4.6 Article

Spatial Energetics Integrating Data From GPS, Accelerometry, and GIS to Address Obesity and Inactivity

期刊

AMERICAN JOURNAL OF PREVENTIVE MEDICINE
卷 51, 期 5, 页码 792-800

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.amepre.2016.06.006

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资金

  1. National Cancer Institute (NCI) Centers for Transdisciplinary Research on Energetics and Cancer (TREC) [U54 CA155626, U54 CA155435, U54 CA155850, U54 CA155796, U01 CA116850]
  2. NCI as part of the TREC initiative
  3. Harvard National Heart, Lung, and Blood Institute Cardiovascular Epidemiology Training Grant [T32 HL 098048]
  4. NIH [UM1 CA176726, R01 ES017017]

向作者/读者索取更多资源

To address the current obesity and inactivity epidemics, public health researchers have attempted to identify spatial factors that influence physical inactivity and obesity. Technologic and methodologic developments have led to a revolutionary ability to examine dynamic, high-resolution measures of temporally matched location and behavior data through GPS, accelerometry, and GIS. These advances allow the investigation of spatial energetics, high-spatiotemporal resolution data on location and time-matched energetics, to examine how environmental characteristics, space, and time are linked to activity-related health behaviors with far more robust and detailed data than in previous work. Although the transdisciplinary field of spatial energetics demonstrates promise to provide novel insights on how individuals and populations interact with their environment, there remain significant conceptual, technical, analytical, and ethical challenges stemming from the complex data streams that spatial energetics research generates. First, it is essential to better understand what spatial energetics data represent, the relevant spatial context of analysis for these data, and if spatial energetics can establish causality for development of spatially relevant interventions. Second, there are significant technical problems for analysis of voluminous and complex data that may require development of spatially aware scalable computational infrastructures. Third, the field must come to agreement on appropriate statistical methodologies to account for multiple observations per person. Finally, these challenges must be considered within the context of maintaining participant privacy and security. This article describes gaps in current practice and understanding and suggests solutions to move this promising area of research forward. (C) 2016 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

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