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NCT Number: NCT07633171

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.

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Key information

Conditions

Type 2 Diabetes Diabetes Mellitus Diabetes Mellitus, Type 2 Endocrine System Diseases Glucose Metabolism Disorders Metabolic Diseases Nutritional and Metabolic Diseases

Age range

18 year–75 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Digital Health Data Collection System

Device

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.

Other names: Welldoc, Samsung Galaxy Watch, Continuous glucose monitor, Dexcom G7, FreeStyle Libre 3

Primary outcomes

  1. Root Mean Square Error of CGM Glucose Prediction Model

    Time frame: Up to 3 Month follow-up

    Model performance will be evaluated using root mean square error to compare predicted continuous glucose monitor glucose values with observed continuous glucose monitor glucose values. Model performance using continuous glucose monitor data alone will be compared with model performance using continuous glucose monitor data plus behavioral measures, including physical activity and diet logs.

Secondary outcomes

  1. Number of Meal Logs Submitted Per Participant

    Time frame: Up to 3 Month follow-up

    The total number of meal logs submitted by each participant in the study app will be summarized. A higher number indicates more frequent meal logging.

  2. Number of Physical Activity Logs Submitted Per Participant

    Time frame: Up to 3 Month follow-up

    The total number of physical activity logs submitted by each participant in the study app will be summarized. A higher number indicates more frequent physical activity logging.

  3. Number of Medication Logs Submitted Per Participant

    Time frame: 3 month follow-up

    The total number of medication logs submitted by each participant in the study app will be summarized. A higher number indicates more frequent medication logging.

  4. Number of Mood Logs Submitted Per Participant

    Time frame: Up to 3 Month follow-up

    The total number of mood logs submitted by each participant in the study app will be summarized. A higher number indicates more frequent mood logging.

  5. Percent of Expected Continuous Glucose Monitor Data Captured Per Participant

    Time frame: Up to 3 Month follow-up

    The percentage of expected continuous glucose monitor data captured during the study period will be summarized for each participant. A higher percentage indicates greater continuous glucose monitor use.

  6. Mean Daily Samsung Smartwatch Wear Time Per Participant

    Time frame: Up to 3 Month follow-up

    Mean daily Samsung smartwatch wear time will be summarized as the average number of hours per day that each participant wears the Samsung smartwatch. A higher number indicates greater smartwatch wear.

  7. Percent of Study Days With Study App Use Per Participant

    Time frame: Up to 3 Month follow-up

    The percentage of study days with any recorded study app use will be summarized for each participant. A higher percentage indicates greater study app use.

  8. Clinician-Rated Accuracy of Artificial Intelligence-Generated Content as Assessed by a Study-Specific 5-Point Likert Scale

    Time frame: 3 month follow-up

    Artificial intelligence-generated research content will be reviewed by the study team for accuracy using a study-specific 5-point Likert scale. Scores range from 1 to 5, with higher scores indicating greater accuracy. These outputs will not be delivered to participants.

  9. Clinician-Rated Safety of Artificial Intelligence-Generated Content as Assessed by a Study-Specific 5-Point Likert Scale

    Time frame: 3 month follow-up

    Artificial intelligence-generated research content will be reviewed by the study team for safety using a study-specific 5-point Likert scale. Scores range from 1 to 5, with higher scores indicating greater safety. These outputs will not be delivered to participants.

  10. 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

    Artificial intelligence-generated research content will be reviewed by the study team for communication quality using a study-specific 5-point Likert scale. Scores range from 1 to 5, with higher scores indicating better communication quality. These outputs will not be delivered to participants.

Study contacts

Contact information is provided by the study sponsor or research team.

Gordon Gao, PhD

CONTACT

[email protected]

410-234-9450.

Nestoras Mathioudakis, MD, MHS

CONTACT

[email protected]

410-955-3663

Sponsors and collaborators

Lead sponsor

Johns Hopkins University

Other

Collaborators

  • Samsung Research America
  • Welldoc

Registry information

Official study title

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

Important dates

Study start
2026
Primary completion
2027
Study completion
2027
First posted
Jun 8, 2026
Registry last updated
Jun 9, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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