Research

Using built environment characteristics to predict walking for exercise

Gina S Lovasi¹ , Anne V Moudon^2,3 , Amber L Pearson⁴ , Philip M Hurvitz² , Eric B Larson⁵ , David S Siscovick^6,7 , Ethan M Berke⁸ , Thomas Lumley⁹ and Bruce M Psaty^6,7,10

¹  Institute for Social and Economic Research and Policy, Columbia University, New York, NY, USA

²  Urban Design and Planning, University of Washington, Seattle, WA, USA

³  Architecture, Landscape Architecture, University of Washington, Seattle, WA, USA

⁴  Department of Geography, University of Washington, Seattle, WA, USA

⁵  Center for Health Studies, Group Health Cooperative, Seattle, WA, USA

⁶  Department of Epidemiology, University of Washington, Seattle, WA, USA

⁷  Department of Medicine, University of Washington, Seattle, WA, USA

⁸  Community and Family Medicine, Dartmouth Medical School, Hanover, NH, USA

⁹  Department of Biostatistics, University of Washington, Seattle, WA, USA

¹⁰  Department of Health Services, University of Washington, Seattle, WA, USA

author email corresponding author email

International Journal of Health Geographics 2008, 7:10doi:10.1186/1476-072X-7-10

Published:	29 February 2008

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.