## 5 Conclusions  
Our study demonstrated that CGM devices, particularly Eversense, can effectively monitor glucose levels during high-intensity PA, albeit with some limitations in accuracy. Our findings revealed significant differences in glucose measurements between these periods, with a noticeable impact on the performance of CGM devices. The results indicate that both Eversense and FSL show higher deviations in glucose values during PA, particularly in hyperglycemic and hypoglycemic ranges. This suggests that strenuous PA affects the accuracy of CGM devices, making them less reliable compared to capillary measurements. Under scenarios with high-intensity PA, the use of CGM devices should be cautious since glucose measures could not be sufficiently accurate. In addition, we developed an automated approach using ML techniques yielding promising results and achieving an accuracy value of 0.93. This approach could help to reduce the workload of clinicians by tracking PA in T1D patients and controlling medical recommendations. The findings underscore the importance of considering the impact of PA on glucose monitoring and the potential of ML methods to improve diabetes management.  
Conceptualization, D.C.-M. and C.S.-R.; methodology, D.C.-M. and C.S.-R.; software, H.C.-D. and D.C.-M.; validation, E.A., H.F. and C.S.-R.; formal analysis, D.C.-M. and C.S.-R.; investigation, H.C.-D. and D.C.-M.; resources, C.S.-R.; data curation, P.R.v.D. and H.C.-D.; writing--original draft preparation, H.C.-D. and D.C.-M.; writing--review and editing, C.S.-R., E.A., H.F., P.R.v.D. and C.S.-R.; visualization, H.C.-D. and D.C.-M.; supervision, C.S.-R.; project administration, C.S.-R.; funding acquisition, C.S.-R. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the European Commission through the H2020-EU3.1.4.2, European Project WARIFA (Watching the risk factors: Artificial intelligence and the prevention of chronic conditions) under the Grant Agreement 101017385; by the Spanish Government by the Grant AAVis-BMR PID2019-10/7768 RA-I00/AEI/10.13039/50110 0011033; by the Community of Madrid with the European Social Fund through the Youth Employment Operational Program and the Youth Employment Initiative (YED) under the grant TIC-11649; and by Rey Juan Carlos University (grant 2023/SOLCON-132212).  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Medical Ethical committees in Spain (Hospital Universitario Central de Asturias; 163/18) and the Netherlands (Isala Hospital; NL66388.075.18/180603) and registered in the Dutch trial register ([[https://onderzoekmetmensen.nl/en/trial/45979](https://onderzoekmetmensen.nl/en/trial/45979)]([https://onderzoekmetmensen.nl/en/trial/45979](https://onderzoekmetmensen.nl/en/trial/45979)), accessed on 8 August 2018, number NL7133).  
**Informed Consent Statement:** Written informed consent has been obtained from the patient(s) to publish this paper.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
**Abbreviations**  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CEG & Clarke error grid \\ CGM & Continuous glucose monitoring \\ DT & Decision tree \\ KNN & K-nearest neighbors \\ LR & Logistic regression \\ PA & Physical activity \\ MAD & Mean absolute deviation \\ MARD & Mean absolute relative deviation \\ NDA & Normal daily activity \\ ML & Machine learning \\ MLP & Multilayer perceptron \\ RF & Random forest \\ SHAP & Shapley additive explanations \\ SVM & Support vector machine \\ T1D & Type 1 diabetes \\ XGB & Extreme gradient boosting \\ \end{tabular}