An Open-Source Tool To Identify Active Travel From Hip-Worn Accelerometer, Gps And Gis Data

Procter, D, Page, A, Cooper, A and Giles-Corti, B., et al, 2019, 'An Open-Source Tool To Identify Active Travel From Hip-Worn Accelerometer, Gps And Gis Data', The International Journal of Behavioral Nutrition and Physical Activity, vol. 15, pp. 1-10.


Document type: Journal Article
Collection: Journal Articles

Title An Open-Source Tool To Identify Active Travel From Hip-Worn Accelerometer, Gps And Gis Data
Author(s) Procter, D
Page, A
Cooper, A
Giles-Corti, B., et al,
Year 2019
Journal name The International Journal of Behavioral Nutrition and Physical Activity
Volume number 15
Start page 1
End page 10
Total pages 10
Publisher BioMed Central Ltd.
Abstract Background: Increases in physical activity through active travel have the potential to have large beneficial effects on populations, through both better health outcomes and reduced motorized traffic. However accurately identifying travel mode in large datasets is problematic. Here we provide an open source tool to quantify time spent stationary and in four travel modes(walking, cycling, train, motorised vehicle) from accelerometer measured physical activity data, combined with GPS and GIS data. Methods: The Examining Neighbourhood Activities in Built Living Environments in London study evaluates the effect of the built environment on health behaviours, including physical activity. Participants wore accelerometers and GPS receivers on the hip for 7 days. We time-matched accelerometer and GPS, and then extracted data from the commutes of 326 adult participants, using stated commute times and modes, which were manually checked to confirm stated travel mode. This yielded examples of five travel modes: walking, cycling, motorised vehicle, train and stationary. We used this example data to train a gradient boosted tree, a form of supervised machine learning algorithm, on each data point (131,537 points), rather than on journeys. Accuracy during training was assessed using five-fold cross-validation. We also manually identified the travel behaviour of both 21 participants from ENABLE London (402,749 points), and 10 participants from a separate study (STAMP-2, 210,936 points), who were not included in the training data. We compared our predictions against this manual identification to further test accuracy and test generalisability. Results: Applying the algorithm, we correctly identified travel mode 97.3% of the time in cross-validation (mean sensitivity 96.3%, mean active travel sensitivity 94.6%). We showed 96.0% agreement between manual identification and prediction of 21 individuals' travel modes (mean sensitivity 92.3%, mean active travel sensitivity 84.9%) and 96.5%
Subject Urban and Regional Planning not elsewhere classified
Epidemiology
DOI - identifier 10.1186/s12966-018-0724-y
Copyright notice © The Author(s). 2018. Creative Commons Attribution 4.0 International License.
ISSN 1479-5868
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