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Using Built Environmental Observation Tools: Comparing Two Methods of Creating a Measure of the Built Environment
Journal article   Open access   Peer reviewed

Using Built Environmental Observation Tools: Comparing Two Methods of Creating a Measure of the Built Environment

Erin M Keast, Nichole E Carlson, Nancy J Chapman and Yvonne L Michael
American journal of health promotion, v 24(5), pp 354-361
May 2010
PMID: 20465151
url
https://doi.org/10.4278/ajhp.080603-QUAN-81View
Published, Version of Record (VoR) Open

Abstract

Residence Characteristics Research purpose: instrument development Manuscript format: research Outcome measure: behavioral Health focus: fitness/physical activity Prevention Research Walking Strategy: built environment Target population: adults Target population circumstances: geographic location Environment Principal Component Analysis Research Design Setting: local community Study design: nonexperimental
Purpose. Identify an efficient method of creating a comprehensive and concise measure of the built environment integrating data from geographic information systems (GIS) and the Senior Walking Environmental Assessment Tool (SWEAT). Design. Cross-sectional study using a population sample. Setting. Eight municipally defined neighborhoods in Portland, Oregon. Subjects. Adult residents (N = 120) of audited segments (N = 363). Measures. We described built environmental features using SWEAT audits and GIS data. We obtained information on walking behaviors and potential confounders through in-person interviews. Analysis. We created two sets of environmental measures, one based on the conceptual framework used to develop SWEAT and another using principal component analysis (PCA). Each measure's association with walking for transportation and exercise was then assessed and compared using logistic regression. Results. A priori measures (destinations, safety, aesthetics, and functionality) and PCA measures (accessibility, comfort/safety, maintenance, and pleasantness) were analogous in conceptual meaning and had similar associations with walking. Walking for transportation was associated with destination accessibility and functional elements, whereas walking for exercise was associated with maintenance of the walking area and protection from traffic. However, only PCA measures consistently reached statistical significance. Conclusion. The measures created with PCA were more parsimonious than those created a priori. Performing PCA is an efficient method of combining and scoring SWEAT and GIS data.

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Collaboration types
Domestic collaboration
Web of Science research areas
Public, Environmental & Occupational Health
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