Obesity and associated factors in youth with an autism spectrum disorder

Abstract

Weight status on children and youth with autism spectrum disorder is limited. We examined the prevalence of overweight/obesity in children and youth with autism spectrum disorder, and associations between weight status and range of factors. Children and youth with autism spectrum disorder aged 2-16 years (n = 208) and their parents participated in this study. Body mass index was calculated using the Centers for Disease Control and Prevention growth charts and the International Obesity Task Force body mass index cut-offs. The Autism Diagnostic Observation Schedule was administered. Parents completed questionnaires about socio-demographics, diagnosed comorbidities, sleep disturbances, social functioning and medication of youth with autism spectrum disorder. The prevalence of overweight/obesity in participants with autism spectrum disorder was 35%. One quarter of obese children and youth (25.6%) had obese parents. There was a significant association between children and youth's body mass index and maternal body mass index (r = 0.25, n = 199, p < 0.001). The gender and age, parental education, family income, ethnicity, autism spectrum disorder severity, social functioning, psychotropic and complementary medication use of children and youth with autism spectrum disorder were not statistically associated with their weight status. Findings suggest the need for clinical settings to monitor weight status of children and youth with autism spectrum disorder in a bid to manage or prevent overweight/obesity in this population. Incorporating a family system approach to influence health behaviours among children and youth with autism spectrum disorder especially for specific weight interventions is warranted and should be further explored.

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Autism
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DOI: 10.1177/1362361315616345
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Introduction
Childhood overweight and obesity has become a signifi-
cant public health problem. The last three decades have
seen a significant increase in the prevalence of overweight
and obesity (Janssen et al., 2005; Lobstein and Frelut, 2003;
Wang and Lobstein, 2006), and approximately 20%–25%
of Australian children and youth fall into either of these cat-
egories (Australian Institute of Health and Welfare (AIHW),
2012; Olds et al., 2009), which is a similar figure to other
developed nations (Lobstein and Frelut, 2003; Reilly and
Kelly, 2010; Skinner and Skelton, 2014; Veugelers and
Fitzgerald, 2005). Childhood overweight and obesity
increases the risk for weight problems in adulthood and can
lead to adverse physical, physiological and psychosocial
effects, including premature mortality (Freedman et al.,
2001; Schiel et al., 2006; Singh et al., 2008).
Autism spectrum disorder (ASD) is a condition that has
also significantly increased in prevalence over the past
three decades (King and Bearman, 2009; Williams et al.,
2014). ASD affects approximately 1% of the population
(Williams et al., 2006, 2014) and is characterized by core
deficits in social and communication ability, and the pres-
ence of restricted and repetitive behaviours (Williams et al.,
2014). Given the substantial increase in overweight/obesity
among typically developing children and youth, the weight
status of young people with ASD has also started to receive
research attention. In Table 1, existing published data show-
ing high prevalence of overweight/obesity among children
Obesity and associated factors in youth
with an autism spectrum disorder
Joanna Granich1, Ashleigh Lin1, Anna Hunt1, John Wray2,
Alena Dass1 and Andrew JO Whitehouse1
Abstract
Weight status on children and youth with autism spectrum disorder is limited. We examined the prevalence of overweight/
obesity in children and youth with autism spectrum disorder, and associations between weight status and range of factors.
Children and youth with autism spectrum disorder aged 2–16 years (n = 208) and their parents participated in this study.
Body mass index was calculated using the Centers for Disease Control and Prevention growth charts and the International
Obesity Task Force body mass index cut-offs. The Autism Diagnostic Observation Schedule was administered. Parents
completed questionnaires about socio-demographics, diagnosed comorbidities, sleep disturbances, social functioning
and medication of youth with autism spectrum disorder. The prevalence of overweight/obesity in participants with
autism spectrum disorder was 35%. One quarter of obese children and youth (25.6%) had obese parents. There was a
significant association between children and youth’s body mass index and maternal body mass index (r = 0.25, n = 199,
p < 0.001). The gender and age, parental education, family income, ethnicity, autism spectrum disorder severity, social
functioning, psychotropic and complementary medication use of children and youth with autism spectrum disorder were
not statistically associated with their weight status. Findings suggest the need for clinical settings to monitor weight status
of children and youth with autism spectrum disorder in a bid to manage or prevent overweight/obesity in this population.
Incorporating a family system approach to influence health behaviours among children and youth with autism spectrum
disorder especially for specific weight interventions is warranted and should be further explored.
Keywords
adolescents, autism, body mass index, children, overweight, parents, youth
1Telethon Kids Institute, Australia
2Child and Adolescent Health Service, Australia
Corresponding author:
Joanna Granich, Telethon Kids Institute, The University of Western
Australia, 100 Roberts Rd, Subiaco, WA 6872, Australia.
Email: Joanna.Granich@telethonkids.org.au
616345AUT0010.1177/1362361315616345AutismGranich et al.
research-article2015
Original Article
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2 Autism
and youth with ASD across several countries are presented.
To date, the only Australian study in this area included
youth with ASD in a broader sample of children and ado-
lescents with a range of developmental disabilities (De
et al., 2008). In this study, the prevalence of overweight/
obesity was higher among children and youth with devel-
opmental disorders and intellectual disability (40%) when
compared to the general paediatric population (23%).
Overall, the evidence suggests a growing public health
problem associated with overweight/obesity among chil-
dren and youth with ASD.
A key goal of research in this area is to identify factors
associated with overweight/obesity in ASD to better
understand contributing mechanisms. In the few studies
focused on children and youth with ASD, and weight sta-
tus, several known and potential contributing risk factors
have been identified. These included sleep problems, gross
and fine motor impairments (Curtin et al., 2014), food
selectivity and sensory sensitivities, (Cermak et al., 2010;
Lane et al., 2010), gastrointestinal problems (Adams et al.,
2011; Mazurek et al., 2013), adaptive functioning and mal-
adaptive behaviours and family functioning in relation to
household levels of stress (Curtin et al., 2014). Motor
impairments coupled with social communication deficits
pose as additional barriers for children and youth with
ASD. These are likely to have a negative impact on their
involvement in physical activities (Curtin et al., 2014).
Increased time spent in sedentary behaviours such as elec-
tronic media use has been shown to be more prevalent
among children and youth with ASD than in typically
developing peers (Mazurek and Wenstrup, 2013). Given
the known health implications of a sedentary lifestyle
(Jones et al., 2013; Tremblay et al., 2010, 2011), this may
also be an important factor for weight gain in ASD. The
use of psychotropic medication in some cases can be asso-
ciated with weight gain (Curtin et al., 2005; Rosenberg
et al., 2010), in particular for atypical anti-psychotics
(Klein et al., 2006; Mackin et al., 2005). However, in the
context of weight status among children and youth with
ASD, this is yet to be examined.
To date, no study has estimated the prevalence of over-
weight/obesity, or examined relevant correlates in an
Australian sample of children and youth with ASD. The
aims of this study were to determine the prevalence of
overweight/obesity in a large Australian paediatric sample
with ASD. We also aimed to examine whether individual
and family factors, ASD characteristics (including autism
severity and social functioning), ASD comorbidities (gas-
trointestinal track (GIT) and sleep problems) and medica-
tion use were associated with overweight/obesity in this
sample.
Method
Participants
The Western Australian Autism Biological Registry
(WAABR) is an ongoing study of children and adolescents
with ASD and their biological parents at the Telethon Kids
Institute in Perth, Australia. Participant recruitment takes
place through advertisements, local diagnosticians and cli-
nicians, and private, government and non-for-profit ser-
vice providers. The study is open to children from birth
through to 17 years and 11 months with a clinical diagnosis
of Autistic Disorder, Asperger’s Disorder, Pervasive
Developmental Disorder–Not Otherwise Specified (PDD-
NOS) or ASD. The diagnosis of these conditions in
Western Australia mandates assessment by a clinical team
Table 1. Summary of overweight/obesity prevalence in children and youth with ASD in previous studies (n = 14).
Published
year
First author Country Sample
size
Age range or
mean age
Overweight
%
Obesity
%
Overweight
or obese %
BMI classification
criteria used
1991 Krebs Japan 140 7–18 – 25.0 – CDC growth charts
1994 Takeuchi Japan 413 6–17 – 33.0 – CDC growth charts
2004 Whiteley United Kingdom 50 6.6 42.0 10.0 52.0 IOTF
2005 Curtin United States 42 2–18 31.0 16.0 47.0 CDC growth charts
2007 Xiong China 429 2–11 33.0 18.0 51.0 CDC growth charts
2010 Chen United States 247 10–17 – 21.1 – CDC growth charts
2010 Rimmer United States 159 14.7 42.5 24.6 67.1 CDC growth charts
2010 Xia China 111 2–9 – – 35.0 CDC growth charts
2012 Evans United States 53 6.6 26.0 17.0 43.0 CDC growth charts
2012 Memari Iran 113 7–14 13.3 27.4 40.7 CDC growth charts
2013 Bica Turkey 164 4–18 22.5 29.4 51.9 WHO standards
2013 Egan United States 273 3.8 17.1 21.8 38.9 CDC growth charts
2014 Broder-Fingert United States 2976 2–20 12.9 24.2 37.1 CDC growth charts
2014 Zuckerman United States 376 5.5 18.1 17.0 35.1 CDC growth charts
ASD: autism spectrum disorder; BMI: body mass index; CDC: Centers for Disease Control and Prevention; IOTF: International Obesity Taskforce;
WHO: World Health Organization.
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Granich et al. 3
comprising a paediatrician, clinical psychologist and
speech pathologist according to Diagnostic and Statistical
Manual of Mental Disorders (DSM-IV-TR) criteria, and a
diagnosis is only made when there is consensus among this
multi-disciplinary team.
The current study included participants who enrolled in
the registry between November 2012 and September 2014,
who were 2–16 years of age, had their height and weight
measured and their biological primary parent completed
a study specific Autism Research: Family History
Questionnaire (FHQ). The final sample comprised 208 chil-
dren and youth, with a majority of males (n = 176, 80.3%).
Characteristics of the sample are presented in Table 2. Ethics
approval for this study was granted by the Human Research
Ethics Committee at Princess Margaret Hospital for
Children in Perth, Western Australia (#1845/EP).
Procedure
Participants were invited to the Telethon Kids Institute
where informed consent was sought from a biological par-
ent prior to testing for the WAABR study. The first part of
testing involved the parent completing several question-
naires, including the FHQ, while the second part involved
children and youth undergoing a clinical pro-forma.
Measures
Autism characteristics and severity. To confirm clinical diag-
nosis of ASD, participants were assessed on the Autism
Observation Schedule–Generic (ADOS-G) (Lord et al.,
2000) administered by the same research-accredited pro-
fessional (A.H.). The ADOS-G is a semi-structured assess-
ment that uses simple activities and questions to elicit and
observe the communicative and social behaviours relevant
to the diagnosis of ASD. Each participant’s total of ADOS-
G raw scores was mapped onto a calibrated severity score
classification derived from an algorithm module and the
participant’s chronological age (⩽16 years) within the
respective modules (1, 2 and 3 only) (Gotham et al., 2009).
Higher scores indicate greater severity of ASD symptoms.
Socio-demographics, comorbidities and medication use. Par-
ents were asked to self-complete the FHQ which asks
information on family socio-demographics, child’s clinical
diagnosis, previously diagnosed comorbidities, gastroin-
testinal problems, prescribed medication and complemen-
tary or alternative medication (CAM) use. There were also
questions related to the health of biological parents.
Sleep problems. Information about children’s sleeping
habits was derived from the Children’s Sleep Habits
Questionnaire (CSHQ) (Owens et al., 2000). The CSHQ
is a 45-item parent questionnaire that examines sleep
behaviour in children and youth. It contains eight sleep
subscales that capture behavioural and clinically relevant
(⩾41 cut-off total score) sleep disturbances in children
and youth. The CSHQ yields a total sleep disturbance
score that is derived from the sum of the eight sleep sub-
scale scores (bedtime resistance, sleep onset delay, sleep
duration, sleep anxiety, night wakings, parasomnias, sleep
disorder breathing and daytime sleepiness).
Social functioning. The social reciprocity and adaptability
of participants with ASD was measured by the Social
Responsiveness Scale (SRS), a 65-item parent-rated scale
that assesses ASD-related social communication deficits.
The SRS can also be used as a screener for ASD severity
(Constantino and Gruber, 2002). The SRS yields a total
Table 2. Characteristics of children and youth with ASD
(n = 208) and their parents.
Characteristics M (SD) or n (%)
Child’s gender
Male 167 (80.3)
Female 41 (19.7)
Child’s age at research assessment 7.6 (3.4)
Child’s age range (years)
< 6 103 (49.5)
7–12 83 (39.9)
13–16 22 (10.6)
Child’s age at clinical diagnosis 4.5 (2.3)
Autism spectrum (clinical) diagnosis
Autistic disorder 160 (87.0)
PDD-NOS 16 (8.7)
Asperger’s Disorder 8 (4.3)
ADOS modules completed at research assessment
Module 1 60 (24.6)
Module 2 95 (38.9)
Module 3 73 (29.9)
ADOS classifications at research assessment
Non-spectrum 40 (19.2)
ASD 34 (16.3)
Autism 134 (64.4)
Maternal age at child’s birth 31.4 (5.2)
Maternal ethnicity
Caucasian 146 (91.3)
Non-Caucasian 14 (8.8)
Paternal age at child’s birth 33.5 (5.1)
Paternal ethnicity
Caucasian 141 (88.7)
Non-Caucasian 18 (11.3)
Family incomea
<AUD$47,800 17 (10.1)
AUD$47,801 152 (89.9)
ASD: autism spectrum disorder; SD: standard deviation; PDD-NOS:
Pervasive Developmental Disorder–Not Otherwise Specified; ADOS:
Autism Observation Schedule.
aThreshold for the ‘family poverty line’ in Australia (Melbourne Institute
of Applied Economic and Social Research, 2014).
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4 Autism
raw score and five subscale scores (awareness, cognition,
communication, motivation and autistic mannerisms). The
SRS manual recommends the use of SRS-Raw in research
studies for comparison across studies using the SRS as a
measure of ASD severity and as an indicator of general
levels of impairment among individuals (Hus et al., 2013).
Body mass index and weight status. Parental height (in cen-
timetres) and weight (in kilograms) was self-reported in
the FHQ. Height and weight of participants with ASD was
measured without shoes using a portable rigid stadiometer
by one researcher (A.H.) and recorded to the nearest centi-
metre. Weighing was conducted in light clothing without
shoes to the nearest 50 grams using a metric digital scale.
The scale was calibrated prior to each participant. The
body mass index (BMI) is a widely accepted index for
identifying indirect total body fatness and subsequent
weight categories that can be used to screen weight-related
problems (Centers for Disease Control and Prevention
(CDC), 2000; World Health Organization (WHO), 1999).
The BMI of each participant was calculated as weight in
kilograms divided by the squared height in metres (weight/
height2). For each child, age- and sex-specific percentiles
were calculated based on the CDC population norms
(Ogden et al., 2002). Participants were classified as healthy
weight (HWT; 5 ⩽ BMI/age < 85 percentile), overweight
(OWT; 85 ⩽ BMI/age < 95 percentile) or obese (OBY;
BMI/age ⩾ 95 percentile) for group weight status compari-
sons. Those who were underweight (BMI < 5 percentile;
n = 12, 5.4%) were excluded from all analyses due to small
sub-sample size. BMI was also converted to z-scores
(±standard deviation (SD)). In addition, extended Interna-
tional Obesity Taskforce (IOTF) age- and sex-specific
BMI cut-points were used to determine whether partici-
pants fell into the HWT (based on adult BMI < 25 kg/m2),
OWT (based on adult BMI = 25–29.99 kg/m2) or OBY
(based on adult BMI ⩾ 30 kg/m2) categories for the com-
parison of BMI indices and respective weight status esti-
mates (Cole and Lobstein, 2012). Parents BMI was mapped
against standardized BMI range categories and the adult
classification system for overweight and obesity (WHO,
2000).
Statistical analyses
Descriptive statistics are presented as mean (±SD) or per-
cent to describe participants and respective BMI and
weight status. The CDC BMI classification was used to
create weight status groups (HWT, OWT, OBY) to test for
associations with socio-demographic characteristics, clin-
ical diagnosis, autism severity, parents’ weight status,
social functioning, sleep problems, GIT problems, previ-
ously diagnosed comorbidities, psychotropic medication
and CAM. Univariate and bivariate methods were used to
identify variables that differed significantly among the
weight status groups. Differences between the groups
were assessed using Chi-square tests (χ2) for categorical
variables. To compare continuous variables between the
three weight status groups, one-way analyses of variance
(ANOVAs) were performed. Bonferroni post hoc testing
was conducted to compare all weight groups. Pearson’s
correlation (r) was used to examine associations between
parental and child BMI. To further examine the associa-
tion between BMI and a range of potentially influencing
factors, a general linear model (GLM) was performed
with BMI z-score (±SD) as the dependent variable with
the remaining factors entered into the model as explana-
tory variables. The level of significance was p-value < 0.05
throughout analyses.
Results
On entry to the study, the majority of children and youth
had a clinical diagnosis of an autistic disorder (based on
DSM-IV-TR) (n = 160, 87.0%), with a small proportion
having a diagnosis of PDD-NOS (n = 16, 8.7%) or
Asperger’s Disorder (n = 8, 4.3%). The mean age of chil-
dren at clinical diagnosis was 4.52 years (SD = 2.32;
Median = 3.9). ADOS-G scores indicated that 168 of 208
children (80.7%) met criteria for autistic disorder or ASD
at the time of assessment for this study. The mean age of
children at assessment time was 7.67 years (SD = 3.46).
Other characteristics of the sample are shown in Table 2.
Overweight and obesity prevalence
Using both the CDC and IOTF child BMI classification
systems, we found that approximately one-third of chil-
dren and youth with ASD were overweight/obese (35.1%;
29.9% respectively). Over 65% of children and youth with
ASD had a healthy weight, while only 5% were under-
weight. The mean BMI for mothers was 27.49 (SD = 6.37)
and for fathers was 29.52 (SD = 6.61). More fathers than
mothers were obese (78.3% vs 60.4%), and about one
quarter of children and youth with ASD (26.1%) had both
parents who were obese (BMI ⩾ 30.00). A small but statis-
tically significant correlation was found between mothers’
BMI and participants’ BMI (r = 0.25, n = 199, p < 0.001)
but not between fathers’ BMI and participants’ BMI
(r = 0.17, n = 106, p = 0.081) (Table 3).
Body weight status and related characteristics
Table 4 shows that there were no significant gender differ-
ences in the proportion of children and youth with ASD
across the different weight groups (HWT vs OWT vs
OBY). The prevalence of overweight was highest in the
early years (2–6 years), while the prevalence of obesity was
highest in the middle childhood (7–12 years), although dif-
ferences between age categories did not achieve statistical
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Granich et al. 5
significance. The highest prevalence of overweight (70.6%)
and obesity (69.2%) was found among children and youth
who were classified as having an autistic disorder at the
time of research assessment. Over one-third (36.8%) of
children and youth who were obese had both parents who
were classified as obese, but differences among parental
weight status categories were not statistically significant.
The majority (64%) of children and youth who were over-
weight appeared to come from high family income house-
holds, but this result was not statistically significant.
Similarly, no statistically significant differences were found
between children and youth weight status groups for paren-
tal education or ethnicity (Table 4).
Body weight status, autism severity and health-
related characteristics
Autism severity according to weight status is presented in
Table 4. There were no statistically significant differences
in ASD severity between weight status categories. On the
SRS, all participants fell above the cut-off score of ⩾74
(total raw score) for social impairments. While this indi-
cates a strong association with a clinical diagnosis of an
autistic disorder, there was no significant effect of social
deficits on weight status across the healthy, overweight or
obese groups (Table 5).
The analysis of previously diagnosed comorbidities
showed a higher proportion of medical conditions among
the obese children and youth with ASD, despite this trend
not reaching statistical significance. Similar proportions of
participants were found to have GIT problems across the
three weight groups. All youth in this sample exhibited
clinically significant sleep problems (using the total CSHQ
cut-off score of ⩽41). There was a main effect for sleep
onset delay between the weight groups. Post hoc analysis
showed that on average, participants in the obese group
took longer to fall asleep when compared with the healthy
weight group. However, the overall effect of sleep distur-
bance on weight status was not significant (Table 5).
Slightly higher proportions of obese children and youth
(20.5%) were using CAM relative to the overweight chil-
dren and youth (18.2%). A total of 33 children and youth
(17.6%) were taking psychotropic medication (anti-
depressants, anti-psychotics and psychostimulants). The
use of psychotropic medication was higher in the obese
group (18.8%) relative to the overweight (9.1%) or the
healthy weight (61.4%) children and youth. Overall, the
differences between the groups were not statistically sig-
nificant. More specifically, a total of nine participants were
taking atypical anti-psychotic medication. Of these, three
were overweight, one was obese and the remaining five
were a healthy weight.
Predictors of BMI z-score
Visual inspection identified an outlying BMI data point
(z-score of 7.8, with the next lowest BMI z-score of 3.2).
This extreme z-score was excluded from the GLM analy-
sis. Participants with ASD who had one or both parents
with a healthy weight had an average BMI z-score 0.68
lower than children and youth of parents who were over-
weight or obese. Participants who were non-CAM users on
average had a BMI z-score 0.5 lower compared with those
who used CAM (Table 6).
Discussion
In the current study, we present data on weight status in a
large sample of Australian youth with ASD. We found that
35% of our sample were either overweight or obese
according to the CDC growth charts (or 30% according to
IOTF BMI cut-offs). This proportion of overweight and
obesity is approximately 10% higher than the Australian
paediatric national average (AIHW, 2012).
The proportion of overweight (16%) or obese (19%)
young people with ASD identified in this study is consist-
ent with prevalence found in recent studies from the United
States (Broder-Fingert et al., 2014; Egan et al., 2013;
Zuckerman et al., 2014) and China (Xia et al., 2010) (Table
1), although the recent prevalence is lower than those pub-
lished prior to 2010 (Curtin et al., 2005; Whiteley et al.,
Table 3. BMI of children and youth with ASD (n = 220a) and
their parents (n = 298) defined according to the World Health
Organization formula, and weight status according to the
guidelines provided by the IOTF and the CDC.
BMI or weight statusan (%)
Mother’s BMI
Healthy 73 (36.7)
Overweight 60 (30.2)
Obese 60 (30.0)
Father’s BMI
Healthy 22 (20.8)
Overweight 45 (42.5)
Obese 38 (35.8)
Parents
One or both healthy 58 (28.0)
Mother overweight or obese 66 (31.9)
Father overweight or obese 29 (14.0)
Both obese 54 (26.1)
Youth with ASDaCDC n (%) IOTF n (%)
Underweight 12 (5.4) 12 (5.4%)
Healthy 135 (64.9) 146 (70.2)
Overweight 34 (16.3) 39 (18.8)
Obese 39 (18.8) 23 (11.1)
BMI: body mass index; ASD: autism spectrum disorder; IOTF: Inter-
national Obesity Taskforce; CDC: Centers for Disease Control and
Prevention.
aIncludes children and youth with ASD who were underweight, n = 12.
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6 Autism
2004; Xiong et al., 2009) (see Table 1). A possible expla-
nation for the discrepancy in the overweight/obesity fig-
ures might be related to geographical, racial and ethnical
differences across the studies. In addition, other influenc-
ing factors such as patterns of physical activity, sedentary
behaviour, diet, sample size, age of children and the use of
different BMI cut-offs to classify children’s weight status
may help explain these discrepancies.
A substantial proportion of parents of youth with ASD
had BMIs that placed them in the overweight/obese range
(69%), which is higher when compared with the Australian
adult general population (63%) (AIHW, 2014). A positive
association between young peoples’ and mothers’ BMI
was found in this sample, which may have future adverse
health implications for both the child and the parent.
Longitudinal evidence showed that children’s BMI
increases with age. Those who are overweight during
childhood are twice as likely to be overweight in adult-
hood (Lagström et al., 2008; Singh et al., 2008) Although
our study did not show a statistically significant age effect,
the risk of future overweight in children also increases
with increasing weight status of parents (Danielzik et al.,
2002; Lake et al., 1997; Magarey et al., 2003).
We also examined an array of socio-demographic,
ASD severity, social functioning, comorbidities and
medical factors that may influence the weight of youth
with ASD. Consistent with previous study of overweight
in ASD (Egan et al., 2013; Memari et al., 2012;
Zuckerman et al., 2014), but in contrast to some coun-
tries’ national representative paediatric populations
(Burke et al., 2001; Cui et al., 2010; Ogden et al., 2012),
socio-economic status as measured by family income
and parental education was not associated with weight
status in our sample. Consistent with another study that
Table 4. Children and youth with ASD and their parents’ characteristics by child’s weight status group.
HWT (n = 135) OWT (n = 34) OBY (n = 39) χ2p
Gender 1.87 0.4
Male 112 (67.1) 25 (15.0) 30 (18.0)
Female 23 (56.1) 9 (22.0) 9 (22.0)
Child’s age (years) 2.60 0.6
< 6 69 (67.0) 19 (18.4) 15 (14.6)
7–12 52 (62.7) 12 (14.5) 19 (22.9)
13–16 14 (63.6) 3 (13.6) 5 (22.7)
ADOS-G classification 2.62 0.5
Non-spectrum 26 (65.0) 6 (15.0) 8 (20.0)
ASD 26 (76.5) 4 (11.8) 4 (11.8)
Autistic disorder 83 (61.9) 4 (11.8) 27 (20.1)
Maternal education level 3.43 0.5
Secondary completion or less 41 (62.1) 11 (16.7) 14 (21.2)
Trade/Technical certificate 26 (76.5) 3 (8.8) 5 (14.7)
Bachelor degree or higher 56 (65.1) 17 (19.8) 13 (15.1)
Maternal ethnicity 0.14 0.9
Caucasian 97 (66.4) 25 (17.1) 24 (16.1)
Non-Caucasian 10 (71.4) 2 (14.3) 2 (14.3)
Paternal education level 2.21 0.7
Secondary completion or less 34 (59.6) 11 (19.3) 12 (21.1)
Trade/Technical Certificate 36 (65.5) 8 (14.5) 11 (20.0)
Bachelor Degree or Higher 47 (71.2) 10 (15.2) 9 (13.6)
Paternal ethnicity 0.77 0.7
Caucasian 95 (67.4) 22 (15.6) 24 (17.0)
Non-Caucasian 12 (66.7) 4 (22.2) 2 (11.1)
Family incomea7.01 0.3
<AUD$47,800 12 (70.0) 2 (11.8) 3 (17.6)
>AUD$47,801 101 (66.4) 27 (17.8) 24 (15.8)
Parental weight status 10.0 0.1
One or both healthy 47 (81.0) 6 (10.3) 5 (8.6)
Mother overweight or obese 41 (62.1) 12 (18.2) 13 (19.7)
Father overweight or obese 17 (58.6) 6 (20.7) 6 (20.7)
Both obese 30 (55.6) 10 (18.5) 14 (25.9)
ASD: autism spectrum disorder; HWT: healthy weight; OWT: overweight; OBY: obese; ADOS-G: Autism Observation Schedule–Generic.
aThreshold for the ‘family poverty line’ in Australia (Melbourne Institute of Applied Economic and Social Research, 2014).
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Granich et al. 7
used the same classification system as this study to
determine severity of ASD (Zuckerman et al., 2014), we
found no association between weight status and autism
severity. Similarly, there was no difference between
weight status and social functioning of youth with ASD.
This strengthens this study’s null findings for the asso-
ciation between ASD severity and weight status, as the
level of social functioning was measured by the SRS
which is a proxy for autism traits and severity (Bölte
et al., 2008).
We found a significant association between sleep onset
delay and weight status; these difficulties were more com-
mon among youth with ASD who were obese. Studies
showed that between 40% and 80% of children with ASD
experienced sleeping difficulties (Cortesi et al., 2010),
with insomnia being the most commonly reported problem
(Cortesi et al., 2010; Richdale and Schreck, 2009; Tsai
et al., 2012). Although evidence points to circadian rhythm
abnormalities among ASD individuals (Melke et al., 2008;
Tsujino et al., 2007), others suggest a complex interaction
between biological, psychological, socio-environmental
and child rearing practices (Cortesi et al., 2010). Studies of
typically developing children have reported links between
obesity and sleep onset delay (Chen et al., 2008, 2010),
with the current study indicating a similar link among
youth with ASD.
Table 5. ASD severity, social functioning, comorbidities and medication use by child’s weight status group.
Continuous: M (SD) HWT OWT OBY F p
ASD severity 5.49 (2.1) 5.74 (1.7) 5.72 (1.9) 0.31 0.7
SRSa
Total 160.2 (22) 156 (23.2) 156 (25.2) 0.73 0.5
Awareness 17.6 (2.5) 16.9 (2.9) 17.4 (2.7) 0.83 0.4
Cognition 30.5 (4.8) 29.1 (4.0) 28.5 (4.9) 2.70 0.1
Communication 52.6 (7.9) 50.3 (9.4) 51.5 (8.6) 1.00 0.4
Motivation 26.3 (4.3) 26.9 (4.9) 26.1 (5.0) 0.26 0.8
Autistic mannerisms 33.6 (7.2) 32.6 (7.1) 32.3 (8.5) 0.13 0.9
Sleep problems
Bedtime resistance 8.3 (3.2) 8.0 (3.1) 8.5 (2.7) 0.17 0.8
Sleep onset delay 1.9 (.83) 2.3 (.79) 2.4 (.75) 5.04 0.007*
Sleep duration 4.7 (2.2) 4.6 (2.0) 4.8 (1.9) 0.03 0.9
Sleep anxiety 6.0 (2.6) 5.5 (2.4) 6.5 (2.1) 1.18 0.3
Night wakings 4.4 (2.0) 4.6 (1.9) 4.9 (1.4) 0.62 0.5
Parasomnias 9.8 (3.3) 9.9 (3.0) 9.7 (2.9) 0.02 0.9
Disordered breathing 3.4 (1.2) 3.4 (1.3) 3.9 (1.4) 2.02 0.1
Daytime sleepiness 10.9 (3.6) 10.0 (3.3) 10.1 (2.5) 1.13 0.3
Total sleep disturbance 48.3 (13.3) 47.3 (12.8) 49.5 (8.8) 0.23 0.8
Categorical: n (%) χ2p
Gastrointestinal problemsb0.33 0.3
None 60 (60.6) 20 (20.2) 19 (19.2)
At least one problem 75 (69.4) 14 (13.0) 19 (17.6)
Diagnosed comorbiditiesc1.31 0.5
None 78 (66.7) 17 (14.5) 22 (18.8)
At least one comorbidity 46 (65.7) 14 (20.0) 10 (14.3)
Psychotropic medication use 1.63 0.4
No 100 (64.9) 28 (18.2) 26 (16.9)
Yes 24 (72.7) 3 (9.1) 6 (18.2)
Complimentary or alternative medication use 1.04 0.6
No 81 (68.1) 17 (14.3) 21 (17.6)
Yes 54 (61.4) 16 (18.2) 18 (20.5)
ASD: autism spectrum disorder; SD: standard deviation; HWT: healthy weight; OWT: overweight; OBY: obese; SRS: Social Responsiveness Scale;
GIT: gastrointestinal track; ADHD/ADD: attention deficit hyperactivity disorder/attention deficit disorder; ANOVA: analysis of variance.
aSRS-Raw scores.
bGIT problems parent reported included: constipation, diarrhoea, gastro-oesophageal reflux or vomiting, abdominal discomfort/irritability, feeding
issues or food selectivity. Numbers were too small to examine separately.
cDiagnosed comorbidities parent reported included intellectual disability, global developmental delay, epilepsy, cerebral palsy and ADHD/ADD.
Numbers were too small to examine separately.
*p < 0.05, one-way ANOVA with Bonferroni post hoc testing and correction.
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8 Autism
We found no statistically significant association
between gastrointestinal complaints and weight status.
Zuckerman and colleagues (Zuckerman et al., 2014) also
generated null findings for gastrointestinal problems and
BMI group among children with ASD. We also found that
previously diagnosed comorbidities were not significantly
associated with the weight status of youth with ASD. In
general, more obese individuals in our sample were found
to use psychotropic and CAM than overweight partici-
pants with ASD, but the overall relationship between med-
ication use and weight status was non-significant. This
finding corroborates with a previous study (Zuckerman
et al., 2014). However, our study also found that youth
with ASD who have parents with a healthy weight or those
who did not take CAM showed a reduction in their BMI.
These findings highlight the importance of parents and
caregivers as significant role models for healthy weight
status among youth with ASD. Incorporating a family sys-
tem approach to influence health behaviours among youth
with ASD may be warranted and should be further
explored. The association between weight status and CAM
use may be explained by the fact that those who do not use
CAM are less likely to have severe ASD core symptoms
(Granich et al., 2014). It is also plausible that youth who
do not take CAM are also less prone to metabolic imbal-
ances as implicated in a number of neurodevelopmental
disorders, which in turn are linked to a metabolic endophe-
notype that compounds the heterogeneity among neurobe-
havioural conditions such as ASD (Melnyk et al., 2012;
Rossignol and Frye, 2012).
Overall, overweight/obesity is a significant risk factor
for a number of chronic health conditions. The high preva-
lence of overweight/obesity in youth with ASD presents
these individuals with greater risk for a range of adverse
health outcomes throughout their life course. The findings
of this study have important implications for future
research, clinical practice and public health policy. The
identified high prevalence of overweight/obesity among
youth with ASD suggests the need for clinical services to
routinely incorporate height, weight and BMI calculations
into intervention strategies. To better facilitate future inter-
study comparisons of overweight/obesity among youth
with ASD, we also recommend that both the IOTF BMI
cut-offs and the CDC growth charts need to be adopted as
standard reporting. Since both BMI classification systems
add value and allow for comparisons of overweight and
obesity across clinical research and practice (Kuczmarski
et al., 2000, 2002; National Health and Medical Research
Council (NHMRC), 2013). In addition, there is growing
evidence for the assessment of waist-to-hip, waist-to-
height ratio or waist circumference. These are deemed to
be better measures of adiposity among children than the
BMI because the ratios are clinically more relevant for the
status of central obesity and thus related cardio-metabolic
risk (Mokha et al., 2010; Warren et al., 2007).
The mechanisms underlying overweight/obesity are the
result of energy expenditure imbalance that is influenced
by an array of biological, physiological, behavioural and
socio-environmental factors. The finding that some sleep
problems may be related to weight suggests the need for
sleep problems and interventions to be better investigated
among children with ASD, especially among those whom
are obese. Future studies also need to include direct meas-
ures of physical activity, sedentary behaviours, screen-
time and dietary intake to improve our understanding of
overweight in youth with ASD. Physical activity and die-
tary habits are commonly shaped by proximal factors like
the home environment (Granich et al., 2010, 2011;
Timperio et al., 2008) and parental behaviours (Golan,
2006; Kitzman-Ulrich et al., 2010). The finding of a posi-
tive relationship between youth and mothers’ BMI lends
itself to include assessments of parental weight status as
parental BMI is likely to have an impact on young people’s
weight as also previously evidenced (Kitzman-Ulrich
et al., 2010; Sung-Chan et al., 2013). Furthermore, it sup-
ports to the inclusion of parents of youth with ASD in
healthy lifestyle interventions to aid in the prevention of
overweight or obesity among youth and parents. Despite
the latter, in this study, parental height and weight was
self-reported, and thus parental BMI calculations are vul-
nerable to social-desirability bias, reflecting a methodo-
logical weakness of this study.
Overweight/obesity is an increasing problem across the
world and it might be particularly detrimental to the health
and well-being of youth with ASD. Given the limited
knowledge about weight status among individuals with
ASD in Australia, this article specifically provides evi-
dence on the high prevalence of overweight and obesity
among youth with ASD and their parents. Consequently,
further research is warranted to elucidate risk factors for
weight status in youth with ASD.
Table 6. Predictors of BMI z-score among children and youth
with ASD.
Explanatory variablesaBMI z-score (SD)
B 95% CI p-value*
Parents healthy weight −0.685 −1.262, −0.107 0.02
Non-users of CAM −0.506 −0.903, −0.109 0.01
BMI: body mass index; ASD: autism spectrum disorder; SD: standard
deviation; B: standardized coefficient; CI: confidence interval; CAM:
complimentary or alternative medication; SRS: Social Responsiveness
Scale; CSHQ: Children’s Sleep Habits Questionnaire.
R2 = 0.254 (adjusted R2 = 0.085).
aAdjusted univariate general linear model controlling for youth gender
and age, maternal and paternal education, family income, comorbidi-
ties, SRS – social awareness, cognition, communication, motivation and
mannerism domains, CSHQ all domains.
*p < 0.05.
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Granich et al. 9
Funding
The author(s) received no financial support for the research,
authorship and/or publication of this article.
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