Activity spaces in place and health research: Novel exposure measures, data collection tools, and designs. (July 2019)

• Record Type: Journal Article
• Authors: Perchoux, Camille; Chaix, Basile; Kestens, Yan
• Is Part Of: Health & place. Volume 58(2019:Jul.)
• Publication Date: 2019-07
• Subjects: Activity space -- Selective daily mobility bias -- Neighborhood effects -- Causal inference; Health -- Social aspects -- Periodicals; Health services accessibility -- Periodicals; Public health -- Periodicals; Political planning -- Periodicals; Social medicine -- Periodicals; Epidemiology -- Periodicals; Health Policy -- Periodicals; Health Services Accessibility -- Periodicals; Public Health -- Periodicals; Public Policy -- Periodicals; Sociology, Medical -- Periodicals; Épidémiologie -- Périodiques; Politique sanitaire -- Périodiques; Santé, Services de -- Accessibilité -- Périodiques; Health services accessibility; Health -- Social aspects; Political planning; Public health; Social medicine; Periodicals; 613
• DOI: 10.1016/j.healthplace.2019.05.008
• Languages: English
• ISSNs: 1353-8292

Abstract

Abstract: Activity space research provides a framework to consider mobility while linking environments to behaviors in the study of neighborhood effects on health. Increased use of wearable location sensors provides new opportunities to observe and analyze fine-grained spatial and temporal information on individuals' mobility patterns, environmental exposures and behaviors; however, these analysis does not easily translate into causal inference. Additional dimensions underlying behavioral decision-making likely influence or even modify environmental effects on behaviors. This commentary discusses how further progresses in exposure measurement, integration of data collection tools, and development of study designs could support future interventions to optimize how environments shape health profiles and inequities. Highlights: Activity space research accounts for mobility when linking environments to behaviors. Spatio-temporal data on behaviors do not automatically translate in causal inference. Behavioral decision-making information beyond exposure could improve causal inference. Novel data collection tools and designs will further inform health interventions.