Abstract Body

Regular exercise has numerous health and fitness benefits for people living with type 1 diabetes, however the acute management of glycemia during and after a bout of exercise remains a major clinical challenge. The Type 1 Diabetes Exercise Initiative (T1Dexi) is a real-world study designed to create a shareable dataset that will help researchers better understand modifiable and non-modifiable factors that may influence the glycemic responses to different types of exercise in those living with type 1 diabetes. This session will highlight results of a one-month observational study of exercise-related glycemia from 497 adults in the US living with type 1 diabetes (n= 183 on standard pump therapy; n=226 on hybrid closed loop; n= 88 on multiple daily injections). Participants self-reported physical activity events, including randomized assignment to study-designed aerobic, resistance or interval type exercise, and food intake using a custom smart phone application. Data collection also included insulin delivery and activity monitors (Polar heart rate chest strap, Verily Health Watch) to contextualize each activity event and relate to exercise-associated changes in glycemia as assessed by continuous glucose monitoring (Dexcom G6).

Abstract Body

Background: Exercise in type 1 diabetes (T1D) remains challenging. Intensity and duration impact glucose levels but this varies with exercise type. Separating increased glucose uptake due to muscle action from increased insulin effectiveness allows us to model glucose changes and better adjust insulin delivery.

Methods: Participants with T1D performed aerobic (n = 26) and resistance exercise (n = 25) during two clamp studies to obtain rate of appearance (Ra) and disappearance (Rd) of glucose. Participants were divided into 2 cohorts, moderate and intense exercise, and engaged in three experiments at different, constant insulin infusion rates (basal, 1.5* basal, and 3*basal). A model of glucose dynamics estimated Ra and Rd, and linear regression across the three experiments per participant obtained insulin-mediated effect. Non-insulin mediated effect was the intercept. We determined area under the curve for endogenous glucose production (AUC_EGP) and Rd (AUC_Rd) over 45 min of exercise.

Results: During aerobic exercise, AUC_Rd increased 12.45 mmol/L and 13.13 mmol/L (P < 0.001) whereas AUC_EGP increased 1.66 mmol/L and 3.46 mmol/L (P < 0.001) above baseline during moderate and intense exercise, respectively. AUC_EGP increased during intense exercise by 2.14 mmol/L (P < 0.001) compared with moderate exercise. Insulin-mediated glucose uptake rose during exercise and persisted hours afterward, whereas non-insulin-mediated effect was limited to the exercise period (figure 1). Preliminary data from resistance exercise is shown in Figure 2.

Conclusions: Separating insulin and non-insulin glucose uptake during exercise in T1D has not been done before. This method allows visualization of these changes for the first time.

Abstract Body

Background: The objective was to evaluate an automated insulin delivery (AID) system that responds automatically to physical activity.

Methods: We evaluated an exercise-aware model predictive control (ExMPC) AID using iPancreas developed at OHSU, which includes a Dexcom G6 CGM, an Insulet Omnipod, a control algorithm running on a Samsung S9 smart-phone, and a Polar M600 smart watch. Another exercise-aware algorithm, fading-memory-proportional-derivative (FMPD) was also evaluated. Heart rate and accelerometer data from the smart-watch were combined to calculate metabolic equivalent of task (MET). METs were continuously used in ExMPC to adjust insulin delivery. FMPD notified when METS exceeded a threshold of 4 METs and then shut off insulin for 30 minutes, then reduced insulin by 50% for 1-hour. We compared ExMPC with FMPD in a 2-arm, randomized 3-day outpatient study that included an in-clinic 30-minute aerobic exercise video on day 1. Wilcoxon rank-sum test determined difference in % time-in-range (TIR: 70-180 mg/dL), % time-low (TL: <70 mg/dL), and % time very low (TVL: <54 mg/dL) between algorithms during in clinic exercise and across the entire study.

Results: From start of in-clinic exercise to 2-hours post-exercise, ExMPC (n=18) had higher TIR than FMPD (n=20), (87.5% vs. 76.3%, P=.046) and trended towards less TVL (0.0% vs. 1.5%, P=.09). Across the entire study, TIR (74.5% vs. 75.7%) and TL (1.0% vs. 1.4%) were comparable between algorithms.

Conclusions: An exercise-aware MPC AID can safely control glucose levels during exercise and under free-living conditions without the need for notifications and confirmations from a user.

Abstract Body

While the benefits of regular physical activity are well established for individuals with type 1 diabetes, glucose control remains a challenge with conventional therapeutic tools, especially during and after physical activity. Factors affecting glycemic control include activity type (aerobic, anaerobic or mixed), intensity and duration of the activity, level of hydration, the secretion of counter-regulatory hormones as well as the amount of insulin and nutrients in the body, when the physical activity is performed.

Glucose-responsive automated insulin (and glucagon) delivery is now a routine clinical reality for many individuals living with type 1 diabetes. There are several automated insulin delivery systems are already available, at the same time there are several other devices extensively evaluated at home, mainly unsupervised, and for longer periods. The performance of automated insulin delivery devices has been challanged with different types of physical activity, using different exercise settings and duration, adding additional signals to detect physical activity, such as activity and heart rate monitoring, and including individuals with type 1 diabetes of different ages.

In this presentation, we will present current data on automated insulin delivery in type 1 diabetes challenged by physical activity.