The Association between Objectively Measured Neighborhood Features and Walking in Middle-Aged Adults

Abstract

Purpose.

Explore the role of the neighborhood environment in supporting walking.

Design.

Cross-sectional study of 10,286 residents of 200 neighborhoods. Participants were selected using a stratified two-stage cluster design. Data were collected by mail survey (68.5% response rate).

Setting.

Brisbane City Local Government Area, Australia, 2007.

Subjects.

Brisbane residents aged 40 to 65 years.

Measures.

Environmental: street connectivity, residential density, hilliness, tree coverage, bikeways, and streetlights within a 1-km circular buffer from each resident's home; and network distance to nearest river or coast, public transport, shop, and park. Walking: minutes walked in the previous week: <30 minutes, ≥30 to <90 minutes, ≥90 to <150 minutes, ≥150 to <300 minutes, and ≥300 minutes.

Analysis.

The association between each neighborhood characteristic and walking was examined using multilevel multinomial logistic regression, and the model parameters were estimated using Markov chain Monte Carlo simulation.

Results.

After adjustment for individual factors, the likelihood of walking for more than 300 minutes (relative to <30 minutes) was highest in areas with the most connectivity (odds ratio [OR] = 1.93; 99% confidence intervals [CI], 1.32–2.80), greatest residential density (OR = 1.47; 99% CI, 1.02–2.12), least tree coverage (OR = 1.69; 99% CI, 1.13–2.51), most bikeways (OR = 1.60; 99% CI, 1.16–2.21), and most streetlights (OR = 1.50; 99% CI, 1.07–2.11). The likelihood of walking for more than 300 minutes was also higher among those who lived closest to a river or the coast (OR = 2.06; 99% CI, 1.41–3.02).

Conclusion.

The likelihood of meeting (and exceeding) physical activity recommendations on the basis of walking was higher in neighborhoods with greater street connectivity and residential density, more streetlights and bikeways, closer proximity to waterways, and less tree coverage. Interventions targeting these neighborhood characteristics may lead to improved environmental quality as well as lower rates of overweight and obesity and associated chromic disease. (Am J Health Promot 2011;25[4]:e12–e21.)

Keywords

Walking Environment Neighborhood GIS Prevention Research Manuscript format: research Research purpose: modeling/relationship testing Study design: nonexperimental Outcome measure: behavioral Setting: local community Health focus: fitness/physical activity Strategy: built environment Target population age: adults Target population circumstances: geographic location

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