Multimodal Glucose Prediction in Type 2 Diabetes

The primary objective of this research, funded by Samsung Strategic Alliance for Research and Technology, is to develop multi-modal foundation models that integrate Continuous Glucose Monitoring (CGM) data with patient behavior data (food intake, medication, and physical activity) to improve real-time glucose prediction and personalized diabetes management for patients with Type 2 diabetes (T2D), delivered via mobile apps and digital health tools.

Inclusion Criteria: * 18-75 years old * Registered patient under Johns Hopkins Medicine (JHM) * Type 2 Diabetes diagnosis * Diabetes managed by a primary care physician or endocrinologist at JHM * Android Smartphone user * Must have a Dexcom G7 or FreeStyle Libre 3 CGM and using a mobile app to access their CGM data (G7 or Libre 3 apps) * 2 weeks of usage (with at least 50% wear time) prior to study participation required * CGM Time in Range of \ 4% (i.e. hypoglycemia) in the 14 days prior to enrollment. * Hospitalization for Diabetic Ketoacidosis (DKA) or severe hypoglycemic episode within the previous 6 months.