Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Class |
|---|---|
| Samsung Research America | INDUSTRY |
| Welldoc | INDUSTRY |
Not provided
Not provided
Not provided
Not provided
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.
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | Up to 3 Month follow-up |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | Up to 3 Month follow-up |
| Number of Physical Activity Logs Submitted Per Participant |
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
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.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Nestoras Mathioudakis, MD, MHS | Contact | 410-955-3663 | nmathio1@jhmi.edu | |
| Gordon Gao, PhD | Contact | 410-234-9450. | ggao8@jh.edu |
| Name | Affiliation | Role |
|---|---|---|
| Nestoras Mathioudakis, MD, MHS | Johns Hopkins University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Johns Hopkins Medicine | Baltimore | Maryland | 21287 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 39774031 | Background | 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. |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| ID | Term |
|---|---|
| D003924 | Diabetes Mellitus, Type 2 |
| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
Not provided
Not provided
Not provided
Not provided
Not provided
|
|
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. |
| 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 3 month follow-up |
| D004700 | Endocrine System Diseases |