To investigate the association between outdoor light at night (LAN) exposure and sleep quality among primary and secondary school students, and to examine the pathways of negative affects including depressive, stress and anxiety symptoms, so as to provide a theoretical basis for optimizing the school environment and enhancing the physical and mental well-being of students.

In December 2024, a total of 36 885 students from 154 primary and secondary schools in Suzhou, Nantong, and Changzhou were included via a stratified cluster sampling method. Sleep quality and negative affect were assessed by using the Pittsburgh Sleep Quality Index (PSQI), Center for Epidemiologic Studies Depression Scale (CES-D), Generalized Anxiety Disorder-7 (GAD-7), and Depression, Anxiety and Stress Scale-21 (DASS-21), respectively. Outdoor LAN exposure data were obtained from the national polar-orbiting partnership visible infrared imaging radiometer suite nighttime light(NPP-VIIRS NTL) dataset provided by the National Earth System Science Data Center. Multivariate Logistic regression model was employed to analyze the association between LAN exposure and sleep quality across different regions, with stratification by monitoring site. Spearman correlation analysis was used to examine the relationships between LAN, negative affect, and sleep quality. The mediating role of negative affect was tested by using Model 4 of the PROCESS macro.

The detection rates among students were 13.95%(n=5 147) for depressive symptom, 16.72%(n=6 166) for stress symptom, and 17.49%(n=6 451) for anxiety symptom. The median outdoor LAN exposure was 28.85(19.10, 41.44)nW/(cm² · sr). After adjusting for confounders, multivariate Logistic regression analysis showed that high LAN exposure (Q4) was positively associated with sleep problems (urban areas: OR=1.28, 95%CI=1.17-1.41; rural areas: OR=1.21, 95%CI=1.07-1.36; both P < 0.05). Spearman correlation analysis revealed significant positive correlations between LAN exposure, depressive symptoms, stress symptoms, anxiety symptoms, and sleep quality (r=0.03-0.75, all P < 0.01). The mediation analysis indicated that all dimensions of negative affect significantly mediated the relationship between high LAN exposure and poor sleep quality (all P < 0.01). Specifically, the indirect effects were 0.03 (95%CI=0.02-0.05) for depressive symptom, 0.05(95%CI=0.03-0.08) for stress symptom, and 0.07(95%CI=0.05-0.09) for anxiety symptom. Overall, 31.9% of the total effect was mediated by negative affect, with anxiety (14.89%) being the strongest mediator, followed by stress (10.64%) and depression(6.38%).

Reducing high levels of outdoor LAN exposure and implementing interventions targeting negative affect may contribute to improved sleep quality among primary and secondary school students.

To analyze the association of participation in non-sports extracurricular tutoring classes with the prevalence of screening myopia, axial length (AL) and axial length to corneal radius ratio (AL/CR) among primary school students, so as to provide evidences for formulating myopia prevention and control policies.

In December 2024, combination of convenience and cluster sampling method was used to select 2 273 students from two primary schools in Hefei City, Anhui Province. Ophthalmic examinations and questionnaire surveys were conducted to obtain information on myopia, AL, AL/CR and participation in various types of extracurricular tutoring. A binary Logistic regression model was used to analyze the association between non-sports tutoring and screening myopia, and multiple linear regression models were used to examine the associations between non-sports tutoring and AL and AL/CR.

Among the surveyed students, the participation rate in non-sports extracurricular tutoring classes was 64.9%, and the overall prevalence of screening myopia was 39.1%. The average AL and AL/CR were (23.60±1.01) mm and (3.00±0.12), respectively. Univariate analysis showed that students who attended non-sports, music, or academic tutoring classes for ≥2 h per week had higher risks of screening myopia and greater AL/CR values than non-participants (screening myopia: OR=1.38, 1.82, 1.55; AL/CR: β=0.01, 0.03, 0.03; all P < 0.05). After adjusting for sex, grade, and participation in sports tutoring, multivariate analysis indicated that participation in non-sports and musical instrument tutoring classes for ≥2 h per week remained significantly associated with higher risks of screening myopia (OR=1.26, 1.49, both P < 0.05). Multiple linear regression showed that participation in musical instrument tutoring for ≥2 h per week was positively correlated with AL (β=0.14, P < 0.05).

s Participation in non-sports extracurricular tutoring is common among primary school students. Attending non-sports tutoring classes for ≥2 h per week increases the risk of screening myopia.

To analyze the changes in classroom lighting environment of schools in Suzhou and their impact on refractive progression among primary and secondary school students, so as to provide the basis for accurate provention and control of myopia.

A baseline investigation was conducted in October 2022 by using a stratified cluster random sampling method to recruit primary and secondary school students from Suzhou.A follow-up visit was performed in October 2023.A total of 12 302 students and 360 classrooms that participated in both surveys were included analysis.The visual acuity progression over one year and classroom lighting conditions were assessed.Group comparisons were performed by using the Wilcoxon or Kruskal-Wallis rank-sum, and Chi-square tests.Multivariate Logistic regression was employed to identify the major factors influencing refractive changes.

The compliance rate of average illuminance on classroom blackboard surface increased from 72.22% to 75.28%, while the compliance rate of average illuminance on desks decreased from 89.44% to 87.22%, the overall myopia rate among students rose from 59.63% to 66.99% from 2022 to 2023.The average annual progression of equivalent spherical power (SE) in the right eye of students was-0.25(-0.75, 0.06) D.Significant statistical differences were observed in the annual mean changes across different school levels, regions, baseline refractive statuses, and classroom lighting environment change groups (Z/H=316.59, -8.27, 38.80, 51.01, all P < 0.05).Multivariate Logistic regression analysis showed that pre-myopia, low myopia, junior high school, senior high school, vocational high school, and improved classroom lighting environment were protective factors of reducing the risk of rapid progression in refractive error (OR=0.58, 0.69, 0.81, 0.50, 0.28, 0.82, all P < 0.05).Conversely, female students and rural students had higher risks of rapid myopia progression (OR=1.09, 1.42, both P < 0.05).

Over one-year follow-up, the complance rate of classroom lighting indicators in Suzhou remaines stable, while students'refractive status shows a trend toward myopia.Improving classroom lighting environment can reduce the risk of rapid myopia progression.

Based on the theoretical framework of Risk Perception-Efficacy Appraisal-Behavioral Response, the study aims to explore the group heterogeneity in high-temperature health risk perception and behavioral responses among college students in Chongqing, so as to provide a scientific basis for implementing differentiated health interventions.

A multi-stage cluster sampling method was used to select 856 students from five universities in Chongqing.Data were collected using a validated questionnaire.One-way analysis of variance and independent samples t-test were used to analyze individual differences, Pearson correlation analysis was employed to examine relationships between variables, multiple linear regression was used to identify influencing factors, a structural equation model was constructed to validate the theoretical pathways, and the Bootstrap method was applied to test mediating effects.

In the risk perception dimension, the severity score of high-temperature health hazards among college students (3.28±0.89) was higher than that of susceptibility score (2.94±0.93).Efficacy appraisal showed that the response efficacy score was the highest (3.91±0.81).In behavioral responses, adaptive behaviors were most prominent (5.43±2.75), while emergency preparedness behaviors were the lowest (2.71±1.33).The structural equation model validated the pathway of "threat appraisal → efficacy appraisal → behavioral response" (χ²/df=2.49, RMSEA=0.05).Self-efficacy played a fully mediating role between threat appraisal and behavioral response, with a mediation effect value of 0.10(95%CI=0.02-0.19).K-means cluster analysis categorized the subjects into three groups, with the core barriers to behavior being economic constraints (22.3%), lack of motivation (34.8%), and insufficient cognition (34.1%), respectively.

Decision-making regarding high-temperature health behaviors among college students follows psychological pathway of "cognition-appraisal-action" with self-efficacy serving as a key mediating variable driving behavioral change.Targeted interventions should be implemented for groups with different characteristics.

To analyze the iodine nutritional status of children aged 8-10 in Baoshan City, so as to comprehensively evaluate the effectiveness of eliminating and consolidating iodine deficiency disorders in Baoshan City.

From 2018 to 2024, a stratified random sampling method was used to sample 7 363 non-boarding children aged 8-10 from 35 survey sites in 5 counties of Baoshan City (Longyang County, Shidian County, Changning County, Tengchong City, Longling County).The salt iodine content and urinary iodine concentration were detected, and the thyroid volume of children was measured by ultrasound.Group comparison was conducted by using Mann-Whitney U test, Kruskal-Wallis H test, and Chi-square test.Spearman rank correlation analysis was used to investigate the correlation of salt iodine, urinary iodine and thyroid volume.

A total of 7 361 samples of household edible salt for children were detected.The iodized salt coverage rate was 99.70%, the qualified iodized salt consumption rate was 97.02%.The proportion of unqualified iodized salt fluctuated and decreased from 3.14% in 2018 to 2.14% in 2024.The median iodine content of household edible salt for children was 23.70(21.60, 25.80) mg/kg.The median urinary iodine of children was 217.41(152.40, 294.59)μg/L, and the proportions of iodine deficiency, adequate iodine, and iodine excess were 9.75%, 66.66%, and 23.58%, respectively.There were statistically significant differences in the median urinary iodine of children among different years, ages, genders and before and after the supply of non-iodized salt (Z/H=134.88, 11.04, -4.28, -2.66, all P < 0.01).An average thyroid volume of children was 3.32(2.77, 3.93) mL, with a goiter rate of 1.91%.Before and after the implementation of non-iodized salt supply in Baoshan City in 2023, there were no statistically significant differences in the median iodine content of household edible salt and the goiter rate of children (Z/χ²=-1.54, 3.25, both P>0.05), but there were statistically significant differences in the qualified status of iodized salt, the median urinary iodine, and the frequency distribution of urinary iodine (χ²/Z=15.53, -2.66, 10.14, all P < 0.05).Salt iodine was positively correlated with urinary iodine (r=0.04) and negatively correlated with thyroid volume (r=-0.07), and urinary iodine was negatively correlated with thyroid volume (r=-0.03)(all P < 0.05).The thyroid volume of children consuming iodized salt was larger than that of children consuming non-iodized salt (H=9.99), and there were statistically significant differences in thyroid volume among children with different urinary iodine levels (H=15.13)(both P < 0.01).

From 2018 to 2024, the overall iodine nutritional level of children aged 8-10 in Baoshan City is at an adequate level.The elimination status of iodine deficiency disorders has been continuously consolidated.

To analyze the correlation between the levels of vitamin A, vitamin D, vitamin E with body fat mass percentage (FMP) as well as lifestyle factors among primary school students, so as to provide references for exploring the vitamin nutritional status of primary school students and its potential influencing factors.

From September 1 to October 30, 2021, a cluster sampling method was used to select 750 third-grade students from eight primary schools in Luohu District, Shenzhen.Their body composition was measured, and blood samples were collected to detect the serum levels of vitamin A, vitamin D, and vitamin E using a mass spectrometer.Dietary and exercise habits were collected through questionnaires.Mann-Whitney U test and Kruskal-Wallis H rank sum test were used for inter-group comparisons, spearman correlation was used for correlation analysis, and Logistic regression model was used to analyze the association between lifestyle and vitamin content.

The overall level of vitamin A in school-aged children was 0.4(0.4, 0.5) mg/L, with a deficiency rate of 0 and a marginal deficiency rate of 5.1%;the level of vitamin D was 26.0(22.0, 30.0) ng/mL, with a deficiency rate of 0.4% and an insufficiency rate of 12.7%;the content of vitamin E was 11.8(10.1, 13.5) mg/L, with an insufficiency rate of 0.8%.Spearman correlation analysis showed that vitamin A was positively correlated with FMP in the total population, boys, girls, and normal weight population (r=0.18, 0.18, 0.20, 0.10), and vitamin D was positively correlated with FMP in the total population and obese population (r=0.08, 0.16)(all P < 0.05).Logistic regression analysis showed that marginal deficiency of vitamin A was associated with consumption of animal, snack, and dairy/egg/bean foods (OR=0.45, 0.55, 0.59);whether vitamin D was deficient was influenced by gender (OR=2.65) and exercise (OR=1.96)(all P < 0.05).

Vitamin A, vitamin D and vitamin E levels are associated with body fat percentage, with significant variations in vitamin status among individuals of different body types, necessitating targeted supplementation.

To investigate the weight loss effect of a comprehensive intervention model combining caloric restriction (CR), physical activity (PA), behavioral therapy (BT), breathing exercise (BE), and functional movement corrective training (FMCT)-referred to as the"CPBBF "model in overweight and obese female college students, so as to provide a reference for scientific weight loss interventions for college students.

From March to May 2022, 46 overweight and obese female college students from Chongqing Water Resources and Electric Engineering College were recruited and randomly divided into an experimental group (24 participants) and a control group (22 participants).The control group received CR (prohibiting ad libitum snacking), PA in the first week, high-intensity interval training (HIIT) for 30 s, and moderate-intensity continuous training (MICT) for 1-5 min alternate 4 sets, duration 15-20 min.From the second week, adjust to HIIT and MICT alternating 3 min each for 5 sets, totaling 30 min, 4 times/week, 70 min/time and BT (60-90 min/session, 3 times/week).The experimental group incorporated FMCT (10-15 min of focused training per session, integrated with PA and daily life) and BE (advocating a gradual transition to proper breathing methods in daily life and low-intensity training, 5 sessions/day, 10 min each).Body oxygen level test (BOLT), Functional Movement Screen (FMS), sports exercise attitude, and body composition indicators were measured at baseline (T0), after 12 weeks of intervention (T1), and after one year of follow-up (T2).The differences were analyzed between groups through generalized estimation equations, and mixed effect model analysis was employed to explore predictive relationships among variables.

The results of the generalized estimation equation showed that time main effects of BOLT values, FMS scores, and exercise attitude among female college students were statistically significant (Wald χ²=18.75, 14.89, 12.45, all P < 0.01);further intragroup comparisons revealed that BOLT, functional motor screening (FMS) scores, and physical exercise attitudeof female college students in the experimental group increased compared to T0, while the control group only showed an increase at T1(all P < 0.05).The group main effects for the aforementioned three indicators were statistically significant (Wald χ²=6.33, 5.21, 4.88), and the time-by-group interactions of BOLT values and FMS scores were also statistically significant (Wald χ²=4.56, 3.97)(all P < 0.05).The time main effects of body weight, body mass index (BMI), and body fat ratio (BFR) in female college students were statistically significant (Wald χ²=44.27, 13.90, 82.33);further intragroup comparisons revealed that the experimental group of female college students showed a decrease in body weight, BMI and BFR at T1 and T2 compared to T0, while the control group only showed a decrease in these indicators at T1(all P < 0.05).The group main effects of weight and BFR were statistically significant (Wald χ²=4.11, 6.46), and the time-by-group interaction of BFR was statistically significant (Wald χ²=8.73)(all P < 0.05).The results of mixed effect model analysis showed that BOLT (β=1.52) and FMS (β=1.81) could both positively predict physical exercise attitude, and physical exercise attitude had statistically significant negative predictive effects on weight, BMI, and BFR (β=-0.08, -0.03, -0.03)(all P < 0.01).

The" CPBBF"comprehensive intervention effectively maintains weight loss effects by modulating the energy compensation mechanism with strong robustness.

To systematically evaluate the prevalence and influencing factors of screen exposure among preschool children in China, so as to provide evidence for formulating scientific intervention strategies.

Retrieve relevant studies on screen exposure among preschool children from PubMed, Web of Science, Embase, Cochrane Library, China Biomedical Literature Database, CNKI, VIP, and Wanfang databases from the time of estaldishment to June 29, 2025.Meta-analysis was performed using Stata 16.0 software.

A total of 43 studies were included.Meta-analysis showed that the prevalence of screen exposure among preschool children in China was 46.0%(95%CI=38.9%-53.1%, P < 0.01).Girls, non-only child, father's age < 35 years, both parents having an educational level of high school or below, being cared for by grandparents, rural residence, parents having no exercise habit, parental support for the use of screen devices, and parental screen time>1 h/d were influencing factors for screen exposure among preschool children[OR(95%CI) were 0.85(0.78-0.92), 1.09(1.04-1.15), 1.45(1.22-1.71), 1.38(1.24-1.54), 1.78(1.32-2.40), 1.39(1.16-1.65), 1.38(1.13-1.69), 1.67(1.40-1.98), 1.70(1.38-2.10), 1.59(1.04-2.43), all P < 0.05].

The prevalence of screen exposure among preschool children in China is relatively high, and relevant child health promotion strategies should be formulated to reduce its occurrence.

To investigate the mediating role of vaccine literacy between vaccine knowledge and vaccine hesitancy and the moderating role of parental education level, so as to provide references for adjusting vaccination strategies.

From May to December 2024, a stratified random sampling method was used to select 10 community hospitals in Guiyang and Zunyi City, Guizhou Province. A total of 1 401 parents of children aged 0-6 years were surveyed regarding their socio-demographic characteristics, vaccine knowledge, vaccine literacy, and vaccine hesitancy levels. Data were analyzed using common method bias tests, Spearman correlation analysis, mediation and moderation effects tests.

The mean score for vaccine knowledge, vaccine literacy and vaccine hesitancy were (2.96±1.11, 14.25±2.64, 39.12±4.93) among the 1 401 participants. Mediating effect analysis showed that both parental vaccine knowledge (β=1.28, 95%CI=1.08-1.49) and vaccine literacy (β=0.75, 95%CI=0.66-0.84) positively predicted vaccine hesitancy (both P < 0.01). Meanwhile, vaccine literacy accounted for 28.1% of the total effect of mediation between knowledge and vaccine hesitancy. In the moderated effects analysis, education level positively predicted vaccine literacy (β=0.40, 95%CI=0.24-0.57), and education level moderated the pathway of vaccine knowledge on vaccine hesitancy (β=0.28, 95%CI=0.05-0.52) (both P < 0.01).

Vaccine literacy partially mediates the relationship between vaccine knowledge and vaccine hesitancy. Parental education level positively moderates the prediction of vaccine knowledge on vaccine hesitancy score.

To develop multiple machine learning models to identify college students at high risk of social network addiction (SNA) and to select key predictors, so as to provide evidences for tiered screening and interventions.

A multi-stage cluster sampling was conducted among 12 541 undergraduates at five universities in Guizhou Province from September to October 2024. The Chinese version of Social Networking Addiction Scale (SNAS-C) was used to classify high-risk individuals. Candidate variables included demographic characteristics, the total scores of Adult ADHD Self-report Scale (ASRS) and the Chinese version of Diagnostic and Statistical Manual of Mental Disorders, 5th Edition Self-rated level 1 Cross-cutting Symptom Measure (DSM-5 CCSM), academic stress and academic satisfaction and 24-hour Movement Guidelines. Four machine learning models including multilayer perceptron neural network (MLP), XGBoost, K-nearest neighbors (KNN), and gradient boosting decision trees (GBDT) were trained with an 8∶2 train-test split. Model performance was evaluated using accuracy, recall, F1 score, and the area under the ROC curve (AUC), followed by feature importance analysis.

The detection rate of high-risk SNA was 8.60%, higher in girls (10.10%) than boys (6.74%), with a statistically significant difference (χ²=44.42, P < 0.01). Compared with the low-risk group for SNA, the high-risk group had higher ASRS scores, more severe of depression and anxiety, greater academic stress, and lower academic satisfaction (t=-21.23, -16.12, -16.73, -13.81, 11.58, all P < 0.01). Regarding 24-hour movement behaviors (physical activity, sleep, sedentary/screen time), the high-risk SNA group showed lower compliance rates (11.77%, 5.65%, 57.92%) than the low-risk group (15.57%, 13.16%, 75.32%) (χ²=11.04, 50.72, 154.40, all P < 0.01). All four models demonstrated good discrimination; XGBoost performed the best (accuracy 88.18%, F1=0.881), while KNN achieved the highest AUC (0.90). Feature importance analysis indicated ASRS as the most critical predictor of SNA, followed by academic stress, depression, age, and academic satisfaction.

Machine learning models can effectively identify college students at high risk for SNA. ADHD symptoms, internalizing problems, and academic stress are major risk factors, while 24-hour movement behaviors are key intervention targets, especially sleep and screen time.