Abstract The incidence of health related behaviors in adolescents is on the rise, directly or potentially threatening the current and future health of adolescents. Adolescents health related behaviors are closely related to low level systemic inflammation. Based on relevant previous literature, this paper briefly reveals the relationship between single form and clustering of health related behaviors (diet, physical activity, substance use, and sleep) and low level systemic inflammation. Moreover, the role of inflammation played in health related behaviors and mental health in adolescents is clarified. This study provides a scientific evidence for promoting the physical and mental health of adolescents.

Objective: To describe the prevalence of cell phone dependence and anxiety-depression co morbidity among college students, and to explore the association between cell phone dependence and anxiety-depression co morbidity among college students, in order to provide a reference basis for promoting the development of college students mental health. Methods: Using a prospective study design, baseline survey was conducted in April-May 2019 among a random whole group sample of 1 135 individuals in two universities in Hefei, Anhui Province and Shangrao, Jiangxi Province, and a follow up survey was conducted in November 2019 among the sampled population, with a valid number of 1 110 individuals after matching with the baseline survey. The Self rating Questionnaire for Adolescent Problematic Mobile Phone Use (SQAPMPU) was used to assess college students cell phone dependence, and the Depression Anxiety Stress Self Rating Scale (DASS-21) to assess depression, anxiety, and anxiety depression co morbidity symptoms, and the self administered Health Related Behavior and Mental Health Questionnaire for College Students to report sleep duration. Results: The detection rates of cell phone dependence among college students at baseline and follow up surveys were 24.5% and 27.7%, respectively, and the detection rates of anxiety symptoms, depressive symptoms, and anxiety depression co morbidities were 28.7%, 21.3%, and 18.4%, respectively. The results of the binary Logistic regression model showed that cell phone dependence was positively associated with the risk of anxiety depression co morbid symptoms among college students at baseline ( OR =5.79, 95% CI =4.06-8.24) and after six months of follow up ( OR =2.62, 95% CI =1.86-3.69) ( P <0.01). The results of the moderating effect analysis showed that sleep duration moderated the association between cell phone dependence and anxiety depression co morbidities, and the interaction term was statistically significant ( β =0.08, 0.04, P <0.01). Conclusion Cell phone dependence in college students increases the risk of depressive and anxiety co morbid symptoms, and sleep mitigates effect of cell phone dependence on depressive and anxiety co morbid symptoms in college students.

Objective: To describe the association of different sleep characteristics and cardiometabolic risk among college students, so as to provide reference for health promotion of college students. Methods: By random cluster sampling method, a questionnaire survey and physical examination including blood pressure, waist circumference and blood lipid indicators, which were conducted in April and May of 2019 among a total of 1 179 college students from the first grade in two universities in Hefei City of Anhui Province and Shangrao City of Jiangxi Province. A total of 729 college students with valid questionnaires were included into analysis. The Pittsburgh Sleep Quality Index (PSQI) and Insomnia Severity Index (ISI) were used to investigate sleep behavior, and the Morning And Evening Questionnaire-5 (MEQ-5) was used to investigate sleep characteristics. The cardiometabolic risk score was derived using the sum of the standardized sex specific Z scores of waist circumference, mean arterial pressure, HDL cholesterol (multiplied by -1), triglycerides, and insulin resistance index. The rank sum tests were used to compare differences in cardiometabolic risk scores across demographic characteristics. Generalized linear models were used to compare the association of different sleep characteristics with cardiometabolic risk scores among college students. Results: The average cardiovascular metabolic risk score of college students was -0.32(-2.03, 1.58). There were statistically significant differences in cardiovascular metabolic risk scores among college students in variables such as smoking, health status, and physical activity levels ( t/F=-3.41, 12.88, 51.07, P <0.01). The results of the generalized linear model showed that nighttime preference ( B=1.89, 95%CI =1.02-3.49), insomnia symptoms ( B=3.25, 95%CI =1.79-5.90), and short or long sleep duration ( B=1.92, 95%CI =1.21-3.05) were positively correlated with the cardiovascular metabolic risk score of college students ( P <0.05). Conclusions Poor sleep patterns among college students are positively correlated with the risk of cardiovascular metabolism. The sleep behavior of college students should be actively changed to reduce the risk of cardiovascular disease.

Objective: The study aimed to describe the prevalence of mobile phone use and depressive symptoms and to examine the bidirectional associations between the two among college students, providinb evidence for mental health promotion among college students. Methods: A longitudinal study with follow up at 6 month intervals was conducted in 1 135 students from 2 universities in Hefei, Anhui Province and Shangrao, Jiangxi Province who were selected between April and May 2019. The last follow up was conducted between April and May 2021 based on questionnaire survey, and 999 valid participants were obtained after matching. The self designed questionnaire was used to investigate the duration of cellular phone use and use of cellular phone functions among college students. The Self rating Questionnaire for Adolescent Problematic Mobile Phone Use (SQAPMPU) and the Patient Health Questionnaire-9 (PHQ-9) were used to assess cellular phone dependence and depressive symptoms among college students. Pearson correlation analysis was used to examine the correlation between cellular phone use behavior and depressive symptoms at baseline and 2 years later; linear regression model was used to analyze the linear association between cellular phone use behavior and depressive symptoms scores; autoregressive cross lagged model was used to analyze the bidirectional associations between cellular phone use behaviors and depressive symptoms among college students over time. Results: The prevalence of mobile phone dependence and depressive symptoms among college students at baseline were 24.3% and 42.4%, respectively. The mean duration of mobile phone use among college students at baseline and the 2 year follow up were (2.84±0.90)h/d and (2.02±1.05)h/d, respectively; the mean scores of mobile phone dependence were (23.30±9.00) and (23.29±10.45), respectively; the mean scores of mobile phone function use were (30.12±6.66) and (29.12±7.27), respectively; and the mean scores of depressive symptoms were (4.51±4.76) and (2.61±4.40), respectively. Pearson correlation analysis showed there were significant positive correlations between duration of cellular phone use, cellular phone dependence, use of cellular phone functions at baseline or 2 years later and depressive symptoms 2 years later( r =0.08-0.50, P <0.05). Linear regression models showed a significant positive association between cellular phone dependence at baseline and depressive symptoms ( β=0.26, 95%CI =0.23-0.29) at baseline and 2 years later ( β=0.12, 95%CI =0.09-0.15). Autoregressive cross lagged models showed that cellular phone dependence at baseline positively predicted depressive symptoms 2 years later ( β =0.04) and depressive symptoms at baseline positively predicted cellular phone dependence 2 years later( β =0.23)( P <0.05). Conclusion There was a bidirectional association between cellular phone dependence and depressive symptoms among college students. Reducing cellular phone dependence is of positive significance for improving college students mental health.

Objective: To describe the prevalence and association of sleep quality and anxiety-depression co-morbid symptoms among nursing students, in order to provide a reference basis for promoting the development of nursing students mental health. Methods: Using a prospective study design, baseline survey was conducted in January 2019 among a random cluster sample of 1 716 individuals in three medical universities in Hefei, Anhui Province, and a follow-up survey was conducted in October 2019, with a valid number of 1 573 individuals after matching with the baseline survey. The Pittsburgh Sleep Quality Index (PSQI) was used to assess nursing students sleep quality, and the Depression Anxiety Stress Scale (DASS-21) to assess the anxiety-depression comorbid symptoms. Results: The detection rates of anxiety-depression co-morbidities among nursing students at baseline and follow-up survey were 16.9% and 18.2%, respectively, and the detection rates of poor sleep quality among nursing students at baseline and follow-up survey were 10.1% and 10.3%, respectively. The results of the binary Logistic regression model showed that baseline PSQI score were positively associated with the risk of anxiety-depression co-morbid symptoms among nursing students at baseline ( OR=1.49, 95%CI =1.40-1.59) and after nine months of follow-up ( OR=1.22, 95%CI =1.16-1.28). Furthermore, the influence of baseline sleep quality on the risk of anxiety-depression co-morbid symptoms were mainly concentrated in the five dimensions of sleep time, sleep efficiency, sleep disorders, hypnotic drugs and daytime dysfunction, and such effects of sleep time, sleep disorders and daytime dysfunction still existed in the follow-up investigation. Conclusion Poor sleep quality of nursing students can increase the risk of anxiety-depression co-morbidities. Improving sleep quality of nursing students has a positive effect on improving their mental health.