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Meta-Analysis
. 2015 Oct 18;9(6):1185-91.
doi: 10.1177/1932296815607864.

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

Affiliations
Meta-Analysis

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

Najib Ben Brahim et al. J Diabetes Sci Technol. .

Abstract

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.

Keywords: T1DM; artificial pancreas; exercise; hypoglycemia; physical activity.

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Conflict of interest statement

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Correlation between the slope change of blood glucose levels at exercise and the blood glucose levels (BG start) at the beginning of exercise in patients with type 1 diabetes.
Figure 2.
Figure 2.
Correlation between the slope change of blood glucose levels at exercise and the body exposure to insulin, expressed as IOBabs/TDI, at the beginning of exercise in patients with type 1 diabetes. IOBabs, absolute insulin on board, referring to insulin delivery for the 4 hours preceding start of exercise; TDI, total daily insulin dose.
Figure 3.
Figure 3.
Model diagnosis of the multiple linear regression for the identification of factors determining slope change of blood glucose levels at exercise in patients with type 1 diabetes.
Figure 4.
Figure 4.
Prediction of blood glucose based on the multiple linear regression model obtained in the identification of factors determining slope change of blood glucose levels at exercise in patients with type 1 diabetes.

References

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