Multimodal Glucose Prediction in Type 2 Diabetes
ConditionType 2 Diabetes
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-75
SponsorJohns Hopkins University
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
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
Disqualifiers
Pregnant
Non-English speaker
Has hearing or vision impairment
Use of an insulin pump (i.e. automated insulin delivery system)
Trial 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.
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
Digital Health Data Collection System
DeviceParticipants 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.
Treatment groups
36 Participants
are divided into 1 treatment groupGroup A: Adults With Type 2 Diabetes Using CGM1 intervention
Trial outcomes
Primary outcomes
1
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.
Time frame
Up to 3 Month follow-upSecondary outcomes
1
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.
Time frame
Up to 3 Month follow-up2
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.
Time frame
Up to 3 Month follow-up3
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.
Time frame
3 month follow-up4
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.
Time frame
Up to 3 Month follow-upSponsors and contacts
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Source ClinicalTrials.gov