Not yet recruiting NCT07633171
Multimodal Glucose Prediction in Type 2 Diabetes
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Digital Health Data Collection System.
- Who it may be relevant to
- Registry conditions: Type 2 Diabetes. Basic parameters: 18 years — 75 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- United States
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
CGM- and Behavior-based Large Health Model for Just-in-time Diabetes Management
Overview
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.
Interventions
- Device 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
Primary outcome measures
- Root Mean Square Error of CGM Glucose Prediction Model [Time frame: Up to 3 Month follow-up]
Secondary outcome measures (10)
- Number of Meal Logs Submitted Per Participant [Time frame: Up to 3 Month follow-up]
- Number of Physical Activity Logs Submitted Per Participant [Time frame: Up to 3 Month follow-up]
- Number of Medication Logs Submitted Per Participant [Time frame: 3 month follow-up]
- Number of Mood Logs Submitted Per Participant [Time frame: Up to 3 Month follow-up]
- Percent of Expected Continuous Glucose Monitor Data Captured Per Participant [Time frame: Up to 3 Month follow-up]
- Mean Daily Samsung Smartwatch Wear Time Per Participant [Time frame: Up to 3 Month follow-up]
- Percent of Study Days With Study App Use Per Participant [Time frame: Up to 3 Month follow-up]
- Clinician-Rated Accuracy of Artificial Intelligence-Generated Content as Assessed by a Study-Specific 5-Point Likert Scale [Time frame: 3 month follow-up]
- Clinician-Rated Safety of Artificial Intelligence-Generated Content as Assessed by a Study-Specific 5-Point Likert Scale [Time frame: 3 month follow-up]
- Clinician-Rated Communication Quality of Artificial Intelligence-Generated Content as Assessed by a Study-Specific 5-Point Likert Scale [Time frame: 3 month follow-up]
Eligibility criteria
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
United States · 1 center
- Johns Hopkins Medicine — Baltimore
Publications
- Healey E, Tan ALM, Flint KL, Ruiz JL, Kohane I. A case study on using a large language model to analyze continuous glucose monitoring data. Sci Rep. 2025 Jan 7;15(1):1143. doi: 10.1038/s41598-024-84003-0. PMID 39774031
Identifiers
NCT: NCT07633171 · IRB00523137