Glycaemic Control During School Days and Holidays in Children and Adolescents With Type 1 Diabetes Using Open‐Source Android Artificial Pancreas Systems: A Real‐World Study
Year: 2026
Type: article
Study design: Observational Study
Abstract: OBJECTIVE: Open-source Android artificial pancreas systems (AAPS) have demonstrated benefits in glycaemic control among children and adolescents with type 1 diabetes (T1D). However, evidence comparing glycaemic outcomes between school days and holidays remains limited. This study investigated whether glycaemic outcomes differed between school days and holidays in children and adolescents with T1D using AAPS. METHODS: This real-world retrospective study included school-aged participants with T1D aged 6-19 years who had used AAPS for more than 3 months. Continuous glucose monitoring (CGM) data were categorized as school days or holidays, and glycaemic outcomes were analysed. RESULTS: 55 participants included, of whom 20 (36.36%) were male. Median total duration of AAPS use was 209.39 [170.66, 289.97] days. Glycaemic metrics, including time in range (TIR), time in tight range (TITR), time above range (TAR), time below range (TBR), glucose management indicator (GMI), mean glucose, standard deviation (SD), coefficient of variation (CV), low blood glucose index (LBGI), and high blood glucose index (HBGI), showed no significant differences between school days and holidays across whole day, daytime, and nighttime periods (p > 0.05). Multivariable logistic regression analyses showed that longer nighttime sleep duration was associated with higher TITR (adjusted OR 2.29, 95% CI 1.29-4.47, p = 0.008) and TIR (adjusted OR 2.07, 95% CI 1.17-3.96, p = 0.018) achievement during school days. CONCLUSIONS: CGM-derived glycaemic outcomes were comparable between school days and holidays in participants with T1D using AAPS. Longer nighttime sleep during school days was associated with higher TITR and TIR achievement.
Source: Diabetes Obesity and Metabolism
DOI: 10.1111/dom.70970
Authors: Xianming Li, Xinyi Ye, Jie Tang, Qin Wang, Wenhao Zhang, Yanqin Xu, Yu Ding, Sihui Luo
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7165839494