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

NCT05041621 | Completed

• 1 other identifier: 2021-47375
• Study Type: interventional
• Target: 15
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Type 1 Diabetes

Outcome Measures

Primary Outcomes (2)

• Comparison of 5 hours postprandial incremental area under the curve of glucose (for high-fat meals and/or postprandial exercise) of the last month algorithm recommendations with the first month recommendations

  First and last month of intervention, approximately 4 months

• Comparison of 5 hours postprandial percentage of time below 3.9 mmol/L (for high-fat meals and/or postprandial exercise) of the last month algorithm recommendations with the first month recommendations

  First and last month of intervention, approximately 4 months

Secondary Outcomes (26)

• Comparison of 5 hours postprandial percentage of time between 3.9 and 10 mmol/L (for high-fat meals and/or postprandial exercise) of the last month algorithm recommendations with the first month recommendations

  First and last month of intervention, approximately 4 months

• Comparison of 5 hours postprandial percentage of time between 3.9 and 7.8 mmol/L (for high-fat meals and/or postprandial exercise) of the last month algorithm recommendations with the first month recommendations

  First and last month of intervention, approximately 4 months

• Comparison of 5 hours postprandial percentage of time below 3.3 mmol/L (for high-fat meals and/or postprandial exercise) of the last month algorithm recommendations with the first month recommendations

  First and last month of intervention, approximately 4 months

• Comparison of 5 hours postprandial percentage of time below 2.8 mmol/L (for high-fat meals and/or postprandial exercise) of the last month algorithm recommendations with the first month recommendations

  First and last month of intervention, approximately 4 months

• Comparison of 5 hours postprandial percentage of time above 7.8 mmol/L (for high-fat meals and/or postprandial exercise) of the last month algorithm recommendations with the first month recommendations

  First and last month of intervention, approximately 4 months

Study Arms (1)

Sensor augmented MDI therapy plus mobile application with reinforcement learning algorithm

EXPERIMENTAL

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.

Device: Sensor augmented MDI therapy plus mobile application

Interventions

Sensor augmented MDI therapy plus mobile application DEVICE

Participants will use the mobile application to calculate their basal dose and to calculate their meal bolus dose by entering their glucose value, carbs (if applicable), fat composition (high fat or not), and type and timing of postprandial exercises. Participants will receive their dosing parameters weekly upon adjustments made by the reinforcement learning algorithm. Participants will be contacted by telephone on Weeks 1, 3, 5, and 7 in case of any technical difficulties or questions. All participants will be asked to complete the: (i) Diabetes treatment satisfaction questionnaire (DTSQ) and hypoglycemia fear survey-II (HFS-II) at baseline, halfway through the intervention, and post-intervention. (ii) mHealth usability questionnaire (MAUQ) at post-intervention.

Sensor augmented MDI therapy plus mobile application with reinforcement learning algorithm

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Signed and dated informed consent form
• Females and males ≥ 18 years old
• Diagnosis of type 1 diabetes of ≥ 12 months based on the clinical investigator's judgement
• Undergoing MDI therapy
• 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

You may not qualify if:

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

Sponsors & Collaborators

• McGill University: lead

Study Sites (1)

Clinique Médicale Hygea

Montreal, Quebec, H4A 3T2, Canada

Location

MeSH Terms

Conditions

Diabetes Mellitus, Type 1

Condition Hierarchy (Ancestors)

Diabetes Mellitus; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases; Autoimmune Diseases; Immune System Diseases

Study Officials

• Ahmad Haidar, PhD

  McGill University Health Centre/Research Institute of the McGill University Health Centre

  STUDY CHAIR

• Michael Tsoukas, MD

  McGill University Health Centre/Research Institute of the McGill University Health Centre

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: TREATMENT
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 26, 2021
• First Posted: September 13, 2021
• Study Start: July 7, 2021
• Primary Completion: February 21, 2023
• Study Completion: February 21, 2023
• Last Updated: November 9, 2023

Record last verified: 2023-11

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, ICF
• Time Frame: Raw data and consent form: Anytime upon reasonable request. Protocol: After publication
• Access Criteria: The requested data could be accessed from the corresponding author, ahmad.haidar@mcgill.ca, upon reasonable request for academic purposes. Protocol is available with publication

Locations

CA (1)