Exercise-Induced Hypoglycemia Prevention in Adults With Type 1 Diabetes Using an Artificial Pancreas



Status:Not yet recruiting
Conditions:Endocrine, Diabetes, Diabetes
Therapuetic Areas:Endocrinology
Healthy:No
Age Range:18 - 65
Updated:3/6/2019
Start Date:March 12, 2019
End Date:September 30, 2019
Contact:Jennifer Pinnata, RN
Email:jp3te@hscmail.mcc.virginia.edu
Phone:4342436681

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Hypoglycemia Prevention During and After Moderate Exercise in Adults With Type 1 Diabetes Using an Artificial Pancreas With Exercise Behavior Recognition

This is a randomized crossover trial with 1:1 randomization to the admission sequence of
using the Control AP system (rMPC - Naïve Model Predictive Control) vs. Experimental AP
system (EnMPC - Ensemble Model Predictive Control) over approximately 4 months. Eligible
participants will proceed to the Data Collection Phase for approximately 28 days, during
which they will participate in regimented exercise activities. If the participant collected
adequate data during the Data Collection Phase, they will be randomized and undergo the study
admissions in the assigned sequence. Each admission is approximately 36 hours in length and
will consist of one afternoon of exercise and one without.

Exercise remains a challenge to AP systems; more specifically, by the time exercise is
detected it is often too late to avoid hypoglycemia without the ingestion of rapid
carbohydrates or the use of rescue injections, such as glucagon. To this avail, the
investigators propose to add a novel Model Predictive Control module to the proven USS
system. This module is designed to compute insulin doses every 5 minutes that are designed to
"optimally" maintain glycaemia around a target of 120mg/dL. The optimality is defined
mathematically as minimizing deviations from basal rate injections and the distance between
current and future (up to 2h) glycaemia from a physiologically feasible trajectory back down
(or up) to a pre-specified target. Furthermore, the novel control system, labelled Multi
Stage MPC or Ensemble MPC, accounts for a preset number of exercise scenarios during the
prediction horizon, these scenarios being derived from the user historical record; this setup
allows the control system to anticipate expected exercise bouts up to 2h in advance while
maintaining the condition for optimal glycemic control.

By adding such module to a well validated system, the investigators expect an improvement in
protection against hypoglycemia during and immediately after physical activity without
increase in hyperglycemia. To demonstrate the feasibility of this approach, the novel
anticipatory system will be compared to a naïve AP system during highly supervised hotel
admissions with afternoon exercise. Participants will be asked to exercise regularly in the
late afternoon during a month of data collection to generate the patterns to be anticipated.

Inclusion Criteria:

- Age ≥18 and ≤65 years

- Clinical diagnosis of Type 1 Diabetes for at least one year

- Currently using an insulin pump for at least 6 months

- Uses insulin parameters such as carbohydrate ratio and correction factors consistently
on their insulin pump in order to dose insulin for meals or corrections

- Access to internet and willingness to upload data during the study

- Willingness to be physically active for at least 30 minutes per day at least 4 times
per week

- Willingness to perform the required exercise regimen during Data Collection Period

- Willingness to not perform regular exercise outside of the study-regimented exercise
window

- For females, not currently pregnant or breastfeeding. If a female is of child-bearing
potential and sexually active, she must agree to use a form of contraception to
prevent pregnancy while participating in the study.

- An understanding and willingness to follow the protocol and sign informed consent.

Exclusion Criteria:

- History of diabetic ketoacidosis (DKA) in the 12 months prior to enrollment.

- Severe hypoglycemia resulting in seizure or loss of consciousness in the 12 months
prior to enrollment.

- Pregnancy or intent to become pregnant during the trial.

- Currently being treated for a seizure disorder

- Coronary artery disease or heart failure, unless written clearance is received from a
cardiologist or primary care provider and documentation of a negative stress test
within the year

- History of cardiac arrhythmia (except for benign premature atrial contractions and
benign premature ventricular contractions which are permitted)

- Clinically significant electrocardiogram (ECG) at time of Screening, as interpreted by
the study medical physician.

- Use of non-insulin glucose-lowering agent (including GLP-1 agonists, pramlintide,
DPP-4 inhibitors, SGLT-2 inhibitors, biguanides, sulfonylureas and naturaceuticals)
with the exception of participants who have been on a stable dose of Metformin for at
least 3 months.

- A known medical condition that in the judgment of the investigator might interfere
with the completion of the protocol such as the following examples:

- Inpatient psychiatric treatment in the past 6 months

- Presence of a known adrenal disorder

- Abnormal liver function test results (Transaminase >2 times the upper limit of
normal); testing required for subjects taking medications known to affect liver
function or with diseases known to affect liver function

- Uncontrolled thyroid disease

- Use of an automated insulin delivery mechanism that is not FDA approved during the
data collection phase

- Use of an automated insulin delivery mechanism that is not downloadable by the subject
or study team

- Inability to be physically active for at least 30 minutes per day for at least 4 times
per week

- Current enrollment in another clinical trial, unless approved by the investigators of
both studies or if clinical trial is a non-interventional registry trial.
We found this trial at
1
site
Charlottesville, Virginia 22903
(434) 924-0311
Principal Investigator: Marc Breton, PhD
University of Virginia The University of Virginia is distinctive among institutions of higher education. Founded...
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Charlottesville, VA
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