Type 1 Diabetes Clinical Trial
Official title:
Testing and Tuning a Multiparameter Exercise Detection Algorithm
The risk of hypoglycemia in individuals with type 1 diabetes increases considerably during
exercise. As a result, many patients with type 1 diabetes experience fear of and reluctance
to pursue physical activity, in order to avoid the discomforting symptoms associated with
hypoglycemia. The bi-hormonal 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. The investigator's group has recently completed
a study of the bi-hormonal artificial pancreas system during exercise, suggesting reduced
hypoglycemia around the exercise period. 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.
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 in both healthy
subjects and subjects with type 1 diabetes. Additionally, the investigators will observe the
change in glucose levels before and after exercise in subjects with type 1 diabetes.
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investigators most recent inpatient study, as yet unpublished, shows that adjusting insulin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.
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