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

Abstract

Purpose: This study aims to investigate the impact of personalized health insights generated from wearable device data on users' health behaviors. The primary objective is to assess whether user feedback-driven algorithms enhance the relevance and effectiveness of health insights, ultimately influencing positive changes in users' daily activities. Methods: A two-month field study was conducted with 25 healthy volunteers using Mi Band 6 wearable devices. Participants were divided into test and control groups, and the test group received personalized insights recommended by a neural network-based algorithm fine-tuned by user feedback. The data collected included various health parameters such as calories burned, step count, heart rate, heart minutes, active minutes, sleep duration, sleep time and sleep segments. Insights were provided through a Telegram chatbot and user feedback was collected through a rating system. Results: The study revealed that the test group, which considered user feedback for insight recommendations, showed a significant improvement in daily activity compared to the control group. The relevance of the insights over time, as evidenced by feedback regression trends, showed a notable increase in the test group. Additional analyzes explored the relationship between insight delivery timing, user feedback, and delays, providing insights into user engagement patterns. Conclusion: This research highlights the effectiveness of personalized health insights generated from wearable data in positively influencing user health behaviors. Incorporating feedback from users into recommendation algorithms greatly enhances the relevance and effectiveness of insights, encouraging behavioral improvements. The results emphasize the significance of timing when providing insights and propose potential areas for future investigation, such as utilizing Graph Neural Networks to improve recommendation systems. In general, personalized insights from wearables have the potential to empower individuals to manage their health and well-being effectively.