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Electronic screens have become an integral part of modern life, accompanied with growing concerns for children's neuropsychological development. This study aimed to evaluate the associations between measures of early life screen exposure and hyperactive behaviors among preschool children. The study also aimed to investigate their cumulative effects and the critical window for these associations. A cross-sectional survey was conducted among 52 625 mother-child dyads at preschools in LongHua District of Shenzhen, China. A self-administered structured questionnaire was used to assess socio-demographic characteristics, duration of children's electronic screen exposure in each of the first 3 years following birth and the presence of current hyperactive behaviors. A series of logistic regression models were used to examine the relationship between previous screen time and current hyperactive behaviors. A crossover analysis was used to explore the critical window for a significant relationship between screen time and hyperactive behaviors. We found that exposure to electronic screens in the first 3 years of life was associated with hyperactive behaviors in preschool children. A cumulative effect was shown in children with an average daily screen time less than 60 min, with adjusted ORs increasing from 1.262 to 1.989 as screen exposure years increased from 1 to 3 years. A critical window was identified in that children in the first 2 years after birth were vulnerable to electronic screen exposure. Exposure to televisions, mobile phones, and computers were all related to elevated risks for hyperactive behaviors. In conclusion, early screen exposure appears to increase the risk for hyperactive behaviors in preschool children with the presence of a cumulative effect, a critical window and different electronic screens having similar effects. [ABSTRACT FROM AUTHOR]

Background and purpose: With the acceleration of the aging process of society, stroke has become a major health problem in the middle-aged and elderly population. A number of new stroke risk factors have been recently found. It is necessary to develop a predictive risk stratification tool using multidimensional risk factors to identify people at high risk for stroke. Methods: The study included 5,844 people (age ≥ 45 years) who participated in the China Health and Retirement Longitudinal Study in 2011 and its follow-up up to 2018. The population samples were divided into training set and validation set according to 1:1. A LASSO Cox screening was performed to identify the predictors of new-onset stroke. A nomogram was developed, and the population was stratified according to the score calculated through the X-tile program. Internal and external verifications of the nomogram were performed by ROC and calibration curves, and the Kaplan-Meier method was applied to identify the performance of the risk stratification system. Results: The LASSO Cox regression screened out 13 candidate predictors from 50 risk factors. Finally, nine predictors, including low physical performance and the triglyceride-glucose index, were included in the nomogram. The nomogram's overall performance was good in both internal and external validations (AUCs at 3-, 5-, and 7-year periods were 0.71, 0.71, and 0.71 in the training set and 0.67, 0.65, and 0.66 in the validation set, respectively). The nomogram was proven to excellently discriminate between the low-, moderate-, and high-risk groups, with a prevalence of 7-year new-onset stroke of 3.36, 8.32, and 20.13%, respectively (P < 0.001). Conclusion: This research developed a clinical predictive risk stratification tool that can effectively identify the different risks of new-onset stroke in 7 years in the middle-aged and elderly Chinese population. [ABSTRACT FROM AUTHOR]