CGM- and Behavior-based Large Health Model for Just-in-time Diabetes Management
Johns Hopkins University
Summary
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.
Eligibility
- Age range
- 18–75 years
- Sex
- All
- Healthy volunteers
- No
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 * Wi…
Interventions
- DeviceDigital Health Data Collection System
Participants will use a digital health data collection system that includes the Welldoc app, a Samsung smartwatch, and the participant's existing continuous glucose monitor. The system will collect CGM data, smartwatch-derived activity, sleep, and vital sign data, and app-based behavioral information such as meals, physical activity, and medication use. Participants will continue usual diabetes care and will not receive treatment recommendations from the study team. Data will be used to develop and validate glucose prediction models and Artificial Intelligence (AI)-generated research outputs that will be reviewed by the study team and not delivered to participants.
Location
- Johns Hopkins MedicineBaltimore, Maryland