title: The association between objectively measured neighborhood features and walking in middle-aged adults.
abstract: OBJECTIVE
  Explore the role of the neighborhood environment in supporting walking.
  
  
  METHODS
  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).
  
  
  METHODS
  Brisbane City Local Government Area, Australia, 2007.
  
  
  METHODS
  Brisbane residents aged 40 to 65 years.
  
  
  METHODS
  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.
  
  
  METHODS
  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] 5 1.93; 99% confidence intervals [CI], 1.32-2.80), greatest residential density (OR 5 1.47; 99% CI, 1.02-2.12), least tree coverage (OR 5 1.69; 99% CI, 1.13-2.51), most bikeways (OR 5 1.60; 99% CI, 1.16-2.21), and most streetlights (OR 5 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 5 2.06; 99% CI, 1.41-3.02).
  
  
  CONCLUSIONS
  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.
authors.affiliation: Queensland University of Technology, Australia. lm.wilson@qut.edu.au
authors.forename: Lee-Ann M
authors.initials: LA
authors.lastname: Wilson
authors.forename: Billie
authors.initials: B
authors.lastname: Giles-Corti
authors.forename: Nicola W
authors.initials: NW
authors.lastname: Burton
authors.forename: Katrina
authors.initials: K
authors.lastname: Giskes
authors.forename: Michele
authors.initials: M
authors.lastname: Haynes
authors.forename: Gavin
authors.initials: G
authors.lastname: Turrell
mesh_terms: D000328:Adult; D000368:Aged; D001315:Australia; D003430:Cross-Sectional Studies; D004779:Environment Design / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D012111:Residence Characteristics / Q000706:statistics & numerical data*; D012931:Social Environment; D016138:Walking / Q000706:statistics & numerical data*