Cargando…

Emerging Technologies for Assessing Physical Activity Behaviors in Space and Time

Precise measurement of physical activity is important for health research, providing a better understanding of activity location, type, duration, and intensity. This article describes a novel suite of tools to measure and analyze physical activity behaviors in spatial epidemiology research. We use i...

Descripción completa

Detalles Bibliográficos
Autores principales: Hurvitz, Philip M., Moudon, Anne Vernez, Kang, Bumjoon, Saelens, Brian E., Duncan, Glen E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3904281/
https://www.ncbi.nlm.nih.gov/pubmed/24479113
http://dx.doi.org/10.3389/fpubh.2014.00002
_version_ 1782301203531563008
author Hurvitz, Philip M.
Moudon, Anne Vernez
Kang, Bumjoon
Saelens, Brian E.
Duncan, Glen E.
author_facet Hurvitz, Philip M.
Moudon, Anne Vernez
Kang, Bumjoon
Saelens, Brian E.
Duncan, Glen E.
author_sort Hurvitz, Philip M.
collection PubMed
description Precise measurement of physical activity is important for health research, providing a better understanding of activity location, type, duration, and intensity. This article describes a novel suite of tools to measure and analyze physical activity behaviors in spatial epidemiology research. We use individual-level, high-resolution, objective data collected in a space-time framework to investigate built and social environment influences on activity. First, we collect data with accelerometers, global positioning system units, and smartphone-based digital travel and photo diaries to overcome many limitations inherent in self-reported data. Behaviors are measured continuously over the full spectrum of environmental exposures in daily life, instead of focusing exclusively on the home neighborhood. Second, data streams are integrated using common timestamps into a single data structure, the “LifeLog.” A graphic interface tool, “LifeLog View,” enables simultaneous visualization of all LifeLog data streams. Finally, we use geographic information system SmartMap rasters to measure spatially continuous environmental variables to capture exposures at the same spatial and temporal scale as in the LifeLog. These technologies enable precise measurement of behaviors in their spatial and temporal settings but also generate very large datasets; we discuss current limitations and promising methods for processing and analyzing such large datasets. Finally, we provide applications of these methods in spatially oriented research, including a natural experiment to evaluate the effects of new transportation infrastructure on activity levels, and a study of neighborhood environmental effects on activity using twins as quasi-causal controls to overcome self-selection and reverse causation problems. In summary, the integrative characteristics of large datasets contained in LifeLogs and SmartMaps hold great promise for advancing spatial epidemiologic research to promote healthy behaviors.
format Online
Article
Text
id pubmed-3904281
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-39042812014-01-29 Emerging Technologies for Assessing Physical Activity Behaviors in Space and Time Hurvitz, Philip M. Moudon, Anne Vernez Kang, Bumjoon Saelens, Brian E. Duncan, Glen E. Front Public Health Public Health Precise measurement of physical activity is important for health research, providing a better understanding of activity location, type, duration, and intensity. This article describes a novel suite of tools to measure and analyze physical activity behaviors in spatial epidemiology research. We use individual-level, high-resolution, objective data collected in a space-time framework to investigate built and social environment influences on activity. First, we collect data with accelerometers, global positioning system units, and smartphone-based digital travel and photo diaries to overcome many limitations inherent in self-reported data. Behaviors are measured continuously over the full spectrum of environmental exposures in daily life, instead of focusing exclusively on the home neighborhood. Second, data streams are integrated using common timestamps into a single data structure, the “LifeLog.” A graphic interface tool, “LifeLog View,” enables simultaneous visualization of all LifeLog data streams. Finally, we use geographic information system SmartMap rasters to measure spatially continuous environmental variables to capture exposures at the same spatial and temporal scale as in the LifeLog. These technologies enable precise measurement of behaviors in their spatial and temporal settings but also generate very large datasets; we discuss current limitations and promising methods for processing and analyzing such large datasets. Finally, we provide applications of these methods in spatially oriented research, including a natural experiment to evaluate the effects of new transportation infrastructure on activity levels, and a study of neighborhood environmental effects on activity using twins as quasi-causal controls to overcome self-selection and reverse causation problems. In summary, the integrative characteristics of large datasets contained in LifeLogs and SmartMaps hold great promise for advancing spatial epidemiologic research to promote healthy behaviors. Frontiers Media S.A. 2014-01-28 /pmc/articles/PMC3904281/ /pubmed/24479113 http://dx.doi.org/10.3389/fpubh.2014.00002 Text en Copyright © 2014 Hurvitz, Moudon, Kang, Saelens and Duncan. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Hurvitz, Philip M.
Moudon, Anne Vernez
Kang, Bumjoon
Saelens, Brian E.
Duncan, Glen E.
Emerging Technologies for Assessing Physical Activity Behaviors in Space and Time
title Emerging Technologies for Assessing Physical Activity Behaviors in Space and Time
title_full Emerging Technologies for Assessing Physical Activity Behaviors in Space and Time
title_fullStr Emerging Technologies for Assessing Physical Activity Behaviors in Space and Time
title_full_unstemmed Emerging Technologies for Assessing Physical Activity Behaviors in Space and Time
title_short Emerging Technologies for Assessing Physical Activity Behaviors in Space and Time
title_sort emerging technologies for assessing physical activity behaviors in space and time
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3904281/
https://www.ncbi.nlm.nih.gov/pubmed/24479113
http://dx.doi.org/10.3389/fpubh.2014.00002
work_keys_str_mv AT hurvitzphilipm emergingtechnologiesforassessingphysicalactivitybehaviorsinspaceandtime
AT moudonannevernez emergingtechnologiesforassessingphysicalactivitybehaviorsinspaceandtime
AT kangbumjoon emergingtechnologiesforassessingphysicalactivitybehaviorsinspaceandtime
AT saelensbriane emergingtechnologiesforassessingphysicalactivitybehaviorsinspaceandtime
AT duncanglene emergingtechnologiesforassessingphysicalactivitybehaviorsinspaceandtime