Latent Profile Analysis of Depression and Its Association with Problematic Social Media Use among Chinese College Students

Latent Profile Analysis of Depression and Its Association with Problematic Social Media Use among Chinese 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 Latent Profile Analysis of Depression and Its Association with Problematic Social Media Use among Chinese College Students Junliang Zhang, Mengqi Fang, Shuang Zheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8501676/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 19 You are reading this latest preprint version Abstract This study gave the Depression Scale and the Problematic Social Media Use Scale to 3,540 college students in order to examine the categorical distribution of depression levels among them as well as the predictive roles of problematic social media use (PSMU) and demographic characteristics. According to the findings, college students' depression levels fall into three latent categories: low, moderate, and high.Depression was significantly predicted by PSMU, and higher PSMU levels were linked to higher depression levels. Additionally, demographic factors including gender, place of origin, and grade also exerted significant influences on depression levels. depression problematic social media use latent profile analysis college students Figures Figure 1 1. Introduction Depression is a complex, multi-dimensional clinical state of mental health marked by extensive deficits in cognitive and physiological functioning, lost motivation and energy, and a chronic poor mood 1 , 2 . It transcends transient emotional fluctuations and can profoundly affect an individual's social functioning, academic performance, and overall quality of life 3 , 4 . Depression is one of the main causes of disability and disease burden worldwide, and its prevalence among teenagers and young adults has significantly increased, making it a critical public health issue 5 .College students, who are navigating a critical transitional period of life,encounter a variety of difficulties including the development of one's identity,the demands of school, social adaptations, job preparation, and independent living 6 , 7 . While this stage offers strong psychological plasticity, it also entails heightened vulnerability, making this group particularly susceptible to emotional disorders like depression. The Report on National Mental Health Development in China (2021–2022) states that a significant percentage of Chinese college students are at varied degrees of risk for depression, and the detection rate of depression is consistently high 8 . Factors such as intensified academic competition, financial stress, peer comparison, and the uncertainties and altered social support networks in the post-pandemic era further exacerbate the psychological burden on this population 9 , 10 . If left unrecognized and unaddressed, depression among college students may lead to academic disengagement, social withdrawal, and even raise the possibility of extreme actions like self-harm and suicide, posing potential threats to personal development, family well-being, and social stability 11 , 12 . Therefore, in-depth investigation into the heterogeneous manifestations of depression in college students and its underlying mechanisms is essential for advancing targeted mental health services and improving the psychological support systems within higher education. Due to the quick development of digital technology in recent years, social media has become an integral part of college students' everyday life, acting as their main means of obtaining information, interacting with others, and expressing themselves 13 . However, the use of social media also exhibits a “double-edged sword” effect 14 . PSMU typically defined as the loss of control over social media engagement leading to significant physiological, psychological, social, or occupational impairment, is particularly prevalent among young students 15 . A large amount of empirical evidence has revealed a significant and complex relationship between PSMU and depression 16 , 17 .On the one hand, theories such as the Uses and Gratifications Theory and Social Comparison Theory offer partial explanations for this association. People may use social media excessively to escape real-life stress or to seek social support, yet frequent online social comparisons—such as exposure to curated and romanticized depictions of other people's life—can easily trigger feelings of inferiority, envy, and disappointment18,19. On the other hand, passive, browse-style usage, as well as nighttime use leading to sleep deprivation, have been shown to positively predict depressive symptoms.Furthermore, literature indicates a clear bidirectional relationship between the frequency of social media use and depression, which in specific circumstances may even be linked to suicidal thoughts 20 , 21 . In fact, A person's PSMU traits are positively correlated with how often they use social media, and this association is especially strong when it comes to depression rates 22 . Despite the wealth of existing research, several areas remain underexplored and warrant further investigation. First, the majority of studies have treated depression among college students as a homogeneous continuous variable, examining its average levels and related factors using a variable-centered approach. However, depression likely exhibits a heterogeneous subgroup structure within the student population, meaning that there may exist distinct subgroups characterized by different symptom patterns, severity levels, and risk profiles 23 . This heterogeneity may be obscured by aggregate mean-based analyses.A person-centered statistical technique called Latent Profile Analysis (LPA) can divide people into discrete latent categories according to how they respond to various depression markers 24 . This allows for a more nuanced depiction of the population structure of depression and enables the examination of differences in etiology and outcomes across subgroups.Second, the direct linear connection or predicted effects of PSMU and depression have been the main focus of study on this relationship. Few studies have integrated an LPA perspective to examine the specific influence of PSMU in differentiating between latent classes of depression. That is, does PSMU—and if so, how—affect an individual’s transition from a “low-risk” to a “high-risk” depressive profile? Addressing this question is crucial for shifting from universal intervention strategies toward targeted and precise prevention approaches. In summary, this study aims to integrate both person-centered and variable-centered research perspectives by employing Latent Profile Analysis to explore the latent class structure of depressive symptoms among Chinese college students. Building on this classification, the study further examines the predictive roles of PSMU as well as demographic factors such as gender and family background across different depressive subgroups. By revealing the heterogeneous manifestations of depression in college students and their association with specific behavioral risks in the digital era—namely PSMU—this research seeks to provide university mental health practitioners with a more differentiated assessment framework and more targeted intervention strategies. Ultimately, it aims to contribute empirical evidence toward constructing effective pathways for promoting the mental health of college students in the digital era. 2. Participants and Methods 2.1 Participants Undergraduate students from a Chinese institution were the focus of this investigation. An effective response rate of 97.18% was obtained by recruiting 3,540 students and keeping 3,440 valid questionnaires.Before answering the questionnaire, each participant gave written informed consent and volunteered to participate in the study. With ages ranging from 17 to 24, the sample included 1,358 female students (39.48%) and 2,082 male students (60.52%). The sample's average age was 19.8 years (SD = 1.5). 2.2 Measures 2.2.1 Depression Self-Rating Scale The College Student Depression Scale, created by Gong et al., was used to measure depressive symptoms 25 . Each of the seven items on the measure is scored on a 4-point Likert scale (1 = never, 4 = often). More severe depressed symptoms are indicated by higher overall scores. 2.2.2 PSMU Scale The Chinese version of the Problematic Social Media Use Scale, which was first created and approved by Franchina et al., was used to gauge problematic social media use 26 . Higher total scores reflect more pronounced maladaptive social-media-use tendencies. The scale has been shown to possess satisfactory psychometric properties in Chinese college student populations. Additionally, the study's internal consistency was acceptable (Cronbach's α = 0.77),fulfilling standard psychometric criteria. 2.3 Data Analysis Mplus 8.3 and SPSS 26.0 were used for statistical analysis of the data. First, The participants were categorized using LPA according to how they answered the seven questions on the college student depression scale. The analysis started with an initial model, and the quantity of latent classes was progressively expanded until an optimal model was identified. Model fit was evaluated using the following indicators: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), adjusted Bayesian Information Criterion (aBIC), entropy, Lo-Mendell-Rubin &Likelihood Ratio Test (LMR&LRT), and the Bootstrap Likelihood Ratio Test (BLRT). Generally, lower values of AIC, BIC, and aBIC, higher entropy, and significant LMR& BLRT results indicate better model fit. In determining the best-fitting model, apart from the statistical indices, the interpretability of the classes, the distribution characteristics of the latent profile plots, and their practical relevance were also considered to comprehensively determine the appropriate number of latent classes.After identifying the optimal latent class model, demographic variables and PSMU scores were incorporated into the analysis. The predictive effects of PSMU and these demographic variables on the membership in various latent depression classes were next investigated using multinomial logistic regression. 3. Results 3.1 Descriptive Statistics and Correlation Analysis All of the depression scale's item scores showed strong positive associations, according to the correlation analysis( To see Table 1 ). Table 1. Descriptive and Correlation Analyses of the University Depression Inventory. Item M SD Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q1 1.80 0.68 1 Q2 2.01 0.82 0.041** 1 Q3 1.79 0.82 0.43** 0.52** 1 Q4 1.77 0.72 0.47** 0.45** 0.62** 1 Q5 1.75 0.75 0.43** 0.47** 0.65** 0.69** 1 Q6 1.58 0.76 0.41** 0.41** 0.63** 0.64** 0.64** 1 Q7 1.41 0.70 0.10** 0.43** 0.56** 0.56** 0.56** 0.69** 1 注：** p <0.01. 3.2 LPA of Depression among College Students LPA based on individual scores was conducted on depression data from 3,440 college students. Using the scores on the seven scale items as manifest indicators, models ranging from one to five latent classes were fitted sequentially, starting from a baseline model with one class. Table 2 displays the findings. With an entropy value of 0.999, the Model 3 demonstrated the greatest reductions in AIC, BIC, and aBIC values among the models under comparison. Both the LMR and BLRT tests indicated p <0.001, and the proportions of the classes demonstrated good interpretability. Considering the statistical indices and theoretical interpretability collectively, the best model was determined to be the Model 3 for classifying depression among college students. This indicates the presence of three distinct latent classes of depression within the sample. Table 2 Comparison of fit parameter indices of different latent profile models(n=3440) Model K AIC BIC aBIC Entropy LMR BLRT Proportions 1 14 54365.23 54391.23 54346.75 2 22 47240.21 47375.36 47305.45 0.968 <0.001 <0.001 0.89/0.11 3 30 41077.15 41261.44 41166.12 0.999 <0.001 <0.001 0.36/0.55/0.09 4 38 38391.56 38625.00 38504.26 0.927 <0.016 <0.001 0.44/0.24/0.26/0.06 5 46 37543.10 37825.69 37679.53 0.937 <0.001 <0.001 0.42/0.26/0.23/0.06/0.03 3.3 Characteristics of Latent Depression Subgroups among College Students Three latent subgroups of college students with depression may be identified based on the LPA results: Class 1, Class 2, and Class 3, which comprise 36% (1,248 participants), 55% (1,876 participants), and 9% (316 participants) of the sample, respectively. To analyze the depressive characteristics of each latent subgroup, a line graph depicting the mean scores of all items was plotted (Figure 1). Based on the fluctuation patterns of the mean item scores across the three groups, the subgroups were labeled as "Low Depression" (Class 1), "Moderate Depression" (Class 2), and "High Depression" (Class 3). In terms of depression scores, the means and standard deviations for the three subgroups were as follows: "Low Depression" (1.37 ± 0.32), "Moderate Depression" (1.99 ± 0.28), and "High Depression" (2.87 ± 0.59). 3.4 Univariate Analysis of Latent Depression Subgroups among College Students The univariate analysis's findings showed that the three subgroups exhibited significant differences in gender, grade level, family location, and major, as presented in Table 3. Consequently, these four demographic variables—gender, grade, family location, and major—were taken into account as covariates in the subsequent multinomial logistic regression analysis. Table 3. Univariate Analysis of Latent Classes in College Students Model AIC Low Depression Moderate Depression High Depression X 2 P Gender Male 1115 747 220 12.11 <0.01 Female 761 501 96 Grade Freshman 1209 650 118 116.02 <0.001 Sophomore 164 151 54 Junior 431 411 132 Senior 72 36 12 Location Rural 1266 908 226 10.38 <0.01 Urban 610 340 90 Major HASS 721 485 93 10.34 <0.01 STEM 1155 763 223 Note,Humanities and Social Sciences(HASS),Science,Technology,Engineering and Mathematics(STEM). 3.5 Multivariate Analysis of Latent Depression Subgroups among College Students Using the latent categories of depression in college students as the dependent variable (with the "Low Depression" subtype as the reference group), a multinomial logistic regression model was constructed for multivariate analysis. PSMU was included as a covariate, and independent variables were those that demonstrated statistical significance in the univariate analysis. The findings (presented in Table 4) revealed that, compared to the "Low Depression" subtype, higher levels of PSMU significantly increased the likelihood of belonging to the "Moderate Depression" subtype (OR = 2.10, p < 0.001) and the "High Depression" subtype (OR= 3.58, p < 0.001). Compared to female students, male students had a higher probability of belonging to the "High Depression" subtype (OR=1.62, p =0.004). Relative to fourth-year students, third-year students showed a greater likelihood of belonging to both the "Low Depression" subtype (OR=2.17, p <0.001) and the "High Depression" subtype (OR=2.43, p = 0.011). Additionally, compared to their urban counterparts, college students from rural areas were more probable to fall into the "Moderate Depression" subtype(OR = 1.32, p < 0.001). Table 4. Multivariate Analysis of the Latent Classes in College Students variable Moderate Depression High Depression OR P 95%CI OR P 95%CI Gender Male 1.09 0.33 [0.91, 1.32] 1.62 0.004 [1.16,2.26] Grade Freshman 1.02. 0.93 [0.66, 1.58] 0.84 0.62 [0.42,1.69] Sophomore 1.37 0.21 [0.84, 2.25] 2.17 0.045 [1.02,4.62] Junior 2.17 <0.001 [1.40, 3.37] 2.43 0.011 [1.23,4.79] Location Rural 1.32 <0.001 [1.12, 1.55] 1.20 0187 [0.91,1.59] Major HASS 1.23 0.05 [0.99, 1.50] 0.93 0.705 [0.64,1.35] PSMU 2.10 <0.001 [1.87, 2.35] 3.58 <0.001 [3.00,4.27] Note. Gender was coded with female as the reference group; grade level was coded with senior year (fourth year) as the reference; family residence was coded with urban as the reference; and academic major was coded with STEM (Science, Technology, Engineering, and Mathematics) as the reference. 4. Discussion 4.1 Latent Subgroups of Depression among College Students Through latent profile analysis (LPA), this study identified three latent subtypes of depressive symptoms among college students: low-depression (36%,n = 1,248), moderate-depression (55%,n = 1,876), and high-depression (9%,n = 316). This classification aligns with previous domestic and international research. For instance, relevant literature categorizes depression into non-depressed (40%), mildly depressed (42%), moderately depressed (14%), and severely depressed (4%) groups 27 . Notably, the moderate-depression subtype accounted for the largest proportion (55%) in this study, indicating that moderate depressive symptoms are relatively common among college students and highlighting the need for focused intervention efforts for this group. Significant variations in symptom presentations were noted among subgroups, as seen in Fig. 1 . The high-depression subtype exhibited prominent scores on core symptoms such as low mood, psychomotor retardation, and reduced volitional activity, along with marked somatic symptoms. In contrast, the moderate-depression subtype was primarily characterized by emotional fluctuations and mild cognitive impairment. These findings are consistent with classification logic distinguishing "low-depression stable" and "low-depression fluctuating" subgroups, suggesting that depressive categories reflect not only symptom severity but also qualitative differences in symptom combination patterns. Based on the above findings, a tiered intervention framework can be constructed. For the most prevalent moderate-depression subtype, universal measures such as group counseling and online assessments are recommended to enhance emotional regulation skills 28 . For the least prevalent but clinically severe high-depression subtype, a closed-loop "screening–assessment–intervention" system should be established, incorporating pharmacological and cognitive-behavioral therapies for systematic intervention 29 .The low-depression subtype would benefit from preventive approaches, such as mental health education, to mitigate symptom progression 30 . The study's findings improve knowledge of the variety of depression experienced by college students and provide a basis for implementing precise mental health services. Future research may investigate the longitudinal developmental trajectories of different subtypes and develop more targeted intervention strategies. 4.2 Prediction of Latent Depression Subgroups by PSMU This study found that the likelihood of being classified as "moderate depression" and "high depression" increases by 2.1 times and 3.58 times, respectively, for each incremental increase in the severity of PSMU compared to the "low depression" group. This finding not only confirms the significance of PSMU as a risk factor for depression but also suggests that varying degrees of PSMU may correspond to different levels of depression risk, providing a quantitative basis for targeted interventions. The results of this study are consistent with the majority of the literature indicating that PSMU is an important predictor of depression, while further refining the stratification of risk levels 31 , 32 .The risk stratification effect observed in this study may stem from the gradient impact of varying degrees of PSMU on the depletion of psychological resources. Mild PSMU may only trigger temporary emotional fluctuations, whereas moderate to severe PSMU can gradually erode an individual’s emotional regulation capacity by forming a vicious cycle of "negative emotions—social media dependence," ultimately leading to exacerbated depressive symptoms. This aligns with the findings of Peng & Liao, who reported that the most severe PSMU symptoms, including stress, anxiety, and depression, are displayed by PSMU users 33 . In a research of 986 high school students in Italy, Lin & Longobardi similarly demonstrated a strong correlation between high social media involvement and depressed symptoms and validated the variability of social media use patterns 34 . Furthermore, Excessive social media use during the COVID-19 pandemic has been linked to negative mental health consequences, namely raising the likelihood of anxiety and depression, according to studies 35 .Therefore, the study's risk categorization findings offer a solid scientific foundation for creating stepped-care intervention plans. For instance, individuals with mild PSMU could benefit from enhanced mental health literacy and improved time-management skills to prevent the onset of depression 36 . For those with moderate PSMU, interventions should focus on addressing emotional dysregulation and breaking the cycle of dependence. For individuals with severe PSMU and a high risk of depression, comprehensive measures—such as restricting social media use, implementing cognitive behavioral therapy, and simultaneously improving mental health literacy—should be adopted to block the interaction of risk factors. 4.3 Demographic Factors Predicting Latent Depression Subgroups In terms of gender, according to this study, male college students were much more likely than female students to fall into the "high depression" grouping. This finding contrasts with the traditional notion that “females are at higher risk for depression,” yet it is supported by specific research 37 , 38 , 39 .On one hand, society continues to place significant expectations on men to be “strong” and “successful,” particularly in areas such as academic competition, career prospects, and economic self-sufficiency. As a result, men may experience more internalized pressure and are less likely to openly express emotional distress or proactively seek psychological support 40 .On the other hand, the widespread adoption of digital social media, shifts in interpersonal relationship patterns, and the gradual evolution of traditional gender roles may create new adaptive challenges for some young men in terms of emotional regulation, identity formation, and social connectedness, thereby increasing their vulnerability to depression 41 . In terms of grade-level differences, junior students showed a higher probability of falling into both the “low depression” and “high depression” categories compared to senior students, indicating a distinct polarization pattern. This conclusion is consistent with the findings of an LPA carried out at a Shanghai City university,which also reported significantly higher proportions of junior students in the severe psychological problems group and the academic stress group 42 .The junior year represents a critical transitional phase in undergraduate education, during which students face multiple pressures, including academic specialization, planning for further education or employment, and stabilizing interpersonal relationships. Students with strong psychological resilience may maintain low levels of depression through self-regulation, whereas those with weaker adaptive capacities are more susceptible to severe psychological distress 43 . Compared to seniors, whose post-graduation trajectories are often more settled, juniors face greater uncertainty about the future. This psychological conflict may exacerbate differentiation in depression categories, underscoring the need for targeted interventions such as career planning guidance and stress management training. The analysis of family location revealed that students from rural areas were more likely to belong to the “moderate depression” subgroup, a finding consistent with conclusions from relevant scholars—namely, that students from rural backgrounds face significantly higher risks of depression compared to their urban counterparts, primarily driven by economic pressures and cultural adaptation challenges.After entering university, rural-origin students often encounter difficulties in adapting to differences in lifestyle, consumption patterns, and social norms 44 . Coupled with potential financial burdens, these challenges may exacerbate feelings of inferiority and social isolation, which may further develop into moderate depression. Therefore, establishing support platforms for cultural adaptation and expanding economic assistance channels for rural-origin students represent urgent priorities for educational administration and mental health intervention. 4.4 Implications and Limitations By using LPA to get beyond the drawbacks of conventional research methods, this study deepens our understanding of the internal structure of depression and reveals a variety of subtypes of depressed symptoms among Chinese college students. The study clarifies the differential risk processes of "who is more affected" by examining the relationship between PSMU and various depression subtypes. This enhances theoretical understanding of the connection between social media and mental health. The findings provide direct evidence for universities to implement precise psychological screening and classified interventions (such as designing cognitive behavioral therapy or behavioral activation programs tailored to different subtypes), and offer scientific reference for promoting digital literacy education and guiding healthy online usage, demonstrating significant practical value. The following are the limitations of the current investigation. First, it is impossible to draw conclusions about a causal relationship between problematic social media use and depressive subtypes because to the cross-sectional methodology.Second, potential regional and institutional biases in the sample warrant caution when generalizing the conclusions to the broader college student population. Third, social desirability may have an impact on the use of self-reported data. Moreover, the study did not delve into the underlying mediating mechanisms (e.g., social comparison, sleep quality) or moderating factors (e.g., family functioning), nor did it differentiate the varying impacts of different patterns or platforms of social media use. Future research could be improved through longitudinal designs, multi-method measurements, and mechanistic analyses. 5. Conclusion Based on latent profile analysis, this study identified the population structure of depression levels among college students, classifying them into three subtypes: low, moderate, and high depression. The findings indicate that depression levels are jointly influenced by PSMU and demographic factors. Specifically, PSMU emerged as a statistically significant negative predictor of depression levels among college students. Furthermore,gender, region of origin, and grade level were among the demographic factors that significantly contributed to the differentiation of various depression subtypes. Declarations Data Availability Statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Human Ethics and Consent to Participate declarations The studies involving human participants were reviewed and approved by Institutional Review Board at Science and Technology College of Nanchang Hangkong University(NHKY202503). The research was conducted in accordance with the Declaration of Helsinki .The participants provided their online informed consent to participate in this study. Author Contributions J.Z. conceived and designed the the research. J.Z. and M.F. carried out the protocol and the questionnaire survey. J.Z. analyzed the data. J.Z. and S.Z. wrote the manuscript. J.Z. and M.F. revised the manuscript. All authors have read and agreed to the published version of the manuscript. Funding This research was research results of Jiangxi Provincial College Ideological and Political Education Research Association in 2024:A study on the influence of social media on depression of college students and its intervention strategies(XLJK24208) and Jiangxi Provincial Education Science Planning Project (2025ZX032),. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Zandbagleh, A., Sanei, S., & Azami, H.Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives. Sensors (Basel,Switzerland) .2024, 24 (18),6103. Li, G., Cai, X., & Wang, Y.Theta Rhythm-Based Attention Switch Training Effectively Modified Negative Attentional Bias. CNS neuroscience & therapeutics .2024, 30 (12), e70157. 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M., Engels, R. C. M. E., & Creemers, D. H. M. Secondary Outcomes of Implemented Depression Prevention in Adolescents:A Randomized Controlled Trial. Frontiers in psychiatry .2021,12, 643632. Kircaburun, K., & Griffiths, M. D. The dark side of internet: Preliminary evidence for the associations of dark personality traits with specific online activities and problematic internet use. Journal of behavioral addictions .2018,7(4),993–1003. Shafi, R. M. A., Nakonezny, P. A., Miller, K. A., Desai, J., Almorsy, A. G., Ligezka, A. N., Morath, B. A., Romanowicz, M., & Croarkin, P. E.An exploratory study of clinical and physiological correlates of problematic social media use in adolescents. Psychiatry research .2021,302, 114020. Peng P, Liao Y. Six addiction components of problematic social media use in relation to depression, anxiety, and stress symptoms: a latent profile analysis and network analysis. BMC Psychiatry . 2023 ,23(1):321. Lin S, Longobardi C. Adolescent Social Media Use and Depression: A Person-Centered Approach. Child Psychiatry Hum Dev . 2025 ,8. Lopes, L. S., Valentini, J. P., Monteiro, T. H., Costacurta, M. C. F., Soares, L. O. N., Telfar-Barnard, L., & Nunes, P. V. Problematic Social Media Use and Its Relationship with Depression or Anxiety: A Systematic Review. Cyberpsychology, behavior and social networking . 2022,25(11), 691–702. Lopez, V., Sanchez, K., Killian, M. O., & Eghaneyan, B. H.Depression screening and education: an examination of mental health literacy and stigma in a sample of Hispanic women. BMC public health .2018,18(1), 646. Wang, J. M., Yang, K. D., Wu, S. Y., Zou, X. G., Liao, Y. S., Yang, B., Xie, B. N., Huang, Y., Li, S. J., & Ma, H. J.Platelet Parameters, C-Reactive Protein, and Depression: An Association Study. International journal of general medicine.2022 ,15, 243–251. Quinn, M. E., Grant, K. E., & Adam, E. K. Negative cognitive style and cortisol recovery accentuate the relationship between life stress and depressive symptoms. Stress (Amsterdam, Netherlands) .2018,21(2), 119–127. Thiemann, P., Brimicombe, J., Benson, J., & Quince, T. When investigating depression and anxiety in undergraduate medical students timing of assessment is an important factor - a multicentre cross-sectional study. BMC medical education .2020,20(1)，125. Lindsey E. W. Emotion Regulation with Parents and Friends and Adolescent Internalizing and Externalizing Behavior. Children (Basel, Switzerland). 2021,8(4), 299. Oliffe, J. L., Hanberg, D., Hannan-Leith, M. N., Bergen, C., & Martin, R. E. "Do You Want to Go Forward or Do You Want to Go Under?" Men's Mental Health in and Out of Prison. American journal of men's health .2018,12(5), 1235–1246. Jing,Y., Yuanhua,S.College Students' Depression and Its Relationship with Social Inhibition. Innovation and Entrepreneurship Education .2015, 16(05): 88-94. Yotsidi, V., Nikolatou, E. K., Kourkoutas, E., & Kougioumtzis, G. A. Mental distress and well-being of university students amid COVID-19 pandemic: findings from an online integrative intervention for psychology trainees. Frontiers in psychology .2023,14, 1171225. Li, Z., Qin, S., Zhu, Y., Zhou, Q., Yi, A., Mo, C., Gao, J., Chen, J., Wang, T., Feng, Z., & Mo, X.Social support mediates the relationship between depression and subjective well-being in elderly patients with chronic diseases: Evidence from a survey in Rural Western China. PloS one .2025,20(6), e0325029. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8501676","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580412833,"identity":"3a8e6614-91d5-445f-bdc9-73402df29132","order_by":0,"name":"Junliang Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYFCCww0MjA0JjA3sjY0PPhCn5SBUC8/hZsMZxGlhhGqRSG+T5iBGg3zjwbYHP3ekyW64+bBBmoHBTk63gaAdB9sNe8/kGM+cndhgXMCQbGx2gIAWZoaDbdKMbRWJ/dKJDckzGA4kbiOkhQ2mpU3yYMNhHmK08EC05CT2SzA2NhOlRQKoRbK3Lc14Zk9iM+MMAyL8Ij/j8DGJn23JshuOH3/+40OFnRxBLQwSKCoMCCkHAf4GYlSNglEwCkbBiAYAI25Jz4HKSoEAAAAASUVORK5CYII=","orcid":"","institution":"Science and Technology College of Nanchang Hangkong University","correspondingAuthor":true,"prefix":"","firstName":"Junliang","middleName":"","lastName":"Zhang","suffix":""},{"id":580412834,"identity":"302e6db4-5e7a-4c79-874f-502345f6dc25","order_by":1,"name":"Mengqi Fang","email":"","orcid":"","institution":"Science and Technology College of Nanchang Hangkong University","correspondingAuthor":false,"prefix":"","firstName":"Mengqi","middleName":"","lastName":"Fang","suffix":""},{"id":580412835,"identity":"8f09c899-146c-48ac-a81b-73d9dc3b36bc","order_by":2,"name":"Shuang Zheng","email":"","orcid":"","institution":"Science and Technology College of Nanchang Hangkong University","correspondingAuthor":false,"prefix":"","firstName":"Shuang","middleName":"","lastName":"Zheng","suffix":""}],"badges":[],"createdAt":"2026-01-02 14:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8501676/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8501676/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101325584,"identity":"a60e27d7-4ead-4c06-a05a-6391b14a8463","added_by":"auto","created_at":"2026-01-28 13:51:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":115813,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of 3 Latent Classes of Depression in College Students\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8501676/v1/ceea0bf31ea8c42c60096763.png"},{"id":101325585,"identity":"8d74cb17-1357-45c8-a369-a0193a2c70c0","added_by":"auto","created_at":"2026-01-28 13:51:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1140168,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8501676/v1/58dbcb3b-00a3-487d-990b-fa80ef2108e8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Latent Profile Analysis of Depression and Its Association with Problematic Social Media Use among Chinese College Students","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDepression is a complex, multi-dimensional clinical state of mental health marked by extensive deficits in cognitive and physiological functioning, lost motivation and energy, and a chronic poor mood\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. It transcends transient emotional fluctuations and can profoundly affect an individual's social functioning, academic performance, and overall quality of life\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Depression is one of the main causes of disability and disease burden worldwide, and its prevalence among teenagers and young adults has significantly increased, making it a critical public health issue\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.College students, who are navigating a critical transitional period of life,encounter a variety of difficulties including the development of one's identity,the demands of school, social adaptations, job preparation, and independent living\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. While this stage offers strong psychological plasticity, it also entails heightened vulnerability, making this group particularly susceptible to emotional disorders like depression. The Report on National Mental Health Development in China (2021\u0026ndash;2022) states that a significant percentage of Chinese college students are at varied degrees of risk for depression, and the detection rate of depression is consistently high\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Factors such as intensified academic competition, financial stress, peer comparison, and the uncertainties and altered social support networks in the post-pandemic era further exacerbate the psychological burden on this population\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. If left unrecognized and unaddressed, depression among college students may lead to academic disengagement, social withdrawal, and even raise the possibility of extreme actions like self-harm and suicide, posing potential threats to personal development, family well-being, and social stability\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Therefore, in-depth investigation into the heterogeneous manifestations of depression in college students and its underlying mechanisms is essential for advancing targeted mental health services and improving the psychological support systems within higher education.\u003c/p\u003e \u003cp\u003eDue to the quick development of digital technology in recent years, social media has become an integral part of college students' everyday life, acting as their main means of obtaining information, interacting with others, and expressing themselves\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, the use of social media also exhibits a \u0026ldquo;double-edged sword\u0026rdquo; effect\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. PSMU typically defined as the loss of control over social media engagement leading to significant physiological, psychological, social, or occupational impairment, is particularly prevalent among young students\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. A large amount of empirical evidence has revealed a significant and complex relationship between PSMU and depression\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.On the one hand, theories such as the Uses and Gratifications Theory and Social Comparison Theory offer partial explanations for this association. People may use social media excessively to escape real-life stress or to seek social support, yet frequent online social comparisons\u0026mdash;such as exposure to curated and romanticized depictions of other people's life\u0026mdash;can easily trigger feelings of inferiority, envy, and disappointment18,19. On the other hand, passive, browse-style usage, as well as nighttime use leading to sleep deprivation, have been shown to positively predict depressive symptoms.Furthermore, literature indicates a clear bidirectional relationship between the frequency of social media use and depression, which in specific circumstances may even be linked to suicidal thoughts \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In fact, A person's PSMU traits are positively correlated with how often they use social media, and this association is especially strong when it comes to depression rates\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite the wealth of existing research, several areas remain underexplored and warrant further investigation. First, the majority of studies have treated depression among college students as a homogeneous continuous variable, examining its average levels and related factors using a variable-centered approach. However, depression likely exhibits a heterogeneous subgroup structure within the student population, meaning that there may exist distinct subgroups characterized by different symptom patterns, severity levels, and risk profiles\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. This heterogeneity may be obscured by aggregate mean-based analyses.A person-centered statistical technique called Latent Profile Analysis (LPA) can divide people into discrete latent categories according to how they respond to various depression markers\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. This allows for a more nuanced depiction of the population structure of depression and enables the examination of differences in etiology and outcomes across subgroups.Second, the direct linear connection or predicted effects of PSMU and depression have been the main focus of study on this relationship. Few studies have integrated an LPA perspective to examine the specific influence of PSMU in differentiating between latent classes of depression. That is, does PSMU\u0026mdash;and if so, how\u0026mdash;affect an individual\u0026rsquo;s transition from a \u0026ldquo;low-risk\u0026rdquo; to a \u0026ldquo;high-risk\u0026rdquo; depressive profile? Addressing this question is crucial for shifting from universal intervention strategies toward targeted and precise prevention approaches.\u003c/p\u003e \u003cp\u003eIn summary, this study aims to integrate both person-centered and variable-centered research perspectives by employing Latent Profile Analysis to explore the latent class structure of depressive symptoms among Chinese college students. Building on this classification, the study further examines the predictive roles of PSMU as well as demographic factors such as gender and family background across different depressive subgroups. By revealing the heterogeneous manifestations of depression in college students and their association with specific behavioral risks in the digital era\u0026mdash;namely PSMU\u0026mdash;this research seeks to provide university mental health practitioners with a more differentiated assessment framework and more targeted intervention strategies. Ultimately, it aims to contribute empirical evidence toward constructing effective pathways for promoting the mental health of college students in the digital era.\u003c/p\u003e"},{"header":"2. Participants and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eUndergraduate students from a Chinese institution were the focus of this investigation. An effective response rate of 97.18% was obtained by recruiting 3,540 students and keeping 3,440 valid questionnaires.Before answering the questionnaire, each participant gave written informed consent and volunteered to participate in the study. With ages ranging from 17 to 24, the sample included 1,358 female students (39.48%) and 2,082 male students (60.52%). The sample's average age was 19.8 years (SD\u0026thinsp;=\u0026thinsp;1.5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measures\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Depression Self-Rating Scale\u003c/h2\u003e \u003cp\u003eThe College Student Depression Scale, created by Gong et al., was used to measure depressive symptoms \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Each of the seven items on the measure is scored on a 4-point Likert scale (1\u0026thinsp;=\u0026thinsp;never, 4\u0026thinsp;=\u0026thinsp;often). More severe depressed symptoms are indicated by higher overall scores.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 PSMU Scale\u003c/h2\u003e \u003cp\u003eThe Chinese version of the Problematic Social Media Use Scale, which was first created and approved by Franchina et al., was used to gauge problematic social media use \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Higher total scores reflect more pronounced maladaptive social-media-use tendencies. The scale has been shown to possess satisfactory psychometric properties in Chinese college student populations. Additionally, the study's internal consistency was acceptable (Cronbach's α\u0026thinsp;=\u0026thinsp;0.77),fulfilling standard psychometric criteria.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Analysis\u003c/h2\u003e \u003cp\u003eMplus 8.3 and SPSS 26.0 were used for statistical analysis of the data. First, The participants were categorized using LPA according to how they answered the seven questions on the college student depression scale. The analysis started with an initial model, and the quantity of latent classes was progressively expanded until an optimal model was identified. Model fit was evaluated using the following indicators: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), adjusted Bayesian Information Criterion (aBIC), entropy, Lo-Mendell-Rubin \u0026amp;Likelihood Ratio Test (LMR\u0026amp;LRT), and the Bootstrap Likelihood Ratio Test (BLRT). Generally, lower values of AIC, BIC, and aBIC, higher entropy, and significant LMR\u0026amp; BLRT results indicate better model fit. In determining the best-fitting model, apart from the statistical indices, the interpretability of the classes, the distribution characteristics of the latent profile plots, and their practical relevance were also considered to comprehensively determine the appropriate number of latent classes.After identifying the optimal latent class model, demographic variables and PSMU scores were incorporated into the analysis. The predictive effects of PSMU and these demographic variables on the membership in various latent depression classes were next investigated using multinomial logistic regression.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Descriptive Statistics and Correlation Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll of the depression scale\u0026apos;s item scores showed strong positive associations, according to the correlation analysis(\u003cstrong\u003eTo see Table 1\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Descriptive and Correlation Analyses of the University Depression Inventory.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eQ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eQ6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eQ7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.041**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.43**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.52**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.47**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.45**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.62**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eQ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.43**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.47**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.65**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.69**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eQ6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.41**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.41**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.63**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.64**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.64**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eQ7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.10**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.43**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.56**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.56**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.56**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.69**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e注：**\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 LPA\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eof Depression among College Students\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLPA based on individual scores was conducted on depression data from 3,440 college students. Using the scores on the seven scale items as manifest indicators, models ranging from one to five latent classes were fitted sequentially, starting from a baseline model with one class. Table 2 displays the findings.\u003c/p\u003e\n\u003cp\u003eWith an entropy value of 0.999, the Model 3 demonstrated the greatest reductions in AIC, BIC, and aBIC values among the models under comparison. Both the LMR and BLRT tests indicated \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001, and the proportions of the classes demonstrated good interpretability. Considering the statistical indices and theoretical interpretability collectively,\u0026nbsp;the best model was determined to be the\u0026nbsp;Model\u0026nbsp;3\u0026nbsp;for classifying depression among college students. This indicates the presence of three distinct latent classes of depression within the sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Comparison of fit parameter indices of different latent profile models(n=3440)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"647\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eaBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eEntropy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eBLRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003eProportions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e54365.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e54391.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e54346.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e47240.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e47375.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e47305.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e0.89/0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e41077.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e41261.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e41166.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e0.36/0.55/0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e38391.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e38625.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e38504.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e0.44/0.24/0.26/0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e37543.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e37825.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e37679.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e0.42/0.26/0.23/0.06/0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Characteristics of Latent Depression Subgroups among College Students\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThree latent subgroups of college students with depression may be identified based on the LPA results: Class 1, Class 2, and Class 3, which comprise 36% (1,248 participants), 55% (1,876 participants), and 9% (316 participants) of the sample, respectively. To analyze the depressive characteristics of each latent subgroup, a line graph depicting the mean scores of all items was plotted (Figure 1). Based on the fluctuation patterns of the mean item scores across the three groups, the subgroups were labeled as \u0026quot;Low Depression\u0026quot; (Class 1), \u0026quot;Moderate Depression\u0026quot; (Class 2), and \u0026quot;High Depression\u0026quot; (Class 3). In terms of depression scores, the means and standard deviations for the three subgroups were as follows: \u0026quot;Low Depression\u0026quot; (1.37 \u0026plusmn; 0.32), \u0026quot;Moderate Depression\u0026quot; (1.99 \u0026plusmn; 0.28), and \u0026quot;High Depression\u0026quot; (2.87 \u0026plusmn; 0.59).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Univariate Analysis of Latent Depression Subgroups among College Students\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe univariate analysis\u0026apos;s findings showed that the three subgroups exhibited significant differences in gender, grade level, family location, and major, as presented in Table 3. Consequently, these four demographic variables\u0026mdash;gender, grade, family location, and major\u0026mdash;were taken into account as covariates in the subsequent multinomial logistic regression analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Univariate Analysis of Latent Classes in College Students\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"659\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eLow Depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003eModerate Depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eHigh Depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e1115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 52px;\"\u003e\n \u003cp\u003e12.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eFreshman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e1209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e116.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eSophomore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eJunior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eSenior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eRural\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e1266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e10.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eMajor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eHASS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e10.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eSTEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e1155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote,Humanities and Social Sciences(HASS),Science,Technology,Engineering and Mathematics(STEM).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Multivariate Analysis of Latent Depression Subgroups among College Students\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the latent categories of depression in college students as the dependent variable (with the \u0026quot;Low Depression\u0026quot; subtype as the reference group), a multinomial logistic regression model was constructed for multivariate analysis. PSMU was included as a covariate, and independent variables were those that demonstrated statistical significance in the univariate analysis. The findings (presented in Table 4) revealed that, compared to the \u0026quot;Low Depression\u0026quot; subtype, higher levels of PSMU significantly increased the likelihood of belonging to the \u0026quot;Moderate Depression\u0026quot; subtype (OR = 2.10, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and the \u0026quot;High Depression\u0026quot; subtype (OR= 3.58,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt; 0.001). Compared to female students, male students had a higher probability of belonging to the \u0026quot;High Depression\u0026quot; subtype (OR=1.62,\u003cem\u003ep\u003c/em\u003e =0.004). Relative to fourth-year students, third-year students showed a greater likelihood of belonging to both the \u0026quot;Low Depression\u0026quot; subtype (OR=2.17, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) and the \u0026quot;High Depression\u0026quot; subtype (OR=2.43,\u003cem\u003ep\u003c/em\u003e = 0.011). Additionally, compared to their urban counterparts, college students from rural areas were more probable to fall into the \u0026quot;Moderate Depression\u0026quot; subtype(OR = 1.32, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Multivariate Analysis of the Latent Classes in College Students\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"694\" style=\"margin-right: calc(25%); width: 75%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 22.3008%;\"\u003e\n \u003cp\u003evariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 21.3259%;\"\u003e\n \u003cp\u003eModerate Depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 27.1818%;\"\u003e\n \u003cp\u003eHigh Depression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 22.3008%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.4838%;\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7992%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0429%;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1397%;\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6271%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8084%;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.749%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5518%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.4838%;\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7992%;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0429%;\"\u003e\n \u003cp\u003e[0.91, \u0026nbsp; \u0026nbsp; 1.32]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1397%;\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.6271%;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8084%;\"\u003e\n \u003cp\u003e[1.16,2.26]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.749%;\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5518%;\"\u003e\n \u003cp\u003eFreshman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.4838%;\"\u003e\n \u003cp\u003e1.02.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7992%;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0429%;\"\u003e\n \u003cp\u003e[0.66, \u0026nbsp; \u0026nbsp; 1.58]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1397%;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8971%;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0643%;\"\u003e\n \u003cp\u003e[0.42,1.69]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.749%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5518%;\"\u003e\n \u003cp\u003eSophomore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.4838%;\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7992%;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0429%;\"\u003e\n \u003cp\u003e[0.84, \u0026nbsp; \u0026nbsp; 2.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1397%;\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8971%;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0643%;\"\u003e\n \u003cp\u003e[1.02,4.62]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.749%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5518%;\"\u003e\n \u003cp\u003eJunior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.4838%;\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7992%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0429%;\"\u003e\n \u003cp\u003e[1.40, \u0026nbsp; \u0026nbsp; 3.37]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1397%;\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8971%;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0643%;\"\u003e\n \u003cp\u003e[1.23,4.79]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.749%;\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5518%;\"\u003e\n \u003cp\u003eRural\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.4838%;\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7992%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0429%;\"\u003e\n \u003cp\u003e[1.12, \u0026nbsp; \u0026nbsp; 1.55]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1397%;\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8971%;\"\u003e\n \u003cp\u003e0187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0643%;\"\u003e\n \u003cp\u003e[0.91,1.59]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.749%;\"\u003e\n \u003cp\u003eMajor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5518%;\"\u003e\n \u003cp\u003eHASS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.4838%;\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7992%;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0429%;\"\u003e\n \u003cp\u003e[0.99, \u0026nbsp; \u0026nbsp; 1.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1397%;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8971%;\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0643%;\"\u003e\n \u003cp\u003e[0.64,1.35]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.749%;\"\u003e\n \u003cp\u003ePSMU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5518%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.4838%;\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7992%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0429%;\"\u003e\n \u003cp\u003e[1.87, \u0026nbsp; \u0026nbsp; 2.35]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1397%;\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8971%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.0643%;\"\u003e\n \u003cp\u003e[3.00,4.27]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote.\u003c/strong\u003eGender was coded with female as the reference group; grade level was coded with senior year (fourth year) as the reference; family residence was coded with urban as the reference; and academic major was coded with STEM (Science, Technology, Engineering, and Mathematics) as the reference.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Latent Subgroups of Depression among College Students\u003c/h2\u003e \u003cp\u003eThrough latent profile analysis (LPA), this study identified three latent subtypes of depressive symptoms among college students: low-depression (36%,n\u0026thinsp;=\u0026thinsp;1,248), moderate-depression (55%,n\u0026thinsp;=\u0026thinsp;1,876), and high-depression (9%,n\u0026thinsp;=\u0026thinsp;316). This classification aligns with previous domestic and international research. For instance, relevant literature categorizes depression into non-depressed (40%), mildly depressed (42%), moderately depressed (14%), and severely depressed (4%) groups \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Notably, the moderate-depression subtype accounted for the largest proportion (55%) in this study, indicating that moderate depressive symptoms are relatively common among college students and highlighting the need for focused intervention efforts for this group. Significant variations in symptom presentations were noted among subgroups, as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The high-depression subtype exhibited prominent scores on core symptoms such as low mood, psychomotor retardation, and reduced volitional activity, along with marked somatic symptoms. In contrast, the moderate-depression subtype was primarily characterized by emotional fluctuations and mild cognitive impairment. These findings are consistent with classification logic distinguishing \"low-depression stable\" and \"low-depression fluctuating\" subgroups, suggesting that depressive categories reflect not only symptom severity but also qualitative differences in symptom combination patterns.\u003c/p\u003e \u003cp\u003eBased on the above findings, a tiered intervention framework can be constructed. For the most prevalent moderate-depression subtype, universal measures such as group counseling and online assessments are recommended to enhance emotional regulation skills\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. For the least prevalent but clinically severe high-depression subtype, a closed-loop \"screening\u0026ndash;assessment\u0026ndash;intervention\" system should be established, incorporating pharmacological and cognitive-behavioral therapies for systematic intervention\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.The low-depression subtype would benefit from preventive approaches, such as mental health education, to mitigate symptom progression\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The study's findings improve knowledge of the variety of depression experienced by college students and provide a basis for implementing precise mental health services. Future research may investigate the longitudinal developmental trajectories of different subtypes and develop more targeted intervention strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Prediction of Latent Depression Subgroups by PSMU\u003c/h2\u003e \u003cp\u003eThis study found that the likelihood of being classified as \"moderate depression\" and \"high depression\" increases by 2.1 times and 3.58 times, respectively, for each incremental increase in the severity of PSMU compared to the \"low depression\" group. This finding not only confirms the significance of PSMU as a risk factor for depression but also suggests that varying degrees of PSMU may correspond to different levels of depression risk, providing a quantitative basis for targeted interventions. The results of this study are consistent with the majority of the literature indicating that PSMU is an important predictor of depression, while further refining the stratification of risk levels \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.The risk stratification effect observed in this study may stem from the gradient impact of varying degrees of PSMU on the depletion of psychological resources. Mild PSMU may only trigger temporary emotional fluctuations, whereas moderate to severe PSMU can gradually erode an individual\u0026rsquo;s emotional regulation capacity by forming a vicious cycle of \"negative emotions\u0026mdash;social media dependence,\" ultimately leading to exacerbated depressive symptoms. This aligns with the findings of Peng \u0026amp; Liao, who reported that the most severe PSMU symptoms, including stress, anxiety, and depression, are displayed by PSMU users \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In a research of 986 high school students in Italy, Lin \u0026amp; Longobardi similarly demonstrated a strong correlation between high social media involvement and depressed symptoms and validated the variability of social media use patterns \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Furthermore, Excessive social media use during the COVID-19 pandemic has been linked to negative mental health consequences, namely raising the likelihood of anxiety and depression, according to studies \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.Therefore, the study's risk categorization findings offer a solid scientific foundation for creating stepped-care intervention plans. For instance, individuals with mild PSMU could benefit from enhanced mental health literacy and improved time-management skills to prevent the onset of depression\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. For those with moderate PSMU, interventions should focus on addressing emotional dysregulation and breaking the cycle of dependence. For individuals with severe PSMU and a high risk of depression, comprehensive measures\u0026mdash;such as restricting social media use, implementing cognitive behavioral therapy, and simultaneously improving mental health literacy\u0026mdash;should be adopted to block the interaction of risk factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Demographic Factors Predicting Latent Depression Subgroups\u003c/h2\u003e \u003cp\u003eIn terms of gender, according to this study, male college students were much more likely than female students to fall into the \"high depression\" grouping. This finding contrasts with the traditional notion that \u0026ldquo;females are at higher risk for depression,\u0026rdquo; yet it is supported by specific research\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.On one hand, society continues to place significant expectations on men to be \u0026ldquo;strong\u0026rdquo; and \u0026ldquo;successful,\u0026rdquo; particularly in areas such as academic competition, career prospects, and economic self-sufficiency. As a result, men may experience more internalized pressure and are less likely to openly express emotional distress or proactively seek psychological support \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.On the other hand, the widespread adoption of digital social media, shifts in interpersonal relationship patterns, and the gradual evolution of traditional gender roles may create new adaptive challenges for some young men in terms of emotional regulation, identity formation, and social connectedness, thereby increasing their vulnerability to depression\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn terms of grade-level differences, junior students showed a higher probability of falling into both the \u0026ldquo;low depression\u0026rdquo; and \u0026ldquo;high depression\u0026rdquo; categories compared to senior students, indicating a distinct polarization pattern. This conclusion is consistent with the findings of an LPA carried out at a Shanghai City university,which also reported significantly higher proportions of junior students in the severe psychological problems group and the academic stress group\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.The junior year represents a critical transitional phase in undergraduate education, during which students face multiple pressures, including academic specialization, planning for further education or employment, and stabilizing interpersonal relationships. Students with strong psychological resilience may maintain low levels of depression through self-regulation, whereas those with weaker adaptive capacities are more susceptible to severe psychological distress\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Compared to seniors, whose post-graduation trajectories are often more settled, juniors face greater uncertainty about the future. This psychological conflict may exacerbate differentiation in depression categories, underscoring the need for targeted interventions such as career planning guidance and stress management training.\u003c/p\u003e \u003cp\u003eThe analysis of family location revealed that students from rural areas were more likely to belong to the \u0026ldquo;moderate depression\u0026rdquo; subgroup, a finding consistent with conclusions from relevant scholars\u0026mdash;namely, that students from rural backgrounds face significantly higher risks of depression compared to their urban counterparts, primarily driven by economic pressures and cultural adaptation challenges.After entering university, rural-origin students often encounter difficulties in adapting to differences in lifestyle, consumption patterns, and social norms\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Coupled with potential financial burdens, these challenges may exacerbate feelings of inferiority and social isolation, which may further develop into moderate depression. Therefore, establishing support platforms for cultural adaptation and expanding economic assistance channels for rural-origin students represent urgent priorities for educational administration and mental health intervention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Implications and Limitations\u003c/h2\u003e \u003cp\u003eBy using LPA to get beyond the drawbacks of conventional research methods, this study deepens our understanding of the internal structure of depression and reveals a variety of subtypes of depressed symptoms among Chinese college students. The study clarifies the differential risk processes of \"who is more affected\" by examining the relationship between PSMU and various depression subtypes. This enhances theoretical understanding of the connection between social media and mental health. The findings provide direct evidence for universities to implement precise psychological screening and classified interventions (such as designing cognitive behavioral therapy or behavioral activation programs tailored to different subtypes), and offer scientific reference for promoting digital literacy education and guiding healthy online usage, demonstrating significant practical value.\u003c/p\u003e \u003cp\u003eThe following are the limitations of the current investigation. First, it is impossible to draw conclusions about a causal relationship between problematic social media use and depressive subtypes because to the cross-sectional methodology.Second, potential regional and institutional biases in the sample warrant caution when generalizing the conclusions to the broader college student population. Third, social desirability may have an impact on the use of self-reported data. Moreover, the study did not delve into the underlying mediating mechanisms (e.g., social comparison, sleep quality) or moderating factors (e.g., family functioning), nor did it differentiate the varying impacts of different patterns or platforms of social media use. Future research could be improved through longitudinal designs, multi-method measurements, and mechanistic analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eBased on latent profile analysis, this study identified the population structure of depression levels among college students, classifying them into three subtypes: low, moderate, and high depression. The findings indicate that depression levels are jointly influenced by PSMU and demographic factors. Specifically, PSMU emerged as a statistically significant negative predictor of depression levels among college students. Furthermore,gender, region of origin, and grade level were among the demographic factors that significantly contributed to the differentiation of various depression subtypes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by Institutional Review Board at Science and Technology College of Nanchang Hangkong University(NHKY202503). The research was conducted in accordance with the Declaration of Helsinki .The participants provided their online informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.Z. conceived and designed the the research. J.Z. and M.F. carried out the protocol and the questionnaire survey. J.Z. analyzed the data. J.Z. and S.Z. wrote the manuscript. J.Z. and M.F.\u0026nbsp;revised the manuscript. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was research results of Jiangxi Provincial College Ideological and Political Education Research Association in 2024:A study on the influence of social media on depression of college students and its intervention strategies(XLJK24208) and Jiangxi Provincial Education Science Planning Project (2025ZX032),.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eZandbagleh, A., Sanei, S., \u0026amp; Azami, H.Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives.\u003cem\u003eSensors (Basel,Switzerland)\u003c/em\u003e.2024,\u003cem\u003e24\u003c/em\u003e(18),6103.\u003c/li\u003e\n \u003cli\u003eLi, G., Cai, X., \u0026amp; Wang, Y.Theta Rhythm-Based Attention Switch Training Effectively Modified Negative Attentional Bias.\u003cem\u003eCNS neuroscience \u0026amp; therapeutics\u003c/em\u003e.2024,\u003cem\u003e30\u003c/em\u003e(12), e70157.\u003c/li\u003e\n \u003cli\u003eXi, C., Jiang, X., He, Y., Liu, Y., An, H., Shang, K., Ma, X., \u0026amp; Ren, D.Integrating Dialectical Behaviour Therapy Into the Treatment of Adolescent Depression: A Retrospective Study.\u003cem\u003eActas espanolas de psiquiatria\u003c/em\u003e.2025,\u003cem\u003e53\u003c/em\u003e(4), 701\u0026ndash;714.\u003c/li\u003e\n \u003cli\u003eMuntean, R. 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Mental distress and well-being of university students amid COVID-19 pandemic: findings from an online integrative intervention for psychology trainees.\u003cem\u003eFrontiers in psychology\u003c/em\u003e.2023,14, 1171225.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLi, Z., Qin, S., Zhu, Y., Zhou, Q., Yi, A., Mo, C., Gao, J., Chen, J., Wang, T., Feng, Z., \u0026amp; Mo, X.Social support mediates the relationship between depression and subjective well-being in elderly patients with chronic diseases: Evidence from a survey in Rural Western China.\u003cem\u003ePloS one\u003c/em\u003e.2025,20(6), e0325029.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"depression, problematic social media use, latent profile analysis, college students","lastPublishedDoi":"10.21203/rs.3.rs-8501676/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8501676/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study gave the Depression Scale and the Problematic Social Media Use Scale to 3,540 college students in order to examine the categorical distribution of depression levels among them as well as the predictive roles of problematic social media use (PSMU) and demographic characteristics. According to the findings, college students' depression levels fall into three latent categories: low, moderate, and high.Depression was significantly predicted by PSMU, and higher PSMU levels were linked to higher depression levels. 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