Modified control algorithms for patients with type 1 diabetes mellitus undergoing exercise

• We modify two control algorithms for type 1 diabetic patients undergoing postprandial exercise.
• The goal of this work is to investigate the effect based on minimally modified algorithms rather than complicated modifications.
• The simulations based on a more realistic type 1 diabetes mellitus (T1DM) model incorporating an exercise model.
• In silico control performance of exercise scenarios is significantly improved when the modified algorithms are applied.
• Postprandial hypoglycemia does not occur for all testing exercise scenarios.

In this article, two previously published control algorithms for type 1 diabetes mellitus (T1DM) are modified for patients undergoing exercise. These two control algorithms include the hybrid neural network model predictive control employed on continuous subcutaneous insulin infusion (CSII) therapy, and the fuzzy-logic-based supervisor applied to multiple daily injections (MDI) therapy. A simulation model incorporating a well-acknowledged meal glucose–insulin model and an additional model of physical activity is employed to create virtual subjects for testing. The main notion of the modifications of the control algorithms is to subtract extra carbohydrate for aerobic exercise (ExCarbs) to prevent the exercise-induced hypoglycemia. This residual of CHO, after subtracting ExCarbs from an estimated carbohydrate (CHO) of a meal, is a more appropriate quantity of the CHO which should be compensated by external insulin. The two modified control algorithms are tested for subjects with various intensities and durations of exercise. Simulation results of the virtual subjects show that the algorithms are effective to prevent exercise-induced hypoglycemia. In addition, the simulation results also reveal that the modified methodologies are still robust with blood glucose (BG) level of subjects maintaining within safe region.