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NOT YET RECRUITING
NCT07517770
NA

Effectiveness of Artificial-Intelligence (AI) Bolus Priming Added to an Existing Fully Automated Control Algorithm (AIDANET)

Sponsor: Sue Brown

View on ClinicalTrials.gov

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

Interventions

DEVICE

Hybrid Closed Loop (HCL) x 2 weeks

During the HCL session, participants will be using their own HCL systems for 2-weeks.

DEVICE

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.

DEVICE

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