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Effectiveness of Artificial-Intelligence (AI) Bolus Priming Added to an Existing Fully Automated Control Algorithm (AIDANET)
Sponsor: Sue Brown
Summary
Bolus Priming (BP) based on Artificial Intelligence (AI) learning of meal patterns, added to our established Automated insulin delivery as Adaptive Network (AIDANET) algorithm and running on iPhone Diabetes Assistant (iDiAs) phone wirelessly connected to Tandem Mobi insulin pump and Dexcom Continuous Glucose Monitor (CGM).
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
INTERVENTIONAL
Enrollment
50
Start Date
2026-05-01
Completion Date
2027-04-30
Last Updated
2026-04-08
Healthy Volunteers
No
Conditions
Interventions
Hybrid Closed Loop (HCL) x 2 weeks
During the HCL session, participants will be using their own HCL systems for 2-weeks.
AIDANET x 2 weeks
Participant will use the AIDANET algorithm on the Mobi system with the standard Bolus Priming System (BPS) automated bolus that does not require announcement of meals.
AIDANET AI x 4 weeks
Participant will use the AIDANET algorithm with the addition of the Bolus Priming (BP) based on AI learning of meal patterns.
Locations (1)
University of Virginia Center for Diabetes Technology
Charlottesville, Virginia, United States