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Biobehavioral Human-Machine Co-adaptation of the Artificial Pancreas

人工胰腺的生物行为人机协同适应

基本信息

批准号：
10613967
负责人：
SUE A BROWN
金额：
$ 70.02万
依托单位：
UNIVERSITY OF VIRGINIA
依托单位国家：
美国
项目类别：
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/10613967
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 Continuous Glucose Monitor 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 biobehavior clinical practice control trial design efficacy evaluation experience glycemic control hypoglycemia unawareness improved metabolic profile mobile application next generation novel pilot test preference preservation randomized trial response trial comparing trial enrollment virtual

项目摘要

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.

项目总结