Incorporating an Exercise Detection, Grading, and Hormone Dosing Algorithm Into the Artificial Pancreas Using Accelerometry and Heart Rate

Abstract

In this article, we present several important contributions necessary for enabling an artificial endocrine pancreas (AP) system to better respond to exercise events. First, we show how exercise can be automatically detected using body-worn accelerometer and heart rate sensors. During a 22 hour overnight inpatient study, 13 subjects with type 1 diabetes wearing a Zephyr accelerometer and heart rate monitor underwent 45 minutes of mild aerobic treadmill exercise while controlling their glucose levels using sensor-augmented pump therapy. We used the accelerometer and heart rate as inputs into a validated regression model. Using this model, we were able to detect the exercise event with a sensitivity of 97.2% and a specificity of 99.5%. Second, from this same study, we show how patients’ glucose declined during the exercise event and we present results from in silico modeling that demonstrate how including an exercise model in the glucoregulatory model improves the estimation of the drop in glucose during exercise. Last, we present an exercise dosing adjustment algorithm and describe parameter tuning and performance using an in silico glucoregulatory model during an exercise event.

Cross-Validation of a Glucose-Insulin-Glucagon Pharmacodynamics Model for Simulation Using Data From Patients With Type 1 Diabetes

“…Few patients achieve this even with intensive insulin treatment [1]. New approaches with automatic glucose controlled insulin and glucagon delivery, known as a dual-hormone artificial pancreas (AP), may offer a solution to improve glycemic control [2][3][4][5][6]. To design and tune control algorithms for AP devices prior to in vivo tests, a validated simulation model capturing the dynamics between glucose, insulin and glucagon is needed to perform helpful in silico experiments [7][8][9].…”

Efficacy of single-hormone and dual-hormone artificial pancreas during continuous and interval exercise in adult patients with type 1 diabetes: randomised controlled crossover trial

“…Several approaches have been investigated for the management of exercise during the operation of the single-hormone artificial pancreas [18][19][20][21][22]. A fully reactive system that is driven by glucose sensor data alone fails to prevent exerciseinduced hypoglycaemia, particularly because the delay in insulin absorption reduces the effectiveness of its suspension after the start of exercise [21].…”

“…A fully reactive system that is driven by glucose sensor data alone fails to prevent exerciseinduced hypoglycaemia, particularly because the delay in insulin absorption reduces the effectiveness of its suspension after the start of exercise [21]. The addition of a heart rate or activity sensor to the single-hormone artificial pancreas might reduce the risk of hypoglycaemia, although at the expense of an increased burden for the patient (via wearing multiple sensors) [22]. The use of glucagon in the dual-hormone artificial pancreas might be particularly beneficial during exercise, but limited data exist to quantify the additional benefits compared with the single-hormone artificial pancreas.…”

Fog Computing and Edge Computing Architectures for Processing Data From Diabetes Devices Connected to the Medical Internet of Things

“…The glucose information along with additional types of information from accelerometers, and other sensors can be combined to deliver a recommended insulin infusion rate that can change almost continuously in response to changes in the metabolic milieu. 28 In such a system, it is necessary for decisions to be made locally with a processor located either worn by or carried by the patient (or somehow in the vicinity of the patient at all times) rather than by a cloud-based server which is going to suffer from potential delays in processing or temporary data dropout in case of data transmission problems. 29 Artificial pancreas systems in use and under development all use fog or edge computing systems.…”