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Using global positioning systems to study health-related mobility and participation

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  • Brusilovskiy, Eugene
  • Klein, Louis A.
  • Salzer, Mark S.

Abstract

Community participation, as indicated by mobility and engagement in socially meaningful activities, is a central component of health based on the International Classification of Health, Functioning, and Disease (WHO, 2001). Global positioning systems (GPS) technology is emerging as a tool for tracking mobility and participation in health and disability-related research. This paper fills a gap in the literature and provides a thorough description of a method that can be used to generate a number of different variables related to the constructs of mobility and participation from GPS data. Here, these variables are generated with the help of ST-DBSCAN, a spatiotemporal data mining algorithm. The variables include the number of unique destinations, activity space area, distance traveled, time in transit, and time at destinations. Data obtained from five individuals with psychiatric disabilities who carried GPS-enabled cell phones for two weeks are presented. Within- and across- individual variability on these constructs was observed. Given the feasibility of gathering data with GPS, larger scale studies of mobility and participation employing this method are warranted.

Suggested Citation

  • Brusilovskiy, Eugene & Klein, Louis A. & Salzer, Mark S., 2016. "Using global positioning systems to study health-related mobility and participation," Social Science & Medicine, Elsevier, vol. 161(C), pages 134-142.
    • Handle: RePEc:eee:socmed:v:161:y:2016:i:c:p:134-142
    DOI: 10.1016/j.socscimed.2016.06.001
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    References listed on IDEAS

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    1. Rogalsky, Jennifer, 2010. "The working poor and what GIS reveals about the possibilities of public transit," Journal of Transport Geography, Elsevier, vol. 18(2), pages 226-237.
    2. Ron N. Buliung & Pavlos S. Kanaroglou, 2006. "Urban Form and Household Activity‐Travel Behavior," Growth and Change, Wiley Blackwell, vol. 37(2), pages 172-199, June.
    3. Li Yin & Samina Raja & Xiao Li & Yuan Lai & Leonard Epstein & James Roemmich, 2013. "Neighbourhood for Playing: Using GPS, GIS and Accelerometry to Delineate Areas within which Youth are Physically Active," Urban Studies, Urban Studies Journal Limited, vol. 50(14), pages 2922-2939, November.
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    Cited by:

    1. Jason Y. Scully & Anne Vernez Moudon & Philip M. Hurvitz & Anju Aggarwal & Adam Drewnowski, 2019. "A Time-Based Objective Measure of Exposure to the Food Environment," IJERPH, MDPI, vol. 16(7), pages 1-14, April.
    2. Brusilovskiy, Eugene & Klein, Louis A. & Townley, Greg & Snethen, Gretchen & McCormick, Bryan & Hiremath, Shivayogi V. & Salzer, Mark S., 2020. "Examining the relationship between community mobility and participation using GPS and self-report data," Social Science & Medicine, Elsevier, vol. 265(C).
    3. Fillekes, Michelle Pasquale & Röcke, Christina & Katana, Marko & Weibel, Robert, 2019. "Self-reported versus GPS-derived indicators of daily mobility in a sample of healthy older adults," Social Science & Medicine, Elsevier, vol. 220(C), pages 193-202.
    4. Chambers, T. & Pearson, A.L. & Kawachi, I. & Rzotkiewicz, Z. & Stanley, J. & Smith, M. & barr, M. & Ni Mhurchu, C. & Signal, L., 2017. "Kids in space: Measuring children's residential neighborhoods and other destinations using activity space GPS and wearable camera data," Social Science & Medicine, Elsevier, vol. 193(C), pages 41-50.

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