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

NCT07517770 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 50

Last updated 2026-04-08

No results posted yet for this study

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).

Conditions

  • Type 1 Diabetes Mellitis

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.

Sponsors & Collaborators

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

    collaborator NIH

  • Tandem Diabetes Care, Inc.

    collaborator INDUSTRY

  • Sue Brown

    lead OTHER

Principal Investigators

  • Sue Brown, MD · University of Virginia Center for Diabetes Technology

Study Design

Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
NONE
Model
CROSSOVER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-05-01
Primary Completion
2027-04-30
Completion
2027-04-30
FDA Device
Yes

Countries

  • United States

Study Locations

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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 NCT07517770 on ClinicalTrials.gov