Multivariable Artificial Pancreas System to Detect and Mitigate the Effects of Unannounced Physical Activities and Acute Psychological Stress

IIT项目摘要/摘要 本建议的目的是证明一个可行的，功能集成的多变量 人工胰腺，将减轻膳食，体力活动和急性心理压力 在没有任何手动输入的情况下，更好地调节糖尿病患者的葡萄糖水平是一项挑战。 第一代人工胰腺（AP）系统，即混合闭环AP， 并且依赖于手动用户输入来减轻 饮食和体力活动的影响。严重的心理压力和许多其他生理压力 除了有计划的锻炼之外的活动也会影响血糖水平， 1型糖尿病患者的饮食习惯各种体力活动和急性 心理应激事件以不同方式影响胰岛素的代谢和敏感性。 因此，它们的类型、强度和持续时间，以及它们的单独和同时存在， 以确定最佳胰岛素施用。多变量人工 胰腺方法提供了一种集成良好且用户友好的技术，负担最小 并减轻意外的身体活动和急性心理 压力诱因此外，这项技术通过减少数量和 低血糖和高血糖事件的持续时间，与仅基于 连续葡萄糖监测设备数据。这种多变量AP系统只能是 通过使用复杂的多变量方法开发，该方法使用来自 连续葡萄糖监测设备和从可穿戴设备获得的生理变量 在日常生活中自由使用的设备。

IIT Project Summary/Abstract The objective of this proposal is to demonstrate a viable, functionally integrated multivariable artificial pancreas that will mitigate meal, physical activity and acute psychological stress challenges without any manual inputs to better regulate glucose levels of people with diabetes. The first generation of artificial pancreas (AP) systems, the hybrid closed-loop AP, collects data from continuous glucose monitoring devices and relies on manual user inputs for mitigating the effects of meals and physical activities. Acute psychological stress and many other physical activities besides planned exercise can affect blood glucose levels and cause challenges to maintaining euglycemia for people with type 1 diabetes. Various physical activities and acute psychological stress events affect the metabolism and sensitivity to insulin in different ways. Hence, their types, intensities and durations, and their individual and concurrent presence must be detected in order to determine the optimal insulin administration. The multivariable artificial pancreas approach provides a well-integrated and user-friendly technology with minimal burden on the user and mitigates the effects of unexpected physical activities and acute psychological stress inducements. Additionally, this technology would be safer by reducing the number and duration of hypoglycemic and hyperglycemic events, compared to APs based exclusively on continuous glucose monitoring device data. Such multivariable AP systems can only be developed by using a sophisticated multivariable approach that uses real-time information from continuous glucose monitoring devices and physiological variables obtained from wearable devices worn in free-living daily life.