Perceived Barriers and Facilitators of Participation in After-School ...

J Dev Phys Disabil (2011) 23:195–211 DOI 10.1007/s10882-010-9215-z O R I G I N A L A RT I C L E

Perceived Barriers and Facilitators of Participation in After-School Physical Activity by Children with Autism Spectrum Disorders Iva Obrusnikova & Albert R. Cavalier

Published online: 21 September 2010 # Springer Science+Business Media, LLC 2010

Abstract This study used Photovoice methodology to assess barriers to and facilitators of after-school participation in moderate to vigorous physical activity as perceived by children with ASD and determine if physical activity patterns exist in relation to these barriers. Participants were a convenience sample of 12 boys and two girls with autism spectrum disorders (ASD), ages 8–14 years. Participants wore an accelerometer and completed an activity log for 7 days. Data were analyzed using qualitative techniques and fitted in a socio-ecological model. Participants reported 143 (44%) barriers and 181 (56%) facilitators. The most frequently cited barriers were intrapersonal, followed by interpersonal, physical, community, and institutional. The most frequent facilitators were physical, followed by intrapersonal and interpersonal, community, and institutional. The study gives support to the use of a multipronged approach when designing physical activity interventions for children and adolescents with ASD. Keywords Autism spectrum disorders . Physical activity . Photovoice . Barriers . Ecological model The prevalence of obesity in children in the United States has tripled within the last 20 years (Hedley et al. 2004). The health consequences of obesity, including type two diabetes, cardiovascular disease, cancer, and arthritis, are serious and expected to worsen (Dietz 1998). Obesity also has become a health issue in individuals with autism spectrum disorders (ASD) (Curtin et al. 2010; Curtin et al. 2005; Pan and Frey 2006). For example, Curtin et al. (2005) found that 36% of participants in their study, ages 2–19 years, were at risk for overweight and 19% were overweight. These I. Obrusnikova (*) Behavioral Health & Nutrition, University of Delaware, 26 North College Avenue, Newark, DE 19716, USA e-mail: [email protected] A. R. Cavalier School of Education, University of Delaware, Newark, DE, USA

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percentages were substantially larger among older children. In addition, Curtin et al. (2010) reported that the unadjusted odds of obesity in children with ASD were 1.42 compared to children without ASD. The most commonly cited factors contributing to obesity in children with ASD are related to decreased opportunities for physical activity (Curtin et al. 2005; Pan and Frey 2005, 2006; Rosser-Sandt and Frey 2005). The Healthy People of 2010 initiative spearheaded by the U.S. Department of Health and Human Services recommends that children and adolescents aged 6–17 years accumulate 60 min or more of moderate to vigorous physical activity (MVPA) every day and 20-min bouts of continuous, vigorous activity on at least 3 days (USDHHS 2002). Research indicates, however, that children with ASD engage in very few sustained bouts of MVPA, particularly in adolescence (Pan and Frey 2005, 2006; Rosser-Sandt and Frey 2005), and therefore fail to meet this standard. Pan and Frey (2005) suggested that the decline of MVPA in older children with ASD is due to the loss of recess time, decreased physical education requirements, and limited engagement in MVPA after school. They also suggested that community barriers such as lack of physical activity programs might influence participation of children with ASD in MVPA. Limited attention span, poor coordination, difficulty coping with certain auditory, visual, and tactile stimuli in large, open spaces, deficits in interpersonal relationships, and narrow interests (Rosenthal-Malek and Mitchell 1997) may also contribute to avoidance of MVPA in this population. Collectively, these factors, if not controlled, have a high likelihood of leading to obesity in adolescents with ASD (Curtin et al. 2005; Pan and Frey 2005, 2006; Rosser-Sandt and Frey 2005). Taking into account the health benefits of MVPA (USDHHS 2002) and the limited amount of time children with ASD spend in MVPA, detailed examination of factors that facilitate or impede MVPA in children with ASD is needed. Although recent research has provided a starting point for examining levels of MVPA in children with ASD (Pan and Frey 2005, 2006; Rosser-Sandt and Frey 2005), no research, to date, has looked at the salient barriers and facilitators of MVPA from the point of view of children with ASD. Identifying factors that facilitate or impede MVPA will be important to improving the efficacy of interventions aimed at increasing levels of MVPA in children with ASD (Brawley et al. 1998). Various models have been used to conceptualize levels of behavioral influence in physical activity research, but the most applicable to this study is the socioecological model that was developed by McLeroy et al. (1988) and used in studies with children with disabilities by Gyurcsik et al. (2004, 2006). The socio-ecological model acknowledges the interplay between personal and environmental factors and specifically posits that behavior is shaped by factors that can be classified into five categories: (a) intrapersonal, (b) interpersonal, (c) institutional, (d) community, and (e) public policy. Intrapersonal or individual factors include characteristics of an individual that are modifiable, such as lack of motivation, and non-modifiable, such as, chronological age and gender. Interpersonal factors are formal and informal social networks and social support systems (e.g., availability of an exercise partner). Institutional factors are formal and informal contexts within social institutions (e.g., too much homework). Community factors are relationships among organizations and informal networks within defined geographic boundaries (e.g., availability of physical activity programs in the community). Public policy includes local, state,


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and national laws and policies that affect physical activity participation. Although the model of McLeroy et al. (1988) did not capture physical factors, they also are critical elements of an ecological model of physical activity (Gyurcsik et al. 2004, 2006; Sallis et al. 1998). Physical factors involve the actual physical context in which activity takes place (e.g., lack of equipment). The purpose of this study was to assess barriers to and facilitators of after-school participation in MVPA as perceived by children with ASD and determine if physical activity patterns exist in relation to these barriers. The assumption is that if something is perceived by the individual as a barrier, self-efficacy to perform a behavior in the face of this barrier becomes an important predictor of future behavior (Bandura 1997).

Methods Participants The participants were a convenience sample of 12 boys and two girls with ASD, ages 8–14 years (M=10.64, SD=1.65). The participants were recruited from a community organization serving children and adolescents with ASD in a suburban area in the state of Delaware. Ten of the participants were diagnosed with Asperger syndrome, three with Pervasive Developmental Disorder—Not Otherwise Specified, and one with autism. The children received their diagnosis as a result of the formal diagnostic protocol employed by the Delaware public school system. This protocol is typically spearheaded by a licensed school psychologist and employs multiple diagnostic measures. Of the participants, 12 were Caucasian, one Asian, and one Filipino. The recruitment criteria were: (a) receiving school-based Special Education services under the diagnostic category of autism (Pierangelo and Giuliani 2007), (b) age between 8 and 14 years, (c) verbal skills to communicate with the researchers, and (d) ability to use a digital camera. Body Mass Index (BMI) scores ranged from 16.2 to 25.3 kg/m2 (M=21.3, SD=2.9) and were calculated from participants’ actual height and weight. While BMI of most participants in the study was within the normal range, atypical physical activity patterns and other intrapersonal factors in this population maybe uniquely associated with the development of obesity (Curtin et al. 2010). The substantially higher ratio of boys in the sample reflected gender prevalence of the population in the community. Participant demographic information and characteristics are provided in Table 1. Measures Definition of MVPA A children’s version of the definition of MVPA was developed by the researchers and its readability was evaluated by a panel of five judges (two parents and a special educator from children participating in the study and two professors in physical education pedagogy). Based on the panel’s input, it was decided to replace the term physical activity with exercise to improve readability. The Flesch-Kincaid grade level of the final version of the definition as calculated

8

9

10

10

10

10

10

10

10

11

11

13

13

14

P1

P2

P3

P4

P5

P6

P7

P8

P9

P10

P11

P12

P13

P14

8

7

6

5

5

5

4

4

4

4

4

3

3

2

Grade

Boy

Boy

Boy

Boy

Boy

Girl

Boy

Boy

Boy

Boy

Boy

Boy

Boy

Girl

Sex

Asperger

Asperger

Asperger

Asperger

Asperger

Asperger

Asperger

Asperger

Asperger

PDD-NOS

PDD-NOS

PDD-NOS

Asperger

Autism

Disability

Homeschool

General PS

General PS

Special PS

Homeschool

General PS

General PS

Charter

General PS

Homeschool

Homeschool

General PS

General PS

Special PS

School

19.66

21.56

21.23

23.72

22.97

17.36

16.15

21.80

25.30

22.46

23.72

24.58

16.91

20.50

BMI

103

107

81

108

121

97

107

81

89

99

81

102

83

103

SRS (T-score)

180.3±94.3

22.6±12.7

93.7±49.3

46.6±28.8

102.25±55.3

26.25±14.08

21.05±16.68

189.8±98.6

34.35±19.5

37.7±24.0

73.2±48.0

72.8 ±59.2

184.0±91.6

55.4±30.9

MVPA (min/day)a

9

3

6

10

24

11

9

11

3

21

5

1

15

11

Barriersb

13

9

13

4

13

16

7

19

11

10

18

11

18

18

Facilitatorsb

b

a

Values represent a number of responses

Values represent means and standard deviations

PDD-NOS Pervasive Developmental Disorder-Not Otherwise Specified; PS Public School; BMI Body Mass Index; MVPA Moderate to Vigorous Physical Activity; SRS Overall Social Responsiveness Score

Age (yrs)

Particip.

Table 1 Participant characteristics

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using the Microsoft Office software was 3.00 and the Flesch Reading Ease score was 84.6, which represents third-grade level readability. The following definition was presented to the participants: Exercise is when you move your arms and legs. Your heart beats faster. You breathe harder. It is good for you to exercise. It makes your heart and muscles stronger. Example—playing soccer, running, or chasing a friend. The Social Responsiveness Scale (SRS) To validate participants’ diagnosis, the SRS was administered to parents. The SRS consists of 65 items that are rated on a fourpoint scale ranging from 0 (not at all true) to 3 (very true). It generates an overall social impairment score and five subscale scores: Social Awareness, Social Cognition, Social Communication, Social Motivation, and Autistic Mannerisms. In this study, according to the SRS manual, raw scores were converted to T-scores. Table 1 shows that all participants’ scores were in the severe range of social impairment (T≥76). This range is associated with a clinical diagnosis of Autistic Disorder, Asperger Disorder, or more severe cases of Pervasive Developmental Disorder-Not Otherwise Specified (PDD-NOS) (Constantino et al. 2004). Accelerometry Physical activity levels were measured with the Actical accelerometer (Mini Mitter, Bend, OR). Actical is a small (28×27×10 mm), lightweight (17 g) device containing an omnidirectional sensor that monitors the occurrence and intensity of motion in all directions. It measures acceleration ranging in magnitude from 0.5 to 2.00 G with frequency response from .35 to 35 Hz. These data were collected in 30-s intervals for 10 h per day for 7 days (five weekdays and two weekend days) within a 14-day period (Rowlands 2007). The researchers met with the parents to ascertain that they understood the accelerometer guidelines and instruct them to select days that were typical and convenient for them to wear the Actical. Participants wore the Actical on an elastic belt on the right hip (anterior to the iliac crest) from 10:00 a.m. to 8:00 p.m. and removed it only for swimming or bathing. If participants got up after 10:00 a.m. (two cases), they were instructed to put it on as soon as they got up and wear it for a minimum of 10 h. They were asked to avoid touching the Actical and maintain normal schedules and activities during the monitored period. Parents and their children were given seven activity logs to record which days were monitored, when the accelerometer was placed on and taken off, and what types of activities the participants engaged in during 30-min intervals. The researchers communicated with the parents throughout the study to increase validity of the findings. Photovoice Photovoice methodology was used to elicit children’s perceptions of barriers to and facilitators of after-school MVPA. Photovoice is a community and participatory action research tool that was developed by Wang and Burris (1994). Grounded in Freire’s (1973) critical education theory, feminist theory, and documentary photography, photovoice allows people who are not typically represented in programmatic planning (e.g., children with disabilities) to take photos of their reality, needs, and settings, and use them for reflection (Wang and Burris 1994). Photovoice has been used to promote dialogue between researchers and

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individuals with disabilities, including those with ASD, learning disabilities, and intellectual disabilities (e.g., Jurkowski 2008). Three data collection techniques were used in this study as a part of the photovoice methodology: (a) digital photography, (b) an online questionnaire, and (c) a semi-structured interview. First, participants were instructed to take photos of the things that made it easy or hard for them to exercise after school in the previous week. A 1-week recall was used in this study based on the recommendations of Brawley et al. (1998). Participants took a total of 182 photos, which were uploaded into a computer and analyzed by a panel of three researchers. Of the photos taken, 160 (87%) were usable. Photos were considered unusable if they were unclear (n=7), redundant (n=5), not taken by the participant for the purpose of the study (n=5), if the participant was unable to identify a barrier or facilitator in the photo (n=2), or if the photo was taken due to the reported influence of their parents (n=3). The online questionnaire prompted three sets of responses regarding each uploaded photo: (a) describe the objects in the photo and why you photographed them, (b) rate whether the objects in the photo would make it easier or harder for you to exercise after school next week using a four-point scale ranging from very easy (4) to very hard (1), and (c) rate how much the object in the picture would make it exciting for you to exercise after school next week using a four-point scale ranging from very much (4) to not much at all (1). The third prompt was included in the questionnaire to identify participants’ motivation to exercise under the given circumstances (Brawley et al. 1998). A four-point scale was used rather than a five-point scale to force children to think about their photos and to avoid neutral responses (Dillman et al. 2009). Children who could not comprehend the four-point scale (n=2) were given a two-point scale. In addition, the scaled items had a response category of “I don’t know” at the end of the scale. When participants could not answer the easy-or-hard question during the interview, the photo was removed from the analysis. The same panel that evaluated the definition of MVPA evaluated readability and format of the questionnaire. Furthermore, it was pilot tested with five children with ASD (ages 8–9 years) and their parents to ensure the format and wording was appropriate for this population. After all comments were addressed, the questionnaire was sent to the panel for final review. Following the questionnaire, a semi-structured audio-recorded interview was conducted individually with each participant. A paper-pencil version of the online questionnaire guided the interviewer. The purpose of the interviews was to: (a) confirm that the photos were taken by the participants for this study, (b) establish reliability of the questionnaire data, (c) validate our interpretation of the photographic data, and (d) provide additional and more detailed information about the six factors of the socioecological model (McLeroy et al. 1988). The nature and combination of the questions in the questionnaire and interview, and the interviewer’s familiarity with the participants minimized the possibility of responses motivated by social desirability.

Procedure The treatment of participants was in accordance with the ethical standards of the American Psychological Association. Children and their parents who consented to


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participate in the study then participated in two 45-min training sessions. Depending on the child’s level of attention, the sessions were conducted individually in a lab or in a small group in a classroom. The purpose of this training was to (a) explain the purpose and procedures of the study in greater detail, (b) define and provide examples of MVPA, (c) explain how to use a digital camera, wear the Actical, and fill out activity logs, and (d) brainstorm the types of items that participants might photograph for the study. The potential issues that might be raised by taking photos in the community also were discussed. For example, the concept of privacy and the importance of safeguarding other people’s privacy were explained. After the training, participants were given 2 weeks in the summer to take their photos, wear their accelerometers, and complete the seven activity logs. The online questionnaire was completed with assistance of a researcher in a lab, 2 to 7 days after the last accelerometer recording. The researcher rephrased any words the participants did not understand and helped the participants stay on task via reminders and other prompts. Furthermore, they reminded the participants to respond based on their feelings and thoughts rather than how their parents would like them to respond. Four participants completed the questionnaire during one 45-min administration and the other 11 required two 30-min administrations. All interviews took place in a lab and were audio recorded. The interviewer and research assistants knew the participants from the community organization for at least 2 months prior to the study. Data Analysis Recorded accelerometer data files were downloaded into the Actical software 2.0 (Mini-Mitter Co.). The participants were asked to wear the Actical for an additional day within the 14-day period if (a) the files showed signs of multifunction (i.e., all activity counts less than 1,952 or more than 5,724, and continuous data with the same value), (b) the Actical was taken off for more than 5 min during the 10:00 a.m. to 8:00 p.m. period (this excluded swimming and bathing), and (c) the total wearing time was less than 10 h per a day (Rowlands 2007). The average metabolic equivalents (METs) values were calculated using a two-regression model developed for the Actical (Crouter and Bassett 2007). Activity counts were analyzed to determine minutes and percentages of time spent in light (6 METs) physical activity for each 60-min segment of the 7-day monitoring period. A daily and weekly summary of MVPA (>3 METs) was calculated in minutes and percentages (Trost et al. 1998). Activity logs were analyzed to determine the types of activities the participants engaged in during the monitored period. All interviews in the study were transcribed verbatim. Using NVivo 8.0 software, the photos were inserted into the corresponding narratives and linked to a brief written summation detailing key points from the photo-elicitation interview, along with the preliminary interpretations. A panel of three researchers analyzed the data in three stages (Hruschka et al. 2004). The goal of the first stage of analysis was to conduct systematic open coding and to create a codebook. The first author divided all responses into two categories so that responses rated by the participants as Very Easy or Easy were placed in a category of facilitators and responses rated as Very Hard or Hard were placed in a

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category of barriers. The three researchers then independently viewed the three sources of data (i.e., photos, interview narratives and notes, and questionnaire ratings directly related to the images) and created a set of concepts. The panel met to (a) compare the concepts, (b) agree on the initial codes, and (c) define each code and rules according to which codes were going to be assigned to the responses. After the initial codebook was created, each panel member independently coded the participants’ photos and interviews. The goal of the second stage was assessment of intercoder reliability. Using Cohen’s kappa (Cohen 1960), reliability scores between the first author and two researchers were initially .76 and .85, respectively. After all disagreements between the coders were discussed, the codebook was modified and the three researchers recoded the data. This resulted in Cohen’s kappa reliability scores of .94 and .98, respectively. The third stage of data analysis involved categorizing concepts and fitting them into six levels of the socioecological model (McLeroy et al. 1988). Finally, frequencies of student responses and photos taken under each concept and level of the socio-ecological model were calculated.

Results Levels of MVPA An intraclass correlation coefficient demonstrated a strong relationship (p