###### Abstract
Despite these benefits, glucose concentrations during PA are highly variable, increasing the use of glucose storage and the risk of glycemic events [11]. For T1D patients, maintaining glycemic control during and after PA is challenging because the risk of hypoglycemic is increased for up to 24 h after the bout of exercise [12]. Additionally, strenuous exercise produces some metabolic changes, particularly in the microcirculation and oxygen tension, which may negatively affect the sensor accuracy of CGM devices [13]. Therefore, it is relevant to evaluate the performance of CGM devices in different scenarios with PA and compare glucose measurements during normal daily activity (NDA). This would help one to make better decisions about the amount of food to consume and amount of exogenous insulin required, thus mitigating the onset of acute events. Several studies have investigated the performance of CGM devices during different activities, including, but not limited to high-intensity interval training, long-distance running, and skiing [14; 15; 16; 17]. However, the performance of CGM devices during strenuous PA is still under study, and it is crucial to perform studies that evaluate the accuracy of these devices in other scenarios with high-intensity PA.  
Over recent years, machine learning (ML) methods have attracted great attention in both academia and industry for the outstanding predictive performance in multiple applications [18; 19; 20]. Many researchers have applied ML models in diabetes research for detecting glycemic events [21; 22], predicting glucose levels [23; 24], and identifying clinical patterns [25], among others. ML-based models are promising to automatically detect and classify whether a patient has performed PA or not, which can help T1D individuals in the management of exercise-induced glycemic changes, thus mitigating long-term health risks [26]. In the literature, most of the studies proposed to detect PA rely on the use of heart rate sensors, accelerometers, and other movement sensors [27; 28; 29]. This study makes sense given that many patients have easy access to these technologies and only few studies have considered glucose data to detect and classify PA [30; 31]. In this work, two main contributions are presented. First, we extend the analysis of the impact of high-intensity PA (particularly, mountain biking) on glucose concentrations measured by two CGM devices, the FreeStyle Libre Flash (FSL) and Eversense, which are based on glucose oxidase and fluorescence, respectively. A statistical and comparative analysis were conducted by employing glucose measurements from each of these CGM devices against reference values obtained from capillary glucose measurements using two-group tests. The impact of PA is investigated during two periods, including PA and NDA, and considering (i) different glucose ranges (hypoglycemia, euglycemia, and hyperglycemia); and (ii) four time intervals throughout the day (morning, afternoon, evening, and night). Second, we evaluate the effectiveness of using CGM data and supervised ML-based models to automatically detect PA in T1D individuals. To achieve this, we employed data from the Bas van de Goor Foundation challenge, where 23 T1D subjects measured glucose with devices during periods of strenuous PA and NDA. T-tests and Mann-Whitney U tests were performed to statistically measure differences in glucose ranges and time intervals during NDA and PA. Logistic regression (LR), K-nearest neighbors (KNN), and support vector machine (SVM) were used to classify PA using glucose measurements.