###### Abstract  
Continuous glucose monitoring (CGM) represents a significant advancement in diabetes management, playing an important role in glycemic control for patients with type \(1\) diabetes (T1D). Despite their benefits, their performance is affected by numerous factors such as the carbohydrate intake, alcohol consumption, and physical activity (PA). Among these, PA could cause hypoglycemic episodes, which might happen after exercising. In this work, two main contributions are presented. First, we extend the performance evaluation of two glucose monitoring devices, Eversense and Free Style Libre (FSL), for measuring glucose concentrations during high-intensity PA and normal daily activity (NDA). The impact of PA is investigated considering (1) different glucose ranges (hypoglycemia, euglycemia, and hyperglycemia); and (2) four time periods throughout the day (morning, afternoon, evening, and night). Second, we evaluate the effectiveness of machine learning (ML) models, including logistic regression, K-nearest neighbors, and support vector machine, to automatically detect PA in T1D individuals using glucose measurements. The performance analysis showed significant differences between glucose levels obtained in the PA and NDA period for Eversense and FSL devices, specially in the hyperglycemic range and two time intervals (morning and afternoon). Both Eversense and FSL devices present measurements with large variability during strenuous PA, indicating that their users should be cautious. However, glucose recordings provided by monitoring devices are accurate for NDA, reaching similar values to capillary glucose device. Lastly, ML-based models yielded promising results to determine when an individual has performed PA, reaching an accuracy value of 0.93. The results can be used to develop an individualized data-driven classifier for each patient that categorizes glucose profiles based on the time interval during the day and according to it a patient performs PA. Our work contributes to the analysis of PA on the performance of CGM devices. continuous glucose monitoring; type 1 diabetes; physical activity; machine learning; TabFFN  
treat this disease and maintain normal glycemic levels, patients require exogenous insulin administration via either multiple injections or continuous subcutaneous insulin infusion such as pumps [2]. The insulin dose is adjusted according to metabolic needs defined mainly by glucose concentrations, carbohydrate intake, and physical activity (PA) [3]. The monitoring and control of glucose is essential to achieve target glycemic control, determining more accurate insulin doses, and diminishing insulin-related complications such as hypoglycemia [4]. Capillary blood glucose monitoring devices have historically been the standard and most established technology for the management of T1D. However, continuous glucose monitoring (CGM) devices have become one of the most relevant advancements, showing similar accuracy to capillary glucose devices [5]. CGM devices use subcutaneous tissue sensors that provide interstitial fluid glucose measurements at regular time intervals and without need for frequent capillary blood glucose measurements [6]. CGM devices provide periodic information about the glycemic levels and may serve as an alarming tool in the onset of hyperglycemic and hypoglycemic events. Several clinical trials have demonstrated that CGM devices help to better adjust insulin doses, improve glycemic control and maintain target glycated hemoglobin [7]. However, the glucose measurements and the performance of CGM devices can be affected by several factors such as administered insulin, carbohydrate intake, and PA [8]. A balanced diet and regular exercise are highly recommended by physicians to maintain a healthy lifestyle. Several studies have shown that PA improves blood lipid profiles, psychological well-being, and may reduce cardiovascular disease risk [9; 10; 11].