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Using built environment characteristics to predict walking for exercise

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dc.contributor.author Lovasi, Gina S. en_US
dc.contributor.author Moudon, Anne V. en_US
dc.contributor.author Pearson, Amber L. en_US
dc.contributor.author Hurvitz, Philip M. en_US
dc.contributor.author Larson, Eric B. en_US
dc.contributor.author Siscovick, David S. en_US
dc.contributor.author Berke, Ethan M. en_US
dc.contributor.author Lumley, Thomas en_US
dc.contributor.author Psaty, Bruce M. en_US
dc.date.accessioned 2010-04-21T15:59:36Z
dc.date.available 2010-04-21T15:59:36Z
dc.date.issued 2008 en_US
dc.identifier.citation Lovasi G, Moudon A, Pearson A, et al. Using built environment characteristics to predict walking for exercise. International Journal of Health Geographics. 2008;7(1):10. en_US
dc.identifier.other 10.1186/1476-072X-7-10 en_US
dc.identifier.uri http://www.ij-healthgeographics.com/content/7/1/10 en_US
dc.identifier.uri http://hdl.handle.net/1773/15802
dc.description.abstract Background: Environments conducive to walking may help people avoid sedentary lifestyles and associated diseases. Recent studies developed walkability models combining several built environment characteristics to optimally predict walking. Developing and testing such models with the same data could lead to overestimating one's ability to predict walking in an independent sample of the population. More accurate estimates of model fit can be obtained by splitting a single study population into training and validation sets (holdout approach) or through developing and evaluating models in different populations. We used these two approaches to test whether built environment characteristics near the home predict walking for exercise. Study participants lived in western Washington State and were adult members of a health maintenance organization. The physical activity data used in this study were collected by telephone interview and were selected for their relevance to cardiovascular disease. In order to limit confounding by prior health conditions, the sample was restricted to participants in good self-reported health and without a documented history of cardiovascular disease. Results: For 1,608 participants meeting the inclusion criteria, the mean age was 64 years, 90 percent were white, 37 percent had a college degree, and 62 percent of participants reported that they walked for exercise. Single built environment characteristics, such as residential density or connectivity, did not significantly predict walking for exercise. Regression models using multiple built environment characteristics to predict walking were not successful at predicting walking for exercise in an independent population sample. In the validation set, none of the logistic models had a C-statistic confidence interval excluding the null value of 0.5, and none of the linear models explained more than one percent of the variance in time spent walking for exercise. We did not detect significant differences in walking for exercise among census areas or postal codes, which were used as proxies for neighborhoods. Conclusion: None of the built environment characteristics significantly predicted walking for exercise, nor did combinations of these characteristics predict walking for exercise when tested using a holdout approach. These results reflect a lack of neighborhood-level variation in walking for exercise for the population studied. en_US
dc.description.sponsorship University of Washington Royalty Research fund award; by contracts R01-HL043201, R01-HL068639, and T32-HL07902 from the National Heart, Lung, and Blood Institute; and by grant R01-AG09556 from the National Institute on Aging. en_US
dc.language.iso en_US en_US
dc.title Using built environment characteristics to predict walking for exercise en_US
dc.type Article en_US


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