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Probabilistic walking models using built environment and sociodemographic predictors.

Anne Vernez Moudon1, Ruizhu Huang2, Orion T Stewart3,4

  • 1Architecture, Landscape Architecture, and Urban Design and Planning, University of Washington, 1107 NE 45th St, Suite 535, Box 354802, Seattle, WA, 98195, USA. moudon@uw.edu.

Population Health Metrics
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Summary
This summary is machine-generated.

Sociodemographic factors, not built environment features, best predicted physical activity levels in Seattle adults. Adding neighborhood data did not improve prediction models for walking or moderate-to-vigorous physical activity (MVPA).

Keywords:
Active travelHome neighborhood domainsPhysical activity

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Area of Science:

  • Public Health
  • Urban Planning
  • Epidemiology

Background:

  • Individual sociodemographic factors and neighborhood built environment (BE) characteristics influence physical activity engagement.
  • Effective methods are needed to model the complex associations between these factors and health-enhancing physical activity.
  • Understanding these relationships is crucial for promoting walking and moderate-to-vigorous physical activity (MVPA).

Purpose of the Study:

  • To develop parsimonious models predicting physical activity (walking and MVPA) based on sociodemographic and built environment (BE) factors.
  • To assess the added value of objective BE measures in predicting physical activity beyond sociodemographic predictors.
  • To identify specific BE and sociodemographic variables associated with physical activity across different neighborhood sizes.

Main Methods:

  • Logistic regression models were fitted using data from 2392 adults in the Seattle region.
  • Objective BE measures from four domains (regional context, neighborhood composition, destinations, transportation) were assessed at two neighborhood sizes (833m and 1666m).
  • Backward elimination identified key predictors, and receiver operating characteristic (ROC) curves compared model predictive performance.

Main Results:

  • The number of relevant BE variables was significantly reduced, with 5 or fewer included in final models.
  • Sociodemographic and BE variables from all four domains were associated with activity, but associations varied by activity type and neighborhood size.
  • Adding BE variables to a sociodemographic model did not significantly improve the prediction of walking or MVPA, as indicated by ROC curve comparisons.

Conclusions:

  • The proposed modeling approach can guide the estimation of activity prediction models tailored to specific populations, activity types, and neighborhood characteristics.
  • Predictive variables for walking and MVPA are specific to the population studied and the built environment characteristics examined.
  • Sociodemographic factors appear to be stronger predictors of physical activity than the built environment features assessed in this study.