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Набор скоро начнётся NCT07633171

Multimodal Glucose Prediction in Type 2 Diabetes

— Наблюдательное Type 2 Diabetes

Ориентир для пациента и семьи

Простыми словами

Автоматическая сводка по структурированным данным реестра. Она помогает сориентироваться, но не заменяет официальный протокол или оценку врача.

Что изучают
В протоколе указаны: Digital Health Data Collection System.
Кому может быть актуально
Состояния в реестре: Type 2 Diabetes. Базовые параметры: 18 лет — 75 лет · Все.
Что важно проверить
Возраст, диагноз и пол — только базовые ориентиры. Предыдущее лечение, анализы и другие обязательные условия указаны ниже в критериях участия.
Где проводится
США
Следующий шаг
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Официальное название

CGM- and Behavior-based Large Health Model for Just-in-time Diabetes Management

Обзор

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.

Вмешательства

  • Устройство 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

Первичные конечные точки

  • Root Mean Square Error of CGM Glucose Prediction Model [Срок оценки: Up to 3 Month follow-up]
Вторичные конечные точки (10)
  • Number of Meal Logs Submitted Per Participant [Срок оценки: Up to 3 Month follow-up]
  • Number of Physical Activity Logs Submitted Per Participant [Срок оценки: Up to 3 Month follow-up]
  • Number of Medication Logs Submitted Per Participant [Срок оценки: 3 month follow-up]
  • Number of Mood Logs Submitted Per Participant [Срок оценки: Up to 3 Month follow-up]
  • Percent of Expected Continuous Glucose Monitor Data Captured Per Participant [Срок оценки: Up to 3 Month follow-up]
  • Mean Daily Samsung Smartwatch Wear Time Per Participant [Срок оценки: Up to 3 Month follow-up]
  • Percent of Study Days With Study App Use Per Participant [Срок оценки: Up to 3 Month follow-up]
  • Clinician-Rated Accuracy of Artificial Intelligence-Generated Content as Assessed by a Study-Specific 5-Point Likert Scale [Срок оценки: 3 month follow-up]
  • Clinician-Rated Safety of Artificial Intelligence-Generated Content as Assessed by a Study-Specific 5-Point Likert Scale [Срок оценки: 3 month follow-up]
  • Clinician-Rated Communication Quality of Artificial Intelligence-Generated Content as Assessed by a Study-Specific 5-Point Likert Scale [Срок оценки: 3 month follow-up]

Критерии участия

Критерии включения

  • 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

Критерии исключения

  • 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.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения
Когортное

Центры проведения

США · 1 центр
  • Johns Hopkins Medicine — Baltimore

Публикации

  • 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. PMID 39774031

Идентификаторы

NCT: NCT07633171 · IRB00523137

Первоисточники (государственные реестры)

Открыть это исследование на ClinicalTrials.gov ↗