Type 2 DiabetesNCT07633171
Multimodal Glucose Prediction in Type 2 Diabetes
An observational study of Type 2 Diabetes, run by Johns Hopkins University at 1 site in United States.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Type 2 Diabetes
- What is being tested?
- Digital Health Data Collection System
- Type of treatment
- Medical device
- How many people
- 36 participants
- Where
- 1 siteUnited States
- Study dates
- 7 Oct 2026to 26 Feb 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Participants will be selected from the Johns Hopkins Medicine adult clinical population. The study population will include adults with type 2 diabetes who receive diabetes care through Johns Hopkins Medicine, including primary care and endocrinology clinics. Participants will be recruited from patients whose routine diabetes care includes use of continuous glucose monitoring and who may be eligible to contribute glucose, wearable, app-based behavioral, and electronic medical record data for development and validation of glucose prediction models.
Who may be able to join
- 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)
Show all 16Show fewer
- 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
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
Johns Hopkins Medicine
Baltimore, Maryland, 21287
Nestoras Mathioudakis, MD, MHS, 410-955-3663, nmathio1@jhmi.edu
Gordon Gao, PhD, 410-234-9450., ggao8@jh.edu
About this study
Official title: 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.
- Conditions
- Type 2 Diabetes
- Being tested
- Digital Health Data Collection System
- Medical subject headings
- Diabetes Mellitus, Type 2
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Digital Health Data Collection SystemDevice
Also called Welldoc, Samsung Galaxy Watch, Continuous glucose monitor, Dexcom G7
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.
| Study group | Role | What they receive |
|---|---|---|
| Adults With Type 2 Diabetes Using CGM | Adults with type 2 diabetes receiving care through Johns Hopkins Medicine will participate in a single site observational cohort study. Participants will continue usual diabetes care and will not receive a treatment intervention from the study team. Participants will contribute CGM, smartwatch, app-based behavioral, and electronic medical record data for development and validation of glucose prediction models. Study-generated messages and summary reports will be reviewed by the study team and will not be delivered to participants. Receives: Device: Digital Health Data Collection System |
What is being measured
The primary outcome is the main question the study is designed to answer.
Root Mean Square Error of CGM Glucose Prediction Model
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.
Measured: Up to 3 Month follow-up
Secondary outcomes (10)
Number of Meal Logs Submitted Per Participant
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.
Measured: Up to 3 Month follow-up
Number of Physical Activity Logs Submitted Per Participant
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.
Measured: Up to 3 Month follow-up
Number of Medication Logs Submitted Per Participant
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.
Measured: 3 month follow-up
Number of Mood Logs Submitted Per Participant
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.
Measured: Up to 3 Month follow-up
Percent of Expected Continuous Glucose Monitor Data Captured Per Participant
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.
Measured: Up to 3 Month follow-up
Mean Daily Samsung Smartwatch Wear Time Per Participant
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.
Measured: Up to 3 Month follow-up
Percent of Study Days With Study App Use Per Participant
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.
Measured: Up to 3 Month follow-up
Clinician-Rated Accuracy of Artificial Intelligence-Generated Content as Assessed by a Study-Specific 5-Point Likert Scale
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.
Measured: 3 month follow-up
Clinician-Rated Safety of Artificial Intelligence-Generated Content as Assessed by a Study-Specific 5-Point Likert Scale
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.
Measured: 3 month follow-up
Clinician-Rated Communication Quality of Artificial Intelligence-Generated Content as Assessed by a Study-Specific 5-Point Likert Scale
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.
Measured: 3 month follow-up
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Jun 2026
- Study start7 Oct 2026
- Primary completion15 Jan 2027 (estimated)
- Study completion26 Feb 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Type 2 DiabetesStudies of the same condition, open and under way.
- More trials run by Johns Hopkins UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 36 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Johns Hopkins University
- Sponsor type
- University, hospital or other
- Collaborators
- Samsung Research America, Welldoc
- Responsible party
- Sponsor
- Investigators
- Nestoras Mathioudakis, MD, MHS, Principal investigator, Johns Hopkins University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07633171
- Sponsor's protocol ID
- IRB00523137
- Status last verified
- Oct 2026
- First submitted
- 2 Jun 2026
- Last updated
- 8 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
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.
PubMed 39774031
Study at a glance
From the registry record, last updated by the sponsor on 8 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- CGM- and Behavior-based Large Health Model for Just-in-time Diabetes Management
- ClinicalTrials.gov ID
- NCT07633171
- Acronym / sponsor ID
- IRB00523137
- Conditions
- Type 2 Diabetes
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 36 participants (estimated)
- Lead sponsor
- Johns Hopkins UniversityUniversity, hospital or other
- Collaborators
- Samsung Research America, Welldoc
- Study start
- 7 Oct 2026 (actual)
- Primary completion
- 15 Jan 2027 (estimated)
- Study completion
- 26 Feb 2027 (estimated)
- First posted
- 8 Jun 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Adults With Type 2 Diabetes Using CGM | – | Device: Digital Health Data Collection SystemAdults with type 2 diabetes receiving care through Johns Hopkins Medicine will participate in a single site observational cohort study. Participants will continue usual diabetes care and will not receive a treatment intervention from the study team. Participants will contribute CGM, smartwatch, app-based behavioral, and electronic medical record data for development and validation of glucose prediction models. Study-generated messages and summary reports will be reviewed by the study team and will not be delivered to participants. |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Participants will be selected from the Johns Hopkins Medicine adult clinical population. The study population will include adults with type 2 diabetes who receive diabetes care through Johns Hopkins Medicine, including primary care and endocrinology clinics. Participants will be recruited from patients whose routine diabetes care includes use of continuous glucose monitoring and who may be eligible to contribute glucose, wearable, app-based behavioral, and electronic medical record data for development and validation of glucose prediction models.
Key inclusion criteria (16 listed)
- 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
Key exclusion criteria (8 listed)
- 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)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Digital Health Data Collection SystemAlso: Welldoc, Samsung Galaxy Watch, Continuous glucose monitor, Dexcom G7Participants 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. | Device | Adults With Type 2 Diabetes Using CGM |
Primary outcome
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.
10 secondary outcomes
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Oct 2026.
- Background: PubMed 39774031Healey 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Nestoras Mathioudakis, MD, MHS principal investigator
Johns Hopkins University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Type 2 Diabetes, run by Johns Hopkins University at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 16 inclusion and 8 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 8 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Nestoras Mathioudakis, MD, MHS (410-955-3663, nmathio1@jhmi.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Root Mean Square Error of CGM Glucose Prediction Model (time frame: Up to 3 Month follow-up). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07633171, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Oct 2026; AzTrials last refreshed its copy on 2 Nov 2026.