Development of Artificial Intelligence Tools for the Detection of Stress Markers and Consideration of Stress States in the Monitoring of Subjects With Type 1 Diabetes

Stress refers to all the reactions of an organism subjected to exogenous or endogenous stress. In the context of diabetes, stress plays a critical role. There are two forms of stress: acute and chronic, both of which can have a significant impact on patients' glycaemic control. Acute stress, if repeated, can cause rapid increases in blood glucose levels, while chronic stress can lead to insulin resistance. It is therefore essential to develop tools for recognising and quantifying stress states specific to patients with diabetes. These tools would provide a better understanding of the role of stress in diabetes management, paving the way for more targeted therapeutic interventions and improving patients' quality of life. We are currently training algorithms using advanced machine learning and artificial intelligence techniques to recognise and quantify stress states using existing databases, including voice and physiological data. These technological advances will make it possible to identify moments of stress more accurately and provide appropriate responses, thereby contributing to better diabetes management. The SMART-T1D study is an ancillary study of the EVASTRESS study.