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Using GPS-derived speed patterns for recognition of transport modes in adults

BACKGROUND: Identification of active or sedentary modes of transport is of relevance for studies assessing physical activity or addressing exposure assessment. We assessed in a proof-of-principle study if speed as logged by GPSs could be used to identify modes of transport (walking, bicycling, and m...

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Autores principales: Huss, Anke, Beekhuizen, Johan, Kromhout, Hans, Vermeulen, Roel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4320483/
https://www.ncbi.nlm.nih.gov/pubmed/25304171
http://dx.doi.org/10.1186/1476-072X-13-40
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author Huss, Anke
Beekhuizen, Johan
Kromhout, Hans
Vermeulen, Roel
author_facet Huss, Anke
Beekhuizen, Johan
Kromhout, Hans
Vermeulen, Roel
author_sort Huss, Anke
collection PubMed
description BACKGROUND: Identification of active or sedentary modes of transport is of relevance for studies assessing physical activity or addressing exposure assessment. We assessed in a proof-of-principle study if speed as logged by GPSs could be used to identify modes of transport (walking, bicycling, and motorized transport: car, bus or train). METHODS: 12 persons commuting to work walking, bicycling or with motorized transport carried GPSs for two commutes and recorded their mode of transport. We evaluated seven speed metrics: mean, 95(th) percentile of speed, standard deviation of the mean, rate-of-change, standardized-rate-of-change, acceleration and deceleration. We assessed which speed metric would best identify the transport mode using discriminant analyses. We applied cross validation and calculated agreement (Cohen’s Kappa) between actual and derived modes of transport. RESULTS: Mode of transport was reliably classified whenever a person used a mode of transport for longer than one minute. Best results were observed when using the 95(th) percentile of speed, acceleration and deceleration (kappa 0.73). When we combined all motorized traffic into one category, kappa increased to 0.95. CONCLUSIONS: GPS-measured speed enable the identification of modes of transport. Given the current low costs of GPS devices and the built-in capacity of GPS tracking in most smartphones, the use of such devices in large epidemiological studies may facilitate the assessment of physical activity related to transport modes, or improve exposure assessment using automated travel mode detection. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1476-072X-13-40) contains supplementary material, which is available to authorized users.
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spelling pubmed-43204832015-02-08 Using GPS-derived speed patterns for recognition of transport modes in adults Huss, Anke Beekhuizen, Johan Kromhout, Hans Vermeulen, Roel Int J Health Geogr Methodology BACKGROUND: Identification of active or sedentary modes of transport is of relevance for studies assessing physical activity or addressing exposure assessment. We assessed in a proof-of-principle study if speed as logged by GPSs could be used to identify modes of transport (walking, bicycling, and motorized transport: car, bus or train). METHODS: 12 persons commuting to work walking, bicycling or with motorized transport carried GPSs for two commutes and recorded their mode of transport. We evaluated seven speed metrics: mean, 95(th) percentile of speed, standard deviation of the mean, rate-of-change, standardized-rate-of-change, acceleration and deceleration. We assessed which speed metric would best identify the transport mode using discriminant analyses. We applied cross validation and calculated agreement (Cohen’s Kappa) between actual and derived modes of transport. RESULTS: Mode of transport was reliably classified whenever a person used a mode of transport for longer than one minute. Best results were observed when using the 95(th) percentile of speed, acceleration and deceleration (kappa 0.73). When we combined all motorized traffic into one category, kappa increased to 0.95. CONCLUSIONS: GPS-measured speed enable the identification of modes of transport. Given the current low costs of GPS devices and the built-in capacity of GPS tracking in most smartphones, the use of such devices in large epidemiological studies may facilitate the assessment of physical activity related to transport modes, or improve exposure assessment using automated travel mode detection. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1476-072X-13-40) contains supplementary material, which is available to authorized users. BioMed Central 2014-10-11 /pmc/articles/PMC4320483/ /pubmed/25304171 http://dx.doi.org/10.1186/1476-072X-13-40 Text en © Huss et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology
Huss, Anke
Beekhuizen, Johan
Kromhout, Hans
Vermeulen, Roel
Using GPS-derived speed patterns for recognition of transport modes in adults
title Using GPS-derived speed patterns for recognition of transport modes in adults
title_full Using GPS-derived speed patterns for recognition of transport modes in adults
title_fullStr Using GPS-derived speed patterns for recognition of transport modes in adults
title_full_unstemmed Using GPS-derived speed patterns for recognition of transport modes in adults
title_short Using GPS-derived speed patterns for recognition of transport modes in adults
title_sort using gps-derived speed patterns for recognition of transport modes in adults
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4320483/
https://www.ncbi.nlm.nih.gov/pubmed/25304171
http://dx.doi.org/10.1186/1476-072X-13-40
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