Enhanced Accuracy of Continuous Glucose Monitoring during Exercise through Physical Activity Tracking Integration

Abstract

Current Continuous Glucose Monitors (CGM) exhibit increased estimation error during periods of aerobic physical activity. The use of readily-available exercise monitoring devices opens new possibilities for accuracy enhancement during these periods. The viability of an array of physical activity signals provided by three different wearable devices was considered. Linear regression models were used in this work to evaluate the correction capabilities of each of the wearable signals and propose a model for CGM correction during exercise. A simple two-input model can reduce CGM error during physical activity (17.46% vs. 13.8%, p < 0.005) to the magnitude of the baseline error level (13.61%). The CGM error is not worsened in periods without physical activity. The signals identified as optimal inputs for the model are “Mets” (Metabolic Equivalent of Tasks) from the Fitbit Charge HR device, which is a normalized measurement of energy expenditure, and the skin temperature reading provided by the Microsoft Band 2 device. A simpler one-input model using only “Mets” is also viable for a more immediate implementation of this correction into market devices.

A Comprehensive Review of Continuous Glucose Monitoring Accuracy during Exercise Periods

“…One of the cases presented a negative MARD value, which means that the MARD for exercise was lower (12.4%) than the pre-exercise value (16.8%). With respect to the resistance exercises, a greater accuracy of the sensors can be observed, as it was concluded in individual studies that compared resistance exercise and other exercises [ 8 , 46 ]. One of the cases also presented a negative MARD value, the MARD for exercise being 6.96% and the MARD for rest being 8.15%.…”

“…Patients need to know that they can rely on CGM systems during any kind of situation with a fast and accurate response. Further work needs to be done exploring the impact of CGM accuracy on aerobic exercise, and possibly correcting that impact, as proposed by Laguna et al [ 8 ].…”

“…Even though the use of CGM is widespread among people with T1D, several studies showed [ 2 , 3 , 4 , 5 , 6 , 7 ] an important lack of accuracy under rapid and unexpected glucose rate changes, as occurs while doing exercise, commonly increasing the risk of hypoglycaemia. As these studies suggest, an understanding of this loss of accuracy during exercise might lead to compensation schemes mitigating its impact [ 8 ].…”

Personalized Approach for the Management of Exercise-Related Glycemic Imbalances in Type 1 Diabetes: Comparison with Reference Method

“…CGM systems may serve as a tool to reduce the risk of exercise-induced hypoglycemia. 30 Nevertheless, it has been shown that their accuracy drops during physical activity when compared to reference glucose measurements, 31 even so among the more recent devices, 32 likely because of the rapidly changing blood glucose concentrations. 33,34…”

Accuracy of Flash Glucose Monitoring During Postprandial Rest and Different Walking Conditions in Overweight or Obese Young Adults

“…Therefore, additional studies are needed to better explore the performance of CGM systems under different exercise conditions (Forlenza et al, 2019). A better understanding of CGM accuracy under exercise conditions may provide evidence to develop CGM algorithms that may be able to compensate for mitigating its impact (Laguna Sanz et al, 2019).…”