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Combining sensor tracking with a GPS-based mobility survey to better measure physical activity in trips: public transport generates walking

BACKGROUND: Policymakers need accurate data to develop efficient interventions to promote transport physical activity. Given the imprecise assessment of physical activity in trips, our aim was to illustrate novel advances in the measurement of walking in trips, including in trips incorporating non-w...

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Autores principales: Chaix, Basile, Benmarhnia, Tarik, Kestens, Yan, Brondeel, Ruben, Perchoux, Camille, Gerber, Philippe, Duncan, Dustin T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6781383/
https://www.ncbi.nlm.nih.gov/pubmed/31590666
http://dx.doi.org/10.1186/s12966-019-0841-2
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author Chaix, Basile
Benmarhnia, Tarik
Kestens, Yan
Brondeel, Ruben
Perchoux, Camille
Gerber, Philippe
Duncan, Dustin T.
author_facet Chaix, Basile
Benmarhnia, Tarik
Kestens, Yan
Brondeel, Ruben
Perchoux, Camille
Gerber, Philippe
Duncan, Dustin T.
author_sort Chaix, Basile
collection PubMed
description BACKGROUND: Policymakers need accurate data to develop efficient interventions to promote transport physical activity. Given the imprecise assessment of physical activity in trips, our aim was to illustrate novel advances in the measurement of walking in trips, including in trips incorporating non-walking modes. METHODS: We used data of 285 participants (RECORD MultiSensor Study, 2013–2015, Paris region) who carried GPS receivers and accelerometers over 7 days and underwent a phone-administered web mobility survey on the basis of algorithm-processed GPS data. With this mobility survey, we decomposed trips into unimodal trip stages with their start/end times, validated information on travel modes, and manually complemented and cleaned GPS tracks. This strategy enabled to quantify walking in trips with different modes with two alternative metrics: distance walked and accelerometry-derived number of steps taken. RESULTS: Compared with GPS-based mobility survey data, algorithm-only processed GPS data indicated that the median distance covered by participants per day was 25.3 km (rather than 23.4 km); correctly identified transport time vs. time at visited places in 72.7% of time; and correctly identified the transport mode in 67% of time (and only in 55% of time for public transport). The 285 participants provided data for 8983 trips (21,163 segments of observation). Participants spent a median of 7.0% of their total time in trips. The median distance walked per trip was 0.40 km for entirely walked trips and 0.85 km for public transport trips (the median number of accelerometer steps were 425 and 1352 in the corresponding trips). Overall, 33.8% of the total distance walked in trips and 37.3% of the accelerometer steps in trips were accumulated during public transport trips. Residents of the far suburbs cumulated a 1.7 times lower distance walked per day and a 1.6 times lower number of steps during trips per 8 h of wear time than residents of the Paris core city. CONCLUSIONS: Our approach complementing GPS and accelerometer tracking with a GPS-based mobility survey substantially improved transport mode detection. Our findings suggest that promoting public transport use should be one of the cornerstones of policies to promote physical activity. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12966-019-0841-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-67813832019-10-17 Combining sensor tracking with a GPS-based mobility survey to better measure physical activity in trips: public transport generates walking Chaix, Basile Benmarhnia, Tarik Kestens, Yan Brondeel, Ruben Perchoux, Camille Gerber, Philippe Duncan, Dustin T. Int J Behav Nutr Phys Act Research BACKGROUND: Policymakers need accurate data to develop efficient interventions to promote transport physical activity. Given the imprecise assessment of physical activity in trips, our aim was to illustrate novel advances in the measurement of walking in trips, including in trips incorporating non-walking modes. METHODS: We used data of 285 participants (RECORD MultiSensor Study, 2013–2015, Paris region) who carried GPS receivers and accelerometers over 7 days and underwent a phone-administered web mobility survey on the basis of algorithm-processed GPS data. With this mobility survey, we decomposed trips into unimodal trip stages with their start/end times, validated information on travel modes, and manually complemented and cleaned GPS tracks. This strategy enabled to quantify walking in trips with different modes with two alternative metrics: distance walked and accelerometry-derived number of steps taken. RESULTS: Compared with GPS-based mobility survey data, algorithm-only processed GPS data indicated that the median distance covered by participants per day was 25.3 km (rather than 23.4 km); correctly identified transport time vs. time at visited places in 72.7% of time; and correctly identified the transport mode in 67% of time (and only in 55% of time for public transport). The 285 participants provided data for 8983 trips (21,163 segments of observation). Participants spent a median of 7.0% of their total time in trips. The median distance walked per trip was 0.40 km for entirely walked trips and 0.85 km for public transport trips (the median number of accelerometer steps were 425 and 1352 in the corresponding trips). Overall, 33.8% of the total distance walked in trips and 37.3% of the accelerometer steps in trips were accumulated during public transport trips. Residents of the far suburbs cumulated a 1.7 times lower distance walked per day and a 1.6 times lower number of steps during trips per 8 h of wear time than residents of the Paris core city. CONCLUSIONS: Our approach complementing GPS and accelerometer tracking with a GPS-based mobility survey substantially improved transport mode detection. Our findings suggest that promoting public transport use should be one of the cornerstones of policies to promote physical activity. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12966-019-0841-2) contains supplementary material, which is available to authorized users. BioMed Central 2019-10-07 /pmc/articles/PMC6781383/ /pubmed/31590666 http://dx.doi.org/10.1186/s12966-019-0841-2 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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 Research
Chaix, Basile
Benmarhnia, Tarik
Kestens, Yan
Brondeel, Ruben
Perchoux, Camille
Gerber, Philippe
Duncan, Dustin T.
Combining sensor tracking with a GPS-based mobility survey to better measure physical activity in trips: public transport generates walking
title Combining sensor tracking with a GPS-based mobility survey to better measure physical activity in trips: public transport generates walking
title_full Combining sensor tracking with a GPS-based mobility survey to better measure physical activity in trips: public transport generates walking
title_fullStr Combining sensor tracking with a GPS-based mobility survey to better measure physical activity in trips: public transport generates walking
title_full_unstemmed Combining sensor tracking with a GPS-based mobility survey to better measure physical activity in trips: public transport generates walking
title_short Combining sensor tracking with a GPS-based mobility survey to better measure physical activity in trips: public transport generates walking
title_sort combining sensor tracking with a gps-based mobility survey to better measure physical activity in trips: public transport generates walking
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6781383/
https://www.ncbi.nlm.nih.gov/pubmed/31590666
http://dx.doi.org/10.1186/s12966-019-0841-2
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