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.
Biobehavioral Human-Machine Co-Adaptation of the Artificial Pancreas Large-scale artificial pancreas studies, including a multi-center pivotal trial recently completed by our team, have established the current capabilities of closed-loop control (CLC) and identified its deficiencies, e.g. inferior control during the day due to slow insulin response to fast biobehavioral perturbations. We therefore propose to develop and test a next-generation CLC system ? Adaptive Biobehavioral Control (ABC) based on the novel concept of human-machine co-adaptation ? which will use a stochastic-process approximation of the day-to-day variation in human physiology and behavior to individualize and optimize CLC. The ABC concept recognizes 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; thus, ABC is expected to be superior to current state-of-the art CLC systems in terms of glycemic control outcomes, particularly during the day, and technology acceptance by the user.
| Brown, Sue A; Breton, Marc D; Anderson, Stacey M et al. (2017) Overnight Closed-Loop Control Improves Glycemic Control in a Multicenter Study of Adults With Type 1 Diabetes. J Clin Endocrinol Metab 102:3674-3682 |
| Campos-Náñez, Enrique; Kovatchev, Boris P (2016) Impact of Meal Constituents on Artificial Pancreas Algorithms. Diabetes Technol Ther 18:607-609 |
| Kovatchev, Boris P; Patek, Stephen D; Ortiz, Edward Andrew et al. (2015) Assessing sensor accuracy for non-adjunct use of continuous glucose monitoring. Diabetes Technol Ther 17:177-86 |
| Kovatchev, Boris P (2015) Hypoglycemia Reduction and Accuracy of Continuous Glucose Monitoring. Diabetes Technol Ther 17:530-3 |
| Brown, Sue A; Kovatchev, Boris P; Breton, Marc D et al. (2015) Multinight ""bedside"" closed-loop control for patients with type 1 diabetes. Diabetes Technol Ther 17:203-9 |
| Gonder-Frederick, Linda (2014) Lifestyle modifications in the management of type 1 diabetes: still relevant after all these years? Diabetes Technol Ther 16:695-8 |
| Kovatchev, Boris P; Renard, Eric; Cobelli, Claudio et al. (2014) Safety of outpatient closed-loop control: first randomized crossover trials of a wearable artificial pancreas. Diabetes Care 37:1789-96 |
| Cobelli, Claudio; Renard, Eric; Kovatchev, Boris (2014) The artificial pancreas: a digital-age treatment for diabetes. Lancet Diabetes Endocrinol 2:679-81 |
| Kovatchev, Boris P; Wakeman, Christian A; Breton, Marc D et al. (2014) Computing the surveillance error grid analysis: procedure and examples. J Diabetes Sci Technol 8:673-84 |
| Renard, Eric; Cobelli, Claudio; Kovatchev, Boris P (2013) Closed loop developments to improve glucose control at home. Diabetes Res Clin Pract 102:79-85 |
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