Do sedentary behavior and physical activity spatially cluster? Analysis of a population-based sample of Boston adolescents
Sedentary behavior and lack of physical activity are key modifiable behavioral risk factors for chronic health problems, such as obesity and diabetes. Little is known about how sedentary behavior and physical activity among adolescents spatially cluster. The objective was to detect spatial clustering of sedentary behavior and physical activity among Boston adolescents. Data were used from the 2008 Boston Youth Survey Geospatial Dataset, a sample of public high school students who responded to a sedentary behavior and physical activity questionnaire. Four binary variables were created: (1) TV watching (>2 h/day), (2) video games (>2 h/day), (3) total screen time (>2 h/day); and (4) 20 min/day of physical activity (≥5 days/week). A spatial scan statistic was utilized to detect clustering of sedentary behavior and physical activity. One statistically significant cluster of TV watching emerged among Boston adolescents in the unadjusted model. Students inside the cluster were more than twice as likely to report >2 h/day of TV watching compared to respondents outside the cluster. No significant clusters of sedentary behavior and physical activity emerged. Findings suggest that TV watching is spatially clustered among Boston adolescents. Such findings may serve to inform public health policy-makers by identifying specific locations in Boston that could provide opportunities for policy intervention. Future research should examine what is linked to the clusters, such as neighborhood environments and network effects.
KeywordsTV viewing Exercise Youth Spatial scan statistics Urban city Geographical patterns
We thank the participants for their contributions to the project. The 2008 Boston Youth Survey was funded by a grant from the Centers for Disease Control and Prevention (Grant U49CE00740) to the Harvard Youth Violence Prevention Center at the Harvard School of Public Health. The Robert Wood Johnson Foundation’s Active Living Research Program (Grant 67129 to Dr. Dustin T. Duncan) supported the development of the Boston Youth Survey Geospatial Dataset. This study was funded also by the National Institute of Diabetes and Digestive and Kidney Diseases and the National Institutes of Health (Grant to R01DK097347 to Dr. Brian Elbel).
Compliance with ethical standards
Conflict of interest
The authors (Kosuke Tamura, Dustin Duncan, Jessica Athens, Marc Scott, Michael Rienti, Jr., Jared Aldstadt, Laurie Brotman, and Brian Elbel) declare that they have no conflict of interest.
The Human Subject Committee (i.e., the Institutional Review Board) at the Harvard School of Public Health approved the ethics of the original study protocols.
We obtained passive consent from parents (i.e., they had the opportunity to opt out from the study) and students read the informed assent before the survey administration. The Human Subject Committee (i.e., the Institutional Review Board) approved the ethics of the original study protocols.
- Almeida, J., Duncan, D. T., & Sonneville, K. R. (2015). Obesogenic behaviors among adolescents: The role of generation and time in the United States. Ethnicity and Disease, 25(1), 58–64.Google Scholar
- American Academy of Pediatrics. (2016). American Academy of Pediatrics Announces New Recommendations for Children’s Media Use. https://www.aap.org/en-us/about-the-aap/aap-press-room/pages/american-academy-of-pediatrics-announces-new-recommendations-for-childrens-media-use.aspx.
- Centers for Disease Control and Prevention (1990). Guidelines for investigating clusters of health events (Vol. 39, pp. 1–16), MMWR, Morbidity and Mortality Weekly Report.Google Scholar
- Duncan, D. T., Rienti Jr, M., Kulldorff, M., Aldstadt, J., Castro, M. C., Frounfelker, R., et al. (2016). Local spatial clustering in youths' use of tobacco, alcohol, and marijuana in Boston. The American Journal of Drug and Alcohol Abuse, 1–10. doi:10.3109/00952990.2016.1151522.
- Harvard T.H. Chan School of Public Health. (2017). Harvard Youth Violence Prevention Center. https://www.hsph.harvard.edu/hyvpc/research/. June 23, 2017.
- Kann, L., Kinchen, S., Shanklin, S. L., Flint, K. H., Kawkins, J., Harris, W. A., et al. (2014). Youth risk behavior surveillance–United States, 2013. MMWR Supplements, 63(4), 1–168.Google Scholar
- Kulldorff, M. (2005). SaTScan™: Software for the spatial, temporal, and space-time scan statistics, http://www.satscan.org/. Accessed June 24, 2016.
- Lou, D. (2014). Sedentary behaviors and youth: Current trends and the impact on health. San Diego, CA: Active Living Research.Google Scholar
- Physical Activity Guidelines Advisory Committee (2008). Physical activity guidelines advisory committee report, 2008. Washington, DC: U.S. Department of Health and Human Services.Google Scholar
- Prince, S. A., Adamo, K. B., Hamel, M. E., Hardt, J., Gorber, S. C., & Tremblay, M. (2008). A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. International Journal of Behavioral Nutrition and Physical Activity, 5, 56. doi:10.1186/1479-5868-5-56.CrossRefGoogle Scholar
- Tamura, K., Puett, R. C., Hart, J. E., Starnes, H. A., Laden, F., & Troped, P. J. (2014). Spatial clustering of physical activity and obesity in relation to built environment factors among older women in three U.S. states. BMC Public Health, 14, 1322. doi:10.1186/1471-2458-14-1322.
- Tamura, K., Duncan, D. T., Athens, J. K., Bragg, M. A., Rienti Jr, M., Aldstadt, J., Scott, M. A., & Elbel, B. (2017). Geospatial clustering in sugarsweetened beverage consumption among Boston youth. International Journal of Food Sciences and Nutrition, 68(6), 719–725. doi:10.1080/09637486.2016.1276519.
- Tremblay, M. S., Aubert, S., Barnes, J. D., Saunders, T. J., Carson, V., Latimer-Cheung, A. E., et al. (2017). Sedentary behavior research network (SBRN)—Terminology consensus project process and outcome. International Journal of Behavioral Nutrition and Physical Activity, 14(1), 75. doi:10.1186/s12966-017-0525-8.CrossRefGoogle Scholar