Models, signals, and distributed bio-behavioral control of exercise in diabetes

 描述（由申请人提供）：体力活跃的 1 型糖尿病 (T1D) 患者在血糖管理方面面临着非常特殊的挑战：体力活动可以在几个小时内急剧改变葡萄糖稳态。这种行为引发的生理紊乱的程度变化很大，取决于多种因素，例如胰岛素、膳食状态和健康状况等。这些复杂的相互作用和相关的对低血糖的恐惧常常导致避免体力活动。我们建议通过医疗设备网络为患者提供帮助，将其组装成自适应人工胰腺（AP）平台，根据每位患者的需求和选择量身定制，从而解决糖尿病患者的这一特定障碍。该项目联合了人工胰腺开发领域的两个领导小组，即弗吉尼亚大学中心 糖尿病技术和伊利诺伊理工学院糖尿病研究和教育中心。我们建议利用我们在 AP 平台、闭环算法、运动检测和量化以及建模方面的广泛技术组合来实现以下具体目标：1. 患者特定的运动风险警报系统，在运动开始时通知患者可能发生低血糖，其基于：(i) 使用 DiAs 云功能为每位患者调整风险检测器，以及 (ii) 基于个性化模拟的治疗适应建议。 2. 运动信息自动胰岛素剂量：升级至 AP 控制系统，使用运动传感来跟踪代谢风险并调整胰岛素以维持安全的 BG 水平。 3. 完全集成的运动适应 AP 系统：我们假设具有前馈 (1) 和反馈 (2) 组件的运动知情 AP 系统将患者从强制性附加设备中解放出来，将提高血糖安全性和技术接受度。我们将通过三项人体临床试验，独立和协调地证明每个拟议模块的可行性、安全性和有效性：两项短期住院示范试验和一项最终长期（4 个月）家庭试验。这项最终试验还将探索使用我们的平台锻炼的心理社会方面，并开始解决安全的关键方面，考虑到这样一个复杂系统的依从性和技术接受度。我们期望所提出的系统能够提高 AP 在现实生活条件下的安全性和有效性。通过创建新颖的数据基础设施和最佳运动控制算法，该项目还有可能为其他治疗模式生成临床相关的衍生产品。

 DESCRIPTION (provided by applicant): Physically active patients with type 1 diabetes (T1D) face a very specific challenge in their management of glycaemia: physical activity can dramatically alter glucose homeostasis both acutely and over a period of several hours. The magnitude of this behaviorally triggered physiological disturbance is highly variable and depends on a number of factors such as insulin- on-board, prandial state, and fitness to name only a few. These complex interactions and associated fear of hypoglycemia often lead to avoidance of physical activity. We propose to address this specific hurdle of living with diabetes by empowering patients through a network of medical devices, assembled into an adaptive artificial pancreas (AP) platform, tailored to the needs and choices of each patient. This project unites two leading groups in artificial pancreas development, the University of Virginia Center for Diabetes Technology and the Illinois Institute of Technology Center for Diabetes Research and Education. We propose to leverage our extensive technology portfolio in AP platform, closed loop algorithms, exercise detection and quantification, and modelling to address the following specific aims: 1. Patient-specific exercise risk alert system informing patients at the onset of exercise of likely hypoglycemia based on: (i) tuning a risk detector to each patient using DiAs cloud functionalities, and (ii) personalized simulation-based advice on treatment adaptation. 2. Exercise-informed automated insulin dosing: Upgrade to AP control system using exercise sensing to track metabolic risk and adjust insulin to maintain safe BG levels. 3. Fully-integrated Exercise-adapted AP system: We hypothesize that an exercise-informed AP system with both feed-forward (1) and feedback (2) components, freeing the patient from obligatory additional devices will improve glycemic safety and technology acceptance. We will demonstrate feasibility, safety, and efficacy of each of the proposed modules, independently and in concert, through three human clinical trials: two short term inpatient demonstration trials and one final longer term (4 months) home trial. This final trial will also explore psychosocial aspects of exercising with our platform, and start addressing key aspects of safety, accounting for adherence and technology acceptance of such a complex system. We expect the proposed system to enhance the safety and efficacy of AP in real life conditions. By creating a novel data infrastructure and optimal exercise control algorithms, this project has also the potential to generate clinically relevant derivatives for other mode of treatment.