Objective: To explore the influencing factors of exposure to campus bullying among junior and senior school students, and to establish a column line diagram model for risk prediction, while providing a theoretical basis for campus bullying prevention and control in secondary schools. Methods: A total of 22 034 junior and senior school students were selected via direct sampling technique from September to November 2021 in 13 cities in Jiangsu Province, China, and questionnaires were administered using the Student Health Behavior Questionnaire. The Chi squared test and multifactor Logistic regression analysis were used to derive the influencing factors of exposure to campus bullying, and a column line graph prediction model was drawn. Results: A total of 540 students reported that they had experienced campus bullying, with a prevalence rate of 2.45%. Being in a non conventional family ( OR =1.30,95% CI =1.02-1.65), overweight/obesity ( OR =1.35,95% CI =1.09-1.67), scolding by parents in the past 30 days ( OR =2.27,95% CI =1.82-2.84), cigarette smoking in the past 30 days ( OR =1.54,95% CI =1.11-2.15), Internet addiction ( OR =2.03，95% CI =1.34-3.08), and depressive symptoms( OR =5.24，95% CI =4.16-6.61), all of which were positively correlated with exposure to campus bullying among junior and senior school students ( P <0.05). Furthermore, the following factors were negatively associated with junior and senior school students protection from campus bullying in female students ( OR = 0.58 , 95% CI =0.46-0.72)，senior school students ( OR =0.68，95% CI =0.54-0.83), eating breakfast sometimes ( OR =0.37，95% CI = 0.22 -0.62), and eating breakfast everyday ( OR =0.28，95% CI =0.17-0.49) ( P <0.05). The column line graph established based on the above influencing factors had an area under the curve of 0.792 (95% CI =0.769-0.815), and the calibration curve showed that the predicted value was basically the same as the measured value. Conclusions Non conventional families, overweight/obesity, male students, junior school students, scolding by parents, cigarette smoking, Internet addiction, and depressive symptoms are correlated with school bullying among middle school students. The predictive model constructed in the study can provide an effective basis to predict the risk of school bullying and facilitate the implementation of proactive interventions for junior and senior school students.

Objective: To assess the effectiveness of online and offline myopia prevention and control health education interventions using wearable behavior monitoring tools for non myopic elementary school students,so as to provide evidence based medical support for public health practices. Methods: From May to June in 2021, two schools were selected within the same county in Jiangsu Province. School 1 conducted online and offline parental health education ( n =111), while school 2 exclusively conducted offline health education activities, representing the traditional intervention group ( n =122). Students from both schools underwent monitoring through wearable behavior tracking tools, with feedback reports provided (eye distance, eye duration, ambient light, and outdoor exposure time). Both schools relied on activities to carry out health education interventions, and organized the distribution of promotional materials and display boards. The intervention group also established WeChat groups to conduct online "Healthy Parents Action" (answering and providing feedback on health knowledge related to myopia prevention and control, myopia prevention and control, science popularization, etc. raised by parents). Evaluation criteria included myopia rates, post dilation refractive error, and axial length, with a tracking period of two years (from 2021 to 2023). Additionally, the study collected refractive parameters from non myopic students who did not participate in wearable tool monitoring in the 12 classes across the two schools. Results: The baseline results indicated that there were no significant differences between the two groups in terms of refractive parameters and wearable tool monitoring results (including screen time, viewing distance, outdoor exposure time, and homework light exposure)( t/Z/χ 2=1.94,1.17,0.58,0.40,0.80,0.69,0.32, P >0.05). After a two-year follow up, in the first and second year, the myopia rate of the online Healthy Parents Action group (11.4%, 29.7%) were lower than that of the traditional group (26.2%, 50.9%), and the degree of refractive change in the intervention group [0.63(0.38,1.19)D] was lower than that of the traditional group [0.91(0.40,1.50)D], and all the differences were statistically significant( χ 2/ Z =4.93，10.37，2.29， P <0.05). However, there were no significant differences ( P >0.05) in axial length changes between the two groups over the twoyear intervention period. Nevertheless, in the second year, the axial length change in the traditional group [0.35(0.20,0.65)mm] was lower than that in the natural observation group [0.55(0.30,0.75)mm], and this difference was statistically significant ( Z =1.92, P <0.05). Conclusions Online and offline myopia prevention and control health education can effectively reduce myopia rates. The intervention mode combining wearable behavior monitoring tools with online health education may have better effects, but further large sample and multi center studies are needed to provide additional evidence and confirmation.