Detection and Control of Unannounced Exercise in the Artificial Pancreas without Additional Physiological Signals.

The purpose of this study was to develop an algorithm that detects aerobic exercise and triggers disturbance rejection actions to prevent exercise-induced hypoglycemia. This approach can provide a solution to poor glycemic control during and after aerobic exercise, a major hindrance in the participation of exercise by patients with type 1 diabetes. This novel exercise-induced hypoglycemia reduction algorithm (EHRA) detects exercise using a threshold on a disturbance term, a parameter estimated from an augmented minimal model using an Unscented Kalman Filter. After detection, the EHRA triggers three actions: 1) a carbohydrate suggestion, 2) a reduction in basal insulin and the insulin-on-board maximum limit, and 3) a 30% reduction of the next insulin meal bolus. The EHRA was tested in silico using a 15-day scenario with 8 exercise sessions of 50 min at 60% VO2max on alternating days. The EHRA was able to obtain improved results when compared to strategies with and without exercise announcement. The unannounced, announced, and EHRA strategies all obtained an overall percentage of time in range (70-180 mg/dl) of 94% and a percentage of time <70 mg/dl of 2%, 0% and 0%, respectively. The EHRA was tested for robustness during exercise sessions of +25% and -25% intensity and results suggest that the EHRA is able to account for variability in exercise intensity, duration, and patient dynamics such as glucose uptake rate and insulin sensitivity.