[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"trial:NCT07633171":3,"trial-entities:NCT07633171":145,"trial-summary:NCT07633171":149},{"id":4,"nct_id":4,"org_study_id":5,"brief_title":6,"official_title":7,"overall_status":8,"completion_date":9,"status_verified_date":10,"last_update_date":11,"start_date":12,"sponsor_name":13,"lead_sponsor_class":14,"has_dmc":15,"brief_summary":16,"detailed_description":17,"conditions":18,"keywords":20,"study_type":21,"primary_purpose":17,"phases":22,"enrollment_info":23,"interventions":26,"primary_outcomes":39,"secondary_outcomes":44,"sex":76,"minimum_age":77,"maximum_age":78,"healthy_volunteers":15,"eligibility_criteria":79,"std_ages":107,"locations":110,"central_contacts":134,"overall_officials":137,"references":139,"see_also_links":144},"NCT07633171","IRB00523137","Multimodal Glucose Prediction in Type 2 Diabetes","CGM- and Behavior-based Large Health Model for Just-in-time Diabetes Management","NOT_YET_RECRUITING","2027-02-26","2026-06","2026-06-09","2026-06-15","Johns Hopkins University","OTHER",false,"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.",null,[19],"Type 2 Diabetes",[],"OBSERVATIONAL",[],{"count":24,"type":25},36,"ESTIMATED",[27],{"type":28,"name":29,"description":30,"armGroupLabels":31,"otherNames":33},"DEVICE","Digital Health Data Collection System","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.",[32],"Adults With Type 2 Diabetes Using CGM",[34,35,36,37,38],"Welldoc","Samsung Galaxy Watch","Continuous glucose monitor","Dexcom G7","FreeStyle Libre 3",[40],{"measure":41,"description":42,"timeFrame":43},"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",[45,48,51,55,58,61,64,67,70,73],{"measure":46,"description":47,"timeFrame":43},"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.",{"measure":49,"description":50,"timeFrame":43},"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.",{"measure":52,"description":53,"timeFrame":54},"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",{"measure":56,"description":57,"timeFrame":43},"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.",{"measure":59,"description":60,"timeFrame":43},"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.",{"measure":62,"description":63,"timeFrame":43},"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.",{"measure":65,"description":66,"timeFrame":43},"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.",{"measure":68,"description":69,"timeFrame":54},"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.",{"measure":71,"description":72,"timeFrame":54},"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.",{"measure":74,"description":75,"timeFrame":54},"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.","ALL","18 Years","75 Years",{"inclusion":80,"exclusion":97,"raw_text":106},[81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96],"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 \\\u003C70% 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",[98,99,100,101,102,103,104,105],"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.","Inclusion Criteria:\n\n* 18-75 years old\n* Registered patient under Johns Hopkins Medicine (JHM)\n* Type 2 Diabetes diagnosis\n* Diabetes managed by a primary care physician or endocrinologist at JHM\n* Android Smartphone user\n* 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)\n* 2 weeks of usage (with at least 50% wear time) prior to study participation required\n* CGM Time in Range of \\\u003C70% in 14 days prior to enrollment\n* Must be able to read, understand, and communicate in English\n* Must not have hearing or vision impairments\n* Willingness to Download the Welldoc app\n* Agree to wear a SAMSUNG Galaxy Watch at least 12 hours per day\n* Download SAMSUNG Health (Non-SAMSUNG Phone user)\n* Download Google Health Connect\n* Use CGM at least 80% of the time\n* Take a photo of all meals\n\nExclusion Criteria:\n\n* Pregnant\n* Non-English speaker\n* Has hearing or vision impairment\n* Use of an insulin pump (i.e. automated insulin delivery system)\n* 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)\n* Non-Android smartphone user (i.e., Apple iOS)\n* CGM time-below-range \\> 4% (i.e. hypoglycemia) in the 14 days prior to enrollment.\n* Hospitalization for Diabetic Ketoacidosis (DKA) or severe hypoglycemic episode within the previous 6 months.",[108,109],"ADULT","OLDER_ADULT",[111],{"facility":112,"city":113,"state":114,"zip":115,"country":116,"contacts":117,"geoPoint":131},"Johns Hopkins Medicine","Baltimore","Maryland","21287","United States",[118,123,127,130],{"name":119,"role":120,"phone":121,"email":122},"Nestoras Mathioudakis, MD, MHS","CONTACT","410-955-3663","nmathio1@jhmi.edu",{"name":124,"role":120,"phone":125,"email":126},"Gordon Gao, PhD","410-234-9450.","ggao8@jh.edu",{"name":128,"role":129},"Nestoras Mathioudakis, MD","PRINCIPAL_INVESTIGATOR",{"name":124,"role":129},{"lat":132,"lon":133},39.29038,-76.61219,[135,136],{"name":119,"role":120,"phone":121,"email":122},{"name":124,"role":120,"phone":125,"email":126},[138],{"name":119,"affiliation":13,"role":129},[140],{"pmid":141,"type":142,"citation":143},"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\u002Fs41598-024-84003-0.",[],{"nct_id":4,"conditions":146,"biomarkers":148},[147],"Type 2 Diabetes Mellitus",[],{"nct_id":4,"found":15,"summary":17,"prompt_version":17}]