Associations Between the Built Environment in GPS-Derived Activity Spaces and Sedentary Behavior, Light Physical Activity, and Moderate-to-Vigorous Physical Activity

Dante G. Vittor, Jeffrey S. Wilson, Scott E. Crouter, Benjamin G. Ethier, Ling Shi, Sarah M. Camhi and Philip J. Troped ()
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Dante G. Vittor: Manning College of Nursing and Health Sciences, University of Massachusetts Boston, Boston, MA 02125, USA
Jeffrey S. Wilson: Department of Geography, Indiana University Indianapolis, Indianapolis, IN 46202, USA
Scott E. Crouter: Department of Kinesiology, Recreation, and Sport Studies, University of Tennessee Knoxville, Knoxville, TN 37996, USA
Benjamin G. Ethier: Manning College of Nursing and Health Sciences, University of Massachusetts Boston, Boston, MA 02125, USA
Ling Shi: Manning College of Nursing and Health Sciences, University of Massachusetts Boston, Boston, MA 02125, USA
Sarah M. Camhi: College of Arts and Sciences, University of San Francisco, San Francisco CA 94117, USA
Philip J. Troped: Manning College of Nursing and Health Sciences, University of Massachusetts Boston, Boston, MA 02125, USA

IJERPH, 2025, vol. 22, issue 4, 1-14

Abstract: Built environment and physical activity (PA) studies have predominantly used fixed or home-centric approaches to identify environmental exposures. In this study, GPS-derived daily activity spaces were used to examine the relationships between the built environment and sedentary behavior (SB), light PA (LPA), and moderate-to-vigorous PA (MVPA). Thirty-one adults were assessed with activity monitors and GPS units. Three types of activity spaces were created: 50 m buffered GPS tracks, minimum convex hulls (MCHs), and standard deviational ellipses (SDEs). The environmental variables included land use mix, greenness, and intersection, multi-use trail, bike infrastructure, and bike station densities. Repeated measures regression was used to test the associations for 141 person-days, controlling for age, gender, income, body mass index, crime, precipitation, and temperature. Greenness within MCH activity spaces was positively associated with LPA ( p = 0.02). The bike infrastructure density within SDE spaces had a significant positive association with MVPA ( p = 0.04). Multi-use trail, bike infrastructure, and bike station densities had significant negative associations with LPA ( p ≤ 0.05). There were no significant adjusted associations with SB. The few significant associations in this study varied by outcome and type of activity space. Further studies are needed to determine optimal, yet flexible methods for activity spaces in built environment and PA research.

Keywords: accelerometry; built environment; environment; motion sensors; physical activity; sedentary behavior; activity space (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2025
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