Objective:To investigate the current status of screen exposure among children aged 3-4 years in Shanghai and its relationship with children developmental risks.Methods:A cross-sectional study was conducted, and the stratified cluster random sampling method was used to select 22 102 children of 3-4 years of age across 16 districts in Shanghai in 2023, and their parents were surveyed online. The screen exposure behavior questionnaire (ScreenQ) was used to assess children′s screen exposure behaviors. The Chinese edition early human capability index (eHCI) was used to evaluate whether children were at developmental risk, and the overall characteristics of newly enrolled 3-4 years of age children in Shanghai were calculated by using sampling weights. After controlling for confounding factors, a stepwise Logistic regression model was used to analyze the association between screen exposure behaviors and children′s developmental status (whether at developmental risk).Results:A total of 21 454 children completed the survey, with an age of (3.8±0.3) years, including 11 275 boys (52.6%) and 10 179 girls (47.4%). After weighting, 38.4% of newly enrolled children aged 3-4 years in Shanghai had daily screen time ≥1 h; 55.3% had screen devices in their bedrooms; 40.8% and 62.5% used screens to assist with falling asleep and emotional regulation, respectively; 19.2% of children were frequently exposed to fast-paced screen content (e.g., content with rapid actions or scene changes); 10.4% of parents never discussed or asked questions about content during screen viewing; and 9.2% of parents never discussed screen content or reasons for preferences after screen use. After confirming no multicollinearity among screen exposure behaviors and controlling confounding factors, stepwise Logistic regression analysis revealed that daily screen time ≥1 h (standardized OR=1.98, P<0.001), using screens for emotional regulation (standardized OR=1.59, P<0.001), lack of parent-child interaction after screen use (standardized OR=1.38, P=0.002), presence of screen in children′s bedrooms (standardized OR=1.27, P=0.012), and exposure to fast-paced screen media (standardized OR=1.23, P=0.010) were the top 5 influencing factors of children developmental risks. Conclusions:Screen exposure among preschool children is prevalent and significantly associated with developmental risks. Early screen exposure behaviors should be addressed, daily screen time should be strictly controlled, and healthy screen use habits should be established to mitigate their impact on child development.

Objective:To investigate the current status of screen exposure among children aged 3-4 years in Shanghai and its relationship with children developmental risks.Methods:A cross-sectional study was conducted, and the stratified cluster random sampling method was used to select 22 102 children of 3-4 years of age across 16 districts in Shanghai in 2023, and their parents were surveyed online. The screen exposure behavior questionnaire (ScreenQ) was used to assess children′s screen exposure behaviors. The Chinese edition early human capability index (eHCI) was used to evaluate whether children were at developmental risk, and the overall characteristics of newly enrolled 3-4 years of age children in Shanghai were calculated by using sampling weights. After controlling for confounding factors, a stepwise Logistic regression model was used to analyze the association between screen exposure behaviors and children′s developmental status (whether at developmental risk).Results:A total of 21 454 children completed the survey, with an age of (3.8±0.3) years, including 11 275 boys (52.6%) and 10 179 girls (47.4%). After weighting, 38.4% of newly enrolled children aged 3-4 years in Shanghai had daily screen time ≥1 h; 55.3% had screen devices in their bedrooms; 40.8% and 62.5% used screens to assist with falling asleep and emotional regulation, respectively; 19.2% of children were frequently exposed to fast-paced screen content (e.g., content with rapid actions or scene changes); 10.4% of parents never discussed or asked questions about content during screen viewing; and 9.2% of parents never discussed screen content or reasons for preferences after screen use. After confirming no multicollinearity among screen exposure behaviors and controlling confounding factors, stepwise Logistic regression analysis revealed that daily screen time ≥1 h (standardized OR=1.98, P<0.001), using screens for emotional regulation (standardized OR=1.59, P<0.001), lack of parent-child interaction after screen use (standardized OR=1.38, P=0.002), presence of screen in children′s bedrooms (standardized OR=1.27, P=0.012), and exposure to fast-paced screen media (standardized OR=1.23, P=0.010) were the top 5 influencing factors of children developmental risks. Conclusions:Screen exposure among preschool children is prevalent and significantly associated with developmental risks. Early screen exposure behaviors should be addressed, daily screen time should be strictly controlled, and healthy screen use habits should be established to mitigate their impact on child development.

Objective:To construct and validate a nomogram predictive model for readmission risk within 1 year of children with type 1 diabetes.Methods:A total of 395 children with type 1 diabetes who were hospitalized in four hospitals in Xi′an City from February 2019 to February 2021 were selected as the research subjects. The children were divided into training set ( n = 219) and verification set ( n = 175) in 5∶4 ratios. A nomogram prediction model for readmission risk within 1 year of children with type 1 diabetes was constructed based on the training set data, and internal validation was carried out. The external validation was carried out based on validation set data. Results:A total of 85 (21.5%) children were readmitted within 1 year. Mean glycohemoglobin A1c ≥ 7.5%, co-infection, complications of diabetes, and family history of diabetes were risk factors for readmission within 1 year of children with type 1 diabetes ( OR values were 4.010 - 5.510, P<0.05), and age of onset >7 years old was a protective factor ( OR = 0.070, P<0.05). The internal verification of nomogram model showed that the area under ROC curve was 0.778 (95% CI 0.703- 0.853), and the observed curve in calibration curve was basically consistent with the predicted curve. The external verification showed that the area under ROC curve was 0.748 (95% CI 0.642- 0.854), and the observed curve in calibration curve was basically consistent with the predicted curve. Conclusions:The nomogram predictive model for readmission risk within 1 year of children with type 1 diabetes is scientific and practical, and has certain clinical value in guiding targeted prevention and intervention of readmission of children with type 1 diabetes within one year.