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

NCT06728059 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 16

Last updated 2025-05-15

No results posted yet for this study

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.

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.

Sponsors & Collaborators

  • National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

    collaborator NIH

  • DexCom, Inc.

    collaborator INDUSTRY

  • Sue Brown

    lead OTHER

Principal Investigators

  • Sue Brown, MD · University of Virginia

Study Design

Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
NONE
Model
CROSSOVER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-02-05
Primary Completion
2025-07-31
Completion
2025-07-31
FDA Device
Yes

Countries

  • United States

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06728059 on ClinicalTrials.gov