Biobehavioral Human-Machine Co-adaptation of the Artificial Pancreas

项目总结 人工胰腺的生物行为人-机共适应 闭环控制(CLC)目前正在过渡到临床实践和最先进的系统之一，以 Date-Control-IQ-使用由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系统现已投入临床，我们的目标是设计和测试下一代 《中图法》解决方案，借鉴经验，利用迄今积累的大型数据库。 因此，我们将这个项目的重点放在自适应生物行为控制(ABC)的新概念上--一流的 该系统将采用人机协同适应的《中图法》，认识到两者控制的必要性 适应人体生理变化的算法，以及人适应《中图法》动作的必要性。 为了实现其目标，ABC系统将在目前最先进的基础上增加两个新组件 Control-IQ：行为适应模块(BAM)-部署在移动应用程序中的行为干预 通过主要关于膳食和体力活动的信息和风险评估，一个人对CLC的适应情况， 和生理适应模块(PAM)-跟踪风险状态和 用户的代谢情况，并实时采取行动，以适应CLC算法的胰岛素控制参数。 利用这些技术，我们现在提议在一项随机交叉试验中，比较90名参与者 对于1型糖尿病，目前的CLC(对照智商)为三种新的治疗方式：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序列被随机分为两组 治疗方式：CLCCLC BAMCLC PAMABC和ABCCLC PAMCLC BAMCLC。每个人 治疗模式将持续2个月，治疗将以2周的冲洗期分开。 这一设计在我们之前的研究中得到了成功的应用，并实现了四种交叉比较：CLC与ABC (主要)和CLC BAM与CLC；CLC PAM与ABC；CLC BAM与CLC PAM(次要)。我们预计： (1)ABC将在以下方面优于目前的《中图法》：缩短目标范围70-180 mg/dl的时间 通过持续血糖监测(CGM)；降低低血糖风险，并更好地接受技术； (2)行为适应(CLC BAM)在改善CGM测量的时间方面优于CLC 白天的目标范围和减少运动期间/之后CGM测量的低血糖发生率； (3)生理适应(CLC PAM)将在一夜之间占据ABC的大部分血糖益处，将是 在运动期间/之后的餐后血糖变异性和低血糖方面低于BAM，并将 对于那些喜欢全自动控制的人来说，在技术接受度方面优于BAM。 总体而言，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《中图法》的具体情况 《中图法》技术对用户生理的处理和适应，单独和组合。建议数 该项目将设计和测试一个监测和控制这些适应过程的系统。

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.