Identification of Main Factors Explaining Glucose Dynamics During and Immediately After Moderate Exercise in Patients With Type 1 Diabetes.

BACKGROUND: Physical activity is recommended for patients with type 1 diabetes (T1D). However, without proper management, it can lead to higher risk for hypoglycemia and impaired glycemic control. In this work, we identify the main factors explaining the blood glucose dynamics during exercise in T1D. We then propose a prediction model to quantify the glycemic drop induced by a mild to moderate physical activity. METHODS: A meta-data analysis was conducted over 59 T1D patients from 4 different studies in the United States and France (37 men and 22 women; 47 adults; weight, 71.4 ± 10.6 kg; age, 42 ± 10 years; 12 adolescents: weight, 60.7 ± 12.5 kg; age, 14.0 ± 1.4 years). All participants had physical activity between 3 and 5 pm at a mild to moderate intensity for approximately 30 to 45 min. A multiple linear regression analysis was applied to the data to identify the main parameters explaining the glucose dynamics during such physical activity. RESULTS: The blood glucose at the beginning of exercise ([Formula: see text]), the ratio of insulin on board over total daily insulin ([Formula: see text]) and the age as a categorical variable (1 for adult, 0 for adolescents) were significant factors involved in glucose evolution at exercise (all P < .05). The multiple linear regression model has an R-squared of .6. CONCLUSIONS: The main factors explaining glucose dynamics in the presence of mild-to-moderate exercise in T1D have been identified. The clinical parameters are formally quantified using real data collected during clinical trials. The multiple linear regression model used to predict blood glucose during exercise can be applied in closed-loop control algorithms developed for artificial pancreas.

Multinight "bedside" closed-loop control for patients with type 1 diabetes.

BACKGROUND: Studies of closed-loop control (CLC) systems have improved glucose levels in patients with type 1 diabetes. In this study we test a new CLC concept aiming to "reset" the patient overnight to near-normoglycemia each morning, for several consecutive nights. SUBJECTS AND METHODS: Ten insulin pump users with type 1 diabetes (mean age, 46.4±8.5 years) were enrolled in a two-center (in the United States and Italy) randomized crossover trial comparing 5 consecutive nights of CLC (23:00-07:00 h) in an outpatient setting versus sensor-augmented insulin pump therapy of the same duration at home. Primary end points included time spent in 80-140 mg/dL as measured by continuous glucose monitoring overnight and fasting blood glucose distribution at 7:00 h. RESULTS: Compared with sensor-augmented pump therapy, CLC improved significantly time spent between 80 and 140 mg/dL (54.5% vs. 32.2%; P<0.001) and between 70 and 180 mg/dL (85.4% vs. 59.1%; P<0.001); CLC reduced the mean glucose level at 07:00 h (119.3 vs. 152.9 mg/dL; P<0.001) and overnight mean glucose level (139.0 vs. 170.3 mg/dL; P<0.001) using a marginally lower amount of insulin (6.1 vs. 6.8 units; P=0.1). Tighter overnight control led to improved daytime control on the next day: the overnight/next-day control correlation was r=0.52, P<0.01. CONCLUSIONS: Multinight CLC of insulin delivery (artificial pancreas) results in significant improvement in morning and overnight glucose levels and time in target range, with the potential to improve daytime control when glucose levels were "reset" to near-normoglycemia each morning.

DiAs web monitoring: a real-time remote monitoring system designed for artificial pancreas outpatient trials.

BACKGROUND: Developments in an artificial pancreas (AP) for patients with type 1 diabetes have allowed a move toward performing outpatient clinical trials. "Home-like" environment implies specific protocol and system adaptations among which the introduction of remote monitoring is meaningful. We present a novel tool allowing multiple patients to monitor AP use in home-like settings. METHODS: We investigated existing systems, performed interviews of experienced clinical teams, listed required features, and drew several mockups of the user interface. The resulting application was tested on the bench before it was used in three outpatient studies representing 3480 h of remote monitoring. RESULTS: Our tool, called DiAs Web Monitoring (DWM), is a web-based application that ensures reception, storage, and display of data sent by AP systems. Continuous glucose monitoring (CGM) and insulin delivery data are presented in a colored chart to facilitate reading and interpretation. Several subjects can be monitored simultaneously on the same screen, and alerts are triggered to help detect events such as hypoglycemia or CGM failures. In the third trial, DWM received approximately 460 data per subject per hour: 77% for log messages, 5% for CGM data. More than 97% of transmissions were achieved in less than 5 min. CONCLUSIONS: Transition from a hospital setting to home-like conditions requires specific AP supervision to which remote monitoring systems can contribute valuably. DiAs Web Monitoring worked properly when tested in our outpatient studies. It could facilitate subject monitoring and even accelerate medical and technical assessment of the AP. It should now be adapted for long-term studies with an enhanced notification feature.