure time sports activities. All questions
could be answered on ve-point bipolar answering scales. Attitude was assessed with two
items asking if the adolescent considers sports and PA in leisure time as very good (+)
or very bad (-) and as very pleasant (+) or very unpleasant (-). e mean item score
(Cronbach’s alpha = ., Intraclass correlation = .) for these items was calculated [].
Subjective norm was assessed with one item: ‘if I engage in sports and PA in leisure time,
my parents consider that as very good (+) – very bad (-)’. Perceived behaviour control was
assessed with one item asking how easy or dicult it is to engage in sports and PA in leisure
time with an answering scale ranging from very easy (+) to very dicult (-). Intention to
perform the behaviour was assessed with a single item: ‘Do you intend to engage in sports
and PA in leisure time in the next six months?’ with an answering scale ranging from Yes, I
certainly do (+) to no, I certainly do not (-).
180 Chapter 8180 Chapter 8
Physical environmental variables
We assessed availability of PA opportunities at home by providing a list of ten sport ‘at-
tributes’ (i.e. bicycle, dog, home trainer /treadmill, running shoes, stationary aerobic
equipment, step aerobics, skates, balls, racquets, jumping rope). is list was translated
from Sallis et al. []. e adolescents could tick which of these were available in their
home. A score of PA attributes available at home was calculated by adding up the “yes”
responses to these questions. Perceived neighbourhood pleasantness was assessed with two
questions: “I think my neighbourhood provides a pleasant living environment”, and “I think
my neighbourhood is attractive”, that could be answered on -point scales ranging from
totally agree (+) to totally disagree (-). e mean item score (Cronbachs alpha = .,
Intraclass correlation = .) of these two items was calculated. Perceived neighbourhood
safety was assessed with four questions: “ere is a lot of trac in my neighbourhood”, “It is
unsafe to bicycle in my neighbourhood”, “I feel safe when I am in my neighbourhood”, and
“It is unsafe to be outside in my neighbourhood”, using the same ve point answering scale
format as neighbourhood attractiveness. e mean item score (Cronbachs alpha = .) of
these four items was calculated. Perceived availability of PA facilities in the neighbourhood
was assessed by asking the adolescents to indicate whether or not (yes/no answering format)
there were parks, sports clubs, sports/playing grounds present in the neighbourhood where
they lived. e yes responses were summed to form one score for these four items.
Social environmental variables
Parental rule about PA was assessed with one question in the questionnaire for parents: “is
it a rule in your household that your child has to participate in sport activities?” in a yes/
no answering format. Parents own sports behaviour was assessed in the questionnaire for
parents with two questions assessing frequency and duration, using the relevant questions
from the SQUASH questionnaire []. e Spearman correlation for overall reproducibility
of the SQUASH was . ( CI = .-.), and correlations for the reproducibility of
leisure time sport was .. Spearman’s correlation coecient between activity monitor
readings and the total activity score was . ( CI = .-.) []. Frequency was
assessed with: “How many days per week do you engage in sports activities?” on a -point
scale from = day per week to = every day. e duration was assessed with “On a day
that you participate in sports activities, how long do you do this on average?” and hours and
minutes could be reported. e frequency and duration of the activities were multiplied,
and then divided by the total number of days to provide the average minutes per day. As
this variable was highly skewed, two categories were created: engaging in sports activities
(coded as ) and not engaging in sports activities.
Environmental factors and sports participation 181
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Environmental factors and sports participation 181
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Demographics
To establish ethnicity according to the Statistics Netherlands denition, adolescents were
asked to report in which country their parents had been born []. Adolescents were consid-
ered to be from a Western ethnic background if both parents had been born in a European
country, North America, Oceania, Indonesia or Japan. Based on the socio-economic and
cultural position of immigrants in the Netherlands from Oceania, Japan and Indonesia (a
former colony of the Netherlands), children from these immigrants were also included in
the Western ethnic group. Adolescents were considered to be from a non-Western ethnic
background if one or both parents had been born in other countries. e school type the
adolescents attended was categorized into two levels: vocational schools and higher-level
secondary education (pre-academic). e schools provided educational level information.
Age was determined based on the date of the measurements and the date of birth that were
provided by the schools.
Data analyses
Possible multi-collinearity problems were examined with bivariate correlations and not
encountered; all inter-correlations between predictors were below ..
Mediation analyses according to suggestions of MacKinnon () were used to identify
total eects, direct eects and mediated eects in the associations of physical environmental
factors (availability of PA attributes at home, availability of PA facilities in the neighbour-
hood, perceived neighbourhood pleasantness and safety) and socio-cultural environmental
factors (parents sports behaviour and parental rule about sport participation) with ado-
lescents’ leisure time sports participation[]. To do so, we explored associations between
the environmental variables and TPB variables with multivariate linear regression analyses
(step , path a in gure ). Next, we examined if the potential mediators from the TPB were
associated with leisure time sports, aer adjustment for the environmental variables (step ,
path b in gure ). e total eect of physical environmental factors and socio-cultural envi-
ronmental factors on adolescents’ leisure time sports participation (step , path c), and aer
adjustment for the possible mediator, the direct eect of environmental variables on sports
participation (step , path c’), were examined in various models. As suggested by MacKin-
non [] and also outlined by Cerin and MacKinnon [], a signicant association between
environmental variables (predictor variables) and sports participation (outcome variable)
is not a requirement for mediation to occur, since absence of an overall relationship may
be due to suppression eects. erefore also non-signicant environmental factors were
included in the mediation analyses. Steps - were examined by means of multi-level multi-
variate logistic regression analyses. All analyses were adjusted for gender, age, ethnicity and
school level, as these are possible confounding factors. All analyses were performed with
MLwiN version .. A three-level structure was used to take into account that adolescents
182 Chapter 8182 Chapter 8
were nested within classes and classes within schools []. Because of the dichotomous
outcome variables there are dierent scales across the (logistic) regression analyses that
makes it incorrect to use the ‘dierence-of-coecients estimate’ as an estimate of the media-
tion eect [, ]. One solution to overcome this dierence in scaling is to standardize the
regression coecients before mediation is estimated [, ]. e standardized coecients
were subsequently used to estimate the proportion mediated ((cstandardized – c’standardized)/cstandard-
ized) and were additionally entered in the Sobel test [] to formally test the mediation eect.
RESULTS
Sixty percent of the adolescents reported to participate in leisure time sports for more than
minutes per day. e adolescents reported positive cognitions regarding leisure time
sports participation. On average adolescents reported to have four of the listed PA attributes
available at home (range -) and to have three PA facilities available in their neighbor-
hood (range -), and reported positive perceptions of neighborhood safety (mean = .,
SD = .) and pleasantness (mean = ., SD = .). A majority of the parents reported
that it was a rule in the household that the adolescent had to do some kind of sports ()
and of the parents participated in leisure time sports activities themselves (Table .).
Associations between environmental factors and TPB variables
Multivariate analyses showed that most of the physical and social environmental variables
were signicantly positively associated with TPB variables (Table .). No associations were
found for neighborhood facilities with attitude, perceived behavior control and intention.
PA attributes at home and neighborhood safety were not associated with perceived behavior
control. Parents’ sport behavior was not associated with subjective parental norm.
Figure 8.1 Conceptual model of the direct and indirect association of physical and socio-cultural environmental
factors
Figure 9.1 Moderating contextual influence of parenting style
Cphys
a
b
Csocial
Physical environment
PA attributes at home
Neighborhood safety
Neighborhood pleasantness
Neighborhood facilities
Socio-cultural environment
Parental rule
Parent behavior
TPB variables
Attitude
Parental norm
Perceived behavioral
control
Intention
Sports
participation
a
Parenting practices Adolescent
outcome
Parenting style
- Strictness
- Involvement
Figure 8.1 Conceptual model of the direct and indirect association of physical and socio-cultural
environmental factors
Environmental factors and sports participation 183
Chapter 8
Environmental factors and sports participation 183
Chapter 8
Table 8.1. Characteristics of the study population (N=584)
Variable Percentage / Mean; SD Range Cronbachs alpha
/ Intraclass
correlation
Behavior
Sports participation (>= 30 min/week) 59.8%
Demographics
Gender (girls) 45.2%
Ethnicity (non-Western) 36.6%
Age M=13.91; SD=1.13 11.9 – 17.6
Educational level (lower) 49.1%
Individual cognitions
Attitude M=1.29; SD=0.64 -2 – 2 0.79 / 0.66
Parental norm M=1.50; SD=0.61 -2 – 2
Perceived behavior control M=1.12; SD=0.81 -2 – 2
Intention M=1.57; SD=0.79 -2 – 2
Physical environment
PA attributes at home M=3.97; SD=2.08 0 – 10
Neighborhood safety M=0.64; SD=0.76 -2 – 2 0.64
Neighborhood pleasantness M=0.76; SD=0.99 -2 – 2 0.78 / 0.64
Neighborhood facilities M=3.26; SD=0.95 0 – 4
Socio-cultural environment
Parental rule to play sports (yes) 65.8%
Parent behavior (parent does practice sports) 59.4%
Associations of TPB variables with adolescents’ leisure time sports participation
To establish a mediation eect, the potential mediators must be associated with the outcome
variable aer adjustment for de independent, environmental variables []. Multivariate
analyses (Table .) showed that attitude and intention were found to be signicantly as-
sociated with a higher likelihood of participating in leisure time sports aer adjustment
for physical environmental variables (attitude OR = .; intention OR = .) and aer
adjustment for social environmental variables (attitude OR = .; intention OR = .).
Mediating eects of TPB variables
As neighborhood facilities were not associated with attitude and intention, this factor was
not included in the mediation models (Table .). e association of PA attributes at home
with a higher likelihood of participating in leisure time sports was partly mediated by at-
titude (.) and intention (.), as indicated by the signicant Sobel test results. e
184 Chapter 8184 Chapter 8
association between neighborhood safety and sports participation was signicantly medi-
ated by intention; however, both direct and indirect associations were not signicant. e
direction of the association between neighborhood pleasantness and sports participation
changed aer adjustment for attitude, which suggests an inconsistent mediation model.
e associations of parental rule and parents’ sports behavior with a higher likelihood of
participating in leisure time sports were also partly mediated by attitude and intention with
Table 8.2 Results of multivariate linear regression analyses (unstandardized regression coecients) of physical
and social environmental variables with TPB variables as dependent variables, adjusted for age, gender,
ethnicity, school type and clustering within classes and schools
Attitude Parental norm Perceived behavioral
control
Intention
PA attributes at home 0.039** 0.042** 0.026 0.056***
Neighborhood safety 0.146*** 0.120*** 0.016 0.186***
Neighborhood pleasantness 0.168*** 0.090*** 0.105** 0.115***
Neighborhood facilities 0.035 0.059** 0.028 0.049
Parental rule 0.141*0.121*0.174*0.166*
Parent behavior 0.135* -0.001 0.185** 0.180**
*p<0.05; **p<0.01, ***p<0.001
Table 8.3 Results of multivariate logistic regression analysis (odds ratios) examining the association between
potential mediators (TPB variables) and leisure time sports participation (>= 30 min), adjusting for the
physical (model 1) and social (model 2) environmental factorsa
Model 1 Model 2
OR 95% CI OR 95% CI
Physical environmental factors
PA attributes at home 1.20 1.07-1.35
Neighborhood safety 1.22 0.86-1.73
Neighborhood pleasantness 0.89 0.68-1.16
PA facilities in the neighborhood 1.02 0.83-1.23
Socio-cultural environmental factors
Parental rule 1.37 0.85-2.20
Parents sports behavior 1.79 1.14-2.80
TPB variables
Attitude 2.30 1.46-3.61 2.24 1.48-3.39
Parental norm 1.21 0.82-1.80 1.29 0.87-1.92
Perceived behavioral control 1.00 0.76-1.33 0.96 0.72-1.27
Intention 2.10 1.47-3.02 2.03 1.42-2.91
a Multivariate logistic regression analyses adjusted for age, gender, ethnicity, school type and clustering within
classes and schools
Environmental factors and sports participation 185
Chapter 8
Environmental factors and sports participation 185
Chapter 8
percentages ranging between . and . (Table .). e associations of PA attributes at
home and parent behavior with leisure time sports remained statistically signicant, while
the association of parental rule lost signicance in the mediation models.
DISCUSSION
In this study, associations of socio-cultural and physical environmental factors with ado-
lescents’ leisure time sports participation were examined and it was explored if these as-
sociations were mediated by individual cognitions such as attitudes and intentions. Results
showed that parents’ sport behaviour, parental rule about sports behaviour and availability
of PA attributes at home were associated with a higher likelihood that adolescents engaged
in sports behaviour. We cannot draw conclusions upon the ndings that resulted from an
inconsistent mediation model []. e inconsistent model is a result of the fact that the
direct association between neighbourhood pleasantness and sports participation was weak
but positive, while the indirect association was also weak and non signicant, but negative.
Table 8.4 Results of logistic regression analyses to examine the mediation of the association between physical
environmental factors with leisure time sports participation (>= 30 min) by attitude (model 2) and intention
(model 3) a
Model 1 Model 2 Sobel
test
Proportion
mediated b
Model 3 Sobel
test
Proportion
mediated b
OR 95% CI OR 95% CI z score % OR 95% CI z score %
Physical
environmental
factors
PA attributes at
home
1.26 1.13-
1.42
1.24 1.10-
1.39
2.54 ** 17.4 1.22 1.09-
1.37
2.86** 21.6
Neighborhood
safety
1.28 0.93-
1.77
1.32 0.94-
1.86
3.40 *** -3.4c1.16 0.83-
1.63
3.37*** 44.3
Neighborhood
pleasantness
1.06 0.83-
1.36
0.87 0.66-
1.13
4.40*** 312.2d1.01 0.78-
1.31
2.92** 82.2
TPB variables
Attitude 3.44 2.33-
5.08
Intention 2.71 1.93-
3.80
a Multivariate logistic regression analyses adjusted for age, gender, ethnicity, school type and clustering within
classes and schools
b as calculated with the standardized coecients (see methods section)
c,d Negative values and values > 100% indicate inconsistent mediation models and the results cannot be
interpreted.
* p< 0.05; ** p< 0.01; *** p< 0.001
186 Chapter 8186 Chapter 8
Evidence was found for partial mediation of social and physical environmental factors
by attitude and intention. However, also a direct signicant association remained of PA
attributes at home and parental behaviour with leisure time sports participation. ese nd-
ings are in accordance with those of earlier studies where attitudes were found to be strong
mediators of the association between physical environmental factors and PA [-, , ].
e results suggests that both direct, automatic inuences of the environment and more
reasoned cognitive processes are important in adolescents’ sports participation as suggested
in the EnRG framework [] However, as we found rather strong associations of attitude
and intention with leisure time sports participation in this study, adolescents leisure time
sports behaviour seems to be, at least partly, also the result of a more reasoned, deliberate
process, that is not inuenced by the environmental factors considered in the present study.
is does make sense, since sport activities are less likely to be part of routine habits, more
likely to need to be planned in advance and to be dependent on explicit positive motivation,
than, for example, daily activities. e present results support this hypothesis to a certain
extend, as we found signicant but small correlations between environmental factors and
cognitions. is association of cognitions, independent from the environmental factors is
not clearly stated in the EnRG framework that focuses on cognitions as mediators of envi-
ronmental inuences. Two of the four cognitions included in the present study, i.e. parental
subjective norm and perceived behavioural control, were not found to be associated with
Table 8.5 Results of logistic regression analyses to examine the mediation of the association between social
environmental factors with leisure time sports participation (>= 30 min) by attitude (model 2) and intention
(model 3) a
Model 1 Model 2 Sobel
test
Proportion
mediated b
Model 3 Sobel
test
Proportion
mediated b
OR 95% CI OR 95% CI z score % OR 95% CI z score %
Social
environmental
factors
Parental rule 1.64 1.05-
2.56
1.51 0.95-
2.42
2.20* 23.1 1.45 0.91-
2.78
2.43* 31.7
Parent behavior 2.03 1.26-
3.25
1.85 1.19-
2.87
2.29* 20.1 1.80 1.16-
2.78
2.13* 23.3
TPB variables
Attitude 3.38 2.32-
4.91
Intention 2.77 2.00-
3.85
a Multivariate logistic regression analyses adjusted for age, gender, ethnicity, school type and clustering within
classes and schools
b as calculated with the standardized coecients (see methods section)
* p<0.05
Environmental factors and sports participation 187
Chapter 8
Environmental factors and sports participation 187
Chapter 8
sports behaviour. is might indicate that not all cognitions as suggested by the eory
of Planned Behaviour are important mediators or play a role in the suggested reasoned
process for this particular behaviour in this population group. On the other hand, the non-
signicant results might be caused by the limited assessment of these constructs with only
one or two items.
In accordance with other studies, social factors seem to be more strongly associated with
physical activity behaviour than physical environmental factors [-]. e direct associa-
tions of parents’ sports behaviour and the availability of PA equipment at home, support
earlier evidence that parental example and support (for instance through providing good
sports equipment at home), are important for PA promotion. e range of social factors
considered in the present study was narrow, and factors such as social networks, friends’
support and behaviour and the perceived behaviour of parents should be included in future
studies to provide further insight into the specic aspects of the social environment that are
most important for adolescents PA behaviours [, ]. Next to this, future research should
address the moderating eects of socio-demographic factors mentioned in the EnRG
framework as there are clear gender dierences in sports participation and correlates of
physical activity might be dierent for boys and girls. For example, mothers’ physical activ-
ity appears to be more oen associated with girls’ rather than boys’ physical activity [].
One possible limitation of this study was that we used perceptions of the environment
instead of more objective measures of the physical environment. Perceived environmental
factors are, of course, also cognitive representations (i.e. of environmental factors), and
dierent mediating pathways may be apparent with more objective assessments of the
environment. Evidence points out that perceived and objective environmental factors are
dierent constructs [] and that perceptions of the environment only partly depend on
what is objectively available in the environment []. Studies exploring TPB variables as
well as perceived environmental factors as mediators of the associations between objective
environmental characteristics and PA behaviour may help to unravel the interplay between
individual and environmental factors in inuencing energy balance-related behaviours as
proposed in the EnRG framework.
e following limitations should be taken into account when interpreting the results of this
study.
e cross-sectional design of the study did not allow us to determine causal eects and
is an important limitation in research examining mediation pathways. Physically active
adolescent might be more aware of physical activity equipment in their environment and
they might select more or less the neighbourhood they are active in by having a specic
denition about how large the neighbourhood is. Having positive cognitions towards sports
188 Chapter 8188 Chapter 8
might shape the adolescents environment. For instance, adolescents might inuence their
parents by promoting sports activities and asking for more equipment. Next to this, the
sample size of this study was restricted because of the rather low response rate for the parent
questionnaire. e fact that adolescents from non-Western ethnic background and lower
educational level were underrepresented in the sample suggests selection bias. Several limi-
tations relate to the measurement instruments used in the study. First, adolescents sports
behaviour was based on self-report and in a validation study with use of accelerometers was
shown that the questionnaire had limited validity and that adolescents over-reported their
activity levels []. Second, the TPB variables were assessed with only one or two items
leading to limited reliability. e TPB variables, particularly perceived behaviour control,
might have been not robust enough to demonstrate associations and to show up as a media-
tor. Environmental constructs were oen measured with only one ore two items with only
moderate reliability. In explorative research more eort needs to be done to construct better
scales that examine all aspects of the perceived environmental factors. More qualitative
research is needed to improve existing measurement instruments and scales. Next to this,
only a limited set of perceived environmental variables was used in this study. Especially
other social environmental inuences such as encouragement of parents and friends might
be important in explaining physical activity behaviours [, , ]. Parents’ sports behaviour
was also assessed with other questions compared to adolescents’ sports participation and
other cut-o points were used. is could also have aected the associations found. ird,
adolescents with overweight or lack of physical activity might have given social desirable
answers on sports behaviour and on the theory of planned behaviour items as well, which
could have inuenced the associations between cognitions and behaviour.
Nevertheless, this explorative study contributes to the structured examination of the as-
sociations between environmental factors and physical activity and the suggested mediation
by TPB variables as supposed by the EnRG framework.
Conclusion
Dutch adolescents were more likely to engage in leisure time sports when PA attributes
were available at home, when parents participated in sports activities and when parents had
the rule in their household that the child has to play a sport. ese associations were partly
mediated by attitude and intention. is indicates that parents are important actors in shap-
ing the environmental factors of interest by making sports activities accessible and a family
routine. erefore, not only adolescents, but also parents should be targeted in interven-
tions aiming to improve PA among adolescents. However, the cross-sectional design of this
study should be taken into account and the ndings have to be veried in longitudinal and
experimental studies. Eort needs to be done to construct better measurement instruments
and scales to examine perceived environmental factors.
Environmental factors and sports participation 189
Chapter 8
Environmental factors and sports participation 189
Chapter 8
Acknowledgements
is study was part of CEPHIR: the Center for Eective Public Health in the larger Rot-
terdam Area. Financial support for this study was provided by grants from ZonMw, the
Netherlands Organization for Health Research and Development (grant ID no. .)
and e World Cancer Research Fund - WCRF-NL (Grant number; /). is paper
has been facilitated by the EU funded HOPE project: “Health-promotion through Obe-
sity Prevention across Europe (the Commission of the European Communities, SPA-
CT--). e study does not necessarily reect the Commission’s views and in no
way anticipates the Commission’s future policy in this area’.
190 Chapter 8190 Chapter 8
REFERENCES
. Boreham C, Riddoch C: e physical activity, tness and health of children. J Sports Sci ,
():-.
. Wareham NJ, van Sluijs EM, Ekelund U: Physical activity and obesity prevention: a review of the
current evidence. e Proceedings of the Nutrition Society , ():-.
. Lampert T, Mensink GB, Romahn N, Woll A: [Physical activity among children and adolescents
in Germany. Results of the German Health Interview and Examination Survey for Children and
Adolescents (KiGGS)] Korperlich-sportliche Aktivitat von Kindern und Jugendlichen in Deutsch-
land. Ergebnisse des Kinder- und Jugendgesundheitssurveys (KiGGS). Bundesgesundheitsblatt
Gesundheitsforschung Gesundheitsschutz , (-):-.
. Scully M, Dixon H, White V, Beckmann K: Dietary, physical activity and sedentary behaviour among
Australian secondary students in . Health Promot Int , ():-.
. Tammelin T, Ekelund U, Remes J, Nayha S: Physical activity and sedentary behaviors among Finnish
youth. Med Sci Sports Exerc , ():-.
. Prevention CfDCa: Youth Risk Behavior Surveillance - United States, . MMWR Surveill Summ
, (SS-):-.
. Telama R, Yang X, Viikari J, Valimaki I, Wanne O, Raitakari O: Physical activity from childhood to
adulthood: a -year tracking study. Am J Prev Med , ():-.
. Ajzen I: e eory of Planned Behavior. Organizational Behavior and Human Decision Processes
, :-.
. Kremers SP, De Bruijn GJ, Visscher TL, Van Mechelen W, De Vries NK, Brug J: Environmental inu-
ences on energy balance-related behaviors: A dual-process view. Int J Behav Nutr Phys Act ,
():.
. Sallis JF, Owen N: Ecological models of health behavior. In Health behavior and health education.
edition. Edited by Glanz K, Rimer BK, Lewis FM. San Fransisco: Jossey-Bass.
. Stokols D: Establishing and maintaining healthy environments. Toward a social ecology of health
promotion. Am Psychol , ():-.
. Ferreira I, van der Horst K, Wendel-Vos W, Kremers S, van Lenthe F, Brug J: Environmental cor-
relates of physical activity in youth - A review and update. Obesity Reviews , ():-.
. Sallis JF, Prochaska JJ, Taylor WC: A review of correlates of physical activity of children and adoles-
cents. Med Sci Sports Exerc , ():-.
. Van Der Horst K, Paw MJ, Twisk JW, Van Mechelen W: A brief review on correlates of physical
activity and sedentariness in youth. Med Sci Sports Exerc , ():-.
. Rhodes RE, Brown SG, McIntyre CA: Integrating the perceived neighborhood environment and the
theory of planned behavior when predicting walking in a Canadian adult sample. Am J Health Promot
, ():-.
. Rhodes RE, Courneya KS, Blanchard CM, Plotniko RC: Prediction of leisure-time walking: an
integration of social cognitive, perceived environmental, and personality factors. Int J Behav Nutr
Phys Act , :.
. de Bruijn GJ, Kremers SP, Lensvelt-Mulders G, de Vries H, van Mechelen W, Brug J: Modeling in-
dividual and physical environmental factors with adolescent physical activity. Am J Prev Med ,
():-.
. Motl RW, Dishman RK, Saunders RP, Dowda M, Pate RR: Perceptions of physical and social environ-
ment variables and self-ecacy as correlates of self-reported physical activity among adolescent girls.
Journal of pediatric psychology , ():-.
Environmental factors and sports participation 191
Chapter 8
Environmental factors and sports participation 191
Chapter 8
. Van der Horst K, Timperio A, Crawford D, Roberts R, Brug J, Oenema A: e school food environ-
ment: associations with adolescent so drink and snack consumption. American Journal of Preventive
Medicine in press.
. van der Horst K, Kremers S, Ferreira I, Singh A, Oenema A, Brug J: Perceived parenting style and
practices and sugar-sweetened beverage consumption in adolescents. Health Educ Res , :-
.
. van der Horst K, Oenema A, van de Looij-Jansen P, Brug J: e ENDORSE study: research into
environmental determinants of obesity related behaviors in Rotterdam schoolchildren. BMC Public
Health , ():.
. Slootmaker SM, Schuit AJ, Chinapaw MJ, Seidell JC, van Mechelen W: Disagreement in physical
activity assessed by accelerometer and self-report in subgroups of age, gender, education and weight
status. Int J Behav Nutr Phys Act , :.
. Sallis JF, Johnson MF, Calfas KJ, Caparosa S, Nichols JF: Assessing perceived physical environmental
variables that may inuence physical activity. Res Q Exerc Sport , ():-.
. Wendel-Vos GC, Schuit AJ, Saris WH, Kromhout D: Reproducibility and relative validity of the short
questionnaire to assess health-enhancing physical activity. J Clin Epidemiol , ():-.
. Statistics Netherlands [ http://statline.cbs.nl]
. Mackinnon DP: Introduction to statistical mediation analysis: Routledge Academic; .
. Cerin E, Mackinnon DP: A commentary on current practice in mediating variable analyses in behav-
ioural nutrition and physical activity. Public health nutrition :-.
. Twisk JWR: Applied Multilevel Analysis: A Practical Guide: Cambridge University Press; .
. MacKinnon DP, Lockwood CM, Brown CH, Wang W, Homan JM: e intermediate endpoint eect
in logistic and probit regression. Clinical trials (London, England) , ():-.
. Jasti S, Dudley WN, Goldwater E: SAS macros for testing statistical mediation in data with binary
mediators or outcomes. Nursing research , ():-.
. Baron RM, Kenny DA: e moderator-mediator variable distinction in social psychological research:
conceptual, strategic, and statistical considerations. J Pers Soc Psychol , ():-.
. MacKinnon DP, Krull JL, Lockwood CM: Equivalence of the mediation, confounding and suppres-
sion eect. Prev Sci , ():-.
. Brug J, Kremers SP, van Lenthe F, Ball K, Crawford D: Environmental determinants of healthy eating:
in need of theory and evidence. Proceedings of the Nutrition Society in press.
. McGinn AP, Evenson KR, Herring AH, Huston SL, Rodriguez DA: Exploring associations between
physical activity and perceived and objective measures of the built environment. J Urban Health ,
():-.
. Scott MM, Evenson KR, Cohen DA, Cox CE: Comparing perceived and objectively measured access
to recreational facilities as predictors of physical activity in adolescent girls. J Urban Health ,
():-.
. Hohepa M, Scragg R, Schoeld G, Kolt GS, Schaaf D: Social support for youth physical activity:
Importance of siblings, parents, friends and school support across a segmented school day. Int J
Behav Nutr Phys Act , :.
9 Perceived parenting
style and practices and
sugar-sweetened beverage
consumption by adolescents
van der Horst K, Kremers S, Ferreira I, Singh A, Oenema A, Brug J.
Perceived parenting style and practices and sugar-sweetened beverage
consumption by adolescents.
Health Education Research , : -.
194 Chapter 9194 Chapter 9
ABSTRACT
Purpose: To investigate whether perceived parenting practices and parenting style dimen-
sions (strictness and involvement) are associated with adolescents’ consumption of sugar-
sweetened beverages.
Methods: In this cross-sectional study, secondary-school students (n = , mean age .)
completed a self-administered questionnaire on their consumption of sugar-sweetened
beverages, attitude, social inuences, self-ecacy, habit strength, food-related parenting
practices, and the general parenting style dimensions of ‘strictness’ and ‘involvement’. Data
were analyzed using multiple linear regression analyses.
Results: More restrictive parenting practices were associated with lower consumption of
sugar-sweetened beverages (β = -. ml, CI = -.; -.). is association was highly
mediated (about ) by attitude, self-ecacy, and modeling from parents. Nevertheless, a
signicant direct eect remained (β = -. ml, CI = -.; -.). Interactions between
perceived parenting style and parenting practices showed that the association between
parenting practices and sugar-sweetened beverage consumption was stronger among ado-
lescents who perceived their parents as being moderately strict and highly involved.
Conclusions: Parents inuence their children’s sugar-sweetened beverage consumption and
should therefore be involved in interventions aimed at changing dietary behaviors. Inter-
ventions aimed at the promotion of healthy parenting practices will improve when they are
tailored to the general parenting style of the participants.
Parenting and so drink consumption 195
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Parenting and so drink consumption 195
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INTRODUCTION
e prevalence of overweight among adolescents has increased rapidly over the last decades
[, ]. Overweight is caused by an imbalance between energy intake and energy expenditure.
Various behaviors, such as watching television [-], fast food consumption [, ], and con-
sumption of large serving portions [, ] have been identied as determinants of a positive
energy balance. Additionally, consumption of sugar-sweetened so drinks has been found
to be positively associated with adolescent obesity [-]. Eective promotion of healthful
eating requires a detailed understanding of the factors inuencing dietary behavior. is
is especially important for sugar-sweetened drinks, given the large increase in so drink
consumption in adolescents in recent years and the increase in so drink consumption
throughout adolescence []. Between and , the consumption of carbonated so
drinks by school-aged children in the United States (aged - years) has increased from
uid oz. ( ml) to uid oz. ( ml) per day [], contributing to the total daily
energy intake of adolescents (. and . in overweight males and females respectively)
[, ]. In Dutch adolescents (aged - years), similar trends have been reported between
and , with sugar sweetened carbonated and non-carbonated so drink consump-
tion increasing by . ( ml - ml) and . ( ml - ml) per day for boys and
girls, respectively [].
Research indicates that parents play an important role in the eating behavior of adolescents
[]. Parents inuence the availability of so drinks at home, but can also exert their in-
uence through food related parenting practices [-]. Parenting practices are directly
related to specic behaviors of their children, such as the consumption of so drinks, and
parents use dierent parenting practices for dierent behaviors. Studies on food-related
parenting practices have reported contradictory results: on the one hand, the results of
some studies indicate that strict parenting practices may increase children’s preference
for (and the intake of) the restricted foods [, , ], whereas an other study suggests
that adolescents have a healthier diet and consume less so drinks when they report more
food related rules in their family []. ese mixed results suggest that additional factors
play a role. For instance Darling & Steinberg [] postulated that parenting style modies
the association between parenting practices and adolescent behavior (gure ). Parent-
ing style refers to general patterns of parenting and the emotional climate in which the
parents’ behaviors are expressed. In contrast to parenting practices, parenting style refers
to parent-child interactions in general, whereas parenting practices are related to specic
behaviors, and are reected in things like food rules []. us, parenting practices operate
in the context of parenting style. Parenting styles are classied according to two dimen-
sions of parental behavior: ‘strictness’ or parental control, and ‘involvement’ or parental
warmth and acceptance []. Food-related parenting practices might have a dierent eect
196 Chapter 9196 Chapter 9
on adolescents’ behavior depending on the parenting style of their parents. Few studies
have proposed this interaction eect between parenting style and parental child-feeding
practices [, ]. Elaborating on the model of Darling & Steinberg, we aimed to examine
the interactive nature of parenting style dimensions in more detail.
Cognitive variables, such as attitude, social inuence, self-ecacy or habit strength may
also be predictors of the consumption of sugar-sweetened beverages [, ]. Whether
behavior-specic cognitions and habit strength can explain the link between parenting
practices and adolescents’ sugar-sweetened beverage consumption is not known, but most
social cognitive theories assume that environmental inuences are mediated by behavior-
specic cognitions.
In this cross-sectional study, we investigated (a) whether behavior-specic cognitions
from the Attitude, Social inuence, self-Ecacy (ASE) model and habit strength are associ-
ated with sugar-sweetened beverage consumption by adolescents, (b) whether perceived
parenting practices are associated with adolescent sugar-sweetened beverage consumption
and whether cognitions and habit strength explain such an association, (c) possible interac-
tions between perceived parenting style dimensions and perceived parenting practices. We
combined the contextual model of parenting style [] with the ASE model [] and habit
strength [] to investigate sugar-sweetened beverage consumption by adolescents.
METHODS
Study population and procedure
is study was part of the Dutch Obesity Intervention in Teenagers. e medical ethical
committee of the VU University Medical Center granted ethical approval for this study.
Figure 8.1 Conceptual model of the direct and indirect association of physical and socio-cultural environmental
factors
Figure 9.1 Moderating contextual influence of parenting style
Cphys
a
b
Csocial
Physical environment
PA attributes at home
Neighborhood safety
Neighborhood pleasantness
Neighborhood facilities
Socio-cultural environment
Parental rule
Parent behavior
TPB variables
Attitude
Parental norm
Perceived behavioral
control
Intention
Sports
participation
a
Parenting practices Adolescent
outcome
Parenting style
- Strictness
- Involvement
Figure 9.1 Moderating contextual model of parenting style
Parenting and so drink consumption 197
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Parenting and so drink consumption 197
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Data were collected at Dutch secondary schools in May and June . e subjects were
adolescents from rst and second grades of ve secondary schools. e question-
naires were completed in the classroom. No refusals to complete the questionnaire were
reported. e mean age (SD; range) of the respondents was . years (.; to );
(.) were female; and (.) were of recent immigrant origin, dened as one or both
parents born abroad.
Measurements
e questionnaire was based on other validated questionnaires that assessed dietary intakes
and behavior-specic cognitions, habit strength and parenting variables in adolescent popu-
lations [-]. e self-administered questionnaire was pre-tested for clarity and length,
by means of cognitive interviewing among four adolescents not participating in the study.
Outcome measure: Sugar-sweetened beverage consumption
Sugar-sweetened beverages were dened as carbonated so drinks, other non-carbonated
sugar-sweetened drinks (water-based beverages that contain sugar), and so-called sport
drinks. e consumption of sugar-sweetened beverages was assessed by two questions: ‘On
how many days a week do you drink sugar-sweetened (not ‘light’ or ‘diet’) beverages?’, with
answering categories ranging from zero to seven days per week, and ‘On days that you drink
sugar-sweetened beverages, how many glasses, cans, and/or bottles do you drink?’, with the
amount to be lled in by hand. Total sugar-sweetened beverage consumption was expressed
in milliliters per day and calculated from these two questions according to Dutch standard
serving sizes ( glass = ml; can = ml; bottle = ml). Reported consumption of
more than l per day (n = ) was recoded as l.
Behavior-specic cognitions
Cognitions specic to sugar-sweetened beverage consumption, i.e. attitude, subjective
norm, social modeling, social pressure and self-ecacy were assessed according to the ASE
Model []. All cognitions were assessed by two questions on a ve-point bipolar scale. e
internal consistency of the constructs was assessed using Cronbach’s alpha (α). In the case
of Cronbach’s alpha > ., the items were combined in one scale by calculating the mean
item score [].
Attitude was assessed using the statements ‘I think it is good to drink a lot of sugar-
sweetened beverages’ and ‘I think it is pleasant to drink a lot of sugar-sweetened beverages’.
Answering categories ranged from: ‘I completely agree’ () to ‘I completely disagree’ (-)
(a=.). Social inuences were assessed by three constructs: subjective norms, modeling
and social pressure. Subjective norm was assessed by: ‘My friends think that I should drink
sugar-sweetened beverages’ and ‘My parents think that I should drink sugar-sweetened
198 Chapter 9198 Chapter 9
beverages’, with answering categories ranging from ‘Yes, denitely’ (+) to ‘No, denitely
not’ (-) (a=.). Modeling was assessed by: ‘Do your friends drink sugar-sweetened bev-
erages?’ and ‘Do your parents drink sugar-sweetened beverages?’, with answering categories
ranging from ‘Yes, a lot’ (+) to ‘No, very little’ (-) (a=.). Social pressure was assessed
by two questions: ‘Do your parents encourage you to drink sugar-sweetened beverages?’,
and ‘Do your friends encourage you to drink sugar-sweetened beverages?’, with answering
categories ranging from ‘Yes, a lot’ (+) to ‘No, very little’ (-) (a=.). Self-ecacy was
assessed by asking ‘Do you think you are able to drink less sugar-sweetened beverages?’,
and ‘Does drinking less sugar-sweetened beverages seem dicult to you?’, with answering
categories ranging from ‘Yes, denitely’ (+) to ‘No, denitely not’ (-) (a=.).
Habit strength
We assessed habit strength of sugar-sweetened beverage consumption by means of the Self
Report Habit Index []. is questionnaire assesses three features of habitual behavior:
() the extent to which a behavior is automatic, e.g. ‘drinking sugar-sweetened beverages is
something I do without thinking’, () the repeated character of the behavior, e.g. ‘drinking
sugar-sweetened beverages is something I have been doing for a long time’, and () the sense
of identity the behavior reects, e.g. ‘drinking sugar-sweetened beverages, that’s typically
“me”’. ese three features were assessed by twelve questions on a ve-point bipolar scale,
ranging from ‘I completely agree’ (+) to ‘I completely disagree’ (-). An overall score for
habit strength was constructed by summing the item scores (a = .).
Perceived parenting practices and parenting style dimensions
Based on the parent–child food control questionnaire developed by Cullen et al.[], we as-
sessed perceived parenting practices using nine items. Four questions (identical for fathers
and mothers) assessed specic practices regarding sugar-sweetened beverage consumption
(e.g. ‘My father/mother tells me how much sugar-sweetened beverages I am allowed to
consume’, ‘My father/mother tells me which sugar-sweetened beverages I am allowed to
consume’). An additional item assessed the availability of so drink in the home environ-
ment: ‘My mother always has my favorite sugar-sweetened beverage available at home’. All
parenting items were measured on ve-point bipolar scales ranging from completely agree
(+) to completely disagree (-). A single score was computed by summing the scores on
these items (a = .), in such a way that a higher score reects more restrictive parenting
practices.
We assessed two parenting style dimensions, perceived strictness and perceived involve-
ment, according to den Exter and colleagues [, ]. Strictness was assessed by seven items,
e.g.: ‘My parents know exactly where I am most aernoons aer school’ and ‘At what time do
you have to be at home at night on weekdays?’. Involvement was assessed by ten items, e.g.:
‘My parents make time to talk to me’ and ‘When I get a poor grade in school, my parents
Parenting and so drink consumption 199
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Parenting and so drink consumption 199
Chapter 9
encourage me to do better’. Composite scores were computed for involvement (a = .) and
strictness (a = .) by summing the scores on these items. Higher scores meant perceiving
parents as more involved or stricter. e two dimensions of strictness and involvement can
be used to dene four parenting styles: authoritarian, authoritative, indulgent and neglect-
ful, by dichotomizing the scores on both dimensions. In this study, however, we used the
two continuous dimensions of strictness and involvement.
Data analyses
Missing data on the cognitions, habit strength and parenting variables were imputed using
the median value of all respondents without missing values. e missing data on sugar-
sweetened beverage consumption were replaced by the group mean. e highest frequency
of missing values was (.), for a parenting practice item. Multi-collinearity problems
were not encountered; all inter-correlations between predictors were below . [].
In all conducted analyses, we used multiple linear regression analyses to examine whether
the associations between the determinants of interest and sugar-sweetened beverage con-
sumption diered with age, sex, and ethnicity. Since no signicant interactions were found
(all p>.), data are presented for the whole sample with adjustments for these variables
as potential confounders.
e rst set of multiple linear regression analyses examined whether cognitions and habit
strength were associated with sugar-sweetened beverage consumption. A second set of mul-
tiple regression analyses investigated whether perceived parenting practices were associated
with sugar-sweetened beverage consumption, and the possible mediating eects of the
behavior-specic cognitions. It used the following requirements for establishing mediation
eects: (a) perceived parenting practices must be associated with sugar-sweetened beverage
consumption, (b) the potential mediators must be associated with sugar-sweetened beverage
consumption, and (c) the mediators must cause a signicant reduction in the association
between perceived parenting practices and sugar-sweetened beverage consumption, aer
controlling for the mediator []. A p-value below . was considered to be signicant. A
Sobel test was conducted [] to examine whether the strength of the association between
perceived parenting practices and sugar-sweetened beverage consumption (given by the
regression coecient) decreased signicantly aer a potential mediator was added to the
model. Finally, we investigated the interaction between perceived strictness and perceived
parenting practices, and between perceived involvement and perceived parenting practices.
To this end, interaction terms between parenting practices and the strictness and involve-
ment dimensions were added to the regression model testing the association between
parenting practices and sugar-sweetened beverage consumption. If the interactions had
p-values below ., stratied analyses were conducted for the quartiles of strictness and
involvement.
200 Chapter 9200 Chapter 9
RESULTS
Table . shows the mean scores of the studied variables for boys and for girls. A signicant
dierence between boys and girls in perceived strictness, attitude, and self-ecacy was
found. Overall, respondents reported to perceive their parents using restrictive parenting
practices regarding sugar-sweetened beverage consumption and they perceived the parent-
ing style of their parents as low in strictness and high in involvement.
Behavior-specic cognitions, habit strength, and associations with sugar-sweetened
beverage consumption
In the rst set of regression analyses (adjusting for age, gender and ethnicity), all cognitions,
except the social norm of friends, were signicantly associated with sugar-sweetened bever-
age consumption (Table .). When all signicant cognitions were included in the regres-
sion model, only attitude, self-ecacy, and modeling from parents remained signicantly
associated with sugar-sweetened beverage consumption. Habit strength was also associated
with sugar-sweetened beverage consumption (β=., CI=.; .).
Mediation of the association between perceived parenting practices and sugar-
sweetened beverage consumption
Perceiving more restrictive parenting practices was associated with less consumption of
sugar-sweetened beverages (Table ., model ). Further adjustments for habit strength
and cognitions signicantly reduced the strength of this association, which nevertheless
remained signicant. Habit strength explained of the association between perceived
parenting practices and sugar-sweetened beverage consumption, as can be inferred from
the reduction of the unstandardized regression coecient from -. ml/day to -. ml/
day. Among the cognitions, attitude was the strongest mediator (.; β -. to β -.),
followed by modeling from parents (.; β -. to β -.) and self-ecacy (.; β -.
to β -.) (Table .). e cognitions, age, gender, ethnicity, and habit strength together
(model ) explained . of the association between perceived parenting practices and
sugar-sweetened beverage consumption (β changed from -. to b -. ml/day).
Interaction between perceived parenting style dimensions and parenting practices
We further examined whether the perceived dimensions of parenting style (involvement
and strictness) modied the associations between parenting practices and sugar-sweetened
beverage consumption. P-values of interaction terms between perceived parenting prac-
tices and strictness (p=.), and between perceived parenting practices and involvement
Parenting and so drink consumption 201
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Parenting and so drink consumption 201
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(p=.), were below .. Further stratied analyses revealed that the association between
perceived parenting practices and sugar-sweetened beverage consumption varied by dif-
ferent quartiles of strictness and involvement (Table .): parenting practices were most
eective (i.e. associated with less sugar-sweetened beverage consumption) in the second
and third quartiles of strictness and in the highest quartile of involvement.
Table 9.1 General characteristics of the study population.
Variable (scale range) Mean (SD) P-value*
T-test
Boys (n=172) Girls (n=211)
Age 13.4 (.542) 13.5 (.679) NS
Ethnicity (% native) 86.6 84.8 NS
Sugar-sweetened beverage consumption (ml /day) 809 (854) 674 (677) NS
Parenting practices (-18; 18) -7.0 (7.25) -7.1 (7.15) NS
Strictness (-14; 14) 2.0 (5.42) 3.8 (4.50) .000
Involvement (-20; 20) 10.5 (5.68) 11.1 (5.78) NS
Habit strength (-24; 24) -2.8 (10.2) -3.5 (10.0) NS
Attitude (-2; 2) 0.39 (.883) 0.12 (.904) .003
Self-ecacy (-2; 2) 0.54 (1.19) 0.82 (1.03) .013
Modeling from parents (-2; 2) -0.23 (.887) -0.34 (.950) NS
Modeling from friends (-2; 2) 0.90 (.726) 0.90 (.654) NS
Social norm of parents (-2; 2) -0.20 (.942) -0.28 (.978) NS
Social norm of friends (-2; 2) -0.08 (.812) -0.13 (.779) NS
Social pressure (-2; 2) -1.2 (.903) -1.3 (.724) NS
* P-values represent the dierences between genders
Table 9.2 Associations between cognitions and sugar-sweetened beverage consumption.
‘Univariate’aMultivariateb
Cognitions ßc 95% CI ßc 95% CI
Attitude 282.7f201.6; 363.7 189.3f105.8; 272.8
Self-ecacy -204.2f-269.7; -138.7 -128.2f-194.1; -62.3
Modeling from parents 269.8f191.6; 347.9 191.2f110.6; 271.8
Modeling from friends 117.7d7.5; 228.0 -28.1 -132.7; 76.4
Social norm of parents 103.9e24.8; 183.0 -12.2 -88.7; 64.2
Social pressure 161.8e68.0; 255.5 36.1 -56.3; 128.5
Social norm of friends 17.3 -78.8; 113.4 - -
Abbreviation: 95% CI = 95% condence interval
a Univariate = model adjusted for age, gender, and ethnicity.
b Multivariate = model further adjusted for cognitions.
c β (unstandardized coecient) indicates the change in so drink consumption (in ml) for a 1 unit increase in
the ASE variable.
d p<.05, e p<.01, f p<.001
202 Chapter 9202 Chapter 9
DISCUSSION
e present study investigated the association between perceived parenting practices and
sugar-sweetened beverage consumption by adolescents on the one hand, and the inuence
of perceived parenting style dimensions on this association on the other. Perceiving more
restrictive parenting practices was found to be associated with less so drink consumption,
which is in agreement with the ndings of the study by De Bourdeaudhuij & Van Oost
(). []. However, ndings from studies among younger children suggest that strict
Table 9.3 Mediation of the association between parenting practices and daily sugar-sweetened beverage
consumption
Model ß (ml/day) 95% CI R2Sobel test
z-score
Percentage of the total eect that
is mediated
1 -38.0c-48.1; -28.0 .154 NA NA
2 -21.3c-31.4; -11.2 .280 -5.88c44.0
3 -29.4c-39.8; -19.0 .203 -3.97c22.7
4 -34.3c-44.0; -24.5 .217 -2.44a9.9
5 -31.3c-41.3; -21.3 .241 -3.63c17.6
6 -17.1b-27.2; -6.90 .325 NA 55.0
Abbreviation: NA, not applicable
Model 1: adjusted for age, gender, and ethnicity
Model 2: model 1 + adjusted for habit strength
Model 3: model 1 + adjusted for attitude
Model 4: model 1 + adjusted for self-ecacy
Model 5: model 1 + adjusted for modeling from parents
Model 6: model 1 + adjusted for habit strength, attitude, self-ecacy, and modeling from parents
a p<.05, b p<.01, c p<.001
Table 9.4 Associations between parenting practices and sugar-sweetened beverage consumption, stratied by
the quartiles of strictness and involvement.
Quartilesa
Strictness Involvement
ßb (ml/day) 95% CI R2ßb
(ml/day)
95% CI R2
1 (lowest) -11 -36; 14 .500 -9 -30; 13 .397
2 -27c-49; -5 .404 -18 -40; 3 .395
3 -35d-55; -14 .319 -15 -38; 8 .325
4 (highest) -15 -32; 3 .182 -28d-46; -10 .433
a Ranges for strictness (-14; 14) per quartile: (1) -14;-1 , (2) 0;3, (3) 4;6, (4) 7;14, Ranges for involvement (-20; 20)
per quartile: (1) -12;7, (2) 8;11, (3) 12;15, (4) 16;20
b Unstandardized beta, adjusted for age, sex, ethnicity, habit strength, attitude, modeling from parents, and self-
ecacy
c p<.05
d p<.01
Parenting and so drink consumption 203
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Parenting and so drink consumption 203
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parental practices can in fact increase children’s preferences for, and intake of, the restricted
foods [, , ]. ese contrasting outcomes may have been caused by the dierence
between parenting practices that are used in childhood and in adolescence. For instance,
parents might use pressure to get their young children to eat, or may restrict access to foods.
For adolescents, parents might use clearly dened rules about the times when a certain food
can be eaten and how much of a certain food they can eat.
In our study, the model with age, gender, ethnicity, habit strength, modeling from parents,
attitude, and self-ecacy explained of the relationship between perceived parenting
practices and sugar-sweetened beverage consumption, the largest contribution being
that by habit strength (). Nevertheless, perceived parenting practices had a direct
association with sugar-sweetened beverage consumption unmediated by cognitions and
habit strength. Sugar-sweetened beverage consumption may thus not always be reasoned
behavior, and this nding has some theoretically important implications. Potential distal
determinants of intakes in the social, cultural or physical environment may increase our
understanding of sugar-sweetened beverage consumption in adolescents. In addition, since
perceived parenting practices were still independently associated with the consumption of
sugar-sweetened beverages, other factors may also be involved in the association between
perceived parenting practices and sugar-sweetened beverage consumption by adolescents,
for instance the inuence of taste preferences []. Parents shape their children’s eating
environment in dierent ways. Parental feeding practices in early childhood, for instance
using foods as a reward or to comfort [], exposure to foods [], and parental control
of how much and what children eat [] can inuence a child’s taste preferences which
may persist into adolescence. In addition, the amount and diversity of sugar-sweetened
beverages parents make available and accessible at home can inuence the amount of such
beverages adolescents consume [, ]. It has been suggested that the exposure to so drink
advertising may lead to a higher consumption of so drinks during TV watching [, ],
an activity which constitutes a considerable part of adolescents’ leisure time activity. Finally,
the availability of so drink vending machines in the adolescents’ immediate environment
(e.g. schools) could also contribute to a higher consumption of so drinks [, , ].
We also explored whether the association between perceived parenting practices (specic
rules about sugar-sweetened beverage consumption) and sugar-sweetened beverage con-
sumption by adolescents was dierent depending on the perceived parenting style of their
parents. We therefore examined if the dimensions of parenting style strictness (parental
control) and involvement (parental warmth and acceptance) modied the association be-
tween parenting practices and the consumption of such beverages. e results indicated that
the eect of parenting practices in this respect was most pronounced in those families with
a highly involved or moderately strict parenting style. Kremers et al. () also found that
204 Chapter 9204 Chapter 9
adolescents raised in a family with authoritative parenting style (highly strict and highly
involved) showed the most favorable consumption of fruits and vegetables []. In the high-
est quartile of strictness, we found no signicant association between perceived parenting
practices and sugar-sweetened beverage consumption. is indicates that if parents use a
very strict parenting style, parenting practices relating to sugar-sweetened beverage con-
sumption may not have an additional direct limiting eect on their children’s consumption
of these beverages. e strongest association between perceived parenting practices and
sugar-sweetened beverage consumption was found in the highest quartile of involvement
indicating a stronger direct eect of parenting practices on adolescent behavior, in the case
of involved parents.
e results of our study and that by Kremers et al. () are not entirely comparable. We
used the continuous measures of the two perceived parenting style dimensions instead of
the four categories of parenting style. ere were several reasons why we chose to use the
two dimensions of strictness and involvement instead of the four parenting style categories.
First, the skewed distribution on these dimensions would have caused misclassication
when dichotomized into categories. Second, dichotomization itself is quite arbitrary since
there are no ‘universal’ cut-o values for those dimensions and the cut-os will therefore
vary for dierent populations. ird, classication of two continuous variables into four
categorical parenting styles means loss of information. Since we were interested in the role
of various types and dimensions of parenting we chose to include the two dimensions as
continuous variables and investigate the existence of interactions.
To our knowledge, this is the rst study to indicate the role of parenting styles as an environ-
mental context factor that can inuence the eectiveness of food-related parenting practices
in terms of adolescents’ consumption of sugar-sweetened beverages. As such, it contributes
to theory development of the inuence of parents on adolescents’ dietary behaviors. In
contrast to assumptions that underlie theories such as the eory of Planned Behavior
[] the results indicate that sugar-sweetened beverage consumption may not always be
reasoned or planned []. Additionally, contextual variables may moderate the associations
between cognitive variables and intake levels. Notably, Bandura’s Social Cognitive eory
[] does include the reciprocal interaction of person, environment and behavior. Such a
theoretical approach may guide future research aimed at examining potential moderators of
the environment – behavior relationship.
ere are several limitations to the interpretation of the results of this study. Since the
design of this study was cross-sectional, inferences regarding cause and eect must be made
with caution and will not be conclusive. Parenting practices could determine, but also be a
result of children’s behavior regarding sugar-sweetened beverage consumption (and indeed
that of other food items). Another limitation is that the schools and classes included in this
Parenting and so drink consumption 205
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Parenting and so drink consumption 205
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study were not randomly selected. In addition, the study population included few children
from the various ethnic minorities in the Netherlands, which made it impossible to examine
the potential role ethnic background in sugar-sweetened beverage consumption. We used
adolescents’ reports of parenting practices and parenting style dimensions. erefore, it
could be that parental reports of practices and style would dier from their children’s per-
ceptions. In addition, what adolescents perceive as ‘strict’ and ‘involved’ may vary among
individuals. Obtaining data from multiple sources (adolescents, parents and siblings) would
probably result in data that are more valid. Finally, the assessment of sugar-sweetened bev-
erage consumption relied on self-report and was not supported by any objective assessment.
Assessment of validity and reliability data were not available for this measure. Although
the measurement instrument used in this study was designed to be as clear as possible, it
is not known whether adolescents under-reported or over-reported their sugar-sweetened
beverage consumption. Validation studies on measures of sugar-sweetened beverage con-
sumption are clearly needed, and might be undertaken as part of future research to improve
the assessment of this behavior.
Despite these limitations, our ndings emphasize the importance of parental rules and
the interaction between these rules and parenting style dimensions for sugar-sweetened
beverage consumption by adolescents. e central role parents can play on the primary
prevention of obesity-related behaviors was clearly illustrated: a one ‘unit’ decrease on the
parenting practice scale accounted for an increase of ml per day in sugar-sweetened
beverage consumption. Small increases in energy intake, not accompanied by concomitant
increasing energy expenditure, will substantially contribute to weight gain.
Parents are thus important intermediates for changing dietary behaviors of adolescents
and should therefore be involved in interventions aimed at changing dietary behaviors, and
reducing overweight. e present study showed that interventions aimed at the promotion
of healthy parenting practices will improve when they are tailored to the general parenting
style of the participants.
206 Chapter 9206 Chapter 9
REFERENCES
. Hedley AA, Ogden CL, Johnson CL, Carroll MD, Curtin LR, Flegal KM: Prevalence of overweight
and obesity among US children, adolescents, and adults, -. JAMA , ():-.
. Lobstein T, Frelut ML: Prevalence of overweight among children in Europe. Obes Rev , ():-
.
. Andersen RE, Crespo CJ, Bartlett SJ, Cheskin LJ, Pratt M: Relationship of physical activity and televi-
sion watching with body weight and level of fatness among children: results from the ird National
Health and Nutrition Examination Survey. JAMA , ():-.
. Eisenmann JC, Bartee RT, Wang MQ: Physical activity, TV viewing, and weight in U.S. youth:
Youth Risk Behavior Survey. Obes Res , ():-.
. Berkey CS, Rockett HR, Field AE, Gillman MW, Frazier AL, Camargo CA, Jr., Colditz GA: Activity,
dietary intake, and weight changes in a longitudinal study of preadolescent and adolescent boys and
girls. Pediatrics , ():E.
. Diet, nutrition and the prevention of chronic diseases. World Health Organ Tech Rep Ser , :i-
viii, -, backcover.
. Levitsky DA, Halbmaier CA, Mrdjenovic G: e freshman weight gain: a model for the study of the
epidemic of obesity. Int J Obes Relat Metab Disord :-.
. Kral TV, Rolls BJ: Energy density and portion size: their independent and combined eects on energy
intake. Physiol Behav , ():-.
. McConahy KL, Smiciklas-Wright H, Mitchell DC, Picciano MF: Portion size of common foods
predicts energy intake among preschool-aged children. J Am Diet Assoc , ():-.
. Mrdjenovic G, Levitsky DA: Nutritional and energetic consequences of sweetened drink consump-
tion in - to -year-old children. J Pediatr , ():-.
. Ludwig DS, Peterson KE, Gortmaker SL: Relation between consumption of sugar-sweetened drinks
and childhood obesity: a prospective, observational analysis. Lancet , ():-.
. James J, omas P, Cavan D, Kerr D: Preventing childhood obesity by reducing consumption of
carbonated drinks: cluster randomised controlled trial. Bmj , ():.
. Giammattei J, Blix G, Marshak HH, Wollitzer AO, Pettitt DJ: Television watching and so drink
consumption: associations with obesity in - to -year-old schoolchildren. Arch Pediatr Adolesc Med
, ():-.
. Rampersaud GC, Bailey LB, Kauwell GP: National survey beverage consumption data for children
and adolescents indicate the need to encourage a shi toward more nutritive beverages. J Am Diet
Assoc , ():-.
. French SA, Lin BH, Guthrie JF: National trends in so drink consumption among children and
adolescents age to years: prevalence, amounts, and sources, / to /. J Am Diet
Assoc , ():-.
. Troiano RP, Briefel RR, Carroll MD, Bialostosky K: Energy and fat intakes of children and adolescents
in the united states: data from the national health and nutrition examination sur veys. Am J Clin Nutr
, ( Suppl):S-S.
. Briefel RR, Johnson CL: Secular trends in dietary intake in the United States. , :-.
. Gezondheidsraad: Enkele belangrijke ontwikkelingen in de voedselconsumptie. Den Haag: Gezond-
heidsraad. Commissie Trends Voedselconsumptie; .
. Golan M, Crow S: Parents are key players in the prevention and treatment of weight-related problems.
Nutr Rev , ():-.
Parenting and so drink consumption 207
Chapter 9
Parenting and so drink consumption 207
Chapter 9
. Grimm GC, Harnack L, Story M: Factors associated with so drink consumption in school-aged
children. J Am Diet Assoc , ():-.
. Kassem NO, Lee JW: Understanding so drink consumption among male adolescents using the
theory of planned behavior. J Behav Med , ():-.
. Kassem NO, Lee JW, Modeste NN, Johnston PK: Understanding so drink consumption among
female adolescents using the eory of Planned Behavior. Health Educ Res , ():-.
. Birch LL, Fisher JO: Mothers’ child-feeding practices inuence daughters’ eating and weight. Am J
Clin Nutr , ():-.
. Robinson TN, Kiernan M, Matheson DM, Haydel KF: Is parental control over children’s eating as-
sociated with childhood obesity? Results from a population-based sample of third graders. Obes Res
, ():-.
. Fisher JO, Birch LL: Restricting access to palatable foods aects children’s behavioral response, food
selection, and intake. Am J Clin Nutr , ():-.
. Brown R, Ogden J: Children’s eating attitudes and behaviour: a study of the modelling and control
theories of parental inuence. Health Educ Res , ():-.
. De Bourdeaudhuij I, Van Oost, P.: Personal and family determinants of dietary behaviour in adoles-
cents and their parents. Psychology and Health , :-.
. Darling N, Steinberg L: Parenting style as context: An integrative model. Psychological Bulletin ,
():-.
. Maccoby EE, Martin JA: Socialization in the context of the family: parent-child interaction. In Hand-
book of Child Psychology Personality and Social Development. Volume . Edited by Hetherington EM.
New York: Wiley; :-.
. Kremers SP, Brug J, de Vries H, Engels RC: Parenting style and adolescent fruit consumption. Appetite
, ():-.
. de Vries H, Dijkstra M, Kuhlman P: Self-ecacy: e third factor besides attitude and subjective
norm as a predictor of behavioural intentions. Health Educ Res , ():-.
. Verplanken B, Orbell S: Reections on past behavior: A self-report index of habit strength. J Appl Soc
Psychol , ():-.
. Cullen KW, Baranowski T, Rittenberry L, Cosart C, Hebert D, de Moor C: Child-reported family and
peer inuences on fruit, juice and vegetable consumption: reliability and validity of measures. Health
Educ Res , ():-.
. Steinberg L, Elmen JD, Mounts NS: Authoritative parenting, psychosocial maturity, and academic
success among adolescents. Child Dev , ():-.
. Nunnaly JC: Psychometric theory. edition. New York: McGraw Hill; .
. den Exter Blokland EAW, Engels RCME, Finkenauer C: Parenting styles, self-control and male
juvenile delinquency: e mediation role of self-control. In Prevention and control of aggression
and the impacts on its victims. Edited by Martinez M. Dordrecht/New York: Kluwer/Plenum Press;
:-.
. Kleinbaum DG, Kupper LL, Muller KE: Applied regression analysis and other multivariable methods.
Boston: PWS-KENT Publishing Company; .
. Baron RM, Kenny DA: e moderator-mediator variable distinction in social psychological research:
conceptual, strategic, and statistical considerations. J Pers Soc Psychol , ():-.
. MacKinnon DP, Dwyer JH: Estimating mediated eects in prevention studies. Evaluation Review,
():-.
. Birch LL: Psychological inuences on the childhood diet. J Nutr , ( Suppl):S-S.
208 Chapter 9208 Chapter 9
. Liem DG, de Graaf C: Sweet and sour preferences in young children and adults: role of repeated
exposure. Physiol Behav , ():-.
. Fisher JO, Mitchell DC, Smiciklas-Wright H, Birch LL: Parental inuences on young girls’ fruit and
vegetable, micronutrient, and fat intakes. J Am Diet Assoc , ():-.
. Van Den Bulck J, Van Mierlo J: Energy intake associated with television viewing in adolescents, a
cross sectional study. Appetite , ():-.
. Vereecken CA, Bobelijn K, Maes L: School food policy at primary and secondar y schools in Belgium-
Flanders: does it inuence young people’s food habits? Eur J Clin Nutr , ():-.
. Ajzen I: Attitudes, personality, and behavior: Homewood, IL, US: Dorsey Press; .
. Kremers SP, De Bruijn GJ, Visscher TL, Van Mechelen W, De Vries NK, Brug J: Environmental inu-
ences on energy balance-related behaviors: A dual-process view. Int J Behav Nutr Phys Act ,
():.
. Bandura A: Social foundations of thought and action: a social cognitive theory.: Englewood Clis, NJ:
Prentice-Hall; .
10 General Discussion
210 Chapter 10210 Chapter 10
ABSTRACT
e studies described in this thesis aimed at gaining insight into important individual and
environmental correlates of energy balance-related behaviors among adolescents. e aims
of this thesis were based on the EnRG framework [] and were to (I) examine individual
and environmental correlates of energy balance-related behaviors and (II) explore to what
extent the association between environmental factors and energy balance-related behaviors
is mediated by individual cognitions. In this chapter the main ndings of this thesis are
summarized. Next, methodological issues are discussed, followed by an integration of
ndings. Finally implications for research and practice will be formulated to inform future
research and intervention development.
General Discussion 211
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General Discussion 211
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10.1 MAIN FINDINGS
Environmental correlates of energy balance-related behaviors: reviews of the literature
Two systematic reviews (chapters and ) were conducted to get an overview of the exist-
ing evidence-base regarding important environmental correlates of energy balance-related
behaviors and to inform the development of measurement instruments. e ANGELO
framework was used in these reviews to categorize the environmental factors []. e review
on dietary behaviors revealed that consistent evidence was found for associations between
parent and sibling intakes with adolescent’s energy and fat intake, and between parents’
educational level and adolescent’s fruit and vegetable intake. In the review on adolescents’
physical activity behaviors, support from signicant others, mother’s education level, family
income, non-vocational school attendance, and low crime incidence were found to be as-
sociated with higher physical activity. Several gaps in the available evidence of associations
between environmental factors and energy balance-related behaviors were identied, such
as the lack of high-quality studies and study replications. Many potentially relevant envi-
ronmental factors have not been studied at all [] and the available research focused mainly
on factors in the home environment such as parental inuences. Only a limited number of
studies assessing environmental correlates of snack and so drink intakes were retrieved,
while these two behaviors may be of specic importance in obesity prevention [, ].
Demographic correlates of energy balance-related behaviors
e second part of this thesis reports on two studies in which associations between de-
mographic correlates and overweight and energy balance-related behaviors were explored.
Chapter reports on gender, ethnic and educational dierences in overweight and energy
balance-related behaviors. e study ndings indicate important ethnic and educational
dierences in overweight and energy balance-related behaviors. In line with previous nd-
ings, youth from non-Western ethnic backgrounds, especially girls and those attending
vocational schools were more likely to be overweight and to engage in unfavorable energy
balance-related behaviors [, ].
Chapter reports on adolescents’ active commuting to school and in this chapter we
explored potential socio-demographic correlates of active commuting to school. Almost
half of the adolescents reported to actively commute to school. Adolescents of non-Western
ethnic background were more likely to be walkers and non-active commuters than cyclists
compared to native Dutch adolescents. A further distance from home to school was inversely
associated with being a walker or cyclist and positively associated with being a non-active
commuter.
212 Chapter 10212 Chapter 10
Individual and environmental correlates of energy balance-related behavior
e nal part of this thesis explored possible mediating eects of individual cognitions.
According to the EnRG framework [] we examined the direct association of environmental
correlates on energy balance-related behaviors, but also the suggested mediating eect of
individual cognitions between environmental factors and energy balance-related behaviors.
In chapter associations of the availability of foods/drinks in school canteens, the pres-
ence of food stores around schools and individual cognitions with so drink and snack
consumption were examined. Mediation of the environment – behavior relationship by
individual cognitions was also examined. is study indicated that individual cognitions
appeared to be stronger correlates of intakes than physical school environmental factors
and little evidence for associations of environmental factors in the school environment with
so drink and snack consumption was found. ere was an inverse association between dis-
tance to the nearest store and the number of small food stores with so drink consumption
and these associations were partly mediated by cognitions. erefore, this study provides
some evidence for the hypothesis that environmental factors are associated with so drink
consumption via the cognitions, as proposed in the EnRG framework.
e possible mediation eect of cognitions was also examined in chapter eight and in this
study signicant mediation eects were also found for the individual cognitions. A direct
association of environmental factors on sports participation was found for availability of
physical activity attributes at home, parents sports behavior and parental rule about sports
participation. ese associations were partly mediated by attitude and intention. is study
provided evidence for the indirect eect of environmental factors on adolescent sports
behavior
In chapter , we used another dataset to investigate the possible mediating role of cogni-
tions in further detail. In this study we examined whether cognitions and habit strength
mediated the association between parenting practices and sugar-sweetened beverage
intake and possible moderating eects of the parenting style dimensions “strictness” and
“involvement”, because Darling and Steinberg [] postulated that parenting style modies
the association between parenting practices and adolescent behavior. e results of this
study indicated that more restrictive parenting practices were associated with lower sugar-
sweetened beverage intake. Mediation was found for the cognitions attitude, self-ecacy
and modeling. Nevertheless, a signicant direct association between environmental par-
enting practices and intake also remained. A possible moderating eect of parenting style
was found, showing that the association between parenting practices and sugar-sweetened
beverage consumption was stronger among adolescents who perceived their parents as be-
ing moderately strict and highly involved.
General Discussion 213
Chapter 10
General Discussion 213
Chapter 10
10.2 METHODOLOGICAL ISSUES
e studies presented in this thesis have several limitations and the results and conclusions
should be interpreted in the light of these limitations. In this section, considerations con-
cerning the study design, sampling and subjects, the assessment of energy balance-related
behaviors and individual and environmental factors are discussed.
Cross sectional study design
e studies in this thesis were based on the baseline data collection from the ENDORSE
project as the longitudinal data collection took place in / and was not yet available.
erefore, causal conclusions cannot be drawn and caution is needed interpreting the nd-
ings reported in the studies. For instance, we found parental inuences, such as parenting
practices to have an important association with energy balance-related behaviors. However
parenting practices could also be a result of the adolescents’ behavior or weight status [,
]. Reciprocal determinism, where the causal relationships are bi-directional also exists
for the associations found between perceptions of the environment and physical activity
behavior. Physically active adolescent might be more aware of physical activity equipment
in their environment. is makes discussion of traditional “causal” pathways more complex
[].
e cross sectional design of the study also made the investigation of mediating eects
more dicult as mediation eects refer to causal mechanisms. We used the method sug-
gested by Baron & Kenny [, ] which species a series of tests of the links in a causal
chain to investigate mediation eects in a cross sectional design. Despite its widespread use,
the Baron–Kenny method has some limitations. For instance, it does not provide a direct
estimate of the size of the indirect (mediated) eect, and the Baron–Kenny approach has
low statistical power in studies with a relatively small sample size, even in the presence of
large mediation eects [, ]. Nevertheless, the Baron–Kenny approach is useful in under-
standing mediating eects of environmental factors on energy balance-related behaviors as
proposed in the EnRG framework because it specically tests the direct and indirect associa-
tions of environmental factors on energy balance-related behavior. e mediation analyses
that were conducted for this thesis should preferably be repeated with a longitudinal design.
A cross-sectional approach to mediation can generate substantially biased estimates even
under the ideal conditions when mediation is complete []. However, a cross-sectional
study design is ecient for exploring and identifying environment – behavior associations
in a relatively new research eld. With the longitudinal data from the ENDORSE project,
the results from various studies can be veried and further examined.
214 Chapter 10
Sampling and subjects
City
e data collection process for the ENDORSE study took place in close cooperation with
the Municipal Health Service Rotterdam area and was an integral part of their ongoing
health surveillance, the Youth Monitor Rotterdam. erefore, all participating schools were
located in Rotterdam. Rotterdam is a city with a relatively high proportion of people from
lower socio-economic positions and foreign ethnicity compared to the rest of the Nether-
lands. e results can thus not be generalized. e study described in chapter was based
on another dataset. is study was part of the Dutch Obesity Intervention in Teenagers [].
Data was collected at Dutch secondary schools in May and June . ese schools were
located in the eastern part of the Netherlands in smaller cities compared to Rotterdam. e
subjects were adolescents from rst and second grades of ve secondary schools.
e ethnic background of this study population was representative for the Dutch general
adolescent population.
Schools
e study results must also be interpreted with the possibility of selection bias at the school
level. Only schools that already participated in the Youth Monitor Rotterdam were con-
tacted to participate in the study. Of these schools, were interested in participating.
ese schools might be more motivated and already involved in promoting healthy lifestyles
than the other schools in Rotterdam. Subsequently, a random sample of school locations
was drawn from the pool of schools that were willing to participate, aer stratication of the
schools according to four city areas in which they were located. Stratication was done, to
ensure variation in physical and cultural environments. No schools located in the western
part of the city with more deprived neighborhoods were willing to participate in the study.
Students
No inclusion criteria were dened for participating in the ENDORSE study. Per school ap-
proximately ve classes were selected to participate in the study. All adolescents in one class
participated in the study, unless they or their parents indicated that they were not willing to
participate or if they were absent on the day of the assessment. We did not obtain data from
the adolescents that refused to participate, and thus we were not able to determine to what
extent selection bias at this level may have occurred. However it is likely that overweight
and obese adolescents were less likely to participate in the anthropometrical measurements.
ere were no signicant dierences in the number of missing values on weight status for
gender, ethnicity and educational level.
General Discussion 215
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General Discussion 215
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Parents
To study the eects of parental behavior and parental rules on sports behavior (chapter
) we used data from the parent questionnaire. However, the response rate for the parent
questionnaire was quite low. Within the sample of full parent-adolescent combinations
respondents from non-Western ethnic background were underrepresented, contrary to
the other studies in which the full adolescent sample was used. is under-representation
might have occurred because all materials were in the Dutch language. As the parents were
mostly rst generation immigrants, language problems could have been the main problem
for answering and returning the questionnaire.
Measurement of energy balance-related behaviors
Validity and reliability
Measuring dietary intake and physical activity behaviors by self-reports is a major problem
as the assessment relies on a child’s recall of behaviors. No really valid questionnaires are
available and the use of these self-reported measures of energy balance-related behaviors
could have caused an overestimation or underestimation of behaviors. erefore, the qual-
ity of the measurement instrument should be taken into account in evaluating the results
of this study.
Food frequency questions to assess food intake are oen used for epidemiological studies
since they are relatively easy to administer and less expensive than other methods such
as -hour recall and food records, but food frequency questionnaires tend to over- and
underestimate energy and nutrient intakes []. A validation study to test the validity of the
food frequency questions used in this study was not undertaken. However validation meth-
ods are also imperfect, since a gold standard does not exist for assessing dietary behaviors
and developing a good food frequency questionnaire is therefore very dicult. e dietary
intake measures used in the ENDORSE project were developed by adapting validated Dutch
questionnaires on dietary intake to the ENDORSE study population and dietary behaviors
[, , ]. e test-retest reliability for the dietary intake variables was considered to be
reasonably good (so drink r=., breakfast consumption r=.; sweet & savory snack
consumption r=.).
Also the assessment of physical activity and sedentary behaviors in adolescents is dif-
cult. In our study we used the Activity Questionnaire for Adults & Adolescents (AQuAA)
[], which is a short questionnaire to assess physical activity at school and during leisure
time, active transportation to school and sedentary behaviors in leisure time. e AQuAA
refers to activities in the past week (-day recall). e test-retest reproducibility was fair to
moderate for this questionnaire; with intra-class correlations ranging from . to .. e
validation study with use of accelerometers showed that adolescents over-reported their
activity levels and validity of this measure was low []. Examination of the self-reported
216 Chapter 10216 Chapter 10
amounts of time adolescents spent on the various physical activity and sedentary behav-
iors in the ENDORSE dataset made clear that the overestimation of physical activity and
sedentary behaviors was also present in the ENDORSE data. e average minutes per day
spent on most of the behaviors was highly skewed, with some adolescents reporting very
high amounts of physical activity and sedentary behaviors. We did not use more objective
measurements such as accelerometers. Accelerometer data give an indication of overall
physical activity and for the ENDORSE study we investigated various physical activity
sub-behaviors such as walking in leisure time and active commuting to school. Another
important weakness of accelerometers is that they are insensitive for many forms of activity,
including cycling [].
In spite of the limitations discussed, questionnaires are oen used to assess behavior and
they are considered to be easy and inexpensive in use. However, the quality of the mea-
surement instruments should be taken into account in evaluating the results of this study.
e development of valid and reliable questionnaires to examine energy balance-related
behaviors needs more attention in future research.
Context specic assessment of behaviors
As described in the ANGELO framework, energy balance-related behaviors can occur in a
wide range of behavioral settings, such as homes, schools, restaurants, and neighborhoods
[, , ]. People may behave dierently in dierent settings and it is important to incor-
porate the behavioral setting in the assessment of energy balance-related behaviors. For
example, physical environmental correlates are likely to be dierent for physical activity at
school and physical activity at home []. Research has begun to focus on specic behaviors
such as walking to school or walking for recreation instead of a generic measure of walk-
ing, but still most research focuses on context-free behavioral outcomes. is is also the
case for the ENDORSE study as in chapter environmental correlates were assessed at the
school level, but the behaviors of interest, so drink and snack consumption were assessed
in general measures as the average intake per day. If adolescents consume most so drinks
and snacks in other settings than at school, for instance at home, this might explain why no
associations were found in this study.
e context specic approach may underestimate the association between environmental
and behavioral variables []. However a context-specic approach to the assessment of
energy balance-related behaviors does have its own limitations. Questionnaires will be too
long and give insight in determinants of behaviors in specic situations while the results of
this kind of research is oen used to inform interventions aimed at changing the behavior
regardless the context in which the behavior takes place.
General Discussion 217
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General Discussion 217
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Measurement of individual and environmental correlates
e following measurement instruments were used in the ENDORSE project: adolescent
and parent questionnaires, interviews with school representatives and canteen managers,
audits of the school environment, census data collection and adolescent body measure-
ments. In this paragraph, the use of questionnaires, the audit instrument and information
from Geographic Information Systems (GIS) to assess individual and environmental cor-
relates will be discussed.
Assessment of individual correlates
e assessment of individual correlates of energy balance-related behaviors was based on
the eory of Planned Behavior (TPB). According to the TPB the assessment of cogni-
tions should be action, target, time and context specic [, ]. e questions used in the
ENDORSE study did not meet all of these criteria. For instance the questions on sports and
physical activity were specic on action (sports and physical activity) and the context (in
leisure time), but did not address the target and time in the question. Similar to measure-
ments of behavior, pursuing such specicity would have made the questionnaire even longer,
making administration within one school hour impossible. Besides, it would have made the
questions too complicated and long for the study population and therefore could have led
to incomplete or unreliable data. erefore, we also assessed the cognitions through direct
measures (good-bad, pleasant-unpleasant, easy-dicult) instead of underlying beliefs [].
Some attempts to test the reliability and validity of the TPB questions were undertaken.
e questions were formulated as much as possible according to generally accepted in-
structions provided from the TPB. In the developing phase, we pre-tested the adolescent
questionnaire among ten adolescents by means of cognitive interviewing to examine the
questions on clarity and comprehensibility. Subsequently, the questionnaire was completed
twice by schoolchildren (aged –) ten-days apart to assess the test-re-test reliability
and other psychometrics of the questionnaire. Items with low reliability were adjusted or
deleted from the questionnaire.
Assessment of environmental correlates
In the ENDORSE project, environmental correlates were assessed with questionnaires, by
audits of the schools and objective Geographic Information System (GIS) data. Potential
important environmental correlates were categorized according to the ANGELO frame-
work. In the ENDORSE study physical, socio-cultural, economic and policy correlates were
examined at the micro level (home, school and neighborhood level), and combinations
of perceived and objectively measured environmental correlates were used. Assessing
environmental correlates is dicult as there is not much known about which correlates are
important and how these correlates need to be measured. As it is impossible to assess all
218 Chapter 10218 Chapter 10
potential important environmental factors in one study, several considerations should be
taken into account. First, one should decide to what extent the complexity of the environ-
ment could be assessed. Second, one can assess environmental factors in a subjective way
with questionnaires or more objectively with an audit instrument or census data. However
the development of a valid audit instrument is dicult because of the limited available
literature and instruments. ird, when we speak of assessing environmental factors in the
neighborhood around school or homes, it is important to dene what is considered as a
neighborhood. ese points will be further explained in the following paragraphs.
Complexity of environmental factors
A problem in environmental research is which aspects of the environment we should focus
on. Most research has primarily focused on the availability of facilities and the attractiveness
of neighborhoods. However, other or more complex measures of the environment might be
necessary to assess environmental inuences on behavior []. For example, in chapter we
focused on the school food environment using crude measures such as counting the avail-
ability of food products, the number of food shops and the distance to the nearest food shop.
However, the nearest food shop might be desirable in that it is nearby school, but could have
also undesirable characteristics for adolescents we did not assess, such as high prices, low
availability of preferred foods and drinks, and inconvenient opening hours, so that the shop
is never used. In chapter crude measures of availability of physical activity equipment and
facilities were used, while the quality of the facilities might also be important. ese more
complex aspects of environmental factors were not taken into account in this study.
Perceived versus objectively measured environmental factors
e development of objective measures of environmental factors is an important direc-
tion for research, as well as studies that compare self-reported perceptions with objective
environmental factors. Studies that report the use of objective environmental measures to
assess the food environment are limited. erefore, we used objective measures to assess
the availability of products in the school canteens and the distance and number of shops in
the school neighborhood (Chapter ). However, the perceived availability of products might
also be important in explaining dietary behaviors []. In chapter eight we used perceived
environmental measures such as perceived neighborhood safety and pleasantness. As we
did not nd signicant associations with sports behavior it might be that more objective
factors might play a role. Currently there is limited evidence about whether “objective” or
“perceived” environmental factors are more strongly associated with energy balance-related
behaviors. Previous studies have found associations between objective environmental mea-
sures with adolescents physical activity [-], active commuting to school [, ], fruit
General Discussion 219
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General Discussion 219
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and vegetable consumption [, ] and snack and so drink consumption and purchases
[]. Studies that examined associations of perceived environmental factors and physical
activity found also signicant associations [-]. In a study in which perceived and objec-
tive environmental factors were compared, only the perceived environment was related to
adolescent girls physical activity and perceived and objectively measured environmental
factors were associated with each other []. In a recent study of Prins et al. [] no agree-
ment between the perceived and objective environmental factors was found, which is in line
with studies conducted among adults [-]. is indicates that perceived and objective
environmental factors are dierent constructs [] and recent ndings suggest that percep-
tions of the environment may depend on what is objectively available in the environment
[, ]. It is therefore very relevant to combine the assessment of objective and perceived
environmental factors in one study to establish their inuence on each other and on energy
balance-related behaviors.
Validity of the audit instrument and GIS data
Another limitation of the ENDORSE study was that the audit instrument to assess physical
environmental correlates in the school environment was not tested on validity. Testing the
validity of the audit instrument was not possible since a gold standard does not exist for as-
sessing environmental factors in the school environment. Most existing instruments focus
on the assessment of facilities for physical activities, aesthetics, trac safety and food shops
in the neighborhood instead of the school environment. It was also not possible to compare
the results of the school canteen observations with product lists or sales records because
these records were not always available.
In chapter GIS data was used to examine the number of food stores around schools.
However, the data in these kinds of databases might be an under- or over-representation of
the actual number of stores, depending on the quality of the databases.
Denition of neighborhood
A problem that arises with assessing environmental factors in the school and home neigh-
borhood is how neighborhoods are dened. Perceptions of the neighborhood environment
are highly individual [] and it is still unclear what people dene as their “neighborhood”
[, ]. To examine the perceptions of the neighborhood environment, we did not specify
what we meant by “your neighborhood” in the questionnaire. e denition adolescent have
of “their neighborhood” may depend on other environmental characteristics than distance,
such as access to vehicles and public transport [], the kind of facility one is traveling to []
or living in rural or urban neighborhoods [], making the area that adolescents perceive as
their neighborhood quite variable []. It is therefore important to assess perceptions of the
220 Chapter 10220 Chapter 10
neighborhood by explicit items such as “Are there any parks within a minute walk from
school” instead of “Are there any parks in your school neighborhood”.
We used a neighborhood boundary of meters from school to examine the food en-
vironment. However, there is little agreement in the literature as to what might constitute
a good boundary from school or home. e choice for a neighborhood boundary might
depend on the behavior of interest, the likelihood of the behavior to occur close to school
or home and the age of the study population. Other problems with dening boundaries
is that key factors located just outside the dened boundary are missed and that factors
located inside the boundary are examined even if they are not used by the subjects because
of the presence of main barriers such as busy roads. Further research should therefore not
only assess facilities within the dened boundary but also combine these measures with the
assessment of the use of facilities within and outside the neighborhood boundary.
10.3 INTEGRATION OF STUDY FINDINGS
is thesis provides important information on the associations between environmental
factors and energy balance-related behaviors as proposed in the EnRG framework. e
research questions for this thesis were:
I What are important individual and environmental correlates of energy balance-related
behaviors?
II To what extent is the association between environmental factors and energy balance-
related behaviors mediated by individual cognitions?
e associations as supposed by the EnRG framework (Figure .) were used to facilitate
the interpretation of the ndings.
What are important individual correlates of energy balance-related behaviors?
is part of research question I relates to the association between cognitive factors and
energy-balance related behaviors. Cognitive factors have been found to be associated with
various health behaviors in many studies. is study contributes to the consistent evidence
for a strong association between cognitive factors from the TPB and energy balance-related
behaviors. Especially attitude was found to be a consistent correlate for various behaviors
such as so drink consumption, snack consumption and leisure time sports participation.
However, perceived behavioral control was not found to be a correlate of so drink and
snack consumption and leisure time sports participation. An explanation for this might be
that most adolescents perceive the dietary and physical activities as very easy to perform.
General Discussion 221
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General Discussion 221
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Next to the changeable cognitive correlates we also examined demographic factors as in-
dividual correlates of energy balance-related behaviors. Ethnicity and the school type ado-
lescents attended (vocational or higher education) were found to be important individual
correlates of energy balance-related behaviors. Demographic factors might be important
moderators of the environment – behavior relationship [] as the environment – behavior
association might be dierent for various demographic sub-groups of the population [-
]. As there were relatively few published obesity-prevention and treatment studies that are
designed for specic educational or ethnic groups, the results of these studies can be used to
distinguish specic risk groups and target groups for preventive interventions.
What are important environmental correlates of energy balance-related behaviors?
is part of research question I relates to the direct association between environmental fac-
tors and energy-balance related behaviors. Literature reviews have shown that evidence on
environmental factors was inconsistent or lacking for some energy balance-related behaviors
such as snack and so drink consumption [, ]. erefore, the ENDORSE study focused
on these gaps in the literature and mainly examined factors in the school and neighborhood
environment to replicate studies and build evidence on environmental correlates of energy
balance-related behaviors. In Table . an overview according to the ANGLO framework
is given of the environment – behavior relationships that were investigated in this thesis.
Figure 10.1 Environmental Research framework for weight Gain prevention [1]
Figure 10.1 Environmental Research framework for weight Gain prevention [1].
222 Chapter 10222 Chapter 10
In line with results from the reviews, few signicant associations between physical environ-
mental factors and energy balance-related behaviors were found. We found availability of
sports attributes associated with leisure time sports; the distance from home to school as-
sociated with the mode of commuting, and the distance to the nearest shop and the number
of small food stores with so drink consumption. Not all of these associations were very
clear to explain, for example the inverse association found between the presence of small
food shops nearby school and so drink consumption.
In accordance with the review studies, our further studies suggest that social factors more
strongly associated with physical activity and dietary behaviors than physical environ-
mental factors [, ] as we found parenting practices or rules associated with so drink
consumption and leisure time sports and parents own sports behavior was associated with
adolescents’ leisure time sports. e range of social factors considered in this thesis was
Table 10.1 Investigated environment – behavior relationships, categorized according to the ANGELO
framework
Levels
Types
Home School Neighborhood
Physical Availability of sport
attributes – leisure time
sports
Availability of snacks and so
drinks and drinks in school
canteens – so drink and
snack consumption
Availability of snacks and so
drinks and drinks in vending
machines – so drink and
snack consumption
Distance from home
to school – mode of
commuting
Availability of food stores
in the school neighborhood
– so drink and snack
consumption
Distance to the nearest food
store – so drink and snack
consumption
Neighborhood safety –
leisure time sports
Neighborhood pleasantness
– leisure time sports
Neighborhood facilities –
leisure time sports
Economic
Political
Socio-cultural Parents behavior – leisure
time sports
Parenting practices – leisure
time sports and so drink
consumption
General Discussion 223
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General Discussion 223
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narrow, and factors such as social networks, social capital, friends’ support and behavior
should be included in future studies to provide further insight into the specic aspects of
the social environment that are most important for adolescents behaviors [, ].
e weak evidence found in the various studies should however not be interpreted as
an absence of a direct environment – energy balance-related behavior relationship. e
research eld of environmental correlates of energy balance-related behavior is relatively
new and in a developing phase. e environment is very large which means that there are
many possible environmental factors that can be important for energy balance-related
behaviors. Most research has focused on just a part of the potentially relevant environment.
erefore, future studies should focus on all aspects of the environment, also on economic
and political factors such as pocket money and school policies to reveal to what extent
environmental factors are associated with energy balance-related behaviors. Next to this,
the development of valid and reliable measures of the environment is necessary. As the
research eld develops, more and more dierent instruments and denitions are used to
assess environmental factors. is hinders the comparison of results between studies and
makes the systematic development of theory for the relation of environment with energy
balance-related behaviors dicult.
To what extent is the association between environmental factors and energy balance-
related behaviors mediated by individual cognitions?
e second aim of this thesis was to investigate mediating factors to gain insight into the
mechanisms that underlie environment – energy balance-related behaviors relationships.
In this thesis cognitions partly mediated the association of the distance to the nearest shop
and the number of small food shops (chapter ) and of parenting practices (chapter ) with
adolescents so drink consumption. Attitude and intention partly mediated the associa-
tions of physical activity attributes at home, parents’ sport behavior and parental rule about
sports participation with leisure time sports (chapter ). is mediating eect of cognitions
was also found in other studies. In a study of Jago et al. was found that the association
between distance to a small food store and low fat vegetable consumption was mediated by
low fat vegetable preferences []. De Bruijn and colleagues found that the association of
environmental aesthetics and distance to PA facilities on PA among adolescents was medi-
ated by intention to be physically active []. Motl and colleagues found that the association
of equipment accessibility with adolescent girls PA was mediated by self-ecacy []. In
these studies it is shown that not all cognitions from the eory of Planned Behavior serve
as a mediator. e studies described above indicate that some TPB variables may be more
likely to serve as mediators in environment – behavior relationships than others [, ],
with the strongest evidence for attitude as a potential mediating variable [-]. Next to an
indirect association of environmental factors, in all studies on mediation, the environmen-
224 Chapter 10224 Chapter 10
tal factors remained signicantly associated with the outcome behavior aer controlling for
the mediating eect of cognitions. is is also shown in other studies that have combined
environmental and cognitive factors [-]. According to the EnRG framework this partly
unmediated environmental eect is an important explanatory mechanism and it can be
argued that energy balance-related behaviors result partly from automatic and unconscious
processes [, ].
As the EnRG-framework is specically developed to generate hypotheses regarding when,
how and for whom environmental factors might be inuential, more research should focus
on these questions instead of examining which environmental factors are important for
energy balance-related behaviors. e studies in this thesis show that environmental factors
can have both an indirect and direct association with energy balance-related behaviors.
However, new questions also arise such as whether other mediating factors than cognitions
for example preferences and environmental barriers play a role in the indirect association
between environment and behavior. Baranowski et al. [], proposed that there can be a
range of mediating processes and cascading sequences of mediating processes. For instance,
a child’s self-ecacy for asking to be active aer school will aect the likelihood that the
child will ask to be active aer school at home, which increases the likelihood that a parent
will play with the child at home. Most previous studies have investigated mediation path-
ways for physical environmental factors. However, the studies in this thesis indicate that
also social factors such as parents own behavior and parenting practices show an indirect
association with energy balance-related behaviors. As the body of evidence on possible
mediators grows, important questions for further research are whether mediating eects
dier for dierent target groups and if some energy balance-related behaviors are more
under the inuence of automatic processes than others.
10.4 IMPLICATIONS OF THE STUDY FINDINGS
Findings from the studies reported in this thesis indicate that both individual and environ-
mental factors are important for energy balance-related behaviors. However more research
is needed to examine the relative inuence and mechanisms behind these inuences.
erefore, the results presented in this thesis have several implications for future research,
theory and practice to assist further research and the development of obesity prevention
interventions, which are described in this paragraph.
Implications for research
e research eld of examining associations between environmental factors and energy
balance-related behaviors is still in its infancy and needs further development and matura-
General Discussion 225
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General Discussion 225
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tion. More insight is needed in how specic aspects of the environment can be assessed for
specic behaviors, what the best measurement instruments are, how the environment can
be dened for dierent target groups, and how gathered data can be used in analyses.
First, a large variety of potential environmental determinants of energy balance-related
behaviors have been studied, with few replicated studies for environment – behavior com-
binations. Further examination of potential relevant environmental factors is needed to
provide public health practitioners with recommendations for intervention development
and to improve theory and models on environmental determinants of health behaviors.
Research should not only be restricted to just a part of the potentially relevant environment,
but on all aspects listed in the ANGELO framework. Currently there is limited evidence
about whether “objective” or “perceived” environmental factors are more strongly associ-
ated with energy balance-related behaviors. Objective measures are generally regarded as
being superior to subjective self-reports. However, people may perceive their environments
dierently even if they live in the same “objective” environment. e assessment of objective
and perceived factors should be combined in one study to establish their inuence on each
other and on behavior to gain insight in which kind of determinants should be targeted in
preventive interventions: perceptions of the environment, the objective environment or a
combination of both.
Second, most studies on environmental determinant still apply relatively weak study
designs and measurement instruments. Longitudinal and intervention research is needed
to gain better insight into the relative importance of individual cognitive determinants
and environmental (physical, socio-cultural, economic, political) determinants for energy
balance-related behaviors. With these studies it can be examined if and how changes in the
environment lead to changes in behavior.
ird, valid and reliable measurement instruments to assess objective and subjective
environmental factors should be developed. More eort should be put into the develop-
ment of validated instruments that assess perceived environmental factors. erefore, also
qualitative research is needed to examine how the target group perceives their neighborhood
taking the behavior of interest into account. More and more detailed objective measures of
environmental factors are available, for example, those documented in geographic infor-
mation systems (GIS). e use of GIS can be helpful to examine objective environmental
factors as environmental factors within dened boundaries can be examined. Next to this
research should also focus on developing good validated and reliable measurement instru-
ments for assessing dietary and physical activity behaviors as in adolescents these behaviors
are oen over reported.
Finally, advanced research techniques need to be used to identify mediating and moderat-
ing factors of the environment – behavior relationship. It is important to examine which
cognitive factors mediate the environment – behavior relationship and if there are other
important mediators such as environmental barriers or parental inuences.
226 Chapter 10226 Chapter 10
Implications for theory development
e EnRG framework we used in this study was developed as a tool to help disentangle the
role of obesogenic environments. It is specically directed at generating questions regarding
when, how and for whom environmental factors may be inuential []. In this framework,
environmental inuences are supposed to inuence behavior both directly and indirectly.
In this thesis, individual cognitions were important correlates of so drink and snack
consumption and sports participation but also mediators of the environment – behavior
relationship conrming the supposed indirect inuence of the environment on behavior
in the EnRG framework. is indicates that socio-ecological models in which individual
cognitions and environmental factors are combined need specicity in the hypothesized
pathways between individual and environmental factors. To further develop theory on envi-
ronment – behavior relationships the mediation eects should be examined in longitudinal
research and for other environment – behavior relationships. Also other possible mediators
such as barriers and preferences should be examined. Future research is also needed to
examine if mediating eects dier for dierent target groups and if some energy balance-
related behaviors are more under the inuence of automatic processes than others.
e direct association of environmental factors with energy balance-related behaviors
indicates also that many behaviors are more or less ‘automatic’. Environmental features can
prompt behavioral choices, without mediation by conscious decision making and theoreti-
cal models are needed that focus also on environmentally cued habitual behavioral patterns
[].
Recommendations for practice
e ndings and implications lead to the following recommendations for practice and
intervention development.
Intervention approach
e partly mediated association between environmental factors and energy balance-related
behaviors indicate that both a health education approach and a health protection approach
by changing the environment might be appropriate strategies for behavior change interven-
tions []. Both intervention approaches are important, but since individual cognitions are
important correlates of energy balance-related behaviors, these factors remain important to
be targeted in interventions.
Intervention ingredients
Parental inuences, including their own behavior and parenting rules seem to be important
factors for sports participation, so drink and snack consumption. It is therefore recom-
General Discussion 227
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General Discussion 227
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mended to not only aim interventions at adolescents but also at the parents. Parents should
be made aware of their important role in promoting healthy dietary and physical activity be-
haviors. Possibly it is important to start to target parents at an early stage in the development
of the child, to teach parents to set clear rules and give a good example. e importance of
parents for energy balance-related behaviors also means that intervention should not only
take place at schools but also in the home environment. e results of this thesis do not
give a clear view on which factors in the school environment should be changed to promote
healthy energy balance-related behaviors. However, schools should promote healthy behav-
ior and healthy food should be made available and accessible in school.
Target groups
Adolescents at lower educational levels and adolescents with a non-Western ethnic back-
ground, mainly girls, engage in the most risk behaviors for overweight. Interventions are
needed that are eective in these groups. erefore, studies to develop and evaluate inter-
ventions aimed at the promotion of healthy dietary and physical activity behaviors should
study the eects for these dierent target groups.
10.5 GENERAL CONCLUSION
ere is only little evidence for associations between objective or perceived physical envi-
ronmental factors and energy balance-related behaviors. e evidence for social factors,
mainly parental factors is stronger and these factors might be more important for energy
balance-related behaviors among adolescents. Parents can importantly promote healthy en-
ergy balance-related behaviors among their ospring by shaping the environment by setting
clear rules, setting a good example and creating opportunities for the behavior. Next to this
we can conclude that environmental factors can have both an indirect and direct association
with energy balance-related behaviors. Especially attitudes and intentions are likely to be
important mediators of environment – behavior relationships. However, new questions also
arise, such as if other mediating factors than individual cognitions such as preferences and
environmental barriers play a role in the indirect association between environment and
behavior. More research is also needed on the investigation of mediating eects for dierent
target groups and if some energy balance-related behaviors are more under the inuence of
automatic processes than others.
228 Chapter 10228 Chapter 10
REFERENCES
. Kremers SP, De Bruijn GJ, Visscher TL, Van Mechelen W, De Vries NK, Brug J: Environmental inu-
ences on energy balance-related behaviors: A dual-process view. Int J Behav Nutr Phys Act ,
():.
. Swinburn B, Egger G, Raza F: Dissecting obesogenic environments: the development and application
of a framework for identifying and prioritizing environmental interventions for obesity. Prev Med
, ( Pt ):-.
. Brug J, Kremers SP, van Lenthe F, Ball K, Crawford D: Environmental determinants of healthy eating:
in need of theory and evidence. Pro Nutr Soc , ():-.
. Rennie KL, Johnson L, Jebb SA: Behavioural determinants of obesity. Best Pract Res Clin Endocrinol
Metab , ():-.
. Swinburn BA, Caterson I, Seidell JC, James WP: Diet, nutrition and the prevention of excess weight
gain and obesity. Public Health Nutr , (A):-.
. Crespo CJ, Smit E, Andersen RE, Carter-Pokras O, Ainsworth BE: Race/ethnicity, social class and
their relation to physical inactivity during leisure time: results from the ird National Health and
Nutrition Examination Survey, -. Am J Prev Med , ():-.
. Hosper K, Nierkens V, Nicolaou M, Stronks K: Behavioural risk factors in two generations of non-
Western migrants: do trends converge towards the host population? Eur J Epidemiol , ():-
.
. Darling N, Steinberg L: Parenting style as context: An integrative model. Psychological Bulletin ,
():-.
. Birch LL, Fisher JO: Mothers’ child-feeding practices inuence daughters’ eating and weight. Am J
Clin Nutr , ():-.
. Costanzo PR, Woody, E.Z.: Domain-specic parenting styles and their impact on the child’s develop-
ment op particular deviance: e example of obesity proneness. J Soc Clin Psychol , ():-.
. Bauman AE, Sallis JF, Dzewaltowski DA, Owen N: Toward a better understanding of the inuences
on physical activity: the role of determinants, correlates, causal variables, mediators, moderators, and
confounders. Am J Prev Med , ( Suppl):-.
. Baron RM, Kenny DA: e moderator-mediator variable distinction in social psychological research:
conceptual, strategic, and statistical considerations. J Pers Soc Psychol , ():-.
. Cerin E, Mackinnon DP: A commentary on current practice in mediating variable analyses in behav-
ioural nutrition and physical activity. Public Health Nutr :-.
. Cerin E, Taylor LM, Leslie E, Owen N: Small-scale randomized controlled trials need more powerful
methods of mediational analysis than the Baron-Kenny method. J Clin Epidemiol , ():-
.
. MacKinnon DP, Lockwood CM, Homan JM, West SG, Sheets V: A comparison of methods to test
mediation and other intervening variable eects. Psychol Methods , ():-.
. Maxwell SE, Cole DA: Bias in cross-sectional analyses of longitudinal mediation. Psychol Methods
, ():-.
. Singh AS, Chin APMJ, Kremers SP, Visscher TL, Brug J, van Mechelen W: Design of the Dutch Obesity
Intervention in Teenagers (NRG-DOiT): systematic development, implementation and evaluation of
a school-based intervention aimed at the prevention of excessive weight gain in adolescents. BMC
public health , :.
. McPherson S, Hoelscher DM, Alexander M, Scanlon KS, Serdula MK: Dietary assessment methods
among school-aged children: validity and reliability. Prev Med , :S-S.
General Discussion 229
Chapter 10
General Discussion 229
Chapter 10
. Bogers RP, Van Assema P, Kester AD, Westerterp KR, Dagnelie PC: Reproducibility, validity, and
responsiveness to change of a short questionnaire for measuring fruit and vegetable intake. Am J Epid
, ():-.
. Van Assema P, Brug J, Ronda G, Steenhuis I, Oenema A: A short dutch questionnaire to measure fruit
and vegetable intake: relative validity among adults and adolescents. Nutrition and health (Berkham-
sted, Hertfordshire) , ():-.
. Chin A Paw MJM, Slootmaker SM, Schuit AJ, van Zuidam M, Van Mechelen W: Test-retest reliability
and concurrent validity of the Activity Questionnaire for Adults and Adolescents (AQuAA). Submit-
ted
. Trost SG: Objective measurement of physical activity in youth: current issues, future directions. Exerc
Sport Sci Rev , ():-.
. Sallis JF, Johnson MF, Calfas KJ, Caparosa S, Nichols JF: Assessing perceived physical environmental
variables that may inuence physical activity. Res Q Exerc Sport , ():-.
. Giles-Corti B, Timperio A, Bull F, Pikora T: Understanding physical activity environmental cor-
relates: increased specicity for ecological models. Exerc Sport Sci Rev , ():-.
. Humpel N, Owen N, Leslie E: Environmental factors associated with adults’ participation in physical
activity: a review. Am J Prev Med , ():-.
. Ajzen I, Fishbein M: Understanding attitudes and predicting social behavior: Englewood Clis NJ:
Prentice-Hall; .
. Fishbein M, Ajzen I: Belief, attitude, intention and behavior. Don Mills (NY): Addison-Wesley; .
. Kremers SP, Visscher TL, Seidell JC, van Mechelen W, Brug J: Cognitive determinants of energy
balance-related behaviours: measurement issues. Sports medicine (Auckland, NZ , ():-.
. Wiecha JL, Finkelstein D, Troped PJ, Fragala M, Peterson KE: School vending machine use and fast-
food restaurant use are associated with sugar-sweetened beverage intake in youth. J Am Diet Assoc
, ():-.
. Gordon-Larsen P, Nelson MC, Page P, Popkin BM: Inequality in the built environment underlies key
health disparities in physical activity and obesity. Pediatrics , ():-.
. Cohen DA, Ashwood JS, Scott MM, Overton A, Evenson KR, Staten LK, Porter D, McKenzie TL,
Catellier D: Public parks and physical activity among adolescent girls. Pediatrics , ():e-
.
. Powell LM, Chaloupka FJ, Slater SJ, Johnston LD, O’Malley PM: e availability of local-area com-
mercial physical activity-related facilities and physical activity among adolescents. Am J Prev Med
, ( Suppl):S-.
. Timperio A, Ball K, Salmon J, Roberts R, Giles-Corti B, Simmons D, Baur LA, Crawford D: Personal,
family, social, and environmental correlates of active commuting to school. Am J Prev Med ,
():-.
. Kerr J, Rosenberg D, Sallis JF, Saelens BE, Frank LD, Conway TL: Active commuting to school: As-
sociations with environment and parental concerns. Med Sci Sports Exerc , ():-.
. Timperio A, Ball K, Roberts R, Campbell K, Andrianopoulos N, Crawford D: Children’s fruit and
vegetable intake: Associations with the neighbourhood food environment. Prev Med , ():
-.
. Kubik MY, Lytle LA, Hannan PJ, Perry CL, Story M: e association of the school food environment
with dietary behaviors of young adolescents. Am J Public Health , ():-.
. Neumark-Sztainer D, French SA, Hannan PJ, Story M, Fulkerson JA: School lunch and snacking
patterns among high school students: associations with school food environment and policies. Int J
Behav Nutr Phys Act , ():.
230 Chapter 10230 Chapter 10
. Mota J, Almeida M, Santos P, Ribeiro JC: Perceived Neighborhood Environments and physical activ-
ity in adolescents. Prev Med , (-):-.
. Scott MM, Evenson KR, Cohen DA, Cox CE: Comparing perceived and objectively measured access
to recreational facilities as predictors of physical activity in adolescent girls. J Urban Health ,
():-.
. Evenson KR, Scott MM, Cohen DA, Voorhees CC: Girls’ perception of neighborhood factors on
physical activity, sedentary behavior, and BMI. Obesity , ():-.
. Prins R, Oenema A, van der Horst K, Brug J: Objective and perceived availability of physical activity
opportunities: dierences in associations with Physical Activity behavior among adolescents. Sub-
mitted
. Boehmer TK, Hoehner CM, Deshpande AD, Brennan Ramirez LK, Brownson RC: Perceived and
observed neighborhood indicators of obesity among urban adults. Int J Obes (Lond) , ():-
.
. Kirtland KA, Porter DE, Addy CL, Neet MJ, Williams JE, Sharpe PA, Ne LJ, Kimsey CD, Ainsworth
BE: Environmental measures of physical activity supports. Perception versus reality. Am J Prev Med
, ():-.
. McGinn AP, Evenson KR, Herring AH, Huston SL, Rodriguez DA: Exploring associations between
physical activity and perceived and objective measures of the built environment. J Urban Health ,
():-.
. Hume C, Salmon J, Ball K: Children’s perceptions of their home and neighborhood environments,
and their association with objectively measured physical activity: a qualitative and quantitative study.
Health Educ Res , ():-.
. Ball K, Timperio AF, Crawford DA: Understanding environmental inuences on nutrition and physi-
cal activity behaviors: where should we look and what should we count? Int J Behav Nutr Phys Act
, :.
. McCormack GR, Giles-Corti B, Bulsara M, Pikora TJ: Correlates of distances traveled to use recre-
ational facilities for physical activity behaviors. Int J Behav Nutr Phys Act , :.
. Colabianchi N, Dowda M, Pfeier KA, Porter DE, Almeida MJ, Pate RR: Towards an understanding
of salient neighborhood boundaries: adolescent reports of an easy walking distance and convenient
driving distance. Int J Behav Nutr Phys Act , ():.
. Giles-Corti B, Donovan RJ: Socioeconomic status dierences in recreational perceived activity levels
and real and perceived access to a supportive physical environment. Prev Med , :-.
. Suminski RR, Poston WS, Petosa RL, Stevens E, Katzenmoyer LM: Features of the neighborhood
environment and walking by U.S. adults. Am J Prev Med , ():-.
. Humpel N, Marshall AL, Leslie E, Bauman A, Owen N: Changes in neighborhood walking are related
to changes in perceptions of environmental attributes. Ann Behav Med , ():-.
. Humpel N, Owen N, Iverson D, Leslie E, Bauman A: Perceived environment attributes, residential
location, and walking for particular purposes. Am J Prev Med , ():-.
. Garcia Bengoechea E, Spence JC, McGannon KR: Gender dierences in perceived environmental
correlates of physical activity. Int J Behav Nutr Phys Act , :.
. Ferreira I, van der Horst K, Wendel-Vos W, Kremers S, van Lenthe F, Brug J: Environmental cor-
relates of physical activity in youth - A review and update. Obesity Reviews , ():-.
. van der Horst K, Oenema A, Ferreira I, Wendel-Vos W, Giskes K, van Lenthe F, Brug J: A systematic
review of environmental correlates of obesity-related dietary behaviors in youth. Health Educ Res
, :-.
General Discussion 231
Chapter 10
General Discussion 231
Chapter 10
. Motl RW, Dishman RK, Saunders RP, Dowda M, Pate RR: Perceptions of physical and social environ-
ment variables and self-ecacy as correlates of self-reported physical activity among adolescent girls.
Journal of pediatric psychology , ():-.
. Jago R, Baranowski T, Baranowski JC, Cullen KW, ompson D: Distance to food stores & adolescent
male fruit and vegetable consumption: mediation eects. Int J Behav Nutr Phys Act , ():.
. de Bruijn GJ, Kremers SP, Lensvelt-Mulders G, de Vries H, van Mechelen W, Brug J: Modeling in-
dividual and physical environmental factors with adolescent physical activity. Am J Prev Med ,
():-.
. Van der Horst K, Timperio A, Crawford D, Roberts R, Brug J, Oenema A: e school food environ-
ment: associations with adolescent so drink and snack consumption. American Journal of Prev Med
, (): -.
. Rhodes RE, Brown SG, McIntyre CA: Integrating the perceived neighborhood environment and the
theory of planned behavior when predicting walking in a Canadian adult sample. Am J Health Promot
, ():-.
. Rhodes RE, Courneya KS, Blanchard CM, Plotniko RC: Prediction of leisure-time walking: an
integration of social cognitive, perceived environmental, and personality factors. Int J Behav Nutr
Phys Act , :.
. van der Horst K, Kremers S, Ferreira I, Singh A, Oenema A, Brug J: Perceived parenting style and
practices and sugar-sweetened beverage consumption in adolescents. Health Educ Res , :-
.
. de Bruijn GJ, Kremers SP, Schaalma H, van Mechelen W, Brug J: Determinants of adolescent bicycle
use for transportation and snacking behavior. Prev Med , ():-.
. Lewis BA, Marcus BH, Pate RR, Dunn AL: Psychosocial mediators of physical activity behavior
among adults and children. Am J Prev Med , ( Suppl):-.
. De Bourdeaudhuij I, Sallis J: Relative contribution of psychosocial variables to the explanation of
physical activity in three population-based adult samples. Prev Med , ():-.
. Aarts H, Dijksterhuis A: e automatic activation of goal-directed behaviour: e case of travel habit.
J Environm Psychol , ():-.
. Chartrand T-L: e Role of Conscious Awareness in Consumer Behavior. J Consumer Psychol ,
:-.
. Baranowski T, Anderson C, Carmack C: Mediating variable framework in physical activity interven-
tions. How are we doing? How might we do better? Am J Prev Med , ():-.
. Owen N, Humpel N, Leslie E, Bauman A, Sallis JF: Understanding environmental inuences on
walking; Review and research agenda. Am J Prev Med , ():-.
Summary 233
Chapter 10Summary
Summary
Over the past decades the numbers of children and adults being overweight or obese have
increased so rapidly, that overweight and obesity are among the most important and chal-
lenging public health problems. It is therefore important to prevent overweight in all age
groups. Children and adolescents may however be especially important groups to target.
Obesity at young age is associated with a higher likelihood of the development of chronic
diseases at an early age or later in life. Furthermore, overweight or obese children and
adolescents are more likely to become overweight or obese adults.
It is already known that weight gain is the result of a positive energy balance in which the
energy intake (via diet) exceeds energy expenditure (mainly via physical activity). So, to be
able to successfully prevent overweight among children and adolescents, it is important to
know which specic dietary and physical activity behaviors (energy balance-related behav-
iors - EBRB) contribute most to weight gain among children and adolescents.
Another important element for successful prevention of overweight is a detailed under-
standing of the factors that determine these EBRB. Individual determinants of behavior
such as knowledge, attitudes, social inuences, and motivation have been found to be
relevant in earlier research. However, it is also likely that physical and social environmental
factors such as availability of food products, opportunities to be active and parents can
inuence dietary and physical activity behaviors of children and adolescents.
To study this, and the interplay between potential personal and environmental deter-
minants of behavioral nutrition as well as physical activity among young adolescents, the
ENDORSE project (ENvironmental Determinants of Obesity in Rotterdam Schoolchil-
drEn) was initiated in . e main goals of the ENDORSE project were: () to identify
which energy balance-related behaviors are associated with overweight and obesity; () to
examine important individual and environmental correlates of these energy balance-related
behaviors; () to investigate the associations with and the interactions between these cor-
relates and energy balance-related behaviors; and () to formulate objectives to be targeted
in interventions aimed at the prevention of overweight in adolescents aged - years. is
thesis presents a series of studies in which these questions are addressed.
In the general introduction (Chapter ), the background, aims and theoretical framework
used in the ENDORSE study are presented.
In Chapter , the study protocol of the ENDORSE study is described together with the
design and the measurement instruments. Data were collected among adolescents in the
rst (- to -year-olds) and third (- to -year-olds) years of secondary school. Seventeen
schools participated in the ENDORSE study and adolescents and their parents were
selected to participate in the study. In this chapter the results of the explorative research on
234 Summary
the identication of risk behaviors for overweight and obesity are described as well. Based
on a review of the literature and the opinion of consulted experts, the following EBRB were
selected to be addressed in the ENDORSE study: active transport to school, leisure time
activities, sports, watching television, computer use, so drink consumption, sweet snack
consumption, savory snack consumption and breakfast consumption.
Research into environmental inuences on behavior is relatively new. erefore, we started
by searching literature on existing evidence on environmental correlates of energy balance-
related behaviors (Chapters and ). Evidence was found for associations between parents’
intake and the intakes of their children, especially for energy and fat intake. If parents had
higher educational levels, their adolescent’s had a higher fruit and vegetable intake. Social
support, mother’s education level, family income, non-vocational school attendance, and
low crime incidence were associated with higher physical activity among adolescents. Both
reviews showed that there were only a limited number of studies available that examined
physical environmental factors, such as the availability of food products or facilities to be
physically active. Although many studies were school-based studies, mainly home and
neighborhood environmental factors were examined in these studies. School environmental
factors were oen le out of consideration.
e Chapters and focus on identifying important socio-demographic factors.
In Chapter dierences in energy balance-related behaviors and overweight according
to gender, ethnicity and school-level are studied. e results of this study showed that
youth from a non-Western ethnic background and those attending lower-level secondary
education were more likely to be overweight and to engage in unfavorable energy balance-
related behaviors than youth from a Western ethnic background and higher-level secondary
education.
In Chapter these dierences are examined for commuting to school. Adolescents of
a non-Western ethnic background were more likely to walk to school and use non-active
transportation (public transport, scooter) compared to native Dutch adolescents, who were
more likely to cycle to school. Adolescents were less likely to walk or cycle to school if
they lived further away from school, in which case they were more likely to use non-active
transportation.
e studies that are presented in chapters - examine the association of individual and en-
vironmental factors with energy balance-related behaviors. Moreover these studies explore
how the environment may inuence energy balance-related behaviors.
e study in Chapter examines whether the availability of snacks and drinks in school
canteens and the presence of food stores in the school neighborhood were associated with
a higher so drink and snack consumption. e results showed that if the distance to the
nearest food store was greater than meters, and if there were more small food stores in
Summary 235
Chapter 10Summary
the school neighborhood, the adolescents drank less so drinks. e availability of school
canteen products and the availability food stores in the proximity of the school were not
associated with snack consumption.
Chapter focuses on whether the availability of sports facilities, neighborhood factors
(availability of facilities, safety and pleasantness) and parental factors were associated with
adolescent sports participation. e results showed that adolescents participated more in
leisure time sports if they had more sports equipment at home, if their parents participated
more in sports and if their parents endorsed the rule that their child should participate in
sports. e inuence of these environmental factors was not only direct (i.e., more equip-
ment was associated with higher sports participation), but also indirect via personal factors.
Adolescents with more sports equipment at home, with parents doing sports and the rule
to participate in sports, had also a more positive attitude and intention towards sports, and
were in turn also more likely to participate in sports.
Chapter reports on a study about potential parenting inuences on adolescent so
drink consumption. e results of this study indicated that more restrictive parenting
practices (rules) were associated with less so drink consumption. It appeared that the
working mechanism was both direct (i.e., strict rules were associated with lower so drink
consumption) and indirect. Having stricter rules about consuming so drinks at home was
associated with a more negative attitude and cognitions towards so drink consumption.
ese more negative cognitions in turn were associated with lower so-drink consumption.
Next to this, it was found that the association between parenting practices and so drink
consumption was stronger among adolescents who perceived the parenting style of their
parents as being moderately strict and highly involved (authoritative parenting style).
In the general conclusion (Chapter ) the ndings of all studies are integrated and conclu-
sions and recommendations for practice and future research are given.
ere is only little evidence for associations between physical environmental factors and
energy balance-related behaviors. e evidence for socio-cultural factors and in particular
parental inuences is stronger and might be more important for the prevention of overweight
among adolescents. Parents can promote healthy energy balance-related behaviors by giving
the right example, by setting clear rules and by creating possibilities for healthy behaviors.
Environmental factors can have both an indirect (via cognitions) and a direct (automatic,
unconscious) association with energy balance-related behaviors. Future research should ex-
amine if some energy balance-related behaviors are more under the inuence of automatic
processes than others.
Adolescents attending lower level education and adolescents from a non-Western ethnic
background are important target groups for the prevention of overweight. Interventions that
target these groups should be developed and investigated. Next to this, parents should be
made aware of their important role in the promotion of healthy behaviors of their children.
Samenvatting 237
Chapter 10Samenvatting
Samenvatting
Het aantal mensen dat in Nederland en andere Westerse landen aan overgewicht of obesitas
lijdt, is de laatste decennia zo sterk gestegen dat overgewicht en obesitas inmiddels behoren
tot de belangrijkste volksgezondheid problemen. Daarom is het van belang overgewicht in
alle leeijdsgroepen te voorkómen. Met name kinderen en adolescenten zijn daarbij een
belangrijke doelgroep. Obesitas op jonge leeijd gaat gepaard met een verhoogde kans op
het ontstaan van chronische ziekten op jonge en latere leeijd. Daarnaast hebben kinderen
en jongeren met obesitas een grotere kans om ook overgewicht of obesitas te hebben als ze
volwassen zijn.
Het is al bekend dat gewichtsstijging het gevolg is van een positieve energiebalans waarbij
de energie-inname (via de voeding) groter is dan het energieverbruik (met name via licha-
melijke activiteit). Voor de succesvolle preventie van overgewicht is het belangrijk te weten
welke specieke energiebalans gerelateerde gedragingen bijdragen aan gewichtsstijging bij
kinderen en jongeren.
Een ander belangrijk element voor succesvolle preventie van overgewicht is het verkrijgen
van een gedetailleerd overzicht van de factoren die deze energiebalans gerelateerde gedra-
gingen beïnvloeden. Uit voorgaand onderzoek is gebleken dat persoonlijke determinanten
van gedrag zoals kennis, attitudes, sociale invloed en motivatie belangrijk zijn. Echter, ook
omgevingsfactoren zoals de beschikbaarheid van voedingsmiddelen, de mogelijkheden tot
lichamelijke activiteit en de ouders kunnen voedings- en beweeggedrag van kinderen en
jongeren beïnvloeden.
Om meer zicht te krijgen op het samenspel tussen de persoonlijke en omgevingsdeter-
minanten van voedings- en beweeggedrag bij jongeren, is in het ENDORSE project
(ENvironmental Determinants of Overweight in Rotterdam SchoolchildEn) geïnitieerd. De
doelen van het ENDORSE project waren () het identiceren welke energiebalans gerela-
teerde gedragingen gerelateerd zijn aan overgewicht, () het onderzoeken van individuele
en omgevingsfactoren van deze energiebalans gerelateerde gedragingen, () het onderzoe-
ken van het verband en het samenspel tussen deze factoren en energiebalans gerelateerde
gedragingen en () het formuleren van doelstellingen voor interventies die gericht zijn op
de preventie van overgewicht bij - jarige jongeren. In dit proefschri worden studies
gepresenteerd die de resultaten van dit onderzoek beschrijven.
In de algemene introductie (hoofdstuk ) worden de achtergrond, de doelen en het theore-
tische raamwerk van de ENDORSE studie beschreven.
In hoofdstuk wordt het ENDORSE onderzoeksprotocol beschreven samen met de
onderzoeksopzet en de meetinstrumenten. Het ENDORSE onderzoek vond plaats onder
jongeren in de eerste ( tot jaar oud) en derde ( tot jaar oud) klas van het voortgezet
238 Samenvatting
onderwijs. Zeventien scholen deden mee aan de ENDORSE studie en jongeren en
hun ouders werden geselecteerd om deel te nemen. Daarnaast wordt in dit hoofdstuk het
resultaat van een verkennend onderzoek beschreven waarin de risicogedragingen voor
het ontstaan van overgewicht en obesitas werden verkend. Een literatuuronderzoek èn
het raadplegen van experts resulteerde in de selectie van de energiebalans gerelateerde
gedragingen die in het ENDORSE project nader onderzocht zouden worden: transport naar
school, lichamelijke activiteit in de vrije tijd, sporten, televisie kijken, computer gebruik en
de consumptie van suikerhoudende frisdranken, tussendoortjes en ontbijt.
Onderzoek naar omgevingsinvloeden op gedrag is relatief nieuw. Daarom is er eerst een
literatuuronderzoek uitgevoerd om het beschikbare bewijs over de relatie tussen omge-
vingsfactoren en energiebalans gerelateerde gedragingen te inventariseren (Hoofdstuk
en ). Met dit literatuuronderzoek werd consistent bewijs gevonden dat er een verband is
tussen de inname van energie en vet door ouders en de inname van energie en vet door hun
kinderen. Ook liet dit onderzoek zien dat bij een hoger opleidingsniveau van de ouders de
jongeren meer groenten en fruit eten.
Sociale steun, het opleidingsniveau van de moeder, gezinsinkomen, het volgen van een
hogere middelbare schoolopleiding en een lage criminaliteitsincidentie in de woonomge-
ving waren geassocieerd met meer lichamelijke activiteit bij jongeren. Het literatuuronder-
zoek liet verder zien dat er maar een beperkt aantal onderzoeken beschikbaar waren die
fysieke omgevingsdeterminanten onderzoeken. Ondanks dat veel studies op scholen zijn
uitgevoerd, werden voornamelijk factoren in de thuis- en woonomgeving onderzocht en
bleef de schoolomgeving vaak buiten beschouwing.
De hoofdstukken en richten zich op het identiceren van belangrijke sociaaldemogra-
sche factoren.
In hoofdstuk werd onderzocht of er verschillen zijn in energiebalans gerelateerde
gedragingen en overgewicht voor geslacht, etniciteit en schoolniveau. De resultaten van
deze studie lieten zien dat jongeren met een niet westerse achtergrond en jongeren van het
VMBO vaker overgewicht hadden en ongezondere energiebalans gerelateerde gedragingen
vertoonden.
In hoofdstuk werden deze verschillen onderzocht voor vervoer naar school. Jongeren
van een niet Westerse aomst wandelden vaker naar school of gebruikten vaker niet actieve
transportmiddelen (openbaar vervoer, scooter) dan Nederlandse jongeren, die vaker de
ets gebruikten. Jongeren gingen minder vaak etsend of lopend naar school naarmate de
afstand tot de school groter was.
Samenvatting 239
Chapter 10Samenvatting
De studies in hoofdstuk - onderzoeken het verband tussen de individuele en omgevings-
factoren met de energiebalans gerelateerde gedragingen. Daarnaast wordt in deze studies
verkend hoe de omgevingsfactoren de energiebalans gerelateerde gedragingen beïnvloeden.
In hoofdstuk werd onderzocht of de beschikbaarheid van frisdranken en snacks in
schoolkantines en de aanwezigheid van levensmiddelenwinkels rond scholen samenhangen
met de inname van frisdrank en snacks. De resultaten lieten zien dat als de afstand van
school tot de dichtstbijzijnde winkel meer dan meter was en als er meer kleine winkels
in de schoolomgeving waren, de jongeren minder frisdrank dronken. Er werd geen verband
gevonden voor het aanbod in schoolkantines en de winkels in de schoolomgeving met
snackconsumptie.
In hoofdstuk onderzochten we of de beschikbaarheid van sportfaciliteiten, buurtfac-
toren, en de invloed van ouders verband hielden met het sportgedrag van jongeren. De
jongeren sportten meer in de vrije tijd als ze thuis meer sportfaciliteiten hadden, als de ou-
ders zelf meer sportten en als de ouders de regel hadden dat hun kind aan sport moet doen.
De relatie van deze omgevingsfactoren was niet alleen direct (b.v. meer faciliteiten hielden
verband met meer sporten), maar ook indirect, via de persoonlijke factoren. Jongeren met
meer sportfaciliteiten thuis, ouders die sporten en met de regel dat ze aan sport moeten
doen, hadden ook een positievere attitude en intentie tot sporten, en vervolgens ook een
grotere kans om meer te sporten.
Hoofdstuk beschrij een onderzoek naar de invloed van de ouders op het frisdrank-
gebruik van jongeren. De resultaten van dit onderzoek lieten zien dat meer restrictieve
regels over frisdrankgebruik waren geassocieerd met minder frisdrank consumptie. Het
werkingsmechanisme leek ook hier niet alleen direct (restrictieve regels resulteerden in
minder frisdrank consumptie) maar ook indirect. Jongeren die meer regels ondervonden,
hadden ook een negatievere attitude en andere cognities ten aanzien van frisdranken, die
vervolgens verband hielden met minder frisdrank consumptie. Daarnaast werd gevonden
dat het verband tussen opvoedingsregels en frisdrankconsumptie sterker was bij jongeren
die hun ouders als matig strikt en zeer betrokken ervaren (autoritatieve opvoedingsstijl).
In de algemene conclusie (hoofdstuk ) worden de resultaten uit de onderzoeken geïn-
tegreerd en conclusies en aanbevelingen gegeven voor de praktijk en verder onderzoek.
De belangrijkste conclusies zijn dat er weinig bewijs is voor een verband tussen fysieke
omgevingsfactoren en energiebalans gerelateerde gedragingen. Voor sociaal-culturele om-
gevingsfactoren, met name de invloed van ouders, is het bewijs sterker en is mogelijk
belangrijker voor de preventie van overgewicht bij jongeren. Ouders kunnen gezonde
energiebalans gerelateerde gedragingen bevorderen, door het goede voorbeeld te geven,
duidelijke regels te formuleren en mogelijkheden te creëren voor gezond gedrag.
Omgevingsfactoren kunnen op een indirecte (via individuele cognities) en directe
(automatische en onbewuste) manier verband houden met energiebalans gerelateerde ge-
240 Samenvatting
dragingen. In toekomstig onderzoek moet onderzocht worden in hoeverre de verschillende
energiebalans gerelateerde gedragingen door automatische processen beïnvloed worden.
Voor de preventie van overgewicht zijn met name jongeren van het VMBO en jongeren
met een niet-westerse achtergrond belangrijke doelgroepen. Interventies die gericht zijn
op deze groepen moeten ontwikkeld en onderzocht worden. Daarnaast moeten ouders
bewust gemaakt worden van hun belangrijke rol in het bevorderen van gezond gedrag bij
hun kinderen.
Dankwoord 241
Chapter 10Dankwoord
Dankwoord
Toen ik besloot om in Maastricht Gezondheidsvoorlichting te gaan studeren wist ik dat ik
hier verder mee wilde, maar het hoe en wat moest nog vorm krijgen. Nu, niet eens zo heel
veel jaren later, ligt dit proefschri er. Stef, je enthousiasme tijdens mijn jaarwerkstuk en
afstudeeronderzoek was besmettelijk en doorslaggevend. Je overhandigde mij mijn diploma
in Maastricht en ook nu ben je er weer bij. Dit keer in de grote promotiecommissie. Wie
weet waar we elkaar de volgende keer treen.
Bovenal wil ik Hans en Anke bedanken. Tenslotte hebben zij de grootste bijdrage geleverd
aan dit proefschri. Hans, je dacht altijd in oplossingen en zelden in problemen. Dat zijn
voor een promotor goede kenmerken. Je zag overal kansen en mogelijkheden, zowel binnen
als buiten je functie. Ik hoop dat ik daar wat van heb opgepikt! Anke, het was eerst even
aasten, maar we hebben altijd goed kunnen samenwerken. Je maakte wel veel werk van het
begeleiden, ….waardoor de papers altijd beter zijn geworden. Bedankt voor de vele hulp en
de jne tijd samen.
Lottie & Nellie, mijn dappere onderzoeksassistenten! Gewapend met weegschaal, meetlat
en vragenlijsten gingen jullie op pad om pubers te meten en te wegen. Meestal was het leuk,
soms lastig. Met jullie enthousiasme, energie en blonde haren hebben jullie de leerlingen
en leraren voor jullie gewonnen. Zonder jullie had ik het niet gekund! Hartelijk dank voor
jullie inzet.
De samenwerking met de GGD Rotterdam is voor het ENDORSE onderzoek erg belangrijk
geweest. Ik kon gebruik maken van de structuur van “de Jeugdmonitor” waardoor veel scho-
len, leraren, leerlingen en ouders bereid waren om deel te nemen. Petra en Wilma bedankt!
Een leuke sfeer en veel koepauzes maken werken makkelijker en leuker. Kamergenotes
Birgitte, Willemieke en Wendy, bedankt voor alle gezelligheid. Paranimf Tinneke, wat heb-
ben wij veel koe gedronken en gekletst. Het was altijd gezellig om het leven van alledag
met je te bespreken. Congresmaatje Carlijn; walvissenjacht in Boston, luxe kamer met
ontbijt in Olso en de Gondola van Ban... we kwamen tenslotte niet alleen voor het congres.
Appel Gert Jan, je favoriete koedrinkstagiaire is klaar! We hebben alleen nog steeds geen
paper met elkaar geschreven, dus… toch nog maar een koe date? Saskia bedankt voor
de hulp met statistische problemen, het schrijven van papers en de jne samenwerking.
Nannah, Merel, Marianne, Rick, Hein en andere (ex) DGG-ers: het was leuk om jullie als
collega’s te hebben, bedankt voor de gezelligheid.
David and Anna, thanks for inviting me for a stay in Melbourne and the opportunity to
work at the Centre for Physical Activity and Nutrition Research. It was a great life and work
242 Dankwoord
experience with a nice paper as a result. Jo, Karen, Abbie, Clare, Kylie and Rebecca, thank
you for making my stay so enjoyable!
Familie & vrienden, het ‘gewoon mijn werk’ is klaar. Steun bij mijn proefschri heb ik niet
echt nodig gehad, dat liep eigenlijk op rolletjes. Maar er is veel gebeurd de afgelopen jaren,
leuke en verdrietige gebeurtenissen. Dank voor alle steun, maar vooral voor alle gezellige
momenten.
Pa, misschien komt het er nog eens van, maar de eerste Dr. dat ben ik! Ma, bedankt voor de
goede zorgen en gezelligheid! Het is altijd jn om thuis te komen.
Bas, bedankt voor alles. Voor mij ben je de beste man die er is. Door jou is de combinatie
carrière & Femke perfect!
C V 243
Chapter 10C V
Curriculum Vitae
Klazine van der Horst is geboren op december in Zwolle. Na de middelbare school
volgde ze de opleiding Voeding & Diëtetiek aan de Hogeschool van Arnhem en Nijmegen.
Na haar afstuderen in volgde ze de Master Gezondheidsvoorlichting van de opleiding
Gezondheidswetenschappen aan de universiteit van Maastricht. Tijdens en na haar afstu-
deeronderzoek werkte Klazine als onderzoeksmedewerker en junior onderzoeker bij het
EMGO instituut van het VU Medisch Centrum in Amsterdam. In augustus kon ze
beginnen met haar promotieonderzoek bij de afdeling Maatschappelijke Gezondheidszorg
van het Erasmus MC Rotterdam. Daar voltooide zij gelijktijdig de Master of Public Health
van het NIHES (). Vanaf mei werkte ze in Rotterdam als wetenschappelijk me-
dewerker verder aan de follow-up studie van het ENDORSE project. Sinds december
werkt ze als postdoctoraal onderzoeker in Zwitserland aan de ETH te Zürich bij de afdeling
Consumer Behavior.
C V
List of Publ. 245
Chapter 10List of Publ.
List of Publications
Hume C, van der Horst K, Brug J, Salmon J, Oenema A. Understanding the correlates of
adolescents’ TV viewing: a social ecological approach. International Journal of Pediatric
Obesity (in press).
van der Horst K, Oenema A, te Velde SJ, Brug J. Gender, ethnic and school type dierences
in overweight and energy balance-related behaviours among Dutch adolescents. Interna-
tional Journal of Pediatric Obesity , May :-. [Epub ahead of print].
DeJong CS, van Lenthe F, van der Horst K, Oenema A. Environmental and cognitive cor-
relates of adolescent breakfast consumption. Preventive Medicine , (): -.
van der Horst K, Oenema A, van de Looij-Jansen P, Brug J. e ENDORSE study: Research
into environmental determinants of obesity related behaviors in Rotterdam Schoolchildren.
BMC Public Health , ;: .
van der Horst K, Timperio A, Crawford D, Roberts R, Brug J, Oenema A. e school food
environment: associations with adolescent so drink and snack consumption. American
Journal of Preventive Medicine , (): -.
Bere E, van der Horst K, Oenema A, Prins R, Brug J. Socio-demographic factors as cor-
relates of active commuting to school in Rotterdam, the Netherlands. Preventive Medicine
, (): -.
van der Horst K, Oenema A, Ferreira I, Wendel-Vos W, Giskes K, Van Lenthe F, Brug J. A
review of environmental correlates of obesity-related dietary behaviors in youth. Health
Education Research , : -.
van der Horst K, Kremers S, Ferreira I, Singh A, Oenema A, Brug J. Perceived parenting
style and practices and sugar-sweetened beverage consumption by adolescents. Health
Education Research , : -
van der Horst K, Chin a Paw M, Twisk J, Van Mechelen W. A brief review on correlates of
physical activity and sedentariness in youth. Medicine & Science in Sports & Exercise ,
(): -.
246 List of Publications
Ferreira I, van der Horst K, Wendel-Vos W, Kremers S, van Lenthe F, Brug J. Environmental
correlates of physical activity in youth - A review and update. Obesity Reviews , ():
-.
Kremers SPJ, van der Horst K, Brug J. Adolescent screen-viewing behaviour is associated
with consumption of sugar-sweetened beverages: the role of habit strength and perceived
parental norms. Appetite , : -.
SUBMITTED
van der Horst K, Oenema A, te Velde SJ, Brug J. Do individual cognitions mediate the
association of socio-cultural and physical environmental factors with adolescent sports
participation? Public Health Nutrition (submitted).
Prins R, Oenema A, van der Horst K, Brug J. Objective and perceived availability of physi-
cal activity opportunities: dierences in associations with physical activity behavior among
adolescents. International Journal for Behavioral Nutrition and Physical Activity (submitted).
van der Horst K, Siegrist M, Orlow P, Giger, M. Residents’ reasons for specialty choice:
Gender, time, patient and career aspects. Medical Education (submitted)
PhD Portfolio 247
Chapter 10PhD Portfolio
PhD Portfolio
Summary of PhD training and teaching activities
Name PhD student: Klazine van der Horst
Erasmus MC Department: Public Health
PhD period: 2004-2008
Promotor: Prof.dr.ir. J. Brug
Supervisor: Dr. A. Oenema
Year Workload
(Hours/
ECTS)
1. PhD training
General courses
Master of Science in Health Sciences, specialization Public Health, NIHES, Erasmus MC
Rotterdam
2006 70 ECTS
Specic courses
eory construction and statistical modelling, Faculty of Social and Behavioural Sciences,
Utrecht University
2006 1 ECTS
Presentations
Van der Horst K, Kremers S, Ferreira I, Singh A, Oenema A, Brug J. “Perceived parenting
practices and parenting styles and adolescent so drink consumption”. Paper presented at
the European Congress on Obesity, Athens, Greece (June 1-4)
2005 1 ECTS
Van der Horst K, Oenema A, Ferreira I, Brug J. “Environmental determinants of weight
gain-related behaviours in youth”. Paper presented at the sixth Conference on Psychology
and Health, Kerkrade, the Netherlands (May 8-10)
2006 1 ECTS
Van der Horst K, Oenema A, Ferreira I, Wendel-Vos W, Giskes K, Van Lenthe F, Brug J.
“Environmental correlates of obesity related behaviors in youth”. Paper presented at the
h conference of the International Society of Behavioral Nutrition and Physical Activity,
Boston, USA (July 13-16)
2006 1 ECTS
Research Meeting, CPAN Deakin University, Melbourne.
‘e ENDORSE Study’
2007 1 ECTS
Van der Horst K, Oenema A, Brug J. “Exploring environmental determinants of (in)
activity in adolescents: the ENDORSE study”. Paper presented at the European Health
Psychology Society conference, Maastricht, the Netherlands. (August 15-18).
2007 1 ECTS
Van der Horst K, Oenema A, te Velde SJ, Brug J. “e inuence of parenting styles and
practices on adolescents’ energy balance related behaviours in the Netherlands”. Paper
presented at the sixth conference of the International Society of Behavioral Nutrition and
Physical Activity, Oslo, Norway (June 20-23)
2007 1 ECTS
Nederlands Congres Volksgezondheid, Groningen
‘Frisdrank en snack consumptie bij jongeren: De invloed van voedingsmiddelen in
schoolkantines & winkels in de schoolomgeving’
2008 1 ECTS
Van der Horst K, Oenema A, Brug J. “Are school physical activity policies and schoolyard
facilities associated with sports and active commuting to school?” Paper presented at
the seventh conference of the International Society of Behavioral Nutrition and Physical
Activity, Ban, Canada. (May 21-24).
2008 1 ECTS
248 PhD Portfolio
International Conferences
Fourth conference of the International Society of Behavioral Nutrition and Physical
Activity, Amsterdam, the Netherlands (July 16-18)
2004 1 ECTS
European Congress on Obesity, Athens, Greece (June 1-4) 2005 1 ECTS
Sixth Conference on Psychology and Health, Kerkrade, the Netherlands (May 8-10) 2006 1 ECTS
Fih conference of the International Society of Behavioral Nutrition and Physical Activity,
Boston, USA (July 13-16)
2006 1 ECTS
Sixth conference of the International Society of Behavioral Nutrition and Physical Activity,
Oslo, Norway (June 20-23)
2007 1 ECTS
Seventh conference of the International Society of Behavioral Nutrition and Physical
Activity, Ban, Canada. (May 21-24).
2008 1 ECTS
2. Teaching activities
Supervising Bachelor thesis 2007 15 hours
Curriculum Medical students, 2nd year, Erasmus MC Rotterdam
eme 2.2: ‘Disorders in nutrition, metabolism and hormonal regulation’. Supervising
students.
2007 5 hours
Curriculum Medical students, 4th year, Erasmus MC Rotterdam
eme 4.2: ‘e population as a patient’. Coordination and supervising students.
2006
2007
15 hours
20 hours
