Performance of continuous glucose monitors (CGMs) may be lower when glucose levels are changing rapidly, such as occurs during physical activity. Our aim was to evaluate accuracy of a current-generation CGM during moderate-intensity exercise in type 1 diabetes (T1D) pregnancy.
Subjects and methods:
As part of a study of 24-h closed-loop insulin delivery in 12 women with T1D (disease duration, 17.6 years; glycosylated hemoglobin, 6.4%) during pregnancy (gestation, 21 weeks), we evaluated the Freestyle Navigator(®) sensor (Abbott Diabetes Care, Alameda, CA) during afternoon (15:00-18:00 h) and morning (09:30-12:30 h) exercise (55 min of brisk walking on a treadmill followed by a 2-h recovery), compared with sedentary conditions (18:00-09:00 h). Plasma (reference) glucose, measured at regular 15-30-min intervals with the YSI Ltd. (Fleet, United Kingdom) model YSI 2300 analyzer, was used to assess CGM performance.
Sensor accuracy, as indicated by the larger relative absolute difference (RAD) between paired sensor and reference glucose values, was lower during exercise compared with rest (median RAD, 11.8% vs. 18.4%; P<0.001). These differences remained significant when correcting for plasma glucose relative rate of change (P<0.001). Analysis by glucose range showed lower accuracy during hypoglycemia for both sedentary (median RAD, 24.4%) and exercise (median RAD, 32.1%) conditions. Using Clarke error grid analysis, 96% of CGM values were clinically safe under resting conditions compared with only 87% during exercise.
Compared with sedentary conditions, accuracy of the Freestyle Navigator CGM was lower during moderate-intensity exercise in pregnant women with T1D. This difference was particularly marked in hypoglycemia and could not be solely explained by the glucose rate of change associated with physical activity.
[Show abstract][Hide abstract] ABSTRACT: One way of constructing a control algorithm for an artificial pancreas is to identify a model capable of predicting plasma glucose (PG) from interstitial glucose (IG) observations. Stochastic differential equations (SDEs) make it possible to account both for the unknown influence of the continuous glucose monitor (CGM) and for unknown physiological influences. Combined with prior knowledge about the measurement devices, this approach can be used to obtain a robust predictive model. A stochastic-differential-equation-based gray box (SDE-GB) model is formulated on the basis of an identifiable physiological model of the glucoregulatory system for type 1 diabetes mellitus (T1DM) patients. A Bayesian method is used to estimate robust parameters from clinical data. The models are then used to predict PG from IG observations from 2 separate study occasions on the same patient. First, all statistically significant diffusion terms of the model are identified using likelihood ratio tests, yielding inclusion of [Formula: see text], [Formula: see text], and [Formula: see text]. Second, estimates using maximum likelihood are obtained, but prediction capability is poor. Finally a Bayesian method is implemented. Using this method the identified models are able to predict PG using only IG observations. These predictions are assessed visually. We are also able to validate these estimates on a separate data set from the same patient. This study shows that SDE-GBs and a Bayesian method can be used to identify a reliable model for prediction of PG using IG observations obtained with a CGM. The model could eventually be used in an artificial pancreas.
Journal of diabetes science and technology 03/2014; 8(2):321-330. DOI:10.1177/1932296814523878
[Show abstract][Hide abstract] ABSTRACT: Background:
We present a clinical trial establishing the feasibility of a control-to-range (CTR) closed-loop system informed by heart rate (HR) and assess the effect of HR information added to CTR on the risk for hypoglycemia during and after exercise.
Subjects and methods:
Twelve subjects with type 1 diabetes (five men, seven women; weight, 68.9 ± 3.1 kg; age, 38 ± 3.3 years; glycated hemoglobin, 6.9 ± 0.2%) participated in a randomized crossover clinical trial comparing CTR versus CTR+HR in two 26-h admissions, each including 30 min of mild exercise. The CTR algorithm was implemented in the DiAs portable artificial pancreas platform based on an Android(®) (Google, Mountainview, CA) smartphone. We assessed blood glucose (BG) decline during exercise, the Low BG Index (LBGI) (a measure of hypoglycemic risk), number of hypoglycemic episodes (BG <70 mg/dL) and overall glucose control (percentage time within the target range 70 mg/dL ≤ BG ≤ 180 mg/dL).
Using HR to inform the CTR algorithm reduced significantly the BG decline during exercise (P=0.022), indicated marginally lower LBGI (P=0.3) and fewer hypoglycemic events during exercise (none vs. two events; P=0.16), and resulted in overall higher percentage time within the target range (81% vs. 75%; P=0.2). LBGI and average BG remained unchanged overall, during recovery, and overnight.
HR-informed closed-loop control can be implemented in a portable artificial pancreas. Although closed loop has been shown to reduce hypoglycemia, adding HR signal may further limit the risk for hypoglycemia during and immediately after exercise. The most prominent effect of adding HR information is reduced BG decline during exercise, without deterioration of overall glycemic control.
[Show abstract][Hide abstract] ABSTRACT: To provide an understanding of both the preclinical and clinical aspects of closed-loop artificial pancreas systems, we provide a discussion of this topic as part of this two-part Bench to Clinic narrative. Here, the Bench narrative provides an in-depth understanding of insulin-glucose-glucagon physiology in conditions that mimic the free-living situation to the extent possible in type 1 diabetes that will help refine and improve future closed-loop system algorithms. In the Clinic narrative, Doyle and colleagues compare and evaluate technology used in current closed-loop studies to gain further momentum toward outpatient trials and eventual approval for widespread use.
Diabetes care 05/2014; 37(5):1184-90. DOI:10.2337/dc13-2066 · 8.42 Impact Factor
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