Heart Rate Data Put Artificial Pancreas on Even Keel
For patients with type 1 diabetes, heart rate information could be used to help regulate the blood glucose levels during physical activity.
By Kristina Fiore, MedPage Today
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Adding data on a patient's heart rate to the algorithm controlling "artificial pancreas" systems may help prevent blood glucose dips during mild exercise, researchers reported.
In an inpatient trial involving 12 patients with type 1 diabetes, including such information in the control algorithm mitigated declines in blood glucose when participants were working out, Marc Breton, PhD, of the University of Virginia, and colleagues reported inDiabetes Technology & Therapeutics.
It also dropped the number of definite hypoglycemic events, but not significantly so, they reported.
"Upon detecting maintained increased heart rate, the system increases the perceived risk for hypoglycemia, which leads to earlier insulin attenuation if the blood glucose goes down," Breton told MedPage Today. "It will do nothing different than a non-exercise-informed system in the rare, but real, case of glucose going up."
Although closed-loop insulin delivery systems have come a long way, Breton said they haven't worked quite as well at preventing hypoglycemia during physical activity. That's probably because of the lack of reactivity of these systems to exercise, he said.
"It will take some time for the effect of exercise to be reflected in glucose measurements," he said, "so insulin will be attenuated a little late."
But indicators of physical activity, such as heart rate monitors or accelerometers, have the potential to alert the system to an increased risk of hypoglycemia earlier and allow for more proactive insulin modulation, Breton said.
To assess whether data on heart rate could inform the algorithm and improve protection against hypoglycemia during exercise, the researchers studied 12 patients with type 1 diabetes in a randomized crossover study.
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During the 26-hour inpatient period, patients did 30 minutes of mild exercise.
The system involved an Omnipod insulin pump and a Dexcom continuous glucose monitor (CGM), run by the DiAs control algorithm on an Android smartphone.
When heart rate data were added to the algorithm, declines in blood glucose during mild exercise were significantly reduced: the researchers found an average maximum decline of 29 mg/dL without heart rate data compared with a drop of 5 mg/dL when tracking heart rate.
However, adding heart rate didn't significantly lower the low blood glucose index (LBGI), nor did it significantly decrease the number of overt hypoglycemic events during exercise -- although there were numerically fewer hypoglycemic events with the new algorithm (two events versus no events).
Breton and colleagues also found a nonsignificant trend toward higher percentage of time spent within the target range when taking heart rate into account (81 percent versus 75 percent).
Going forward, Breton said he and his colleagues may explore other means of tracking exercise, including accelerometers or pedometers.
Steven Russell MD, PhD, of Massachusetts General Hospital in Boston, who recently reported data on the first longer-term outpatient trial of a bionic pancreas, expressed caution about including such data in predictive algorithms.
"Exercise has an unpredictable effect on blood glucose," Russell told MedPage Today after reviewing the paper. "Mild exercise, the only type examined in this study, typically leads to a drop in blood glucose, but more intense exercise can actually make blood glucose rise. And things other than exercise can make the heart rate rise, such as anger or excitement, and those can elevate blood glucose, so there are limits to how much you can do with information about heart rate."
"The approach used by this study makes the control system more sensitive to the risk of hypoglycemia when the heart rate is elevated." Russell added.
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