Ruben Brondeel1, Bruno Pannier, Basile Chaix. 1. 1Institut National de la Santé et de la Recherche Médicale, UMR_S 1136, Pierre Louis Institute of Epidemiology and Public Health, Research Team in Social Epidemiology, Paris, FRANCE; 2Sorbonne Universités, Université Pierre et Marie Curie Univ Paris 06, UMR_S 1136, Pierre Louis Institute of Epidemiology and Public Health, Research Team in Social Epidemiology, Paris, FRANCE; 3Ecole des Hautes études en Santé Publique School of Public Health, Rennes, FRANCE; and 4IPC Medical Centre, Paris, FRANCE.
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 d, including the transportation mode, global positioning system, geographical information systems, and accelerometer data. A prediction model of transportation modes was constructed using the random forests method. Finally, we investigated the performance of models on the basis of 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 global positioning system explanatory variables combined with geographical information systems variables performed nearly as well. Relatively good predictions could be made using a model based on the 991 trips of the first 30 participants. CONCLUSIONS: 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 on the basis of a limited number of observations, this method may decrease the workload for participants/researchers and provide relevant trip-level data to investigate relations between transportation and health.
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 d, including the transportation mode, global positioning system, geographical information systems, and accelerometer data. A prediction model of transportation modes was constructed using the random forests method. Finally, we investigated the performance of models on the basis of 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 global positioning system explanatory variables combined with geographical information systems variables performed nearly as well. Relatively good predictions could be made using a model based on the 991 trips of the first 30 participants. CONCLUSIONS: 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 on the basis of a limited number of observations, this method may decrease the workload for participants/researchers and provide relevant trip-level data to investigate relations between transportation and health.
Authors: Jerzy Eisenberg-Guyot; Anne V Moudon; Philip M Hurvitz; Stephen J Mooney; Kathryn B Whitlock; Brian E Saelens Journal: J Transp Health Date: 2019-08-03
Authors: Chelsea Steel; Katie Crist; Amanda Grimes; Carolina Bejarano; Adrian Ortega; Paul R Hibbing; Jasper Schipperijn; Jordan A Carlson Journal: J Meas Phys Behav Date: 2021-10-25
Authors: Minna Aittasalo; Johanna Tiilikainen; Kari Tokola; Jaana Suni; Harri Sievänen; Henri Vähä-Ypyä; Tommi Vasankari; Timo Seimelä; Pasi Metsäpuro; Charlie Foster; Sylvia Titze Journal: Int J Environ Res Public Health Date: 2019-05-13 Impact factor: 3.390
Authors: Duncan S Procter; Angie S Page; Ashley R Cooper; Claire M Nightingale; Bina Ram; Alicja R Rudnicka; Peter H Whincup; Christelle Clary; Daniel Lewis; Steven Cummins; Anne Ellaway; Billie Giles-Corti; Derek G Cook; Christopher G Owen Journal: Int J Behav Nutr Phys Act Date: 2018-09-21 Impact factor: 6.457