A Clustering Approach for Modeling and Analyzing Changes in Physical Activity Behaviors From Accelerometers

Abstract

To evaluate the impact of Health interventions promoting physical activity, researchers typically conduct pre- and post-assessments using accelerometers. While aggregated metrics such as daily counts, daily steps and time spent at various intensity levels are commonly used, very few studies exploit the richness of the data often collected with a very fine granularity. We investigate the benefit of a deeper analysis of wrist accelerometry data to understand physical activity behaviours throughout the day, as well as how these may change overtime. To analyse physical activity behaviour changes, we propose a methodology that extracts bouts of physical activity characterised by their activity levels and duration, and uses these as features to cluster participants' daily and hourly behaviours. We then compare these clusters to assess changes following an intervention promoting physical activity in children. We demonstrate that this approach provides a more insightful analysis of the physical activity behaviours because it highlights the nature and the timing of behaviour changes, when present. We illustrate this methodology using data from research-grade activity trackers (GENEActiv) and explain the insights discovered in the context of an intervention aimed at educating school children about healthy behaviours.

Keywords

• Accelerometer
• activity trackers
• behavior clustering
• data mining
• health education
• physical activity