Keep It Moving: Factors to Consider in Establishing an Interprofessional Approach to Promote Physical Activity Among US Adults in the Northeast
American Journal of Health Research
Volume 4, Issue 2-1, March 2016, Pages: 28-36
Received: Sep. 14, 2015; Accepted: Jan. 26, 2016; Published: Jun. 17, 2016
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Sariyamon Tiraphat, ASEAN Institute for Health Development, Mahidol University, Salaya, Nakhon Pathom, Thailand
Koren S. Goodman, Department of Health and Nutrition Sciences, Montclair State University, Montclair, New Jersey, USA
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Physical inactivity is a major public health concern. In the United States (US), only 21% of adults meet the established guidelines [1]. Recommendations for adults aged 18 to 64 years include 150 minutes of moderate activity, with 2 days of muscle-strengthening to improve overall health and to lower the risk for diseases such as diabetes, heart disease, and stroke [1]. Sedentary and inactive lifestyles increase the risks for developing many chronic and cardiovascular diseases and some cancers [1]. A growing body of literature focuses on built environments and its impact on physical activity using multilevel models. However, limited published research exists on cross level interaction effects between individual characteristics and environments. The purpose of this study was to examine environmental factors associated with physical activity for adults living in the Northeastern region of the United States (US) and to investigate whether these influences differ by subgroups of the population. The current study employed a cross-sectional research design among 45,251 adults, aged 18 years and older living in approximately 66 US counties. The dependent variable was physical activity level, measured as a dichotomous variable based on CDC’s recommended physical activity guidelines. Data from the 2007 Behavioral Risk Factor Surveillance System (BRFSS) was linked with the US Census Bureau, the US Department of Agriculture (USDA), and the National Outdoor Recreation Supply Information System (NORSIS) databases. Multilevel logistic regression was used to examine direct effects of five environmental factors and to examine cross level interactions between individual characteristics and environmental influences. Findings from this study indicate that effective interprofessional solutions and appropriate interventions are needed to promote regular physical activity among adults.
Physical Activity, Built Environments, Multilevel Models
To cite this article
Sariyamon Tiraphat, Koren S. Goodman, Keep It Moving: Factors to Consider in Establishing an Interprofessional Approach to Promote Physical Activity Among US Adults in the Northeast, American Journal of Health Research. Special Issue: Interprofessional Education and Collaboration is a Call for Improvement Across the Board in the Health Sciences. Vol. 4, No. 2-1, 2016, pp. 28-36. doi: 10.11648/j.ajhr.s.2016040201.14
Centers for Disease Control and Prevention (CDC). (2016). Physical Activity. Retrieved from
King, A. C., Stokols, D., Talen, E., Brassington, G. S., & Killingsworth, R. 2002. Theoretical approaches to the promotion of physical activity: Forging a transdisciplinary paradigm. American Journal of Preventive Medicine, 23(2 Suppl), 15-25.
Li, W., Lee, A., & Solmon, M. (2006). Gender differences in beliefs about the influence of ability and effort in sport and physical activity. Sex Roles, 54(1/2), 147-156.
Palmer, C. (2005). Exercise as a treatment for depression in elders. Journal of the American Academy of Nurse Practitioners, 17(2), 60-66.
Lee, L. L., Arthur, A., & Avis, M.(2008). Using self-efficacy theory to develop interventions that help older people overcome psychological barriers to physical activity: A discussion paper. International Journal of Nursing Studies, 45(11), 1690-1699.
Humpel, N., Owen, N., & Leslie, E. (2002). Environmental factors associated with adults' participation in physical activity: A review. American Journal of Preventive Medicine, 22(3), 188-199.
Lepore, S. J., Revenson, T. A., Weinberger, S. L., Weston, P., Frisina, P. G., Robertson, R., Portillo, M., Jones, H., & Cross, W. (2006). Effects of social stressors on cardiovascular reactivity in Black and White women. Annals of Behavioral Medicine, 31(2), 120-127.
Stokols, D. (1996). Translating social ecological theory into guidelines for community health promotion. American Journal of Health Promotion, 10(4), 282-298.
Kim, D., Subramanian, S. V., Gortmaker, S. L., & Kawachi, I. (2006). US state- and county-level social capital in relation to obesity and physical inactivity: A multilevel, multivariable analysis. Social Science & Medicine, 63(4), 1045-1059.
Boone-Heinonen, J., Diez Roux, A. V., Kiefe, C. I., Lewis, C. E., Guilkey, D. K., & Gordon-Larsen, P., (2011). Neighborhood socioeconomic status predictors of physical activity through young to middle adulthood: The CARDIA study. Social Science & Medicine, 72(5), 641-649.
Yang, W., Spears, K. Zhang, F., Lee, W., & Himler H. L. (2012). Evaluation of personal and built environment attributes to physical activity: A multilevel analysis on multiple population-based data sources. Journal of Obesity, 548910.
Fisher, K., Michael, Y., & Cleveland, M. (2004). Neighborhood-level influences on physical activity among older adults: A multilevel analysis." Journal of Aging and Physical Activity, 12(1), 45-63.
Center for Disease Control and Prevention (CDC), SMART: BRFSS City and County Data Documentation. Retrieved June 17, 2010 from
IBM Corp. Released 2010. (2010). IBM SPSS Statistics for Windows, Version 19.0. Armonk, NY: IBM Corp.
National Outdoor Recreation Supply Information System (NORSIS). (2010). NORSIS 1997: Codebook and Documentation. Retrieved from
United States Census Bureau-County and City Data Book. (2010). County and City Data Book: 2007. Retrieved from
United States Department of Agriculture’s (USDA) Economic Research Service. Retrieved from
Field, A. (2009). Discovering statistics using SPSS. (3rd ed.) CA: Sage Publications.
Larsen, K., & Merlo, J. (2005). Appropriate assessment of neighborhood effects on individual health: integrating random and fixed effects in multilevel logistic regression. American Journal of Epidemiology, 161(1), 81-88.
Larsen, K., Petersen, J. H., Budtz-Jorgensen, E., & Endahl, L. (2000). Interpreting parameters in the logistic regression model with random effects. Biometrics, 56(3), 909-914.
Canizares, M., Power, J., Perruccio, A., & Badley, E. (2008). Association of regional racial/cultural context and socioeconomic status with arthritis in the population: A multilevel analysis. Arthritis Rheumatism, 59(3), 399-407.
Ronald, H., Scott, T., & Lynn, T. (2010). Multilevel and longitudinal modeling with IBM SPSS. NY: Routledge.
Zhou, X.-H., Perkins, A., & Hui, S. (1999). Comparisons of software packages for generalized linear multilevel models. The American Statistician, 53(3), 1999.
McLeroy, K., Bibeau, D., Steckler, A., & Glanz, K. (1988). An ecological perspective on health promotion programs. Health Education Quarterly, 15(4), 351-77.
Sallis, J., & Owen, N. (1999). Physical Activity & Behavioral Medicine. Thousand Oaks, CA: Sage Publications.
Chen, T., Lee, J., Kawakubo, K., Watanabe, E., Mori, K., Kitaike, T., & Akabayashi, A. (2013). Features of perceived neighborhood environment associated with daily walking time or habitual exercise: Differences across gender, age, and employment status in a community–dwelling population of Japan. Environmental Health and Preventive Medicine, 18(5), 368-376.
Cain, K., Millstein, R., Sallis, J., Conway, T., Gavand, K., Frank, L. & King, A.(2014). Contribution of streetscape audits to explanation of physical activity in four age groups based on the Microscale Audit of Pedestrian Streetscapes (MAPS). Social Science & Medicine, 116, 82-92.
Ross, C. E., & Mirowsky, J. (2001). Neighborhood disadvantage, disorder, and health. Journal of Health and Social Behavior, 42(3), 258-276.
Yang, Y., Roux, A. V., & Bingham, C. R. (2011). Variability and seasonality of active transportation in USA: Evidence from the 2001 NHTS. International Journal of Behavioral Nutrition and Physical Activity, 8, 96.
USDA: ERS., Natural Amenities and Regions. Retrieved from:
Feinglass, J., Lee, J., Semanik, P., Song, J., Dunlop, D., & Chang, R. (2011). The effects of daily weather on accelerometer-measured physical activity. Journal of Physical Activity and Health, 8(7), 934-943.
Lindström, M., Hanson, B. & Östergren, P. (2001). Socioeconomic differences in leisure-time physical activity: the role of social participation and social capital in shaping health related behaviour. Social Science & Medicine, 52(3), 441-451.
Chinn, D. J., White, M., Harland, J., Drinkwater, C., & Raybould, S. (1999). Barriers to physical activity and socioeconomic position: implications for health promotion. Journal of Epidemiology and Community Health, 53(3), 191.
Chaudhury, H., Mahmood, A., Michael, Y., Campo, M., & Hay, K. (2012). The influence of neighborhood residential density, physical and social environments on older adults' physical activity: An exploratory study in two metropolitan areas. Journal of Aging Studies, 26(1), 35-43.
Kelly-Schwartz, A. C., Stockard, J., Doyle, S., & Schlossberg, M. (2004). Is sprawl unhealthy? A multilevel analysis of the relationship of metropolitan sprawl to the health of individuals. Journal of Planning Education and Research, 24(2), 184-196.
Ross, S., Larson, N., Graham, D., & Neumark-Sztainer, D. (2014). Longitudinal changes in physical activity and sedentary behavior from adolescence to adulthood: comparing U. S.-born and foreign-born populations. Journal of Physical Activity and Health, 11(3), 519-527.
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