1. Results Correlates and Contributing Factors to Obesity in Autism Spectrum Disorder Populations Kelsey Borner MA 1,2, Cy Nadler, PhD 3, Katrina Poppert 1,2, Cathleen Odar Stough MA 1,2, Meredith Dreyer Gillette PhD 1,3, Rebecca Swinburne Romine PhD 4 1 Center for Children’s Healthy Lifestyles & Nutrition, Kansas City, Missouri 2 University of Kansas, Clinical Child Psychology Program, Lawrence, Kansas 3 Children’s Mercy Hospitals and Clinics, Kansas City, Missouri 4 The Schiefelbusch Institute for Life Span Studies, University of Kansas Background Youth with autism spectrum disorder (ASD) are at increased risk for obesity compared to their typically developing peers (Eaves & Ho, 2008). Youth with ASD are also at risk for poor health behaviors, including lower levels of physical activity (Memari et al., 2013), increased screen time use (Must et al., 2014), and more sleep problems (Goldman et al., 2012). However, an exploration of how health risk factors are related to overweight and obesity among youth with ASD does not yet exist in a large sample. This study examines the relationships between child weight status and health behaviors for children with and without ASD. Conclusions Children with ASD are significantly more likely to be obese than their non-ASD peers; however, a clear understanding of the contributing factors remains unknown. Screen time and sleep patterns as assessed in this survey were not different between children with and without ASD, and did not differentiate between weight statuses among children with ASD. Children with ASD are more likely to be sedentary (i.e., zero days of physical activity per week) than their non-ASD peers, which suggests that physical activity among children with ASD should be prioritized as a potential intervention strategy. population. Acknowledgments Data used from the 2011/12 National Survey of Children’s Health. Maternal and Child Health Bureau in collaboration with the National Center for Health Statistics. 2011/12 NSCH Indicator Data Set prepared by the Data Resource Center for Child and Adolescent Health, Child and Adolescent Health Measurement Initiative. www.childhealthdata.org Methods Procedures: Participants were drawn from the 2011/2012 National Survey of Children’s Health, a nationally representative (50 states, the District of Columbia, and U.S. Virgin Islands) telephone-based survey designed to provide national- and state-level estimates of child health indicators. The survey randomly sampled parents of children ages 0-17. Measures: Parent reports of child anthropometrics (height/weight), frequency of physical activity, sleep, and screen time use, and whether or not a doctor had ever told the parent that the child had an ASD. Participants: 45,217 children (ages 10-17 years) with BMI information. 925 (1 in 49) reportedly had a current diagnosis of ASD. Children with ASD : 84.0% male. White: 62.4%; Hispanic: 21.3%; Black:10.2%; Multiracial/Other: 6.1%. Mean age = 13.1 years (SD =.175). References Eaves, L. C., & Ho, H. H. (2008). Young adult outcome of autism spectrum disorders. Journal of Autism and Developmental Disorders, 38(4), 739-747. Memari, A. H., Ghaheri, B., Ziaee, V., Kordi, R., Hafizi, S., & Moshayedi, P. (2013). Physical activity in children and adolescents with autism assessed by triaxial accelerometry. Pediatric Obesity, 8(2), 150-158. Must, A., Phillips, S. M., Curtin, C., Anderson, S. E., Maslin, M., Lividini, K., & Bandini, L. G. (2014). Comparison of sedentary behaviors between children with autism spectrum disorders and typically developing children. Autism, 18, 376-384. Goldman, S. E., Richdale, A. L., Clemons, T., & Malow, B. A. (2012). Parental sleep concerns in autism spectrum disorders: Variations from childhood to adolescence. Journal of Autism and Developmental Disorders, 42(4), 531-538. Data Analysis Non-response and unequal selection probabilities were accounted for by using sampling weights to help establish population-based estimates. Analyses were conducted using AM Statistical Software (Beta Version 0.06.04. (c) The American Institutes for Research and Jon Cohen, 2011). Aside from descriptive analyses, logistic regression was used for all analyses. The Wald test was used to determine significance. All Children (10 – 17 Years)T-test; (p-value) Vigorous Physical Activity ASDNon-ASD 0 Days/Week18.8%10.2% 2.527; (.01) 1-3 Days/Week30.0%26.8% 0.985; (.33) 4-6 Days/Week31.3%39.8%-2.960; (<.01) 7 Days/Week19.9%23.2%-0.932; (.35) Adequate Sleep 0 Days/Week3.5%3.1% 0.236; (.81) 1-3 Days/Week10.0%7.4% 1.298; (.20) 4-6 Days/Week27.2%34.9%-1.858; (.07) 7 Days/Week59.3%54.7% 1.110; (.27) Screen Time TV/Video/Video Games: <1 Hr/Day41.4%40.5% 0.194; (.85) 1-4 Hrs/Day42.2%44.6%-0.512; (.61) ≥ 4 Hrs/Day16.4%14.9% 0.833; (.41) Computer/Cell Phone/Electronics: Never19.8%8.7% 2.749; (<.01) <1 Hr/Day42.7%46.8%-1.759; (.82) 1-4 Hrs/Day25.5%28.9%-1.045; (.30) ≥ 4 Hrs/Day12.0%15.6%-1.658; (.10) Electronics in Bedroom: Yes57.7%61.7%-1.061; (.29) No42.3%38.2% 1.064; (.29) Children with ASD (10 - 17 Years)Wald-test; (p-value) Normal WeightOverweightObese Vigorous Physical Activity 0 Days/Week20.3%12.0%21.1%<.001; (.97) 1-3 Days/Week26.5%38.0%33.2%1.221; (.27) 4-6 Days/Week28.7%34.7%29.8% 0.196; (.66) 7 Days/Week24.5%15.3%15.8% 0.377; (.54) Adequate Sleep 0 Days/Week3.8%3.4%1.2% 4.818; (.03) 1-3 Days/Week6.6%8.7%20.0% 9.494; (<.01) 4-6 Days/Week27.3%27.5%27.0%<.001; (.99) 7 Days/Week62.3%60.5%51.9% 2.210; (.14) Screen Time TV/Video/Video Games: <1 Hr/Day39.7%53.7%35.1% 0.214; (.64) 1-4 Hrs/Day45.5%30.9%44.5% 0.027; (.87) ≥ 4 Hrs/Day14.7%15.4%20.4% 0.676; (.41) Computer/Cell Phone/Electronics: Never24.9%6.6%21.0% 0.353; (.55) > 1 Hr/Day43.5%43.8%36.9% 1.504; (.22) 1-4 Hrs/Day23.9%26.9%29.0% 1.779; (.18) ≥ 4 Hrs/Day7.7%22.7%13.1% 1.134; (.29) Electronics in Bedroom: Yes55.5%57.7%61.0% 0.103; (.75) No44.4%42.3%39.0% 0.097; (.76) Results Continued Non-ASD ASD ASD vs. Non-ASD Health Behaviors: Health Behaviors of Children with ASD by Weight Status: Prevalence of Weight Status