Enabling Data-Driven Decision Making for Personalized Care in Type 2 Diabetes with Clinical and Lifestyle Journey Data Using a Digital Therapeutic

Background: Lack of data on patient lifestyle between consultation visits is a major limitation in practice of data-driven clinical decision making in type 2 diabetes (T2DM). We showcase Wellthy Diabetes (WD), a digital therapeutic, as an effective tool for the amplification of patient's clinical and lifestyle journey data between visits to aid doctors in practice of data-driven decision-making. Methods: Wellthy Diabetes (WD) smartphone app is a digital therapeutic that delivers an artificial intelligence (AI) augmented lifestyle modification program for people with T2DM, developed in scientific collaboration with the Research Society for Study of Diabetes India (RSSDI). The WD app uses an AI-based digital persuasion model to encourage users through personalized reminders and nudges to self-report data on blood glucose, weight, meals, and physical activity. The app also tracks the duration of physical activity through inbuilt sensors on the phone. This study presents data from a cohort (N=102) of completers of a 16-week program. Results: During the study 121,675 diabetes-related clinical and lifestyle data points (including 112,960 mins of sensor-tracked physical activity) were reported by 102 participants over 17,052 person days. The mean duration of a participant on WD was 167 days during which, on average, 452 clinical and lifestyle data points were tracked per participant at a mean velocity of 5.6 data points per day. On average each participant reported 68.8, 11.4, 5.1, and 1107.7 instances of meals, blood sugar, weight, and activity respectively. Conclusion: The results confirm WD amplifies self-reporting of clinically relevant lifestyle data by users during the journey between doctor consultations. First-hand data on clinical trends and lifestyle between consultations provide personalized insight into patient behavior enabling doctors to make data-driven decisions for personalizing evidence-based care for T2DM.