Type 1 Diabetes Mellitis
Conditions
Keywords
Type 1 Diabetes (T1D), Artificial-Intelligence (AI), Hybrid Closed Loop (HCL), Fully Closed Loop (FCL), Mobi insulin pump, iPhone Diabetes Assistant (iDiAs), Automated insulin delivery as Adaptive Network (AIDANET) algorithm, Bolus Priming System (BPS), Mobi Research App (MRA)
Brief 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).
Detailed description
A randomized cross-over trial assessing glycemic control on AIDANET equipped with its standard bolus vs AIDANET AI. A secondary comparison of AIDANET AI vs a current commercial Hybrid Closed Loop (HCL) system is planned as well. Following enrollment and screening, participants will be randomized 1:1 to two 8-week sequences: Group A participants will continue to use their home Hybrid closed loop (HCL) system for 2 weeks, then switch to AIDANET for two weeks, and the switch to AIDANET AI for another 4 weeks. Group B participants will begin with 4 weeks of AIDANET AI, then switch to AIDANET for 2 weeks and then revert to their home HCL systems for the last 2 weeks of the study (Figure 1). The last two weeks of the 4-week AIDANET AI session will be used for analysis, comparing standard Ambulatory Glucose Profile (AGP) metrics across AIDANET vs AIDANET AI, with primary outcome Time in Range (TIR) (70-180 mg/dL) during the day. Secondary analyses will compare AIDANET AI 2-week AGP to HCL AGP metrics. Both analyses use randomized crossover design.
Interventions
DEVICEHybrid Closed Loop (HCL) x 2 weeks
During the HCL session, participants will be using their own HCL systems for 2-weeks.
DEVICEAIDANET 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.
DEVICEAIDANET 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
Sue Brown
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Tandem Diabetes Care, Inc.
Study design
Allocation
RANDOMIZED
Intervention model
CROSSOVER
Primary purpose
TREATMENT
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Age ≥18.0 years old at time of consent 2. Clinical diagnosis, based on investigator assessment, of Type 1 Diabetes (T1D) for at least one year. 3. Currently using an automated insulin delivery (AID) system. 4. 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. 5. Has one or more supportive companions knowledgeable about emergency procedures for severe hypoglycemia and able to contact emergency services and study staff that either live with participant or located within approximately 30 minutes of participant and able to locate participant in the event of an emergency. 6. Participant not currently known to be pregnant or breastfeeding. 7. If participant can become pregnant, they must agree to use a form of contraception to prevent pregnancy while a participant in the study. A negative serum or urine pregnancy test will be required for all females of childbearing potential. Participants who become pregnant will be discontinued from the study. Also, participants who during the study develop and express the intention to become pregnant within the timespan of the study will be discontinued. 8. Willingness to use the study AIDANET system (CGM, insulin pump, and phone) during the study period. 9. Willingness not to start any new non-insulin glucose-lowering agent during the course of the trial. 10. Willingness to participate in all study procedures. 11. Access to internet at home and willingness to upload data during the study as needed. 12. Investigator has confidence that the participant can successfully operate all study devices and is capable of adhering to the protocol. 13. Participant is proficient in reading and writing English.
Exclusion criteria
1. Plans to start a new non-insulin glucose-lowering agent (e.g., Glucagon-like peptide-1 (GLP-1) receptor agonists, Symlin, DPP-4 inhibitors, sulfonylureas). Participants may be on a stable dose of such an agent for at least the past month. 2. Current use of an sodium-glucose transport protein 2 (SGLT-2) or SGLT-1/2 inhibitor due to risk of euglycemic diabetic ketoacidosis (DKA). 3. Hemophilia or any other bleeding disorder. 4. History of severe hypoglycemic events with seizure or loss of consciousness in the last 12 months. 5. History of DKA event in the last 12 months. 6. Unstable Stage 4 chronic renal disease or currently on peritoneal or hemodialysis. 7. Currently being treated for adrenal insufficiency. 8. Currently being treated for a seizure disorder. 9. Hypothyroidism or hyperthyroidism that is not adequately treated. 10. Use of oral or injectable steroids at the time of enrollment or within the last 2 weeks. 11. Planned surgery during the study period that results in prolonged disconnection from study devices. 12. Known ongoing adhesive intolerance that is not well managed. 13. A condition, which in the opinion of the investigator or designee, would put the participant or study at risk. 14. Participation in another interventional trial at the time of enrollment. 15. Participant with a direct supervisor involved in the conduct of the trial.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Time in Range (TIR) 70-180 mg/dL for 2-week free-living at home periods on AIDANET vs AIDANET AI. | two weeks | The last two weeks of the 4-week AIDANET AI session will be used for analysis, comparing standard Ambulatory Glucose Profile metrics across AIDANET vs AIDANET AI, with primary outcome TIR (70-180 mg/dL) during the day. |
Countries
United States
Contacts
CONTACTCarlene Alix
CONTACTLaura Kollar, RN
PRINCIPAL_INVESTIGATORSue Brown, MD
University of Virginia Center for Diabetes Technology
Outcome results
None listed