## 2 Materials and Methods
### Statistical Tests and Metrics to Measure Differences in Glucose Concentrations  
To statistically measure the differences in glucose concentrations between PA and NDA periods, and to identify the time interval and glucose range more affected by high-intensity PA, several parametric and nonparametric tests were considered. Specifically, the following statistical tests were considered: (i) the Shapiro Wilk test was used to perform the normality test; (ii) the two-proportion z-test was used to determine whether two proportions from two independent samples are significantly different; (iii) the (parametric) two-sample \(t\)-test was used to compare and determine whether the difference between the means of two populations exists; and (iv) the (nonparametric) rank-based Mann-Whitney U test was used to identify differences between groups by considering their medians or means. The latter method is commonly used when sample sizes are small and for non-normally distributed data, being less sensitive to the non-homogeneity of the variance. For all these statistical tests, a level of significance \(alpha=0.05\) was considered.  
To assess the differences between glucose concentrations obtained from CGM devices (Eversense and FSL) against the capillary glucose measurements (FSLCstrip), two approaches were considered: (i) the deviation metrics mean absolute deviation (MAD) and mean absolute relative deviation (MARD); and (ii) the proportion of glucose measurements in zone A of the Clarke error grid (CEG) plot [34]. Given \(\textbf{y}=[y_{1},\dots,y_{N}]\) as the vector representing the glucose measurements by FSLCstrip, and \(\hat{\textbf{y}}=[y_{1},\dots,y_{N}]\) as the vector indicating the glucose measurements of FSL and Eversense. MAD and MARD are defined as follows.  
\[MAD=\frac{1}{N}\sum_{i=1}^{N}|y_{i}-\hat{y}_{i}| \tag{1}\]  
\[MARD=\frac{1}{N}\sum_{i=1}^{N}\left|\frac{y_{i}-\hat{y}_{i}}{\hat{y}_{i}}\right| \tag{2}\]  
where \(N\) is the total number of glucose measurements, \(y_{i}\) is the \(i\)th measure obtained by the reference device FSLCstrip, and \(\hat{y}_{i}\) is the \(i\)th measure obtained using FSL or Eversense.  
The CEG is a visual tool used to quantify the accuracy of measured glucose values compared to reference values, represented as a grid divided into five zones: A, B, C, D, and E. Figure 1 shows an example of a CEG plot with the different zones. Zone A contains values considered clinically accurate, indicating that the predicted glucose values would lead to the correct treatment of the patient. Zone B indicates altered clinical action with little or no effect on clinical outcome. The values in zone C indicate overcorrecting acceptable blood glucose levels and those in zone D suppose dangerous failures since they are predicted as being in an acceptable range when they are outside normoglycemia. Zone E values wouldalso lead to erroneous treatment since the predicted values are opposite to the accurate BG levels. Values in zones A and B are deemed clinically acceptable, while those in zones C, D, and E are considered potentially unsafe. Since it is the most representative and contains the largest amount of information, the analysis in this work is focused on studying the values that fall in zone A.