Multimodal Glucose Prediction in Type 2 Diabetes

CGM- and Behavior-based Large Health Model for Just-in-time Diabetes Management

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 <70% in 14 days prior to enrollment
• Must be able to read, understand, and communicate in English
• Must not have hearing or vision impairments
• Willingness to Download the Welldoc app
• Agree to wear a SAMSUNG Galaxy Watch at least 12 hours per day
• Download SAMSUNG Health (Non-SAMSUNG Phone user)
• Download Google Health Connect
• Use CGM at least 80% of the time
• Take a photo of all meals

Exclusion Criteria:

• Pregnant
• Non-English speaker
• Has hearing or vision impairment
• Use of an insulin pump (i.e. automated insulin delivery system)
• Diagnosed with other forms of diabetes (e.g. Type 1 Diabetes, Latent Autoimmune Diabetes in Adults (LADA), Maturity-Onset Diabetes of the Young (MODY), or Gestational diabetes)
• Non-Android smartphone user (i.e., Apple iOS)
• CGM time-below-range > 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.