Feedback-Driven Insight Generation and Recommendation for Health Self-Management

This paper introduces a framework aimed at improving the lifestyle of individuals engaged in health self-management programs by providing them with valuable and actionable insights derived from their personal health data. The framework is designed to autonomously discover, optimize, and deliver these insights to the participants. To evaluate the effectiveness of the framework, an experiment was conducted where participants were provided with insights related to their sleep and physical activity. The results demonstrate that the proposed framework, which incorporates feedback-driven optimization, effectively recommends insights that align closely with the behavior and preferences of the participants. These findings highlight the potential of the framework to enhance the impact of health self-management programs.