Exercise Detection Study

The artificial pancreas, a device used for automatic delivery of insulin and glucagon subcutaneously to subjects with type 1 diabetes, is paving the way to revolutionize the management of this disease. Already, the benefit of improved glycemic control compared to current open-loop pump therapy has been demonstrated in several trials. The investigator's group has shown that artificial pancreas algorithm dual hormone system effectively manages blood glucose in a clinic setting and the investigators have specifically shown great progress using glucagon to reduce hypoglycemic episodes outside of exercise. The investigators most recent inpatient study, as yet unpublished, shows that adjusting insulin and glucagon delivery during closed loop treatment, after announcing exercise, effectively reduces mean time below a glucose level of 70 mg/dl when compared to closed loop control without adjustments. The investigators utilized initial open-loop data from this study to help devise dosing changes for the artificial pancreas algorithm. In order to prepare for a future home study, the ability to detect, grade, and classify physical activity so as to appropriately adjust system parameters is vital in helping to prevent exercise induced hypoglycemia in the home setting. Currently, our closed-loop system transmits heart rate and accelerometry outputs from a Zephyrlife BioPatch monitoring device to a Nexus 5 smart phone master controller via Bluetooth. The algorithm then converts the heart rate and accelerometry data into modified estimated energy expenditure - accounting for age, weight, height, sex, resting and sitting heart rates - to determine if exercise is present. However, further data collection is needed to hone the specificity and sensitivity of the detection algorithm to account for a wide variety of subject characteristics and activities. This study is designed to collect 3-axis accelerometry data and heart rate data during a variety of different home activities, as well as during formal exercise, which included aerobic exercise (on a calibrated treadmill) and resistance exercise (straight-leg raises or equivalent) in healthy subjects as well as subjects with type 1 diabetes. Optionally VO2 data from a portable VO2 mask will be obtained. The data collected will be used to further enhance our algorithm that, in future closed-loop studies, will detect exercise and automatically trigger algorithmic adjustments to reduce exercise-related hypoglycemia during and after exercise in individuals with type 1 diabetes.