Date

May 2007

Document Type

Thesis

Degree Name

M.P.H.

Department

Dept. of Public Health and Preventive Medicine

Institution

Oregon Health & Science University

Abstract

Research supporting associations between built environment and physical activity is limited by the absence of a reliable, valid, and objective measure of the built environment. This study assesses the validity of the Senior Walking Environmental Assessment Tool (SWEAT), a reliable 35-question instrument measuring a variety of street, sidewalk, and building characteristics. Neighborhood built environment was objectively measured through observation of street-segments in the quarter-mile surrounding each participant's home with SWEAT and Geographic Information Software (GIS). Two methods of describing the built environment in relation to walking using SWEAT and GIS variables were compared. Principal Components Analysis (PCA) was used to create four indices: Functionality, Maintenance, Comfort/Safety, and Pleasantness. A priori hypotheses in the conceptual model behind SWEAT identified four separate indices: Functional, Safety, Aesthetics, and Destinations. Walking behavior (destination walking and exercise walking) data and relevant covariates from a cross-sectional survey of community-dwelling adults (n=120) in Portland, Oregon were used to evaluate the association between each index and walking behavior, while adjusting for confounding variables. All indices from the conceptual model were independently associated with walking for transportation as part of daily routine, while only Aesthetics was associated with walking for exercise. Of the PCA indices Functionality and Pleasantness were associated with walking for transportation; Comfort/Safety and Maintenance were associated with walking for exercise. While the conceptual model explains transportation walking, the PCA indices better explain both transportation and exercise walking behaviors. The results of this study establish SWEAT as a valid audit tool and propose a method of operationalizing street-level data into neighborhood-level variables.

Identifier

doi:10.6083/M4TB14XV

School

School of Medicine

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