Exercise Effects for Children With Autism Spectrum Disorder

Article
Exercise Effects for
Children With Autism
Spectrum Disorder:
Metabolic Health,
Autistic Traits, and
Quality of Life
Perceptual and Motor Skills
2018, Vol. 125(1) 126–146
! The Author(s) 2017
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DOI: 10.1177/0031512517743823
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Chrystiane V. A. Toscano1,2,
Humberto M. Carvalho3, and José P. Ferreira1
Abstract
This study examined the effects of a 48-week exercise-based intervention on the
metabolic profile, autism traits, and perceived quality of life in children with autism
spectrum disorder (ASD). We randomly allocated 64 children with ASD (aged 6–12
years) to experimental (n ¼ 46) and control groups (n ¼ 18) and used multilevel
regression modeling to examine responses to receiving or not receiving the intervention. The experimental group showed beneficial effects on metabolic indicators
(high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and total
cholesterol), autism traits, and parent-perceived quality of life. Our results provide
support for exercise and physical activity, including basic coordination and strength
exercises, as important therapeutic interventions for children with ASD.
Keywords
obesity, multilevel modeling, mental health, developmental disorders, psychometric
tests, ASD
1
Faculty of Physical Education, University of Coimbra, Portugal
Faculty of Physical Education, Federal University of Alagoas, Maceió, AL, Brazil
3
Faculty of Physical Education, Federal University of Santa Catarina, Florianópolis, SC, Brazil
2
Corresponding Author:
Chrystiane V. A. Toscano, Physical Education Course, Federal University of Alagoas, Av. Lourival Melo
Mota, s/n, Tabuleiro dos Martins, 57072-900 Maceió, AL, Brazil.
Email: [email protected]
Toscano et al.
127
Introduction
Autism spectrum disorder (ASD) is a neurodevelopmental disability, characterized by deficits in social and emotional reciprocity, and by the presence of
repetitive, restricted and stereotyped patterns of behavior and interests
(American Psychiatric Association, 2013). Limited levels of physical activity
and delayed motor skills and physical fitness, particularly in children and adolescents, may accompany these deficits and lead to a higher incidence of overweight and obesity, and associated health complications in this population,
relative to typically developing youth (Curtin, Jojic, & Bandini, 2014; Must
et al., 2014; Xia, Zhou, Sun, Wang, & Wu, 2010).
Past research has documented that children with ASD may have a higher
incidence of obesity than children without a disability (Broder-Fingert,
Brazauskas, Lindgren, Iannuzzi, & Van Cleave, 2014; Curtin et al., 2014;
Shedlock et al., 2016), and obesity among children with ASD may be particularly problematic for its further negative impact on limited social motivation or
motivation to participate in structured physical activities with other children
(Zuckerman, Hill, Guion, Voltolina, & Fombonne, 2014).
ASD-related symptoms, in turn, may also impact body mass and body composition through food selectivity (Curtin et al., 2014), gastrointestinal disturbances (Williams et al., 2011), sleep problems (Wachob & Lorenzi, 2015), and
psychotropic medication (Must et al., 2014) or metabolic disturbances
(Obrusnikova & Miccinello, 2012; Shedlock et al., 2016).
Metabolic dysfunction and use of psychotropic medication are more common
among those with ASD, compared with populations with other disorders
(Cheng, Rho, & Masino, 2017), and such metabolic dysfunction and psychotropic medication use may further contribute to short- and long-term central
obesity and cardiometabolic risks (Barnhill, Tami, Schutte, Hewitson, & Olive,
2016). For example, lipid profiles appear to be more heterogenic in populations
with ASD compared with the general population (Dziobek, Gold, Wolf, &
Convit, 2007; Rossignol & Frye, 2012; Kim, Neggers, Shin, Kim, & Kim,
2010). Medication interference with lipid profiles, for example, total-cholesterol,
high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides, has been evident in adults with ASD (Moses,
Katz, & Weizman, 2014), though data considering lipid profiles in children and
adolescents with ASD has been more limited. Kim et al. (2010) reported a tendency toward dysplidemia in children with ASD, but their sample size was small
and details about medication use were sparse, limiting general interpretations.
Given the pervasive nature of ASD characteristics in children with this disorder, there are greater risks of ASD interference with health-related quality of
life in multiple domains (physical, psychological, and social). These risks have a
multidimensional effect on an individual’s life perception and inclinations
toward daily physical participation (Berntsson & Kohler, 2001; Felce & Perry,
1995). Parents have reported that children with ASD, relative to children with
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Perceptual and Motor Skills 125(1)
typical development, have a lower quality of life in all domains (De Vries &
Geurts, 2015). Even among children with various psychiatric disorders, parents
of those with pervasive developmental delay reported that their children had
significantly worse psychosocial health and emotional functioning than did parents of children with other psychiatric disorders (Bastiaansen, Koot, Bongers,
Varni, & Verhulst, 2004).
On a more hopeful note, recent data suggest that adding exercise and other
physical activities to intervention programs for children with ASD may be beneficial. Physical exercise exerts a positive influence on such different symptom
concerns as physical motor deficits (Batey et al., 2014), obesity and overweight
problems (Dickinson & Place, 2014; Fragala-Pinkham, Haley, & O’Neil, 2008),
time executing tasks (Oriel, George, Peckus, & Semon, 2011), cognitive functioning (Bremer, Crozier, & Lloyd, 2016; Tan, Pooley, & Speelman, 2016),
behavioral stereotypy (Celiberti, Bobo, Kelly, Harris, & Handleman, 1997;
Watters & Watters, 1980), aggressive behaviors (Neely, Rispoli, Gerow, &
Ninci, 2015), and socioemotional functioning (Bremer et al., 2016).
Walking and running programs have been the most common modes of
delivering physical activity interventions (Kern, Koegel, & Dunlap, 1984;
Neely et al., 2015), followed by water-based activities (Fragala-Pinkham et al.,
2008). A few studies have manipulated the intensity of physical exercise, both
aerobic and resistance, during interventions for populations with ASD (S. M.
Srinivasan, Pescatello, & Bhat, 2014). Generally, interventions with physical
exercise have ranged in duration from 8–36 weeks, with a session frequency of
2–3 times per week and a session duration of 20–40 minutes (S. M. Srinivasan
et al., 2014).
The present research study built on prior research in this area by examining
the effects of a 48-week exercise-based intervention on weight status, metabolic
profile, ASD symptoms profile, and parent-perceived quality of life in children
with ASD, accounting for the influence of medication on body size.
Method
Study Design and Participants
This study employed a pre–post randomized controlled trial. We recruited both
boys and girls with ASD aged 6–12 years (n ¼ 90) from a pediatric center for
populations with ASD located in Maceió/Alagoas, Brazil. It is well documented
that there is a higher prevalence of boys with ASD (Duvekot et al., 2016), and
only eight girls were included in the present study (with no cluster effect
observed). Considering potential dropout/poor compliance in the intervention
group, participants were randomly assigned unequally in a ratio of 3:1
(Dumville, Hahn, Miles, & Torgerson, 2006) to the intervention group
(n ¼ 67) and control group (n ¼ 23). The final sample considered for data
Toscano et al.
129
analysis was composed of 46 and 18 children in experimental and control
groups, respectively. All participants and their families received information
about the protocol and signed an informed consent form. The study was
approved by the Federal University of Alagoas Ethical Committee (CAAE:
1.091.864). A schematic map of the study design is shown in Figure 1.
Both the intervention and control groups were heterogenic in terms of etiologic characteristics (e.g., Asperger’s syndrome, autism, or developmental disorder without specification) and use psychotropic medication. The diagnoses of
ASD were established by an experienced psychiatrist, based on DSM-IV criteria
(American Psychiatric Association, 2000). Nine children were classified with
Asperger’s syndrome, 43 were classified with autism, and 12 were classified
with developmental disorder without specification. Information about the psychotropic medication (antiepileptics, antipsychotics, and stimulants) used by the
participants was obtained from the participants’ medical charts. Twenty-one
children used no prescribed medication; three children had prescriptions
of stimulants and antiepileptics, and 40 were medicated with antipsychotics
Approached to participate
n= 146
Did not meet the intervension criteria
n=56
Randomized
n= 90
Allocated control group
n= 23
Lost to post-intervention
n= 5
Reason:
Did not attend the application
of the evaluation protocols.
Allocated intervention group:
n= 67
Lost to post-intervention
n= 21
Reasons:
Did not complete the application of
the evaluation protocols
Did not meet 90% of the total
frequency in the Program
Analysed
n= 18
Analysed
n= 46
Figure 1. A cluster-randomized, controlled trial of the impact of exercise-based intervention on metabolic health, autistic traits, and perceived quality of life among children with
autism spectrum disorders.
130
Perceptual and Motor Skills 125(1)
(27 only with antipsychotics and the remaining 13 with a combination of antipsychotics, stimulants, and antiepileptics).
Procedure
The intervention group was exposed to a 48-week physical activity program,
based on basic coordination and strength exercises (Table 1) consuming 40 minutes per session, with sessions occurring twice a week and totaling 96 over the
observation period. All intervention sessions were directed by a physical educator with experience working with children with ASD (first author). All sessions
included a maximum of three children and their parents/legal representatives.
The physical exercise intervention sessions had the following structure: (a) preparatory phase (5 minutes)—in which children were prepared for the exercise
session; (b) development phase (30 minutes)—in which children performed a
brief warm-up and then performed strength, balance, and coordination exercises; and (c) return to calm phase (5 minutes), in which parents and legal representatives assisted with the relaxation exercises using tactile slip skills (i.e., soft
massage) on the child’s back and belly aiming to return the child to calm.
Control group participants were exposed to the same care within the specialized
pediatric center for populations with ASD as the intervention group, but they
did not participate in the intervention exercise sessions. Control group participants maintained their usual levels of daily activity, without additional exercise
components.
Only participants who attended at least 90% of the total 96 sessions across
the observation period were considered for data analysis. Following random
group assignment, compliance with attending and participating in the intervention was 69%. Thus, 46 participants were retained for analysis in the intervention group, while 21 participants did not meet the program attendance minimum
or were absent from the post-intervention assessment. Five participants from the
control group showed worsening ASD symptoms that prohibited their participation in post-intervention measurements, and data from these five control
group members were not considered for analysis. Thus, data from 18 participants in the control group were included in data analysis.
Measurements
All anthropometry measurements were taken by a single experienced observer
following standardized procedures (Lohman, Roche, & Martorell, 1988).
Stature was measured with a portable stadiometer (Seca model 206, Hanover,
MD, USA) to the nearest 0.1 cm. Body mass (BM) was measured with a calibrated portable balance (Seca, Model 770, Hanover, MD, USA) to the nearest
0.1 kg. Body mass index (BMI) was calculated as BM (kg) divided by squared
stature (m). Waist circumference was measured with a nonextendable flexible
Toscano et al.
131
Table 1. Description Exercise Program.
Exercise
Climbing and
support in
the bar
Physical ability
Upper limb
strength
Description
The child should raise a vertical
backrest in order to reach the
last bar and hold the body for
5.0 seconds
Release to the Upper limb
Starting from an initial position
basket
strength
with the medicinal mini-ball
close to the chest, the child
should perform a shoulder lift
(180 ) followed by an elbow
flexion, positioning the ball
over the head. In this position,
you should then do the full
extension of the upper limbs
(elbow and forearm) followed
by a slight flexion of the wrist,
completing the ball throwing
movement
Work with
Strength of the The task will be performed in
pairs. The child and the
elastics
lower and
parent/legal representative
upper limbs
are upright, face to face,
maintaining a distance of
approximately one-half meter.
The child, with the arms suspended along the body, picks
up the elastic by the cuffs,
which is fixed to the ground
under the feet of the parent/
legal representative. The child
should perform a simultaneous flexing of the forearms
by bringing their hands close
to their shoulders with each
repetition
Strength and
The child should perform the
Walking on
coordination
ascent of three steps and
steps and
inclined plane (hip and knee
inclined
flexion movement)
plane
Resource
Vertical back (standard)
with 1.5 m of height,
fixed to 0.50 cm of the
ground.
Basketball table (fixed to
1.75 m from the floor),
three-bench with different dimensions (base
0.50 cm2, height
0.50 cm, 1 m, 1.50) to
support the launch.
Medicated mini-ball
with different weights
(0.5, 1.0, and 2.0 kg)
Elastic extender
L-shaped wooden staircase with three steps
(12 15 cm), inclined
plane of 0.78 cm in
length and 30 cm in
height, with handrail
throughout its length
(continued)
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Perceptual and Motor Skills 125(1)
Table 1. Continued
Exercise
Physical ability
Description
Resource
Step box with Strength and
The child should scale three sets Six steps Model EVA with
the dimension of
target
coordination
of sequenced steps. Upon
60 28 14 cm. The
reaching the last set, they
steps should be placed
should perform a plantar
in the form of a ladder
flexion of the ankle so as to
(first step consisting of
reach the target fixed to the
one step, second step
wall above their head
consisting of two overlapping steps, and third
step consisting of three
overlapping steps,
respectively)
Sequenced
Coordination
The child should perform front Five plastic bows with
50 cm in diameter
march
running on a sequence of five
arcs arranged sequentially on
the ground
tape (Seca, Model 201, Hanover, MD, USA) to the nearest 0.1 cm. The tape was
applied above the iliac crest, parallel to the ground, with the participant standing
with the abdomen relaxed, arms along the body, and feet together. Waist-toheight ratio (WHtR) was calculated as an anthropometric index to detect central
obesity and to assess associations between cardiometabolic risk factor variables
and central intra-abdominal obesity.
To assess biological markers, participants undertook blood sample tests collected after a 12-hour overnight fasting to assess plasma glucose, triglycerides,
total cholesterol, high-density lipoprotein cholesterol (HDL-C) and low-density
lipoprotein cholesterol (LDL-C) levels. LDL-C was calculated using
Friedewald’s formula (Friedewald, Levy, & Fredrickson, 1972) and glucose
was measured by enzymatic method (Glucose Oxidase—lab test). Blood collections were taken at the specialized pediatric center for populations with ASD by
a health professional experienced in collecting blood from children with ASD.
One blood collection was made at each pre- and post-intervention measurement
(i.e., twice: before any exercise sessions and then after all 96 exercise sessions).
Children’s parents or guardians completed the Portuguese version (Correr
et al., 2008) of the Child Health Questionnaire—CHQ-PF50 (Raat, Bonsel,
Essink-Bot, Landgraf, & Gemke, 2002), following instructions to answer the
50 items in reference to their own child as recommended in the questionnaire
protocol. The 50 items were scaled in 15 domains, yielding a total score presented on a scale of 0 to 100, with the highest score representing the best state of
Toscano et al.
133
health, well-being, and satisfaction (see www.healthactchq.com). Physical health
score was calculated as the sum of the scales scores: physical functioning, role/
social–physical, general health, bodily pain, parental impact-time, and parental
impact-emotional. Psychosocial health score was calculated as the sum of the
scales scores: parental impact-time, parental impact-emotional, role/social-emotional/behavioral, self-esteem, mental health, and behavior. Parents completed
the questionnaire twice—once before and once after the intervention.
The Portuguese version (Pereira, Riesgo, & Wagner, 2008) of the Childhood
Autism Rating Scale (Moulton, Bradbury, Barton, & Fein, 2016; Schopler,
Reichler, & Renner, 2002; Schopler, Reichler, DeVellis, & Daly, 1980) was
completed by an experienced clinical psychologist from the pediatric center
for populations with ASD (pre- and post-intervention). The observer was
blinded as to whether children were allocated to control or intervention
groups. The scale examines children with ASD behavior in 14 domains affected
by ASD. Scores are assigned between 1 and 4 for each domain where 1 indicates
normal behavior appropriate for age level (no signs of autism), and 4 indicates a
severe deviance with respect to the normal behavior (severe symptoms of autism).
The total score is the sum of the single items, with a maximum score of 60.
Statistical Analysis
Descriptive statistics for all measures are presented as means and standard deviations. The assumption of normality was checked by visual inspection of normality plots. Initially, we used unconditional means models, which include only
the random parameters, to measure the proportion of the total variance which is
between-participants grouped in intervention and control groups (i.e., the variance partition coefficient; Goldstein, 2011). The variance components model
allowed determining whether baseline values were clustered by intervention or
control group. Later, we used multilevel modeling to examine the responses to
the 48-week exercise-based intervention program (dummy valuable—control
group coded 0, intervention group coded 1) on HDL-C, LDL-C, total cholesterol, triglycerides, glucose, the autistic traits scale, the motor profile scale, physical health, and psychosocial health, accounting for the psychotropic medication
influence on BM and metabolic profile (Kingsbury, Fayek, Trufasiu, Zada, &
Simpson, 2001; Malone, Delaney, Hyman, & Cater, 2007). Hence, we assumed
measurements pre- and post-intervention (Level 1 unit) nested by children (Level
2 unit), nested by psychotropic medication use (Level 3 unit; binary variable—no medication or other nonpsychotropic medication coded 0, psychotropic medication use coded 1). Constant (intercept term) and time of
measurement (binary variable—preintervention coded 0, post-intervention
coded 1) parameters were considered as fixed parameters, and were allowed
to vary randomly at Level 2 (between participants) and at Level 3 (between-participants group by psychotropic medication use). We also included as fixed
134
Perceptual and Motor Skills 125(1)
parameter an interaction term between time of measurement and participant
group, to examine differences in the response to the intervention between
groups. To make inferences about the true (population) values of the effect of
the 48-week intervention on dependent variables, the size of the standard deviations for individual responses was interpreted in relation to the baseline
between-participant standard deviation (Atkinson & Batterham, 2015). Plots
of residual versus predicted values from the analyses were examined to inspect
the validity of the multilevel models. Considering the sample size, maximum
likelihood estimation was used to obtain the unknown parameters. Multilevel
regression models were obtained using the ‘‘nlme’’ package (Pinheiro & Bates,
2000), within the R statistical language.
Results
The descriptive statistics of children with ASD for the total sample and grouped
as intervention or control groups are summarized in Table 2. Variance partition
coefficients derived from multilevel null models (i.e., variance partition coefficient > .05) indicate a significant variation by group allocation for body dimensions, LDL-C, total cholesterol, triglycerides, and glucose. Also, variance
Table 2. Baseline Characteristics of Children With Autism Spectrum Disorder Grouped as
Intervention and Control Groups.
Intervention
group (n ¼ 46)
Chronological age (years)
Stature (cm)
Body mass (kg)
Body mass index (kg/m2)
Waist circumference (cm)
HDL-C (mg/dL)
LDL-C (mg/dL)
Total cholesterol (mg/dL)
Triglycerides (mg/dL)
Glucose (mg/dL)
Autistic traits scale (#)
Motor profile scale (#)
Physical health (#)
Psychosocial health (#)
8.2
128.2
35.9
21.4
67.9
50.3
100.9
167.4
91.7
71.6
60.1
6.3
41.7
23.1
(1.7)
(14.7)
(13.6)
(6.2)
(16.3)
(14.4)
(29.16)
(31.3)
(47.0)
(10.2)
(13.9)
(2.1)
(13.5)
(14.0)
Control group
(n ¼ 18)
8.9
148.7
51.2
21.1
48.6
45.6
119.9
201.0
139.2
89.2
61.0
6.8
40.3
25.6
(2.0)
(27.2)
(29.1)
(5.4)
(11.1)
(15.5)
(34.8)
(49.1)
(57.2)
(11.7)
(14.0)
(2.0)
(8.8)
(13.6)
HDL-C ¼ high-density lipid cholesterol; LDL-C ¼ low-density lipid cholesterol.
Partition variance
coefficient
.05
.35
.22
.00
.44
.01
.13
.27
.23
.57
.00
.00
.00
.00
Toscano et al.
135
partition coefficient values suggest that the magnitude of differences between
participants allocated by group were small to moderate (Tymms, 2004).
Therefore, baseline differences between participants allocated to intervention
and control groups were included in the multilevel models examining changes
across the 48-week exercise-based intervention.
Multilevel modeling results of changes across the 48-week exercise-based
intervention are summarized in Table 3 and Figures 2 and 3. Growth in stature
across the 12 months of observation was 3.7 cm (95% CI 1.0–6.4 cm) and 2.4 cm
(95% CI 0.1–4.7 cm) for the intervention and control group, respectively. No
substantial changes were observed for BM and BMI across the observation
period. Substantial variation between participants by use of psychotropic medication was observed for body dimensions.
Changes in metabolic indicators are presented in Figure 2. The intervention
group showed increases for HDL-C (5.2 mg/dL, 95% CI 2.2–8.1 mg/dL, effect
size ¼ 0.67), and decreases for LDL-C (7.7 mg/dL, 95% CI14.5 and 0.9 mg/
dL, effect size ¼ 0.43) and total cholesterol (10.1 mg/dL, 95% CI 19.0 to
1.3 mg/dL, effect size ¼ 0.88) compared with the control group. No changes
were observed for glucose and triglycerides. No substantial variation between
participants by use of psychotropic medication was observed for metabolic
indicators.
Changes in the observer-completed autistic characteristics scale and the
parent-completed quality of life scale in response to the intervention are presented in Figure 3. For the autistic traits scale (8.1, 95% CI 12.2 to 4.0,
effect size ¼ 1.05), there was a decrease across the 48-week intervention. There
was substantial variation on the autistic traits scale between participants on the
basis of their use of psychotropic medication. As for parental perceived quality
of life, the intervention group showed increases, both for physical health score
(13.3, 95% CI 7.7–18.9, effect size ¼ 1.05) and psychosocial health score (15.2,
95% CI 9.8–20.7, effect size ¼ 1.66). There was no substantial variation in
parent-perceived quality of life between participants on the basis of their use
of psychotropic medication.
Discussion
In our examination of the effects of a 48-week exercise-based intervention on
metabolic profile, autism traits, and perceived quality of life in children with
ASD, we found that children exposed to the intervention program, compared
with those in a control group, showed significant positive effects in their
improved metabolic health and reduced autistic traits. Consequently, parents’
perceptions of their children’s quality of life increased significantly more for
those in the intervention program, relative to those in the control group.
Given the probable higher risk of incidence of obesity in children with ASD
(Broder-Fingert et al., 2014; Shedlock et al., 2016), and consequent potential for
136
60.4 (56.7–63.9)
6.5 (5.9–7.0)
Autistic traits scale (#)
Motor profile scale (#)
19.3 (11.3–27.4)
–
–
–
–
15.5 (22.9 to 12.1)
86.4 (130.9 to 42.0)
33.6 (53.4 to 13.8)
19.0 (34.7 to 3.3)
–
–
15.3 (26.0 to 4.7)
20.5 (30.8 to 10.2)
Average difference
between control
and experimental
(95% CI)
2.4 (0.1–4.7)
Average change
in the control
group (95% CI)
2.4 (7.1 to 2.4)
3.1 (1.8 to 8.1)
0.1 (0.7 to 0.9)
1.4 (4.9 to 2.1)
2.8 (0.2–5.5)
38.1 (73.5 to 2.72)
0.2 (7.2 to 7.7)
2.0 (3.8 to 7.8)
0.9 (1.6 to 3.4)
1.8 (1.6 to 5.2)
0.5 (2.1 to 1.0)
0.2 (2.1 to 2.6)
HDL-C ¼ high-density lipid cholesterol; LDL-C ¼ low-density lipid cholesterol.
23.8 (20.4–27.2)
89.2 (84.6–93.8)
Glucose (mg/dL)
Psychosocial health (#)
178.2 (140.5–215.8)
Triglycerides (mg/dL)
41.4 (38.4–44.2)
201.0 (184.3–217.8)
Total cholesterol (mg/dL)
Physical health (#)
48.9 (45.1–52.8)
48.6 (41.8–55.4)
Waist circumference (cm)
119.9 (106.5–133.1)
21.3 (19.9–22.8)
Body mass index (kg/m2)
LDL-C (mg/dL)
51.2 (42.2–60.3)
Body mass (kg)
HDL-C (mg/dL)
148.7 (140.0 to 157.4)
Stature (cm)
Preintervention
average—control
group (95% CI)
15.2 (9.8–20.7)
13.3 (7.7–18.9)
2.4 (3.3 to 1.5)
8.1 (12.2 to 4.0)
1.5 (4.7 to 1.6)
33.1 (8.6 to 74.9)
10.1 (19.0 to 1.3)
-7.7 (14.5 to 0.9)
5.2 (2.2–8.1)
0.4 (3.6 to 4.5)
0.2 (2.0 to 1.6)
1.9 (0.9 to 4.7)
3.7 (1.0–6.4)
Average change in
the experimental
group (95% CI)
Table 3. Multilevel Modeling Results of Changes Pre- and Post-Exercise Based Intervention For All Sample.
7.6 (6.3–9.3)
8.2 (6.9–9.8)
1.3 (1.1–1.5)
5.4 (4.5–6.4)
4.0 (3.4–4.8)
53.1 (44.5–63.3)
11.2 (9.4–13.4)
8.7 (7.3–10.3)
3.8 (3.2–4.5)
5.1 (4.3–6.1)
2.4 (2.0–2.8)
3.5 (3.0–4.2)
3.4 (2.9–4.1)
Random
within-patient
SD: Level-1
(95% CI)
11.2 (9.0–14.0)
18.0 (6.1–10.7)
1.9 (1.5–2.3)
13.1 (10.8–15.8)
8.9 (7.2–10.8)
59.8 (46.3–77.1)
33.8 (28.1–40.7)
26.9 (22.4–32.4)
14.9 (12.4–17.9)
13.5 (11.2–16.3)
5.2 (4.3–6.3)
18.9 (15.8–22.6)
18.2 (15.2–21.8)
Random
between-patient
SD: Level-2
intercept (95% CI)
Toscano et al.
137
Figure 2. Mean changes in metabolic indicators pre- and post-intervention in the experimental group and control group: (a) HDL-C, (b) LDL-C, (c) Total cholesterol, (d)
Triglycerides, and (e) Glucose. HDL-C ¼ high-density lipid cholesterol; LDL-C ¼ low-density
lipid cholesterol.
obesity-related complications in development (Santosh & Singh, 2016), the present results have important implications for both the short- and long-term health
effects of including exercise programs and regular physical activity in therapeutic
approaches for children with ASD.
Stature and yearly growth within the observation period of the present
sample was comparable with the 50th specific-age percentile or higher for reference data (Cole, Bellizzi, Flegal, & Dietz, 2000; Kuczmarski et al., 2000).
Baseline values for BM and BMI were comparable with the 97th specific-age
percentile for reference data (Cole et al., 2000; Cole, Flegal, Nicholls, & Jackson,
2007; Kuczmarski et al., 2000), and comparable with obese Brazilian specific
BMI cut-off values (Conde & Monteiro, 2006). This is consistent with observations of a suspected higher prevalence of overweight and obesity in children with
ASD (Broder-Fingert et al., 2014), though research in this area remains limited.
Many of the risk factors for children with ASD are likely the same as for
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Figure 3. Mean changes in autistic trait scale and parental perceived quality of life indicators pre- and post-intervention in the experimental group and control group: (a) Autistic
traits scale, (b) Physical health, (c) Psychosocial health, and (d) CHQ-PF50 score.
children with typical development, but the needs and challenges of children with
ASD may make them more susceptible and vulnerable to those same risk factors
(Curtin et al., 2014).
Defining overweight as age-adjusted, sex-specific BMI between the 85th and
the 95th percentile, and obese as a BMI at or above the 95th percentile
(Kuczmarski et al., 2000), about 27% and 33% of the children with ASD in
this sample were overweight and obese, respectively. Data available on weight
status of children with ASD are limited. Present results are consistent with recent
observations based on 2,978 children and youngsters with ASD aged 2–20 years
showing that 25% were overweight and 48% were obese (Broder-Fingert et al.,
2014). A study considering a small sample of children with ASD reported an
overweight and obesity prevalence of 35.7% and 19.0% for boys and girls,
respectively (Curtin et al., 2014). In addition, a prevalence of 33% overweight
and 18% obese was reported in a clinical sample of 380 boys and 49 girls with
autism aged 2–11 years in China (Xiong et al., 2009).The prevalence of overweight and obesity in children with ASD (2–9 years old) was 31.5%, based on
the World Health Organization 1995 standards (Xia et al., 2010). The prevalence
of obesity in children with ASD using data from the National Study of
Children’s Health indicates that children with ASD were 40% more likely to
be obese than children in the general population (Curtin et al., 2014). All these
findings suggest that children with ASD are at risk for obesity at the same or
higher rate than are typically developing children. Thus, this particular issue
Toscano et al.
139
deserves both research and clinical attention because of the adverse health effects
known to be associated with obesity (Curtin et al., 2014).
The present results also showed that no changes in BM and BMI were
observed after participation in the intervention, implying that this exercisebased program did not lead to weight status improvements in children and
youngsters with ASD. As children with obesity have an increased risk of
developing related metabolic disorders (Shedlock et al., 2016), research regarding metabolic variables may also have relevance for obesity. WHtR was used as
a measurable anthropometric index for detecting central obesity and to assess
associations between cardiometabolic risk factors and central intra-abdominal
obesity (Nambiar, Hughes, & Davies, 2010; S. R. Srinivasan et al., 2009), particularly in adults. Although studies relating BMI and WHtR to cardiovascular
disease risk factors in children and adolescents are emerging (Kahn, Imperatore,
& Cheng, 2005; Nambiar et al., 2010), information is still scarce on the utility of
WHtR in assessing the status of abdominal obesity and related cardiometabolic
risk profile among normal weight and overweight/obese children. WHtR detects
central obesity and related adverse cardiometabolic risk among normal weight
children, but also identifies those without such conditions among the overweight/obese children (Mokha et al., 2010). Results in the present study of children with ASD are consistent with data from children without autism (Mokha
et al., 2010), lending support to an association between metabolic diseases and
ASD (Shedlock et al., 2016) with implications for pediatric primary care
practice.
ASD is a complicated condition in which nutritional and environmental factors play major roles. A number of risk factors under investigation include
genetic, infectious, metabolic, nutritional, and environmental, with specific
causes for these problems known in less than 10% to 12% of cases (Grether,
Anderson, Croen, Smith, & Windham, 2009). Several studies have reported
abnormalities in lipid profiles of adults with Asperger’s syndrome (Dziobek
et al., 2007), a disorder related to ASD. Alterations in the plasma lipid profile
in boys with ASD have also been reported (Kim et al., 2010). In this study, Kim
et al. found that the triglycerides level was significantly higher, whereas the mean
HDL-C level was significantly lower compared with those in the control group.
There were no differences in total-cholesterol and LDL-C levels between cases
and controls. The LDL/HDL ratio was significantly higher in cases compared
with controls. Such results are similar to those from our study, suggesting that
some lipid fractions in children with autism may be significantly different from
those obtained in healthy children.
Proof of the ability to reduce such metabolic risk in obese children and adolescents through exercise is still scarce (McCormack et al., 2014), particularly in
children with ASD. However, cross-sectional evidence indicates that boys who
are more physically active have higher HDL-C and lower triglycerides (Telford
et al., 2015). In addition, organized sports and exercise have been found to be
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Perceptual and Motor Skills 125(1)
inversely associated with body fat percentage and LDL-C and directly related to
HDL-C and diastolic blood pressure adjusted for age and gender (Väistö et al.,
2014). We found an increase in HDL-C and a decrease in LDL-C and in total
cholesterol as a result of exercise among children with ASD, but we found no
differences for glucose and triglycerides from our exercise intervention.
Quality of life is a multidimensional concept that acknowledges physical,
psychological, and social domains of health and their influence on the individual
(Kheir et al., 2012). It is well established in the literature that parents of children
with ASD experience poorer family functioning including parent mental health
problems, poorer family quality of life, and parenting difficulties (Cooper,
Martin, Langley, Hamshere, & Thapar, 2014; Van Der Meer et al., 2012). It
has been further suggested that higher ASD symptoms are associated with
poorer family quality of life across emotional, family, and time domains
(Green et al., 2016). As it is not known how comorbid ASD symptoms such
as obesity may contribute to family functioning in children with ASD, we sought
to better understand this relationship. We found that children exposed to our
exercise intervention program showed an important decrease in their autistic
traits, including particularly, a reduction of their stereotypical behavioral patterns, and improvements in verbal and nonverbal social communication skills. A
recent meta-analysis evaluating 16 behavioral studies in children and adults with
autism revealed that all exercise programs produced significant progress on the
different measures assessed, particularly motor and social functioning (Sowa &
Meulenbroek, 2012). Individual programs seem to elicit significantly more
improvements than group interventions, both in motor and social domains.
Combined results also revealed that in terms of motor performance and social
skills, children and adults with ASD benefit more from individual than from
group exercise interventions. Furthermore, the impact of both individual and
group exercise interventions on communication deficits is very much influenced
by ASD symptom severity.
The present results revealed positive changes in parent’s perceptions about
repetitive and stereotyped behavior and health-related quality of life in children
with ASD as a response to exercise through increasing physical health and psychosocial scores. Evidence of associations between ASD symptoms and healthrelated quality of life suggests that effective interventions of this kind permit
reductions in specific behavioral characteristics that, in turn, produce gains in
health-related quality of life (Tilford et al., 2012). Our participants showed
reduced general and primary (repetitive and stereotyped behavior) symptoms
of autism as a result of the 48-week exercise intervention program, confirming
the positive influence of exercise-based intervention on primary symptoms of
ASD (Bremer et al., 2016; Tan et al., 2016).
Limitations to the current study include a dependence on parent perceptions
of children’s profile changes. Future research efforts should procure complementary data from direct observation and completion of the Scale of Autistic Traits
Toscano et al.
141
by other trained professionals on the child’s specialized care team. Second, there
is a need for follow-up data to judge the stability of these positive effects over
time. Third, laboratory analyses of insulin resistance status would yield more
complete information about metabolic health.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research,
authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
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Author Biographies
Chrystiane V. A. Toscano has a degree in Physical Education (Federal University of Sergipe, Brazil).
She is a PhD Student in Sport Sciences (since 2013 by University of Coimbra, Portugal). Currently,
she is a professor at the Federal University of Alagoas (Brazil).
Humberto M. Carvalho has a degree in Sports Sciences and Physical Education (University of
Coimbra, Portugal), master’s degree in Youth Sports Training (University of Coimbra, Portugal),
and PhD in Sports Sciences (University of Coimbra, Portugal). Currently, he is a professor and
researcher at the Sports Center of the Federal University of Santa Catarina, and his research interests
include hierarchical/multilevel modeling.
José P. Ferreira has a degree in Physical Education (Special Education or Rehabilitation), master’s
degree in Children Motor Behavior (FMH-UTL, Portugal), and PhD in (University of Bristol, UK).
Currently, he is a professor at the University of Coimbra (Portugal), and his research interests focus
on the study of psychological variables associated with the practice of sport and exercise in special
groups (CIDAF/FCDEF-UC). He teaches European programs Erasmus Mundus Master in Adapted
Physical Activity and European University Diploma in Adapted Physical Activity. Currently, he is
the president of the European Federation of Adapted Physical Activity.