Using Built Environment Characteristics to Predict Walking for Exercise

Simple item page
dc.contributor.authorLovasi, G.S.
dc.contributor.authorMoudon, A.V.
dc.contributor.authorPearson, A.L.
dc.contributor.authorHurvitz, P.M.
dc.contributor.authorLarson, E.B.
dc.contributor.authorSiscovick, D.S.
dc.contributor.authorBerke, E.M.
dc.contributor.authorLumley, T.
dc.contributor.authorPsaty, B.M.
dc.date.accessioned2010-03-21T20:55:03Z
dc.date.available2010-03-21T20:55:03Z
dc.date.issued2008en
dc.description.abstractBackground 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
dc.identifier.citationLovasi, G.S., Moudon, A.V., Pearson, A.L., Hurvitz, P.M., Larson, E.B., Siscovick, D.S., Berke, E.M., Lumley, T., Psaty, B.M. (2008) Using Built Environment Characteristics to Predict Walking for Exercise. International Journal of Health Geographics, 7(10), pp. 1-13.en
dc.identifier.doihttps://doi.org/10.1186/1476-072X-7-10
dc.identifier.urihttp://hdl.handle.net/10092/3610
dc.language.isoen
dc.publisherUniversity of Canterbury. Geographyen
dc.rights.urihttps://hdl.handle.net/10092/17651en
dc.subjectPublic Healthen
dc.subjectGeographyen
dc.subject.marsdenFields of Research::320000 Medical and Health Sciences::321400 Human Movement and Sports Science::321499 Human movement and sports science (not elsewhere classified)en
dc.subject.marsdenFields of Research::370000 Studies in Human Society::370400 Human Geography::370401 Urban and regional studiesen
dc.titleUsing Built Environment Characteristics to Predict Walking for Exerciseen
dc.typeJournal Article
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
12623223_IJHG_Lovasi_022008.pdf
Size:
381.56 KB
Format:
Adobe Portable Document Format
Download
Collections
Science: Journal Articles