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Safety and Feasibility of a Machine-Learning Bolus Priming Added to Existing Control Algorithm
Sponsor: Sue Brown
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
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.
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