Neighborhood Disorder and Physical Activity among Older Adults: A Longitudinal Study
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Neighborhood physical disorder—the visual indications of neighborhood deterioration—may inhibit outdoor physical activity, particularly among older adults. However, few previous studies of the association between neighborhood disorder and physical activity have focused on this sensitive population group, and most have been cross-sectional. We examined the relationship between neighborhood physical disorder and physical activity, measured using the Physical Activity Scale for the Elderly (PASE), in a three-wave longitudinal study of 3497 New York City residents aged 65–75 at baseline weighted to be representative of the older adult population of New York City. We used longitudinal mixed linear regression controlling for a number of individual and neighborhood factors to estimate the association of disorder with PASE score at baseline and change in PASE score over 2 years. There were too few subjects to assess the effect of changes in disorder on activity levels. In multivariable mixed regression models accounting for individual and neighborhood factors; for missing data and for loss to follow-up, each standard deviation increase in neighborhood disorder was associated with an estimated 2.0 units (95% CI 0.3, 3.6) lower PASE score at baseline, or the equivalent of about 6 min of walking per day. However, physical disorder was not related to changes in PASE score over 2 years of follow-up. In this ethnically and socioeconomically diverse population of urban older adults, residents of more disordered neighborhoods were on average less active at baseline. Physical disorder was not associated with changes in overall physical activity over time.
KeywordsCities Neighborhood physical disorder Older adults Physical activity Urban health
The research presented here was supported by National Institute for Mental Health grant 5R01MH085132-05 and by National Institute of Child Health and Human Development grant 5T32HD057822-07. Thelma Mielenz, Alfred Neugut, Shuang Wang, and Ryan Demmer gave helpful comments on an earlier version of this work.
- 7.Centers for Disease Control and Prevention. The state of aging and health in America 2013. Available at: http://www.cdc.gov/aging/pdf/state-aging-health-in-america-2013.pdf. Accessed April 29, 2014.
- 19.Rothman KJ, Greenland S, Lash TL. Modern epidemiology. Philadelphia, PA: Lippincott Williams & Wilkins; 2008.Google Scholar
- 42.Raghunathan TE, Solenberger PW, Van Hoewyk J. IVEware: imputation and variance estimation software. Ann Arbor, MI: Survey Methodology Program, Survey Research Center, Institute for Social Research, University of Michigan; 2002.Google Scholar
- 43.Weather Underground. Historical weather. Available at: http://www.wunderground.com/history/. Accessed 30 July 2015.
- 44.Brice T, Hall T. Heat index. Available at: http://www.srh.noaa.gov/images/epz/wxcalc/heatIndex.pdf. Accessed 30 July 2015.
- 45.Brice T, Hall T. Wind chill. Available at: http://www.srh.noaa.gov/images/epz/wxcalc/windChill.pdf. Accessed 30 July 2015.
- 49.Wilson JQ, Kelling GL. Broken windows. Atlantic Mon. 1982; 249(3): 29–38.Google Scholar
- 50.Kelling GL, Coles CM. Fixing broken windows: restoring order and reducing crime in our communities. New York, NY: Martin Kessler Books; 1996.Google Scholar
- 54.Lumley T. mitools: tools for multiple imputation of missing data. R package version 2.2. Vienna, Austria: R Foundation for Statistical Computing; 2012.Google Scholar
- 58.Moran M, Van Cauwenberg J, Hercky-Linnewiel R, Cerin E, Deforche B, Plaut P. Understanding the relationships between the physical environment and physical activity in older adults: a systematic review of qualitative studies. Int J Behav Nutr Phys Act. 2014; 11: 79.CrossRefPubMedPubMedCentralGoogle Scholar
- 67.Allison PD. Missing data, vol. 136. Thousand Oaks, CA: Sage publications; 2001.Google Scholar
- 68.New York City Department of Health and Mental Hygiene. Survey data on the health of New Yorkers. Available at: http://www.nyc.gov/html/doh/html/data/chs-methods.shtml. Accessed 30 July 2015.
- 69.Sinclair M, O’Toole J, Malawaraarachchi M, Leder K. Comparison of response rates and cost-effectiveness for a community-based survey: postal, internet and telephone modes with generic or personalised recruitment approaches. BMC Med Res Methodol. 2012; 12(1): 132.CrossRefPubMedPubMedCentralGoogle Scholar