Objective: To understand the current status and associated factors of sleep problems among preschool children in Hainan Province, so as to provide scientific evidence for improving sleep health in this population. Methods: From January 2021 to June 2022, a total of 4 105 preschool children aged 3-6 years from 62 kindergartens in Hainan Province were selected using stratified cluster random sampling method. Demographic information and lifestyle habits were collected through the Hainan Province Child Growth and Development Survey Questionnaire. The Children s Sleep Habits Questionnaire (CSHQ) was employed to assess sleep status. Unconditional binary Logistic regression model was applied to investigate the associated factors of sleep problems among preschool children. Results: The overall CSHQ score for children was 58.03±18.84, with 80.95% of preschool children exhibiting sleep related issues. The top three most prevalent sleep problem domains were bedtime resistance (72.42%), sleep anxiety ( 54.88 %), and parasomnias (38.86%). Logistic regression analysis revealed that higher family annual income ( OR=0.60, 95%CI = 0.45-0.79), higher maternal education level ( OR=0.53, 95%CI =0.32-0.89), regular or daily vitamin D supplementation ( OR=0.77, 95%CI =0.60-0.99), and fully self initiated eating behavior ( OR=0.71, 95%CI =0.59-0.85) were negatively related with children s sleep problems; in addition, screen exposure ( OR=1.27, 95%CI =1.06-1.51) and picky eating ( OR= 1.47 , 95%CI =1.21-1.78) were positively related to children s sleep problems (all P <0.05). Conclusion The high detection rate of sleep problems among preschool children in Hainan Province is multifactorially associated with family environment, dietary habits, and lifestyle behaviors.

Objective: To investigate the screen exposure status and influencing factors among 6-12 year-old children in Hainan Province, so as to provide insights into screen exposure intervention for children. Methods: Children aged 6-12 years from 18 counties (cities) in Hainan Province were selected using multi-stage stratified cluster sampling method from December 2020 to July 2021. Demographic information, parents' educational level, family type and screen time was collected using questionnaire surveys. The screen exposure rate of children was analyzed, and factors affecting screen exposure were identified using a multivariable logistic regression model. Results: A total of 27 501 children were surveyed, including 13 901 boys (50.55%) and 13 600 girls (49.45%). The mean age was (9.22±1.86) years. Among them, 3 925 children had screen exposure, with a screen exposure rate of 14.27%. Multivariable logistic regression analysis showed that gender (female, OR=0.859, 95%CI: 0.796-0.926), age (OR=1.078, 95%CI: 1.049-1.108), ethnicity (ethnic minorities, OR=1.147, 95%CI: 1.041-1.254), place of residence (rural area, OR=0.869, 95%CI: 0.801-0.944), father's educational level (high school or technical secondary school, OR=0.879, 95%CI: 0.788-0.981; college degree or above, OR=0.686, 95%CI: 0.589-0.818), mother's educational level (college degree or above, OR=0.706, 95%CI: 0.588-0.846), family type (others, OR=1.250, 95%CI: 1.105-1.414), and annual family income (>100 000 Yuan, OR=0.741, 95%CI: 0.619-0.885) were the influencing factors for screen exposure among children aged 6-12 years. Conclusion The screen exposure among children aged 6-12 years in Hainan Province was affected by gender, age, ethnicity, place of residence, parental education level, family type and annual family income.