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International Journal of Sports Science and Physical Education
2018; 3(3): 32-39
http://www.sciencepublishinggroup.com/j/ijsspe
doi: 10.11648/j.ijsspe.20180303.11
ISSN: 2575-226X (Print); ISSN: 2575-1611 (Online)
Wellness Behaviors and Body Mass Index Among U.S.
Adolescents: A Comparative Study
Timothy Makubuya
Department of Educator Preparation and Leadership, University of Missouri- St. Louis, St. Louis, USA
Email address:
To cite this article:
Timothy Makubuya. Wellness Behaviors and Body Mass Index Among U.S. Adolescents: A Comparative Study. International Journal of
Sports Science and Physical Education. Vol. 3, No. 3, 2018, pp. 32-39. doi: 10.11648/j.ijsspe.20180303.11
Received: September 22, 2018; Accepted: October 15, 2018; Published: November 1, 2018
Abstract:
Behaviors such as sedentariness, poor nutritional choices and inadequate sleeping put youth at risk of illnesses.
Although health and physical education programs are structured to promote the development of various health and physical
skills, they are constantly faced with challenges to their continual existence. As such, issues related to body composition ensue
and manifest differently across gender and race/ethnicity. This study aimed at examining the relationships between multiple
youth risk behaviors and body composition. In addition, gender and racial/ethnic differences between white and black high
school students were examined. Bivariate and multivariate examinations of physical activity, dietary behavior, sleep in relation
to Body Mass Index (BMI) percentiles, was followed by a comparison of two data sets of the 2013 YRBS (n= 13, 363) and
2015 YRBS (n= 15, 624). The results revealed an existence of gender differences in relationships between physical activity,
dietary behavior, sleep duration and BMI percentiles with significant associations in male high school students but not their
female counterparts in both data sets. There were no racial differences in the strength of these relationships between Black or
African American and White or Caucasian high school students. These findings corroborate the need for gender based
interventions and further analyses based on non-subjective measures of those health-risk behaviors, in order to fully understand
the relationships. In addition, family, school and community based interventions to the physical inactivity, poor nutrition and
poor sleep habits are warranted, and should encircle strategies from various stakeholders.
Keywords:
Physical Activity, Dietary Behavior, Sleep, Body Mass Index’ Race/Ethnicity
1. Introduction
A number of schools and school districts across the U.S.,
often contemplate ways of dealing with budgetary challenges
especially during difficult economic times [1]. Health and
physical education (PE) programs often become victims of
these unforeseeable budgetary shortfalls, leading to their
reductions or closures. Physical education should be
emphasized as a critical investment in the nation’s youth,
although monetary decision makers almost often neglect such
investment. Even though U.S. Department of Health and
Human Services’ guidelines for physical activity require
meaningful daily physical activity in children and
adolescents [2], many children just don’t meet the
requirements. In addition, many children live in communities
and homes, that often lack the capability to provide resources
for a healthy living. Often too, the economic gap between
rich and poor, exacerbates the situation and hence leading to
extreme health disparities between the poor and their wealthy
counterparts. Of specific importance though, are the health
disparities in physical activity and diet. In addition, to the
low socio-economic status (SES) as a major factor [3],
African American youth often lack the opportunities to
access community recreational resources compared to their
white counterparts [4]. Similar patterns and trends are also
often evident in schools with higher percentages of diverse
students [5].
In fact, it is through health and physical education that
many youths obtain meaningful levels of physical activity
and other health related skills including nutrition and overall
wellbeing. Even though, there is an emphasis on the adoption
of a framework that promotes high-quality physical
education. In addition to meeting the minimum of 150
minutes of weekly instructions in elementary school and 225
33 Timothy Makubuya: Wellness Behaviors and Body Mass Index Among U.S. Adolescents: A Comparative Study
minutes in middle and high schools, many schools still lag
behind [1].
In some cases, the necessary recommendations
for daily moderate to vigorous physical activity for children
and adolescents are not being met.
Although from the most recent data, rates of overweight
and obesity in adolescents are levelling off, the fact is that,
those rates are still exorbitant. Conversations around body
composition should often require an examination of school
and out of school nutrition practices, with their unavoidable
interrelationship with physical inactivity. Conversely, instead
of only focusing on nutrition and physical inactivity, there is
also need to study the role of other factors outside schools,
such as insufficient sleep and higher risk of obesity among
children. This aspect of health and wellness could better be
addressed through family structures. Available evidence
suggests that, there is an expeditiously increasing literature
on chronic partial sleep [6] from both epidemiological and
clinical studies indicating its link to obesity risk and weight
gain [7]. An analysis of national YRBS data examined the
association between self-reported obesity and self-reported
sleep duration, and found results to exhibit gender
differences, with girls having significant association
compared to their male counterparts [8], although the
recommendations suggested caution on abrupt sleep
adjustment practices, citing the need for further studies.
Physical activity, nutrition and sleep habits have received
heightened attention in studying relationships with body
composition among all individuals. Previous investigations
[9]
conducted to establish the relationship between physical
activity and overweight children in Taiwan suggested that the
emphasis should be placed on extrinsic factors and how they
impact overweight and obesity in children. These influences
are normally within school, community and family control. A
two-year longitudinal study reported both diet-related and
physical activity predictors of obesity in young children
between 6 and 9 years old [10]. About 48% of those
respondents were Latinos and their findings suggested that
there was an interrelatedness between the factors of diet,
physical activity and obesity. Elsewhere, food patterns,
physical activity, sleep, television viewing, and longer sleep
hours were associated with consumption of fast food, the lack
of vegetables and fruit-rich diet among Portuguese children
[11]. Television viewing often increases the sit-down time,
which is fountainhead to sedentariness and sitting disease
syndrome.
Adolescents greatly benefit from physical activity and
physical education classes, and this is particularly under the
guidance of a teacher. Some would also argue that
engagement in other extracurricular activities would provide
similar benefits. A study to compare the outcomes from two
groups with one engaging in physical education and the other
in Junior Reserve Officer Training Corps (JROTC), a
commonly used alternative for Physical Education (PE) time
[12]
revealed significant differences in instruction time,
content and context, with students engaging in more
moderate to vigorous physical activity during PE than
JROTC. Therefore, such revelations should spearhead
advocacy efforts for programs that meet and exceed the daily
recommendation for the right intensity and duration of
physical activity. The purpose of this study was to examine
the gender and racial/ethnic related association between
physical activity, diet, and sleep with body mass index
percentiles and then compare the results of two cross-
sectional data sets. In addition, this paper conceptualizes the
Whole School, Whole Community and Whole Child
approach, that addresses school, and community institutions
through connections that require family engagement in the
exploration of potential school and community resources and
interventions aimed at addressing the health and wellness
behaviors that lead to overweight and obesity.
2. Materials and Methods
2.1. Participants and Setting
Data from participant responses on the 2013 and 2015
youth risk behavior survey (YRBS) were used to identify and
examine health risk behavior items about physical activity,
food and sleep for 9th to 12th grade high school students.
Both national samples for the 2013 and 2015 YRBS were
determined using a three-stage cluster sample design by the
U.S. Center’s for Disease Control and Prevention (CDC).
Participants were from selected private and public schools
that were in the representative pool.
After all multi-level eliminations of missing cases and
removing outliers at three standard deviations from the mean,
13,363 and 15,624 participant questionnaires were used for
both 2013 and 2015 analyses respectively. These participants
identified as both male and females from the 9th and 12th
grade classes that had a varying racial/ethnic make-up.
2.2. Data Collection
YRBS data were collected nationally from sampled
schools following similar procedures in 2013 and 2015 [13,
14]. The collection of data from respondents required passive
consent that was gained through permission slips that were
sent home from schools, requiring adult consent. National
YRBS data were collected by CDC and its vendor trained
collectors who presented students with computer readable
answer sheets with the survey. Once data were collected
through responses to the different questions on survey, the
CDC and its contractor, ICF Macro, Inc., carried out the
preparation and analysis of the initial data [13, 14]. For
purposes of this study, the CDC availed the data, and with the
approval of the University of Missouri-St. Louis Institution
Review Board, for further analysis.
2.3. Methodology
Relationship of BMI percentiles with physical activity,
dietary behavior and sleep was examined, while controlling
for gender and race/ethnicity. Survey responses for physical
activity, dietary behavior and sleep elements were scored
based on the particular response options per question.
Responses on the survey ranged from A to H whereby, A
International Journal of Sports Science and Physical Education 2018; 3(3): 32-39 34
represented 0 and H represented 7 points respectively.
The risk behaviors were then categorized based on an
aggregate score of the values for each question ranging from
0 to 7.
Based on the nature of the health risk behavior question,
responses were then assigned a positive or negative
numerical value 1 to 7 or -1 to -7 for categories of health
risk behavior depending on whether they are positive or
negative behaviors. Behaviors such as drinking soda
(including non- diet soda), watching TV, and playing
video game were assigned a negative numerical value
whereas behaviors such as daily participation in 60 minute
of physical activity were assigned a positive value. In this
study, the question on “how many times did you eat
potatoes” was eliminated from the dietary behaviors
variable computation from both 2013 and 2015 data.
Physical activity was grouped into three categories
(Nearly Always/ Often, Occasional and Little/No Physical
Activity); dietary behavior into three categories (Frequent
Consumption, Moderate Consumption, Little /No
Consumption); and hours of sleep into three categories
(Recommended, Close to Recommended and Far Less
than Recommended). Higher physical activity scores
implied nearly always or often physically active
participation, whereas frequent consumption implied
higher scores for the dietary score. Longer sleep durations
were the equivalent of gaining the recommended sleep
duration and vice versa.
Since YRBS questions on physical activity were mainly
reported based on the frequency of their occurrence. This
study categorized these responses based on the SHAPE
America Physical Activity guidelines for adolescents that
require daily participation in physical activity. Questions on
dietary behaviors were mainly reported based on the
frequency of their occurrence. The study categorized these
responses based on the USDA Dietary Guidelines for
Americans (2015-2020). These guidelines support the
recommendations that require frequent/daily consumption of
nutritious foods across all food groups by children and
adolescents. The National Institute of Health (NIH)
recommendations for adolescent health are in line with the
CDC recommendations for healthy sleep. The National Sleep
Foundations has recommendations for teenagers to obtain 8-
10 hours of sleep daily [15]. Body Mass Index percentiles, an
index for body composition was computed from the self-
reported weight and height by the respondents. Computations
of BMI percentiles were performed by the CDC using the
growth chart measures [14].
Table 1. Categories for Health and Wellness Risk Behaviors.
Physical Activity Dietary Behaviors Sleep Duration
Nearly Always (5- 7 days) Frequent Consumption (5-7 days) Recommended (8-10+ hours)
Occasional (3-4 days) Moderate Consumption (3-4 days) Close to Recommended (6 <8 hours)
Little (<3 days) Little/No Consumption (<3 days) Less than Recommended (<6 hours)
2.4. Statistical Analysis
Bivariate and multivariate measures were used to
determine the relationship between the three independent
variables of physical activity, dietary behaviors and sleep and
the dependent variable of body mass index. Due to the
continuous nature of the variables, Spearman Rho correlation
was applied to establish the relationship between each
independent variable with the dependent variable.
Correlation coefficient, r values range from -1 to +1. In
addition, the relationship between these independent
variables and the dependent variable was examined with
multiple linear regression using Statistical Program for Social
Sciences (SPSS 23).
Multiple linear regression, an extension of simple linear
regression, was used for this study based on the general
equation:
Y=β
0
+β
1
X
1
+β
2
X
2
+β
3
X
3
+………β
n
X
n
+error
Under this analysis, the assumption for an error value of 0
is necessary. Therefore, the estimate value E, of the
dependent variable Y (BMI percentile), for this study was
given by the mathematical equation:
E(Y)=β
0
+β
pa
PA+β
db
DB+β
s
S+βg
Where Y is the predicted, expected or estimated dependent
variable of BMI percentile, pa is Physical Activity, db is
Dietary Behavior, and s is Sleep, all independent predictors
or variables whereas β
0
is the constant, β
pa
, β
db
and β
s
, and β
g
are regression coefficients for physical activity, dietary
behavior, sleep and gender respectively. A model that
accounted for a higher variance in the prediction was more
reliable, and is more acceptable.
For both males and females, emphasis was placed on the
two groups of Race/Ethnicity (White and African
American/Black) that were used as categorical independent
variables in this study. A multiple regression equation that
related the outcome or dependent variable of Y (BMI
Percentile) to each independent variable of Physical Activity
(PA), Dietary Behavior (DB) and Sleep (S) and their product,
while checking for interactions and confounding for gender
and race/ethnicity was estimated. The interactions were
expressed as:
Physical Activity (PA) and Race/Ethnicity (RE) = PA by
RE Interaction Variable (PA. RE)
Dietary Behavior (DB) and Race/Ethnicity (RE) = DB by
RE Interaction (DB. RE)
Sleep (S) and Race/Ethnicity (RE) = S by RE Interaction
(S. RE).
35 Timothy Makubuya: Wellness Behaviors and Body Mass Index Among U.S. Adolescents: A Comparative Study
3. Results
Table 2. BMI Associations with Health Risk Behaviors.
Category 2013 2015
r p-value r p-value
Physical
Activity
All -.31 .001 -.28 .020
Boys .74** .000 -.56** .000
Girls .01 .958 .03 .829
Dietary
Behavior
All -.22 .015 -.10 .270
Boys -.32** .014 -.45** .001
Girls -.15 .167 .17 .200
Sleep
All -.31 .001 -.09 .334
Boys -.47** .000 -.21 .107
Girls -.19 .148 -.04 .778
** P value < .005
3.1. Physical Activity and BMI Percentiles
2013 YRBS
Bivariate analysis using Spearman’s Rho yielded a
significant, negative correlation between BMI percentile and
physical activity score among high school students (r = -.31,
p = .001). This indicates that the higher levels of physical
activity among high school students, the lower the BMI
percentiles. Spearman’s Rho correlation between PA score
and BMI percentile yielded a significant, negative correlation
among high school boys from the 2013 data (r = - .74, p
= .000) and a weak and non-significant correlation among
high school girls (r = .01, p = .958).
2015 YRBS
The correlations between BMI percentiles and physical
activity also yielded a significant, negative correlations
among high school students (r = -.28, p = .020). Among high
school boys, the correlations were significant and negative (r
= -.56, p = .000) and a non-significant correlation among
high school girls (r = .03, p = .829). Consequently, higher
levels of physical activity were found to be associated with
significantly lower BMI percentiles in high school boys and
not among high school girls.
3.2. Dietary Behaviors and BMI Percentiles
2013 YRBS
Spearman’s Rho yielded a weak negative correlation
between dietary behavior score and BMI percentiles from the
2013 data (r = -.22, p = .015). Spearman’s Rho yielded a
weak negative correlation between DB score and BMI
percentiles among high school boys (r = -.32, p = .014).
Spearman’s Rho also yielded a weak negative correlation
between dietary behavior score and BMI percentiles among
high school girls (r = -.15, p = .167). However, this
correlation was non-significant at the set p value. These
results suggest that, higher dietary behavior scores are
associated with lower BMI Percentiles in high school boys
but not in high school girls.
2015 YRBS
Bivariate correlations yielded indirect associations
between dietary behaviors and BMI percentiles (r = -.10, p
= .270) among all high school students. However, the
correlations were different between males (r = -.45, p = .001)
and females (r = .17, p = .200). The associations between
dietary behavior and BMI percentiles among male high
school students were both significant, negative and indirect
in nature. Therefore, this suggests that higher dietary
behavior scores are associated with lower BMI percentiles in
high school boys but not in girls.
3.3. Sleep and BMI Percentiles
2013 YRBS
Spearman’s Rho yielded a negative and weak correlation,
but yet significant at the set p value for the 2013 data (r = -
.31, p = .001). For boys, longer sleep durations were
associated with lower BMI percentiles (r = -.47, p = .000). In
girls, the results were not significant (r = -.19, p = .148). The
differences in correlations between boys and girls were tested
for significance of the difference using a z-test (p = .000) and
this result was significant at p < .05. The results implied that
the longer the sleep duration by high school boys, the lower
their BMI percentiles, unlike in their female counterparts.
2015 YRBS
Analyses from the 2015 data, yielded non-significant
correlation (r = -.09, p = .334) among high school students.
The 2015 data also yielded negative but non-significant
correlations between sleep duration and BMI percentiles (r =
-. 21, p = .107) among males. In addition, there were non-
significant negative correlations in females (r = -.04, p
= .778). This analysis didn’t confirm the significance of the
relationship between sleep duration and BMI percentiles
among high school students overall or between boys and girls
based on the 2015 data. The results of the 2013 and 2015
analyses for sleep duration differ in terms of significance.
3.4. Race/Ethnicity
To examine whether the relationship between the three
health risk behaviors and BMI Percentiles was stronger for
White or Caucasian than Black or African American high
school students depended upon the values as reflected by the
regression coefficients.
E(Y)=β
o
+β
pa
PA+β
db
DB+β
s
S+β
paxre
PA.RE+β
dbxre
DB.RE+β
sxre
S.RE
For both 2013 and 2015, several assumptions for using
linear regression that were met. The assumption of linearity
was met through examining scatter plots, normality of the
distribution was tested using the Kolmogorov-Smirnov test,
independence of errors was met through the exclusion of
outliers within ±3 standard deviations, and homoscedasticity
was met through the examination of the residual and scatter
plots. To test the strength of the relationship between
Caucasian or White and African American or Black, multiple
linear regressions was used initially, and an interaction effect
included depending on the whether the interaction was
significant for the independent variables as selected by the
regression model. Some variables such as gender and sleep
showed initial significance at p < .05, for both 2013 and 2015
data, however, the final model results didn’t provide
International Journal of Sports Science and Physical Education 2018; 3(3): 32-39 36
meaningful estimation models. Other variables were
excluded from the models, due to their lack of significance.
There was inconclusive evidence from this study to suggest
the strength differentials in the relationships between youth
risk behaviors and BMI percentiles among black and white
high school students in 2013 (β
re
= -4.421, p <. 05) and in
2015 (β
re
= -4.543, p < .05) based on the regression
coefficients.
4. Discussion
This study precedes any other in comparing two YRBS
data sets in examining associations between health risk
behaviors of physical activity, dietary behavior and sleep
with BMI Percentile. The health- risk behaviors used as
variables in this study continue to receive heightened
attention in ways that they relate to overall health among
individuals of this group, irrespective of gender or race. The
results of this study encourage further examinations using
non-subjective measures of physical activity, dietary
behavior and sleep. Considerations for additional questions
or items that address specific behaviors, are necessary. From
the results of the analyses, there are specific implications for
each health-risk behavior and body mass index, as they relate
to adolescent health. Gender differences in physical activity
have been documented in previous YRBS research. These
were evident in physical activity and weight status
associations among high school students surveyed with the
1999 YRBS. A previous study [12] utilized a unique
approach that characterized physical activity in different
dichotomous variables. This necessitated the examination of
the link with weight status based on the CDC weight status
categories. The results in that study highlight a similar
pattern revealed in the current study [16].
The current study confirms that the gender differences in
associations between physical activity and BMI percentiles
exist, and this is well supported by a 2016 report by the
World Health Organization (WHO) that purported that
female adolescents aged 11-17 years of age, were less active
than boys in 2010. Whether the chronically low level of
physical activity affects the female adolescent’s other health
outcomes and behaviors, that can indirectly impact body
composition, is not revealed by this study. Physical inactivity
among girls have been linked to serious illnesses that warrant
attention. Previously, a decline in physical activity
participation with aging has been established among girls
[17]. A review of literature also suggests that the decline in
physical activity over the past couple of decades is negatively
impacting life expectancy [18]. Across the board strategies
that address physical attribute concerns among girls, and
peer-pressure related obstacles to physical activity should be
encouraged as well as monitored, in ways that reduce the
sedentary behavior. Of recent, multi- layered approach to
other health issues such as metabolic syndrome is receiving
heightened attention [19].
With the persistence of the overweight and obesity
problem, especially in children and adolescents, schools and
communities at large are continuously implementing
wellness policies that are intended to reduce childhood
obesity, although they are often challenged by budgetary cuts
[1]. To this end, behaviors engaged in away from school,
remain a challenge to boys and girls alike. From the results of
this study, nutrition and weight status are remarkably
important aspects of our daily lives. The Healthy People
2020 objectives address the prevention of excess weight gain
in children and adolescents aged 2 to 19 years. This study
uncovered gender differences in dietary behaviors similar to
those indicated in a previous study of 878 adolescents aged
between 11 and 15 years. These were studied for diet,
physical activity and sedentary behaviors as risk factors for
overweight [20]. There were noticeable differences between
these racially diverse girls and boys in terms of percentage
calories consumed, as total calories expended per day, and
fiber grams per day. Though this revelation seems interesting
in terms of comparisons, the studies did not use a similar
approach to defining dietary behaviors, since the two studies
differ in design and setting. Conversely, the participants in
the current study were slightly older based on the grade
levels of participants in YRBS. A randomized control
approach was used, compared to the self-reported dietary
behaviors that are common with the YRBS [20].
In addition,
the sample size was also very small compared to the national
sample of YRBS. Addressing dietary related problems
requires a wider range of approaches that address the need
for a cultural shift in nutrition, particularly for foods served
in schools and community settings alike. Interestingly also, is
the fact that plant based foods may not be widely appealing
to many adolescents. Yet, their inclusion in daily meals is
fundamental as indicated in the dietary guidelines [21].
Therefore, community and school health educators should
continue to emphasize the advantages of plant based
nutrients, low fat dairy products and lean and non- processed
meats in maintaining health body weights among
adolescents, especially since they are nearing a more critical
entry stage to adulthood.
The current study also revealed noticeable gender
differences in associations between sleep durations and BMI
percentiles. There were significant indirect relationships
between sleep duration and BMI percentiles among boys and
non-significant indirect relationships in their female
counterparts. The results are equivocal with an implication
that further research on sleep duration within different weight
status categories is indispensable. A previous study
highlights that the vagueness may also originate from the fact
that there is merely a single question on the YRBS on sleep
[22].
In addition, relationships within different weight status
categories could possibly reveal valuable information to
address sleep deficits especially in categories that don’t meet
the recommended durations. In addition, the incidence of
early onset of metabolic syndrome, overweight and obesity,
and a cluster of related abnormalities are becoming
increasingly recognizable in adolescents [22, 23]. Other
studies that found associations between children’s weight
status and sleep have addressed other aspects of sleep, such
37 Timothy Makubuya: Wellness Behaviors and Body Mass Index Among U.S. Adolescents: A Comparative Study
as sleep quality. Relationships between sleeping less than 8
hours with higher BMI in adolescents regardless of gender
have been previously established [23]. Significant
associations between sleep duration and BMI, have been
detected in females, although ethnic differences in sleep
durations didn’t exacerbate the BMI disparities [24]. The
present study revealed and affirmed that longer sleep
durations in boys were associated with lower BMI percentiles
unlike in girls. In a previous study, among the factors
identified, were further parental sensitization as well as
addition of more YRBS items addressing other sleep related
aspects [22].
Interventions aimed at addressing low levels of
physical activity, poor nutrition and inadequate sleep can be
implemented through communities, school and at home.
Community-based overweight and obesity interventions
supported by existing infrastructure and resources are able to
address these challenges in diverse communities. Community
interventions that include reforms to policies in schools and
communities, as well as school to home programs have been
modestly successful among U.S. youth aged 2- 19 years of age
[25]. These community interventions are based on a multiple
component approach and guided by the socio-ecological model
in addressing a complex overweight and obesity epidemic,
which has numerous causes [25, 26]. However, few
interventions have used the Community Based Participatory
Research (CBPR) that would otherwise engage various
stakeholders [25]. There is evidence to the dangers of isolating
key elements of a community wide collective approach to
overweight and obesity prevention. Although environmental
and policy interventions have been successful in increasing the
levels of PA in schools, among male students, the failure to
include health instruction, a key contributor to poor eating
habits leads to ineffectiveness in these interventions, especially
if school nutrition is ignored [27].
One of the challenges to schools, especially those in areas
of low socio-economic status (SES), is the ability to maintain
valuable co-curricular programs, especially those that would
otherwise provide youth with the necessary amount of
physical activity, and probably other aspects of health and
wellness education that greatly impacts their weight status.
Girls are particularly concerned of weight status issues, and
often arising from peer pressure. A gender specific school
overweight and obesity interventions for girls in low SES in
New Zealand revealed the possibility to reverse declining
levels of PA with the possibility of preventing unhealthy
weight gain [28]. In the U.S., previous school-based
interventions have been designed to include and emphasize
moderate to vigorous physical activity [29]. What is
interesting however is that this intervention included both
weekday and weekend opportunities for prescribed physical
activity and hence extending the intervention from school to
homes [29]. In Europe, interventions have combined both PA
and nutrition [30]. Overall, reviews of all interventions
advocate for a combination of school-based physical activity
and nutrition with family involvement [31].
Many family-based obesity prevention interventions have
failed to target media and sleep. In addition, the available
literature mostly targets family interventions in non-diverse
family backgrounds [31]. There is need to address
overweight and obesity from a multi-level and multi-layered
approach involving various stakeholders as factors leading to
obesity are interconnected [32].
School and home
collaboration is necessary in obesity prevention intervention,
especially as necessary for adult and parental support in
attaining the recommended levels of PA and good nutrition
[32]. Interventions that are longer and encompass aspects of
PA with family involvement, seem to benefit young and
female participants, and don’t necessarily aim at
environmental policy changes [29]. In addition, there is need
for obesity prevention interventions that address sleep quality
and behaviors in youth in addition to media usage as
controlled by parents of very young and older children alike
[33, 34].
5. Conclusions
This paper indicates the complexity of numerous health
risk behaviors, and their impact on weight status in
adolescents. Although the results from the correlation
analysis, indicated statistical significance, especially in
males, cautionary interpretation is indispensable due to the
existence of a number of factors that are not addressed by the
survey. This study didn’t reveal any differences in the
strengths of the associations between the three health-risk
behaviors and BMI percentiles in black and white high
school students, suggesting that it is possible that the effects
of physical inactivity, poor dietary choices, and lack of
enough sleep affects U.S. high school students in similar
ways. In addition, the factors exacerbating overweight and
obesity are numerous and complex, thereby requiring a
multi-level approach with various stakeholders. Due to the
subjective nature of the YRBS, objective and longitudinal
data collection tools are encouraged to further similar
examinations on these associations.
Acknowledgements
This study was partially supported by a dissertation grant
from the College of Education at University of Missouri- St.
Louis.
Author Contributions
TM solely contributed to this manuscript.
Conflicts of Interest
The authors declare that they have no competing interests.
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