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RECRUITING
NCT06728059
NA

Safety and Feasibility of a Machine-Learning Bolus Priming Added to Existing Control Algorithm

Sponsor: Sue Brown

View on ClinicalTrials.gov

Summary

A randomized crossover trial assessing glycemic control using Reinforcement Learning trained Bolus Priming System (BPS\_RL) added to the the Automated Insulin Delivery as Adaptive NETwork (AIDANET algorithm) compared to the original AIDANET algorithm.

Key Details

Gender

All

Age Range

18 Years - Any

Study Type

INTERVENTIONAL

Enrollment

16

Start Date

2025-02-05

Completion Date

2025-07-31

Last Updated

2025-05-15

Healthy Volunteers

No

Conditions

Interventions

DEVICE

Automated Insulin Delivery Adaptive NETwork (AIDANET)

Group A participants will use the AIDANET system at home for 7 days/6 nights. They will continue use of AIDANET system for 18 hours during the hotel session and then use AIDANET+BPS\_RL for 18 hours during the hotel session.

DEVICE

AIDANET+ BPS_RL→AIDANET

Group B participant will use the AIDANET+BPS\_RL system for 18 hours during the hotel session and will then use AIDANET system for 18 hours during the hotel session. They will continue to use AIDANET+BPS\_RL system at home for 7 days/6 night and then use the AIDANET system at home for 7 days/6 nights.

Locations (1)

University of Virginia Center for Diabetes Technology

Charlottesville, Virginia, United States