Description:
For the more than three million people in the U.S. suffering from Type I diabetes (T1DM), the majority of whom are children, the daily tasks required to manage the disease — such as eight to 10 needle pricks, constant worrying over what to eat and how to control one's blood sugar, and self-injection of medication multiple times daily — can be burdensome and overwhelming. Type I diabetics, in particular, have reason to be enthusiastic with the promising developments in the area of insulin pumps and continuous glucose monitors (CGM). These devices are designed to simulate an artificial pancreas that can measure blood glucose and deliver the appropriate insulin dosage without the painful injections and needle sticks.
Current CGM technology is comprised of continuous glucose monitors coupled with insulin pumps and a control algorithm. The algorithms are mathematical methods that enable the pump and sensor to work in tandem to effectively determine the necessary insulin dose, despite the variety of conditions patients experience in a normal day. While such systems have proven feasible in steady metabolic states, they fail during changing metabolic demands, such as meals and physical activity. Because physical activity is a major trigger of acute hypoglycemia in diabetes, the timely detection of metabolic changes is critical for the success of the closed-loop control. However, increased metabolic demand due to physical activity cannot be reliably detected via glucose monitoring alone.
Boris P. Kovatchev, Ph.D., and Marc D. Breton, Ph.D., have developed an algorithm that correlates changes in heart rate to increased metabolic demand. Using parallel heart rate and glucose monitoring data from 40 Type I diabetics, the researchers developed a mathematical model to compensate for the increased metabolic demand during exercise. The technology overcomes one of the major limitations of closed-loop control: the inability to account for metabolic changes due to physical activity.
