Yan Kestens1, Benoit Thierry1, Basile Chaix2. 1. Département de médecine sociale et préventive, École de Santé Publique de l'Université de Montréal, 7101, rue du Parc, Montréal, QC, Canada H3N 1X9; Centre de recherche du CHUM, 850, rue St-Denis, Montréal, QC, Canada H2X 0A9. 2. Institut national de la santé et de la recherche médicale, Institut Pierre Louis d'Epidémiologie et de Santé Publique, UMR S 1136, 27 rue Chaligny, 75012, Paris, France.
Abstract
BACKGROUND: GPS tracking is increasingly used to document daily mobility, allowing refined analysis of daily exposures and health behaviour. Validation of algorithms processing raw GPS data to identify activity locations and trips are lacking. OBJECTIVE: Propose novel ways to evaluate GPS processing algorithms data while validating an existing kernel-based algorithm with real-life GPS tracks. METHODS: Seven-day GPS tracking and GPS-prompted recall interviews were conducted among 234 adult participants of the RECORD GPS Study. Raw GPS data was transformed using a kernel-based algorithm. Two match and nine mismatch configurations are analysed. Algorithm detection of activity locations and trips were validated. RESULTS: Some 95.8% of available GPS time was correctly classified as an activity location or a trip. The algorithm falsely identified a trip for 2.2% of the tracking time, and falsely identified an activity location 0.7% of time. Missed trips and missed activity locations counted for less than .4% of the time. CONCLUSION: The tested kernel-based algorithm provides histories of activity locations and trips that are highly concordant with GPS-prompted follow-up interviews.
BACKGROUND: GPS tracking is increasingly used to document daily mobility, allowing refined analysis of daily exposures and health behaviour. Validation of algorithms processing raw GPS data to identify activity locations and trips are lacking. OBJECTIVE: Propose novel ways to evaluate GPS processing algorithms data while validating an existing kernel-based algorithm with real-life GPS tracks. METHODS: Seven-day GPS tracking and GPS-prompted recall interviews were conducted among 234 adult participants of the RECORD GPS Study. Raw GPS data was transformed using a kernel-based algorithm. Two match and nine mismatch configurations are analysed. Algorithm detection of activity locations and trips were validated. RESULTS: Some 95.8% of available GPS time was correctly classified as an activity location or a trip. The algorithm falsely identified a trip for 2.2% of the tracking time, and falsely identified an activity location 0.7% of time. Missed trips and missed activity locations counted for less than .4% of the time. CONCLUSION: The tested kernel-based algorithm provides histories of activity locations and trips that are highly concordant with GPS-prompted follow-up interviews.
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