## 4 Discussion
Although CGM devices play an important role in the management of T1D, we confirmed (extending the analysis of the study [32]) that accuracy of these devices is reduced during PA at different time intervals and glucose ranges. The literature has shown that CGM devices support the reduction in hypoglycemia and hyperglycemia, improving the management of T1D. However, most of these studies were not conducted during high-intensity activities. In a similar way to this previous analysis, the frequency of onset of glucose outside the target range, in particular hypoglycemic/hyperglycemic episodes, was studied at different time intervals. Hypoglycemic events appeared to be stable at both periods (PA and NDA). However, hyperglyic events vary significantly at different time intervals during the PA period compared to the NDA period. This makes sense considering that it is known that performing exercise diminishes the glucose storage of the body, therefore avoiding hyperglycemia. Although CGM devices play an important role in the management of T1D, we confirmed that accuracy of these devices are reduced during PA. The reviewed literature has shown that CGM devices support reducing hypoglycemia and hyperglycemia. However, most of these studies were not conducted during high-intensity activities. The use of ML models to determine when an individual has performed PA yielded promising results with an accuracy value of 0.93. The predictive models developed using supervised ML techniques demonstrate high accuracy in distinguishing between PA and NDA, with Eversense showing the best performance. The SHAP analysis provides insights into the importance of different features in the predictive models. It highlights the significant influence of the total number of adverse events (F_Adverse) on the model's predictions, suggesting that the occurrence of glycemic events is a key indicator of PA. This finding is consistent across all devices studied. These predictive 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 if a patient performs PA. Despite the extensive comparative analysis between Eversense and FSL devices against FSLCstrip, it is important to address the limitations of this study. One of the major limitations is related to the dataset size, where only glucose measurements from 23 T1D participants were considered. We acknowledge that the small sample size may affect the generalization of our results since the glycemic variability of individuals may impact both the statistical analyses and extracted features used in the classification of PA. This could limit the broader applicability of our findings to the larger T1D population and other scenarios with PA since our work has been evaluated with glucose measurements in high-intensity PA. In this line, we also have to mention the potential bias in participant selection, and how the specific demographic, clinical, and glycemic characteristics of the participants might limit the extent to which these results can be applied to other studies or cohorts. However, it is worth noting that studies that collect data from individuals performing high-intensity PA are scarce, and our study is one of the few that has conducted an analysis on this, and more particularly using data from mountain biking. To overcome these limitations, future work will focus on expanding the analysis by incorporating larger and more diverse datasets related to glucose, thereby enhancing the reliability of the findings. Additionally, to mitigate potential selection bias, future studies will include not only high-intensity PA but also various forms of aerobic and anaerobic exercises. This approach will allow for a comprehensive evaluation of glucose monitoring devices across a broader range of PA scenarios. Another valuable direction for future research involves the evaluation of other measures to capture the glycemic dynamics, and fusing them with those proposed in this study.