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

Type 1 Diabetes Clinical Trial

Official title: A Single Arm Pilot Study to Assess the Feasibility of a Learning Algorithm to Automatically Adjust Basal and Bolus Recommendations for High Fat Meals and Exercise Management for Individuals With Type 1 Diabetes on MDI Therapy

Status Completed

Clinical Trial 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.

Clinical Trial 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. ;

Related Conditions & MeSH terms

• Diabetes Mellitus
• Diabetes Mellitus, Type 1
• Type 1 Diabetes

• NCT number: NCT05041621
• Study type: Interventional
• Source: McGill University
• Status: Completed
• Phase: N/A
• Start date: July 7, 2021
• Completion date: February 21, 2023