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Multimodal Glucose Prediction in Type 2 Diabetes
Sponsor: 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.
Official title: CGM- and Behavior-based Large Health Model for Just-in-time Diabetes Management
Key Details
Gender
All
Age Range
18 Years - 75 Years
Study Type
OBSERVATIONAL
Enrollment
36
Start Date
2026-06-15
Completion Date
2027-02-26
Last Updated
2026-06-09
Healthy Volunteers
No
Conditions
Interventions
Digital 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.
Locations (1)
Johns Hopkins Medicine
Baltimore, Maryland, United States