Detecting and Mitigating Psychological Stress Effects on Glycemia in Type 1 Diabetes

Physical and psychological stressors have substantial effects on metabolism, challenging treatment decisions for people with type 1 diabetes (TID). Incorporating physical activity (PA) in multivariable automated insulin delivery (mv AID) algorithms using physiological signals improves diabetes treatment decisions, but acute psychological stress (APS) in diabetes therapies has not been explicitly studied. In this work, we develop machine learning (ML) models using clinical experiment data to detect physical and psychological stressors and incorporate this information in mvAID. The ML models can detect PA and APS in independent test data with 97.8% and 96.1 % accuracy, respectively. We develop a mathematical model that characterizes the effects of PA and APS on glycemia and use it to design a predictive control algorithm to regulate blood glucose concentrations (BGC) by manipulating insulin dosing in response to detected PA and APS. In silico studies demonstrate that the mv AID informed of PA and APS results in 2.36 % improvement in time spent in the target glucose range (BGC of 70–180 mg/dL) while reducing the time spent in hypoglycemia (BGC <70 mg/dL) by 3.11 % compared to a mv AID considering PA only. The results show that insulin dosing algorithms that explicitly consider PA and APS in their decisions can improve glucose control and improve the lives of people with TID.