Sensory Patterns, Obesity, and Physical Activity Participation of Children With Autism Spectrum Disorder

Lisa Mische Lawson; Lauren Foster

doi:10.5014/ajot.2016.021535

Abstract

Obesity is a public health concern for the population in general and for children with autism spectrum disorder (ASD) specifically. The purpose of this study was to understand relationships between sensory patterns, obesity, and physical activity engagement of children with ASD (N = 77) sampled from a specialized community-based swimming program. This retrospective correlational study analyzed program data. Results show that almost half (42.2%) of the children were overweight or obese, and sensory avoiding behaviors were related to higher body mass index (BMI). Children participated in few formal and informal physically active recreation activities. Sensory seeking behaviors were associated with increased participation in informal activities, and higher BMI was associated with less participation in both formal and informal activities. Practitioners should consider sensory processing patterns and BMI when developing community-based programs to promote physical activity of children with ASD.

More than one-third of children in the United States are overweight or obese, and rates have increased in recent years, making obesity a public health concern (Ogden, Carroll, Kit, & Flegal, 2014). Childhood obesity is associated with increased risk of physical problems (Daniels et al., 2005) and psychological problems (Pizzi & Vroman, 2013; Strauss, 2000). Aside from negatively affecting children’s health, obesity has social impacts, such as high medical costs associated with the treatment of obesity and related conditions (Finkelstein, Trogdon, Cohen, & Dietz, 2009). Physical activity is an integral component in treatment to promote weight loss and prevent weight regain (Dugan, 2008; Hills, King, & Armstrong, 2007). Increasing the amount of daily physical activity can help prevent weight gain while providing other positive health outcomes, such as increased physical and mental health (Centers for Disease Control and Prevention [CDC], 2015c). Only about one-third of children get the recommended 60 min of daily moderate to vigorous physical activity (Fakhouri et al., 2014).

Children with disabilities participate in physical activity at even lower rates than their peers (Law et al., 2006), as much as 4.5 times less active than children without disabilities (Rimmer & Rowland, 2008). Children with autism spectrum disorder (ASD) are at higher risk of inactivity than their peers with and without disabilities (Bandini et al., 2013); risk of obesity is as much as 40% greater in children with ASD (Curtin, Anderson, Must, & Bandini, 2010).

ASD is characterized by deficits in social communication and restricted or repetitive behaviors, interests, or activities (American Psychiatric Association, 2013), making participation in physical activities challenging (Pan & Frey, 2006; Mische Lawson, Foster, Harrington, & Oxley, 2014). Children with ASD also have unique sensory processing patterns that affect participation in everyday life activities (Ismael, Mische Lawson, & Cox, 2015). Researchers have found relationships between sensory processing patterns and play and recreation choices for toddlers, children, and teens (Ismael et al., 2015; Ismael & Mische Lawson, 2012; Mische-Lawson & Dunn, 2008). The literature suggests that children’s participation increases when their unique sensory processing patterns are supported within activities (Dunn, Cox, Foster, Mische-Lawson, & Tanquary, 2012).

Occupational therapy practitioners promote participation in meaningful activities, including leisure, by matching individual strengths to activities and by modifying and adapting activities to match personal factors (Pizzi & Vroman, 2013). Participation in healthy, meaningful leisure activities can increase quality of life, physical wellness, mood, and well-being (Law, 2002; Penedo & Dahn, 2005). Moreover, participation in healthy, physically active leisure activities (PALs) can increase health and decrease the risk of obesity (Dugan, 2008). For children with ASD, finding meaningful PALs may not only decrease risk of obesity and related conditions but also provide a sense of meaning and purpose (King et al., 2006). Scholars suggest that making the distinction between formal and informal leisure activities is important because each promotes development in different ways (King et al., 2006). Formal leisure activities are activities that are organized or structured, have rules, and are often performed in groups (King et al., 2006; King, Petrenchik, Law, & Hurley, 2009), whereas informal leisure activities are more spontaneous and can be performed alone or with others.

Understanding the interaction between a child’s personal factors and the physical activity can help occupational therapy practitioners develop programming that meets children’s needs. When practitioners use sensory processing knowledge to support children’s participation in activities, children have better outcomes (Dunn et al., 2012). To our knowledge, no evidence exists regarding whether and how sensory processing patterns affect participation in formal and informal PALs and subsequent health outcomes. Therefore, the aims of this study were to find out whether relationships could be identified between sensory processing patterns and obesity, sensory processing patterns and parent-reported engagement in PALs, and body mass index (BMI) and parent-reported engagement in PALs in children with ASD.

Method

This retrospective correlational study analyzed data from children’s Sensory Profile Caregiver Questionnaire, a measure from the Sensory Profile (Dunn, 1999) series, and parent-reported child measurements (height and weight) and leisure participation. The study included parent-reported height and weight rather than direct measure because the research was conducted in a community setting where direct measure was impractical.

Participants

Participants were recruited from KU Sensory Enhanced Aquatics (KUSEA), a community-based swim program for children with ASD run by the University of Kansas Medical Center, to provide a sample of children with ASD engaging in at least some physical activity. Participants of KUSEA participated in 30 min of swimming instruction once per week. Recruitment was based on ASD diagnosis (per parent report), age (4–17 yr), and order of response in which participants signed up for swimming lessons. Children without an ASD diagnosis were referred to other aquatic programs. Children younger than age 4 were excluded from the study because they generally are not developmentally ready to learn independent swimming skills (Parker & Blanksby, 1997) and were not enrolled in KUSEA lessons. Children with multiple diagnoses whose primary diagnosis was not ASD and children with sensory impairments (e.g., blindness) were also excluded from the study.

Materials and Instrumentation

Demographic Form.

Parents reported information about their child’s age, gender, ethnicity, diagnosis, medications, services, health (i.e., height and weight), and leisure participation. This information was collected to inform KUSEA lessons. No additional demographic information was collected.

Recreation Participation Log.

Parents reported information about their child’s current participation in physically active informal and formal recreation activities other than KUSEA. The log was disseminated with the demographic form and included space to write in recreation information.

Sensory Profile Caregiver Questionnaire.

The Sensory Profile is a pediatric assessment tool consisting of a caregiver questionnaire that helps professionals measure the possible contributions of sensory processing to children’s daily performance patterns (Ohl et al., 2012) and is based on the model proposed by Dunn. The Sensory Profile comprises 125 items in the categories of Sensory Processing, Modulation, and Behavioral and Emotional Responses. Psychometric studies (Dunn & Westman, 1997; Ermer & Dunn, 1998; Ohl et al., 2012) have confirmed the Sensory Profile’s validity and internal consistency and test–retest reliability.

Body Mass Index and Percentile.

BMI is a person’s weight in kilograms divided by the square of height in meters. For children and teens, BMI is age and sex specific and is often referred to as BMI-for-age. For this study, we calculated BMI using the BMI percentile calculator on the CDC (2016) website using parent-reported height and weight in inches and pounds.

Weight-for-Age Percentile.

The CDC (2015a) provides clinical growth charts for children and adolescents ages birth to 20. We used a children’s growth chart percentiles calculator (About Health, 2015) to compute weight percentiles and confirmed results with CDC weight-for-age clinical growth charts for boys and girls ages 2–20.

Analysis

Because little is known about the relationships among children’s sensory processing patterns, BMI, and participation in PALs, this study was exploratory in nature. According to Portney and Watkins (2000), when the purpose of research is to evaluate the relationship between two measured variables, researchers should use procedures for exploratory analysis, such as measures of correlation. Pearson’s product–moment correlations were used to analyze parametric data, and Spearman’s rank correlations were used for ordinal data with an α level of .05 for all statistical tests. Variables for analysis included total scores on all four Sensory Profile quadrants, BMI totals and percentages, and parent-reported frequency of participation in formal and informal PALs.

Results

Seventy-seven children (72 male, 5 female) ages 4–13 yr were included in this study. Children were diagnosed before the Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association, 2000) was revised to remove subcategories; thus, all children had a primary diagnosis of autism, with the majority of participants (87%) diagnosed with ASD and others indicating a diagnostic subcategory (e.g., Asperger’s; Table 1). Our sample was primarily White (N = 54, 71%), and 20 (26%) were from underserved populations.

Table 1.

Participant Demographics (N = 77)

Characteristic	n	%
Diagnosis
Autism	67	87.0
Asperger’s	4	5.2
Other subcategory	6	7.8
Age, yr
≤5	29	37.7
6–10	44	57.1
≥11	4	5.2
Weight, lb
≤44	17	22.1
45–55	17	22.1
56–67	17	22.1
≥68	16	20.7
Missing	10	13.0

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Table 1.

Participant Demographics (N = 77)

Characteristic	n	%
Diagnosis
Autism	67	87.0
Asperger’s	4	5.2
Other subcategory	6	7.8
Age, yr
≤5	29	37.7
6–10	44	57.1
≥11	4	5.2
Weight, lb
≤44	17	22.1
45–55	17	22.1
56–67	17	22.1
≥68	16	20.7
Missing	10	13.0

The CDC (2015b) identifies any child with a BMI from the 85th to the 94th percentile as overweight and any child with a BMI of the 95th percentile and above as obese. According to CDC BMI guidelines, 6.7% of our sample was underweight, 51.1% a normal or healthy weight, and 42.2% overweight (17.8%) or obese (24.4%). Some participants did not report height, so we also calculated CDC weight-for-age percentiles; 3.0% of our sample was <5th percentile, 74.6% between the 5th and 85th percentile, and 22.4% >85th percentile. Analysis revealed a significant but weak relationship between BMI and children’s sensory avoiding scores (r_s = −.255, p = .049, 1−β = .52; Table 2). There were no other significant correlations between Sensory Profile scores and weight.

Table 2.

Spearman’s Rank Correlations for Sensory Processing Patterns With Weight and Physical Activity

Factor	Sensory Quadrant
Factor	Low Registration	Seeking	Sensitivity	Avoiding
Weight
BMI	.033	−.094	−.065	−.255* (.52)^a
BMI percentile	.069	−.170	.033	−.176
Weight percentile	−.082	−.097	−.067	−.132
Physically active recreation
Formal	−.039	−.231* (.63)^a **	−.009	−.153
Informal	−.081	−.207* (.55)^a	−.018	−.168

Note. BMI = body mass index.

^aPower (1−β).

*p ≤ .05.

**p ≤ .01.

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Table 2.

Spearman’s Rank Correlations for Sensory Processing Patterns With Weight and Physical Activity

Factor	Sensory Quadrant
Factor	Low Registration	Seeking	Sensitivity	Avoiding
Weight
BMI	.033	−.094	−.065	−.255* (.52)^a
BMI percentile	.069	−.170	.033	−.176
Weight percentile	−.082	−.097	−.067	−.132
Physically active recreation
Formal	−.039	−.231* (.63)^a **	−.009	−.153
Informal	−.081	−.207* (.55)^a	−.018	−.168

Note. BMI = body mass index.

^aPower (1−β).

*p ≤ .05.

**p ≤ .01.

Parents reported their child’s participation in 16 different formal PALs, with 9 specified as “adapted” or “special” group recreation activities. Parents also reported child participation in 26 different informal physically active recreation activities and 8 school- or therapy-related physical activities (Table 3). Children participated in a greater number of informal (mean [M] = 2.13, standard deviation [SD] = 1.6) than formal (M = 0.64, SD = 0.93) PALs; 56.6% of children participated in no formal PALs, and 19.7% participated in no informal PALs.

Table 3.

Parent-Reported Child Participation in Formal and Informal Physically Active Leisure Activities (N = 77)

Activity	n
Formal Activities
General recreation^a
Gymnastics	7
Swim	5
Soccer	5
Horseback riding	4
Tennis	2
Track	1
Karate	1
Special recreation^b
Adapted dance	4
Special Olympics
Soccer	3
Cheerleading	1
Challengers
Baseball	5
Basketball	4
General or unspecified	3
Flag football	2
Bowling	2
Swimming	1
Informal Activities
Swimming or water play	28
Riding a bicycle or tricycle	16
Trampoline or jumping	16
Indoor or outdoor play	12
Running	11
Playing with siblings, friends, or neighbors	11
Playing ball	9
Swinging	9
Playing at the playground	8
Playing at the park	7
Walking	6
Riding a scooter	4
Going to open gym	3
Climbing	4
Wii Dance or Sports	2
Dancing	2
Tumbling or gymnastics	2
Wrestling or roughhousing	2
Skating	1
Fishing	1
Golf	1
Geocaching	1
Tubing or boating	1
Camping or hiking	1
Playing Frisbee	1
Playing with pets	1

^aActivities not specifically identified as adapted or special recreation.

^bActivities specified as adapted or special recreation.

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Table 3.

Parent-Reported Child Participation in Formal and Informal Physically Active Leisure Activities (N = 77)

Activity	n
Formal Activities
General recreation^a
Gymnastics	7
Swim	5
Soccer	5
Horseback riding	4
Tennis	2
Track	1
Karate	1
Special recreation^b
Adapted dance	4
Special Olympics
Soccer	3
Cheerleading	1
Challengers
Baseball	5
Basketball	4
General or unspecified	3
Flag football	2
Bowling	2
Swimming	1
Informal Activities
Swimming or water play	28
Riding a bicycle or tricycle	16
Trampoline or jumping	16
Indoor or outdoor play	12
Running	11
Playing with siblings, friends, or neighbors	11
Playing ball	9
Swinging	9
Playing at the playground	8
Playing at the park	7
Walking	6
Riding a scooter	4
Going to open gym	3
Climbing	4
Wii Dance or Sports	2
Dancing	2
Tumbling or gymnastics	2
Wrestling or roughhousing	2
Skating	1
Fishing	1
Golf	1
Geocaching	1
Tubing or boating	1
Camping or hiking	1
Playing Frisbee	1
Playing with pets	1

^aActivities not specifically identified as adapted or special recreation.

^bActivities specified as adapted or special recreation.

Significant weak correlations between children’s patterns of sensory seeking and participation in formal (r_s = −.231, p = .026, 1−β = .63) and informal (r_s = −.207, p = .041, 1−β = .55) PALs were found. Lower Sensory Profile scores indicate greater preferences for that quadrant; thus, results show that children with greater sensory seeking behaviors participated in more formal and informal PALs. There were also significant relationships between children’s BMI, BMI percentages, and weight percentages and participation in formal PALs. Higher BMI and weight were associated with less participation in informal PALs (BMI, p = .007, 1−β = .80; BMI percentile, p = .022, 1−β = .66; weight percentile, p = .012, 1−β = .74; Table 4).

Table 4.

Pearson’s Product–Moment Correlations for Weight With Physical Activity

Physical Activity	Weight
Physical Activity	BMI	BMI Percentile	Weight Percentile
Physically active recreation
Formal	−.021	−.003	−.061
Informal	−.364* (.80)^a	−.303* (.66)^a	−.275* (.74)^a
Therapy or school	.106	.145	.115

Note. BMI = body mass index.

^aPower (1−β).

*p ≤ .05.

View Large

Table 4.

Pearson’s Product–Moment Correlations for Weight With Physical Activity

Physical Activity	Weight
Physical Activity	BMI	BMI Percentile	Weight Percentile
Physically active recreation
Formal	−.021	−.003	−.061
Informal	−.364* (.80)^a	−.303* (.66)^a	−.275* (.74)^a
Therapy or school	.106	.145	.115

Note. BMI = body mass index.

^aPower (1−β).

*p ≤ .05.

Discussion

The prevalence of childhood obesity has increased dramatically in the past two decades, prompting numerous studies to understand and remedy this significant health threat. Obesity is particularly concerning for children with ASD because they are 40% more likely to be obese than children without disabilities (Curtin et al., 2010). Nearly half of our sample of children with ASD were overweight or obese, which is similar to nationally representative samples of children with ASD (Curtin et al., 2010) and more obese than the general population of children (CDC, 2014). However, in our sample, a greater number of children were obese (24.4%) than overweight (17.8%), whereas previous studies indicated that children with ASD were more likely to be overweight than obese (Curtin, Bandini, Perrin, Tybor, & Must, 2005). The increased prevalence of obesity in our study may indicate that efforts to prevent obesity are inadequate for children with ASD, and research is needed to examine the factors associated with obesity in this population.

Results of our study show that children with avoiding sensory patterns had higher BMI, suggesting that sensory patterns may be related to obesity. Although these correlations were weak, they may have important implications for children. Scholars have found that sensory avoiding negatively affects daily living skills (Jasmin et al., 2009) and activity participation of people with ASD (Koenig & Rudney, 2010). Dunn and Tomchek (2007) postulated that children who display avoiding patterns notice more sensory stimuli and therefore may withdraw from their environments. Thus, children who are sensitive and reactive to environmental input may be inclined to withdraw in everyday life activities, including physical activity.

Other factors might influence why children with avoiding patterns have higher BMI despite the lack of correlation with physical activity participation. For example, children with more severe avoiding patterns are often sensitive to food tastes and textures and therefore eat a less varied and potentially less healthful diet than their peers (e.g., Cermak, Curtin, & Bandini, 2010). Research is needed to fully understand sensory processing preferences as a personal factor contributing to participation in physically active recreation activities.

Our study showed weak correlations between children’s sensory seeking behaviors and their participation in PALs but no correlations with other sensory processing patterns. Children with sensory seeking behaviors (e.g., jumping, swinging, messy eating) participated in a greater number of formal and informal PALs than other children. People who display sensory seeking patterns actively create opportunities to experience more intense and various sensations (Dunn, 2001); thus, they may engage in both formal and informal PALs to meet their sensory seeking needs. Moreover, parents and practitioners may be more likely to recommend activities that provide increased sensory stimulus for children with sensory seeking behaviors.

Parent-reported child participation in PALs was low compared with previous reports for both typically developing children (Bandini et al., 2013; Ismael et al., 2015; Law et al., 2006) and children with ASD (Bandini et al., 2013). In the Bandini et al. (2013) study, parents reported child activity with an 18-item checklist and space to list additional activities, whereas our study prompted parents to list all formal and informal recreation activities. It is possible that the checklist prompted parents to report items they might not have reported on their own; however, our study yielded a greater variety of parent-reported activities, so this is unlikely.

Furthermore, findings of our study support previous research reporting that access and availability of structured activities are a major barrier to physical activity for children with disabilities (Shields, Synnot, & Barr, 2012). Research has indicated that fewer than half of children with disabilities play with neighborhood peers, ultimately leading to fewer friends with whom to participate in structured activities, including physical activities (Solish, Perry, & Minnes, 2010). Although many of the informal physical activities reported in our study could include peers, parents reported only 11 (14.3%) of the children in our study as specifically playing with siblings, friends, or neighbors. Participation of children with disabilities in activities alone, whether through choice or lack of opportunity, may contribute to less participation in sports teams than typically developing peers when children reach adolescence (Rimmer & Rowland, 2008). The low rates of participation in both formal and informal PALs are concerning and indicate that attention to this issue is needed.

Finally, results also show that children with higher BMI and weight participated in fewer PALs compared with children with lower BMI, suggesting a weak but possibly important link between BMI and physical activity. The relationships cannot link cause and effect; thus, children may participate in fewer activities because they are overweight, or they may be overweight because they participate in fewer activities. Our results are consistent with literature suggesting that physical activity prevents weight gain and other related health conditions for children and adolescents with ASD and that low rates of physical activity contribute to obesity (Srinivasan, Pescatello, & Bhat, 2014).

Limitations and Future Research

This study had a large sample representing diverse ethnicities. Participants were purposely recruited from a community-based swimming program, so it is possible that our population was more motivated to participate in physical activity than the general population of children with ASD. We relied on parent-reported height and weight to determine BMI, which is less accurate than direct measures (Akinbami & Ogden, 2009); however, this method allowed us to make comparisons with nationally representative samples of BMI based on parent report (Curtin et al., 2010). Additionally, parent report may be influenced by parent education level, and we did not collect information about parent education. We collected information about medication and therapy in an effort to control these confounding variables, but inconsistent reporting prevented analysis of this information.

This was an exploratory study, and future studies should build on this work. A large-scale survey of physical activity participation among children with ASDs, measurement of physical activity levels during PALs, and research investigating interventions promoting physical activity are necessary to address inactivity and obesity. Additionally, future studies should examine contextual factors related to physical activity because sensory processing is just one of many personal factors that may contribute to performance. To take a truly ecological perspective, one must measure how the context influences physical activity while looking at personal factors and task performance.

Implications for Occupational Therapy Practice

The results of this study have the following implications for occupational therapy practice:

Occupational therapy practitioners are experts on how sensory processing affects life activities. Understanding how a person’s sensory processing patterns influence participation in physical activity allows practitioners to better design interventions for individual clients, groups, and populations.
Using sensory processing knowledge, occupational therapy practitioners can partner with recreation and exercise professionals to increase physical activity among children with ASD by developing programming that meets the need of the clients, matching clients with existing programs, and teaching clients underlying skills to succeed in preferred activities.
Structuring environments with low levels of sensory stimuli can provide a better fit for children who display avoiding patterns. Examples of physical activities with lower levels of sensory input include quiet walking or running, stationary biking, tae kwon do, swimming, and yoga.

Conclusion

Obesity is a public health concern for the population in general and for children with ASD specifically. The findings of this study are consistent with those of other research showing that children with ASD have low rates of participation in formal and informal PALs. Results of this study indicate that sensory processing patterns may influence engagement in physical activity; children with higher BMI and sensory avoiding behaviors may be at greater risk for inactivity than children with other sensory preferences. This exploratory study had several limitations; thus, research is needed to understand the relationship between a child’s personal factors and physical activity. Future research should also investigate the relationship between sensory avoiding preferences and weight because engagement in physical activity did not relate to weight in this study.

Acknowledgments

This study was partially funded by the University of Kansas Medical Center Auxiliary and an Autism Speaks Family Community Grant. Several participants in the study were supported by Autism Speaks through an Autism Speaks Swimming and Water Safety Award. The views expressed in this article do not necessarily express or reflect the views of Autism Speaks or any other funding agency. This study is registered as NCT02747459 at ClinicalTrials.gov.

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Table 1.

Participant Demographics (N = 77)

Characteristic	n	%
Diagnosis
Autism	67	87.0
Asperger’s	4	5.2
Other subcategory	6	7.8
Age, yr
≤5	29	37.7
6–10	44	57.1
≥11	4	5.2
Weight, lb
≤44	17	22.1
45–55	17	22.1
56–67	17	22.1
≥68	16	20.7
Missing	10	13.0

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Table 1.

Participant Demographics (N = 77)

Characteristic	n	%
Diagnosis
Autism	67	87.0
Asperger’s	4	5.2
Other subcategory	6	7.8
Age, yr
≤5	29	37.7
6–10	44	57.1
≥11	4	5.2
Weight, lb
≤44	17	22.1
45–55	17	22.1
56–67	17	22.1
≥68	16	20.7
Missing	10	13.0

Table 2.

Spearman’s Rank Correlations for Sensory Processing Patterns With Weight and Physical Activity

Factor	Sensory Quadrant
Factor	Low Registration	Seeking	Sensitivity	Avoiding
Weight
BMI	.033	−.094	−.065	−.255* (.52)^a
BMI percentile	.069	−.170	.033	−.176
Weight percentile	−.082	−.097	−.067	−.132
Physically active recreation
Formal	−.039	−.231* (.63)^a **	−.009	−.153
Informal	−.081	−.207* (.55)^a	−.018	−.168

Note. BMI = body mass index.

^aPower (1−β).

*p ≤ .05.

**p ≤ .01.

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Table 2.

Spearman’s Rank Correlations for Sensory Processing Patterns With Weight and Physical Activity

Factor	Sensory Quadrant
Factor	Low Registration	Seeking	Sensitivity	Avoiding
Weight
BMI	.033	−.094	−.065	−.255* (.52)^a
BMI percentile	.069	−.170	.033	−.176
Weight percentile	−.082	−.097	−.067	−.132
Physically active recreation
Formal	−.039	−.231* (.63)^a **	−.009	−.153
Informal	−.081	−.207* (.55)^a	−.018	−.168

Note. BMI = body mass index.

^aPower (1−β).

*p ≤ .05.

**p ≤ .01.

Table 3.

Parent-Reported Child Participation in Formal and Informal Physically Active Leisure Activities (N = 77)

Activity	n
Formal Activities
General recreation^a
Gymnastics	7
Swim	5
Soccer	5
Horseback riding	4
Tennis	2
Track	1
Karate	1
Special recreation^b
Adapted dance	4
Special Olympics
Soccer	3
Cheerleading	1
Challengers
Baseball	5
Basketball	4
General or unspecified	3
Flag football	2
Bowling	2
Swimming	1
Informal Activities
Swimming or water play	28
Riding a bicycle or tricycle	16
Trampoline or jumping	16
Indoor or outdoor play	12
Running	11
Playing with siblings, friends, or neighbors	11
Playing ball	9
Swinging	9
Playing at the playground	8
Playing at the park	7
Walking	6
Riding a scooter	4
Going to open gym	3
Climbing	4
Wii Dance or Sports	2
Dancing	2
Tumbling or gymnastics	2
Wrestling or roughhousing	2
Skating	1
Fishing	1
Golf	1
Geocaching	1
Tubing or boating	1
Camping or hiking	1
Playing Frisbee	1
Playing with pets	1

^aActivities not specifically identified as adapted or special recreation.

^bActivities specified as adapted or special recreation.

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Table 3.

Parent-Reported Child Participation in Formal and Informal Physically Active Leisure Activities (N = 77)

Activity	n
Formal Activities
General recreation^a
Gymnastics	7
Swim	5
Soccer	5
Horseback riding	4
Tennis	2
Track	1
Karate	1
Special recreation^b
Adapted dance	4
Special Olympics
Soccer	3
Cheerleading	1
Challengers
Baseball	5
Basketball	4
General or unspecified	3
Flag football	2
Bowling	2
Swimming	1
Informal Activities
Swimming or water play	28
Riding a bicycle or tricycle	16
Trampoline or jumping	16
Indoor or outdoor play	12
Running	11
Playing with siblings, friends, or neighbors	11
Playing ball	9
Swinging	9
Playing at the playground	8
Playing at the park	7
Walking	6
Riding a scooter	4
Going to open gym	3
Climbing	4
Wii Dance or Sports	2
Dancing	2
Tumbling or gymnastics	2
Wrestling or roughhousing	2
Skating	1
Fishing	1
Golf	1
Geocaching	1
Tubing or boating	1
Camping or hiking	1
Playing Frisbee	1
Playing with pets	1

^aActivities not specifically identified as adapted or special recreation.

^bActivities specified as adapted or special recreation.

Table 4.

Pearson’s Product–Moment Correlations for Weight With Physical Activity

Physical Activity	Weight
Physical Activity	BMI	BMI Percentile	Weight Percentile
Physically active recreation
Formal	−.021	−.003	−.061
Informal	−.364* (.80)^a	−.303* (.66)^a	−.275* (.74)^a
Therapy or school	.106	.145	.115

Note. BMI = body mass index.

^aPower (1−β).

*p ≤ .05.

View Large

Table 4.

Pearson’s Product–Moment Correlations for Weight With Physical Activity

Physical Activity	Weight
Physical Activity	BMI	BMI Percentile	Weight Percentile
Physically active recreation
Formal	−.021	−.003	−.061
Informal	−.364* (.80)^a	−.303* (.66)^a	−.275* (.74)^a
Therapy or school	.106	.145	.115

Note. BMI = body mass index.

^aPower (1−β).

*p ≤ .05.