4.1 Article

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

期刊

AMERICAN JOURNAL OF HEALTH PROMOTION
卷 24, 期 5, 页码 354-361

出版社

SAGE PUBLICATIONS INC
DOI: 10.4278/ajhp.080603-QUAN-81

关键词

Environment; Residence Characteristics; Walking; Principal Component Analysis; Research Design; Prevention Research

资金

  1. NCI NIH HHS [R21 CA109920-02, R21 CA109920, CA109920] Funding Source: Medline
  2. NIA NIH HHS [R03 AG022240-01] Funding Source: Medline
  3. PHS HHS [G022240] Funding Source: Medline

向作者/读者索取更多资源

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 inter-views. 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. (Am J Health Promot 2010;24[5]:354-361.)

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