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

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 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.