Samenvatting

One of the main causes of numerous health problems is a lack of physical activity. To promote a more active lifestyle, the Hanze University started a health promotion program. Participants were motivated to reach their daily goal of physical activity by means of an activity tracker in combination with two-weekly coaching sessions. Employing the data of the experiment, we investigated the manners in which the predictability of physical activity of a participant during the day can be improved. The collected step count data was used to construct personalised machine learning models, by taking into account the difference between physical activities during weekdays on the one hand and weekends on the other hand. The training of algorithms per participant in combination with the time-slices weekdays, weekend and the whole week improves the accuracy of the prediction model. The performance of the models improves even further when the individualised time-sliced models are combined. More contextual data, like free time and working hours, might even extend the accuracy. The use of personalised prediction models, based on machine learning and time slices, could become an addition in preventive personalized eHealth systems and mobile activity monitoring. For instance, this can constitute as a viable addition to a virtual coaching system to help the participants to reach their daily goal. As the individualised models allow for predictions of the progression of the physical activity during the day, they enable the virtual coaching system to intervene at the appropriate moment in time.