Safety and Feasibility of a Machine-Learning Bolus Priming Added to Existing Control Algorithm
Trial Parameters
Brief 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.
Eligibility Criteria
Inclusion Criteria: 1. Age ≥18.0 years old at time of consent 2. Clinical diagnosis, based on investigator assessment, of Type 1 Diabetes for at least one year. 3. Having used an AID system equipped with Dexcom G6 or G7 CGM within the last three months (does not need to be continuous use if CGM was unavailable for instance). 4. Currently using insulin for at least six months. 5. Willingness to switch to use a commercially approved personal insulin (e.g., lispro or aspart, or biosimilar approved products) within the study pump as directed by the study team. 6. Has one or more supportive companions knowledgeable about emergency procedures for severe hypoglycemia and able to contact emergency services and study staff that either lives with participant or located within approximately 30 minutes of participant and able to locate participant in the event of an emergency. 7. Participant not currently known to be pregnant or breastfeeding. 8. If participant capable of becoming pregnant, must a