NCT07633171

Multimodal Glucose Prediction in Type 2 Diabetes

Not Yet Recruiting
Not specifiedAges 18–75Observational
Johns Hopkins University
~36 participants
Updated 2026-06-09 on ClinicalTrials.gov
What's tested:Digital Health Data Collection System

At a glance

Recruiting sites
0 of 1 listed site is recruiting right now
RecruitingSuspended, closed, or not yet open
What they're measuring
Root Mean Square Error of CGM Glucose Prediction Model
Measured over Up to 3 Month follow-up
Type 2 Diabetes
1 sites across 1 states
Maryland1
  • Nestoras Mathioudakis, MD, MHS · PRINCIPAL_INVESTIGATOR · Johns Hopkins University

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Eligibility criteria

Inclusion

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

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.
  • Root Mean Square Error of CGM Glucose Prediction ModelUp 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.