Heike Leutheuser ^1,2* Marc Bartholet ² Alexander Marx ^2,3,4 Marc Pfister ^5,6 Marie-Anne Burckhardt ^6,7 Sara Bachmann ^6,7 Julia E. Vogt ^2,4*

• ¹ Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
• ² Department of Computer Science, ETH Zurich, Zurich, Zürich, Switzerland
• ³ Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Zürich, Switzerland
• ⁴ SIB Swiss Institute of Bioinformatics, Switzerland, Switzerland
• ⁵ Pediatric Pharmacology and Pharmacometrics, University Children’s Hospital Basel, Basel, Switzerland
• ⁶ Department of Clinical Research, University Hospital of Basel, Basel, Basel-Stadt, Switzerland
• ⁷ Pediatric Endocrinology and Diabetology, University Children’s Hospital Basel, Basel, Switzerland

Children with type 1 diabetes (T1D) frequently have nocturnal hypoglycemia, daytime physical activity being the most important risk factor. The risk for late post-exercise hypoglycemia depends on various factors and is difficult to anticipate. The availability of continuous glucose monitoring (CGM) enabled the development of various machine learning approaches for nocturnal hypoglycemia prediction for different prediction horizons. Studies focusing on nocturnal hypoglycemia prediction in children are scarce, and none, to the best knowledge of the authors, investigate the effect of previous physical activity. The primary objective of this work was to assess the risk of hypoglycemia throughout the night (prediction horizon 9~h) associated with physical activity in children with T1D using data from a structured setting. Continuous glucose and physiological data from a sports day camp for children with T1D were input for logistic regression, random forest, and deep neural network models. Results were evaluated using the F2 score, adding more weight to misclassifications as false negatives. Data of 13 children (4 female, mean age 11.3 years) were analyzed. Nocturnal hypoglycemia occurred in 18 of a total included 66 nights. Random forest using only glucose data achieved a sensitivity of 71.1% and a specificity of 75.8% for nocturnal hypoglycemia prediction. Predicting the risk of nocturnal hypoglycemia for the upcoming night at bedtime is clinically highly relevant, as it allows appropriate actions to be taken---to lighten the burden for children with T1D and their families.