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Displaying One Session

PARALLEL SESSION
Date
Wed, 02.06.2021
Session Type
PARALLEL SESSION
Session Time
20:10 - 21:40
Room
Hall D
PARALLEL SESSION

Introduction

Date
Wed, 02.06.2021
Lecture Time
20:10 - 20:12
PARALLEL SESSION

Monitoring the elite athlete with type 1

Date
Wed, 02.06.2021
Lecture Time
20:12 - 20:32
PARALLEL SESSION

EASD/ISAP position statement on CGM/exercise in type 1 diabetes

Date
Wed, 02.06.2021
Lecture Time
20:32 - 20:52
PARALLEL SESSION

Decision support and closed loop control during exercise: new findings from clinical studies and larger data sets

Abstract

Abstract Body

Exercise remains a challenge for people with type 1 diabetes. Hypoglycemia during and following exercise is a common problem. People with type 1 diabetes oftentimes have difficulty maintaining normal glucose levels during and following exercise. Automated hormone delivery and decision support systems can provide assistance in adjusting dosing in response to various types of exercise to help people with type 1 diabetes avoid hypoglycemia and maintain glucose within a target range. These automated systems and decision support systems rely on expert knowledge and predictive models that can determine adjustments to hormone dosing, food consumption, or behavior interventions. We show how we used data collected under both free-living conditions and data collected from highly controlled glucose clamp physiology studies during and following different types of exercise to build glucose forecasting models. We show how aerobic and resistance exercise models of metabolism are designed using ordinary differential equations (ODE) and Markov Chain Monte Carlo system identification methods with data collected from two studies of people with type 1 diabetes undergoing three glucose clamp studies under three different insulin loading conditions (1x, 1.5x, and 3x basal infusion rate) and at moderate and intense exercise. And we show how data-driven models are trained using larger data sets and machine learning to predict the impact of free-living exercise on glucose changes during and following exercise, including nocturnal hypoglycemia on nights following exercise. We provide demonstrations of how the ODE and machine learning algorithms are integrated into automated hormone delivery and decision support systems.

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PARALLEL SESSION

Closed-loop and physical activity in youth with type 1 diabetes

Date
Wed, 02.06.2021
Lecture Time
21:12 - 21:32

Abstract

Abstract Body

Closed-loop glycemic control, characterized by glucose-responsive automated insulin, is now a part of regular clinical reality for many individuals living with type 1 diabetes. The management of type 1 diabetes during exercise is complex. At the same time, dosing insulin adequately either in advance of activity or in real-time can generate positive outcomes and reduce the likelihood of hypoglycemia.
The performance of closed-loop glycemic control in individuals with type 1 diabetes during and after the physical activity has been extensively evaluated, especially in the controlled environment, while there is less data regarding unsupervised physical activity in home settings. Closed-loop therapy was in the past challenged with different exercise protocols of different durations and intensity, in heterogeneous age groups, with additional devices to detect physical activity, such as activity and heart rate monitoring, and adding glucagon to prevent hypoglycemia.
In this presentation, we will present contemporary data on closed-loop glycemic control challenged by physical activity in children, adolescents and young adults with type 1 diabetes.
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PARALLEL SESSION

Live Q&A

Date
Wed, 02.06.2021
Lecture Time
21:32 - 21:40