Re-creating daily mobility histories for health research from raw GPS tracks: Validation of a kernel-based algorithm using real-life data

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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.

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