Research Article
Predictors of Participation Difficulties in
Autistic Children
Importance: Participation in meaningful occupations supports quality of life and health. Because quality of life is
lower in autistic children than in children without this diagnosis, it is important to consider aspects contributing to
the participation difficulties this population experiences.
Objective: To identify predictors of participation difficulties in a large data set from autistic children to inform
professionals about potential intervention targets.
Design: Retrospective cross-sectional design using a large data set with multivariate regression models for home
life, friendships, classroom learning, and leisure activities.
Setting: 2011 Survey of Pathways to Diagnosis and Services data set.
Participants: Parents or caregivers of 834 autistic children with co-occurring intellectual disability (ID) and 227
autistic children with no ID.
Results: The strongest participation predictors within the scope of occupational therapy practice were sensory
processing, emotional regulation, behavioral variables, and social variables. Our results are consistent with those of
smaller previous studies and indicate the importance of addressing these areas in occupational therapy
intervention in line with client priorities.
Conclusion and Relevance: Focusing interventions with autistic children on sensory processing, emotional
regulation, behavioral skills, and social skills to address their underlying neurological processing can support their
increased participation in home life, friendships, classroom learning, and leisure activities.
What This Article Adds: Our findings support a focus in occupational therapy interventions on sensory processing
and social skills to increase activity participation in autistic children with and without ID. Emotional regulation and
behavioral skills can be supported by interventions that target cognitive flexibility.
Positionality Statement: This article uses the identity-first language autistic people. This nonableist language
describes their strengths and abilities and is a conscious decision. This language is favored by autistic
communities and self-advocates and has been adopted by health care professionals and researchers
(Bottema-Beutel et al., 2021; Kenny et al., 2016).
Hilton, C. L., Ratcliff, K., & Hong, I. (2023). Predictors of participation difficulties in autistic children. American Journal of Occupational Therapy,
77, 7702205010. https://doi.org/10.5014/ajot.2023.050068
or optimal growth and development, children
and adolescents need to participate in a variety
of activities (Bohnert et al., 2019). Diverse activity
participation results in the creation of neural pathways that aid the development of competence in a
wide range of activities vital for daily life functioning (Demetriou et al., 2018; Reynolds et al., 2011)
and the automatization of movements and social
communication (Hirata et al., 2014), leading to
greater life satisfaction, lifetime health, well-being,
and quality of life (McCollum et al., 2016). Autistic
F
children encounter difficulty participating in many
activities (Hilton et al., 2008) and enjoy a less robust range of developmentally supportive activities
than children without this diagnosis (Hilton et al.,
2019; Taheri et al., 2016). Potvin et al. (2013) found
that the out-of-school activity participation preferences of autistic children were similar to those of
peers without a diagnosis, except for lower physical
activity preferences among the autistic children.
Limited diversity in activity participation is not
exclusively related to person factors; it can also be
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Claudia L. Hilton, Karen Ratcliff, Ickpyo Hong
Areas of Participation
Home Life
As with all of the outcome variables in the current
study, home life is not clearly defined in the data set we
used from the 2011 Survey of Pathways to Diagnosis
and Services that was conducted by the Centers for
Disease Control and Prevention (hereinafter referred to
as “Pathways”; Child and Adolescent Health Measurement Initiative [CAHMI], 2015). However, the
literature generally defines the term to include interactions with family members, family mealtime, meal
preparation, care of other family members, self-care,
chores, homework, personal care, cleanup, and care of
animals or pets (Anaby et al., 2013). Home life spans
several occupations from the Occupational Therapy
Practice Framework: Domain and Process (4th ed.;
OTPF–4; AOTA, 2020), including activities of daily
living (ADLs), instrumental activities of daily living, education, and social participation. Autistic children
participate less in home life than children with no
diagnosis, particularly in the areas of personal care
management, household chores, and school preparation
(McCollum et al., 2016), and difficulties with home life
continue into adulthood (Smith et al., 2012). Studies
have identified lack of skills in daily living, communication, and socialization as major barriers to home life
participation (Liss et al., 2001; Perry et al., 2009).
Classroom Learning
Previously identified areas of concern regarding classroom learning include behavioral and emotional
differences, school performance, social activities, communication, following rules and directions, personal
care awareness, task completion, positive social interactions, and safety (Sparapani et al., 2016). Classroom
learning is part of the occupation of education in the
OTPF–4. Many areas of concern are related to executive
function, a frequent area of difficulty for autistic individuals (Demetriou et al., 2018). Sparapani et al. (2016)
found that autistic children spent less time emotionally
regulated and actively engaged in the classroom than
other children. Autistic children experience behavioral
and emotional differences from their peers without a
diagnosis that are potential predictors of classroom
learning, including shorter attention span, anxiety, depression, and behaviors that can be disruptive in the
classroom (Ashburner et al., 2010). Aspects of the classroom environment contribute to these behaviors and
may be potential targets for intervention (Martin, 2016).
Friendships
The friendships of autistic children without ID differ
from those of children without an ASD diagnosis in
self-, peer-, and teacher-rated quality and reciprocity
(Kasari et al., 2011). Friendships are part of the occupation of social participation in the OTPF–4. In a
study examining the friendships of young adults with
ASD only, significantly more were likely to never see
friends, to never be called by friends, to never be invited to activities, and to be socially isolated compared
with young adults with other diagnoses who received
special education services (Orsmond et al., 2013).
Leisure Activities
Leisure is defined as intrinsically motivated nonobligatory activity that is engaged in during time not
committed to obligatory occupations such as work,
self-care, or sleep (AOTA, 2020; Parham & Fazio,
1997). This definition is consistent with that of the
occupation of leisure in the OTPF–4. Recreation, a category of leisure, consists of an individual’s preferred
pleasurable and enjoyable activities engaged in during
leisure time (Veal, 1992) and includes both physically
active and solitary sedentary activities (e.g., watching
television and videos; playing video games; using
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an outcome of environmental and task-related
factors (Law et al., 1996).
Longitudinal studies of severity trajectories in autistic children have shown mixed outcomes. One study
that examined autistic children ages 3 to 11 yr found
symptom severity changes in 51% of participants, with
27% experiencing increased and 24% decreased severity (Waizbard-Bartov et al., 2022). In a different type
of data analysis, an older group, ages 12 to 23 yr,
showed an increase in intelligence while autism symptoms remained unchanged (Simonoff et al., 2020).
Although autistic people may not consider reducing
symptom severity a priority (Leadbitter et al., 2021),
their symptoms can limit diversity in their activity participation, preventing the creation of neural pathways
that facilitate competence in adult roles and expectations (Reynolds et al., 2011). As autistic children age
into adolescence, their participation differences increase compared with peers without a diagnosis
(Ratcliff et al., 2018). These differences may contribute
to difficulty as adults in living independently, becoming employed, going to college, and getting married
(Baldwin et al., 2014).
Previously, Hilton and colleagues (2021) compared
parent-reported difficulties experienced by children
with intellectual disability (ID) with those experienced
by autistic children with and without ID in four areas:
home life, classroom learning, friendships, and leisure
activities. Friendships was the most commonly reported area of difficulty for all of these children.
Autistic children with ID experienced the greatest
difficulties in all four areas, followed by those with
autism alone. In the current study, we examined predictors of difficulties in these four areas in autistic
children with and without ID to inform potential intervention targets to support broader participation
interests. We selected the same outcome areas as in
the previous study because they are often identified as
participation categories of concern in the autism spectrum disorder (ASD) literature.
computers, cellphones, and other electronic devices).
Although many studies have found lower levels of
physically active recreational activity in autistic children (Askari et al., 2015; Hilton et al., 2008; Potvin
et al., 2013), Ratcliff et al. (2018) found no significant
difference in recreation participation between children
with and without ASD, but they did not differentiate
between physically active and solitary sedentary leisure.
To promote greater diversity of participation by autistic children and adolescents, occupational therapy
practitioners should understand the factors that influence participation so they can design interventions in
collaboration with clients and care providers to address the barriers. We used a large cross-sectional
database to examine predictors of parent-reported participation difficulties in the types of activities in which
autistic children engage. On the basis of prior research
findings, we selected the following predictor variables:
Contextual variables: age, sex, race or ethnicity,
parent education level, household income,
health insurance coverage
ADLs and functional and adaptive skills
Skill-related variables: fine motor, learning (i.e.,
understanding, paying attention)
Behavioral variables: conduct problems, hyperactivity, acting out (i.e., fighting, bullying, arguing)
Emotional regulation variables: emotional regulation problems, anxiety or depression
Social variables: social problems, prosocial
behaviors
Sensory processing variables: sensory seeking,
sensory avoidant, low registration (i.e., disorganized, overreactive), repetitive behaviors.
Our aims in the current study were to identify and
compare the strength of these predictors of activity
participation difficulties in home life, classroom learning, friendships, and leisure activities in autistic
children with and without co-occurring ID in order to
better inform professionals about potential targets of
intervention. We formed five hypotheses:
1. Sensory processing, emotional regulation, and behavioral variables and household income predict
participation difficulties in home life, classroom
learning, friendships, and leisure activities for
both groups.
2. Social variables predict participation difficulties in
home life, friendships, and leisure activities for
both groups.
3. Social variables, parent education level, household
income, and health insurance coverage predict
classroom learning and friendships for both
groups.
4. Sensory processing and emotional regulation variables are stronger predictors than contextual
variables.
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Method
We used a retrospective cross-sectional complex sample design (N 5 4,032) to examine predictive factors
for the four outcome variables of home life, friendships, classroom learning, and leisure activities. We
sought to identify characteristics of autistic children
that can be addressed in intervention targeting more
robust participation and to reveal any potential confounding demographic variables.
Participants
We examined data from the 2011 Survey of Pathways
to Diagnosis and Services data set (CAHMI, 2015). This
survey is part of a repeated cross-sectional survey of parents and caregivers of children with special health care
needs who have ever had a diagnosis of ASD, ID, or developmental delay from all 50 U.S. states; data were
weighted to represent the U.S. population. Survey respondents were 6,090 parents or caregivers of children
ages 6 to 17 yr, and phone interviews were conducted
with 4,032 respondents. We excluded 1,112 respondents
who did not complete questions about home life,
friendships, classroom learning, and leisure activities.
We analyzed data from reports having no missing observations (N 5 2,920) for children with ASD only
(n 5 834), ASD and ID (n 5 227), ID only (n 5 508),
developmental disorder only (n 5 995), and no current
diagnosis (n 5 356; see Appendix Figure A.1). Missing
responses for 236 children were imputed in the data
analyses. This study did not require approval or oversight from the institutional review board at the
University of Texas Medical Branch (No. 18–044)
because we used publicly available, deidentified data.
Study Variables
The Pathways data include 25 social–behavioral items
categorized into five social–behavioral areas: emotional
regulation problems, conduct problems, hyperactivity/
inattention, social problems, and prosocial behavior
(see Appendix Table A.1). Higher scores indicate
greater social behavioral difficulties. We also examined
four sensory domains—sensory seeking, sensory avoidant, low registration/disorganized/overreactive, and
repetitive behaviors—using the 15 Pathways sensory
items (Lee et al., 2019). Lee and colleagues (2019)
calibrated the sensory items with item response theory
models and found good psychometric properties, including structural and known group validity. Higher
scores represent greater sensory differences.
ADL, skill-related, and behavioral variables from
the Pathways data set included ADLs (eating, dressing,
bathing), fine motor difficulty (using hands), learning
(understanding, paying attention), anxiety or
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Potential Predictors of Activity
Participation Difficulties
5. Poorer ADLs and functional and adaptive skills
predict greater participation difficulties in home
life and classroom learning.
depression, and behavior problems (acting out, fighting, bullying, arguing). These items were rated using a
3-point scale (1 5 no difficulty, 2 5 a little difficulty, 3
5 a lot of difficulty). Higher scores represent more difficulties. Contextual variables consisted of parent
education level, race or ethnicity, household income,
and health insurance coverage and were included as
the study covariates.
Outcome Variables
Data Analysis
We conducted population-weighted descriptive data
analyses for the demographic characteristics using
t tests for the continuous variable (age) and chi-square
tests for the categorical variables (sex, race or ethnicity,
Results
Table 1 presents the population-weighted descriptive
statistics for the two diagnosis groups. The estimated
Table 1. Participant Demographics
Weighted % [95% CI]
Variables
ASD
Age, M (SE)
ASD and ID
11.1 (0.18)
12.0 (0.38)
62.6 [60.0, 65.2]
18.2 [16.1, 20.3]
14.4 [12.4, 16.4]
4.7 [3.6, 5.8]
Non-Hispanic White (n 5 808)
55.0 [52.3, 57.7]
12.6 [10.9, 14.3]
Non-Hispanic Black (n 5 53)
6.8 [5.1, 8.5]
2.2 [1.5, 2.9]
Hispanic (n 5 85)
8.5 [6.7, 10.3]
4.2 [2.9, 5.5]
Non-Hispanic other (n 5 115)
6.7 [5.4, 8.0]
3.9 [2.9, 4.9]
Less than high school (n 5 147)
16.7 [14.4, 19.0]
8.0 [6.3, 9.7]
More than high school (n 5 914)
60.3 [57.6, 63.0]
14.9 [13.2, 16.6]
<100% FPL (n 5 141)
12.3 [10.2, 14.4]
4.4 [3.3, 5.5]
≥100% FPL (n 5 920)
64.8 [62.1, 67.5]
18.4 [16.3, 20.5]
75.1 [72.8, 77.4]
22.1 [19.9, 24.3]
2.0 [1.1, 2.9]
0.7 [0.3, 1.1]
Sex
Male (n 5 833)
Female (n 5 227)
Missing (n 5 1)
a
Race or ethnicity
Parent education level
Household income
Health care coverage
Yes (n 5 1,035)
No (n 5 26)
Note. ASD 5 autism spectrum disorder; FPL 5 federal poverty level; ID 5 intellectual disabilities; SE 5 standard error. Data are from the
2011 Survey of Pathways to Diagnosis and Services (Child and Adolescent Health Measurement Initiative, 2015).
a
Data are missing for ASD only. Not applicable for ASD and AD.
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The outcome variables were home life (Item C27CA),
friendships (Item C27CB), classroom learning (Item
C27CC), and leisure activities (Item C27CD). For
those four items, parents or caregivers were asked,
“Do the difficulties interfere with your child’s everyday life?” Respondents used a 4-point rating scale
(1 5 not at all, 2 5 only a little, 3 5 quite a lot,
4 5 a great deal), which we operationally dichotomized as 0 5 not at all/only a little, 1 5 quite a lot/a
great deal to estimate odds ratios.
parent education level, household income, health insurance coverage). Next, we performed four individual
multivariate logistic regression models for the outcome
variables and identified significant predictor variables.
For the multivariate logistic regression analyses, we
examined whether the study variables violated the
multicollinearity assumption, and we checked the
model fit. To control for the multilevel sampling structures of the complex survey database, we conducted
subpopulation approaches using the entire sample
with the sampling weight, strata, and cluster information; however, the primary analyses were produced
using data solely for the autistic children with and
without ID. For the multivariate logistic regression
models, we used the SAS MI and MIANALYZE procedure modules to conduct the fully conditional
specification as multiple imputations for the 236 missing observations in the study variables (Allison, 2002).
In the individual multivariate regression models, we
calculated the point estimations as adjusted odds ratios
and 95% confidence intervals. We used SAS Version
9.4 for data management and all study analyses.
Discussion
Large data research provides opportunities to examine
patterns of responses among a large number of participants. The focus of this study was on parent-reported
participation difficulties specific to children and adolescents; readers should note that parent-identified
priorities may differ from the self-reported priorities of
the children and adolescents themselves and from the
priorities of autistic adults. Our findings support those
of past and current research on areas that clinicians
have targeted in therapeutic interventions, add additional directions for intervention development, and
include some outcomes that were unexpected. We
found the strongest predictors of participation difficulties across the contexts examined to be sensory
processing, emotional regulation, and behavioral variables; social variables; contextual variables; and ADLs
and functional and adaptive skills. Differences between
autistic children with and without ID were generally
minor.
Sensory Processing, Emotional Regulation, and
Behavioral Variables
We hypothesized that sensory processing, emotional
regulation, and behavioral variables would predict participation difficulties in home life, classroom learning,
friendships, and leisure activities for autistic children
with and without ID. This hypothesis was supported
by some of the variables in these categories.
Sensory processing, emotional regulation, and behavioral variables are interconnected and consistent
with theoretical understandings of sensory processing;
sensory information registered from the environment
is processed in the brain, which directs emotional and
behavioral responses (Blair & Diamond, 2008; Smith
Roley et al., 2007). Management of emotional and behavioral responses is governed by executive function
systems that work to control attentional processes,
cognitive flexibility, goal setting, and information processing and enable individuals to control themselves
physically, cognitively, and emotionally (Monteiro,
2021). These processes support a person’s ability to
participate in home life, classroom learning, friendships, and leisure activities and may play a role as
predictors of participation in these activities.
The role of sensory processing as an activity participation predictor was identified by Loh et al. (2021),
who found that sensory processing was related to participation in childhood occupations, and by Choi and
Jung (2021), who found sensory processing to be a
predictor of leisure participation in early adolescents
with no diagnosis. Werkman et al. (2020) examined
the relationships among sensory processing, emotional
and behavioral problems, and social participation and
found that autistic individuals with higher cognitive
abilities and sensory processing difficulties demonstrated more emotional and behavioral problems and
that social participation was restricted by sensory processing difficulties regardless of cognitive abilities.
Other studies have linked emotional and behavioral
responses to participation. In a study examining home
life participation, Reynolds et al. (2011) found that
fewer autistic children completed chores compared
with unaffected peers and that sensory processing contributed to participation in both play and leisure
activities and job or chore activities. Another study
found that the social and cognitive demands of activities limited participation in home life (Egilson et al.,
2018).
Sparapani et al.’s (2016) study supports our findings of the importance of emotional regulation and
behavioral variables, finding less time spent being
emotionally regulated and actively engaged in the
classroom among the autistic children in comparison
with the other children. Freeman et al. (2017) found
that the autistic children who were rated by teachers
as having poor initiation, working memory, and planning and organization skills spent more time engaging
in solitary play when on the playground. Several
studies found that lower emotional regulation and behavioral skills (executive function) contributed the
most to lower participation in classroom learning
(Kheirollahzadeh et al., 2021; Zingerevich & LaVesser,
2009). Sensory processing (Zingerevich & LaVesser,
2009) and motor proficiency (Kheirollahzadeh et al.,
2021) were also identified as contributors to reduced
participation in classroom learning.
The ability to manage emotional and behavioral
responses has been shown to be important for friendship participation. Fong and Iarocci (2020) found the
executive function skills of emotional regulation and
behavioral regulation to be associated with social competence in a study of 77 autistic children. In another
study, increased participation in organized activity and
better friendship quality were demonstrated by youth
with better emotional control (Bohnert et al., 2019).
Findings from the current study support those of
previous research identifying sensory processing as a
predictor of participation. Hochhauser and EngelYeger (2010) found lower leisure participation to be
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population samples were 98,679 (21.5%) for autistic
children with ID and 360,170 (78.5%) for autistic children without ID. The demographics of the two groups
were similar, except for parent education level in the
ASD group (p 5 .017). There was no significant difference in household income between the two groups
(p 5 .675).
The multivariate logistic regression models did not
violate the multicollinearity assumption and revealed
good model fit (all ps > .05). Results indicated some
differences in predictors (emotional regulation variables, social variables, behavioral variables, sensory
processing variables, ADLs and skill-related variables,
and contextual variables) for autistic children with and
without ID and across participation areas. Results are
categorized by participation areas (Table 2).
Predictors
0.82 [0.40, 1.69]
1.02 [0.92, 1.14]
ASD
1.01 [0.54, 1.89]
0.50 [0.20, 1.27]
0.94 [0.37, 2.39]
1.01 [0.21, 4.94]
1.32 [0.78, 2.24]
Learning
1.10 [0.96, 1.27]
1.34 [0.91, 1.97]
Hyperactivity
Acting out
Social problems
1.05 [0.91, 1.21]
1.34 [0.87, 2.07]
Anxiety or depression
Social variables
1.15 [1.02, 1.30]*
Emotional regulation problems
Emotional regulation variables
1.31 [1.09, 1.57]*
Conduct problems
Behavioral variables
1.00 [0.63, 1.60]
1.24 [0.79, 1.95]
Fine motor skills
Skill-related variables
Activities of daily living
No
Health insurance coverage (Ref. 5 yes)
≥100% FPL
Household income (Ref. 5 <100% FPL)
High school or less
Parent education level (Ref. 5 more than high school)
Other
Race or ethnicity (Ref. 5 non-Hispanic White)
Male
Sex (Ref. 5 female)
Age
Contextual variables
ASD + ID
0.92 [0.71, 1.18]
0.79 [0.31, 2.04]
1.32 [0.92, 1.91]
0.94 [0.40, 2.24]
1.18 [0.84, 1.65]
1.54 [1.07, 2.21]*
0.95 [0.20, 4.37]
0.48 [0.23, 1.01]
0.85 [0.36, 1.98]
0.23 [0.03, 1.79]
5.44 [1.16, 25.47]*
0.83 [0.19, 3.59]
1.27 [0.34, 4.66]
2.91 [0.82, 10.34]
0.93 [0.79, 1.10]
Home Life
1.58 [1.35, 1.85]*
0.95 [0.60, 1.48]
1.31 [1.13, 1.52]*
1.43 [0.86, 2.38]
1.09 [0.92, 1.29]
0.98 [0.78, 1.23]
1.29 [0.74, 2.26]
1.35 [0.81, 2.23]
1.15 [0.64, 2.08]
2.48 [0.39, 15.86]
1.69 [0.54, 5.24]
0.56 [0.23, 1.34]
0.90 [0.42, 1.94]
0.56 [0.24, 1.33]
0.99 [0.90, 1.10]
ASD
1.19 [0.51, 2.77]
0.87 [0.41, 1.85]
1.66 [0.83, 3.34]
0.67 [0.34, 1.34]
1.02 [0.93, 1.11]
ASD
1.89 [1.24, 2.89]*
0.79 [0.35, 1.78]
0.79 [0.48, 1.31]
1.67 [0.55, 5.06]
1.54 [1.07, 2.21]*
1.11 [0.77, 1.59]
0.11 [0.01, 1.15]
0.60 [0.22, 1.62]
1.41 [0.44, 4.47]
1.04 [0.92, 1.17]
0.82 [0.55, 1.24]
1.09 [0.96, 1.25]
0.91 [0.57, 1.47]
1.22 [1.06, 1.40]*
1.05 [0.88, 1.26]
1.71 [1.00, 2.91]*
0.96 [0.61, 1.51]
1.36 [0.85, 2.17]
0.53 [0.26, 1.07]
1.04 [0.95, 1.13]
ASD
0.68 [0.35, 1.32]
1.09 [0.83, 1.43]
1.37 [0.48, 3.92]
1.20 [0.81, 1.79]
0.24 [0.09, 0.65]*
1.39 [1.04, 1.85]*
1.34 [0.88, 2.05]
3.08 [0.30, 31.88]
0.82 [0.32, 2.10]
1.71 [0.53, 5.51]
1.07 [0.95, 1.21]
0.93 [0.58, 1.50]
1.22 [1.06, 1.40]*
0.93 [0.61, 1.41]
1.02 [0.90, 1.17]
1.22 [1.04, 1.44]*
1.30 [0.76, 2.21]
0.91 [0.60, 1.39]
1.16 [0.75, 1.79]
1.43 [0.31, 6.65]
4.16 [1.21, 14.36]* 0.94 [0.40, 2.20]
0.49 [0.13, 1.84]
(Continued)
1.07 [0.95, 1.21]
0.41 [0.17, 0.97]*
1.37 [1.07, 1.75]*
0.80 [0.38, 1.68]
1.02 [0.90, 1.17]
1.07 [0.81, 1.40]
0.41 [0.09, 1.93]
0.20 [0.09, 0.45]*
1.66 [0.69, 4.00]
1.19 [0.08, 16.98]
0.82 [0.22, 3.11]
0.94 [0.33, 2.71]
0.24 [0.07, 0.75]*
0.93 [0.31, 2.78]
0.86 [0.71, 1.03]
ASD + ID
Leisure Activities
4.97 [1.12, 22.07]* 1.15 [0.62, 2.15]
1.13 [0.22, 5.73]
1.22 [0.93, 1.60]
ASD + ID
Classroom Learning
1.29 [0.03, 50.33]* 4.90 [1.26, 19.10]* 2.00 [0.07, 58.62]
4.53 [0.95, 21.64]
0.55 [0.07, 4.08]
0.70 [0.21, 2.34]
1.22 [0.27, 5.55]
1.08 [0.83, 1.41]
ASD + ID
Friendships
Adjusted Odds Ratioa [95% CI]
Table 2. Multivariate Regression Models With Multiple Imputations for Predictors of Participation Difficulties in Home Life, Friendships, Classroom Learning, and Leisure Activities
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Note. ASD 5 autism spectrum disorder; CI 5 confidence interval; ID 5 intellectual disabilities; Ref. 5 reference group. Data are from the 2011 Survey of Pathways to Diagnosis and Services (Child and Adolescent Health
Measurement Initiative, 2015).
a
Odds ratios were weighted to account for the complex survey sampling structures.
b
Prosocial behaviors predicted lower difficulty scores, so scores were reversed.
*p < .05.
1.01 [0.72, 1.41]
1.15 [0.98, 1.35]
0.78 [0.64, 0.93]*
1.06 [0.89, 1.26]
Repetitive behaviors
1.32 [0.94, 1.86]
1.02 [0.68, 1.53]
1.15 [0.96, 1.36]
1.17 [0.85, 1.60]
1.12 [0.81, 1.55]
1.12 [0.95, 1.33]
1.11 [0.91, 1.37]
1.13 [0.95, 1.35]
Low registration
1.72 [1.08, 2.74]*
1.61 [0.97, 2.66]
1.27 [1.06, 1.52]*
1.64 [1.10, 2.45]*
0.97 [0.81, 1.17]
1.22 [0.88, 1.69]
1.08 [0.97, 1.21]
0.92 [0.76, 1.12]
0.92 [0.70, 1.22]
1.23 [0.72, 2.10]
0.88 [0.72, 1.07]
1.06 [0.94, 1.20]
0.90 [0.70, 1.16]
0.81 [0.54, 1.22]
1.15 [1.00, 1.32]*
1.02 [0.84, 1.23]
0.73 [0.57, 0.93]*
1.09 [0.76, 1.56]
0.95 [0.85, 1.06)
0.89 [0.75, 1.04]
Sensory seeking
Sensory processing variables
Sensory avoidant
1.57 [1.16, 2.13]*
1.30 [1.15, 1.48]*
1.19 [1.02, 1.39]*
Prosocial behaviors
b
1.14 [1.00, 1.29]*
1.57 [1.17, 2.12]*
1.30 [0.93, 1.81]
1.04 [0.92, 1.17]
0.92 [0.66, 1.27]
ASD + ID
Leisure Activities
ASD
ASD + ID
Classroom Learning
ASD
ASD + ID
Friendships
ASD
ASD + ID
Home Life
ASD
Predictors
Adjusted Odds Ratioa [95% CI]
Social Variables
We hypothesized that social problems and lower prosocial behaviors would predict participation difficulties
in home life, classroom learning, friendships, and leisure activities for both groups. This hypothesis was
supported for all outcomes except classroom learning.
Lower prosocial behaviors was a significant predictor of participation problems in home life and leisure
activities for both groups and of friendships for the
ASD-only group. Our finding regarding friendships
supports the results of previous studies showing that
successful peer relationships require social skills such
as person perception, empathy, and prosocial behavior
(Cillessen & Bellmore, 2011; Vetter et al., 2013). In
other studies, social competence was shown to be
important for obtaining and maintaining friendships
(Flannery & Smith, 2017), and social impairment was
found to be a mediator in the relationship between
executive function and friendship quality (Lieb &
Bohnert, 2017). Social problems, such as preferring to
play alone, not being liked by other children, and
being picked on or bullied, predicted friendship
difficulties for both groups in our study. Salters et al.
(2022) found that instructors reported social challenges
as the largest barrier to participation, although they
identified cognitive and fine motor skills as well. It is
possible that parents in the Pathways study were not
aware of the impact of social skills on difficulties in
classroom learning or interpreted that outcome narrowly to focus only on school attendance and not on
interactions with others.
Contextual Variables
We hypothesized that parent education level, household income, and health insurance coverage would
predict participation difficulties in classroom learning,
friendships, and leisure activities for autistic children
with and without ID, but we did not expect race or
ethnicity to be a predictor. This hypothesis was generally not supported.
Surprisingly, higher household income was a strong
predictor of greater participation difficulties in home
life and classroom learning for autistic children with
and without ID. Poverty has been shown to slow cognitive and language development and is associated
with lower academic achievement (Hernandez, 2011),
and household income has been associated with autistic children’s physical and play activities (Memari
et al., 2015). It is possible that, in our analysis, parents
and caregivers with higher household income were
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Table 2. Multivariate Regression Models With Multiple Imputations for Predictors of Participation Difficulties in Home Life, Friendships, Classroom Learning, and Leisure Activities (Cont.)
correlated with atypical sensory processing in autistic
children, and Choi and Jung (2021) found sensory
processing abilities to be predictive of participation in
leisure activities for autistic adolescents. In a scoping
review, Askari et al. (2015) identified a pattern of sensory, cognitive, and behavioral factors as a barrier to
participation for autistic children.
Activities of Daily Living and Functional and
Adaptive Skills
We hypothesized that the group of skills consisting of
poorer ADLs and functional and adaptive skills would
predict participation difficulties in home life and
classroom learning. This hypothesis was supported, although only fine motor skills were a protective factor
for leisure participation among autistic children with
ID and learning difficulties were a predictor for classroom learning.
Difficulty learning, understanding, and paying attention predicted classroom learning in children with
ASD only. Fine motor skills, including handwriting,
manipulation of materials, and scissor skills, are required in the classroom environment, and difficulty
with fine motor skills curtails a child’s ability to perform these classroom tasks and thus their classroom
learning.
We expected the ability to complete ADLs such as
dressing and toileting to be a predictor of home life
and classroom learning participation. Holloway et al.
(2021) found that better gross motor skills were associated with greater participation in self-care, leisure, and
social interactions. In a study of adolescents, physical
demands were among the factors that parents identified as barriers to participation in home and school
life (Lamash et al., 2020). Surprisingly, we found that
fine motor skills and ADLs were not predictive of participation in home life or classroom learning. Fine
motor difficulties were actually protective for leisure
activity difficulties for the autistic children with ID. It
is possible that, because parents completed the survey
and did not often observe their children in the classroom, they may not have been aware of the impact of
these skills; in addition, they may have been accustomed to managing ADLs at home and did not think
of fine motor problems as a limiter in home life. In
addition, the question asked whether using their hands
interfered with the child’s classroom participation. Parents may have interpreted the question as referring to
school attendance rather than measures of success
such as good writing skills.
Many leisure activities do not require fine motor
skills; research findings indicate that many autistic
children and adolescents play computer and video
games and watch TV, videos, and DVDs (Lamash
et al., 2020; Must et al., 2014; Stiller & M€
oßle, 2018).
The fine motor skills required for watching TV and
videos or playing computer and video games may be
less demanding than the fine motor skills required for
ADLs and other home life activities.
Limitations and Directions for
Future Research
The 2011 Survey of Pathways to Diagnosis and Services provides data from a large number of participants,
but the nature of secondary data sources limits the
precision of some of the data. As with all secondary
analyses of survey data that were collected for another
purpose, inherent bias is a potential limitation of our
analysis. It is also possible that critical sensory items
were not included in the 15 sensory items found in
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more able to focus on their children’s needs and to
identify areas of participation difficulty than those
with income at or below the poverty level, who likely
experienced more money-related distractions.
Having no health insurance coverage predicted
greater participation problems for children with ASD
only in classroom learning and for autistic children
with ID in friendships. It is possible that the families
with no health insurance felt they had inadequate access to services for their children and therefore more
frequently identified participation problems. Only 2%
of families in this data set did not have health insurance, so the data may not adequately represent the
opinions of that group.
Contrary to our hypothesis, identification with a
race or ethnicity other than non-Hispanic White was a
significant predictor for classroom learning participation problems for autistic children with and without
ID but was a protective factor for leisure activities. It is
possible that non-Hispanic White parents have higher
expectations for their children in the classroom and
consider a wider range of activities as leisure than
other parents. A previous study found that some Black
parents were concerned about institutional racism in
schools (Fields-Smith & Williams, 2009), which may
have increased the observations of problems with
classroom learning in this portion of our sample.
Parent education level was not a significant predictor
of any of the participation areas for either group. This
was surprising because higher income and highly educated parents are more likely to be involved in their
children’s education, which is a key factor in educational success (Cabrera et al., 2018). It is possible that
the parent perspective on their child’s difficulty in the
data we used is not the same as actual school performance measured in other studies. It is also possible that
the high representation of educated and higher income
parents in this sample meant that too few lower educated (less than high school) and poverty-level income
parents were surveyed to adequately represent the differences in lower education and lower income conditions.
We also hypothesized that sensory processing and
emotional regulation variables would be stronger predictors of participation difficulties than contextual
factors. This hypothesis was supported; sensory processing and emotional regulation factors were strong
predictors in all participation areas for autistic children
both with and without ID. Previous studies have identified emotional regulation (Pugliese et al., 2016) and
sensory responsiveness (Hilton et al., 2010) as potential
contributors to participation. Memari et al. (2015)
identified financial burden and lack of opportunities as
stronger barriers than specific autistic characteristics,
but they examined only physical activity participation.
Implications for Occupational
Therapy Practice
The findings from this study suggest the following
implications for occupational therapy practice with
autistic children and adolescents:
Incorporating strategies that help them manage
their emotional and behavioral responses, including environmental and occupational adjustments, can support their increased participation
in home life, classroom learning, friendships,
and leisure activities.
Addressing sensory differences, particularly low
registration, disorganized, overresponsive, and
sensory seeking, can help this client group increase their participation in home life and
friendships.
Supporting the development of social skills can
help these clients increase participation in home
life, friendships, and leisure activities.
As a cornerstone of occupational therapy practice,
client-centered approaches are important aspects of assessment and intervention for autistic children and
adolescents. Practitioners need to include collaboration
with clients and their families and care providers in
evaluation and intervention (AOTA, 2020).
䊏
䊏
䊏
Conclusion
Participation in a wide range of activities is important
for development and for quality of life. Autistic children and adolescents participate in fewer activities
than their peers without this diagnosis. Occupational
therapy practitioners work to enhance and promote
client participation, and information about the predictors of limited participation can support them in
designing the most effective intervention plans. Large
data research provides opportunities to examine
patterns of responses among large numbers of participants, and the patterns that emerged from the 2011
Survey of Pathways to Diagnosis and Services data set
through its design and sampling provide generalizable
ideas of factors that influence participation for autistic
children and adolescents.
Most of the predictors of participation difficulties
we found are within the scope of occupational therapy
intervention. The strongest predictors were sensory
processing, emotional regulation, behavioral, and social variables, supporting previous smaller studies and
indicating the importance of addressing these areas in
intervention. The connections between these areas and
their relationship to participation also suggest the
importance of focusing occupational therapy interventions on emotional regulation, sensory processing, and
social skills to address the underlying neurological
processing in autistic children for the purpose of supporting increased participation in occupations.
Acknowledgments
This research was supported in part by the American Occupational Therapy Foundation (AOTF)
Health Services Research program (Grant No.
AOTFHSR2019HILTON) and the Ministry of
Education of the Republic of Korea and the National Research Foundation of Korea (Grant No.
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this data set. However, the sensory scale was validated
for this population survey (Lee et al., 2019). Age and
gender differences across diagnostic groups potentially
bias the findings from this study. Use of parentreported survey data is another potential limitation of
this study and did not allow for examination of interrater reliability. In addition, the survey did not include
questions for the autistic children and adolescents, so
the survey data may not reflect their perspectives on
the activities that were difficult for them. Diagnoses
were based on parent report; confirmatory diagnostic
assessments and behavioral observations might have
improved interrater reliability, but it was not possible
to conduct these types of assessments with secondary
data. Participation variables were based on single questions that were broad in scope, so parent responses
may have been biased because of misinterpretation of
the questions and lack of specific categories for each
(e.g., physically active vs. sedentary leisure activities).
Finally, our findings were limited by the dichotomous
nature of the ID status and the absence of actual IQ
data, which would have allowed for more robust statistical analyses.
Nevertheless, our use of a large cross-sectional data
set was a strength of this study, allowing us to complete the first examination of a range of predictors of
participation difficulties in autistic children. Our results provide professionals with potential intervention
targets to address participation difficulties in this
population.
Future studies that include quantitative IQ data,
diagnostic measures, and sensory assessments would
extend the understanding of the relationships identified in this study. The results of more extensive
participation assessments could better explain the
participation limitations identified in this analysis.
Examining longitudinal data will shed light on the
relationships between the predictor and outcome variables and the impact of interventions. It is possible
that sensory differences are mediators between other
predictors and participation outcomes, and the relationships among emotional regulation, behavioral, and
cognitive strategy use require further exploration.
Other child factors that might affect participation,
such as use of medication to manage ASD symptoms,
presence of siblings, executive function factors, and interoception abilities, are potential topics for future
studies to help practitioners better understand and
support activity participation among autistic children.
Finally, data from surveys of autistic children and adolescents would add a valuable perspective on their
participation difficulties.
NRF-2021S1A3A2A02096338). Its contents are
solely the responsibility of the authors and do not
necessarily represent the official views of AOTF.
This study used a publicly available, deidentified
data set that was supported by the U.S. Data Resource Center for Child and Adolescent Health and
the U.S. National Center for Health Statistics. The
data set and survey questionnaire are available at
www.childhealthdata.org.
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Karen Ratcliff, PhD, OTR, is Associate Professor, Department of
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Ickpyo Hong, PhD, OTR, is Associate Professor, Department of
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ihong@yonsei.ac.kr
Appendix
Figure A.1. Flow diagram of participants through the study.
N = 4,032: Phone interviews with the
Parents/caregivers in the 2011
Survey of Pathways to Diagnosis and
Services
n = 1,112 (27.6%)
Incomplete data for the four
outcome measures
n = 2,920: Analytic cohort
n = 834: ASD alone
n = 227: ASD and ID
n = 508: ID alone
n = 995:
Developmental
disorder alone
n = 356: No current
diagnosis
Note. ASD 5 autism spectrum disorder; ID 5 intellectual disabilities.
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Table A.1. Social–Behavioral Items
Social–Behavioral Area
Emotional regulation problems
Items
▪ Often complains of headaches, stomachaches, sickness
▪ Has many worries or often seems worried
▪ Is often unhappy, depressed, or tearful
▪ Is nervous or clingy in new situations
▪ Has many fears, is easily scared
Conduct problems
▪ Often loses temper
▪ Is generally well-behaved
▪ Often lies or cheats
▪ Steals from home, school, or elsewhere
Hyperactivity/inattention
▪ Is restless, overactive, cannot stay still for long
▪ Is constantly fidgeting or squirming
▪ Is easily distracted
▪ Thinks things out before acting
▪ Has good attention span, finishes chores or homework
Social problems
▪ Prefers to play alone, would rather be alone
▪ Has at least one good friend
▪ Is generally liked by other children/youth
▪ Is picked on or bullied by other children/youth
▪ Gets along better with adults than with other children/youth
Prosocial behavior
▪ Is considerate of other people’s feelings
▪ Shares readily with other children/youth
▪ Is helpful if someone is hurt, upset or feeling ill
▪ Is kind to younger children/youth
▪ Often offers to help others (parents, teachers, other children/youth)
Note. Respondents use a 3-point scale: 1 5 not true, 2 5 somewhat true, 3 5 certainly true. Positive items are reverse scored. Higher
scores indicate more difficulties.
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▪ Often fights with other children or bullies them