The Predictive Role of Anxiety Symptoms in the Psychological Health Adaptation among At-Risk University Freshmen: A Cross-Lagged Network Analysis

The Predictive Role of Anxiety Symptoms in the Psychological Health Adaptation among At-Risk University Freshmen: A Cross-Lagged Network Analysis | 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 The Predictive Role of Anxiety Symptoms in the Psychological Health Adaptation among At-Risk University Freshmen: A Cross-Lagged Network Analysis Jiatong Zhang, Ziang Wang, Teng Zhang, Ran Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7338769/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract University freshmen encounter numerous psychological health challenges during their adaptation to college life, with anxiety symptoms being a pervasive and critical risk factor. Despite recognition of anxiety's impact, the specific dynamic predictive pathways by which anxiety symptoms interact within the symptom network and influence freshmen's psychological adaptation are not yet fully elucidated. This study aimed to elucidate the dynamic predictive patterns and central role of anxiety symptoms within the psychological health adaptation of at-risk freshmen using cross-lagged network analysis. A cohort of 720 at-risk freshmen from a university in Shandong Province, China, was assessed at two time points: one week after enrollment (T1) and seven months later (T2), utilizing the Symptom Checklist-90 (SCL-90). Data were analyzed using R-Studio for network analyses and SPSS for descriptive statistics. Results from cross-sectional network analysis revealed that Anxiety Item 78 ("Feeling restless or fidgety") consistently exhibited the highest bridge centrality alongside other key symptoms, indicating its prominent position and significant bridging role within the symptom network. Crucially, cross-lagged network analysis further demonstrated that Anxiety Item 78 consistently displayed the strongest predictive influence on subsequent symptom activation across time (from T1 to T2), highlighting its unique dynamic predictive value within the freshman psychological adaptation symptom network. These findings provide novel and specific insights into the temporal dynamics of anxiety symptoms in freshmen's psychological adaptation, particularly emphasizing the critical predictive role of "feeling restless or fidgety’. These findings offer concrete guidance for the early identification of at-risk freshmen and hold substantial practical significance for developing more precise and effective mental health intervention strategies for this population. Anxiety Symptoms Cross-Lagged Network University Freshmen University Freshmen Psychological Adaptation Mental Health Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The transition to university life represents a critical period from adolescence to adulthood, offering valuable opportunities for academic and personal growth, while simultaneously presenting challenges such as academic pressure, interpersonal adjustments, and environmental adaptation (Haktanir et al., 2018). Within this context, freshmen face elevated risks to psychological health, with significantly higher rates of emotional distress like anxiety and depression compared to other groups. If not promptly identified and intervened upon, these issues can have long-term negative consequences for future academic performance, career development, and overall well-being (Geng et al., 2020). Therefore, gaining a deeper understanding of the mechanisms underlying the development of psychological health problems in freshmen and exploring effective early-warning and intervention strategies are crucial for safeguarding their healthy development and enhancing the quality of university education. Previous research has primarily explored psychological health problems from a latent variable perspective. Two main latent variable models prevail. One conceptualizes psychological disorders as disease entities causing symptom manifestations (McNally, 2016). The other views psychological disorders as abstract summaries of all symptom presentations (Adam, 2013). Both models posit the latent psychological disorder as the common cause of symptoms, essentially assuming independence between observed variables, thereby neglecting the potential interactions between symptoms (Borsboom & Cramer, 2013; Schmittmann et al., 2013). Within the dynamic network view, the psychological problem network is initiated by a common factor but maintained by the interactions among symptoms (Fried et al., 2017; Kendler et al., 2011). The psychopathological network perspective conceptualizes psychological health problems as complex systems, where micro-level interdependencies between symptoms manifest as macro-level regularities (Fried, 2022). This perspective views networks as dynamic; while strong symptom interconnections maintain network stability in typical environments, intense interactions driven by various factors can alter the network's internal structure and propel its overall development (Borsboom & Cramer, 2013). Thus, longitudinal network analysis is a primary method within this framework for investigating the internal mechanisms of symptom change over time. Cross-lagged network analysis, a key component of network methodologies, differs from traditional longitudinal network approaches often focused on short-term studies. It enhances the accuracy of long-term longitudinal research and employs methods controlling for the influence of other nodes in the network, making results more interpretable (Wysocki et al., 2022). Furthermore, prior research on psychological problems often focused on strength metrics, overlooking bridge symptoms that connect different clusters. Network theory posits that comorbidity arises from symptoms common to multiple disorders, acting as bridges within the overall network that can activate symptoms across different dimensions (Cramer et al., 2010). Therefore, only by comparing key bridge symptoms across different time points within a cross-lagged network analysis can we infer symptom presentations that remain stable across stages. In summary, network analysis allows researchers to identify core predictive symptoms (e.g., those with high centrality) and key symptomatic manifestations (bridge symptoms connecting symptom clusters) within dynamic symptom networks, thereby revealing the internal developmental mechanisms of psychological health problems. This approach facilitates a more comprehensive understanding of the dynamic processes underlying psychological health problems at the symptom level, rather than solely at the diagnostic level, enabling the formulation of more effective and targeted prevention and intervention strategies. Despite the methodological strengths of network analysis, existing research has not yet applied cross-lagged panel network methodology to examine at-risk college freshmen. This gap impedes exploration of the dynamic developmental mechanisms underlying their mental health challenges and precludes identification of core predictive symptoms alongside key symptomatic manifestations.The present study addresses this critical void through network analysis by identifying primary symptomatic manifestations and their core predictive symptoms while revealing latent dynamic predictive pathways between these elements. This approach offers a unique and vital perspective for understanding the evolving trajectory of mental health risks in incoming freshmen. As the first investigation of its kind, this research implements cross-lagged panel network analysis to assess at-risk freshmen across two strategic timepoints: initial enrollment (T1) and 7-month follow-up (T2). It aims to discover core predictive symptoms of emergent post-enrollment psychopathology while characterizing key symptomatic manifestations. These insights establish foundations for early detection protocols and precisely targeted interventions within this vulnerable population. 2. Methods 2.1 Participants This study employed a convenience sampling method, recruiting freshmen identified as at-risk (i.e., scoring ≥ 160 on the SCL-90, or having > 43 positive items, or scoring > 2 on any single factor) from a university in Shandong Province, China. Data collection occurred approximately one week after enrollment (T1) and again seven months later (T2). Participants provided informed consent, ensuring voluntary participation and confidentiality. After excluding incomplete and invalid responses, 8115 participants completed the initial survey (T1), with 720 identified as at-risk at both time points. The study received approval from the university's Research Ethics Committee. 2.2 Measures The Symptom Checklist-90 (SCL-90), developed by Derogatis (1973), serves as a well-established screening instrument for mental health concerns and has been widely implemented across Chinese mainland universities. Accordingly, the Symptom Checklist-90 (SCL-90) was used to screen for at-risk freshmen and assess their psychological health levels at T1 and T2. The scale comprises 9 dimensions: Somatization, Obsessive-Compulsive, Interpersonal Sensitivity, Depression, Anxiety, Hostility, Phobic Anxiety, Paranoid Ideation, Psychoticism, and Additional Items. In this study, Cronbach's α coefficients for each dimension were 0.823, 0.691, 0.740, 0.835, 0.798, 0.790, 0.708, 0.684, 0.731, and 0.620, respectively. All items were rated on a 5-point Likert scale (1 = Not at all, 2 = A little bit, 3 = Moderately, 4 = Quite a bit, 5 = Extremely). 2.3 Statistical Analysis Descriptive statistics were analyzed using SPSS (Version 22.0, IBM Corp., Armonk, NY, USA). All network analyses were performed using R software (Version 4.1.2; R Core Team, 2021), primarily relying on the bootnet package (Epskamp et al., 2018), qgraph package (Epskamp et al., 2012), and ggplot2 package (Wickham, 2016) for network estimation, centrality calculation, and visualization. 2.3.1 Network Analysis The Cross-Lagged Panel Network (CLPN) model was employed to investigate dynamic interactions among symptoms. This model utilizes Least Absolute Shrinkage and Selection Operator (LASSO) regression to shrink coefficients of non-significant predictors to zero, generating a sparse network structure. This approach reduces false-positive probabilities in the predictive relationships between T1 symptoms and T2 symptoms, thereby constructing a more precise directed network (Freijeiro-González et al., 2022). Specifically, for each symptom at T2, LASSO regression was performed with all T1 symptoms as predictors. Regularization strength was controlled by the λ parameter, selected via 10-fold cross-validation using the criterion of minimum cross-validation error plus one standard error (i.e., lambda.1se ). The EBICglasso function within the qgraph and bootnet packages was used for cross-sectional network matrix estimation and visualization (Zhang et al., 2008). Edge weight thresholds were set minimum value of 0.03 across all networks. In the symptom network graphs, nodes represent symptoms. Blue edges indicate positive associations, red edges indicate negative associations, and edge thickness represents the strength of the association. The distance of a node from the center reflects its connectedness within the network; nodes farther from the center have fewer connections. 2.3.2 Centrality Metrics This study computed key centrality metrics for both cross-sectional and cross-lagged networks to identify core symptoms within the networks. Common centrality metrics in cross-sectional network analysis include Strength, Closeness, Betweenness, and Bridge Strength (Bridge Strength).Strength is the sum of the absolute edge weights connected to a node, indicating its overall connectedness within the network. Closeness Measures how efficiently a node reaches all other nodes, calculated as the inverse of the sum of shortest path distances. Betweenness Quantifies the frequency with which a node lies on the shortest paths between other node pairs, directly reflecting its role in facilitating influence flow between node clusters (Opsahl et al., 2010). Bridge Strength Indicates a node's connections to other symptom dimensions (excluding its own), with higher values signifying stronger cross-dimensional links. This metric typically identifies transdiagnostic symptoms shared across comorbid conditions (Borsboom et al., 2011; Cramer et al., 2010). Centrality metrics for cross-lagged network analysis primarily include Out-Expected Influence (Out-EI) and In-Expected Influence (In-EI). Out-Expected Influence (Out-EI) is The sum of the predictive weights of all outgoing edges from a node. It indicates the extent to which a node predicts other nodes in the network over time. In-Expected Influence (In-EI) is The sum of the predictive weights of all incoming edges to a node. It indicates the extent to which a node is predicted by other nodes in the network over time. 2.3.3 Stability Estimation To evaluate the robustness of the network structure and centrality metrics, two types of stability analyses were performed using the bootnet package (Epskamp et al., 2018). First, the accuracy of edge weights was estimated using 95% confidence intervals (CIs) derived from non-parametric bootstrapping (1,000 bootstrapped samples). Less overlap between these CIs indicates higher accuracy. Second, the centrality stability coefficient (CS-coefficient) was calculated using the case-dropping subset bootstrap procedure. A CS-coefficient > 0.50 is considered indicative of good stability, while > 0.25 is considered acceptable. Finally, centrality difference tests were used to assess the statistical significance of differences in centrality metrics between symptoms within the cross-sectional and longitudinal networks (Epskamp et al., 2018). 3. Results 3.1 Descriptive Statistics The final sample of 720 at-risk freshmen consisted of 40% male and 60% female. Table 1 presents the mean scores and standard deviations for all SCL-90 dimensions at T1 and T2. As shown in Table 1 , most symptom dimensions exhibited elevated mean scores at both time points, consistent with the at-risk nature of the sample. Notably, several dimensions showed a slight increase in mean scores from T1 to T2. Table 1 Mean SCL-90 Dimension Scores Across Assessment Timepoints M SD T1 Somatization 1.96 0.56 T1 Obsessive-Compulsive 2.68 0.52 T1 Interpersonal Sensitivity 2.57 0.56 T1 Depression 2.27 0.57 T1 Anxiety 2.25 0.55 T1 Hostility 1.99 0.64 T1 Phobic Anxiety 2.10 0.62 T1 Paranoid Ideation 2.04 0.57 T1 Psychoticism 2.04 0.53 T1 Additional Items 2.07 0.55 T2 Somatization 2.30 0.67 T2 Obsessive-Compulsive T2 Interpersonal Sensitivity T2 Depression T2 Anxiety T2 Hostility T2 Phobic Anxiety T2 Paranoid Ideation T2 Psychoticism T2 Additional Items 2.47 2.84 2.54 2.60 2.27 2.35 2.34 2.32 2.37 0.63 0.51 0.61 0.60 0.65 0.66 0.71 0.67 0.64 3.2 Cross-Sectional Network Structure and Centrality Analysis The estimated network models are shown in Fig. 1 . The symptom networks at both time points were generally stable, although the Anxiety and Depression dimensions shifted towards the network center. Key nodes located centrally and exhibiting strong connections (thick edges) in both cross-sectional networks included: Anxiety Item 72 ("Spells of terror or panic"), Anxiety Item 78 ("Feeling restless or fidgety"), Anxiety Item 33 ("Feeling afraid"), Anxiety Item 23 ("Suddenly scared for no reason"), Psychoticism Item 90 ("Feeling something is wrong with your mind"), Psychoticism Item 87 ("Feeling that you are physically ill"), and Depression Item 32 ("Feeling no interest in things"). Anxiety Items 72, 78, 33, and 23 were stably central at both time points and showed mutual influences. Symptom centrality is depicted in Fig. 2 . To reduce the impact of symptom scale differences, standardized scores were used for centrality estimation. At both time points, Anxiety Item 78 ('Feeling restless or fidgety') consistently exhibited the highest Strength centrality at both T1 and T2, indicating its strong overall connections within the symptom networks. Besides, Other highly central symptoms across both time points included 72 ("Spells of terror or panic") and 71 (Feeling that everything is an effort.) based on their Closeness and Betweenness centrality scores.” 3.3 Bridge Centrality Estimation Bridge Strength estimates are shown in Fig. 3 . At T1, Interpersonal Sensitivity Item 69 ("Feeling others are unsympathetic or dislike you") had the highest Bridge Strength. At T2, Depression Item 79 ("Feeling worthless") had the highest Bridge Strength. This indicates that the key symptomatic manifestations bridging all dimensions were time-specific. However, Interpersonal Sensitivity Item 69 ("Feeling others are unsympathetic or dislike you"), Anxiety Item 78 ("Feeling restless"), Anxiety Item 23 ("Suddenly scared for no reason"), and Somatization Item 48 ("Trouble getting your breath") consistently exhibited the highest Bridge Strength across both time points, signifying their role as common symptomatic manifestations spanning different psychological problem dimensions. Furthermore, Item 23 ("Suddenly scared for no reason"), Item 69 ("Feeling others are unsympathetic or dislike you"), and Item 78 ("Feeling restless") also showed high Strength and Closeness centrality, confirming their significant influence on symptoms across other dimensions and their stability as key symptomatic manifestations. 3.4 Cross-Lagged Network Model and Predictive Centrality Analysis The cross-lagged network model spanning the first semester is depicted in Fig. 4 . Blue arrows indicate positive predictive relationships. Results showed that Anxiety Item 78 ("Feeling restless or fidgety") had the highest number of outgoing predictive paths. Predictive centrality for the cross-lagged network is shown in Fig. 5 . Anxiety Item 78 ("Feeling restless or fidgety") had the highest OutStrength centrality, indicating it exerted the strongest predictive influence on other symptoms over time, predicting the most symptoms, and serving as a stable predictor. Conversely, Anxiety Item 72 ("Spells of terror or panic") had the highest InStrength centrality, indicating it was the most influenced by other symptoms and thus more sensitive to changes within the symptom network. Combined with the results of the cross-sectional network analysis indicate that Anxiety Item 78 ("Feeling restless or fidgety") influences Anxiety Item 33 ("Feeling afraid") and Item 23 ("Suddenly scared for no reason") through the mediating effect of Anxiety Item 72 ("Spells of terror or panic"), elucidating the interaction mechanisms among these four symptoms observed in the cross-sectional network. Furthermore, Anxiety Item 78 ("Feeling restless or fidgety") directly influences Interpersonal Sensitivity Item 69 ("Feeling others are unsympathetic or dislike you") and Somatization Item 48 ("Trouble getting your breath")—both of which exhibited consistently high bridge strength—explaining the internal influence mechanisms underlying these core symptomatic manifestations. 3.5 Reliability and Generalizability of Network Structure and Centrality Estimates Centrality difference tests are shown in Fig. 5 (a). Black squares indicate statistically significant differences between nodes. The subset bootstrap procedure results (Fig. 5 (b)) showed stable centrality values with decreasing sample size. The centrality stability coefficients (CS) for Out-EI and In-EI were 0.594 and 0.284, respectively, indicating acceptable to good stability and supporting the generalizability of the results. 4. Discussion This study employed cross-lagged network analysis to explore the key adaptation symptoms among university freshmen during their first semester and their internal dynamic mechanisms over time. Findings indicated that anxiety and depression significantly influenced the cluster of adaptation symptoms, while interpersonal sensitivity was closely linked to other symptom clusters. These results underscore that anxiety and depression symptoms exhibited the most numerous and strongest connections to all other symptoms. Furthermore, symptoms of interpersonal sensitivity manifested across all other dimensions. This suggests universities should prioritize addressing anxiety and depressive feelings in freshmen and enhance psychological support focused on interpersonal relationships. Cross-sectional network analysis revealed that Anxiety Items 72 ("Spells of terror or panic"), 78 ("Feeling restless or fidgety"), 33 ("Feeling afraid"), and 23 ("Suddenly scared for no reason") occupied central positions within the network, indicating their pivotal role. Consistent with Beck's cognitive model of anxiety, physiological symptoms (e.g., panic) and cognitive biases (e.g., catastrophizing) mutually reinforce each other, forming core nodes in the symptom network (Beck & Clark, 1997). These four symptoms cover core dimensions of anxiety: acute fear (Item 72), persistent unease (Item 78), generalized apprehension (Item 33), and uncued fear (Item 23), encompassing physiological arousal, cognitive worry, and emotional experience. Integrating cross-lagged findings revealed their dynamic interplay: Trait-like daily restlessness (Item 78) more readily triggered state-like panic (Item 72), potentially exacerbated by sympathetic activation (e.g., palpitations, sweating) feeding back into restlessness. Persistent unease likely impaired emotion regulation, heightening sensitivity to ambiguous threats (Item 33). Chronic uncued fear (Item 23) likely reflected anticipatory anxiety, further consolidating its central position (Yi Xia et al, 2025). Notably, a bidirectional reinforcement mechanism existed between Items 23 and 72. Beck's cognitive model of emotional disorders posits catastrophizing as a core mechanism maintaining anxiety (Beck & Emery, 1985). On one hand, intense physiological reactions (e.g., racing heart) during panic spells (Item 72) can be misinterpreted catastrophically (e.g., "I have a heart problem") as "signs of losing control," triggering hypervigilance towards similar situations or daily activities, thereby increasing unexplained, generalized fear (Item 23). On the other hand, according to affective priming theory (LeDoux, 1996), elevated baseline anxiety accelerates amygdala responses to potential threats. Chronic generalized fear (Item 23) maintains hyperactivation of the sympathetic nervous system (e.g., elevated cortisol), keeping individuals in a prolonged stress state. This state significantly lowers the threshold for triggering panic spells (Item 72). Furthermore, Cross-sectional network of central nodes Psychoticism Item 90 ("Feeling something is wrong with your mind") and Items 89 ("Feeling guilty") and 87 ("Feeling that you are physically ill") were closely interrelated. Item 90 acted as a potential pathway connecting Items 89 and 87, reflecting its frequent co-occurrence with both and its potential to exacerbate their symptom severity. Interestingly, at T1, Item 90 showed direct paths to key anxiety nodes Item 23, Item 33 ("Feeling afraid"), and Item 80 ("Feeling that familiar things are strange or unreal"), and indirect paths to Items 72 and 78, but no direct link to Item 87. By T2, Item 87 had established direct connections with Item 72 and Item 23. This pattern suggests a crucial role for shifts in internal attributional style. Attributing failures internally threatens self-concept, lowers self-esteem, and diminishes perceived self-determination. This creates a vicious cycle: fear of failure and internal attribution following setbacks lower self-esteem and thwart psychological need satisfaction; conversely, low self-esteem predisposes individuals to anxiety, making them more reactive to self-threats (Ramón-Arbués et al., 2020). Rollo May (1950) theorized that societal changes threaten independence, fostering feelings of self-alienation and denial of authentic emotions, leading to anxiety. The central positioning of Items 90 and 87 highlights the profound impact of physiological and cognitive self-doubt – the dissonance between ideal and real self – causing cognitive dissonance and exerting a decisive influence within the entire symptom network. Notably, the anxiety symptom 72 ("Spells of terror or panic") was highly sensitive to changes in other symptoms and frequently acted as a mediator. Evolutionary psychology suggests fear is an instinctive survival response to potential threats, activating the "fight-or-flight" response. To satisfy needs for belonging, security, and competence within social groups, individuals develop fears of social rejection. This future-oriented fear of uncertainty can lead to self-centeredness as a coping mechanism, potentially fostering social withdrawal tendencies (Surtees et al., 2024). Existing research links fear of negative evaluation strongly to low self-esteem and perceived high social pressure (Wu et al., 2021; Zhang et al., 2022), which are significant predictors of depression and anxiety (Chen et al., 2018; Niu et al., 2021; Moksnes & Reidunsdatter, 2019). This study further confirms the significant role of fear in negative outcomes like anxiety and depression. The stable role of Interpersonal Sensitivity Item 69 ("Feeling others are unsympathetic or dislike you"), Anxiety Item 78 ("Feeling restless"), Anxiety Item 23 ("Suddenly scared for no reason"), and Somatization Item 48 ("Trouble getting your breath") as bridge symptoms across dimensions confirms their importance as core clinical indicators. Item 69 reflects the core social anxiety feature of evaluation fear (Wallace & Alden, 1995, 1997). According to the Selective Optimization with Compensation Theory (SOC), freshmen strive to adapt using various strategies. However, thwarted basic psychological needs can lead to anxiety, suppression, and somatization (Vansteenkiste & Ryan, 2013), potentially explaining the emergence and bridging role of these specific symptoms. 5. Conclusion This study provides crucial evidence elucidating the internal mechanisms of adaptation symptoms in university freshmen and the specific role of anxiety, offering potential directions for diagnosing and intervening in freshmen adaptation issues. Our findings demonstrate that anxiety symptoms, particularly "feeling restless or fidgety" (Item 78), play a significant predictive role within the symptom network of at-risk freshmen, while interpersonal sensitivity symptoms represent stable key manifestations – an aspect previously overlooked. Future research exploring the role of anxiety in freshmen adaptation should further incorporate the dimension of time. Although this study focused on symptoms present one week post-enrollment, universities can utilize these findings to test the clinical efficacy of precise symptom-targeted interventions and transdiagnostic strategies within psychological support programs for freshmen, aiming to improve current support schemes. 6. Limitations Building upon prior research, this study offers novel insights into the internal dynamic mechanisms of freshmen adaptation over time. However, several limitations warrant acknowledgment. First, the use of the SCL-90, which assesses symptoms over the preceding week, imposes temporal constraints on the findings. Future research should include more frequent assessments to validate these conclusions. Second, relying solely on the SCL-90, despite its breadth, may have missed nuances within specific symptom domains. Future studies should build on this work by incorporating domain-specific, psychometrically robust measures. Third, the absence of pre-enrollment baseline symptom assessments prevents ruling out the influence of prior experiences. Fourth, the reliance on self-report measures introduces potential response biases; future research should consider supplementing with structured clinical interviews for assessing adaptation symptoms. Declarations Declaration of competing interest The authors declared no conflict interest. Compliance with Ethical Standards This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of University Liaocheng (IRBs)(2025.6.23/No.HE2025062301). Informed consent was secured from all participants either in person or via printed or electronic consent forms. Participants were fully informed of the purpose, procedures, potential risks and benefits of the study, and their voluntary participation, with explicit assurance of their right to withdraw at any time without consequence. Confidentiality and anonymity were rigorously maintained throughout the research process, with all data securely stored and transmitted using encrypted systems. Informed Consent Informed Consent Participation was voluntary, and all the participants were instructed to complete the informed consent form by means of paper and pencil. Consent for publication Not Applicable. This study does not include identifying images or personal or clinical details of participants that could compromise anonymity. Funding This study was supported by Nation Center for Mental Health,China. Author Contribution J and R conceived of the study, participated in its design and coordination and drafted the manuscript and performed the measurement; J and Z and T participated in the design and interpretation of the data and performed the statistical analysis. All authors read and approved the final manuscript. Data availability The authors are make sure that all data and materials as well as software application or custom code support their published claims and comply with field standards. References Borsboom , D ., Cramer , A . O . J ., Schmittmann , V . D ., Epskamp , S .,& Waldorp , L . J .(2011). The small world of psychopathology . PLoS One , 6 (11),e27407. Cramer , A . O . J ., Waldorp , L . J ., van der Maas , H . L . J .,& Borsboom , D . (2010) . Comorbidity : A network perspective . Behavioral and Brain Sciences , 33 (2-3),137-150. Derogatis, L. R., Lipman, R. S., & Covi, L. (1973). The SCL-90: An Outpatient Psychiatric Rating Scale. Deutsche Bearbeitung CIPS. Psychopharmacological. Freijeiro‐González, L., Febrero‐Bande, M., & González‐Manteiga, W. (2022). A critical review of LASSO and its derivatives for variable selection under dependence among covariates. International Statistical Review , 90 (1), 118-145. Fried, E. I., & Cramer, A. O. J. (2017). Moving forward: Challenges and directions for psychopathological network theory and methodology. Perspectives on Psychological Science, 12 (6), 999–1020. Fried, E. I. (2022). Studying mental health problems as systems, not syndromes. Current Directions in Psychological Science, 31 (6), 500–508. Geng, Y., Gu, J., Yu, J., & Zhu, X. (2020). Negative life events and depressive symptoms among Chinese adolescents: Mediating role of resilience and moderating role of psychopathy. Current Psychology , 1-8. Geng- feng, N., Xiao- han., SHI., Yuan, TIAN., SUN Xiao- jun., LEI Yu- ju. (2021). The Association between Social Networking Site Use and Older Adults’ Depression: The Effects of Online Social Capital and Loneliness. Chinese Journal of Clinical Psychology 29 (5), 1055-1059. Haktanir, A., Watson, J. C., Ermis-Demirtas, H., Karaman, M. A., Freeman, P. D., Kumaran, A., & Streeter, A. (2021). Resilience, academic self-concept, and college adjustment among first-year students. Journal of college student retention: Research, theory & practice , 23 (1), 161-178.Ingram, I., Kelly, P. J., Deane, F. P., Baker, A. L., & Dingle, G. A. (2020). Perceptions of loneliness among people accessing treatment for substance use disorders. Drug and alcohol review , 39 (5), 484-494. Kendler , K . S ., Zachar , P .,& Craver , C .(2011). What kinds of things are psychiatric disorders ? Psychological Medicine , 41 (6),1143-1150. May, R.. (1950). The meaning of anxiety (pp. 10760-000). New York, NY, US: Ronald Press Company Moksnes, U. K., & Reidunsdatter, R. J. (2019). Self-esteem and mental health in adolescents-level and stability during a school year. Norsk Epidemiologi , 28 (1-2), 59-67. Opsahl T ., Agneessens F .,& Skvoretz , J .(2010). Node centrality in weighted networks : Generalizing degree and shortest paths . Social Networks , 32 (3),245-251. Qingqing- Zhang, Tingting- Chen, Shanshan- Liu, Xinying- Liu, Fengqiong- Yu, Chunyan- Zhu. (2022). Effect of Fear of Negative Evaluation on Depression: Multiple Serial Mediating Role ofCognitive Reappraisal, Expressive Suppression, and Social Anxiety. Chinese Journal of Clinical Psychology, 30 (06), 1292-1296. Ramón-Arbués, E., Gea-Caballero, V., Granada-López, J. M., Juárez-Vela, R., Pellicer-García, B., & Antón-Solanas, I. (2020). The prevalence of depression, anxiety and stress and their associated factors in college students. International journal of environmental research and public health, 17 (19), 7001. Surtees, A. D., Briscoe, H., & Todd, A. R. (2024). Anxiety and Mentalizing: Uncertainty as a Driver of Egocentrism. Current Directions in Psychological Science, 33 (2), 100-107. Tong- Wu, Ningxi- Yang, Li- Cai , Yan- Zhang, Zhiqin- Sang. (2021). Self-Compassion and Social Anxiety: The MediationEffect of Self-Esteem and Fear of Evaluation. Chinese Journal of Clinical Psychology, 29 (1), 169-172. Vansteenkiste, M., & Ryan, R. M. (2013). On psychological growth and vulnerability: Basic psychological need satisfaction and need frustration as a unifying principle. Journal of Psychotherapy Integration, 23 (3), 263–280. Xin Z Q, Zhang M & He L. (2012). Changes in College Students’ Mental Health: A Cross-Temporal Meta-Analysis. Acta Psychologica Sinica, 44 (05), 664-679. Wickham, H. (2016). Programming with ggplot2. In Ggplot2: elegant graphics for data analysis (pp. 241-253). Cham: Springer International Publishing. Wysocki, A., Rhemtulla, M., van Bork, R., & Cramer, A. (2022). Cross-lagged network models. Yi- Xia, Jie- Zhang, Huoyin- Zhang, Yi- Lei, Haoran- Dou. (2025). Understanding approach-avoidance conflict dysregulation in anxiety: Cognitive processes and neural mechanisms. Advances in Psychological Science , 33 (03), 477-493. Zhang, M., Zhang, D., & Wells, M. T. (2008). Variable selection for large p small n regression models with incomplete data: Mapping QTL with epistases. Bmc Bioinformatics , 9 , 251. Zichen- Chen, Huijuan – Zhang, Xinjian – Wang, Xiaokang – Lv. (2018). Three theoretical perspectives on the origin of depression. Advances in Psychological Science ,26 (6), 1041. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-7338769","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":505470336,"identity":"21126d10-9b7f-49a8-9726-96ec93b1fd20","order_by":0,"name":"Jiatong Zhang","email":"","orcid":"","institution":"Liaocheng University","correspondingAuthor":false,"prefix":"","firstName":"Jiatong","middleName":"","lastName":"Zhang","suffix":""},{"id":505470338,"identity":"dba7e531-d3a9-4bc1-8b0e-b4bd885c535d","order_by":1,"name":"Ziang Wang","email":"","orcid":"","institution":"Liaocheng University","correspondingAuthor":false,"prefix":"","firstName":"Ziang","middleName":"","lastName":"Wang","suffix":""},{"id":505470339,"identity":"ac65b5e6-e24d-4c6d-8632-453f69106505","order_by":2,"name":"Teng Zhang","email":"","orcid":"","institution":"Liaocheng University","correspondingAuthor":false,"prefix":"","firstName":"Teng","middleName":"","lastName":"Zhang","suffix":""},{"id":505470340,"identity":"a4b6ea58-3f3f-4ec4-b75c-6640f49e0d51","order_by":3,"name":"Ran Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYBACNvbmgw8//qmRY2xvPkCcFj6eY8nGkg3HjJl7jiUQp0VOIsdMgreBObF9ho8BkQ5jSDCTkNzBxtg7g+fjjTcMdnK6DQS1HEi2KDwjwyw5u3ez5RyGZGOzA4S0MDYcvCHBxsZmOOfsNmkehgOJ2whqYWZskOBhY+axv5HzjEgtbMxMErxtzBKMM3LYiNQCtMFY4swxA8aeY8aWcwyI8Iv8/PcfH36oqKlvbG9+eONNhZ0cQS0oQIKHyKhB1kKqjlEwCkbBKBgRAACBN0DAnWijzwAAAABJRU5ErkJggg==","orcid":"","institution":"Liaocheng University","correspondingAuthor":true,"prefix":"","firstName":"Ran","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-08-10 12:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7338769/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7338769/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89951312,"identity":"19cc5a0c-7eba-4b4e-9e34-3893ecddc066","added_by":"auto","created_at":"2025-08-26 19:17:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":397935,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork Structure of University Students' SCL-90\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7338769/v1/1b56aa28cf7948ba0ea42cb9.png"},{"id":89950994,"identity":"7fcbdfd3-ac59-4f43-a8d7-2a9f48e4f749","added_by":"auto","created_at":"2025-08-26 19:09:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":183240,"visible":true,"origin":"","legend":"\u003cp\u003eCentrality Estimates for SCL-90\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7338769/v1/df8234f5cd16b6794832dd52.png"},{"id":89951313,"identity":"d3c7cbca-b128-4bb3-8894-e4865a3a18ac","added_by":"auto","created_at":"2025-08-26 19:17:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85009,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a).\u003c/strong\u003eBridge Strength Estimates (T1)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b).\u003c/strong\u003e Bridge Strength Estimates (T2)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7338769/v1/c6e76fa219e2c04795a6e5b1.png"},{"id":89950991,"identity":"f8ac5607-5d29-435d-b371-72dc54b6df23","added_by":"auto","created_at":"2025-08-26 19:09:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":169312,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) \u003c/strong\u003eCross-Lagged Networks of University Students' Mental Health Problems (T1-T2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b) \u003c/strong\u003eSCL-90 Cross-Lagged Centrality Estimates (T1-T2)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7338769/v1/9f625f52e7719616eb1234ab.png"},{"id":89950995,"identity":"b5c3561f-e7cb-4eb3-9267-395fcece4252","added_by":"auto","created_at":"2025-08-26 19:09:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":215724,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a).\u003c/strong\u003e Centrality Difference Test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b).\u003c/strong\u003e Centrality Stability Coefficient\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7338769/v1/da004909b4b6064265c53d12.png"},{"id":91332033,"identity":"766daf34-5fed-483a-9a6c-970ede48be9d","added_by":"auto","created_at":"2025-09-15 11:08:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1666040,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7338769/v1/5b1c5fdd-6660-4e90-909d-c1e22d817331.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Predictive Role of Anxiety Symptoms in the Psychological Health Adaptation among At-Risk University Freshmen: A Cross-Lagged Network Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe transition to university life represents a critical period from adolescence to adulthood, offering valuable opportunities for academic and personal growth, while simultaneously presenting challenges such as academic pressure, interpersonal adjustments, and environmental adaptation (Haktanir et al., 2018). Within this context, freshmen face elevated risks to psychological health, with significantly higher rates of emotional distress like anxiety and depression compared to other groups. If not promptly identified and intervened upon, these issues can have long-term negative consequences for future academic performance, career development, and overall well-being (Geng et al., 2020). Therefore, gaining a deeper understanding of the mechanisms underlying the development of psychological health problems in freshmen and exploring effective early-warning and intervention strategies are crucial for safeguarding their healthy development and enhancing the quality of university education.\u003c/p\u003e\u003cp\u003ePrevious research has primarily explored psychological health problems from a latent variable perspective. Two main latent variable models prevail. One conceptualizes psychological disorders as disease entities causing symptom manifestations (McNally, 2016). The other views psychological disorders as abstract summaries of all symptom presentations (Adam, 2013). Both models posit the latent psychological disorder as the common cause of symptoms, essentially assuming independence between observed variables, thereby neglecting the potential interactions \u003cem\u003ebetween\u003c/em\u003e symptoms (Borsboom \u0026amp; Cramer, 2013; Schmittmann et al., 2013).\u003c/p\u003e\u003cp\u003eWithin the dynamic network view, the psychological problem network is initiated by a common factor but maintained by the interactions among symptoms (Fried et al., 2017; Kendler et al., 2011). The psychopathological network perspective conceptualizes psychological health problems as complex systems, where micro-level interdependencies between symptoms manifest as macro-level regularities (Fried, 2022). This perspective views networks as dynamic; while strong symptom interconnections maintain network stability in typical environments, intense interactions driven by various factors can alter the network's internal structure and propel its overall development (Borsboom \u0026amp; Cramer, 2013). Thus, longitudinal network analysis is a primary method within this framework for investigating the internal mechanisms of symptom change over time. Cross-lagged network analysis, a key component of network methodologies, differs from traditional longitudinal network approaches often focused on short-term studies. It enhances the accuracy of long-term longitudinal research and employs methods controlling for the influence of other nodes in the network, making results more interpretable (Wysocki et al., 2022). Furthermore, prior research on psychological problems often focused on strength metrics, overlooking bridge symptoms that connect different clusters. Network theory posits that comorbidity arises from symptoms common to multiple disorders, acting as bridges within the overall network that can activate symptoms across different dimensions (Cramer et al., 2010). Therefore, only by comparing key bridge symptoms across different time points within a cross-lagged network analysis can we infer symptom presentations that remain stable across stages. In summary, network analysis allows researchers to identify core predictive symptoms (e.g., those with high centrality) and key symptomatic manifestations (bridge symptoms connecting symptom clusters) within dynamic symptom networks, thereby revealing the internal developmental mechanisms of psychological health problems. This approach facilitates a more comprehensive understanding of the dynamic processes underlying psychological health problems at the symptom level, rather than solely at the diagnostic level, enabling the formulation of more effective and targeted prevention and intervention strategies.\u003c/p\u003e\u003cp\u003eDespite the methodological strengths of network analysis, existing research has not yet applied cross-lagged panel network methodology to examine at-risk college freshmen. This gap impedes exploration of the dynamic developmental mechanisms underlying their mental health challenges and precludes identification of core predictive symptoms alongside key symptomatic manifestations.The present study addresses this critical void through network analysis by identifying primary symptomatic manifestations and their core predictive symptoms while revealing latent dynamic predictive pathways between these elements. This approach offers a unique and vital perspective for understanding the evolving trajectory of mental health risks in incoming freshmen.\u003c/p\u003e\u003cp\u003eAs the first investigation of its kind, this research implements cross-lagged panel network analysis to assess at-risk freshmen across two strategic timepoints: initial enrollment (T1) and 7-month follow-up (T2). It aims to discover core predictive symptoms of emergent post-enrollment psychopathology while characterizing key symptomatic manifestations. These insights establish foundations for early detection protocols and precisely targeted interventions within this vulnerable population.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Participants\u003c/h2\u003e\u003cp\u003eThis study employed a convenience sampling method, recruiting freshmen identified as at-risk (i.e., scoring\u0026thinsp;\u0026ge;\u0026thinsp;160 on the SCL-90, or having\u0026thinsp;\u0026gt;\u0026thinsp;43 positive items, or scoring\u0026thinsp;\u0026gt;\u0026thinsp;2 on any single factor) from a university in Shandong Province, China. Data collection occurred approximately one week after enrollment (T1) and again seven months later (T2). Participants provided informed consent, ensuring voluntary participation and confidentiality. After excluding incomplete and invalid responses, 8115 participants completed the initial survey (T1), with 720 identified as at-risk at both time points. The study received approval from the university's Research Ethics Committee.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Measures\u003c/h2\u003e\u003cp\u003eThe Symptom Checklist-90 (SCL-90), developed by Derogatis (1973), serves as a well-established screening instrument for mental health concerns and has been widely implemented across Chinese mainland universities. Accordingly, the Symptom Checklist-90 (SCL-90) was used to screen for at-risk freshmen and assess their psychological health levels at T1 and T2. The scale comprises 9 dimensions: Somatization, Obsessive-Compulsive, Interpersonal Sensitivity, Depression, Anxiety, Hostility, Phobic Anxiety, Paranoid Ideation, Psychoticism, and Additional Items. In this study, Cronbach's α coefficients for each dimension were 0.823, 0.691, 0.740, 0.835, 0.798, 0.790, 0.708, 0.684, 0.731, and 0.620, respectively. All items were rated on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;Not at all, 2\u0026thinsp;=\u0026thinsp;A little bit, 3\u0026thinsp;=\u0026thinsp;Moderately, 4\u0026thinsp;=\u0026thinsp;Quite a bit, 5\u0026thinsp;=\u0026thinsp;Extremely).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics were analyzed using SPSS (Version 22.0, IBM Corp., Armonk, NY, USA). All network analyses were performed using R software (Version 4.1.2; R Core Team, 2021), primarily relying on the \u003cb\u003ebootnet\u003c/b\u003e package (Epskamp et al., 2018), \u003cb\u003eqgraph\u003c/b\u003e package (Epskamp et al., 2012), and \u003cb\u003eggplot2\u003c/b\u003e package (Wickham, 2016) for network estimation, centrality calculation, and visualization.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1 Network Analysis\u003c/h2\u003e\u003cp\u003eThe Cross-Lagged Panel Network (CLPN) model was employed to investigate dynamic interactions among symptoms. This model utilizes Least Absolute Shrinkage and Selection Operator (LASSO) regression to shrink coefficients of non-significant predictors to zero, generating a sparse network structure. This approach reduces false-positive probabilities in the predictive relationships between T1 symptoms and T2 symptoms, thereby constructing a more precise directed network (Freijeiro-Gonz\u0026aacute;lez et al., 2022). Specifically, for each symptom at T2, LASSO regression was performed with all T1 symptoms as predictors. Regularization strength was controlled by the λ parameter, selected via 10-fold cross-validation using the criterion of minimum cross-validation error plus one standard error (i.e., \u003cem\u003elambda.1se\u003c/em\u003e). The EBICglasso function within the \u003cem\u003eqgraph\u003c/em\u003e and \u003cem\u003ebootnet\u003c/em\u003e packages was used for cross-sectional network matrix estimation and visualization (Zhang et al., 2008). Edge weight thresholds were set minimum value of 0.03 across all networks.\u003c/p\u003e\u003cp\u003eIn the symptom network graphs, nodes represent symptoms. Blue edges indicate positive associations, red edges indicate negative associations, and edge thickness represents the strength of the association. The distance of a node from the center reflects its connectedness within the network; nodes farther from the center have fewer connections.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2 Centrality Metrics\u003c/h2\u003e\u003cp\u003eThis study computed key centrality metrics for both cross-sectional and cross-lagged networks to identify core symptoms within the networks. Common centrality metrics in cross-sectional network analysis include Strength, Closeness, Betweenness, and Bridge Strength (Bridge Strength).Strength is the sum of the absolute edge weights connected to a node, indicating its overall connectedness within the network. Closeness Measures how efficiently a node reaches all other nodes, calculated as the inverse of the sum of shortest path distances. Betweenness Quantifies the frequency with which a node lies on the shortest paths between other node pairs, directly reflecting its role in facilitating influence flow between node clusters (Opsahl et al., 2010). Bridge Strength Indicates a node's connections to other symptom dimensions (excluding its own), with higher values signifying stronger cross-dimensional links. This metric typically identifies transdiagnostic symptoms shared across comorbid conditions (Borsboom et al., 2011; Cramer et al., 2010).\u003c/p\u003e\u003cp\u003eCentrality metrics for cross-lagged network analysis primarily include Out-Expected Influence (Out-EI) and In-Expected Influence (In-EI). Out-Expected Influence (Out-EI) is The sum of the predictive weights of all outgoing edges from a node. It indicates the extent to which a node predicts other nodes in the network over time. In-Expected Influence (In-EI) is The sum of the predictive weights of all incoming edges to a node. It indicates the extent to which a node is predicted by other nodes in the network over time.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.3.3 Stability Estimation\u003c/h2\u003e\u003cp\u003eTo evaluate the robustness of the network structure and centrality metrics, two types of stability analyses were performed using the \u003cb\u003ebootnet\u003c/b\u003e package (Epskamp et al., 2018). First, the accuracy of edge weights was estimated using 95% confidence intervals (CIs) derived from non-parametric bootstrapping (1,000 bootstrapped samples). Less overlap between these CIs indicates higher accuracy. Second, the centrality stability coefficient (CS-coefficient) was calculated using the case-dropping subset bootstrap procedure. A CS-coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.50 is considered indicative of good stability, while\u0026thinsp;\u0026gt;\u0026thinsp;0.25 is considered acceptable. Finally, centrality difference tests were used to assess the statistical significance of differences in centrality metrics between symptoms within the cross-sectional and longitudinal networks (Epskamp et al., 2018).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Descriptive Statistics\u003c/h2\u003e\n \u003cp\u003eThe final sample of 720 at-risk freshmen consisted of 40% male and 60% female. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the mean scores and standard deviations for all SCL-90 dimensions at T1 and T2. As shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, most symptom dimensions exhibited elevated mean scores at both time points, consistent with the at-risk nature of the sample. Notably, several dimensions showed a slight increase in mean scores from T1 to T2.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMean SCL-90 Dimension Scores Across Assessment Timepoints\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1 \u003cstrong\u003eSomatization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1 \u003cstrong\u003eObsessive-Compulsive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1 \u003cstrong\u003eInterpersonal Sensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1 \u003cstrong\u003eDepression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1 \u003cstrong\u003eAnxiety\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1 \u003cstrong\u003eHostility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1 \u003cstrong\u003ePhobic Anxiety\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1 \u003cstrong\u003eParanoid Ideation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1 \u003cstrong\u003ePsychoticism\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1 \u003cstrong\u003eAdditional Items\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2 \u003cstrong\u003eSomatization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2 \u003cstrong\u003eObsessive-Compulsive\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eT2 \u003cstrong\u003eInterpersonal Sensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eT2 \u003cstrong\u003eDepression\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eT2 \u003cstrong\u003eAnxiety\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eT2 \u003cstrong\u003eHostility\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eT2 \u003cstrong\u003ePhobic Anxiety\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eT2 \u003cstrong\u003eParanoid Ideation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eT2 \u003cstrong\u003ePsychoticism\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eT2 \u003cstrong\u003eAdditional Items\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003cp\u003e2.84\u003c/p\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003cp\u003e2.27\u003c/p\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003cp\u003e2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Cross-Sectional Network Structure and Centrality Analysis\u003c/h2\u003e\n \u003cp\u003eThe estimated network models are shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The symptom networks at both time points were generally stable, although the Anxiety and Depression dimensions shifted towards the network center. Key nodes located centrally and exhibiting strong connections (thick edges) in both cross-sectional networks included: Anxiety Item 72 (\u0026quot;Spells of terror or panic\u0026quot;), Anxiety Item 78 (\u0026quot;Feeling restless or fidgety\u0026quot;), Anxiety Item 33 (\u0026quot;Feeling afraid\u0026quot;), Anxiety Item 23 (\u0026quot;Suddenly scared for no reason\u0026quot;), Psychoticism Item 90 (\u0026quot;Feeling something is wrong with your mind\u0026quot;), Psychoticism Item 87 (\u0026quot;Feeling that you are physically ill\u0026quot;), and Depression Item 32 (\u0026quot;Feeling no interest in things\u0026quot;). Anxiety Items 72, 78, 33, and 23 were stably central at both time points and showed mutual influences.\u003c/p\u003e\n \u003cp\u003eSymptom centrality is depicted in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. To reduce the impact of symptom scale differences, standardized scores were used for centrality estimation. At both time points, Anxiety Item 78 (\u0026apos;Feeling restless or fidgety\u0026apos;) consistently exhibited the highest Strength centrality at both T1 and T2, indicating its strong overall connections within the symptom networks. Besides, Other highly central symptoms across both time points included 72 (\u0026quot;Spells of terror or panic\u0026quot;) and 71 (Feeling that everything is an effort.) based on their Closeness and Betweenness centrality scores.\u0026rdquo;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Bridge Centrality Estimation\u003c/h2\u003e\n \u003cp\u003eBridge Strength estimates are shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. At T1, Interpersonal Sensitivity Item 69 (\u0026quot;Feeling others are unsympathetic or dislike you\u0026quot;) had the highest Bridge Strength. At T2, Depression Item 79 (\u0026quot;Feeling worthless\u0026quot;) had the highest Bridge Strength. This indicates that the key symptomatic manifestations bridging all dimensions were time-specific. However, Interpersonal Sensitivity Item 69 (\u0026quot;Feeling others are unsympathetic or dislike you\u0026quot;), Anxiety Item 78 (\u0026quot;Feeling restless\u0026quot;), Anxiety Item 23 (\u0026quot;Suddenly scared for no reason\u0026quot;), and Somatization Item 48 (\u0026quot;Trouble getting your breath\u0026quot;) consistently exhibited the highest Bridge Strength across both time points, signifying their role as common symptomatic manifestations spanning different psychological problem dimensions. Furthermore, Item 23 (\u0026quot;Suddenly scared for no reason\u0026quot;), Item 69 (\u0026quot;Feeling others are unsympathetic or dislike you\u0026quot;), and Item 78 (\u0026quot;Feeling restless\u0026quot;) also showed high Strength and Closeness centrality, confirming their significant influence on symptoms across other dimensions and their stability as key symptomatic manifestations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Cross-Lagged Network Model and Predictive Centrality Analysis\u003c/h2\u003e\n \u003cp\u003eThe cross-lagged network model spanning the first semester is depicted in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Blue arrows indicate positive predictive relationships. Results showed that Anxiety Item 78 (\u0026quot;Feeling restless or fidgety\u0026quot;) had the highest number of outgoing predictive paths.\u003c/p\u003e\n \u003cp\u003ePredictive centrality for the cross-lagged network is shown in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Anxiety Item 78 (\u0026quot;Feeling restless or fidgety\u0026quot;) had the highest OutStrength centrality, indicating it exerted the strongest predictive influence on other symptoms over time, predicting the most symptoms, and serving as a stable predictor. Conversely, Anxiety Item 72 (\u0026quot;Spells of terror or panic\u0026quot;) had the highest InStrength centrality, indicating it was the most influenced by other symptoms and thus more sensitive to changes within the symptom network.\u003c/p\u003e\n \u003cp\u003eCombined with the results of the cross-sectional network analysis indicate that Anxiety Item 78 (\u0026quot;Feeling restless or fidgety\u0026quot;) influences Anxiety Item 33 (\u0026quot;Feeling afraid\u0026quot;) and Item 23 (\u0026quot;Suddenly scared for no reason\u0026quot;) through the mediating effect of Anxiety Item 72 (\u0026quot;Spells of terror or panic\u0026quot;), elucidating the interaction mechanisms among these four symptoms observed in the cross-sectional network. Furthermore, Anxiety Item 78 (\u0026quot;Feeling restless or fidgety\u0026quot;) directly influences Interpersonal Sensitivity Item 69 (\u0026quot;Feeling others are unsympathetic or dislike you\u0026quot;) and Somatization Item 48 (\u0026quot;Trouble getting your breath\u0026quot;)\u0026mdash;both of which exhibited consistently high bridge strength\u0026mdash;explaining the internal influence mechanisms underlying these core symptomatic manifestations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Reliability and Generalizability of Network Structure and Centrality Estimates\u003c/h2\u003e\n \u003cp\u003eCentrality difference tests are shown in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e(a). Black squares indicate statistically significant differences between nodes. The subset bootstrap procedure results (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e(b)) showed stable centrality values with decreasing sample size. The centrality stability coefficients (CS) for Out-EI and In-EI were 0.594 and 0.284, respectively, indicating acceptable to good stability and supporting the generalizability of the results.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study employed cross-lagged network analysis to explore the key adaptation symptoms among university freshmen during their first semester and their internal dynamic mechanisms over time. Findings indicated that anxiety and depression significantly influenced the cluster of adaptation symptoms, while interpersonal sensitivity was closely linked to other symptom clusters. These results underscore that anxiety and depression symptoms exhibited the most numerous and strongest connections to all other symptoms. Furthermore, symptoms of interpersonal sensitivity manifested across all other dimensions. This suggests universities should prioritize addressing anxiety and depressive feelings in freshmen and enhance psychological support focused on interpersonal relationships.\u003c/p\u003e\u003cp\u003eCross-sectional network analysis revealed that Anxiety Items 72 (\"Spells of terror or panic\"), 78 (\"Feeling restless or fidgety\"), 33 (\"Feeling afraid\"), and 23 (\"Suddenly scared for no reason\") occupied central positions within the network, indicating their pivotal role. Consistent with Beck's cognitive model of anxiety, physiological symptoms (e.g., panic) and cognitive biases (e.g., catastrophizing) mutually reinforce each other, forming core nodes in the symptom network (Beck \u0026amp; Clark, 1997). These four symptoms cover core dimensions of anxiety: acute fear (Item 72), persistent unease (Item 78), generalized apprehension (Item 33), and uncued fear (Item 23), encompassing physiological arousal, cognitive worry, and emotional experience.\u003c/p\u003e\u003cp\u003eIntegrating cross-lagged findings revealed their dynamic interplay: Trait-like daily restlessness (Item 78) more readily triggered state-like panic (Item 72), potentially exacerbated by sympathetic activation (e.g., palpitations, sweating) feeding back into restlessness. Persistent unease likely impaired emotion regulation, heightening sensitivity to ambiguous threats (Item 33). Chronic uncued fear (Item 23) likely reflected anticipatory anxiety, further consolidating its central position (Yi Xia et al, 2025). Notably, a bidirectional reinforcement mechanism existed between Items 23 and 72. Beck's cognitive model of emotional disorders posits catastrophizing as a core mechanism maintaining anxiety (Beck \u0026amp; Emery, 1985). On one hand, intense physiological reactions (e.g., racing heart) during panic spells (Item 72) can be misinterpreted catastrophically (e.g., \"I have a heart problem\") as \"signs of losing control,\" triggering hypervigilance towards similar situations or daily activities, thereby increasing unexplained, generalized fear (Item 23). On the other hand, according to affective priming theory (LeDoux, 1996), elevated baseline anxiety accelerates amygdala responses to potential threats. Chronic generalized fear (Item 23) maintains hyperactivation of the sympathetic nervous system (e.g., elevated cortisol), keeping individuals in a prolonged stress state. This state significantly lowers the threshold for triggering panic spells (Item 72).\u003c/p\u003e\u003cp\u003eFurthermore, Cross-sectional network of central nodes Psychoticism Item 90 (\"Feeling something is wrong with your mind\") and Items 89 (\"Feeling guilty\") and 87 (\"Feeling that you are physically ill\") were closely interrelated. Item 90 acted as a potential pathway connecting Items 89 and 87, reflecting its frequent co-occurrence with both and its potential to exacerbate their symptom severity. Interestingly, at T1, Item 90 showed direct paths to key anxiety nodes Item 23, Item 33 (\"Feeling afraid\"), and Item 80 (\"Feeling that familiar things are strange or unreal\"), and indirect paths to Items 72 and 78, but no direct link to Item 87. By T2, Item 87 had established direct connections with Item 72 and Item 23. This pattern suggests a crucial role for shifts in internal attributional style. Attributing failures internally threatens self-concept, lowers self-esteem, and diminishes perceived self-determination. This creates a vicious cycle: fear of failure and internal attribution following setbacks lower self-esteem and thwart psychological need satisfaction; conversely, low self-esteem predisposes individuals to anxiety, making them more reactive to self-threats (Ram\u0026oacute;n-Arbu\u0026eacute;s et al., 2020). Rollo May (1950) theorized that societal changes threaten independence, fostering feelings of self-alienation and denial of authentic emotions, leading to anxiety. The central positioning of Items 90 and 87 highlights the profound impact of physiological and cognitive self-doubt \u0026ndash; the dissonance between ideal and real self \u0026ndash; causing cognitive dissonance and exerting a decisive influence within the entire symptom network.\u003c/p\u003e\u003cp\u003eNotably, the anxiety symptom 72 (\"Spells of terror or panic\") was highly sensitive to changes in other symptoms and frequently acted as a mediator. Evolutionary psychology suggests fear is an instinctive survival response to potential threats, activating the \"fight-or-flight\" response. To satisfy needs for belonging, security, and competence within social groups, individuals develop fears of social rejection. This future-oriented fear of uncertainty can lead to self-centeredness as a coping mechanism, potentially fostering social withdrawal tendencies (Surtees et al., 2024). Existing research links fear of negative evaluation strongly to low self-esteem and perceived high social pressure (Wu et al., 2021; Zhang et al., 2022), which are significant predictors of depression and anxiety (Chen et al., 2018; Niu et al., 2021; Moksnes \u0026amp; Reidunsdatter, 2019). This study further confirms the significant role of fear in negative outcomes like anxiety and depression.\u003c/p\u003e\u003cp\u003eThe stable role of Interpersonal Sensitivity Item 69 (\"Feeling others are unsympathetic or dislike you\"), Anxiety Item 78 (\"Feeling restless\"), Anxiety Item 23 (\"Suddenly scared for no reason\"), and Somatization Item 48 (\"Trouble getting your breath\") as bridge symptoms across dimensions confirms their importance as core clinical indicators. Item 69 reflects the core social anxiety feature of evaluation fear (Wallace \u0026amp; Alden, 1995, 1997). According to the Selective Optimization with Compensation Theory (SOC), freshmen strive to adapt using various strategies. However, thwarted basic psychological needs can lead to anxiety, suppression, and somatization (Vansteenkiste \u0026amp; Ryan, 2013), potentially explaining the emergence and bridging role of these specific symptoms.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study provides crucial evidence elucidating the internal mechanisms of adaptation symptoms in university freshmen and the specific role of anxiety, offering potential directions for diagnosing and intervening in freshmen adaptation issues. Our findings demonstrate that anxiety symptoms, particularly \"feeling restless or fidgety\" (Item 78), play a significant predictive role within the symptom network of at-risk freshmen, while interpersonal sensitivity symptoms represent stable key manifestations \u0026ndash; an aspect previously overlooked. Future research exploring the role of anxiety in freshmen adaptation should further incorporate the dimension of time. Although this study focused on symptoms present one week post-enrollment, universities can utilize these findings to test the clinical efficacy of precise symptom-targeted interventions and transdiagnostic strategies within psychological support programs for freshmen, aiming to improve current support schemes.\u003c/p\u003e"},{"header":"6. Limitations","content":"\u003cp\u003eBuilding upon prior research, this study offers novel insights into the internal dynamic mechanisms of freshmen adaptation over time. However, several limitations warrant acknowledgment. First, the use of the SCL-90, which assesses symptoms over the preceding week, imposes temporal constraints on the findings. Future research should include more frequent assessments to validate these conclusions. Second, relying solely on the SCL-90, despite its breadth, may have missed nuances within specific symptom domains. Future studies should build on this work by incorporating domain-specific, psychometrically robust measures. Third, the absence of pre-enrollment baseline symptom assessments prevents ruling out the influence of prior experiences. Fourth, the reliance on self-report measures introduces potential response biases; future research should consider supplementing with structured clinical interviews for assessing adaptation symptoms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e\n\u003cp\u003eThe authors declared no conflict interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of University Liaocheng (IRBs)(2025.6.23/No.HE2025062301). Informed consent was secured from all participants either in person or via printed or electronic consent forms. Participants were fully informed of the purpose, procedures, potential risks and benefits of the study, and their voluntary participation, with explicit assurance of their right to withdraw at any time without consequence. Confidentiality and anonymity were rigorously maintained throughout the research process, with all data securely stored and transmitted using encrypted systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed Consent Participation was voluntary, and all the participants were instructed to complete the informed consent form by means of paper and pencil.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable. This study does not include identifying images or personal or clinical details of participants that could compromise anonymity.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was supported by Nation Center for Mental Health,China.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eJ and R conceived of the study, participated in its design and coordination and drafted the manuscript and performed the measurement; J and Z and T participated in the design and interpretation of the data and performed the statistical analysis. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eThe authors are make sure that all data and materials as well as software application or custom code support their published claims and comply with field standards.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBorsboom , D ., Cramer , A . O . J ., Schmittmann , V . D ., Epskamp , S .,\u0026amp; Waldorp , L . J .(2011). The small world of psychopathology .\u003cem\u003e PLoS One\u003c/em\u003e ,\u003cem\u003e6\u003c/em\u003e(11),e27407.\u003c/li\u003e\n\u003cli\u003eCramer , A . O . J ., Waldorp , L . J ., van der Maas , H . L . J .,\u0026amp; Borsboom , D . (2010) . Comorbidity : A network perspective . \u003cem\u003eBehavioral and Brain Sciences\u003c/em\u003e ,\u003cem\u003e 33\u003c/em\u003e(2-3),137-150.\u003c/li\u003e\n\u003cli\u003eDerogatis, L. R., Lipman, R. S., \u0026amp; Covi, L. (1973). The SCL-90: An Outpatient Psychiatric Rating Scale. Deutsche Bearbeitung CIPS. Psychopharmacological.\u003c/li\u003e\n\u003cli\u003eFreijeiro‐Gonz\u0026aacute;lez, L., Febrero‐Bande, M., \u0026amp; Gonz\u0026aacute;lez‐Manteiga, W. (2022). A critical review of LASSO and its derivatives for variable selection under dependence among covariates. \u003cem\u003eInternational Statistical Review\u003c/em\u003e, \u003cem\u003e90\u003c/em\u003e(1), 118-145.\u003c/li\u003e\n\u003cli\u003eFried, E. I., \u0026amp; Cramer, A. O. J. (2017). Moving forward: Challenges and directions for psychopathological network theory and methodology. \u003cem\u003ePerspectives on Psychological Science, 12\u003c/em\u003e(6), 999\u0026ndash;1020. \u003c/li\u003e\n\u003cli\u003eFried, E. I. (2022). Studying mental health problems as systems, not syndromes. \u003cem\u003eCurrent Directions in Psychological Science, 31\u003c/em\u003e(6), 500\u0026ndash;508.\u003c/li\u003e\n\u003cli\u003eGeng, Y., Gu, J., Yu, J., \u0026amp; Zhu, X. (2020). Negative life events and depressive symptoms among Chinese adolescents: Mediating role of resilience and moderating role of psychopathy. \u003cem\u003eCurrent Psychology\u003c/em\u003e, 1-8.\u003c/li\u003e\n\u003cli\u003eGeng- feng, N., Xiao- han., SHI., Yuan, TIAN., SUN Xiao- jun., LEI Yu- ju. (2021). The Association between Social Networking Site Use and Older Adults\u0026rsquo; Depression: The Effects of Online Social Capital and Loneliness. \u003cem\u003eChinese Journal of Clinical Psychology\u003c/em\u003e\u003cem\u003e 29\u003c/em\u003e(5), 1055-1059.\u003c/li\u003e\n\u003cli\u003eHaktanir, A., Watson, J. C., Ermis-Demirtas, H., Karaman, M. A., Freeman, P. D., Kumaran, A., \u0026amp; Streeter, A. (2021). Resilience, academic self-concept, and college adjustment among first-year students. \u003cem\u003eJournal of college student retention: Research, theory \u0026amp; practice\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(1), 161-178.Ingram, I., Kelly, P. J., Deane, F. P., Baker, A. L., \u0026amp; Dingle, G. A. (2020). Perceptions of loneliness among people accessing treatment for substance use disorders. \u003cem\u003eDrug and alcohol review\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(5), 484-494.\u003c/li\u003e\n\u003cli\u003eKendler , K . S ., Zachar , P .,\u0026amp; Craver , C .(2011). What kinds of things are psychiatric disorders ?\u003cem\u003e Psychological Medicine \u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(6),1143-1150.\u003c/li\u003e\n\u003cli\u003eMay, R.. (1950). \u003cem\u003eThe meaning of anxiety\u003c/em\u003e (pp. 10760-000). New York, NY, US: Ronald Press Company\u003c/li\u003e\n\u003cli\u003eMoksnes, U. K., \u0026amp; Reidunsdatter, R. J. (2019). Self-esteem and mental health in adolescents-level and stability during a school year. \u003cem\u003eNorsk Epidemiologi\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(1-2), 59-67.\u003c/li\u003e\n\u003cli\u003eOpsahl T ., Agneessens F .,\u0026amp; Skvoretz , J .(2010). Node centrality in weighted networks : Generalizing degree and shortest paths . \u003cem\u003eSocial Networks\u003c/em\u003e ,\u003cem\u003e32\u003c/em\u003e(3),245-251.\u003c/li\u003e\n\u003cli\u003eQingqing- Zhang, Tingting- Chen, Shanshan- Liu, Xinying- Liu, Fengqiong- Yu, Chunyan- Zhu. (2022). Effect of Fear of Negative Evaluation on Depression: Multiple Serial Mediating Role ofCognitive Reappraisal, Expressive Suppression, and Social Anxiety.\u003cem\u003e Chinese Journal of Clinical Psychology, 30\u003c/em\u003e(06), 1292-1296.\u003c/li\u003e\n\u003cli\u003eRam\u0026oacute;n-Arbu\u0026eacute;s, E., Gea-Caballero, V., Granada-L\u0026oacute;pez, J. M., Ju\u0026aacute;rez-Vela, R., Pellicer-Garc\u0026iacute;a, B., \u0026amp; Ant\u0026oacute;n-Solanas, I. (2020). The prevalence of depression, anxiety and stress and their associated factors in college students. \u003cem\u003eInternational journal of environmental research and public health, 17\u003c/em\u003e(19), 7001.\u003c/li\u003e\n\u003cli\u003eSurtees, A. D., Briscoe, H., \u0026amp; Todd, A. R. (2024). Anxiety and Mentalizing: Uncertainty as a Driver of Egocentrism. \u003cem\u003eCurrent Directions in Psychological Science, 33\u003c/em\u003e(2), 100-107.\u003c/li\u003e\n\u003cli\u003eTong- Wu, Ningxi- Yang, Li- Cai , Yan- Zhang, Zhiqin- Sang. (2021). Self-Compassion and Social Anxiety: The MediationEffect of Self-Esteem and Fear of Evaluation.\u003cem\u003e Chinese Journal of Clinical Psychology, 29\u003c/em\u003e(1), 169-172.\u003c/li\u003e\n\u003cli\u003eVansteenkiste, M., \u0026amp; Ryan, R. M. (2013). On psychological growth and vulnerability: Basic psychological need satisfaction and need frustration as a unifying principle. Journal of Psychotherapy Integration,\u003cem\u003e 23\u003c/em\u003e(3), 263\u0026ndash;280.\u003c/li\u003e\n\u003cli\u003eXin Z Q, Zhang M \u0026amp; He L. (2012). Changes in College Students\u0026rsquo; Mental Health: A Cross-Temporal Meta-Analysis. \u003cem\u003eActa Psychologica Sinica, 44\u003c/em\u003e(05), 664-679.\u003c/li\u003e\n\u003cli\u003eWickham, H. (2016). Programming with ggplot2. In \u003cem\u003eGgplot2: elegant graphics for data analysis\u003c/em\u003e (pp. 241-253). Cham: Springer International Publishing.\u003c/li\u003e\n\u003cli\u003eWysocki, A., Rhemtulla, M., van Bork, R., \u0026amp; Cramer, A. (2022). Cross-lagged network models.\u003c/li\u003e\n\u003cli\u003eYi- Xia, Jie- Zhang, Huoyin- Zhang, Yi- Lei, Haoran- Dou. (2025). Understanding approach-avoidance conflict dysregulation in anxiety: Cognitive processes and neural mechanisms.\u003cem\u003e Advances in Psychological Science\u003c/em\u003e\u003cem\u003e,\u003c/em\u003e\u003cem\u003e \u003c/em\u003e\u003cem\u003e33 (03), \u003c/em\u003e477-493.\u003c/li\u003e\n\u003cli\u003eZhang, M., Zhang, D., \u0026amp; Wells, M. T. (2008). Variable selection for large p small n regression models with incomplete data: Mapping QTL with epistases. \u003cem\u003eBmc Bioinformatics\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e, 251.\u003c/li\u003e\n\u003cli\u003eZichen- Chen, Huijuan \u0026ndash; Zhang, Xinjian \u0026ndash; Wang, Xiaokang \u0026ndash; Lv. (2018). Three theoretical perspectives on the origin of depression. \u003cem\u003eAdvances in Psychological Science\u003c/em\u003e\u003cem\u003e,26\u003c/em\u003e(6), 1041.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Anxiety Symptoms, Cross-Lagged Network, University Freshmen, University Freshmen, Psychological Adaptation, Mental Health","lastPublishedDoi":"10.21203/rs.3.rs-7338769/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7338769/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUniversity freshmen encounter numerous psychological health challenges during their adaptation to college life, with anxiety symptoms being a pervasive and critical risk factor. Despite recognition of anxiety's impact, the specific dynamic predictive pathways by which anxiety symptoms interact within the symptom network and influence freshmen's psychological adaptation are not yet fully elucidated. This study aimed to elucidate the dynamic predictive patterns and central role of anxiety symptoms within the psychological health adaptation of at-risk freshmen using cross-lagged network analysis. A cohort of 720 at-risk freshmen from a university in Shandong Province, China, was assessed at two time points: one week after enrollment (T1) and seven months later (T2), utilizing the Symptom Checklist-90 (SCL-90). Data were analyzed using R-Studio for network analyses and SPSS for descriptive statistics. Results from cross-sectional network analysis revealed that Anxiety Item 78 (\"Feeling restless or fidgety\") consistently exhibited the highest bridge centrality alongside other key symptoms, indicating its prominent position and significant bridging role within the symptom network. Crucially, \u003cstrong\u003ecross-lagged network analysis\u003c/strong\u003e further demonstrated that Anxiety Item 78 consistently displayed the strongest predictive influence on subsequent symptom activation across time (from T1 to T2), highlighting its unique dynamic predictive value within the freshman psychological adaptation symptom network. These findings provide novel and specific insights into the temporal dynamics of anxiety symptoms in freshmen's psychological adaptation, particularly emphasizing the critical predictive role of \"feeling restless or fidgety’. These findings offer concrete guidance for the early identification of at-risk freshmen and hold substantial practical significance for developing more precise and effective mental health intervention strategies for this population.\u003c/p\u003e","manuscriptTitle":"The Predictive Role of Anxiety Symptoms in the Psychological Health Adaptation among At-Risk University Freshmen: A Cross-Lagged Network Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-26 19:09:54","doi":"10.21203/rs.3.rs-7338769/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"95e30d1f-fd03-4de6-822b-03e7c949ba6b","owner":[],"postedDate":"August 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-25T11:08:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-26 19:09:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7338769","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7338769","identity":"rs-7338769","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}