Evidence map›Paper›PMID 31590666›Full record

ArticleThe international journal of behavioral nutrition and physical activity2019

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

Basile Chaix, Tarik Benmarhnia, Yan Kestens, Ruben Brondeel, Camille Perchoux, Philippe Gerber, Dustin T Duncan

Open access · goldAbstract read
In one paragraph

Article in The international journal of behavioral nutrition and physical activity, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 2 pooled it
3.1field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 57 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 5 institutions in 4 countries.

Basile ChaixSorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, Nemesis team, Faculté de Médecine Saint-Antoine, 27 rue Chaligny, 75012, Paris, France. basile.chaix@iplesp.upmc.fr.ORCID 0000-0003-1631-8630
Tarik BenmarhniaDepartment of Family Medicine and Public Health & Scripps Institution of Oceanography, University of California in San Diego, 9500 Gilman Drive #0725, La Jolla, CA, 92093, USA.
Yan KestensDepartment of Social and Preventive Medicine, École de Santé Publique de l'Université de Montréal, Centre de recherche du CHUM, Tour Saint-Antoine, 850 Saint-Denis, S03-280, Montréal, H2X 0A9, Canada.
Ruben BrondeelDepartment of Social and Preventive Medicine, École de Santé Publique de l'Université de Montréal, Centre de recherche du CHUM, Tour Saint-Antoine, 850 Saint-Denis, S03-280, Montréal, H2X 0A9, Canada.
Camille PerchouxLuxembourg Institute of Socio-Economic Research, Maison des Sciences Humaines, 11 Porte des Sciences, L-4366, Esch-sur-Alzette, Luxembourg.
Philippe GerberLuxembourg Institute of Socio-Economic Research, Maison des Sciences Humaines, 11 Porte des Sciences, L-4366, Esch-sur-Alzette, Luxembourg.
Dustin T DuncanSpatial Epidemiology Lab, Department of Population Health, School of Medicine, New York University, 180 Madison Avenue, New York, NY, 10016, USA.
Luxembourg Institute of Socio-Economic Research · LUUniversité de Montréal · CAInserm · FRNew York University · USScripps Institution of Oceanography · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPolicymakers 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.

methodsWe 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.

resultsCompared 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.

conclusionsOur 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.

Indexed as

Geographic Information SystemsPublic HealthTransportationAccelerometryHumansSignal Processing, Computer-AssistedWalkingAccelerometryGlobal positioning systemPublic transportTransportWalking

Identifiers

PMID31590666
PMCPMC6781383
OpenAlexW2978697067

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.