Detecting behaviour changes in accelerometer data

How can the impact of Health Education programs promoting physical activity be analysed? One common way with learning programs is to conduct pre- and post-tests and measure whether/how target knowledge has evolved. In the case of physical activity, unobtrusive accelerometers can capture detailed data about people’s movements, but the challenge is to extract information from these raw data to investigate whether/how physical activity behaviours have evolved. This paper presents a methodology to do so, by extracting bouts of physical activity of specific intensity levels and of various lengths, and by using these as features to cluster students’ daily behaviours before and after intervention. This approach enables a more insightful analysis of the physical activity behaviours of the participants, and point to the nature of behaviour changes, if present. We illustrate this methodology with pre- and post-test data collected in the context of an e-learning program aimed at educating school children about healthy behaviours, with a focus on reaching recommended levels of daily physical activity: the pre- and post-tests were carried out by measuring unobtrusively and continuously their physical activity for five consecutive school days using research-grade accelerometers (GENEActiv).