Liying (Victoria) Qu ¹ and Yu Sun ² , ¹ USA, ² California State Polytechnic University, USA

Diabetes mellitus affects over 537 million adults globally, demanding continuous self-management that conventional pharmacological approaches alone cannot fully address. Music therapy has emerged as a promising complementary intervention, with clinical research demonstrating that slow-tempo music can reduce blood glucose by 15-30 mg/dL through parasympathetic activation and cortisol reduction. BeatSugar is a cross-platform mobile application that integrates real-time blood sugar and heart rate monitoring with personalized, evidence-based music therapy recommendations. The system employs a context-aware algorithm that maps blood glucose levels, measurement timing, and diabetic status to clinically appropriate music tempos, incorporating Traditional Chinese Medicine FiveElement tonal sequences alongside AI-generated therapeutic compositions. A personalized effectiveness scoring engine learns from individual listening sessions, adapting recommendations based on measurable health outcomes. Experimental evaluation demonstrates 94.2% recommendation accuracy and algorithm convergence within 8-12 sessions. BeatSugar offers a scientifically grounded, scalable approach to complementary diabetes management through accessible digital music therapy.

Music Therapy, Diabetes Management, Blood Sugar Regulation, Mobile Health, Personalized Recommendations