Recommendation in persuasive eHealth systems: An effective strategy to spot users' losing motivation to exercise

Persuasive health technologies in the sport domain focus mostly on motivating and supporting people in reaching an active lifestyle. In this paper, we exploit a real-world dataset made available by a commercial persuasive ecosystem called u4fit. u4fit allows coaches to create tailored workout plans and to constantly monitor and support their sportsmen remotely. Occasional sportsmen often and suddenly abandon their workout routines, without giving any prior notice to the coach, frequently because of a decline in motivation. In this paper, we tackle this issue by developing an approach able to spot users' behavioral changes and predict if one will soon stop exercising. These predictions can be further elaborated and provided as a recommendation to the user's coach, to let her get in touch with the sportsmen and prevent such a situation. Experiments, validated through standard accuracy metrics, revealed that behavioral changes in training patterns represent one of the main markers that lead sportsmen to abandon.