A Learning Algorithm for MDI Individuals With Type 1 Diabetes to Adjust Recommendations for High Fat Meals and Exercise Management

McGill University

Status: Completed

Conditions: Type 1 Diabetes

Treatments: Device: Sensor augmented MDI therapy plus mobile application

Study type: Interventional

Funder types: Other

Identifiers

NCT05041621

2021-47375

Details and patient eligibility

About

McGill artificial pancreas lab has developed a learning algorithm using a reinforcement learning approach to adjust basal and bolus recommendations for high-fat meals and exercise management for individuals with type 1 diabetes on multiple daily injections (MDI) therapy. The reinforcement learning algorithm is integrated with a mobile application that gathers insulin, meal information (carbs (if applicable) and high-fat content), mealtime glucose value, glucose trend at mealtime, and type and timing of postprandial exercise.

Full description

The objective of this study is to assess the feasibility of a reinforcement learning algorithm to adjust basal and bolus recommendations for high-fat meals and postprandial exercise management. The investigators hypothesize that the reinforcement learning algorithm will be safe, and participants will get the benefit of improved glucose outcomes and improved patient satisfaction from the start to the end of study.

Participants (aged ≥18) will undergo multiple daily injections (MDI) therapy for 4 months using a freestyle Libre glucose sensor (Abbott Diabetes Care) and a mobile data collection application integrated with the reinforcement learning algorithm.

Enrollment: 15 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

1. Signed and dated informed consent form
2. Females and males ≥ 18 years old
3. Diagnosis of type 1 diabetes of ≥ 12 months based on the clinical investigator's judgement
4. Undergoing MDI therapy
5. A self-reported diet that consists of at least 3 high-fat meals per week or participation in exercise for at least 30 minutes, two times per week

Exclusion criteria

1. Current use of any non-insulin antihyperglycemic medication (SGLT2 inhibitors, GLP 1 receptor agonists, metformin...)
2. Current use of glucocorticoid medication, except inhaled and/or at low stable doses
3. Pregnancy
4. Use of isophane insulin (NPH) or intermediate-acting insulin
5. Significant clinical nephropathy, neuropathy, retinopathy as per the clinical investigator's judgement
6. Acute macrovascular event (ex: acute coronary syndrome or cardiac surgery) within 6 months of admission
7. Severe diabetes ketoacidosis and/or hypoglycemia within one month of admission
8. Other severe medical illness that the clinical investigator considers may interfere with participation in or completion of the study
9. An inability or unwillingness to comply with study procedures as per the clinical investigator's judgement

Trial design

Primary purpose: Treatment

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

---

15 participants in 1 patient group

Sensor augmented MDI therapy plus mobile application with reinforcement learning algorithm

Experimental group

Description:

Participants with type 1 diabetes will undergo sensor-augmented MDI therapy for 4 months using a freestyle libre glucose sensor (Abbott Diabetes Care) and a mobile application integrated with the reinforcement learning algorithm.

Treatment:

Device: Sensor augmented MDI therapy plus mobile application