Adapting Diabetes Treatment Expert Systems to Patient in Type 1 Diabetes
NCT04443153 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 88
Last updated 2025-10-28
Summary
This study is evaluate the superior efficacy of a Continuous Glucose Monitor (CGM)-based advisory system in Type 1 Diabetes Mellitus (T1DM), as compared to Sensor Augmented Mode (SAM) therapy, and with characterizing the impact of psycho-behavioral factors on system performance, which will enable system individualization and lead to automated adaptation of advice delivery to optimize glycemic control and reduce the system's psychological impact.
Conditions
- Diabetes Mellitus, Type 1
Interventions
- DEVICE
-
Sensor Augmented Mode
A mode in Diabetes Assistant (DiAs) that combines Diabetes Assistant, Insulin pump or Multiple Daily Injections, CGM, ketone meters, and glycemic treatment guidelines together to manage diabetes.
- DEVICE
-
Personalized Feedback
Provides tailored information to the user about what glycemic risk they may be facing as well as how glycemic control and system use looked in the past week. Treatment decision and therapy changes are entirely left to the decision of the user.
- DEVICE
-
Decision Support System
CGM-based system that includes Personalized Feedback (PF) and further assists with treatment recommendations for common metabolic challenges
Sponsors & Collaborators
-
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
collaborator NIH -
University of Virginia
lead OTHER
Principal Investigators
-
Marc Breton, PhD · University of Virginia Center for Diabetes Technology
Study Design
- Allocation
- RANDOMIZED
- Purpose
- BASIC_SCIENCE
- Masking
- NONE
- Model
- CROSSOVER
Eligibility
- Min Age
- 18 Years
- Sex
- ALL
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2020-09-04
- Primary Completion
- 2024-09-19
- Completion
- 2024-09-19
- FDA Device
- Yes
Countries
- United States
Study Locations
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