Biobehavioral Human-Machine Co-adaptation of the Artificial Pancreas

人工胰腺的生物行为人机协同适应

• 批准号: 10200019
• 负责人: SUE A BROWN
• 金额: $ 70万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-28 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10200019
• 关键词: Address; Algorithm Design; Algorithms; Artificial Pancreas; Behavior; Behavior Control; Behavior Therapy; Behavioral; Blood Glucose; Caring; Cellular Phone; Classification; Clinic; Clinical Trials; Code; Color; Cross-Over Trials; Crossover Design; Data; Databases; Development; Diabetes Mellitus; Enrollment; Exercise; Funding; Generations; Glucose; Goals; Human; Hyperglycemia; Hypoglycemia; Incidence; Inferior; Insulin; Insulin Infusion Systems; Insulin-Dependent Diabetes Mellitus; Journals; Learning; Measures; Medicine; Modality; Monitor; New England; Outcome; Participant; Persons; Physical activity; Physiological; Physiological Adaptation; Physiology; Procedures; Process; Publishing; Randomized; Recording of previous events; Research; Risk; Risk Assessment; Self Management; Stochastic Processes; System; Technology; Testing; Time; Translating; United States National Institutes of Health; Variant; Work; advanced system; base; biobehavior; clinical practice; control trial; design; experience; glucose monitor; glycemic control; hypoglycemia unawareness; improved; metabolic profile; mobile application; next generation; novel; preference; preservation; randomized trial; response; trial comparing; virtual

PROJECT SUMMARY Biobehavioral Human-Machine Co-adaptation of the Artificial Pancreas Closed-loop control (CLC) is now transitioning to the clinical practice and one of the most advanced systems to date–Control-IQ–uses an algorithm designed and tested by the previous research cycle of this project. With the first generation of our CLC system now translated to the clinic, our objective is to design and test next-generation CLC solutions, learning from the experience and utilizing the large database accumulated to date. Thus, we focus this project on the new concept of Adaptive Biobehavioral Control (ABC) – a first-in-class system that will use human-machine co-adaptation of CLC, recognizing both the necessity for the control algorithm to adapt to changes in human physiology, and the necessity for the person to adapt to CLC action. To achieve its objectives, the ABC system will have two new components added to the current state-of-the art Control-IQ: a Behavioral Adaptation Module (BAM) – a behavioral intervention deployed in a mobile app to assist a person's adaptation to CLC by information and risk assessment primarily regarding meals and physical activity, and a Physiologic Adaptation Module (PAM) – an automated procedure tracking risk status and changes in the user's metabolic profile and acting in real time to adapt the CLC algorithm's insulin control parameters. Using these technologies, we now propose to compare, in a randomized cross-over trial enrolling 90 participants with type 1 diabetes, the current CLC (Control-IQ) to three new treatment modalities: ABC and its components BAM and PAM. To do so, study participants will be randomized to two groups following two different sequences of treatment modalities: CLCCLC+BAMCLC+PAMABC and ABCCLC+PAMCLC+BAMCLC. Each treatment modality will continue for 2 months and the treatments will be separated by 2-week washout periods. This design was used successfully in our previous study and enables four crossover comparisons: CLC vs. ABC (primary) and CLC+BAM vs. CLC; CLC+PAM vs. ABC; CLC+BAM vs. CLC+PAM (secondary). We expect that: (1) ABC will be superior to the current CLC in terms of: improved time in the target range 70-180mg/dl measured by continuous glucose monitoring (CGM); reduced risk for hypoglycemia, and better technology acceptance; (2) Behavioral adaptation (CLC+BAM) will be superior to CLC in terms of improved CGM-measured time in the target range during the day and reduced CGM-measured incidence of hypoglycemia during/after exercise; (3) Physiologic adaptation (CLC+PAM) will account for most of the glycemic benefits of ABC overnight, will be inferior to BAM in terms of postprandial glucose variability and hypoglycemia during/after exercise, and will be superior to BAM in terms of technology acceptance for those who prefer fully-automated control. Overall, we affirm that reliable technology has been developed and sufficient data accumulated to warrant the development of next-generation biobehavioral control, aiming adaptation of user behavior to the specifics of CLC treatment and adaptation of CLC technology to user physiology, separately and in combination. The proposed project will design and test a system for monitoring and control of these adaptation processes.

项目摘要 人工胰腺的生物行为人机协同适应 闭环控制（CLC）现在正在向临床实践过渡，并且是最先进的系统之一， date-Control-IQ-使用本项目前一个研究周期设计和测试的算法。与 我们的第一代CLC系统现在已经应用于临床，我们的目标是设计和测试下一代 CLC解决方案，借鉴经验，利用迄今积累的大型数据库。 因此，我们把这个项目的重点放在自适应生物行为控制（ABC）的新概念-一流的 系统，将使用人机协同适应CLC，认识到双方的必要性，控制 算法要适应人的生理变化，而人的CLC动作必须适应。 为了实现其目标，ABC系统将在现有技术的基础上增加两个新的组成部分 Control-IQ：行为适应模块（BAM）-部署在移动的应用程序中的行为干预， 一个人的适应CLC的信息和风险评估，主要是关于膳食和体力活动， 和生理适应模块（PAM）-跟踪风险状态和 用户的代谢概况，并在真实的时间内采取行动以调整CLC算法的胰岛素控制参数。 使用这些技术，我们现在建议在一项随机交叉试验中比较，招募90名参与者 1型糖尿病，目前的CLC（控制智商），以三种新的治疗方式：ABC及其组成部分 BAM和PAM。为此，研究参与者将按照两种不同的顺序随机分为两组 治疗方式：CLC+BAM CLC+PAM CLC ABC和ABC CLC+PAM CLC+BAM CLC。每个 治疗模式将持续2个月，治疗将间隔2周洗脱期。 这种设计在我们以前的研究中得到了成功的应用，并实现了四个交叉比较：CLC与ABC CLC+BAM对CLC; CLC+PAM对ABC; CLC+BAM对CLC+PAM（次级）。我们期望： (1)ABC在以下方面上级当前的CLC：在目标范围内的时间改善70- 180 mg/dl测量值 通过持续血糖监测（CGM）;降低低血糖风险，提高技术接受度; (2)行为适应（CLC+BAM）在改善CGM测量时间方面上级CLC。 白天的目标范围和运动期间/之后降低的CGM测量的低血糖发生率; (3)生理适应（CLC+PAM）将占ABC过夜的大部分血糖益处， 在餐后血糖变异性和运动期间/之后的低血糖方面劣于BAM， 对于那些喜欢全自动控制的人来说，在技术接受度方面要优于BAM上级。 总的来说，我们确认已经开发了可靠的技术，并积累了足够的数据，以保证 开发下一代生物行为控制，旨在使用户行为适应CLC的具体要求 CLC技术的治疗和适应用户生理，单独和组合。拟议 该项目将设计和测试一个监测和控制这些适应进程的系统。