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Learn more: PMC Disclaimer | PMC Copyright Notice BMC Psychol . 2026 Mar 9;14:538. doi: 10.1186/s40359-026-04287-x Search in PMC Search in PubMed View in NLM Catalog Add to search Anxiety development trajectories in junior high students: effects of family functioning and gender, and prediction of non-suicidal self-injury Junkai Yang Junkai Yang 1 Department of Psychology, Institute of Education, China University of Geosciences, Wuhan, 430074 China Find articles by Junkai Yang 1, # , Juan Zhang Juan Zhang 2 Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong, University of Science and Technology, Wuhan, 430012 China Find articles by Juan Zhang 2, # , Yufan Zhou Yufan Zhou 1 Department of Psychology, Institute of Education, China University of Geosciences, Wuhan, 430074 China Find articles by Yufan Zhou 1 , Yaofei Xie Yaofei Xie 2 Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong, University of Science and Technology, Wuhan, 430012 China Find articles by Yaofei Xie 2 , Taimin Wu Taimin Wu 2 Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong, University of Science and Technology, Wuhan, 430012 China Find articles by Taimin Wu 2 , Juan Qiao Juan Qiao 3 Wuhan Wudong Hospital (Wuhan Second Mental Hospital), Wuhan, 430084 China Find articles by Juan Qiao 3 , Bing Xiang Yang Bing Xiang Yang 4 Center for Wise Information Technology of Mental Health Nursing Research, School of Nursing, Wuhan University, Wuhan, 430071 China Find articles by Bing Xiang Yang 4 , Dan Luo Dan Luo 4 Center for Wise Information Technology of Mental Health Nursing Research, School of Nursing, Wuhan University, Wuhan, 430071 China Find articles by Dan Luo 4 , Lianzhong Liu Lianzhong Liu 3 Wuhan Wudong Hospital (Wuhan Second Mental Hospital), Wuhan, 430084 China 5 School of Medicine, Wuhan University of Science and Technology, Wuhan, 430065 China Find articles by Lianzhong Liu 3, 5, ✉ , Ping Yu Ping Yu 2 Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong, University of Science and Technology, Wuhan, 430012 China Find articles by Ping Yu 2, ✉ Author information Article notes Copyright and License information 1 Department of Psychology, Institute of Education, China University of Geosciences, Wuhan, 430074 China 2 Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong, University of Science and Technology, Wuhan, 430012 China 3 Wuhan Wudong Hospital (Wuhan Second Mental Hospital), Wuhan, 430084 China 4 Center for Wise Information Technology of Mental Health Nursing Research, School of Nursing, Wuhan University, Wuhan, 430071 China 5 School of Medicine, Wuhan University of Science and Technology, Wuhan, 430065 China ✉ Corresponding author. # Contributed equally. Received 2025 Nov 3; Accepted 2026 Feb 27; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13085396 PMID: 41796381 Abstract Background Anxiety is a prevalent mental health issue among junior high students, significantly impacting their physical and psychological well-being. This study aimed to investigate the developmental trajectory of anxiety in this population and its underlying causes and outcomes, providing support for adolescent mental health interventions. Methods A total of 1479 junior high students participated in a three-wave longitudinal study over two years. Latent Growth Curve Modeling was employed to examine the developmental trajectory of anxiety, and analyze the effects of gender and family functioning on the trajectory and the trajectory’s predictive role for Non-Suicidal Self-Injury (NSSI). Additionally, Latent Class Growth Modeling was used to explore heterogeneous subgroups within the anxiety trajectory. Multivariate logistic regression analyzed the predictive validity of gender and family functioning for trajectory class membership and the predictive effect of anxiety trajectory classes on NSSI. Results The overall anxiety trajectory exhibited a linear increasing trend. Female students showed higher initial anxiety levels and faster increases. Family functioning significantly negatively predicted anxiety levels. The anxiety trajectory predicted the occurrence of NSSI. Three distinct latent classes of anxiety trajectories were identified: High/Increasing, Moderate/Increasing, and Low/Decreasing. Gender and family functioning significantly predicted class membership. Individuals in the High/Increasing and Moderate/Increasing classes were more likely to engage in NSSI. Conclusions Anxiety levels in junior high students show an overall linear increase. Gender and family functioning significantly influence the trajectory and predict subgroup membership. The anxiety trajectory predicts subsequent NSSI. Therefore, adolescent mental health interventions should address multiple factors and anxiety-specific risk profiles. Supplementary Information The online version contains supplementary material available at 10.1186/s40359-026-04287-x. Keywords: Anxiety, Junior high students, Developmental trajectory, Family functioning, Non-suicidal self-injury Introduction Adolescence is a critical stage for individual psychological development, characterized by significantly increased susceptibility to emotional disorders like anxiety [ 1 ]. According to the 2019 Global Burden of Disease Study, anxiety ranks as the sixth leading cause of health impairment in adolescents 2 ]. Anxiety exerts significant negative impacts on adolescent students [ 3 ]. In China, the detection rate of anxiety among junior high students over the past decade is approximately 27% [ 4 ], positioning adolescent anxiety as a serious public health concern. Without timely and appropriate intervention, adolescent anxiety often persists into adulthood, increasing the risk of mental illness [ 5 ] and causing lasting negative effects on life [ 6 ]. Existing research presents mixed findings regarding the developmental trend of anxiety in adolescent students, which may be attributable to differences in sample characteristics, measurement tools, and cultural contexts. A cross-temporal meta-analysis indicated an increasing trend in anxiety among Chinese adolescents [ 7 ], and a 5-year longitudinal study in Canada found significant increases in adolescent anxiety over time [ 8 ]. However, other studies have reported decreasing trends in anxiety among middle school students [ 9 , 10 ]. To address these inconsistencies, the present study employs a longitudinal design and examines heterogeneous subgroups. We focus on junior high students, a group experiencing rapid psychological development during adolescence, to analyze their anxiety development trajectory and potential heterogeneous subgroups. Gender differences constitute a crucial dimension in anxiety research. Emotional disorders, such as anxiety and depression, exhibit higher prevalence rates among females [ 11 , 12 ], with this disparity becoming prominent during adolescence [ 13 ]. Females are more likely to experience higher levels of anxiety than males during this period [ 14 ]. Research has also revealed associations between adolescent gender role orientation and anxiety symptoms, with femininity positively correlating with anxiety symptoms [ 15 ]. Therefore, gender differences likely exist in the developmental characteristics of anxiety among junior high school students. The bioecological model of human development posits that individual development depends on interactions between the individual, family, school, community, and other factors (e.g., macrosystem, chronosystem) [ 16 ]. Adolescents’ relationships and socialization within the family significantly influence their interactions with others in school settings and their personal development [ 17 ]. Family functioning, commonly conceptualized as the quality of family communication, support, and problem-solving, is a key contextual factor [ 18 ]. In this study, it is operationalized as perceived family functioning. Anxiety symptoms in middle school students are highly correlated with family functioning [ 19 – 21 ], with adolescents from families with poor functioning exhibiting higher anxiety levels than their peers. This study will explore the influence of family functioning on the development and change of anxiety in junior high school students, providing deeper insight into the role of family factors in adolescent mental health development. Non-suicidal self-injury (NSSI) represents a maladaptive coping mechanism individuals use to alleviate negative emotional experiences [ 22 ] and is highly associated with anxiety [ 23 , 24 ]. Anxiety corresponds to changes in behavioral factors; individuals using multiple NSSI methods report relatively higher anxiety levels [ 25 ], and those meeting criteria for anxiety disorders show higher rates of NSSI [ 26 ]. Adolescents are a high-risk group for NSSI behavior. A cross-sectional study in China reported an annual prevalence rate of 37.1% for NSSI among junior high school students [ 27 ], and NSSI frequency tends to increase over time in younger adolescents [ 28 ], severely impacting their physical and mental health. This study adopts a dynamic perspective to investigate the predictive role of anxiety trajectories on NSSI behavior in junior high students. Theoretically, anxiety as a dynamically developing emotional problem reflects cumulative emotional load and declining regulatory capacity over time, which may elevate the risk of impulsive coping behaviors such as NSSI [ 29 , 30 ]. In contrast, static anxiety levels fail to capture such developmental processes. Empirically, an increasing anxiety trajectory has been linked to emotional dysregulation and impaired impulse control in adolescents [ 31 ], both of which are precursors to NSSI [ 32 ]. Therefore, by examining anxiety trajectories, this study aims to adopt a developmental perspective in predicting NSSI, offering more timely insights for early intervention. To comprehensively understand the development of anxiety, it is essential to examine both the average trajectory across the population and the potential heterogeneous subgroups that may follow distinct patterns. Latent Growth Curve Modeling (LGCM) is used to describe the average initial level and rate of change over time [ 33 ], answering the question of “how does anxiety develop on average”. In contrast, Latent Class Growth Modeling (LCGM) is employed to identify whether there are subgroups of individuals who share similar developmental patterns [ 34 ], addressing the question of “are there different types of anxiety development trajectories”. Utilizing both approaches provides a more nuanced picture—revealing the overall trend while also uncovering meaningful individual differences that might be masked in an average model. Accordingly, this longitudinal study aims to: (1) model the overall developmental trajectory of anxiety among junior high students using LGCM and identify potential heterogeneous subgroups using LCGM; (2) examine the effects of gender and family functioning on the anxiety trajectory (intercept and slope) and on trajectory class membership; and (3) investigate whether the anxiety trajectory and trajectory classes predict subsequent NSSI. Based on the bioecological model and prior literature, we hypothesize that: H1: Family functioning negatively predicted concurrent anxiety levels. H2: Female students will exhibit a higher initial anxiety level and a steeper rate of increase compared to male students. H3: A higher initial anxiety level and a increasing slope will predict a higher likelihood of subsequent NSSI. Furthermore, individuals belonging to anxiety trajectory classes characterized by higher and/or increasing anxiety will be at greater risk for NSSI. Method Procedure This study employed a longitudinal design with three waves of data collection, each spaced one year apart. This study was approved by the Wuhan Mental Health Center Ethics Committee (Ethical approval number: KY2021.11.01). After obtaining ethical approval, we contacted the principals of four public junior high schools in Wuhan, China, all of whom agreed to participate. Following approval from school administrators, informed consent forms were distributed to students and their parents or legal guardians. Students who provided assent and parental consent participated in the study. Surveys were administered during regular school hours in classroom settings by trained research assistants. Participation was voluntary and anonymous, with no monetary compensation provided. The questionnaires required approximately 10–20 min to complete. Participants Using the aforementioned procedure, students from four junior high schools in Wuhan, China, were surveyed. Data collection occurred at three time points: baseline (T1) in October 2021 involved students who had just entered Grade 7 ( N = 1710; 54.7% boys, 45.3% girls). The same cohort was surveyed again in October 2022 (T2, Grade 8) and October 2023 (T3, Grade 9). A total of 1479 students (804 boys, 675 girls) provided complete data at all three waves. Missing data analyses revealed that students with missing data were significantly more likely to report NSSI behavior (χ²(1) = 12.189, p < 0.001) and higher anxiety levels (t = -3.098, p = 0.002) at T1 compared to those with complete data. No differences were found for gender or family functioning. This study has obtained informed consent to participate from all research participants and their parents or legal guardians. Measures Anxiety Assessed using the Generalized Anxiety Disorder 7-item scale (GAD-7) [ 35 ] for screening and severity assessment. Although named for Generalized Anxiety Disorder, the GAD-7 is widely used and validated as a measure of general anxiety symptom severity in adolescent populations, including in Chinese contexts [ 36 ]. We used a validated Chinese version of the scale. The GAD-7 consists of 7 items rated on a 4-point scale (0 = Not at all, 3 = Nearly every day). Total scores range from 0 to 21, with higher scores indicating greater anxiety severity. Administered at T1, T2, and T3. In the current sample, Cronbach’s α was 0.912 at T1, 0.919 at T2, and 0.941 at T3. Family functioning Measured using the Family APGAR Index [ 37 ]. This scale subjectively assesses an individual’s satisfaction with family functioning across dimensions such as adaptation, partnership, growth, affection, and resolve. We employed a widely used Chinese version [ 38 ]. This 5-item scale subjectively assesses satisfaction with family functioning. Items are rated on a 3-point scale (0 = Almost never, 2 = Almost always). Total scores range from 0 to 10, with higher scores indicating better family functioning. Administered at T1, T2, and T3. In the current sample, Cronbach’s α was 0.882 at T1, 0.892 at T2, and 0.912 at T3. Non-suicidal self-injury (NSSI) Assessed by asking participants if they had intentionally injured themselves without suicidal intent in the past 12 months (Yes/No). This single-item measure has been employed in prior adolescent mental health research to screen for NSSI presence [ 39 , 40 ]. Although it does not assess frequency, methods, or severity (a limitation), it serves as a practical and efficient indicator of NSSI behavior, particularly in large-scale school-based surveys where questionnaire brevity is crucial. Its convergent validity with more comprehensive NSSI measures has been documented in previous studies [ 39 ], and is capable of effectively identifying adolescents at risk of self-injury [ 41 , 42 ], though its sensitivity to infrequent or less severe NSSI may be limited. Responses were coded 1 = Yes (presence of NSSI), 0 = No (absence of NSSI). Assessed at T1, T2, and T3. NSSI at T3 was used as the outcome in trajectory prediction analyses. Demographics questionnaire Collected at T1, including gender (coded 1 = boy, 2 = girl), only-child status, paternal education level, maternal education level, and family economic status. Data analyses Descriptive statistics and correlations were analyzed using SPSS 27.0. Latent Growth Curve Modeling (LGCM) [ 33 ] was conducted using Mplus 8.1 to examine the overall initial level (intercept) and rate of change (slope) of anxiety across the three time points. Model fit was evaluated using CFI (Comparative Fit Index), TLI (Tucker-Lewis Index), RMSEA (Root Mean Square Error of Approximation), and SRMR (Standardized Root Mean Square Residual), with commonly accepted thresholds of CFI/TLI > 0.90, RMSEA < 0.08, and SRMR < 0.08 indicating good fit [ 43 ], while values of CFI/TLI > 0.80, RMSEA < 0.10, and SRMR < 0.10 are considered to indicate an acceptable model fit [ 44 , 45 ]. Full Information Maximum Likelihood (FIML) estimation was used to handle missing data, which is robust to data missing at random. We also conducted attrition analyses as reported in the Participants section. Conditional LGCM was then employed, incorporating the time-invariant covariate (gender) and time-varying covariates (family functioning at T1, T2, T3) to test their effects on the anxiety trajectory. Family functioning was modeled as a contemporaneous (time-varying) predictor to examine their concurrent association with anxiety at each wave. Lagged effects were not modeled in this study. NSSI status at T3 was added to test the predictive effect of the anxiety trajectory (intercept and slope) on NSSI (See Fig. 1 ). Finally, Latent Class Growth Modeling (LCGM) [ 34 ] was used to identify trajectory group (class) membership of junior high student anxiety. Model selection was based on AIC, BIC, aBIC, LMR, BLRT, and Entropy [ 46 ], set the conditional probabilities for each category group with a 5% threshold. Multivariate logistic regression was used to examine the predictive power of T1 family functioning and gender for anxiety trajectory class membership and the predictive effect of trajectory classes on T3 NSSI. Other demographic covariates (e.g., parental education, family economics) were initially considered but were not significant predictors in preliminary models and were controlled for parsimony in the final analyses. Fig. 1. Open in a new tab Conditional latent growth curve model Results Descriptive statistics The final sample comprised 1479 students (54.4% boy, 45.6% girl). Descriptive statistics for the key study variables are presented in Table 1 . Correlational analyses revealed significant correlations among all variables. The results of correlational analyses please see the supplementary data. NSSI prevalence was significantly higher among girls than boys at all three time points: T1: 6.2% overall (92/1479; Boy = 28, Girl = 64; χ² = 22.636, p < 0.001); T2: 11.4% overall (168/1479; Boy = 57, Girl = 111; χ² = 31.893, p < 0.001); T3: 13.6% overall (201/1479; Boy = 82, Girl = 119; χ² = 17.252, p < 0.001). Table 1. Means and standard deviations of study variables Anxiety (T1) Anxiety (T2) Anxiety (T3) FF (T1) FF (T2) FF (T3) Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD N 2.66 3.724 3.37 4.309 3.44 4.490 7.66 2.563 6.07 2.974 5.94 3.160 Boys 2.32 3.383 2.78 3.848 3.03 4.349 7.95 2.306 6.36 2.945 6.09 3.109 Girls 3.08 4.057 4.07 4.708 3.93 4.609 7.31 2.800 5.72 2.974 5.76 3.212 Open in a new tab FF Family Functioning Latent growth curve modeling (LGCM) Unconditional LGCM The linear LGCM model provided acceptable fit to the anxiety data (CFI = 0.982, TLI = 0.946, RMSEA = 0.074, SRMR = 0.021, AIC = 24817.067, BIC = 24859.460) and was selected. The mean intercept was significant (Mean = 2.746, p < 0.001), indicating significant anxiety levels at T1. The significant intercept variance (Var = 5.372, p < 0.001) indicated significant individual differences in initial anxiety levels. The mean slope was significant and positive (Mean = 0.386, p < 0.001), indicating a significant linear increase in anxiety over time on average. The significant slope variance (Var = 2.406, p < 0.001) indicated significant individual differences in the rate of anxiety change. The correlation between the intercept and slope was non-significant. The results of LGCM model fit indices and other unconditional LGCM results please see the supplementary data. Conditional LGCM The conditional LGCM including gender, time-varying family functioning, and T3 NSSI demonstrated a marginally acceptable fit (CFI = 0.897, TLI = 0.838, RMSEA = 0.093, SRMR = 0.087, AIC = 24724.738, BIC = 24809.524). Results (Table 2 ) showed that gender significantly predicted both the intercept and slope of the anxiety trajectory. Girls had significantly higher initial anxiety levels (β = 0.412, SE = 0.164, p < 0.05) and a significantly faster rate of increase in anxiety (β = 0.261, SE = 0.116, p < 0.05) compared to boys. Family functioning at each time point significantly negatively predicted concurrent anxiety levels. Both the intercept (β = 0.050, SE = 0.011, p < 0.001) and slope (β = 0.121, SE = 0.023, p < 0.001) of the anxiety trajectory significantly positively predicted NSSI at T3. Higher initial anxiety levels and a faster rate of increase predicted a higher likelihood of engaging in NSSI at T3. Table 2. Conditional LGCM results Predictor Path β(S.E.) Time-Invariant Covariates Gender → Intercept 0.412(0.164) * Gender → Slope 0.261(0.116) * Intercept → T3 NSSI 0.050(0.011) *** Slope → T3 NSSI 0.121(0.023) *** Time-Varying Covariates T1 Family Functioning → T1 Anxiety -0.644(0.029) *** T2 Family Functioning → T2 Anxiety -0.604(0.023) *** T3 Family Functioning → T3 Anxiety -0.525(0.030) *** Open in a new tab * p < 0.05, *** p < 0.001 Latent class growth modeling (LCGM) Heterogeneous subgroups of anxiety trajectory Fit indices for the LCGM models are presented in Table 3 . Based on the indices, theoretical interpretability and conditional probabilities, the 3-class solution was selected as optimal. It showed a high entropy value, significant LMR and BLRT p-values indicating improvement over the 2-class model (Entropy = 0.923, pLMR = 0.0009, pBLRT = 0.000), and yielded conceptually distinct and meaningful classes with adequate sample proportions, the conditional probability of some trajectories in other category groups is less than 5%. The estimated trajectories for the three classes are shown in Fig. 2 . Class 1 (High/Increasing anxiety class; 6.6%): High intercept (β = 4.559, p < 0.001), High positive slope (β = 5.372, p < 0.001). Class 2 (Moderate/Increasing anxiety class; 28.2%): Moderate intercept (β = 3.241, p < 0.001), Positive slope (β = 1.755, p < 0.001). Class 3 (Low/Decreasing anxiety class; 65.2%): Moderate intercept (β = 2.382, p < 0.001), Negative slope (β = -0.786, p < 0.001). Table 3. LCGM fit Indices for anxiety trajectory classes Classes AIC BIC aBIC pLMR pBLRT Entropy Conditional Probabilities 1 C 25256.348 25282.844 25266.960 1.00 2 C 24493.720 24536.113 24510.700 0.0006 0 0.891 0.120/0.880 3 C 24090.034 24148.324 24113.381 0.0009 0 0.923 0.066/0.282/0.652 4 C 23785.929 23860.117 23815.643 0.0001 0 0.950 0.061/0.291/0.029/0.619 5 C 23554.664 23644.749 23590.745 0.1064 0 0.944 0.286/0.062/0.031/0.029/0.592 Open in a new tab Fig. 2. Open in a new tab Estimated developmental trajectories of anxiety for the three latent classes Predictors and outcomes of heterogeneous subgroups Using the largest class (C3: Low/Decreasing anxiety class) as the reference, multivariate logistic regression examined the effects of T1 family functioning and gender on trajectory class membership (Table 4 ). Compared to the Low/Decreasing class, girls were significantly more likely to belong to the Moderate/Increasing class (OR = 1.429, p < 0.01). Lower T1 family functioning significantly predicted membership in both the High/Increasing class (OR = 0.800, p < 0.001) and the Moderate/Increasing class (OR = 0.892, p < 0.001) compared to the Low/Decreasing class. After adjusting for gender and T1 family functioning, logistic regression also revealed significant predictive effects of trajectory class on T3 NSSI. Compared to the Low/Decreasing class (C3), adolescents in the High/Increasing class had a dramatically higher odds of NSSI (OR = 21.230, p < 0.001). This very high OR should be interpreted with caution given the relatively small size of this class (6.6%) and the binary nature of the NSSI measure. Those in the Moderate/Increasing class also had significantly elevated odds of NSSI (OR = 5.963, p < 0.001). Table 4. Multivariate logistic regression results for predictors of anxiety trajectory class membership Comparison (vs. C3 Low/Decreasing) Predictor β OR(Exp(β)) C1: High/Increasing Gender (Girl) 0.246 1.279 T1 Family Functioning -0.223 *** 0.800 C2: Moderate/Increasing Gender (Girl) 0.357 ** 1.429 T1 Family Functioning -0.115 *** 0.892 Open in a new tab Gender reference = Boy ** p < 0.01, *** p < 0.001 Discussion This longitudinal study investigated the developmental trajectory of anxiety in junior high students, the roles of family functioning and gender within this trajectory, and the relationship between the anxiety trajectory and subsequent NSSI. Results indicated an overall increasing trend in anxiety with gender differences. Family functioning and NSSI served as significant predictors and outcomes of the anxiety trajectory, respectively. Three distinct heterogeneous subgroups of anxiety trajectories were identified: Low/Decreasing class, Moderate/Increasing class, and High/Increasing class. Anxiety developmental trajectory and latent subgroups Rapid changes in emotional problems are characteristic of adolescence [ 47 ]. This study found an overall linear increasing trend in anxiety among junior high students, which aligns with some longitudinal studies in both Western and Chinese contexts [ 7 , 8 ]. While students initially experience a relatively less demanding environment upon entering junior high school, they gradually face increasing academic pressure [ 48 , 49 ] and peer pressure [ 50 ] as they progress through grades. Stressful life events are significant contributors to adolescent emotional problems [ 51 ], likely explaining the rise in anxiety during the school years. Although the majority (65.2%) belonged to the Low/Decreasing anxiety class, significant portions exhibited increasing trends (Moderate/Increasing class: 28.2%; High/Increasing class: 6.6%). Interventions should not only target the overall trend but also address these significant individual differences within the population. For instance, universal mental health education can be implemented for all students, while targeted support (e.g., coping skills groups) can be offered to students in increasing-trajectory classes, and intensive intervention (e.g., individual counseling, family therapy) can be provided for the high-risk High/Increasing class. Furthermore, the rate of anxiety change was not predicted by the initial level at school entry, suggesting that anxiety increase is a specific response [ 52 ] unrelated to initial mental health status. Interventions should therefore avoid relying solely on initial screening and implement comprehensive, dynamic monitoring and support mechanisms throughout junior high school. Predictors of anxiety trajectory Consistent with prior research [ 53 – 55 ], family functioning was negatively correlated with anxiety at each time point. Lower family functioning predicted higher concurrent anxiety levels. From a subgroup perspective, lower family functioning significantly increased the likelihood of belonging to the increasing anxiety trajectory classes (Moderate/Increasing and High/Increasing). As a crucial social context [ 56 ], family functioning significantly impacts adolescent emotional problems like anxiety [ 57 ]. For students entering junior high, better family functioning can buffer against anxiety. Family systems theory emphasizes the family as the primary and lifelong environment for health and well-being, and a core component of the social support system [ 58 ]. Family warmth is considered an effective protective factor against anxiety [ 59 ]. Parents should be attentive to their children’s emotional struggles and provide effective coping guidance. Thus, positive family functioning fosters adolescent emotional well-being, while poor functioning predicts mental health difficulties. Girls reported significantly higher anxiety levels than boys at all three time points, aligning with previous findings [ 60 , 61 ]. Girls also exhibited a faster rate of anxiety increase. The interaction of biological and cognitive factors may make girls more vulnerable to developing negative emotions and experiencing distress in response to stressors during adolescence [ 62 ], potentially due to heightened sensitivity in this developmental period [ 63 ]. Gender also differentiated trajectory subgroup membership, with girls more likely to belong to the Moderate/Increasing and High/Increasing anxiety groups. Adolescent girls often report encountering more stressful events and employing a wider range of coping strategies [ 64 , 65 ], and certain maladaptive coping strategies (e.g., avoidance, rumination, self-blame) can contribute to increased anxiety [ 66 ]. Given the gender differences in both the initial state and developmental trend of anxiety, interventions should consider biological factors and focus on fostering adaptive coping skills to promote mental health. While the primary conclusions regarding the existence of significant effects of gender and family functioning are supported by the model and align with theory, the borderline fit indicates that future research should explore alternative model specifications or include additional relevant predictors to better account for the observed data. Predictive role of anxiety trajectory for NSSI This study revealed the prospective influence of anxiety development on NSSI behavior. The predictive effect of high initial anxiety levels on NSSI is associated with the long-term depletion of emotional regulation resources. Individuals with high anxiety are more likely to detect potential threat cues [ 67 ] and allocate processing resources towards threat-related stimuli [ 68 ]. This sustained cognitive and emotional load may reduce coping resilience, by depleting cognitive resources needed for executive control and emotion regulation, thereby lowering the threshold for impulsive behaviors [ 69 , 70 ]. This can increase the likelihood of engaging in behaviors like NSSI that use physical pain to provide immediate distraction from psychological distress [ 71 ]. Accelerating anxiety trends in junior high students, potentially driven by cumulative environmental stressors (e.g., intensifying academic demands) or increased reliance on avoidance strategies [ 30 , 72 ], may heighten the risk of using NSSI to manage emotional distress [ 29 , 73 ], reflecting vulnerability in coping with adversity [ 32 ]. After identifying heterogeneous subgroups, the High/Increasing anxiety group faced an extremely high risk of NSSI, suggesting severe psychological distress and potentially deficient emotion regulation, leading to self-injury as a pressure release [ 74 ]. The Moderate/Increasing anxiety group also showed significantly elevated risk compared to the decreasing group, indicating that even moderate initial anxiety levels become problematic if they rise steadily. Furthermore, the sequential pathway from gender and family functioning predicting anxiety development, which in turn predicts NSSI, underscores the critical influence of biological and familial environmental factors on adolescent behavioral responses and mental health [ 75 ]. Therefore, early intervention during phases of increasing anxiety symptoms is crucial, particularly targeting high-risk trajectory groups. Policy and practice implications The findings offer several implications for policy and practice. First, schools should consider implementing regular, dynamic mental health screenings (e.g., at least annually) to monitor anxiety trajectories, rather than relying on single-point assessments. This would facilitate early identification of students showing increasing anxiety trends. Second, intervention programs should be tailored. Universal programs can promote mental health literacy and adaptive coping for all students. Targeted interventions, such as cognitive-behavioral therapy groups, should be made available for students in the Moderate/Increasing trajectory class. For the small but critical High/Increasing risk group, intensive, individualized support involving school counselors, psychologists, and family is necessary. Third, family-based components are essential. Parenting workshops can educate parents on the importance of family functioning, warm communication, and how to support their adolescent’s emotional development. Finally, the pronounced gender differences highlight the need for gender-sensitive approaches in both assessment and intervention design. For instance, assessment tools should consider gender-specific expressions of anxiety, and intervention programs may benefit from addressing gendered coping styles, socialization patterns, and stigma related to emotional expression in boys and girls. Limitations Several limitations should be acknowledged. First, the relatively long interval between waves (one year) might limit the ability to capture finer-grained fluctuations in adolescent anxiety; shorter intervals could better describe developmental trends. Second, reliance solely on self-report measures introduces potential biases such as social desirability; future research could benefit from multi-informant (e.g., parent, teacher) or multi-method assessment. Third, the use of a single binary item to assess NSSI is a significant limitation, as it precludes analysis of frequency, severity, and methods, which are important for understanding the behavior’s clinical significance. Fourth, the convenience sampling from four schools in a single city in Central China limits the generalizability of the findings to other regions, rural areas, or different socio-cultural contexts. Fifth, the non-random attrition of students with higher initial anxiety and NSSI may have led to an underestimation of the true anxiety levels and their association with NSSI, despite the use of FIML. Sixth, the conditional LGCM showed marginally acceptable fit, indicating that the model specification could be improved in future research. Finally, the observational longitudinal design precludes definitive causal conclusions; the relationships identified are predictive and associative in nature. Conclusion This study examined the developmental characteristics of anxiety and gender differences among adolescent students, while investigating the relationships between family functioning, anxiety, and NSSI. The results indicate that anxiety levels in junior high school students increased progressively over time, with gender differences observed in the patterns of these changes. Family functioning served as a negative predictor of anxiety problems, and the developmental trajectory of anxiety predicted the occurrence of NSSI behavior. It is important to note that these findings and implications should be interpreted within the context of the study’s limitations, including the use of self-report measures, a convenience sample from one urban area, and a single-item assessment for NSSI. Notwithstanding these findings suggest that adolescent mental health interventions should prioritize gender as a significant predictive factor for anxiety, and that early intervention strategies targeting low family functioning may help prevent anxiety problems in junior high students. To prevent negative behavioral responses, it is essential not only to identify individuals with high anxiety but also to focus on high-risk subgroups exhibiting rapidly increasing anxiety levels. In summary, the study reveals interactions between internalizing emotional problems and various factors in junior high students, emphasizing the need to address individual differences in interventions targeting emotional issues. Supplementary Information Supplementary Material 1. (29.1KB, docx) Acknowledgements We sincerely thank all the participants and colleagues who have contributed their time and effort to the data collection work. Authors’ contributions Junkai Yang conceived the study. The data analysis, results interpretation, and original manuscript writing were done by Junkai Yang and Juan Zhang. Yaofei Xie reviewed the original manuscript. Yufan Zhou, Taimin Wu, and Juan Qiao conducted data collection and organization. Bing Xiang Yang and Dan Luo managed and coordinated the planning and execution of research activities. Lianzhong Liu and Ping Yu supervised this work. All authors read and approved the final manuscript. Funding We declare that the study presented here has not received any funding that could influence the findings. Data availability The data that support the findings of the study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. Declarations Ethics approval and consent to participate This study has been submitted to and approved by the local ethics committee, the Wuhan Mental Health Center Ethics Committee (Ethical approval number: KY2021.11.01). The analysis in this paper involves a human questionnaire and conduct human data collection, we declare that we have adhered to the Declaration of Helsinki to this effect. Informed consent has been obtained from all research participants and from the parents or legal guardians of participants under the age of 16. Consent for publication We consent to the publication of this study. The manuscript does not contain any identifiable images or other personal/clinical information that could compromise participant anonymity. Competing interests The authors declare no competing interests. Footnotes Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Junkai Yang and Juan Zhang contributed equally to this work. Contributor Information Lianzhong Liu, Email: [email protected]. Ping Yu, Email: [email protected]. References 1. Young KS, Sandman CF, Craske MG. Positive and negative emotion regulation in adolescence: links to anxiety and depression[J]. Brain Sci. 2019;9(4):76. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. Vos T, Lim SS, Abbafati C, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019[J]. The lancet. 2020;396(10258):1204-22. 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(29.1KB, docx) Data Availability Statement The data that support the findings of the study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. 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