A Comprehensive Review of Continuous Glucose Monitoring Accuracy during Exercise Periods.

Continuous Glucose Monitoring (CGM) has been a springboard of new diabetes management technologies such as integrated sensor-pump systems, the artificial pancreas, and more recently, smart pens. It also allows patients to make better informed decisions compared to a few measurements per day from a glucometer. However, CGM accuracy is reportedly affected during exercise periods, which can impact the effectiveness of CGM-based treatments. In this review, several studies that used CGM during exercise periods are scrutinized. An extensive literature review of clinical trials including exercise and CGM in type 1 diabetes was conducted. The gathered data were critically analysed, especially the Mean Absolute Relative Difference (MARD), as the main metric of glucose accuracy. Most papers did not provide accuracy metrics that differentiated between exercise and rest (non-exercise) periods, which hindered comparative data analysis. Nevertheless, the statistic results confirmed that CGM during exercise periods is less accurate.

A Randomized, Placebo-Controlled Double-Blind Trial of a Closed-Loop Glucagon System for Postbariatric Hypoglycemia.

BACKGROUND: Postbariatric hypoglycemia (PBH) can threaten safety and reduce quality of life. Current therapies are incompletely effective. METHODS: Patients with PBH were enrolled in a double-blind, placebo-controlled, crossover trial to evaluate a closed-loop glucose-responsive automated glucagon delivery system designed to reduce severe hypoglycemia. A hypoglycemia detection and mitigation algorithm was embedded in the artificial pancreas system connected to a continuous glucose monitor (CGM, Dexcom) driving a patch infusion pump (Insulet) filled with liquid investigational glucagon (Xeris) or placebo (vehicle). Sensor/plasma glucose responses to mixed meal were assessed during 2 study visits. The system delivered up to 2 doses of study drug (300/150 µg glucagon or equal-volume vehicle) if triggered by the algorithm. Rescue dextrose was given for plasma glucose <55 mg/dL or neuroglycopenia. RESULTS: Twelve participants (11 females/1 male, age 52 ± 2, 8 ± 1 years postsurgery, mean ± SEM) completed all visits. Predictive hypoglycemia alerts prompted automated drug delivery postmeal, when sensor glucose was 114 ± 7 vs 121 ± 5 mg/dL (P = .39). Seven participants required rescue glucose after vehicle but not glucagon (P = .008). Five participants had severe hypoglycemia (<55 mg/dL) after vehicle but not glucagon (P = .03). Nadir plasma glucose was higher with glucagon vs vehicle (67 ± 3 vs 59 ± 2 mg/dL, P = .004). Plasma glucagon rose after glucagon delivery (1231 ± 187 vs 16 ± 1 pg/mL at 30 minutes, P = .001). No rebound hyperglycemia occurred. Transient infusion site discomfort was reported with both glucagon (n = 11/12) and vehicle (n = 10/12). No other adverse events were observed. CONCLUSION: A CGM-guided closed-loop rescue system can detect imminent hypoglycemia and deliver glucagon, reducing severe hypoglycemia in PBH. CLINICAL TRIALS REGISTRATION: NCT03255629.

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

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.