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

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Chelsea Christie ◽  
Jennifer Vena ◽  
Christine Friedenreich ◽  
Gavin McCormack
Keyword(s):  
Propensity Score ◽  
Built Environment ◽  
Minimal Change ◽  
Multinomial Regression ◽  
Neighbourhood Environment ◽  
Walkability Index ◽  
Self Selection ◽  
Inverse Probability Weight ◽  
Neighbourhood Walkability ◽  
Different Types

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.

scholarly journals Associations between objectively-measured and self-reported neighbourhood walkability on adherence and steps during an internet-delivered pedometer intervention

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242999
Author(s):  
Anna Consoli ◽  
Alberto Nettel-Aguirre ◽  
John C. Spence ◽  
Tara-Leigh McHugh ◽  
Kerry Mummery ◽  
...  
Keyword(s):  
Physical Activity ◽  
Built Environment ◽  
Physical Activity Intervention ◽  
Neighbourhood Environment ◽  
Physical Activity Interventions ◽  
Neighbourhood Walkability ◽  
Walk Score ◽  
Unit Increase ◽  
Steps Per Day ◽  
Objectively Measured

Background Accumulating evidence suggests that the built environment is associated with physical activity. The extent to which the built environment may support adherence to physical activity interventions is unclear. The aim of this study was to investigate whether the neighbourhood built environment constrains or facilitates adherence and steps taken during a 12-week internet-delivered pedometer-based physical activity intervention (UWALK). Method The study was undertaken in Calgary (Canada) between May 2016 and August 2017. Inactive adults (n = 573) completed a telephone survey measuring sociodemographic characteristics and perceived neighbourhood walkability. Following the survey, participants were mailed a pedometer and instructions for joining UWALK. Participants were asked to report their daily pedometer steps into the online program on a weekly basis for 12 weeks (84 days). Walk Score® estimated objective neighbourhood walkability and the Neighbourhood Environment Walkability Scale–Abbreviated (NEWS-A) measured participants self-reported neighbourhood walkability. Regression models estimated covariate-adjusted associations of objective and self-reported walkability with: 1) adherence to the UWALK intervention (count of days with steps reported and count of days with 10000 steps reported), and; 2) average daily pedometer steps. Results On average, participants undertook 8565 (SD = 3030) steps per day, reported steps on 67 (SD = 22.3) of the 84 days, and achieved ≥10000 steps on 22 (SD = 20.5) of the 84 days. Adjusting for covariates, a one-unit increase in self-reported walkability was associated on average with 45.76 (95CI 14.91, 76.61) more daily pedometer steps. Walk Score® was not significantly associated with steps. Neither objective nor self-reported walkability were significantly associated with the UWALK adherence outcomes. Conclusion The neighbourhood built environment may support pedometer-measured physical activity but may not influence adherence to pedometer interventions. Perceived walkability may be more important than objectively-measured walkability in supporting physical activity during pedometer interventions.

Propensity Score Weighting with Generalized Boosted Models to Explore the Effects of the Built Environment and Residential Self-Selection on Travel Behavior

2019 ◽  
Vol 2673 (4) ◽  
pp. 373-383 ◽  
Author(s):  
Yiling Deng ◽  
Yadan Yan
Keyword(s):  
Propensity Score ◽  
Built Environment ◽  
Hierarchical Clustering ◽  
Travel Behavior ◽  
Public Transit ◽  
Average Treatment Effect ◽  
Propensity Score Weighting ◽  
Self Selection ◽  
Environment Variables ◽  
Trip Frequency

Many studies have examined the association between the built environment, residential self-selection, and travel behavior. However, few studies have quantified the relative contribution of the built environment itself. Using the 2012 Nanjing Household Travel Survey data, this study applied hierarchical clustering and propensity score weighting to study the effects of the built environment and residential self-selection on travel behavior. First, residents’ household locations were classified into four built environment patterns using hierarchical clustering based on six built environment variables by loosely following the “5Ds” (i.e., density, diversity, design, destination accessibility, and distance to transit). Second, a powerful machine learning method, generalized boosted model (GBM), was employed to obtain propensity scores. Propensity score weighting, which is more effective for multiple treatments than matching or stratification, was used to control for residential self-selection. Lastly, the observed effect (OBE), the average treatment effect on the population (ATE), and the built environment proportion (BEP) were calculated for the walking trip frequency, bicycle trip frequency, public transit trip frequency, and vehicle kilometers traveled (VKT) of six pairs of built environment patterns. The results show that a high-density, mixed-use, walkable, and transit-accessible built environment is associated with more walking trips and lower VKT but has no impact on bicycle trips and has an inconsistent impact on public transit trips. The effects of some built environment variables on bicycle and public transit trips are tangled. The residential self-selection effect has the greatest impact on VKT (BEP: 48%–77%), followed by the walking trip frequency (BEP: 62%–74%) and the public transit frequency (BEP: 90%–107%).

scholarly journals The role of perceived environment, neighbourhood characteristics, and attitudes in walking behaviour: evidence from a rapidly developing city in China

2019 ◽  
Author(s):  
Eric T. H. Chan ◽  
Tim Schwanen ◽  
David Banister
Keyword(s):  
Built Environment ◽  
Interaction Effects ◽  
Regression Analyses ◽  
Neighbourhood Environment ◽  
Perceived Environment ◽  
Neighbourhood Level ◽  
Different Types ◽  
Neighbourhood Characteristics

Abstract The associations between objective and subjective dimensions of the built environment and walking behaviour have been examined extensively in existing studies. However, the interaction effects of those dimensions of the built environment on walking behaviour are understudied and may be more complex than hitherto suggested. Apart from the subjective dimensions of the built environment, walking attitudes also play a role in moderating these relationships. This paper investigates the independent and joint effects of objective neighbourhood characteristics, people’s perceptions of the neighbourhood environment, and walking attitudes on the frequency of walking by using Shenzhen as a case study. Since those effects may vary across different kinds of walking trips, the analysis looks separately at three major types of walking at the neighbourhood level—walking for work/school, walking for leisure/recreation, and walking for household responsibilities. Logistic regression analyses confirm that the correlates of people’s walking frequency vary considerably among different types of walking. Statistically significant interaction effects of objective neighbourhood characteristics and perceived environment are found. The results suggest that positive perceptions of the environment can compensate for the effect that low objective walkability of neighbourhoods has on people’s walking frequency. When seeking to encouraging walking at the neighbourhood level, policymakers should not only concentrate on improving objective neighbourhood characteristics but also consider people’s perception of the neighbourhood environment and their attitudes towards walking.

Association between residential self-selection and non-residential built environment exposures

2018 ◽  
Vol 54 ◽  
pp. 149-154 ◽  
Author(s):  
Nicholas A. Howell ◽  
Steven Farber ◽  
Michael J. Widener ◽  
Jeff Allen ◽  
Gillian L. Booth
Keyword(s):  
Built Environment ◽  
Self Selection

Associations of the Neighborhood Built Environment with Gestational Weight Gain

2021 ◽  
Author(s):  
William A. Grobman ◽  
Emma G. Crenshaw ◽  
Derek J. Marsh ◽  
Rebecca B. McNeil ◽  
Victoria L. Pemberton ◽  
...  
Keyword(s):  
Physical Activity ◽  
Weight Gain ◽  
Built Environment ◽  
Gestational Weight Gain ◽  
Grocery Stores ◽  
Multinomial Regression ◽  
Lower Odds ◽  
Residential Address ◽  
Gestational Weight ◽  
Nulliparous Women

Objective This study aimed to determine whether specific factors of the built environment related to physical activity and diet are associated with inadequate and excessive gestational weight gain (GWG). Study Design This analysis is based on data from the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-To-Be, a prospective cohort of nulliparous women who were followed from the beginning of their pregnancies through delivery. At each study visit, home addresses were recorded and geocoded. Locations were linked to several built-environment characteristics such as the census tract National Walkability Score (the 2010 Walkability Index) and the number of gyms, parks, and grocery stores within a 3-km radius of residential address. The primary outcome of GWG (calculated as the difference between prepregnancy weight and weight at delivery) was categorized as inadequate, appropriate, or excessive based on weight gained per week of gestation. Multinomial regression (generalized logit) models evaluated the relationship between each factor in the built environment and excessive or inadequate GWG. Results Of the 8,182 women in the analytic sample, 5,819 (71.1%) had excessive GWG, 1,426 (17.4%) had appropriate GWG, and 937 (11.5%) had inadequate GWG. For the majority of variables examined, built environments more conducive to physical activity and healthful food availability were associated with a lower odds of excessive or inadequate GWG category. For example, a higher number of gyms or parks within 3 km of a participant's residential address was associated with lower odds of having excessive (gyms: adjusted odds ratio [aOR] = 0.93 [0.89–0.96], parks: 0.94 [0.90–0.98]) or inadequate GWG (gyms: 0.91 [0.86–0.96]; parks: 0.91 [0.86–0.97]). Similarly, a higher number of grocery stores was associated with lower odds of having excessive GWG (0.94 [0.91–0.97]). Conclusion Among a diverse population of nulliparous women, multiple aspects of the built environment are associated with excessive and inadequate GWG. Key Points

scholarly journals Children’s Transport Built Environments: A Mixed Methods Study of Associations between Perceived and Objective Measures and Relationships with Parent Licence for Independent Mobility in Auckland, New Zealand

2019 ◽  
Vol 16 (8) ◽  
pp. 1361 ◽  
Author(s):  
Melody Smith ◽  
Rebecca Amann ◽  
Alana Cavadino ◽  
Deborah Raphael ◽  
Robin Kearns ◽  
...  
Keyword(s):  
Mixed Methods ◽  
Built Environment ◽  
Parent Perceptions ◽  
Mixed Methods Study ◽  
Objective Measures ◽  
Built Environments ◽  
Independent Mobility ◽  
Neighbourhood Environment ◽  
Mixed Modelling ◽  
Neighbourhood Perceptions

Children’s independent mobility is declining internationally. Parents are the gatekeepers of children’s independent mobility. This mixed methods study investigates whether parent perceptions of the neighbourhood environment align with objective measures of the neighbourhood built environment, and how perceived and objective measures relate to parental licence for children’s independent mobility. Parents participating in the Neighbourhood for Active Kids study (n = 940) answered an open-ended question about what would make their neighbourhoods better for their child’s independent mobility, and reported household and child demographics. Objective measures of the neighbourhood built environment were generated using geographic information systems. Content analysis was used to classify and group parent-reported changes required to improve their neigbourhood. Parent-reported needs were then compared with objective neighbourhood built environment measures. Linear mixed modelling examined associations between parental licence for independent mobility and (1) parent neighbourhood perceptions; and (2) objectively assessed neighbourhood built environment features. Parents identified the need for safer traffic environments. No significant differences in parent reported needs were found by objectively assessed characteristics. Differences in odds of reporting needs were observed for a range of socio-demographic characteristics. Parental licence for independent mobility was only associated with a need for safer places to cycle (positive) and objectively assessed cycling infrastructure (negative) in adjusted models. Overall, the study findings indicate the importance of safer traffic environments for children’s independent mobility.

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