Research on the Interaction between the Mental Health and Physical Health of College Students

Research on the Interaction between the Mental Health and Physical Health of College Students | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Research on the Interaction between the Mental Health and Physical Health of College Students Zange Lin, Wenlong Ding, Meihua Su This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8112287/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background: This study aims to explore the predictive effect of college students' baseline mental health status on their physical health status to provide a scientific basis and practical reference for the formulation of mental health education in colleges and universities and strategies for promoting their physical health. Methods: A total of 6,491 undergraduate graduates from six consecutive cohorts (2019--2024) at a comprehensive university were selected as the research subjects. The Symptom Checklist-90 (SCL-90) was used to assess their mental health status, and their physical health test scores were incorporated into the analysis. Correlation analysis, multiple linear regression, and other methods were employed to examine the relationships between mental health and physical health test results. Results: There were significant differences in the scores of obsessive-compulsive, depressive, anxious, and phobic factors on the SCL-90 between male and female college students, with female students scoring higher than male students. Students with higher physical health test scores in their first year of college had significantly lower scores for factors such as somatization, interpersonal sensitivity, depression, paranoia, and psychoticism than those in the lower-score group did (P<0.05). Additionally, there was a negative correlation between the scores of these factors and the physical health test scores upon enrollment. There was a significant negative correlation between the comprehensive grade point average (GPA) of physical health and mental health level (P<0.01); that is, students with a higher GPA had a better mental health status. Multiple linear regression analysis revealed that somatization, depression, anxiety, and other factors influenced physical health test scores upon enrollment; moreover, depression, anxiety, and psychoticism influenced the comprehensive GPA of physical health. Conclusions: The mental health status of college students has a predictive effect on both their physical health test scores in the first academic year and their comprehensive physical health GPA over the four years of college, with a higher predictive accuracy for the physical health test scores in the first academic year. Mental health Physical health Multiple regression analysis Baseline prediction Longitudinal study Figures Figure 1 Figure 2 Background College years, as a transitional period from adolescence to adulthood and the early stage after reaching adulthood, coincide with a critical phase of physical and psychological development. During this period, an individual’s health status not only affects their current academic performance and daily life but also profoundly affects their future development [ 1 ] . Health issues among college students have become increasingly prominent worldwide, particularly in terms of mental health problems and the decline in physical fitness [ 2 ][ 3 ][ 4 ] . As the country with the largest number of college students in the world, China regards the health of college students as a crucial public health issue. Therefore, understanding and researching the mental health and physical health status of college students holds significant practical significance. In recent years, numerous studies and relevant reports have focused on college students, indicating that there is a significant association between the frequent occurrence of mental health problems and a decline in physical fitness [ 5 ][ 6 ] . Specifically, the vast majority of contemporary college students adopt unhealthy lifestyles, such as staying up late, having irregular diets, and overusing the internet. These habits further impair the maintenance of physical functions, leading to a downward trend in physical fitness. Moreover, college students face multiple sources of distress, including academic pressure, interpersonal relationship issues, emotional problems, and employment pressure. These factors significantly affect mental health [ 7 ] . In turn, mental health status indirectly affects physical fitness test scores and daily performance by influencing students’ daily dietary behaviors and living conditions [ 8 ][ 9 ][ 10 ] . However, existing studies have focused mostly on the correlation analysis between the two, while controversies remain regarding their causal relationships and deeper-level interactions [ 11 ] . For example, scholars hold divergent views on questions such as whether mental health status can predict physical health status and whether physical health status affects mental health status. Therefore, the "mutual influence relationship" between mental health and an individual’s physical fitness requires verification through more longitudinal tracking experiments. Therefore, this study aims to explore the predictive effect of mental health on physical health by conducting a longitudinal follow-up of students’ physical health status and integrating their baseline mental health levels, striving to present the interaction mechanism and general laws between mental health and physical health in a more comprehensive manner. This study provides a scientific basis and practical reference for the formulation of mental health education in colleges and universities and strategies for promoting students’ physical health. 1 Research Objects and Methods 1.1 Research Objects This study selected undergraduate graduates from six consecutive cohorts (2019–2024) of a comprehensive university in Xiamen city as the research objects. A total of 7,281 undergraduate graduates were invited to participate as subjects in this study, with 1,134 from the 2019 cohort, 1,264 from the 2020 cohort, 1,154 from the 2021 cohort, 1,301 from the 2022 cohort, 1,232 from the 2023 cohort, and 1,196 from the 2024 cohort. Among them, there were 3,529 male students and 2,962 female students. The mental health assessment was uniformly organized by the university's Psychological Counseling Center, and the students completed the assessment collectively in the computer lab. The scores of the students' physical fitness assessments were obtained by the university's physical education teachers through physical fitness tests. A total of 789 cases were excluded, including those with invalid questionnaires, missing data (e.g., incomplete physical fitness test records), and students who dropped out, took a leave of absence, or passed away during the study period. Finally, the valid sample size was 6,491, with an effective rate of 89.16%. 1.2 Methods 1.2.1 Mental health status assessment The mental health assessment tool adopted in this study was the Symptom Checklist-90 (SCL-90), which was developed by Derogatis et al. For the current study, the Chinese version translated by Wang was used for relevant assessment purposes. [ 12 ] This scale consists of 90 items and covers a wide range of content related to psychiatric symptomatology. The Symptom Checklist-90 (SCL-90) includes 10 factors in total. Specifically, there are 9 core factors: somatization, obsessive‒compulsive symptoms, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, Paranoid ideation, and psychoticism. Additionally, there is an "Other" factor, which mainly covers sleep and dietary conditions [ 13 ] . This scale uses a 5-point Likert scale for scoring. In this study, a single factor score of < 2.0 was defined as negative (indicating healthy mental status), whereas a factor score of ≥ 2.0 was defined as positive (indicating potential mental health concerns). These criteria were used to evaluate the mental health status of the college students. 1.2.2 Physical fitness health assessment The assessment indicators for physical fitness health status are the physical fitness test scores of college students in their first academic year (hereinafter referred to as "Admission Physical Fitness Scores") and the value obtained by dividing the sum of grade points from physical fitness course tests over four years of college by the total corresponding credits (4 credits) (hereinafter referred to as "Comprehensive Physical Fitness GPA"). These two sets of data serve as the basis for evaluating physical fitness health status. The physical fitness health status was grouped according to research needs: Grouping was conducted on the basis of the physical fitness scores of the first academic year: scores below 60 were categorized into the "Fail Group", scores of 60 or above but below 80 were categorized into the "Pass Group", scores of 80 or above but below 90 were categorized into the "Good Group", and scores of 90 or above were categorized into the "Excellent Group". Grouping was conducted on the basis of the comprehensive physical fitness GPA: GPAs below 2.0 were categorized into the "low GPA group", GPAs of 2.0 or above but below 2.5 were categorized into the "medium GPA group", and GPAs of 2.5 or above were categorized into the "high GPA group". 1.3 Statistical analysis The data were statistically analyzed via SPSS 27.0 software. For measurement data, if they conformed to a normal distribution, they were expressed as the mean ± standard deviation (M ± SD); if they did not conform to a normal distribution, they were expressed as the median. The Kolmogorov‒Smirnov (K-S) test was used for normality testing. First, independent samples t tests and Jonckheere–Terpstra tests were used to conduct difference tests on students of different genders, different admission physical fitness scores, and different comprehensive physical fitness GPAs. Second, Spearman's rank correlation analysis was used to analyze the correlations between the admission physical fitness scores, comprehensive physical fitness GPA, and each factor of the Symptom Checklist-90 (SCL-90). Finally, multiple linear regression analysis was used, with scores of several factors from the Symptom Checklist-90 (SCL-90) as independent variables and admission physical fitness scores and comprehensive physical fitness GPA as dependent variables. Multiple linear regression models were constructed separately to analyze the relationships between each factor of the SCL-90 and admission physical fitness scores, as well as between each factor of the SCL-90 and comprehensive physical fitness GPA, to conduct baseline predictive analysis on admission physical fitness scores and comprehensive physical fitness GPA. All tests were two-tailed, and a P value < 0.05 was considered statistically significant. 2 Results 2.1 Demographic Information The statistical information of the 6491 students was analyzed to reveal the basic characteristics of the study participants, including gender, grade, family economic status, and parents' educational level. The detailed data are shown in Table 1 . As shown in Table 1 , among the college student samples included in this study, 3,529 males (54.4%) and 2,962 females (45.6%) were included. The sample distribution of graduates across different years is as follows: 1,064 graduates from the class of 2019, 1,027 from the class of 2020, 1,099 from the class of 2021, 1,091 from the class of 2022, 1,079 from the class of 2023, and 1,131 from the class of 2024.67.8% of the students had families with an average economic status, while most of their parents had not received higher education, and the majority held an educational level of junior high school and below. Table 1 Demographic characteristics of the study participants Characteristics Situation Count Percentage (%) Year 2019 1064 16.4 2020 1027 15.8 2021 1099 17.0 2022 1091 16.8 2023 1079 16.6 2024 1131 17.4 Gender Male 3529 54.4 Female 2962 45.6 Family Economic Status Difficult 1631 25.1 Average 4399 67.8 Good 422 6.5 Wealthy 39 0.6 Mother's Educational Level Junior High School and Below School and Below 4297 66.2 Senior High School Technical Secondary School 1275 19.6 University Junior College 893 13.8 Master's Degree and Above 26 0.4 Father's Educational Level Junior High School and Below School and Below 3771 58.1 Senior High School Technical Secondary School 1464 22.6 University Junior College 1198 15.4 Master's Degree and Above 58 0.9 2.2 Difference analysis 2.2.1 The impact of sex on mental health status An independent samples t test was conducted on the factor scores of the SCL-90 Symptom Checklist among college students of different genders, and the results are shown in Fig. 1 . As shown in Fig. 1 , among the 10 factors of the SCL-90 Symptom Checklist, the average score of male students was higher than that of female students only for the Paranoid Ideation factor; for the other 9 factors, the average scores of male students were all lower than those of female students. Among these, in terms of the scores on the four factors (Obsessive-Compulsive Symptoms, Depression, Anxiety, and Phobic Anxiety), the scores of the male students were extremely significantly lower than those of the female students (P < 0.001). In terms of the scores for the somatization and interpersonal sensitivity factors, the scores of the male students were also significantly lower than those of the female students (P < 0.01), whereas there was no significant difference between the male and female students in the scores for the remaining factors. This finding indicates that when left home for the first time, the vast majority of female students may be more prone to experiencing emotional distress, tension, and fear of unfamiliar environments, as well as other physiological reactions, than male students are. 2.2.2 The impact of different admission physical health scores on mental health status The Jonckheere–Terpstra test was conducted on the factor scores of the SCL-90 Symptom Checklist among college students in different admission physical health score groups (Fail Group, Pass Group, Good Group, and Excellent Group) to obtain significant results among the 4 groups. The Mann‒Whitney U test was subsequently used to perform pairwise comparisons between two groups. Finally, the Bonferroni correction method was applied to adjust the significance level, and thus, reliable multiple comparison results were obtained, as shown in Table 2 . Table 2 Difference analysis of SCL-90 factor scores among students with different admission physical health scores Variable Standardized J-T Statistic P Post Hoc Multiple Comparisons Somatization -2.483 0.013 A < C * ,A < D * Obsessive-Compulsive Symptoms - - - Interpersonal Sensitivity -2.057 0.040 A < C * ,B < D * Depression -2.493 0.013 B < D * Anxiety - - - Hostility - - - Phobic Anxiety - - - Paranoid Ideation -2.926 0.003 A < D * ,B < D * Psychoticism -2.160 0.031 B < C * Other -3.164 0.002 A < C * ,B < C * Notice: In the table, D represents the "Fail Group", C represents the "Pass Group", B represents the "Good Group", and A represents the "Excellent Group". *** indicates a significant correlation at the P < 0.001 level, ** indicates a significant correlation at the P < 0.01 level, and * indicates a significant correlation at the P < 0.05 level. The results revealed significant differences in 6 factors (Somatization, Interpersonal Sensitivity, Depression, Paranoid Ideation, Psychoticism, and Additional Items/Other) among the 4 groups (P < 0.05). Among these factors, Paranoid Ideation and other items/other factors exhibited extremely significant differences (P < 0.01). There were no significant differences in the 4 factors, namely, obsessive‒compulsive symptoms, anxiety, hostility, and phobic anxiety. The post hoc multiple comparison results in Table 2 indicate that, in terms of the Somatization factor score, the Excellent Group (admission physical health score) score was significantly lower than the Pass Group and the Fail Group. In terms of the Interpersonal Sensitivity factor score, the Excellent Group was significantly lower than the Pass Group, and the Good Group was significantly lower than the Fail Group. In terms of the Depression factor score, the Good Group was significantly lower than the Fail Group. In terms of the Paranoid Ideation factor score, the Excellent Group was extremely significantly lower than the Fail Group, and the Good Group was significantly lower than the Fail Group. In terms of the Psychoticism factor score, the Good Group was significantly lower than the Pass Group. In terms of the Additional Items/Other factor score, the Excellent Group was extremely significantly lower than the Pass Group, and the Good Group was significantly lower than the Pass Group. According to the values of the standardized J-T statistic obtained from the Jonckheere–Terpstra test, for factors such as somatization, interpersonal sensitivity, depression, Paranoid Ideation, psychoticism, and additional items/other, the scores of these factors decrease as the level of the physical health score groups admitted increases. To summarize the above analysis, students with higher admission physical health scores have better conditions in terms of psychological inferiority, sleep and dietary status, and interest in life than do those with poor physical health test scores. 2.2.3 Impact of Different Comprehensive Grade Point Averages (GPAs) for Physical Health on Mental Health Status Taking the scores of the 10 factors in the SCL-90 Symptom Checklist as test variables and the different comprehensive GPA groups for physical health (high-performance group, middle group, low-performance group) as grouping variables, the Jonckheere–Terpstra test was conducted to obtain the significance results among the 3 groups. The same processing method used in Table 2 was subsequently used for post hoc multiple comparisons. See Table 3 for details. The results revealed extremely significant differences in all 10 factors of the SCL-90 scale among the different groups, with varying comprehensive physical health grades (P < 0.01). Among these factors, somatization, interpersonal sensitivity, depression, paranoia, and others exhibited extremely significant differences (P < 0.001). The results of the post hoc multiple comparisons in Table 3 indicate that the scores of somatization, depression, anxiety, and other factors in the high-achievement group were significantly lower than those in the low-achievement group. In terms of the scores of factors such as paranoia and psychoticism, the scores of the middle-income group and the high-achievement group were significantly lower than those of the low-achievement group. The scores of factors such as interpersonal sensitivity, hostility, and phobia were significantly lower in the high-achievement group than in the middle group. The score of the obsessive‒compulsive symptom factor was significantly lower in the middle group than in the low-achievement group. On the basis of the standard J-T statistic values obtained from the Jonckheere–Terpstra test, it can be concluded that for factors such as somatization, interpersonal sensitivity, depression, paranoia, and psychoticism, the scores of these factors decrease as the level of comprehensive physical health grade point groups increases. In summary, the above analysis indicates that mental health status is negatively correlated with comprehensive physical health grades. Students with better comprehensive physical health grades have better mental health status, and there are significant differences in multiple mental health factors among groups with different comprehensive physical health grades. Table 3 Differences in SCL-90 factor scores among students with different comprehensive GPAs for physical health Variable Standardized J-T Statistic P Post Hoc Multiple Comparisons Somatization -3.448 < 0.001 G < E * Obsessive-Compulsive Symptoms -3.068 0.002 F < E * Interpersonal Sensitivity -3.725 < 0.001 G < F * Depression -3.495 < 0.001 G < E * Anxiety -2.625 0.009 G < E * Hostility -2.683 0.007 G < F * Phobic Anxiety -2.644 0.008 G < F * Paranoid Ideation -4.445 < 0.001 F、G < E * Psychoticism -2.916 0.004 F、G < E * Other -3.864 < 0.001 G < E * Note: In the table, "E" represents the "Low-Performance Group", "F" represents the "Middle Group", and "G" represents the "High-Performance Group". *** indicates a significant correlation at the P < 0.001 level, ** indicates a significant correlation at the P < 0.01 level, and * indicates a significant correlation at the P < 0.05 level. 2.3 Correlation analysis Spearman correlation analysis was performed between the comprehensive physical health grade points and the initial physical health test scores and each factor of the SCL-90 Symptom Checklist. See Fig. 2 for details. The results revealed that with respect to comprehensive physical health grades, depression (r=-0.56, P < 0.01), anxiety (r=-0.45, P < 0.01), psychoticism (r=-0.53, P < 0.01), and other factors (r=-0.58, P < 0.01) were negatively correlated with comprehensive physical health grades. The results revealed that in terms of initial physical health test scores, interpersonal sensitivity (r=-0.47, P < 0.01), depression (r=-0.52, P < 0.01), anxiety (r=-0.41, P < 0.05), phobia (r=-0.26, P < 0.05), psychoticism (r=-0.48, P < 0.01), and other factors (r=-0.53, P < 0.01) were negatively correlated with initial physical health test scores. 2.4 Multiple linear regression analysis 2.4.1 The prediction of mental health status on initial physical health test scores Taking initial physical health test scores (continuous variables) as the dependent variable and considering the results of Tables 2 , 3 , and 4 comprehensively, the factors with significant differences (somatization, interpersonal sensitivity, depression, anxiety, psychoticism, and others) were included in the multiple linear regression analysis, as shown in Table 4 . The results of multiple linear regression analysis revealed that four factors—somatization (B=-1.047, P = 0.007), depression (B=-2.799, P < 0.001), anxiety (B = 0.073, P = 0.034), and others (B = 0.031, P = 0.020)—were influencing factors of physical health status. The variance inflation factor (VIF) of the independent variables ranged from 1.537–4.778, all less than 5, indicating that there was no multicollinearity among the variables. For the current regression model, R² = 0.452, adjusted R² = 0.441, and the Durbin–Watson statistic is 1.861. When it is close to 2, the model meets the assumption and has a good fit. Table 4 Multiple linear regression analysis between initial physical health test scores and partial SCL-90 factors Variables Unstandardized Coefficients β Value t Value p Value Collinearity Statistics B Value SE Tolerance VIF (Constant) 16.756 0.026 96.944 0.000 Somatization -1.047 0.382 -0.184 -2.745 0.007 0.595 1.680 Interpersonal Sensitivity 0.018 0.026 0.020 0.704 0.481 0.190 2.266 Depression -2.799 0.340 -0.571 -8.244 < 0.001 0.651 1.537 Anxiety 0.073 0.035 0.067 2.120 0.034 0.153 4.547 Psychoticism -0.030 0.026 -0.025 -1.128 0.259 0.312 3.202 Others 0.031 0.013 0.221 2.336 0.020 0.297 3.364 2.4.2 The prediction of mental health status on comprehensive physical health grade points Taking comprehensive physical health grade points (continuous variables) as the dependent variable, the SCL-90 factors included in the multiple linear regression analysis are the same as those in Table 4 . The results of the multiple linear regression analysis revealed that depression (B=-2.686, P < 0.001), anxiety (B = 0.462, P = 0.019), and psychoticism (B=-0.087, P = 0.007) were influencing factors of physical health status. The variance inflation factor (VIF) of the independent variables ranged from 1.537–4.778, all less than 5, indicating that there was no multicollinearity among the variables. Table 5 Multiple linear regression analysis between comprehensive physical health grade points and partial SCL-90 factors Variables Unstandardized Coefficients β Value t Value p Value Collinearity Statistics B Value SE Tolerance VIF (Constant) 71.405 0.265 269.878 0.000 Somatization -0.380 0.309 -0.026 -1.233 0.218 0.0350 2.857 Interpersonal Sensitivity -0.054 0.248 -0.005 -0.216 0.829 0.238 4.197 Depression -2.686 0.503 -0.457 -5.366 < 0.001 0.363 2.754 Anxiety 0.462 0.356 0.450 2.298 0.019 0.163 3.145 Psychoticism -0.087 0.032 -0.146 -2.738 0.007 0.743 1.345 Others -0.533 0.276 -0.042 -1.934 0.053 0.321 3.120 For the current regression model, R² = 0.216, adjusted R² = 0.194, and the Durbin–Watson statistic is 1.896. When it is close to 2, the model meets the assumption and has a good fit. 3 Discussion This study takes time as the axis, adopts a longitudinal perspective, and focuses on the predictive effect of college students' mental health status on their physical health status. Multiple linear regression methods were used to explore the impact of mental health status on physical health status systematically, providing a new perspective and empirical evidence for research in the field of college students' physical and mental health. The results of this study indicate that mental health status has a predictive effect on physical health status, which is consistent with the findings of numerous previous cross-sectional studies. However, by retaining the baseline mental health status of the research subjects and tracking their physical health course grades over four years of college as the basis for their physical health status during this period, this study conducted multiple linear regression analysis. Furthermore, their baseline mental health status also had a predictive effect on the comprehensive physical health score across the four years of college. The adjusted R² of the multiple linear regression analysis model was 0.194, whereas the adjusted R² of the multiple linear regression analysis model for physical health status in the first year of college was 0.441. Building on Sun Lihai’s [14] finding that physical health and mental health show a concurrent changing trend, this study further reveals a deeper-level association between mental health and physical health. This clearly confirms that among college students, an individual’s mental health status is one of the factors influencing changes in their physical health status. Moreover, this result forms a complete closed loop with Han Ye’s [15] research conclusion that physical health status can significantly predict mental health status. This finding also provides further evidence for Wu Huipan’s [16] research conclusion that there is an association between physical health and mental health. Analysis of R² revealed that mental health status has greater predictive accuracy for physical health status in the first year of college (adjusted R²=0.441), whereas its predictive effect on comprehensive physical health grade points over four years of college is weaker (adjusted R²=0.194). The stronger predictive effect on the first year of college may be related to the direct impact of freshmen’s psychological state, such as exercise habits and social pressure, on their adaptation to college. The weaker predictive effect over four years of college may be due to the failure to track dynamic fluctuations in mental health (such as academic pressure and changes in interpersonal relationships), leading to the attenuation of the explanatory power of the baseline data over time. Studies by Wang Qilin [17] , Yin Chuanzhong [18] , Tian Yong [19] , and others on the correlation between mental health status and physical exercise, as well as Chen Jie’s [20] research on the promotion of physical exercise by mental health, have all shown that a good mental health state helps students engage in physical exercise more effectively, thereby improving their physical health status. This clarifies one of the realization paths for the prediction of physical health status by baseline mental health level. In addition to multiple regression analysis, this study also conducted different analyses on sex, initial physical health scores, and comprehensive physical health scores. In the difference analysis between different genders and mental health statuses, there were significant differences between male and female students in the scores of factors such as obsessive‒compulsive symptoms, depression, anxiety, phobia, somatization, and interpersonal sensitivity (P < 0.05), with males showing significantly lower scores than females for each of these factors. These findings suggest that, compared with male college students, female college students may be more sensitive to psychological stress responses during the initial stage of adapting to college and face a greater risk of mental health problems. Gender differences may thus serve as an important entry point for mental health interventions. In the difference analysis between initial physical health scores and mental health status, the scores of factors such as somatization, interpersonal sensitivity, depression, paranoia, psychoticism, and others decreased as the group level of initial physical health scores increased. These findings indicate that students with higher initial physical health scores have better performance in aspects such as mental state, sleep quality, dietary status, and interest in life than do those with poorer physical health test scores. In the difference analysis between comprehensive physical health grade points and mental health status, the scores of factors such as somatization, interpersonal sensitivity, depression, paranoia, psychoticism, and others decreased as the group level of comprehensive physical health grade points increased. These findings indicate that mental health status is positively correlated with comprehensive physical health grade points—specifically, students with better comprehensive physical health grade points have better mental health status—and that there are significant differences in multiple mental health factors among groups with different comprehensive physical health grade points. Compared with previous cross-sectional studies. First, through a longitudinal follow-up design, this study overcomes the limitation of static associations between mental and physical health, further verifies the dynamic predictive effect of mental health on physical health, and confirms the predictive role of mental health in physical health via follow-up data. Moreover, two factors—depression and anxiety—have the most significant impact on students’ physical health (P < 0.001), and this result is partially consistent with the conclusion of a cross-sectional study based on multischool samples by Shuzhen M et al. [21] Furthermore, by extending the time dimension, this study further reveals the phased differences in predictive validity. In terms of the theoretical mechanism, this study overcomes the traditional understanding of the one-way effect between physical and mental health; for example, the research results of Yin Chuanzhong [18] and Chen Jie [20] emphasize only the impact of physical health on mental health, whereas this study reveals that the significant predictive effect of factors such as somatization and depression on physical health in the first year of college (β = -1.047–2.799) may imply a reverse feedback mechanism of mental health status on physical health issues. This echoes the "dynamic physical‒mental interaction effect" model proposed by Zhang Y et al. [22] but provides more direct evidence through the baseline prediction model. However, compared with similar studies, this study has several limitations: on the one hand, the sample representativeness is limited. Although it has certain advantages in obtaining longitudinal physical health test scores and mental health data, the use of a single university sample may lead to conclusion bias. For example, multischool data from Shuzhen M, Yanqi X et al. [23] indicate that there is heterogeneity in the mental-physical health correlation among students from different types of institutions, whereas this study does not include vocational college or art student groups, which may underestimate the impact of educational background on the results. On the other hand, the dynamic assessment is insufficient. Mental health data were only collected at the time of freshmen enrollment, failing to reflect fluctuations in psychological status during college (such as the impact of academic pressure, social adaptation and other events). This results in the explanatory power of the prediction model for comprehensive physical health grade points over four years of college (R²=0.194) being significantly lower than that of the prediction model for physical health test scores in the first year of college (R²=0.441), which is inconsistent with the "dynamic physical‒mental interaction effect" model proposed by Zhang Y et al. [22] . The reasons for the aforementioned limitations are as follows: first, the research design and resource constraints are insufficient. During the data collection phase, limited by the practical difficulties of university management systems (such as the protection of students’ privacy), the expansion of sample diversity could not be realized. Second, at the practical level, constrained by the costs of longitudinal follow-up and the shortage of staff at the university’s psychological counseling center, no additional psychological assessment nodes were added in subsequent academic years. Third, in terms of theoretical construction, the complexity of the bidirectional interaction mechanism between mental health and physical health was underestimated, leading to limited coverage of the variable system. In response to the above limitations, future research needs to seek breakthroughs in the breadth and depth of data as well as the integration of theories. Future research needs to systematically analyze the dynamic interaction mechanism between mental and physical health through the innovation of multidimensional experimental conditions and the reconstruction of theoretical frameworks. First, it is necessary to expand the coverage of sample heterogeneity, overcome the limitations of a single university sample, and include student groups from vocational colleges, art colleges, and universities in different regions (such as underdeveloped areas in central and western China). This research aims to examine the moderating effects of education type (e.g., academic-oriented vs. skill-practice-oriented) and regional resource endowments (such as the accessibility of mental health services and the density of sports facilities) on mental–physical health correlation, referring to the special student groups (e.g., ethnic minorities, art students) studied by Kaisaer Palita et al. [25] , Xiao Qianjin [26] , and others. Second, it is necessary to construct a refined dynamic follow-up system. On the basis of the existing baseline assessment, additional annual synchronous monitoring nodes for mental and physical health should be added, and ecological records of key life events (such as academic assessment periods and employment preparation periods) should be embedded. Furthermore, the cross-lagged panel model should be used to quantify the time-varying impact of fluctuations in psychological status on the trajectory of physical health—for example, verifying whether the sharp increase in anxiety levels in the second year of college has a lagged inhibitory effect on physical health scores in the third year. In addition, the interdisciplinary integration of assessment tools should be promoted. It is necessary to integrate wearable devices (monitoring daily steps and sleep efficiency), biomarkers (such as salivary cortisol and heart rate variability), and brain function imaging data to construct a multimodal health database. This will help reveal the explanatory power synergy mechanism between subjective psychological scales (SCL-90) and objective physiological indicators; for example, elevated cortisol may partially mediate the negative predictive pathway of depression factors on physical health. 4 Conclusion Through a longitudinal research design, this study compensates for the shortcomings of previous cross-sectional studies and comprehensively reveals the dynamic connections and intrinsic mechanisms between mental health status and physical health scores. The research results indicate that college students’ mental health status has an effect on their physical health test scores both in the first academic year and over the four years of college, with higher prediction accuracy for the physical health test scores in the first academic year. Universities should construct an integrated intervention system for mental health screening and physical health promotion on the basis of the predictive effect of baseline mental health levels to achieve the coordinated development of students’ physical and mental health. However, this study has certain limitations. First, the sample is limited to a single comprehensive university, and the external validity of the conclusions may be restricted by the homogeneity of institution type, regional resources, and student backgrounds. Second, the assessment of mental health status was only conducted upon freshmen enrollment; in the future, mental health assessments could be carried out every academic year with annual follow-ups. Finally, this study explored the impact of mental health status on physical health; future research could further investigate the impact of physical health on mental health, as well as the mutual interaction mechanism between the two. Declarations 5.1 Ethics approval and consent to participate The protocols for the experiments and procedures were approved by the Jimei University Committee on Science and Technology Ethics. ( approval no. JMU202407064 ). After receiving a detailed explanation of the purpose, potential benefits, and risks of participating in the study, each participant gave written informed consent. The study was implemented in accordance with the Declaration of Helsinki. 5.2 Consent for publication Not applicable！ 5.3 Availability of data and materials The data that support the findings of this study are available from Office of Academic Affairs, Jimei University but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Office of Academic Affairs, Jimei University. 5.4 Competing interests The authors declare that they have no competing interests. 5.5 Funding We greatly appreciate the study participants. This study was supported in part by a study on the Mutual Learning and Optimization of Youth Sports Health Promotion System under the Background of Cross-Strait Integration(Grant Number :FJ2024B098) from the Fujian Province General Social Science Project and a research on the Driving Mechanism and Intervention System of College Students' Physical Health under the Perspective of Proactive Health（Grant Number:ZD202306）from the Higher Education Studies in Fujian Province. 5.6 Authors' contributions LZG provided the data sources and proposed the writing methodology; SMH supplied the data and offered suggestions for the revision of the paper; DWL drafted the initial manuscript and took charge of the paper's revision. 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China Sport Sci Technol. 2007;580–2. /10.16470/j.csst.2007.05.018 . Han Y. A study on the correlation between college students’ physical health and mental health. J Phys Educ. 2008;561–3. 10.16237/j.cnki.cn44-1404/g8.2008.05.017 . Wu HP, Yin XJ. A study on the relationship between adolescents’ physical fitness and mental health[J]. Chin J School Health. 2021;42(1):157–60. 10.16835/j.cnki.1000-9817.2021.01.038 . Wang QL. Mental health status of male college students in Gansu and its correlation with physical exercise[J]. Chin J School Health. 2014;35(8):1229–30. 10.16835/j.cnki.1000-9817.2014.08.040 . Yin CZ. An analysis of the correlation between physical exercise and college students’ mental health[j]. Chin Health Service Manage. 2023;40(7):566. Tian Y, Wen Z, Ma WH, et al. The association between physical exercise, exercise motivation and mental health among vocational college students. Chin J School Health. 2024;45(9):1300–3. 10.16835/j.cnki.1000-9817.2024279 . Chen J. The promoting effect of physical exercise on college students' mental health. Chin J School Health. 2024;45(3):467. Ma S, Yang Y, Soh KG, Tan H. Effects of physical fitness on mental health of Chinese college students: across-sectional study[J]. BMC Public Health. 2024;24(1):727. 10.1186/s12889-024-18097-6 . Zhang Y, Hu Y, ,Wang Z et al. Associations of the University Personality Inventory measured mental health symptoms with physical fitness among Chinese college freshmen: a new potential health risk marker[J].Current Psychology,2024,43(48):1–15.DOI10.1007/s12144-024-07136-5. Ma S, Xu Y, Xu S, Guo Z. The effect of physical fitness on psychological health: evidence from Chinese university students. BMC Public Health. 2024;24(1):1365. 10.1186/s12889-024-18841-y . Hong Y, Shen J, Hu Y, Gu Y, Bai Z, Chen Y, Huang S. The association between physical fitness and mental health among college students: a cross-sectional study. Front Public Health. 2024;12:1384035. 10.3389/fpubh.2024.1384035 . Kaisaer PT, Aerkun ABDWYT, Yang ZJ, et al. Comparison of physical fitness status among Uyghur college students with different mental health levels in a college. Chin J School Health. 2016;37(7):1093–4. 10.16835/j.cnki.1000-9817.2016.07.039 . Xiao QJ. The value of sports dance in promoting college students’ physical health. Chin J School Health. 2024;45(7):1068–9. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 Dec, 2025 Reviewers agreed at journal 01 Dec, 2025 Reviewers invited by journal 01 Dec, 2025 Editor assigned by journal 01 Dec, 2025 Editor invited by journal 22 Nov, 2025 Submission checks completed at journal 22 Nov, 2025 First submitted to journal 22 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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09:14:04","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":111889,"visible":true,"origin":"","legend":"","description":"","filename":"8a77200cddc54e178c7f9dc0e645ee501structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8112287/v1/71a4ff2c2ca507c281ddabbd.xml"},{"id":97380592,"identity":"25eea7df-8945-49b3-a51b-dc77f29feb97","added_by":"auto","created_at":"2025-12-03 18:00:59","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122908,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8112287/v1/5f1320c5b17e85cfb3e67820.html"},{"id":97380584,"identity":"ea0da732-afb8-41e7-bb05-ce5e6a32df3e","added_by":"auto","created_at":"2025-12-03 18:00:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":176105,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of SCL-90 factor scores between students of different sexes\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8112287/v1/48659673eea41cbedeef3b1b.png"},{"id":97380586,"identity":"afd6ae8a-f302-4480-8925-7a5781059cbd","added_by":"auto","created_at":"2025-12-03 18:00:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":267181,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between comprehensive physical health grade points, initial physical health test scores and SCL-90 factors\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8112287/v1/15e6f5454c415686a4b8e24e.png"},{"id":98622342,"identity":"6e61c8c6-718c-4b5c-8aef-d63d4799d2df","added_by":"auto","created_at":"2025-12-19 16:52:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1862248,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8112287/v1/8905ce4e-f987-4adb-930b-29504fb33af6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on the Interaction between the Mental Health and Physical Health of College Students","fulltext":[{"header":"Background","content":"\u003cp\u003eCollege years, as a transitional period from adolescence to adulthood and the early stage after reaching adulthood, coincide with a critical phase of physical and psychological development. During this period, an individual\u0026rsquo;s health status not only affects their current academic performance and daily life but also profoundly affects their future development \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Health issues among college students have become increasingly prominent worldwide, particularly in terms of mental health problems and the decline in physical fitness \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e][\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e][\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. As the country with the largest number of college students in the world, China regards the health of college students as a crucial public health issue. Therefore, understanding and researching the mental health and physical health status of college students holds significant practical significance.\u003c/p\u003e\u003cp\u003eIn recent years, numerous studies and relevant reports have focused on college students, indicating that there is a significant association between the frequent occurrence of mental health problems and a decline in physical fitness \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e][\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Specifically, the vast majority of contemporary college students adopt unhealthy lifestyles, such as staying up late, having irregular diets, and overusing the internet. These habits further impair the maintenance of physical functions, leading to a downward trend in physical fitness. Moreover, college students face multiple sources of distress, including academic pressure, interpersonal relationship issues, emotional problems, and employment pressure. These factors significantly affect mental health \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. In turn, mental health status indirectly affects physical fitness test scores and daily performance by influencing students\u0026rsquo; daily dietary behaviors and living conditions \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e][\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e][\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHowever, existing studies have focused mostly on the correlation analysis between the two, while controversies remain regarding their causal relationships and deeper-level interactions \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. For example, scholars hold divergent views on questions such as whether mental health status can predict physical health status and whether physical health status affects mental health status. Therefore, the \"mutual influence relationship\" between mental health and an individual\u0026rsquo;s physical fitness requires verification through more longitudinal tracking experiments.\u003c/p\u003e\u003cp\u003eTherefore, this study aims to explore the predictive effect of mental health on physical health by conducting a longitudinal follow-up of students\u0026rsquo; physical health status and integrating their baseline mental health levels, striving to present the interaction mechanism and general laws between mental health and physical health in a more comprehensive manner. This study provides a scientific basis and practical reference for the formulation of mental health education in colleges and universities and strategies for promoting students\u0026rsquo; physical health.\u003c/p\u003e"},{"header":"1 Research Objects and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e1.1 Research Objects\u003c/h2\u003e\nThis study selected undergraduate graduates from six consecutive cohorts (2019\u0026ndash;2024) of a comprehensive university in Xiamen city as the research objects. A total of 7,281 undergraduate graduates were invited to participate as subjects in this study, with 1,134 from the 2019 cohort, 1,264 from the 2020 cohort, 1,154 from the 2021 cohort, 1,301 from the 2022 cohort, 1,232 from the 2023 cohort, and 1,196 from the 2024 cohort. Among them, there were 3,529 male students and 2,962 female students.\u003cbr /\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe mental health assessment was uniformly organized by the university's Psychological Counseling Center, and the students completed the assessment collectively in the computer lab. The scores of the students' physical fitness assessments were obtained by the university's physical education teachers through physical fitness tests. A total of 789 cases were excluded, including those with invalid questionnaires, missing data (e.g., incomplete physical fitness test records), and students who dropped out, took a leave of absence, or passed away during the study period. Finally, the valid sample size was 6,491, with an effective rate of 89.16%.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e1.2 Methods\u003c/h2\u003e\n\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n\u003ch2\u003e1.2.1 Mental health status assessment\u003c/h2\u003e\nThe mental health assessment tool adopted in this study was the Symptom Checklist-90 (SCL-90), which was developed by Derogatis et al. For the current study, the Chinese version translated by Wang was used for relevant assessment purposes.\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e This scale consists of 90 items and covers a wide range of content related to psychiatric symptomatology. The Symptom Checklist-90 (SCL-90) includes 10 factors in total. Specifically, there are 9 core factors: somatization, obsessive‒compulsive symptoms, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, Paranoid ideation, and psychoticism. Additionally, there is an \"Other\" factor, which mainly covers sleep and dietary conditions \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. This scale uses a 5-point Likert scale for scoring. In this study, a single factor score of \u0026lt;\u0026thinsp;2.0 was defined as negative (indicating healthy mental status), whereas a factor score of \u0026ge;\u0026thinsp;2.0 was defined as positive (indicating potential mental health concerns). These criteria were used to evaluate the mental health status of the college students.\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n\u003ch2\u003e1.2.2 Physical fitness health assessment\u003c/h2\u003e\nThe assessment indicators for physical fitness health status are the physical fitness test scores of college students in their first academic year (hereinafter referred to as \"Admission Physical Fitness Scores\") and the value obtained by dividing the sum of grade points from physical fitness course tests over four years of college by the total corresponding credits (4 credits) (hereinafter referred to as \"Comprehensive Physical Fitness GPA\"). These two sets of data serve as the basis for evaluating physical fitness health status. The physical fitness health status was grouped according to research needs:\u003cbr /\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eGrouping was conducted on the basis of the physical fitness scores of the first academic year: scores below 60 were categorized into the \"Fail Group\", scores of 60 or above but below 80 were categorized into the \"Pass Group\", scores of 80 or above but below 90 were categorized into the \"Good Group\", and scores of 90 or above were categorized into the \"Excellent Group\".\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGrouping was conducted on the basis of the comprehensive physical fitness GPA: GPAs below 2.0 were categorized into the \"low GPA group\", GPAs of 2.0 or above but below 2.5 were categorized into the \"medium GPA group\", and GPAs of 2.5 or above were categorized into the \"high GPA group\".\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e1.3 Statistical analysis\u003c/h2\u003e\nThe data were statistically analyzed via SPSS 27.0 software. For measurement data, if they conformed to a normal distribution, they were expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD); if they did not conform to a normal distribution, they were expressed as the median. The Kolmogorov‒Smirnov (K-S) test was used for normality testing. First, independent samples t tests and Jonckheere\u0026ndash;Terpstra tests were used to conduct difference tests on students of different genders, different admission physical fitness scores, and different comprehensive physical fitness GPAs. Second, Spearman's rank correlation analysis was used to analyze the correlations between the admission physical fitness scores, comprehensive physical fitness GPA, and each factor of the Symptom Checklist-90 (SCL-90). Finally, multiple linear regression analysis was used, with scores of several factors from the Symptom Checklist-90 (SCL-90) as independent variables and admission physical fitness scores and comprehensive physical fitness GPA as dependent variables. Multiple linear regression models were constructed separately to analyze the relationships between each factor of the SCL-90 and admission physical fitness scores, as well as between each factor of the SCL-90 and comprehensive physical fitness GPA, to conduct baseline predictive analysis on admission physical fitness scores and comprehensive physical fitness GPA. All tests were two-tailed, and a P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/div\u003e"},{"header":"2 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Demographic Information\u003c/h2\u003e\nThe statistical information of the 6491 students was analyzed to reveal the basic characteristics of the study participants, including gender, grade, family economic status, and parents' educational level. The detailed data are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, among the college student samples included in this study, 3,529 males (54.4%) and 2,962 females (45.6%) were included. The sample distribution of graduates across different years is as follows: 1,064 graduates from the class of 2019, 1,027 from the class of 2020, 1,099 from the class of 2021, 1,091 from the class of 2022, 1,079 from the class of 2023, and 1,131 from the class of 2024.67.8% of the students had families with an average economic status, while most of their parents had not received higher education, and the majority held an educational level of junior high school and below.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDemographic characteristics of the study participants\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCharacteristics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSituation\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCount\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePercentage (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eYear\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1064\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2021\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1099\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1091\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2023\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1079\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1131\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3529\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e54.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2962\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e45.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eFamily Economic Status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDifficult\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1631\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e25.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAverage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4399\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e67.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e422\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWealthy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eMother's Educational Level\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJunior High School and Below School and Below\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4297\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e66.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSenior High School Technical Secondary School\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1275\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e19.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUniversity Junior College\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e893\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaster's Degree and Above\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eFather's Educational Level\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJunior High School and Below School and Below\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3771\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e58.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSenior High School Technical Secondary School\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1464\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUniversity Junior College\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1198\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaster's Degree and Above\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 Difference analysis\u003c/h2\u003e\n\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n\u003ch2\u003e2.2.1 The impact of sex on mental health status\u003c/h2\u003e\nAn independent samples t test was conducted on the factor scores of the SCL-90 Symptom Checklist among college students of different genders, and the results are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003cbr /\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, among the 10 factors of the SCL-90 Symptom Checklist, the average score of male students was higher than that of female students only for the Paranoid Ideation factor; for the other 9 factors, the average scores of male students were all lower than those of female students. Among these, in terms of the scores on the four factors (Obsessive-Compulsive Symptoms, Depression, Anxiety, and Phobic Anxiety), the scores of the male students were extremely significantly lower than those of the female students (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In terms of the scores for the somatization and interpersonal sensitivity factors, the scores of the male students were also significantly lower than those of the female students (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), whereas there was no significant difference between the male and female students in the scores for the remaining factors. This finding indicates that when left home for the first time, the vast majority of female students may be more prone to experiencing emotional distress, tension, and fear of unfamiliar environments, as well as other physiological reactions, than male students are.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n\u003ch2\u003e2.2.2 The impact of different admission physical health scores on mental health status\u003c/h2\u003e\nThe Jonckheere\u0026ndash;Terpstra test was conducted on the factor scores of the SCL-90 Symptom Checklist among college students in different admission physical health score groups (Fail Group, Pass Group, Good Group, and Excellent Group) to obtain significant results among the 4 groups. The Mann‒Whitney U test was subsequently used to perform pairwise comparisons between two groups. Finally, the Bonferroni correction method was applied to adjust the significance level, and thus, reliable multiple comparison results were obtained, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003cbr /\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDifference analysis of SCL-90 factor scores among students with different admission physical health scores\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStandardized J-T Statistic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePost Hoc Multiple Comparisons\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSomatization\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.483\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA\u0026thinsp;\u0026lt;\u0026thinsp;C\u003csup\u003e*\u003c/sup\u003e,A\u0026thinsp;\u0026lt;\u0026thinsp;D\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eObsessive-Compulsive Symptoms\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInterpersonal Sensitivity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.057\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.040\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA\u0026thinsp;\u0026lt;\u0026thinsp;C\u003csup\u003e*\u003c/sup\u003e,B\u0026thinsp;\u0026lt;\u0026thinsp;D\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDepression\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.493\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eB\u0026thinsp;\u0026lt;\u0026thinsp;D\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnxiety\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHostility\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhobic Anxiety\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eParanoid Ideation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.926\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA\u0026thinsp;\u0026lt;\u0026thinsp;D\u003csup\u003e*\u003c/sup\u003e,B\u0026thinsp;\u0026lt;\u0026thinsp;D\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePsychoticism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.160\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eB\u0026thinsp;\u0026lt;\u0026thinsp;C\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-3.164\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA\u0026thinsp;\u0026lt;\u0026thinsp;C\u003csup\u003e*\u003c/sup\u003e,B\u0026thinsp;\u0026lt;\u0026thinsp;C\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u003cstrong\u003eNotice: In the table, D represents the \"Fail Group\", C represents the \"Pass Group\", B represents the \"Good Group\", and A represents the \"Excellent Group\". *** indicates a significant correlation at the P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 level, ** indicates a significant correlation at the P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 level, and * indicates a significant correlation at the P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level.\u003c/strong\u003e\u0026nbsp;\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThe results revealed significant differences in 6 factors (Somatization, Interpersonal Sensitivity, Depression, Paranoid Ideation, Psychoticism, and Additional Items/Other) among the 4 groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among these factors, Paranoid Ideation and other items/other factors exhibited extremely significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). There were no significant differences in the 4 factors, namely, obsessive‒compulsive symptoms, anxiety, hostility, and phobic anxiety. The post hoc multiple comparison results in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e indicate that, in terms of the Somatization factor score, the Excellent Group (admission physical health score) score was significantly lower than the Pass Group and the Fail Group. In terms of the Interpersonal Sensitivity factor score, the Excellent Group was significantly lower than the Pass Group, and the Good Group was significantly lower than the Fail Group. In terms of the Depression factor score, the Good Group was significantly lower than the Fail Group. In terms of the Paranoid Ideation factor score, the Excellent Group was extremely significantly lower than the Fail Group, and the Good Group was significantly lower than the Fail Group. In terms of the Psychoticism factor score, the Good Group was significantly lower than the Pass Group. In terms of the Additional Items/Other factor score, the Excellent Group was extremely significantly lower than the Pass Group, and the Good Group was significantly lower than the Pass Group. According to the values of the standardized J-T statistic obtained from the Jonckheere\u0026ndash;Terpstra test, for factors such as somatization, interpersonal sensitivity, depression, Paranoid Ideation, psychoticism, and additional items/other, the scores of these factors decrease as the level of the physical health score groups admitted increases. To summarize the above analysis, students with higher admission physical health scores have better conditions in terms of psychological inferiority, sleep and dietary status, and interest in life than do those with poor physical health test scores.\u003c/p\u003e\n\u003cstrong\u003e2.2.3 Impact of Different Comprehensive Grade Point Averages (GPAs) for Physical Health on Mental Health Status\u003c/strong\u003e\u003c/div\u003e\n\u003cdiv class=\"Section3\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"Section3\"\u003eTaking the scores of the 10 factors in the SCL-90 Symptom Checklist as test variables and the different comprehensive GPA groups for physical health (high-performance group, middle group, low-performance group) as grouping variables, the Jonckheere\u0026ndash;Terpstra test was conducted to obtain the significance results among the 3 groups. The same processing method used in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e was subsequently used for post hoc multiple comparisons. See Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e for details.\u003c/div\u003e\n\u003cdiv class=\"Section3\"\u003eThe results revealed extremely significant differences in all 10 factors of the SCL-90 scale among the different groups, with varying comprehensive physical health grades (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Among these factors, somatization, interpersonal sensitivity, depression, paranoia, and others exhibited extremely significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The results of the post hoc multiple comparisons in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e indicate that the scores of somatization, depression, anxiety, and other factors in the high-achievement group were significantly lower than those in the low-achievement group. In terms of the scores of factors such as paranoia and psychoticism, the scores of the middle-income group and the high-achievement group were significantly lower than those of the low-achievement group. The scores of factors such as interpersonal sensitivity, hostility, and phobia were significantly lower in the high-achievement group than in the middle group. The score of the obsessive‒compulsive symptom factor was significantly lower in the middle group than in the low-achievement group. On the basis of the standard J-T statistic values obtained from the Jonckheere\u0026ndash;Terpstra test, it can be concluded that for factors such as somatization, interpersonal sensitivity, depression, paranoia, and psychoticism, the scores of these factors decrease as the level of comprehensive physical health grade point groups increases. In summary, the above analysis indicates that mental health status is negatively correlated with comprehensive physical health grades. Students with better comprehensive physical health grades have better mental health status, and there are significant differences in multiple mental health factors among groups with different comprehensive physical health grades.\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDifferences in SCL-90 factor scores among students with different comprehensive GPAs for physical health\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStandardized J-T Statistic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePost Hoc Multiple Comparisons\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSomatization\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-3.448\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG\u0026thinsp;\u0026lt;\u0026thinsp;E\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eObsessive-Compulsive Symptoms\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-3.068\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF\u0026thinsp;\u0026lt;\u0026thinsp;E\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInterpersonal Sensitivity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-3.725\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG\u0026thinsp;\u0026lt;\u0026thinsp;F\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDepression\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-3.495\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG\u0026thinsp;\u0026lt;\u0026thinsp;E\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnxiety\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.625\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG\u0026thinsp;\u0026lt;\u0026thinsp;E\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHostility\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.683\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG\u0026thinsp;\u0026lt;\u0026thinsp;F\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhobic Anxiety\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.644\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG\u0026thinsp;\u0026lt;\u0026thinsp;F\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eParanoid Ideation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-4.445\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF、G\u0026thinsp;\u0026lt;\u0026thinsp;E\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePsychoticism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.916\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF、G\u0026thinsp;\u0026lt;\u0026thinsp;E\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-3.864\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG\u0026thinsp;\u0026lt;\u0026thinsp;E\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003e\u003cstrong\u003eNote: In the table, \"E\" represents the \"Low-Performance Group\", \"F\" represents the \"Middle Group\", and \"G\" represents the \"High-Performance Group\". *** indicates a significant correlation at the P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 level, ** indicates a significant correlation at the P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 level, and * indicates a significant correlation at the P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level.\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3 Correlation analysis\u003c/h2\u003e\nSpearman correlation analysis was performed between the comprehensive physical health grade points and the initial physical health test scores and each factor of the SCL-90 Symptom Checklist. See Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e for details.\n\u003cp\u003eThe results revealed that with respect to comprehensive physical health grades, depression (r=-0.56, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), anxiety (r=-0.45, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), psychoticism (r=-0.53, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and other factors (r=-0.58, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were negatively correlated with comprehensive physical health grades. The results revealed that in terms of initial physical health test scores, interpersonal sensitivity (r=-0.47, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), depression (r=-0.52, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), anxiety (r=-0.41, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), phobia (r=-0.26, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), psychoticism (r=-0.48, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and other factors (r=-0.53, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were negatively correlated with initial physical health test scores.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4 Multiple linear regression analysis\u003c/h2\u003e\n\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n\u003ch2\u003e2.4.1 The prediction of mental health status on initial physical health test scores\u003c/h2\u003e\nTaking initial physical health test scores (continuous variables) as the dependent variable and considering the results of Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e comprehensively, the factors with significant differences (somatization, interpersonal sensitivity, depression, anxiety, psychoticism, and others) were included in the multiple linear regression analysis, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/div\u003e\n\u003cdiv class=\"Section3\"\u003eThe results of multiple linear regression analysis revealed that four factors\u0026mdash;somatization (B=-1.047, P\u0026thinsp;=\u0026thinsp;0.007), depression (B=-2.799, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), anxiety (B\u0026thinsp;=\u0026thinsp;0.073, P\u0026thinsp;=\u0026thinsp;0.034), and others (B\u0026thinsp;=\u0026thinsp;0.031, P\u0026thinsp;=\u0026thinsp;0.020)\u0026mdash;were influencing factors of physical health status. The variance inflation factor (VIF) of the independent variables ranged from 1.537\u0026ndash;4.778, all less than 5, indicating that there was no multicollinearity among the variables. For the current regression model, R\u0026sup2; = 0.452, adjusted R\u0026sup2; = 0.441, and the Durbin\u0026ndash;Watson statistic is 1.861. When it is close to 2, the model meets the assumption and has a good fit.\u003cbr /\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultiple linear regression analysis between initial physical health test scores and partial SCL-90 factors\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eUnstandardized Coefficients\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026beta; Value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003et Value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ep Value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCollinearity Statistics\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eB Value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTolerance\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVIF\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(Constant)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16.756\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e96.944\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSomatization\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1.047\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.382\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.184\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.745\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.595\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.680\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInterpersonal Sensitivity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.704\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.481\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.190\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.266\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDepression\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.799\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.340\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.571\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-8.244\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.651\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.537\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnxiety\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.073\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.035\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.067\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.120\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.034\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.153\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.547\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePsychoticism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.030\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1.128\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.259\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.312\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.202\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.221\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.336\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.297\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.364\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n\u003ch2\u003e2.4.2 The prediction of mental health status on comprehensive physical health grade points\u003c/h2\u003e\nTaking comprehensive physical health grade points (continuous variables) as the dependent variable, the SCL-90 factors included in the multiple linear regression analysis are the same as those in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The results of the multiple linear regression analysis revealed that depression (B=-2.686, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), anxiety (B\u0026thinsp;=\u0026thinsp;0.462, P\u0026thinsp;=\u0026thinsp;0.019), and psychoticism (B=-0.087, P\u0026thinsp;=\u0026thinsp;0.007) were influencing factors of physical health status. The variance inflation factor (VIF) of the independent variables ranged from 1.537\u0026ndash;4.778, all less than 5, indicating that there was no multicollinearity among the variables.\u0026nbsp;\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultiple linear regression analysis between comprehensive physical health grade points and partial SCL-90 factors\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eUnstandardized Coefficients\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026beta; Value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003et Value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep Value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCollinearity Statistics\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eB Value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTolerance\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVIF\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(Constant)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e71.405\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.265\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e269.878\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSomatization\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.380\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.309\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1.233\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.218\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0350\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.857\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInterpersonal Sensitivity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.054\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.248\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.216\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.829\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.238\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.197\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDepression\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.686\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.503\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.457\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-5.366\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.363\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.754\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnxiety\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.462\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.356\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.450\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.298\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.145\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePsychoticism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.087\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.032\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.146\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.738\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.743\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.345\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.533\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.276\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.042\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1.934\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.053\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.321\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.120\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFor the current regression model, R\u0026sup2; = 0.216, adjusted R\u0026sup2; = 0.194, and the Durbin\u0026ndash;Watson statistic is 1.896. When it is close to 2, the model meets the assumption and has a good fit.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"3 Discussion","content":"\u003cp\u003eThis study takes time as the axis, adopts a longitudinal perspective, and focuses on the predictive effect of college students' mental health status on their physical health status. Multiple linear regression methods were used to explore the impact of mental health status on physical health status systematically, providing a new perspective and empirical evidence for research in the field of college students' physical and mental health. The results of this study indicate that mental health status has a predictive effect on physical health status, which is consistent with the findings of numerous previous cross-sectional studies. However, by retaining the baseline mental health status of the research subjects and tracking their physical health course grades over four years of college as the basis for their physical health status during this period, this study conducted multiple linear regression analysis. Furthermore, their baseline mental health status also had a predictive effect on the comprehensive physical health score across the four years of college. The adjusted R\u0026sup2; of the multiple linear regression analysis model was 0.194, whereas the adjusted R\u0026sup2; of the multiple linear regression analysis model for physical health status in the first year of college was 0.441. Building on Sun Lihai\u0026rsquo;s \u003csup\u003e[14]\u003c/sup\u003e finding that physical health and mental health show a concurrent changing trend, this study further reveals a deeper-level association between mental health and physical health. This clearly confirms that among college students, an individual\u0026rsquo;s mental health status is one of the factors influencing changes in their physical health status. Moreover, this result forms a complete closed loop with Han Ye\u0026rsquo;s \u003csup\u003e[15]\u003c/sup\u003e research conclusion that physical health status can significantly predict mental health status. This finding also provides further evidence for Wu Huipan\u0026rsquo;s \u003csup\u003e[16]\u003c/sup\u003e research conclusion that there is an association between physical health and mental health. Analysis of R\u0026sup2; revealed that mental health status has greater predictive accuracy for physical health status in the first year of college (adjusted R\u0026sup2;=0.441), whereas its predictive effect on comprehensive physical health grade points over four years of college is weaker (adjusted R\u0026sup2;=0.194). The stronger predictive effect on the first year of college may be related to the direct impact of freshmen\u0026rsquo;s psychological state, such as exercise habits and social pressure, on their adaptation to college. The weaker predictive effect over four years of college may be due to the failure to track dynamic fluctuations in mental health (such as academic pressure and changes in interpersonal relationships), leading to the attenuation of the explanatory power of the baseline data over time. Studies by Wang Qilin \u003csup\u003e[17]\u003c/sup\u003e, Yin Chuanzhong \u003csup\u003e[18]\u003c/sup\u003e, Tian Yong \u003csup\u003e[19]\u003c/sup\u003e, and others on the correlation between mental health status and physical exercise, as well as Chen Jie\u0026rsquo;s \u003csup\u003e[20]\u003c/sup\u003e research on the promotion of physical exercise by mental health, have all shown that a good mental health state helps students engage in physical exercise more effectively, thereby improving their physical health status. This clarifies one of the realization paths for the prediction of physical health status by baseline mental health level. In addition to multiple regression analysis, this study also conducted different analyses on sex, initial physical health scores, and comprehensive physical health scores. In the difference analysis between different genders and mental health statuses, there were significant differences between male and female students in the scores of factors such as obsessive‒compulsive symptoms, depression, anxiety, phobia, somatization, and interpersonal sensitivity (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with males showing significantly lower scores than females for each of these factors. These findings suggest that, compared with male college students, female college students may be more sensitive to psychological stress responses during the initial stage of adapting to college and face a greater risk of mental health problems. Gender differences may thus serve as an important entry point for mental health interventions. In the difference analysis between initial physical health scores and mental health status, the scores of factors such as somatization, interpersonal sensitivity, depression, paranoia, psychoticism, and others decreased as the group level of initial physical health scores increased. These findings indicate that students with higher initial physical health scores have better performance in aspects such as mental state, sleep quality, dietary status, and interest in life than do those with poorer physical health test scores. In the difference analysis between comprehensive physical health grade points and mental health status, the scores of factors such as somatization, interpersonal sensitivity, depression, paranoia, psychoticism, and others decreased as the group level of comprehensive physical health grade points increased. These findings indicate that mental health status is positively correlated with comprehensive physical health grade points\u0026mdash;specifically, students with better comprehensive physical health grade points have better mental health status\u0026mdash;and that there are significant differences in multiple mental health factors among groups with different comprehensive physical health grade points.\u003c/p\u003e\n\u003cp\u003eCompared with previous cross-sectional studies. First, through a longitudinal follow-up design, this study overcomes the limitation of static associations between mental and physical health, further verifies the dynamic predictive effect of mental health on physical health, and confirms the predictive role of mental health in physical health via follow-up data. Moreover, two factors\u0026mdash;depression and anxiety\u0026mdash;have the most significant impact on students\u0026rsquo; physical health (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and this result is partially consistent with the conclusion of a cross-sectional study based on multischool samples by Shuzhen M et al. \u003csup\u003e[21]\u003c/sup\u003e Furthermore, by extending the time dimension, this study further reveals the phased differences in predictive validity. In terms of the theoretical mechanism, this study overcomes the traditional understanding of the one-way effect between physical and mental health; for example, the research results of Yin Chuanzhong \u003csup\u003e[18]\u003c/sup\u003e and Chen Jie \u003csup\u003e[20]\u003c/sup\u003e emphasize only the impact of physical health on mental health, whereas this study reveals that the significant predictive effect of factors such as somatization and depression on physical health in the first year of college (\u0026beta; = -1.047\u0026ndash;2.799) may imply a reverse feedback mechanism of mental health status on physical health issues. This echoes the \"dynamic physical‒mental interaction effect\" model proposed by Zhang Y et al. \u003csup\u003e[22]\u003c/sup\u003e but provides more direct evidence through the baseline prediction model.\u003c/p\u003e\n\u003cp\u003eHowever, compared with similar studies, this study has several limitations: on the one hand, the sample representativeness is limited. Although it has certain advantages in obtaining longitudinal physical health test scores and mental health data, the use of a single university sample may lead to conclusion bias. For example, multischool data from Shuzhen M, Yanqi X et al. \u003csup\u003e[23]\u003c/sup\u003e indicate that there is heterogeneity in the mental-physical health correlation among students from different types of institutions, whereas this study does not include vocational college or art student groups, which may underestimate the impact of educational background on the results. On the other hand, the dynamic assessment is insufficient. Mental health data were only collected at the time of freshmen enrollment, failing to reflect fluctuations in psychological status during college (such as the impact of academic pressure, social adaptation and other events). This results in the explanatory power of the prediction model for comprehensive physical health grade points over four years of college (R\u0026sup2;=0.194) being significantly lower than that of the prediction model for physical health test scores in the first year of college (R\u0026sup2;=0.441), which is inconsistent with the \"dynamic physical‒mental interaction effect\" model proposed by Zhang Y et al. \u003csup\u003e[22]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe reasons for the aforementioned limitations are as follows: first, the research design and resource constraints are insufficient. During the data collection phase, limited by the practical difficulties of university management systems (such as the protection of students\u0026rsquo; privacy), the expansion of sample diversity could not be realized. Second, at the practical level, constrained by the costs of longitudinal follow-up and the shortage of staff at the university\u0026rsquo;s psychological counseling center, no additional psychological assessment nodes were added in subsequent academic years. Third, in terms of theoretical construction, the complexity of the bidirectional interaction mechanism between mental health and physical health was underestimated, leading to limited coverage of the variable system. In response to the above limitations, future research needs to seek breakthroughs in the breadth and depth of data as well as the integration of theories.\u003c/p\u003e\n\u003cp\u003eFuture research needs to systematically analyze the dynamic interaction mechanism between mental and physical health through the innovation of multidimensional experimental conditions and the reconstruction of theoretical frameworks. First, it is necessary to expand the coverage of sample heterogeneity, overcome the limitations of a single university sample, and include student groups from vocational colleges, art colleges, and universities in different regions (such as underdeveloped areas in central and western China). This research aims to examine the moderating effects of education type (e.g., academic-oriented vs. skill-practice-oriented) and regional resource endowments (such as the accessibility of mental health services and the density of sports facilities) on mental\u0026ndash;physical health correlation, referring to the special student groups (e.g., ethnic minorities, art students) studied by Kaisaer Palita et al. \u003csup\u003e[25]\u003c/sup\u003e, Xiao Qianjin \u003csup\u003e[26]\u003c/sup\u003e, and others. Second, it is necessary to construct a refined dynamic follow-up system. On the basis of the existing baseline assessment, additional annual synchronous monitoring nodes for mental and physical health should be added, and ecological records of key life events (such as academic assessment periods and employment preparation periods) should be embedded. Furthermore, the cross-lagged panel model should be used to quantify the time-varying impact of fluctuations in psychological status on the trajectory of physical health\u0026mdash;for example, verifying whether the sharp increase in anxiety levels in the second year of college has a lagged inhibitory effect on physical health scores in the third year. In addition, the interdisciplinary integration of assessment tools should be promoted. It is necessary to integrate wearable devices (monitoring daily steps and sleep efficiency), biomarkers (such as salivary cortisol and heart rate variability), and brain function imaging data to construct a multimodal health database. This will help reveal the explanatory power synergy mechanism between subjective psychological scales (SCL-90) and objective physiological indicators; for example, elevated cortisol may partially mediate the negative predictive pathway of depression factors on physical health.\u003c/p\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eThrough a longitudinal research design, this study compensates for the shortcomings of previous cross-sectional studies and comprehensively reveals the dynamic connections and intrinsic mechanisms between mental health status and physical health scores. The research results indicate that college students\u0026rsquo; mental health status has an effect on their physical health test scores both in the first academic year and over the four years of college, with higher prediction accuracy for the physical health test scores in the first academic year. Universities should construct an integrated intervention system for mental health screening and physical health promotion on the basis of the predictive effect of baseline mental health levels to achieve the coordinated development of students\u0026rsquo; physical and mental health.\u003c/p\u003e\n\u003cp\u003eHowever, this study has certain limitations. First, the sample is limited to a single comprehensive university, and the external validity of the conclusions may be restricted by the homogeneity of institution type, regional resources, and student backgrounds. Second, the assessment of mental health status was only conducted upon freshmen enrollment; in the future, mental health assessments could be carried out every academic year with annual follow-ups. Finally, this study explored the impact of mental health status on physical health; future research could further investigate the impact of physical health on mental health, as well as the mutual interaction mechanism between the two.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e5.1 Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protocols for the experiments and procedures were approved by the Jimei University Committee on Science and Technology Ethics. (\u003cstrong\u003eapproval no. JMU202407064\u003c/strong\u003e). After receiving a detailed explanation of the purpose, potential benefits, and risks of participating in the study, each participant gave written informed consent. The study was implemented in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2 Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable！\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.3 Availability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from Office of Academic Affairs, Jimei University but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Office of Academic Affairs, Jimei University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.4 Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.5 Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe greatly appreciate the study participants. This study was supported in part by a study on the Mutual Learning and Optimization of Youth Sports Health Promotion System under the Background of Cross-Strait Integration(Grant Number :FJ2024B098) from the Fujian Province General Social Science Project and a research on the Driving Mechanism and Intervention System of College Students' Physical Health under the Perspective of Proactive Health（Grant Number:ZD202306）from the Higher Education Studies in Fujian Province.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.6 Authors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLZG provided the data sources and proposed the writing methodology; SMH supplied the data and offered suggestions for the revision of the paper; DWL drafted the initial manuscript and took charge of the paper's revision.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCotton NK, Shim RS. 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Chin J School Health. 2024;45(7):1068\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mental health, Physical health, Multiple regression analysis, Baseline prediction, Longitudinal study","lastPublishedDoi":"10.21203/rs.3.rs-8112287/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8112287/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThis study aims to explore the predictive effect of college students' baseline mental health status on their physical health status to provide a scientific basis and practical reference for the formulation of mental health education in colleges and universities and strategies for promoting their physical health.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA total of 6,491 undergraduate graduates from six consecutive cohorts (2019--2024) at a comprehensive university were selected as the research subjects. The Symptom Checklist-90 (SCL-90) was used to assess their mental health status, and their physical health test scores were incorporated into the analysis. Correlation analysis, multiple linear regression, and other methods were employed to examine the relationships between mental health and physical health test results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThere were significant differences in the scores of obsessive-compulsive, depressive, anxious, and phobic factors on the SCL-90 between male and female college students, with female students scoring higher than male students. Students with higher physical health test scores in their first year of college had significantly lower scores for factors such as somatization, interpersonal sensitivity, depression, paranoia, and psychoticism than those in the lower-score group did (P\u0026lt;0.05). Additionally, there was a negative correlation between the scores of these factors and the physical health test scores upon enrollment. There was a significant negative correlation between the comprehensive grade point average (GPA) of physical health and mental health level (P\u0026lt;0.01); that is, students with a higher GPA had a better mental health status. Multiple linear regression analysis revealed that somatization, depression, anxiety, and other factors influenced physical health test scores upon enrollment; moreover, depression, anxiety, and psychoticism influenced the comprehensive GPA of physical health.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe mental health status of college students has a predictive effect on both their physical health test scores in the first academic year and their comprehensive physical health GPA over the four years of college, with a higher predictive accuracy for the physical health test scores in the first academic year.\u003c/p\u003e","manuscriptTitle":"Research on the Interaction between the Mental Health and Physical Health of College Students","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-03 18:00:54","doi":"10.21203/rs.3.rs-8112287/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-12-15T20:52:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97888409124936662574960969450244849383","date":"2025-12-01T21:12:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-01T15:24:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-01T15:20:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-22T17:07:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-22T14:42:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-11-22T14:37:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1eef1a1b-14f6-42f7-8e09-1fadbd9c1f6a","owner":[],"postedDate":"December 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-03T18:00:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-03 18:00:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8112287","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8112287","identity":"rs-8112287","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}