T2D is a condition that combines insulin resistance and relative insulin deficiency. The T2D naturally progresses towards an increasingly pronounced insulin deficiency that leads to the need for pancreatic replacement, by administering insulin.~Type 1 diabetes (T1D) requires a complete and immediate substitution of pancreatic insulin secretion. Currently, patients need to be involved in managing their disease by deciding how much insulin to administer based on the results of glucose monitoring. Artificial intelligence, thanks to a self-learning algorithm, enables the automation and customization of insulin administration. These devices, known as closed loops, bring real benefit to the patients included in the studies, by improving glycemic balance, by decreasing the number of hypo- and hyperglycemia but also by decreasing the mental load associated with the disease, improving their quality of life.~These very significant benefits in the T1D treatment open the possibility of obtaining similar benefits in the T2D treated by the basal-bolus type insulin regimen. This study aims to develop a specific algorithm of T2D to meet its particular characteristics.~The objective of this study is to collect the evolution of blood glucose levels in T2D patients under different conditions of their daily life: physical activity, meals, sleep, etc. This data will be used to develop a test bench to evaluate insulin delivery algorithms to treat patients with insulin-resistant T2D using a closed loop.
The objective of this study is to collect the evolution of blood glucose levels in type 2 diabetes (T2D) patients under different conditions of their daily life: physical activity, meals, sleep, etc. This data will be used to develop a test bench to evaluate insulin delivery algorithms to treat patients with insulin-resistant T2D using a closed loop.