Marc Breton, United States of America

University of Virginia Center for Diabetes Technology

Moderator Of 1 Session

Session Type
21.02.2020, Friday
Session Time
09:00 - 10:00

Presenter Of 2 Presentations

Automated bolus and basal modulation with Control-IQ leads to better glycemic outcomes in adults and adolescents

Session Type
20.02.2020, Thursday
Session Time
16:40 - 18:00
Lecture Time
17:10 - 17:30

Clinical trial results of an artificial pancreas (AP) System that anticipates physical activity patterns in type 1 diabetes


Background and Aims / Part 1

Achieving the glycemic target for people with type 1 diabetes is challenging. Regular exercise improves glycemic control, fitness, body composition and metabolic profiles, with a recommended 150min of exercise weekly, spread over at least 3 days/week. However, glycemic control during and after exercise can be very challenging.

Artificial pancreas (AP) systems can lead to reduced exposure to hypoglycemia and increased time in range; however, preventing hypoglycemia during and immediately after exercise remains a challenge. Additional health monitoring data (e.g. heart rate and step counts) improved system's performance during exercise; nonetheless the risk for hypoglycemia was not eliminated. Furthermore, recent work has shown that an 80% reduction of basal infusion in the 90min leading to moderate exercise may allow for optimal glycemia.

We present the initial testing of such an exercise-informed AP system, capable of anticpating regular physical activity patterns.

Methods / Part 2

Eighteen adults with T1D (>1year) were enrolled in an open-labelled randomized crossover study (15 completers). All subjects were experienced insulin pump users (>6 months), and had no history of severe hypoglycemia or diabetic ketoacidosis in the past 12 months; pregnancy and clinically significant cardiac conditions we also excluding.

Subjects completed a 4-weeks data collection period followed by two 32h supervised hotel admissions. During the data collection period, data from a continuous glucose monitor (Dexcom G6, Dexcom), activity tracker (Fitbit Charge 2, Fitbit) and subjects’ personal insulin pumps were collected. Subjects were instructed to exercise between 4pm and 7pm for at least 30 min/day and at least 4 times per week. Participants were then admitted to the hotel admissions, testing either a standard or an exercise-informed AP system. Admission started at noon on day 1, and included standardized meals at 1pm, 7pm, 8am, and 1pm; a 3x15min moderate exercise bout occurred at 5:30pm on day 1. During the admission, particpants used a prototype AP system consisting of a t: AP pump (Tandem Diabetes Care), a Dexcom G6 (Dexcom), and an activity tracker (Smartband 2, Sony), all connected to the DiAs platform (UVA) running the chosen algorithm.

Results / Part 3

Exercised-informed closed loop system significantly reduced the exposure to hypoglycemia during and immediately after exercise by consistently reducing insulin infusion in the hours leading to the bout, with no increased risk of hyperglycemia.

Conclusions / Part 4

AP systems informed by commercially available activity trackers can learn from users exercise behaviors and approipriately anticipate such glycemic disturbances, potentially leading to reduced glycemic risk, and eventually a more automated system.