Citation

Brondeel R, Pannier B, Chaix B. Med. Sci. Sports Exerc. 2015; 47(12): 2669-2675.

Affiliation

1INSERM, UMR_S 1136, Pierre Louis Institute of Epidemiology and Public Health, Research team in social epidemiology, Paris, France; 2Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1136, Pierre Louis Institute of Epidemiology and Public Health, Research team in social epidemiology, Paris, France; 3EHESP School of Public Health, Rennes, France; 4IPC Medical Centre, Paris, France.

Copyright

(Copyright © 2015, Lippincott Williams and Wilkins)

DOI

10.1249/MSS.0000000000000704

PMID

25984892

Abstract

INTRODUCTION: Active transportation is a substantial source of physical activity, which has a positive influence on many health outcomes. A survey of transportation modes for each trip is challenging, time-consuming, and requires substantial financial investments. This study proposes a passive collection method and the prediction of modes at the trip level using random forests.

METHODS: The RECORD GPS Study collected real-life trip data from 236 participants over 7 days, including the transportation mode, GPS, GIS, and accelerometer data. A prediction model of transportation modes was constructed using the random forests method. Finally, we investigated the performance of models based on a limited number of participants/trips to predict transportation modes for a large number of trips.

RESULTS: The full model had a correct prediction rate of 90%. A simpler model of GPS explanatory variables combined with GIS variables performed nearly as well. Relatively good predictions could be made using a model based on the 991 trips of the first 30 participants.

CONCLUSION: This study uses real-life data from a large sample set to test a method for predicting transportation modes at the trip level, thereby providing a useful complement to time unit-level prediction methods. By enabling predictions based on a limited number of observations, this method may decrease the workload for participants/researchers and provide relevant trip-level data to investigate relationships between transportation and health.

Language: en