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

糖尿病运动的模型、信号和分布式生物行为控制

• 批准号: 8971447
• 负责人: MARC D BRETON
• 金额: $ 297.99万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2020-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8971447
• 关键词: Accounting; Acute; Address; Adherence; Algorithms; Architecture; Artificial Pancreas; Automation; Behavior Control; Blood Glucose; Carbohydrates; Cellular Phone; Clinical Trials; Complex; Data; Data Analyses; Data Collection; Databases; Decision Support Systems; Detection; Development; Devices; Diabetes Mellitus; Dose; Ecosystem; Education; Ensure; Exercise; Exposure to; Face; Feasibility Studies; Feedback; Fright; Glucose; Hearing; Heart Rate; Home environment; Hour; Human; Hyperglycemia; Hypoglycemia; Illinois; Injection of therapeutic agent; Inpatients; Institutes; Insulin; Insulin-Dependent Diabetes Mellitus; Intake; Lead; Left; Life; Medical Device; Metabolic; Modeling; Monitor; Names; Patients; Pattern; Philosophy; Physical activity; Physiological; Physiology; Pump; Research; Research Infrastructure; Risk; Running; Safety; Signal Transduction; Specificity; System; Technology; Testing; Time; Universities; Update; Validation; Variant; Virginia; base; blood glucose regulation; clinically relevant; cloud based; design; detector; empowered; feeding; fitness; glycemic control; improved; novel; pancreas development; programs; psychosocial; public health relevance; safety study; sensor; simulation

 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.

 描述（由申请人提供）：经常运动的 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血糖稳态。引发的生理紊乱变化很大，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低血糖恐惧常常导致回避。我们建议通过医疗设备网络为患者提供帮助，将其组装成人工适应性胰腺（AP）平台，根据每位患者的需求和选择来解决糖尿病患者的这一特定障碍。该项目由两个部分组成。弗吉尼亚大学人工胰腺开发中心的领导小组 我们建议利用糖尿病技术和伊利诺伊理工学院糖尿病研究和教育中心的广泛技术组合、闭环算法、运动检测和量化以及建模来实现以下特定目标： 1. 针对患者的特定目标。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 云功能调整每个患者的风险检测器，以及 (ii) 基于个性化模拟的治疗适应建议 2。运动知情的自动胰岛素剂量：升级到 AP 控制系统，使用运动感应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：我们发现运动知情的 AP 系统具有两者。前馈（1）和反馈（2）组件，将患者从强制性的额外装置中解放出来，将提高血糖安全性和技术接受度，我们将通过以下方式独立和协调地证明每个提议模块的可行性、安全性和有效性。三项人体临床试验：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、依从性和技术等关键方面。我们期望所提出的系统能够通过创建新颖的数据基础设施和最佳运动控制算法来增强 AP 在现实生活中的安全性和有效性，该项目还有可能为其他系统生成临床相关的衍生产品。治疗方式。

项目成果

Improving Glucose Prediction Accuracy in Physically Active Adolescents With Type 1 Diabetes.

提高患有 1 型糖尿病的体力活跃青少年的血糖预测准确性。

• 发表时间: 2019
• 作者: Hobbs, Nicole;Hajizadeh, Iman;Rashid, Mudassir;Turksoy, Kamuran;Breton, Marc;Cinar, Ali
• 通讯作者: Cinar, Ali

Anticipation of Historical Exercise Patterns by a Novel Artificial Pancreas System Reduces Hypoglycemia During and After Moderate-Intensity Physical Activity in People with Type 1 Diabetes.

新型人工胰腺系统对历史运动模式的预测可减少 1 型糖尿病患者中等强度体力活动期间和之后的低血糖。

• 发表时间: 2021
• 影响因子: 5.4
• 作者: Garcia;Brown, Sue A;Laichuthai, Nitchakarn;Colmegna, Patricio;Koravi, Chaitanya L K;Ozaslan, Basak;Corbett, John P;Barnett, Charlotte L;Pajewski, Michael;Oliveri, Mary C;Myers, Helen;Breton, Marc D
• 通讯作者: Breton, Marc D

Closed-Loop Control with Unannounced Exercise for Adults with Type 1 Diabetes using the Ensemble Model Predictive Control.

使用集成模型预测控制对 1 型糖尿病成人进行突击锻炼闭环控制。

• DOI: 10.1016/j.jprocont.2019.05.017
• 发表时间: 2019-08-01
• 期刊: Journal of process control
• 影响因子: 4.2
• 作者: J. Garcia;John P. Corbett;Dimitri Boiroux;J. B. Jørgensen;M. Breton
• 通讯作者: M. Breton

Combining Short-Term Interval Training with Caloric Restriction Improves ß-Cell Function in Obese Adults.

将短期间歇训练与热量限制相结合可改善肥胖成人的细胞功能。

• 发表时间: 2018-06-03
• 影响因子: 5.9
• 作者: Francois, Monique E;Gilbertson, Nicole M;Eichner, Natalie Z M;Heiston, Emily M;Fabris, Chiara;Breton, Marc;Mehaffey, J Hunter;Hassinger, Taryn;Hallowell, Peter T;Malin, Steven K
• 通讯作者: Malin, Steven K

Impact of short-term exercise training intensity on β-cell function in older obese adults with prediabetes.

短期运动训练强度对老年肥胖糖尿病前期患者β细胞功能的影响。

• 发表时间: 2018-12-01
• 作者: Malin, Steven K;Francois, Monique E;Eichner, Natalie Z M;Gilbertson, Nicole M;Heiston, Emily M;Fabris, Chiara;Breton, Marc
• 通讯作者: Breton, Marc