Predicting User Engagement in mHealth Apps with Neighborhood-based Approaches

Health apps have the potential to collect data in real life, which makes them a useful tool. Users can monitor themselves, and researchers can gain comprehensive insights. However, user behavior is subject to fluctuations. On the one hand, this makes it complicated to use machine learning techniques, and on the other hand, there is a need to predict activity to stimulate it when necessary.This paper investigates user participation, focusing on their usage behavior, possible patterns of engagement, and resulting neighborhoods. The goal is to use these insights to predict a user’s engagement. Three ways of modeling patterns of engagement are presented. Neighborhood methods are used to infer conclusions about future behavior from the information of other users. Three methods are proposed to predict the length of a phase of inactivity, the length of the next phase of activity, and its values and structure.Applied to two real-world datasets, the results show that the approaches are suitable for making these predictions.These findings can be used to understand better and predict the behavior of mHealth users and plan possible interventions. This research expands the boundaries of research using self-monitoring mHealth apps, as user behavior can now be more easily formalized and applied. Furthermore, the approach can help to influence user engagement in a targeted way. Thus, this contribution exceeds our previous state-of-the-art work in its possibilities.