Arthur Bertachi1,2, Aleix Beneyto1, Charrise M Ramkissoon1, Josep Vehí1,3. 1. 1 Institute of Informatics and Applications, University of Girona , Girona, Spain . 2. 2 Federal University of Technology - Paraná (UTFPR) , Guarapuava, Brazil . 3. 3 Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM) , Madrid, Spain .
Abstract
BACKGROUND: Moderate physical activity improves overall health conditions in subjects with type 1 diabetes. However, insulin management during and after exercise is challenging due to the effects of exercise on glycemic control. Artificial pancreas (AP) systems aim to automatically control blood glucose levels, but exercise-induced hypoglycemia is a major challenge for these systems, especially in uni-hormonal configurations. The aim of this work was to evaluate the ability of several feed-forward (FF) actions to prevent exercise-induced hypoglycemia in a closed-loop setting. METHODS: A closed-loop control algorithm combined with FF actions aimed at eliminating exercise-induced hypoglycemia was evaluated in silico using the UVa/Padova type 1 diabetes simulator. The simulator was modified with an exercise model fitted to clinical data. The FF actions were evaluated in two scenarios: (1) exercise sessions during postprandial period and (2) exercise sessions during fasting period. RESULTS: The mitigation methods proposed in this work were able to minimize the occurrence of hypoglycemic events related with exercise in both scenarios. The time spent in hypoglycemic range in the 2-h period after exercise decreased from 33.3% to 0.0% (P < 0.01) and from 41.3% to 0.0% (P < 0.01) in both scenarios tested. Besides that, the occurrence of hypoglycemic events after exercise sessions was also reduced. CONCLUSIONS: The combination of the FF actions presented in this article within an AP system showed to be an effective strategy to mitigate the risk of hypoglycemia in front of aerobic exercise.
BACKGROUND: Moderate physical activity improves overall health conditions in subjects with type 1 diabetes. However, insulin management during and after exercise is challenging due to the effects of exercise on glycemic control. Artificial pancreas (AP) systems aim to automatically control blood glucose levels, but exercise-induced hypoglycemia is a major challenge for these systems, especially in uni-hormonal configurations. The aim of this work was to evaluate the ability of several feed-forward (FF) actions to prevent exercise-induced hypoglycemia in a closed-loop setting. METHODS: A closed-loop control algorithm combined with FF actions aimed at eliminating exercise-induced hypoglycemia was evaluated in silico using the UVa/Padova type 1 diabetes simulator. The simulator was modified with an exercise model fitted to clinical data. The FF actions were evaluated in two scenarios: (1) exercise sessions during postprandial period and (2) exercise sessions during fasting period. RESULTS: The mitigation methods proposed in this work were able to minimize the occurrence of hypoglycemic events related with exercise in both scenarios. The time spent in hypoglycemic range in the 2-h period after exercise decreased from 33.3% to 0.0% (P < 0.01) and from 41.3% to 0.0% (P < 0.01) in both scenarios tested. Besides that, the occurrence of hypoglycemic events after exercise sessions was also reduced. CONCLUSIONS: The combination of the FF actions presented in this article within an AP system showed to be an effective strategy to mitigate the risk of hypoglycemia in front of aerobic exercise.
Entities:
Keywords:
Artificial pancreas; Exercise; Physical activity; Type 1 diabetes