Peter G. Jacobs (United States of America)
Oregon Health & Science University
Biomedical Engineering
Peter G. Jacobs, PhD is an Associate Professor in the Department of Biomedical Engineering at Oregon Health & Science University (OHSU) where he directs the Artificial Intelligence for Medical Systems (AIMS) lab (www.ohsu.edu/jacobs, @aimslabnews). He received his Ph.D. in electrical engineering from OHSU, his masters in electrical and computer engineering from the University of Wisconsin in Madison, and his bachelors in engineering from Swarthmore College. His interests are in the area of medical device design, ubiquitous sensing technologies, machine learning, control systems, decision support, and signal processing as applied toward type 1 diabetes technologies. The AIMS lab is focused on developing advanced control systems for automating delivery of insulin, glucagon and other hormones and evaluating these systems in people with type 1 diabetes. In recent years, the focus has been on integrating exercise metrics into decision support and closed loop systems and in developing machine learning algorithms to predict hypoglycemia during sleep, during exercise, and in general under free-living conditions. In addition to his academic work, he has been an early contributor to a number of diabetes technology companies including Dexcom and iSense (now Waveform), and is a co-founder of Pacific Diabetes Technologies.

Moderator of 1 Session

 Conference Calendar

ORAL PRESENTATIONS SESSION 8

Session Type
Oral Presentations Session
Date
Sat, 30.04.2022
Session Time
13:00 - 14:30
Room
Hall 120
Chair(s)

Presenter of 1 Presentation

 Conference Calendar

Integrating metabolic expenditure data from wearable sensors into an automated insulin delivery system: clinical study results

Session Name
0340 - PARALLEL SESSION - SPORTS AND DIABETES (ID 20)
Session Type
Parallel Session
Date
Thu, 28.04.2022
Session Time
16:40 - 18:00
Room
Hall 114
Presenter
Lecture Time
17:20 - 17:40

Abstract

Abstract Body

abstract_image_v2.pngBackground: The objective was to evaluate an automated insulin delivery (AID) system that responds automatically to physical activity.

Methods: We evaluated an exercise-aware model predictive control (ExMPC) AID using iPancreas developed at OHSU, which includes a Dexcom G6 CGM, an Insulet Omnipod, a control algorithm running on a Samsung S9 smart-phone, and a Polar M600 smart watch. Another exercise-aware algorithm, fading-memory-proportional-derivative (FMPD) was also evaluated. Heart rate and accelerometer data from the smart-watch were combined to calculate metabolic equivalent of task (MET). METs were continuously used in ExMPC to adjust insulin delivery. FMPD notified when METS exceeded a threshold of 4 METs and then shut off insulin for 30 minutes, then reduced insulin by 50% for 1-hour. We compared ExMPC with FMPD in a 2-arm, randomized 3-day outpatient study that included an in-clinic 30-minute aerobic exercise video on day 1. Wilcoxon rank-sum test determined difference in % time-in-range (TIR: 70-180 mg/dL), % time-low (TL: <70 mg/dL), and % time very low (TVL: <54 mg/dL) between algorithms during in clinic exercise and across the entire study.

Results: From start of in-clinic exercise to 2-hours post-exercise, ExMPC (n=18) had higher TIR than FMPD (n=20), (87.5% vs. 76.3%, P=.046) and trended towards less TVL (0.0% vs. 1.5%, P=.09). Across the entire study, TIR (74.5% vs. 75.7%) and TL (1.0% vs. 1.4%) were comparable between algorithms.

Conclusions: An exercise-aware MPC AID can safely control glucose levels during exercise and under free-living conditions without the need for notifications and confirmations from a user.

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