596Two approaches for estimating propensity score weights for examining neighbourhood built environment and walking changes

Abstract

Abstract Background Walking is associated with the built environment, however, this association may be biased by residential self-selection. This study examined how walking duration changed with residential relocation, while accounting for unbalanced covariates that may contribute to residential self-selection, using two different propensity score inverse probability weight (IPW) methods. Methods Urban participants (n = 703) of Alberta’s Tomorrow Project with pre- and post-relocation neighbourhood built environment and walking data were included. A walkability index was created by aggregating estimates for population density, street connectivity, and destination diversity. Participants were categorized into three groups based on change in residential walkability (decreased, minimal change, or increased). The association between changes in walkability and walking duration (min/week) was modelled with linear regression. Two types of IPWs were applied: 1) manually generated from multinomial regression models, and 2) generated from generalized boosted models. Results All three groups increased walking duration from pre- to post-relocation, however the largest increase was among participants who had increased walkability (M = 73.2, SD = 388), followed by those with minimal change (M = 60.0, SD = 382) and decreased (M = 50.2, SD = 374) walkability. Longitudinal associations between walkability change and walking were not statistically significant (p < 0.05) in models with or without IPWs. Conclusions Changes in neighbourhood walkability were not associated with changes in walking, regardless of how the sample was weighted. Further research should examine changes in the neighbourhood environment with different types of walking and physical activity behaviours. Key messages IPW methods can be used to account for unbalanced covariates in analyses that involve possible self-selection bias.