Impact of Sleep Quality on Depressive Symptoms in Adolescents: The Mediating Role of Coping Strategies and Limited Moderating Effect of Self-Efficacy

Impact of Sleep Quality on Depressive Symptoms in Adolescents: The Mediating Role of Coping Strategies and Limited Moderating Effect of Self-Efficacy | 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 Impact of Sleep Quality on Depressive Symptoms in Adolescents: The Mediating Role of Coping Strategies and Limited Moderating Effect of Self-Efficacy Juan Zhao, Juanjuan Liu, Ying Li, Yangjie Chen, Xiaoxia You, Junxiang Cheng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5277627/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 Background Adolescence is a critical developmental stage characterized by emotional challenges and an increased vulnerability to depressive symptoms (DS). While poor sleep quality (PSQ) is known to correlate with DS, the roles of coping strategies (CS) and self-efficacy (SE) in this relationship remain underexplored.This study investigates the relationships between sleep quality (SQ), CS, SE and DS among adolescents, emphasizing the mediating role of CS and the moderating role of SE in the SQ-DS relationship. Methods Utilizing a cross-sectional design, data were collected from 1,132 junior high school students in China between January and June 2023.Participants completed self-report questionnaires assessing the Self-Rating Depression Scale (SDS), the Pittsburgh Sleep Quality Index (PSQI), the Simplified Coping Style Questionnaire (SCSQ), and the General Self-Efficacy Scale (GSES). Descriptive statistics, correlation analysis, regression analysis, and mediation-moderation analysis using PROCESS were conducted to examine variable relationships. Results The findings revealed a significant positive relationship between PSQI and SDS (Beta = 0.350, p < 0.001), indicating that PSQ is associated with higher levels of DS. CS acted as a mediator; specifically, positive coping (SCSQ.AR) negatively predicted SDS (Beta = -0.432, p < 0.001), whereas negative coping (SCSQ.NC) positively predicted SDS (Beta = 0.270, p < 0.001). GSES did not significantly moderate the direct relationship between PSQI and SDS (B = -0.0076, p = 0.5437), but it partially moderated the indirect effects through negative coping. Adolescents with lower SE were more prone to adopt negative coping strategies (NCS), which in turn exacerbated their DS. Conclusion PSQ is significantly associated with increased DS in adolescents, with NCS intensifying this relationship, especially among those with lower SE. Although enhancing SE alone may not significantly influence the direct impact of PSQ on DS, interventions that promote positive coping strategies (PCS) and reduce NCS, combined with efforts to enhance SE, could effectively alleviate DS. Future research should adopt a longitudinal approach to further elucidate these relationships and inform targeted mental health interventions for adolescents. Adolescents Sleep Quality Depressive Symptoms Coping Strategies Self-Efficacy Mediation Moderation Figures Figure 1 Background Adolescence is a pivotal developmental stage, bridging childhood and adulthood, characterized by rapid psychological, physiological, and social changes. This period is often fraught with emotional challenges as adolescents navigate various academic, social, and familial pressures that can significantly impact their mental well-being. As Kaman et al. note, "the emotional landscape of adolescents is shaped by multifaceted stressors, leading to an increased vulnerability to depressive symptoms (DS)" ( 1 ) . Recent trends indicate a troubling rise in the prevalence of depression among adolescents, particularly among those facing stressful life changes ( 2 ) . The implications of depression during this critical period extend beyond immediate emotional distress, disrupting daily functioning, academic performance, and potentially influencing long-term mental health trajectories and personality development. As such, it is imperative to identify risk factors associated with adolescent depression and to explore moderating mechanisms that could inform effective interventions ( 3 ) . One critical factor in this context is sleep quality (SQ), which is fundamental to both physical and mental health. High-quality sleep is essential for cognitive functioning, emotional regulation, and behavioral control, especially in adolescents. Vazsonyi et al. emphasize that "adequate sleep is not merely a luxury; it is a vital component of adolescent development" ( 4 ) . Unfortunately, many adolescents experience poor sleep quality (PSQ), which has been linked to a heightened risk of developing DS ( 5 , 6 ) . The physiological changes that occur during adolescence, such as delayed circadian rhythms, further exacerbate this issue, increasing vulnerability to sleep deprivation and impairing emotional regulation ( 7 ) . The existing literature has established a bidirectional relationship between sleep disturbances and depression, where sleep issues not only result from depressive symptoms but also act as contributing factors ( 4 , 5 , 8 , 9 ) . As Roberts and Duong assert, "improving sleep quality can significantly alleviate depressive symptoms across different age groups ( 10 ) . This underscores the critical importance of addressing sleep disturbances as part of comprehensive mental health interventions for adolescents. Coping styles (CS) also play a significant role in how adolescents manage stress and emotional challenges. These strategies can be categorized into positive coping strategies(PCS) and negative coping strategies(NCS). PCS, which include problem-solving and seeking social support, are associated with improved mental health outcomes and reduced psychological distress, as evidenced by lower levels of cortisol and inflammatory markers ( 11 ) . Conversely, NCS, such as avoidance and denial, are linked to increased psychological distress and heightened risks for conditions like depression and anxiety ( 12 ) . Research indicates that adolescents who employ PCS demonstrate resilience and enhanced emotional regulation, whereas those relying on NCS experience exacerbated distress ( 13 ) . As highlighted by Orzechowska et al., "passive coping can serve as a mediator between stress and mental health issues, contributing to disorders like post-traumatic stress disorder (PTSD) and depression" ( 12 ) . Additionally, Self-efficacy (SE), is a vital psychological construct influencing adolescent mental health. According to Bandura as "an individual's belief in their ability to succeed in specific situations," self-efficacy significantly impacts how adolescents cope with stress ( 14 ) . Adolescents with higher SE tend to exhibit greater confidence in dealing with stress and are more likely to adopt positive coping strategies (PCS), which help them better manage negative emotions ( 15 ) . Conversely, adolescents with lower SE often feel overwhelmed and are more likely to resort to NCS, increasing their risk for depression. Yang proposed that SE acts as a protective factor, mitigating the adverse effects of life stressors on emotional well-being ( 16 ) . Thus, SE can play a dual role as both a mediator and moderator in the relationship between stress and DS ( 17 ) . As awareness of adolescent mental health issues grows, there is an increasing emphasis on the myriad factors influencing DS. Key psychological variables—such as SQ, CS, and SE—have emerged as significant determinants of adolescent depression. Zhang et al. identified PSQ as a crucial predictor of DS, while Brink et al. emphasized the impact of SQ on emotional regulation capabilities ( 17 , 18 ) . Concurrently, CS and SE have gained traction as central themes in depression intervention research. Ren et al. demonstrated that PCS can effectively mitigate negative emotions, while NCS may exacerbate DS ( 19 ) . Furthermore, Brink suggested that SE significantly influences how individuals manage stress, thereby enhancing psychological resilience and emotional regulation ( 17 ) . Despite the existing literature highlighting the connections among SQ, DS, CS, and SE, systematic research focusing specifically on adolescent populations remains limited. Particularly, the influence of CS and SE on the relationship between SQ and DS amidst academic, social, and familial pressures warrants further exploration. This study aims to explore the mediating effect of SE and CS on the relationship between SQ and DS in adolescents, employing a cross-sectional research design. Additionally, it will investigate whether SE moderates the use of CS in this context. By exploring these dynamics, this study seeks to deepen our understanding of how these psychological factors collectively influence adolescent mental health, potentially informing the development of targeted interventions to support this vulnerable population. Research Hypotheses Drawing upon the literature review and theoretical framework outlined earlier, this study proposes the following hypotheses: Hypothesis 1 SQ (measured by the Pittsburgh Sleep Quality Index, PSQI) directly influences DS (measured by the Self-Rating Depression Scale, SDS). PSQ will be associated with more severe DS. Hypothesis 2 SE (measured by the General Self-Efficacy Scale, GSES) will indirectly influence the relationship between SQ and DS by moderating the use of CS, specifically PCS and NCS. Hypothesis 3 PCS (active response subscale of the Simplified Coping Style Questionnaire, SCSQ.AR) moderate DS associated with PSQ, whereas NCS (measured by the negative coping subscale of the Simplified Coping Style Questionnaire, SCSQ.NC) will exacerbate DS. Hypothesis 4 SE will have a more significant moderating effect on DS through its influence on NCS. Methods Research Design This study utilized a cross-sectional research design to investigate the relationships among SE, CS, SQ, and DS in adolescents. The analysis specifically examined the correlations between these variables, with a particular focus on the potential mediating effects of CS and the moderating effects of SE on the relationship between SQ and DS. Study Population Data were gathered from 1,200 junior high school students enrolled in three middle schools within a single province in China, between January and June 2023. After excluding responses that did not meet the study’s rigorous criteria, 1,132 valid questionnaires were retained for subsequent analysis. Inclusion criteria mandated that participants demonstrate good mental health, possess normal literacy and comprehension abilities, be free from serious physical illnesses, and provide informed consent from both themselves and their guardians, thereby ensuring voluntary participation. Data Collection and Quality Assurance To uphold the integrity of the data collection process, all investigators underwent standardized training prior to questionnaire distribution. Each questionnaire was collected using anonymous numbering to ensure that no identifying information about participants was recorded or identified. The questionnaire was accompanied by a separate consent form that participants were asked to take home and have their guardian read and sign before completing the questionnaire. If the guardian did not consent, the participant would not complete the questionnaire. The consent form was only used to ensure that participants' participation in the study was voluntary, and the questionnaire itself was anonymized for data collection and did not contain any personally identifiable information. Completed questionnaires were collected by investigators the following day. To ensure data accuracy, a two-person data entry and verification process was implemented, complemented by a third person who randomly checked 20% of the questionnaires to further validate the accuracy and reliability of the data entry process. Research Tool The Self-Rating Depression Scale (SDS) The SDS is a widely utilized tool for assessing depressive symptoms, encompassing emotional, somatic, and psychological dimensions ( 20 ) . This instrument comprises items rated on a 4-point Likert scale, ranging from 1 ("never") to 4 ("often"), with higher scores indicating more severe depressive symptoms. A score of 53 or above serves as the threshold for identifying the presence of depressive symptoms. In this study, the SDS functioned as the dependent variable for evaluating DS in relation to SQ and CS, exhibiting a Cronbach's alpha of 0.836, indicating good internal consistency. The Pittsburgh Sleep Quality Index (PSQI) The PSQI is a standardized measure for assessing sleep quality ( 21 ) . It evaluates seven components: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. Each component is scored from 0 to 3, yielding a total score between 0 and 21, where higher scores signify poorer sleep quality. In this study, the PSQI was employed as the independent variable to explore the association between SQ and DS, with a Cronbach's alpha of 0.902, reflecting excellent reliability.. The General Self-Efficacy Scale (GSES) The GSES assesses an individual's belief in their capability to navigate challenging situations ( 22 ) . The scale consists of items rated on a 4-point Likert scale, with higher scores denoting stronger self-efficacy. In this study, the GSES was used as a moderating variable to examine its influence on the relationship among CS, SQ, and DS. The GSES exhibited a Cronbach's alpha of 0.908, demonstrating high reliability. The Simplified Coping Style Questionnaire (SCSQ) The SCSQ evaluates coping mechanisms and comprises two dimensions: active response (SCSQ.AR) and negative coping (SCSQ.NC) ( 23 ) . Each item is scored on a 4-point Likert scale, ranging from 1 ("never") to 4 ("often"). SCSQ.AR is generally associated with improved mental health outcomes, whereas SCSQ.NC is linked to heightened psychological distress. In this study, the SCSQ served as a mediating variable to assess its impact on the relationship between SQ and DS, achieving a Cronbach's alpha of 0.866, indicating of good internal consistency. Data Analysis Data analysis was conducted using IBM SPSS 25 and the PROCESS macro developed by Hayes, incorporating a comprehensive array of statistical techniques. These techniques included descriptive statistics, correlation analysis, partial correlation analysis, regression analysis, mediation analysis, and moderated mediation analysis. Descriptive statistics were employed to summarize the sample characteristics, calculating medians and interquartile ranges. Non-parametric tests were also utilized to compare differences between depressive symptom subgroups, providing preliminary insights into the distribution and characteristics of the study variables. Spearman's correlation coefficients were calculated to explore the relationships among key variables, including SDS, PSQI, SCSQ, and GSES. To account for potential confounders, partial correlation analyses were performed while controlling for demographic variables such as academic performance and family income. Spearman's correlation coefficients were calculated to explore the relationships among key variables, including SDS, PSQI, SCSQ, and GSES. To account for potential confounders, partial correlation analyses were performed while controlling for demographic variables such as academic performance and family income. Utilizing the PROCESS macro (Model 4) established by Hayes, mediation analysis examined the roles of SCSQ and GSES in the relationship between PSQI and SDS. A bootstrap sample of 5,000 iterations was employed to estimate the indirect effects, with significance tests and bootstrap confidence intervals (CIs) determining whether SCSQ.AR, SCSQ.NC, and GSES partially or fully mediated the impact of PSQI on SDS. The PROCESS macro (Model 8) was employed for moderated mediation analysis to investigate whether GSES moderated the mediating effect of SCSQ in the PSQI-SDS relationship. Path coefficients were assessed at varying levels of GSES to elucidate its moderating role within these mediational pathways. The moderation effect was analyzed by evaluating how the interaction between GSES and PSQI influenced the indirect effects via SCSQ on SDS. Results Descriptive Statistics and Analysis of Variance The descriptive statistics revealed that the overall sample exhibited a median score of 53.75 on SDS and a median score of 4.00 on PSQI. These findings indicate that, while the overall SQ of the sample was generally satisfactory, a notable proportion of participants reported experiencing DS. Analysis of variance (ANOVA) further uncovered significant differences in DS across various demographic factors, including religion, area of residence, grade level, exposure to violence, parental marital status, parental relationships, family economic status, parental literacy, parental occupation, the number of close friends, age, and history of self-harm (i.e. Table 1). Correlation and partial correlation analysis Spearman correlation analyses were conducted to examine the bivariate relationships among the main variables, as presented in Table 2 . The results indicated a statistically significant positive correlation between PSQI and SDS, with a correlation coefficient of r = 0.327 (p < 0.01),, thereby confirming Hypothesis 1 .This finding suggests that PSQ is associated with elevated levels of DS ( 24 ) . Additionally, SCSQ.AR were negatively correlated with SDS, implying that adolescents who employed more PCS tended to experience fewer DS. Conversely, SCSQ.NC showed a positive correlation with SDS, indicating that reliance on NCS exacerbates DS ( 25 ) . Furthermore, GSES exhibited a negative correlation with SDS, suggesting that higher levels of SE are associated with lower levels of DS. To control for potential confounding effects from significant demographic variables identified in the univariate analyses (e.g., grade, family income, parental occupation, and close friendships), rank correlation analyses were conducted alongside partial correlation analyses (i.e. Table 3 ). The results aligned with those of the Spearman correlation analysis, further validating Hypothesis 1 . Table 2 Bivariate correlation matrix of SDS, PSQI, GSES, and SCSQ Variable SDS SCSQ.AR SCSQ.NC SCSQ GSES PSQI SDS 1 SCSQ.AR − .431** 1 SCSQ.NC .208** .138** 1 SCSQ − .505** .669** − .590** 1 GSES − .243** .321** 0.013 .246** 1 PSQI .327** − .175** .198** − .261** − .150** 1 Note: ** p < 0.01; Table 3 Partial correlation analysis by Rank Cases of the main variables Variable RSDS RPSQI RSDS 1 RPSQI 0.166** 1 Note: ** p < 0.01; RSDS, RPSQI are rank-transformed variables of SDS, PSQI, respectively; control variables: Religion & Place of Residence & grades & Traumatic experiences & Divorced parents & Parental relationship & Household financial situation & Father's culture & Mother' s culture & Father's occupation & Mother's occupation & Close friends & history of suicide & Age. Regression Analysis Initially univariate regression analyses assessed the direct effect of PSQI on SDS. The findings indicated that PSQI serves as a significant positive predictor of SDS, suggesting that PSQ was associated with higher levels of DS ( 26 ) . The standardized regression coefficient for PSQI in the model without mediators was Beta = 0.350, p < 0.001, explaining 12.3% of the variance in DS. This supports Hypothesis 1 , which posited a significant relationship between PSQI and SDS. Subsequently, a multiple linear regression model was employed that included the mediating variables: SCSQ.AR, SCSQ.NC, and GSES. The analysis revealed a substantial increase in the explained variance to 34.5% (R² = 0.345, p < 0.001), indicating that CS and SE partially mediate the relationship between PSQI and SDS. Specifically, SCSQ.AR emerged as a significant negative predictor of SDS (Beta = -0.432, p < 0.001), suggesting that adolescents who utilize more PCS experienced fewer DS ( 27 ) . Conversely, SCSQ.CN demonstrated a significant positive predictive effect on SDS (Beta = 0.270, p < 0.001), confirming Hypothesis 3 that NCS exacerbate DS ( 28 ) . Furthermore, GSES significantly predicted SDS (Beta = -0.083, p = 0.001), thereby supporting Hypothesis 2 , which posited that GSES would indirectly affect the relationship between PSQI and SDS by moderating CS ( 29 ) . Notably, the direct effect of PSQI on SDS was diminished (from Beta = 0.350 to Beta = 0.225, p < 0.001) upon the introduction of the mediating variables (SCSQ and GSES), indicating that these factors partially mediate the relationship between SQ and DS (i.e. Table 4 ). Table 4 The Effects of PSQI, SCSQ, and GSES on SDS Model I: PSQI on SDS Predictor B Beta LLCI ULCI SE t P-value VIF Constant 45.571 44.34 46.802 0.627 72.634 < 0.001 PSQI 1.484 0.35 1.252 1.715 0.118 12.578 < 0.001 1 Model Summary R = 0.350;R2 = 0.122; F = 158.204; P < 0.001; Durbin-Watson = 1.741 Model II: PSQI, SCSQ, GSES on SDS Constant 57.461 54.97 59.95 1.269 45.263 < 0.001 PSQI 0.952 0.225 0.743 1.16 0.106 8.959 < 0.001 1.083 SCSQ.AR -0.605 -0.432 -0.678 -0.533 0.037 -16.405 < 0.001 1.195 SCSQ.NC 0.64 0.27 0.522 0.757 0.06 10.675 < 0.001 1.102 GSES -0.144 -0.083 -0.23 -0.058 0.044 -3.276 0.001 1.116 Model Summary: R = 0.587;R2 = 0.345; F = 158.204; P < 0.001; Durbin-Watson = 1.741 Mediation Analysis Utilizing Hayes' PROCESS model, we examined the mediating roles of SCSQ and GSES in the relationship between PSQI and SDS (i.e. Table 5 ). The analysis revealed that both positive coping strategies (SCSQ.AR) and negative coping strategies (SCSQ.NC) significantly mediated this relationship, supporting Hypothesis 3 . Specifically, the indirect effect of SCSQ.AR was B = -0.605, Beta = -0.432 (p < 0.001), indicating that PSQ led to a reduction in the use of PCS, subsequently increasing DS. Likewise, the indirect effect of SCSQ.NC was B = 0.640, Beta = 0.270 (p < 0.001), suggesting that PSQ exacerbated DS through reliance on NCS. These findings underscore the pivotal mediating role of SCSQ in the PSQI-SDS relationship, strongly supporting Hypothesis 3 . Conversely, while the mediating effect of GSES was statistically significant, it was comparatively modest, thereby supporting Hypothesis 2 . T The indirect effect of GSES was B = -0.144, Beta = -0.083 (p = 0.001), indicating that higher SE is associated with improved SQ, which in turn mitigates DS ( 30 ) . Nonetheless, relative to the effects of SCSQ.AR and SCSQ.NC, the mediating role of GSES was less pronounced, suggesting that while it plays a role, its influence on the relationship between PSQI and SDS is limited. Overall, the results illustrate that PSQI exerts a more substantial indirect effect on SDS by decreasing SCSQ.AR and increasing SCSQ.NC, while the mediating effect of GSES, despite being statistically significant, remains weaker. These findings affirm the mediating roles of both SCSQ and GSES in the relationship between PSQI and SDS, thus confirming Hypotheses 2 and 3. Table 5 SCSQ and GSES on the Relationship between PSQI and SDS Mediator B Beta LLCI ULCI SE t P-value SCSQ.AR -0.465 -0.154 -0.680 -0.291 0.089 -5.235 < 0.001 SCSQ.NC 0.319 0.1782 0.2160 0.4214 0.0599 6.0895 < 0.001 GSES -0.323 -0.131 -0.465 -0.181 0.073 -4.452 < 0.001 Model Summary: Total Direct Effect (PSQI on SDS): B = 0.952, Beta = 0.225(95% CI [0.7435, 1.1605], p < 0.001 Total Indirect Effect: B = 0.5320 ,Beta = 0.1256 (95% CI [0.3999, 0.6712]), p < 0.001 Indirect Effect via SCSQ.AR: B= -0.2817, Beta= -0.0665, 95% CI [0.171, 0.405], p < 0.001 Indirect Effect via SCSQ.NC: B = 0.2039, Beta = 0.0482, 95% CI [0.128, 0.286], p < 0.001 Indirect Effect via GSES: B = -0.0464, Beta = -0.0110, 95% CI [0.0142, 0.0885], p = 0.001 Moderated Mediation Analysis To investigate whether GSES moderates the mediating effect of SCSQ on the PSQI-SDS relationship, moderated mediation analyses were conducted using Hayes' PROCESS model 8 (i.e. Table 6 ). The results indicated significant direct effects of PSQI and SCSQ on SDS. Specifically, PSQI was found to directly increase SDS (B = 1.1219, p = 0.0002), while SCSQ.AR significantly attenuated SDS (B = -0.6063, p < 0.001), and SCSQ.NC exacerbated SDS (B = 0.6418, p < 0.001). However, the interaction between GSES and PSQI was not significant (B = -0.0076, p = 0.5437), indicating that GSES does not significantly moderate the direct effect of PSQI on SDS. Thus, GSES does not play a moderating role in the PSQI-SDS relationship when looking solely at the direct pathway. Table 6 Regression Results of PSQI, SCSQ, and GSES on SDS Outcome Predictor B SE t LLCI ULCI p-value SCSQ.AR Constant 11.3039 1.4358 7.873 8.4867 14.1211 < 0.001 PSQI -0.1533 0.2488 -0.616 -0.6414 0.3349 0.538 GSES 0.4014 0.0574 6.995 0.2888 0.5140 < 0.001 PSQI × GSES -0.0086 0.0103 -0.834 -0.0289 0.0116 0.405 SCSQ.NC Constant 7.0787 0.8833 8.014 5.3455 8.8118 < 0.001 PSQI 0.0944 0.1531 0.617 -0.2059 0.3947 0.537 GSES -0.0080 0.0353 -0.226 -0.0772 0.0613 0.822 PSQI × GSES 0.0105 0.0063 1.647 -0.0020 0.0229 0.100 SDS Constant 56.6841 1.8023 31.451 53.1478 60.2204 < 0.001 PSQI 1.1219 0.2993 3.749 0.5347 1.7091 0.0002 SCSQ.AR -0.6063 0.0369 -16.412 -0.6787 -0.5338 < 0.001 SCSQ.NC 0.6418 0.06 10.689 0.524 0.7597 < 0.001 GSES -0.1101 0.0706 -1.56 -0.2487 0.0284 0.119 PSQI × GSES -0.0076 0.0124 -0.607 -0.0319 0.0168 0.544 Note: Direct Effects: These are shown for PSQI, SCSQ.AR, SCSQ.NC, and GSES on SDS and their respective mediators. Moderation Effects: The interaction terms (PSQI × GSES) are included to reflect whether GSES moderates the relationships between PSQI, SCSQ, and SDS. None of these moderation effects are statistically significant (p > 0.05). Table 7 outlines the indirect effects of PSQI on SDS through SCSQ at various levels of GSES. For individuals with lower GSES, the indirect effect of PSQI on SDS via SCSQ.AR was B = 0.1771 (95% CI [0.0309, 0.3326]), suggesting a modest mitigating effect of SCSQ.AR on DS. In contrast, for individuals with higher GSES, the indirect effect increased to B = 0.2505 (95% CI [0.0806, 0.4324]). Despite this increase, the effect did not significantly strengthen with higher GSES, indicating that the protective role of SCSQ.AR was consistent across different levels of GSES but not substantially moderated by it. Regarding SCSQ.NC, although the moderating effect of GSES was not statistically significant, the indirect effect of PSQI on SDS through SCSQ.NC was evident at various levels of GSES, partially supporting Hypothesis 4 . For individuals with low GSES, the indirect effect was B = 0.1690 (95% CI [0.0709, 0.2691]), whereas for those with high GSES, it increased to B = 0.2634 (95% CI [0.1625, 0.3836]). This suggests that while SCSQ.NC consistently exacerbated DS, its negative impact was more pronounced among individuals with higher GSES. Table 7 Conditional Indirect Effects of PSQI on SDS via SCSQ at Different Levels of GSES Moderator (GSES) Direct Effect Indirect Effect (via SCSQ.AR) LLCI (SCSQ.AR) ULCI (SCSQ.AR) Indirect Effect (via SCSQ.NC) LLCI (SCSQ.AR) ULCI (SCSQ.AR) 16.144 (Low) 1.000 0.177 0.031 0.333 0.169 0.071 0.269 23.178 (Medium) 0.947 0.214 0.102 0.337 0.216 0.136 0.302 30.213 (High) 0.894 0.251 0.081 0.4324 0.263 0.163 0.384 Note: VIA indicates the pathway or mediation through which PSQI affects SDS using the coping strategies SCSQ.AR or SCSQ.NC. Figure 1 illustrates these relationships, depicting how PSQI influences SDS via SCSQ.AR and SCSQ.NC. While GSES did not significantly moderate the direct effect of PSQI on SDS, it partially moderated the relationship between SCSQ.NC and SDS, particularly for individuals with higher levels of GSES. In contrast, the role of SCSQ.AR in mitigating SDS remained consistent, regardless of GSES. In summary, SCSQ.NC had the strongest mediating effect between PSQI and SDS, particularly for individuals with higher levels of GSES. In contrast, SCSQ.AR had a weaker mitigating effect on SDS, and its influence did not significantly increase with higher GSES. Therefore, while GSES moderates the effect of SCSQ.NC on SDS, it does not significantly moderate the overall relationship between PSQI and SDS. These findings suggest that Hypothesis 4 is only partially supported, as the moderation effect of GSES is more pronounced for SCSQ.NC than for SCSQ.AR. Discussion This study systematically explored the intricate relationships between SQ, DS, CS, and SE in adolescents. The findings largely supported the proposed hypotheses, while also highlighting specific limitations in the observed effects. Notably, a significant relationship between SQ and DS was confirmed, consistent with Hypothesis 1 . Additionally, the study validated Hypothesis 3 by demonstrating the mediating role of CS in the relationship between SQ and DS. Hypothesis 2 was also supported, revealing a partial mediating effect of SE. Furthermore, results indicated that SE partially moderated the influence of CS on the relationship between SQ and DS, aligning with Hypothesis 4 . The Relationship Between Sleep and Mental Health The study confirmed a robust connection between SQ and DS, underscoring the critical role of sleep in mental health ( 18 ) . This finding reinforces existing theories regarding the bidirectional relationship between sleep disturbances and depression and highlights adolescence as a critical period for implementing sleep interventions ( 10 ) . Addressing sleep issues could yield long-term benefits in preventing or alleviating DS, particularly given that adolescents often face unique sleep challenges, such as delayed circadian rhythms ( 31 ) . This research contributes to the literature emphasizing sleep's foundational role in emotional well-being, expanding it by exploring the indirect effects of CS and SE. The Mediating Role of CS The findings that PCS mitigate DS, while NCS exacerbate them underscore the importance of incorporating coping interventions in adolescent mental health programs ( 18 ) . The study showed that PCS buffer the impact of poor sleep on DS, suggesting that interventions designed to enhance these strategies could significantly alleviate the emotional burden associated with PSQ ( 18 ) . Adolescents struggling with SQ may benefit more from programs focused on adaptive coping skills, such as problem-solving and seeking social support ( 32 ) . However, simply improving SQ may not suffice without addressing how adolescents cope with stress ( 33 ) . Conversely, the detrimental impact of NCS highlights the urgent need for interventions aimed at reducing reliance on maladaptive strategies, such as avoidance and emotional venting ( 34 ) . Educating adolescents to recognize and transition away from NCS could help mitigate the risk of DS, even when SQ is compromised ( 35 ) . This reinforces the necessity for school and community-based mental health programs that integrate coping mechanisms alongside education about sleep. The Mediating Role of SE While SE demonstrated a smaller mediating role than initially expected, it remains an essential construct for emotional regulation ( 36 ) . Adolescents with higher SE are better equipped to manage the emotional stress associated with PSQ, aligning with Bandura’s theory that SE acts as a buffer against emotional distress ( 37 ) . However, the relatively modest mediating effect of SE suggests that it may not be sufficient on its own to significantly alter the relationship between SQ and DS. This observation raises important questions for future interventions. While programs designed to enhance SE, particularly those utilizing cognitive-behavioral therapy, are beneficial, they may need to be paired with coping skills training for a more comprehensive impact ( 38 , 39 ) . Practically, adolescents might derive greater benefits from multi-faceted interventions that simultaneously target both SE and coping skills, thereby maximizing resilience against DS. The Moderating Role of SE The study revealed that SE did not significantly moderate the direct relationship between PSQ and DS, an unexpected finding that merits further investigation. One possible explanation is that the effect of SE may be mediated indirectly through coping strategies rather than directly moderating emotional outcomes ( 40 ) . Adolescents with low self-efficacy may lack the psychological resources needed to transition from NCS to PCS, which could account for the absence of significant moderation in the direct pathway ( 17 ) . Additionally, external factors such as family support, peer relationships, and socioeconomic status may exert stronger influences on the mental health of adolescents, potentially diminishing the observable impact of SE ( 41 ) . The cross-sectional design of this study also limits the capacity to observe dynamic changes in SE over time, which may reveal more nuanced moderating effects. Future longitudinal studies could more effectively assess how SE develops and interacts with SQ and CS over time. Broader implications for mental health interventions The partial support for Hypothesis 4 - suggesting that SE moderates the effects of NCS on DS - highlights the importance of addressing NCS in high-risk groups, particularly adolescents with low SE. While enhancing SE may not directly alter positive coping behaviors, it could help mitigate the adverse effects of NCS ( 42 ) . This indicates a need for targeted interventions that aim not only to boost SE but also to reduce adolescents' reliance on on NCS ( 32 ) . Moreover, the non-significant moderation of SE in the overall SQ-DS pathway suggests that efforts to bolster SE should be complemented by environmental support systems, such as family, school, and peer networks ( 43 ) . These systems may provide the emotional and psychological resources necessary for adolescents to effectively utilize PCS ,thereby enhancing the protective role of SE ( 44 ) . Limitations and Future Directions This study presents several limitations that merit consideration. Firstly, the cross-sectional design constrains the ability to establish causality, complicating the assessment of directional relationships among SQ, CS, SE, and DS ( 45 ) . Additionally, reliance on self-reported data introduces potential biases, as adolescents may underreport symptoms or overestimate the use of PCS. Future research should employ longitudinal designs to better capture the temporal dynamics of these variables and facilitate a more robust assessment of causality ( 16 ) . Another limitation pertains to the specific cultural and geographical context of the study, as the data were collected from adolescents in a single province in Chinese. Cultural factors can significantly influence CS, SE, and mental health, which may limit the generalizability of the findings to other populations. Therefore, extending research to encompass diverse cultural and socio-economic contexts is vital for testing the universality of these findings and enhancing their applicability across different settings. Conclusion This study enhances our understanding of how SQ, CS, and SE interact to influence mental health in adolescents. While SQ remains a critical predictor of DS, addressing CS, particularly by reducing the use of NCS, can significantly mitigate the impact of poor sleep on depression ( 17 ) . Improving SE, while beneficial, may be most effective when combined with interventions that PCS and discourage NCS. Future studies should further explore these relationships in longitudinal settings and across diverse populations to develop more tailored and effective mental health interventions for adolescents. Abbreviations DS - Depressive Symptoms SDS - Self-Rating Depression Scale SQ - Sleep Quality PSQ - Poor Sleep Quality PSQI - Pittsburgh Sleep Quality Index CS - Coping Styles PCS - Positive Coping Strategies NCS - Negative Coping Strategies SCSQ - Simplified Coping Style Questionnaire SCSQ.AR - Simplified Coping Style Questionnaire Active Response SCSQ.NC - Simplified Coping Style Questionnaire Negative Coping SE - Self-Efficacy GSES - General Self-Efficacy Scale PTSD - Post-Traumatic Stress Disorder Declarations Ethics approval and consent to participate This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Ethics Committee of the First Hospital of Shanxi Medical University (approval number: 2021k-149). All participants and their guardians provided informed consent prior to participation, ensuring compliance with ethical standards. Consent for publication The data in this study were collected using anonymous numbering and did not contain any information that could personally identify the participants. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This research was supported by a grant from the Basic Research Program of Shanxi Province, No.202103021223435. Authors' contributions NAS designed the study and revised the manuscript. JZ completed all analyses and was responsible for the first draft of the manuscript. JJL, YL, XXY and JXC participated in data collection and entry. YJC participated in the drafting of the manuscript. All authors have read and approved the final submitted manuscript. Acknowledgements We thank the research team for their contribution to data collection. We are even more sincerely grateful to all the adolescents and their guardians who participated in this study and whose participation was crucial to the success of this research project. References Kaman A, Otto C, Klasen F, Westenhöfer J, Reiss F, Hölling H, et al. Risk and resource factors for depressive symptoms during adolescence and emerging adulthood – A 5-year follow-up using population-based data of the BELLA study. J Affect Disord. 2021;280:258–66. Lu W. Adolescent Depression: National Trends, Risk Factors, and Healthcare Disparities. Am J Health Behav. 2019;43(1):181–94. Michelini G, Perlman G, Tian Y, Mackin DM, Nelson BD, Klein DN, et al. Multiple domains of risk factors for first onset of depression in adolescent girls. J Affect Disord. 2021;283:20–9. Vazsonyi AT, Liu D, Blatny M. Longitudinal bidirectional effects between sleep quality and internalizing problems. 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J Clin Sleep Med. 2019;15(9):1303–10. Zhu J, Sun L, Zhang L, Wang H, Fan A, Yang B et al. Prevalence and Influencing Factors of Anxiety and Depression Symptoms in the First-Line Medical Staff Fighting Against COVID-19 in Gansu. Front Psychiatry [Internet]. 2020 Apr 29 [cited 2024 Sep 28];11. https://www.frontiersin.org/journals/psychiatry/articles/ 10.3389/fpsyt.2020.00386/full Huang Y, Su X, Si M, Xiao W, Wang H, Wang W, et al. The impacts of coping style and perceived social support on the mental health of undergraduate students during the early phases of the COVID-19 pandemic in China: a multicenter survey. BMC Psychiatry. 2021;21(1):530. Volz M, Voelkle MC, Werheid K. General self-efficacy as a driving factor of post-stroke depression: A longitudinal study. Neuropsychol Rehabil. 2019;29(9):1426–38. Volz M, Voelkle MC, Werheid K. General self-efficacy as a driving factor of post-stroke depression: A longitudinal study. Neuropsychol Rehabil. 2019;29(9):1426–38. Zou L, Wu X, Tao S, Xu H, Xie Y, Yang Y et al. Mediating Effect of Sleep Quality on the Relationship Between Problematic Mobile Phone Use and Depressive Symptoms in College Students. Front Psychiatry [Internet]. 2019 Nov 13 [cited 2024 Sep 28];10. https://www.frontiersin.org/journals/psychiatry/articles/ 10.3389/fpsyt.2019.00822/full de Jonge-Heesen KWJ, Rasing SPA, Vermulst AA, Tak YR, Engels RCME, Creemers DHM. Associations Between Coping Strategies and Depressive Symptoms in Adolescence: A Longitudinal Perspective. J Early Adolesc. 2021;41(7):1020–54. Xiong W, Liu H, Gong P, Wang Q, Ren Z, He M, et al. Relationships of coping styles and sleep quality with anxiety symptoms among Chinese adolescents: A cross-sectional study. J Affect Disord. 2019;257:108–15. Benatov J, Klomek AB, Shira B, Apter A, Carli V, Wasserman C, et al. Doing Nothing is Sometimes Worse: Comparing Avoidant versus Approach Coping Strategies with Peer Victimization and Their Association to Depression and Suicide Ideation. J Sch Violence. 2020;19(4):456–69. Tang X, Wong DFK, Xu H, Hou L. Barriers to a classroom-based universal prevention program for depressive symptoms in Chinese adolescents: A qualitative study. Health Soc Care Community. 2022;30(5):e2226–35. Grasaas E, Helseth S, Fegran L, Stinson J, Småstuen M, Haraldstad K. Health-related quality of life in adolescents with persistent pain and the mediating role of self-efficacy: a cross-sectional study. Health Qual Life Outcomes. 2020;18(1):19. Ozer EM, Albert Bandura. (1925–2021). Am Psychol. 2022;77(3):483–4. Lunkenheimer F, Domhardt M, Geirhos A, Kilian R, Mueller-Stierlin AS, Holl RW, et al. Effectiveness and cost-effectiveness of guided Internet- and mobile-based CBT for adolescents and young adults with chronic somatic conditions and comorbid depression and anxiety symptoms (youthCOACHCD): study protocol for a multicentre randomized controlled trial. Trials. 2020;21(1):253. Oud M, de Winter L, Vermeulen-Smit E, Bodden D, Nauta M, Stone L, et al. Effectiveness of CBT for children and adolescents with depression: A systematic review and meta-regression analysis. Eur Psychiatry. 2019;57:33–45. Dehghan P, Aynehchi A, Saleh-Ghadimi S, Asghari Jafarabadi M, Moslemi E. Association of self-efficacy and coping with sleep quality and disturbances with an emphasis on mediating role of eating behaviors and body mass index: A structural equation modeling approach. Curr Psychol. 2022;41(11):7471–81. Li J, Wang J, Li Jyu, Qian S, Ling R, zhe, Jia R et al. xia,. Family socioeconomic status and mental health in Chinese adolescents: the multiple mediating role of social relationships. J Public Health. 2022;44(4):823–33. Cong CW, Ling WS, Aun TS. Problem-focused coping and depression among adolescents: Mediating effect of self-esteem. Curr Psychol. 2021;40(11):5587–94. Mikkelsen HT, Haraldstad K, Helseth S, Skarstein S, Småstuen MC, Rohde G. Health-related quality of life is strongly associated with self-efficacy, self-esteem, loneliness, and stress in 14–15-year-old adolescents: a cross-sectional study. Health Qual Life Outcomes. 2020;18(1):352. Jhang FH. Uncontrollable and controllable negative life events and changes in mental health problems: Exploring the moderation effects of family support and self-efficacy in economically disadvantaged adolescents. Child Youth Serv Rev. 2020;118:105417. Spector PE. Do Not Cross Me: Optimizing the Use of Cross-Sectional Designs. J Bus Psychol. 2019;34(2):125–37. Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5277627","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":370612272,"identity":"16a3df89-d426-4254-a6af-f3ad19a07e59","order_by":0,"name":"Juan Zhao","email":"","orcid":"","institution":"The First Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Zhao","suffix":""},{"id":370612273,"identity":"1c16599a-20d5-44de-8fa8-36affbfe1cc8","order_by":1,"name":"Juanjuan Liu","email":"","orcid":"","institution":"The First Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Juanjuan","middleName":"","lastName":"Liu","suffix":""},{"id":370612274,"identity":"cb2269ff-9fe6-4678-96cf-20ad653ab9cd","order_by":2,"name":"Ying Li","email":"","orcid":"","institution":"The First Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Li","suffix":""},{"id":370612275,"identity":"b6d4754b-150e-4bc9-9b1d-2de657f7c1d9","order_by":3,"name":"Yangjie Chen","email":"","orcid":"","institution":"The Fourth Medical Center of PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yangjie","middleName":"","lastName":"Chen","suffix":""},{"id":370612276,"identity":"1249b3e8-a972-4586-b906-647ae030d358","order_by":4,"name":"Xiaoxia You","email":"","orcid":"","institution":"The First Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxia","middleName":"","lastName":"You","suffix":""},{"id":370612277,"identity":"543913e6-27ee-4621-b0d0-b6bc639f732d","order_by":5,"name":"Junxiang Cheng","email":"","orcid":"","institution":"The First Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junxiang","middleName":"","lastName":"Cheng","suffix":""},{"id":370612278,"identity":"4f33719f-4089-41d1-9d67-0cd2c04ffaff","order_by":6,"name":"Ahmad Naqib Shuid；","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAmElEQVRIiWNgGAWjYJCCwwwVpGs5Q6oWZsY2UpSbsx/eeLhw3mF73QbuNAmitFj2pBUcnrntcOK2A7zbiNNicCDH4DDvtsMJZsRrOf8GqGXOYXsStNwA2dJwmJEEh914VnB4xrH0xG2HeTdbEOmw5M2fC2qs7c2O9268QZQWkC4IxczAQpzDEFqAmj4Qq2UUjIJRMApGFgAA5GEzR6R8RAoAAAAASUVORK5CYII=","orcid":"","institution":"Universiti Sains Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Ahmad","middleName":"Naqib","lastName":"Shuid；","suffix":""}],"badges":[],"createdAt":"2024-10-16 17:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5277627/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5277627/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67846970,"identity":"07e02d5f-4ee6-4d51-9174-8857a40d37bc","added_by":"auto","created_at":"2024-10-30 10:01:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":126105,"visible":true,"origin":"","legend":"\u003cp\u003eMediation effect of coping styles on sleep quality and depressive symptoms and moderating role of self-efficacy\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5277627/v1/d442d12363f1f801f04db624.png"},{"id":84756304,"identity":"84d4e5ea-b1f3-45bf-86a8-d634d5bbe8f9","added_by":"auto","created_at":"2025-06-17 04:31:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1211168,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5277627/v1/63976cf2-6dd4-4bc8-a006-dd27316f0eca.pdf"},{"id":67848574,"identity":"9dad0e27-cd49-4c11-bd52-d815ca7c1126","added_by":"auto","created_at":"2024-10-30 10:09:10","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":37376,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.xls","url":"https://assets-eu.researchsquare.com/files/rs-5277627/v1/c7ad439e0cef17339f6347d3.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Sleep Quality on Depressive Symptoms in Adolescents: The Mediating Role of Coping Strategies and Limited Moderating Effect of Self-Efficacy","fulltext":[{"header":"Background","content":"\u003cp\u003eAdolescence is a pivotal developmental stage, bridging childhood and adulthood, characterized by rapid psychological, physiological, and social changes. This period is often fraught with emotional challenges as adolescents navigate various academic, social, and familial pressures that can significantly impact their mental well-being. As Kaman et al. note, \"the emotional landscape of adolescents is shaped by multifaceted stressors, leading to an increased vulnerability to depressive symptoms (DS)\"\u003csup\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/sup\u003e. Recent trends indicate a troubling rise in the prevalence of depression among adolescents, particularly among those facing stressful life changes\u003csup\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/sup\u003e. The implications of depression during this critical period extend beyond immediate emotional distress, disrupting daily functioning, academic performance, and potentially influencing long-term mental health trajectories and personality development. As such, it is imperative to identify risk factors associated with adolescent depression and to explore moderating mechanisms that could inform effective interventions\u003csup\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOne critical factor in this context is sleep quality (SQ), which is fundamental to both physical and mental health. High-quality sleep is essential for cognitive functioning, emotional regulation, and behavioral control, especially in adolescents. Vazsonyi et al. emphasize that \"adequate sleep is not merely a luxury; it is a vital component of adolescent development\"\u003csup\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/sup\u003e. Unfortunately, many adolescents experience poor sleep quality (PSQ), which has been linked to a heightened risk of developing DS\u003csup\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/sup\u003e. The physiological changes that occur during adolescence, such as delayed circadian rhythms, further exacerbate this issue, increasing vulnerability to sleep deprivation and impairing emotional regulation\u003csup\u003e(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/sup\u003e. The existing literature has established a bidirectional relationship between sleep disturbances and depression, where sleep issues not only result from depressive symptoms but also act as contributing factors\u003csup\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/sup\u003e. As Roberts and Duong assert, \"improving sleep quality can significantly alleviate depressive symptoms across different age groups\u003csup\u003e(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/sup\u003e. This underscores the critical importance of addressing sleep disturbances as part of comprehensive mental health interventions for adolescents.\u003c/p\u003e \u003cp\u003eCoping styles (CS) also play a significant role in how adolescents manage stress and emotional challenges. These strategies can be categorized into positive coping strategies(PCS) and negative coping strategies(NCS). PCS, which include problem-solving and seeking social support, are associated with improved mental health outcomes and reduced psychological distress, as evidenced by lower levels of cortisol and inflammatory markers\u003csup\u003e(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/sup\u003e. Conversely, NCS, such as avoidance and denial, are linked to increased psychological distress and heightened risks for conditions like depression and anxiety\u003csup\u003e(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/sup\u003e. Research indicates that adolescents who employ PCS demonstrate resilience and enhanced emotional regulation, whereas those relying on NCS experience exacerbated distress\u003csup\u003e(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/sup\u003e. As highlighted by Orzechowska et al., \"passive coping can serve as a mediator between stress and mental health issues, contributing to disorders like post-traumatic stress disorder (PTSD) and depression\" \u003csup\u003e(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdditionally, Self-efficacy (SE), is a vital psychological construct influencing adolescent mental health. According to Bandura as \"an individual's belief in their ability to succeed in specific situations,\" self-efficacy significantly impacts how adolescents cope with stress\u003csup\u003e(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/sup\u003e. Adolescents with higher SE tend to exhibit greater confidence in dealing with stress and are more likely to adopt positive coping strategies (PCS), which help them better manage negative emotions\u003csup\u003e(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/sup\u003e. Conversely, adolescents with lower SE often feel overwhelmed and are more likely to resort to NCS, increasing their risk for depression. Yang proposed that SE acts as a protective factor, mitigating the adverse effects of life stressors on emotional well-being\u003csup\u003e(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/sup\u003e. Thus, SE can play a dual role as both a mediator and moderator in the relationship between stress and DS\u003csup\u003e(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs awareness of adolescent mental health issues grows, there is an increasing emphasis on the myriad factors influencing DS. Key psychological variables—such as SQ, CS, and SE—have emerged as significant determinants of adolescent depression. Zhang et al. identified PSQ as a crucial predictor of DS, while Brink et al. emphasized the impact of SQ on emotional regulation capabilities\u003csup\u003e(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/sup\u003e. Concurrently, CS and SE have gained traction as central themes in depression intervention research. Ren et al. demonstrated that PCS can effectively mitigate negative emotions, while NCS may exacerbate DS\u003csup\u003e(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/sup\u003e. Furthermore, Brink suggested that SE significantly influences how individuals manage stress, thereby enhancing psychological resilience and emotional regulation\u003csup\u003e(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite the existing literature highlighting the connections among SQ, DS, CS, and SE, systematic research focusing specifically on adolescent populations remains limited. Particularly, the influence of CS and SE on the relationship between SQ and DS amidst academic, social, and familial pressures warrants further exploration. This study aims to explore the mediating effect of SE and CS on the relationship between SQ and DS in adolescents, employing a cross-sectional research design. Additionally, it will investigate whether SE moderates the use of CS in this context.\u003c/p\u003e \u003cp\u003eBy exploring these dynamics, this study seeks to deepen our understanding of how these psychological factors collectively influence adolescent mental health, potentially informing the development of targeted interventions to support this vulnerable population.\u003c/p\u003e\n\u003ch3\u003eResearch Hypotheses\u003c/h3\u003e\n\u003cp\u003eDrawing upon the literature review and theoretical framework outlined earlier, this study proposes the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eSQ (measured by the Pittsburgh Sleep Quality Index, PSQI) directly influences DS (measured by the Self-Rating Depression Scale, SDS). PSQ will be associated with more severe DS.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 2\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eSE (measured by the General Self-Efficacy Scale, GSES) will indirectly influence the relationship between SQ and DS by moderating the use of CS, specifically PCS and NCS.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 3\u003c/strong\u003e \u003c/p\u003e\u003cp\u003ePCS (active response subscale of the Simplified Coping Style Questionnaire, SCSQ.AR) moderate DS associated with PSQ, whereas NCS (measured by the negative coping subscale of the Simplified Coping Style Questionnaire, SCSQ.NC) will exacerbate DS.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 4\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eSE will have a more significant moderating effect on DS through its influence on NCS.\u003c/p\u003e "},{"header":"Methods","content":"\u003ch2\u003eResearch Design\u003c/h2\u003e\u003cp\u003eThis study utilized a cross-sectional research design to investigate the relationships among SE, CS, SQ, and DS in adolescents. The analysis specifically examined the correlations between these variables, with a particular focus on the potential mediating effects of CS and the moderating effects of SE on the relationship between SQ and DS.\u003c/p\u003e\u003ch3\u003eStudy Population\u003c/h3\u003e\u003cp\u003eData were gathered from 1,200 junior high school students enrolled in three middle schools within a single province in China, between January and June 2023. After excluding responses that did not meet the study’s rigorous criteria, 1,132 valid questionnaires were retained for subsequent analysis. Inclusion criteria mandated that participants demonstrate good mental health, possess normal literacy and comprehension abilities, be free from serious physical illnesses, and provide informed consent from both themselves and their guardians, thereby ensuring voluntary participation.\u003c/p\u003e\u003ch3\u003eData Collection and Quality Assurance\u003c/h3\u003e\u003cp\u003eTo uphold the integrity of the data collection process, all investigators underwent standardized training prior to questionnaire distribution. Each questionnaire was collected using anonymous numbering to ensure that no identifying information about participants was recorded or identified. The questionnaire was accompanied by a separate consent form that participants were asked to take home and have their guardian read and sign before completing the questionnaire. If the guardian did not consent, the participant would not complete the questionnaire. The consent form was only used to ensure that participants' participation in the study was voluntary, and the questionnaire itself was anonymized for data collection and did not contain any personally identifiable information. Completed questionnaires were collected by investigators the following day. To ensure data accuracy, a two-person data entry and verification process was implemented, complemented by a third person who randomly checked 20% of the questionnaires to further validate the accuracy and reliability of the data entry process.\u003c/p\u003e\u003ch3\u003eResearch Tool\u003c/h3\u003e\u003cp\u003eThe Self-Rating Depression Scale (SDS)\u003c/p\u003e\u003cp\u003eThe SDS is a widely utilized tool for assessing depressive symptoms, encompassing emotional, somatic, and psychological dimensions\u003csup\u003e(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/sup\u003e. This instrument comprises items rated on a 4-point Likert scale, ranging from 1 (\"never\") to 4 (\"often\"), with higher scores indicating more severe depressive symptoms. A score of 53 or above serves as the threshold for identifying the presence of depressive symptoms. In this study, the SDS functioned as the dependent variable for evaluating DS in relation to SQ and CS, exhibiting a Cronbach's alpha of 0.836, indicating good internal consistency.\u003c/p\u003e\u003cp\u003eThe Pittsburgh Sleep Quality Index (PSQI)\u003c/p\u003e\u003cp\u003eThe PSQI is a standardized measure for assessing sleep quality\u003csup\u003e(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/sup\u003e. It evaluates seven components: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. Each component is scored from 0 to 3, yielding a total score between 0 and 21, where higher scores signify poorer sleep quality. In this study, the PSQI was employed as the independent variable to explore the association between SQ and DS, with a Cronbach's alpha of 0.902, reflecting excellent reliability..\u003c/p\u003e\u003cp\u003eThe General Self-Efficacy Scale (GSES)\u003c/p\u003e\u003cp\u003eThe GSES assesses an individual's belief in their capability to navigate challenging situations\u003csup\u003e(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/sup\u003e. The scale consists of items rated on a 4-point Likert scale, with higher scores denoting stronger self-efficacy. In this study, the GSES was used as a moderating variable to examine its influence on the relationship among CS, SQ, and DS. The GSES exhibited a Cronbach's alpha of 0.908, demonstrating high reliability.\u003c/p\u003e\u003cp\u003eThe Simplified Coping Style Questionnaire (SCSQ)\u003c/p\u003e\u003cp\u003eThe SCSQ evaluates coping mechanisms and comprises two dimensions: active response (SCSQ.AR) and negative coping (SCSQ.NC)\u003csup\u003e(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/sup\u003e. Each item is scored on a 4-point Likert scale, ranging from 1 (\"never\") to 4 (\"often\"). SCSQ.AR is generally associated with improved mental health outcomes, whereas SCSQ.NC is linked to heightened psychological distress. In this study, the SCSQ served as a mediating variable to assess its impact on the relationship between SQ and DS, achieving a Cronbach's alpha of 0.866, indicating of good internal consistency.\u003c/p\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eData analysis was conducted using IBM SPSS 25 and the PROCESS macro developed by Hayes, incorporating a comprehensive array of statistical techniques. These techniques included descriptive statistics, correlation analysis, partial correlation analysis, regression analysis, mediation analysis, and moderated mediation analysis.\u003c/p\u003e\u003cp\u003eDescriptive statistics were employed to summarize the sample characteristics, calculating medians and interquartile ranges. Non-parametric tests were also utilized to compare differences between depressive symptom subgroups, providing preliminary insights into the distribution and characteristics of the study variables.\u003c/p\u003e\u003cp\u003eSpearman's correlation coefficients were calculated to explore the relationships among key variables, including SDS, PSQI, SCSQ, and GSES. To account for potential confounders, partial correlation analyses were performed while controlling for demographic variables such as academic performance and family income.\u003c/p\u003e\u003cp\u003eSpearman's correlation coefficients were calculated to explore the relationships among key variables, including SDS, PSQI, SCSQ, and GSES. To account for potential confounders, partial correlation analyses were performed while controlling for demographic variables such as academic performance and family income.\u003c/p\u003e\u003cp\u003eUtilizing the PROCESS macro (Model 4) established by Hayes, mediation analysis examined the roles of SCSQ and GSES in the relationship between PSQI and SDS. A bootstrap sample of 5,000 iterations was employed to estimate the indirect effects, with significance tests and bootstrap confidence intervals (CIs) determining whether SCSQ.AR, SCSQ.NC, and GSES partially or fully mediated the impact of PSQI on SDS.\u003c/p\u003e\u003cp\u003eThe PROCESS macro (Model 8) was employed for moderated mediation analysis to investigate whether GSES moderated the mediating effect of SCSQ in the PSQI-SDS relationship. Path coefficients were assessed at varying levels of GSES to elucidate its moderating role within these mediational pathways. The moderation effect was analyzed by evaluating how the interaction between GSES and PSQI influenced the indirect effects via SCSQ on SDS.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics and Analysis of Variance\u003c/h2\u003e \u003cp\u003eThe descriptive statistics revealed that the overall sample exhibited a median score of 53.75 on SDS and a median score of 4.00 on PSQI. These findings indicate that, while the overall SQ of the sample was generally satisfactory, a notable proportion of participants reported experiencing DS. Analysis of variance (ANOVA) further uncovered significant differences in DS across various demographic factors, including religion, area of residence, grade level, exposure to violence, parental marital status, parental relationships, family economic status, parental literacy, parental occupation, the number of close friends, age, and history of self-harm (i.e. Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation and partial correlation analysis\u003c/h2\u003e \u003cp\u003eSpearman correlation analyses were conducted to examine the bivariate relationships among the main variables, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The results indicated a statistically significant positive correlation between PSQI and SDS, with a correlation coefficient of r\u0026thinsp;=\u0026thinsp;0.327 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01),, thereby confirming Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.This finding suggests that PSQ is associated with elevated levels of DS\u003csup\u003e(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdditionally, SCSQ.AR were negatively correlated with SDS, implying that adolescents who employed more PCS tended to experience fewer DS. Conversely, SCSQ.NC showed a positive correlation with SDS, indicating that reliance on NCS exacerbates DS\u003csup\u003e(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/sup\u003e. Furthermore, GSES exhibited a negative correlation with SDS, suggesting that higher levels of SE are associated with lower levels of DS.\u003c/p\u003e \u003cp\u003eTo control for potential confounding effects from significant demographic variables identified in the univariate analyses (e.g., grade, family income, parental occupation, and close friendships), rank correlation analyses were conducted alongside partial correlation analyses (i.e. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results aligned with those of the Spearman correlation analysis, further validating Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Bivariate correlation matrix of SDS, PSQI, GSES, and SCSQ\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSDS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCSQ.AR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSCSQ.NC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSCSQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGSES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSQI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCSQ.AR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.431**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCSQ.NC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.208**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.138**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.505**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.669**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.590**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.243**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.321**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.246**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.327**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.175**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.198**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.261**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.150**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eNote: ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Partial correlation analysis by Rank Cases of the main variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRSDS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRPSQI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRSDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRPSQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.166**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNote: ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; RSDS, RPSQI are rank-transformed variables of SDS, PSQI, respectively;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003econtrol variables: Religion \u0026amp; Place of Residence \u0026amp; grades \u0026amp; Traumatic experiences \u0026amp; Divorced parents \u0026amp; Parental relationship \u0026amp; Household financial situation \u0026amp; Father's culture \u0026amp; Mother' s culture \u0026amp; Father's occupation \u0026amp; Mother's occupation \u0026amp; Close friends \u0026amp; history of suicide \u0026amp; Age.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRegression Analysis\u003c/h2\u003e \u003cp\u003eInitially univariate regression analyses assessed the direct effect of PSQI on SDS. The findings indicated that PSQI serves as a significant positive predictor of SDS, suggesting that PSQ was associated with higher levels of DS\u003csup\u003e(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)\u003c/sup\u003e. The standardized regression coefficient for PSQI in the model without mediators was Beta\u0026thinsp;=\u0026thinsp;0.350, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, explaining 12.3% of the variance in DS. This supports Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which posited a significant relationship between PSQI and SDS.\u003c/p\u003e \u003cp\u003eSubsequently, a multiple linear regression model was employed that included the mediating variables: SCSQ.AR, SCSQ.NC, and GSES. The analysis revealed a substantial increase in the explained variance to 34.5% (R\u0026sup2; = 0.345, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that CS and SE partially mediate the relationship between PSQI and SDS.\u003c/p\u003e \u003cp\u003eSpecifically, SCSQ.AR emerged as a significant negative predictor of SDS (Beta = -0.432, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that adolescents who utilize more PCS experienced fewer DS\u003csup\u003e(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/sup\u003e. Conversely, SCSQ.CN demonstrated a significant positive predictive effect on SDS (Beta\u0026thinsp;=\u0026thinsp;0.270, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming Hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e that NCS exacerbate DS\u003csup\u003e(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, GSES significantly predicted SDS (Beta = -0.083, p\u0026thinsp;=\u0026thinsp;0.001), thereby supporting Hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which posited that GSES would indirectly affect the relationship between PSQI and SDS by moderating CS\u003csup\u003e(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNotably, the direct effect of PSQI on SDS was diminished (from Beta\u0026thinsp;=\u0026thinsp;0.350 to Beta\u0026thinsp;=\u0026thinsp;0.225, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) upon the introduction of the mediating variables (SCSQ and GSES), indicating that these factors partially mediate the relationship between SQ and DS (i.e. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e The Effects of PSQI, SCSQ, and GSES on SDS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eModel I: PSQI on SDS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePredictor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBeta\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLLCI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eULCI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003et\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eVIF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eModel Summary R\u0026thinsp;=\u0026thinsp;0.350;R2\u0026thinsp;=\u0026thinsp;0.122; F\u0026thinsp;=\u0026thinsp;158.204; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Durbin-Watson\u0026thinsp;=\u0026thinsp;1.741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eModel II: PSQI, SCSQ, GSES on SDS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCSQ.AR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-16.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCSQ.NC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eModel Summary: R\u0026thinsp;=\u0026thinsp;0.587;R2\u0026thinsp;=\u0026thinsp;0.345; F\u0026thinsp;=\u0026thinsp;158.204; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Durbin-Watson\u0026thinsp;=\u0026thinsp;1.741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMediation Analysis\u003c/h2\u003e \u003cp\u003eUtilizing Hayes' PROCESS model, we examined the mediating roles of SCSQ and GSES in the relationship between PSQI and SDS (i.e. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The analysis revealed that both positive coping strategies (SCSQ.AR) and negative coping strategies (SCSQ.NC) significantly mediated this relationship, supporting Hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Specifically, the indirect effect of SCSQ.AR was B = -0.605, Beta = -0.432 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that PSQ led to a reduction in the use of PCS, subsequently increasing DS. Likewise, the indirect effect of SCSQ.NC was B\u0026thinsp;=\u0026thinsp;0.640, Beta\u0026thinsp;=\u0026thinsp;0.270 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that PSQ exacerbated DS through reliance on NCS. These findings underscore the pivotal mediating role of SCSQ in the PSQI-SDS relationship, strongly supporting Hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eConversely, while the mediating effect of GSES was statistically significant, it was comparatively modest, thereby supporting Hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. T The indirect effect of GSES was B = -0.144, Beta = -0.083 (p\u0026thinsp;=\u0026thinsp;0.001), indicating that higher SE is associated with improved SQ, which in turn mitigates DS \u003csup\u003e(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/sup\u003e. Nonetheless, relative to the effects of SCSQ.AR and SCSQ.NC, the mediating role of GSES was less pronounced, suggesting that while it plays a role, its influence on the relationship between PSQI and SDS is limited.\u003c/p\u003e \u003cp\u003eOverall, the results illustrate that PSQI exerts a more substantial indirect effect on SDS by decreasing SCSQ.AR and increasing SCSQ.NC, while the mediating effect of GSES, despite being statistically significant, remains weaker. These findings affirm the mediating roles of both SCSQ and GSES in the relationship between PSQI and SDS, thus confirming Hypotheses 2 and 3.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e SCSQ and GSES on the Relationship between PSQI and SDS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMediator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLLCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eULCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCSQ.AR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-5.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCSQ.NC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.0895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-4.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eModel Summary:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eTotal Direct Effect (PSQI on SDS): B\u0026thinsp;=\u0026thinsp;0.952, Beta\u0026thinsp;=\u0026thinsp;0.225(95% CI [0.7435, 1.1605], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eTotal Indirect Effect: B\u0026thinsp;=\u0026thinsp;0.5320 ,Beta\u0026thinsp;=\u0026thinsp;0.1256 (95% CI [0.3999, 0.6712]), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003eIndirect Effect via SCSQ.AR: B= -0.2817, Beta= -0.0665, 95% CI [0.171, 0.405], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003eIndirect Effect via SCSQ.NC: B\u0026thinsp;=\u0026thinsp;0.2039, Beta\u0026thinsp;=\u0026thinsp;0.0482, 95% CI [0.128, 0.286], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003eIndirect Effect via GSES: B = -0.0464, Beta = -0.0110, 95% CI [0.0142, 0.0885], p\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eModerated Mediation Analysis\u003c/h2\u003e \u003cp\u003eTo investigate whether GSES moderates the mediating effect of SCSQ on the PSQI-SDS relationship, moderated mediation analyses were conducted using Hayes' PROCESS model 8 (i.e. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The results indicated significant direct effects of PSQI and SCSQ on SDS. Specifically, PSQI was found to directly increase SDS (B\u0026thinsp;=\u0026thinsp;1.1219, p\u0026thinsp;=\u0026thinsp;0.0002), while SCSQ.AR significantly attenuated SDS (B = -0.6063, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and SCSQ.NC exacerbated SDS (B\u0026thinsp;=\u0026thinsp;0.6418, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eHowever, the interaction between GSES and PSQI was not significant (B = -0.0076, p\u0026thinsp;=\u0026thinsp;0.5437), indicating that GSES does not significantly moderate the direct effect of PSQI on SDS. Thus, GSES does not play a moderating role in the PSQI-SDS relationship when looking solely at the direct pathway.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Regression Results of PSQI, SCSQ, and GSES on SDS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLLCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eULCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCSQ.AR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.3039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.4867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.1211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePSQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.6414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePSQI \u0026times; GSES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCSQ.NC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.0787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.3455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.8118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePSQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.2059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePSQI \u0026times; GSES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.6841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.1478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60.2204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePSQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.7091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCSQ.AR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.6063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-16.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.6787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.5338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCSQ.NC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.2487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePSQI \u0026times; GSES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eNote: Direct Effects: These are shown for PSQI, SCSQ.AR, SCSQ.NC, and GSES on SDS and their respective mediators.\u003c/p\u003e \u003cp\u003eModeration Effects: The interaction terms (PSQI \u0026times; GSES) are included to reflect whether GSES moderates the relationships between PSQI, SCSQ, and SDS. None of these moderation effects are statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e outlines the indirect effects of PSQI on SDS through SCSQ at various levels of GSES. For individuals with lower GSES, the indirect effect of PSQI on SDS via SCSQ.AR was B\u0026thinsp;=\u0026thinsp;0.1771 (95% CI [0.0309, 0.3326]), suggesting a modest mitigating effect of SCSQ.AR on DS. In contrast, for individuals with higher GSES, the indirect effect increased to B\u0026thinsp;=\u0026thinsp;0.2505 (95% CI [0.0806, 0.4324]). Despite this increase, the effect did not significantly strengthen with higher GSES, indicating that the protective role of SCSQ.AR was consistent across different levels of GSES but not substantially moderated by it. Regarding SCSQ.NC, although the moderating effect of GSES was not statistically significant, the indirect effect of PSQI on SDS through SCSQ.NC was evident at various levels of GSES, partially supporting Hypothesis \u003cspan refid=\"FPar4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. For individuals with low GSES, the indirect effect was B\u0026thinsp;=\u0026thinsp;0.1690 (95% CI [0.0709, 0.2691]), whereas for those with high GSES, it increased to B\u0026thinsp;=\u0026thinsp;0.2634 (95% CI [0.1625, 0.3836]). This suggests that while SCSQ.NC consistently exacerbated DS, its negative impact was more pronounced among individuals with higher GSES.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConditional Indirect Effects of PSQI on SDS via SCSQ at Different Levels of GSES\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerator (GSES)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect Effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndirect Effect\u003c/p\u003e \u003cp\u003e(via SCSQ.AR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLLCI\u003c/p\u003e \u003cp\u003e(SCSQ.AR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eULCI\u003c/p\u003e \u003cp\u003e(SCSQ.AR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIndirect Effect\u003c/p\u003e \u003cp\u003e(via SCSQ.NC)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLLCI\u003c/p\u003e \u003cp\u003e(SCSQ.AR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eULCI\u003c/p\u003e \u003cp\u003e(SCSQ.AR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16.144 (Low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23.178 (Medium)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30.213 (High)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eNote: VIA indicates the pathway or mediation through which PSQI affects SDS using the coping strategies SCSQ.AR or SCSQ.NC.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure 1 illustrates these relationships, depicting how PSQI influences SDS via SCSQ.AR and SCSQ.NC. While GSES did not significantly moderate the direct effect of PSQI on SDS, it partially moderated the relationship between SCSQ.NC and SDS, particularly for individuals with higher levels of GSES. In contrast, the role of SCSQ.AR in mitigating SDS remained consistent, regardless of GSES.\u003c/p\u003e \u003cp\u003eIn summary, SCSQ.NC had the strongest mediating effect between PSQI and SDS, particularly for individuals with higher levels of GSES. In contrast, SCSQ.AR had a weaker mitigating effect on SDS, and its influence did not significantly increase with higher GSES. Therefore, while GSES moderates the effect of SCSQ.NC on SDS, it does not significantly moderate the overall relationship between PSQI and SDS. These findings suggest that Hypothesis \u003cspan refid=\"FPar4\" class=\"InternalRef\"\u003e4\u003c/span\u003e is only partially supported, as the moderation effect of GSES is more pronounced for SCSQ.NC than for SCSQ.AR.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study systematically explored the intricate relationships between SQ, DS, CS, and SE in adolescents. The findings largely supported the proposed hypotheses, while also highlighting specific limitations in the observed effects. Notably, a significant relationship between SQ and DS was confirmed, consistent with Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Additionally, the study validated Hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e by demonstrating the mediating role of CS in the relationship between SQ and DS. Hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e was also supported, revealing a partial mediating effect of SE. Furthermore, results indicated that SE partially moderated the influence of CS on the relationship between SQ and DS, aligning with Hypothesis \u003cspan refid=\"FPar4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eThe Relationship Between Sleep and Mental Health\u003c/h2\u003e \u003cp\u003eThe study confirmed a robust connection between SQ and DS, underscoring the critical role of sleep in mental health\u003csup\u003e(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/sup\u003e. This finding reinforces existing theories regarding the bidirectional relationship between sleep disturbances and depression and highlights adolescence as a critical period for implementing sleep interventions\u003csup\u003e(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/sup\u003e. Addressing sleep issues could yield long-term benefits in preventing or alleviating DS, particularly given that adolescents often face unique sleep challenges, such as delayed circadian rhythms\u003csup\u003e(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/sup\u003e. This research contributes to the literature emphasizing sleep's foundational role in emotional well-being, expanding it by exploring the indirect effects of CS and SE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eThe Mediating Role of CS\u003c/h2\u003e \u003cp\u003eThe findings that PCS mitigate DS, while NCS exacerbate them underscore the importance of incorporating coping interventions in adolescent mental health programs \u003csup\u003e(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/sup\u003e. The study showed that PCS buffer the impact of poor sleep on DS, suggesting that interventions designed to enhance these strategies could significantly alleviate the emotional burden associated with PSQ\u003csup\u003e(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/sup\u003e. Adolescents struggling with SQ may benefit more from programs focused on adaptive coping skills, such as problem-solving and seeking social support\u003csup\u003e(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/sup\u003e. However, simply improving SQ may not suffice without addressing how adolescents cope with stress\u003csup\u003e(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConversely, the detrimental impact of NCS highlights the urgent need for interventions aimed at reducing reliance on maladaptive strategies, such as avoidance and emotional venting \u003csup\u003e(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/sup\u003e. Educating adolescents to recognize and transition away from NCS could help mitigate the risk of DS, even when SQ is compromised\u003csup\u003e(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/sup\u003e. This reinforces the necessity for school and community-based mental health programs that integrate coping mechanisms alongside education about sleep.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eThe Mediating Role of SE\u003c/h2\u003e \u003cp\u003eWhile SE demonstrated a smaller mediating role than initially expected, it remains an essential construct for emotional regulation\u003csup\u003e(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/sup\u003e. Adolescents with higher SE are better equipped to manage the emotional stress associated with PSQ, aligning with Bandura\u0026rsquo;s theory that SE acts as a buffer against emotional distress\u003csup\u003e(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/sup\u003e. However, the relatively modest mediating effect of SE suggests that it may not be sufficient on its own to significantly alter the relationship between SQ and DS.\u003c/p\u003e \u003cp\u003eThis observation raises important questions for future interventions. While programs designed to enhance SE, particularly those utilizing cognitive-behavioral therapy, are beneficial, they may need to be paired with coping skills training for a more comprehensive impact\u003csup\u003e(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e)\u003c/sup\u003e. Practically, adolescents might derive greater benefits from multi-faceted interventions that simultaneously target both SE and coping skills, thereby maximizing resilience against DS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eThe Moderating Role of SE\u003c/h2\u003e \u003cp\u003eThe study revealed that SE did not significantly moderate the direct relationship between PSQ and DS, an unexpected finding that merits further investigation. One possible explanation is that the effect of SE may be mediated indirectly through coping strategies rather than directly moderating emotional outcomes\u003csup\u003e(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/sup\u003e. Adolescents with low self-efficacy may lack the psychological resources needed to transition from NCS to PCS, which could account for the absence of significant moderation in the direct pathway\u003csup\u003e(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdditionally, external factors such as family support, peer relationships, and socioeconomic status may exert stronger influences on the mental health of adolescents, potentially diminishing the observable impact of SE\u003csup\u003e(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e)\u003c/sup\u003e. The cross-sectional design of this study also limits the capacity to observe dynamic changes in SE over time, which may reveal more nuanced moderating effects. Future longitudinal studies could more effectively assess how SE develops and interacts with SQ and CS over time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eBroader implications for mental health interventions\u003c/h2\u003e \u003cp\u003eThe partial support for Hypothesis \u003cspan refid=\"FPar4\" class=\"InternalRef\"\u003e4\u003c/span\u003e - suggesting that SE moderates the effects of NCS on DS - highlights the importance of addressing NCS in high-risk groups, particularly adolescents with low SE. While enhancing SE may not directly alter positive coping behaviors, it could help mitigate the adverse effects of NCS\u003csup\u003e(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e)\u003c/sup\u003e. This indicates a need for targeted interventions that aim not only to boost SE but also to reduce adolescents' reliance on on NCS\u003csup\u003e(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMoreover, the non-significant moderation of SE in the overall SQ-DS pathway suggests that efforts to bolster SE should be complemented by environmental support systems, such as family, school, and peer networks\u003csup\u003e(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/sup\u003e. These systems may provide the emotional and psychological resources necessary for adolescents to effectively utilize PCS ,thereby enhancing the protective role of SE\u003csup\u003e(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e \u003cp\u003eThis study presents several limitations that merit consideration. Firstly, the cross-sectional design constrains the ability to establish causality, complicating the assessment of directional relationships among SQ, CS, SE, and DS\u003csup\u003e(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e)\u003c/sup\u003e. Additionally, reliance on self-reported data introduces potential biases, as adolescents may underreport symptoms or overestimate the use of PCS. Future research should employ longitudinal designs to better capture the temporal dynamics of these variables and facilitate a more robust assessment of causality\u003csup\u003e(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAnother limitation pertains to the specific cultural and geographical context of the study, as the data were collected from adolescents in a single province in Chinese. Cultural factors can significantly influence CS, SE, and mental health, which may limit the generalizability of the findings to other populations. Therefore, extending research to encompass diverse cultural and socio-economic contexts is vital for testing the universality of these findings and enhancing their applicability across different settings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study enhances our understanding of how SQ, CS, and SE interact to influence mental health in adolescents. While SQ remains a critical predictor of DS, addressing CS, particularly by reducing the use of NCS, can significantly mitigate the impact of poor sleep on depression\u003csup\u003e(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/sup\u003e. Improving SE, while beneficial, may be most effective when combined with interventions that PCS and discourage NCS. Future studies should further explore these relationships in longitudinal settings and across diverse populations to develop more tailored and effective mental health interventions for adolescents.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003col\u003e\n \u003cli\u003eDS - Depressive Symptoms\u003c/li\u003e\n \u003cli\u003eSDS - Self-Rating Depression Scale\u003c/li\u003e\n \u003cli\u003eSQ - Sleep Quality\u003c/li\u003e\n \u003cli\u003ePSQ - Poor Sleep Quality\u003c/li\u003e\n \u003cli\u003ePSQI - Pittsburgh Sleep Quality Index\u003c/li\u003e\n \u003cli\u003eCS - Coping Styles\u003c/li\u003e\n \u003cli\u003ePCS - Positive Coping Strategies\u003c/li\u003e\n \u003cli\u003eNCS - Negative Coping Strategies\u003c/li\u003e\n \u003cli\u003eSCSQ - Simplified Coping Style Questionnaire\u003c/li\u003e\n \u003cli\u003eSCSQ.AR - Simplified Coping Style Questionnaire Active Response\u003c/li\u003e\n \u003cli\u003eSCSQ.NC - Simplified Coping Style Questionnaire Negative Coping\u003c/li\u003e\n \u003cli\u003eSE - Self-Efficacy\u003c/li\u003e\n \u003cli\u003eGSES - General Self-Efficacy Scale\u003c/li\u003e\n \u003cli\u003ePTSD - Post-Traumatic Stress Disorder\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Ethics Committee of the First Hospital of Shanxi Medical University (approval number: 2021k-149). All participants and their guardians provided informed consent prior to participation, ensuring compliance with ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data in this study were collected using anonymous numbering and did not contain any information that could personally identify the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by a grant from the Basic Research Program of Shanxi Province, No.202103021223435.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNAS designed the study and revised the manuscript. JZ completed all analyses and was responsible for the first draft of the manuscript. JJL, YL, XXY and JXC participated in data collection and entry. YJC participated in the drafting of the manuscript. All authors have read and approved the final submitted manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the research team for their contribution to data collection. We are even more sincerely grateful to all the adolescents and their guardians who participated in this study and whose participation was crucial to the success of this research project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKaman A, Otto C, Klasen F, Westenh\u0026ouml;fer J, Reiss F, H\u0026ouml;lling H, et al. Risk and resource factors for depressive symptoms during adolescence and emerging adulthood \u0026ndash; A 5-year follow-up using population-based data of the BELLA study. 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Child Youth Serv Rev. 2020;118:105417.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpector PE. Do Not Cross Me: Optimizing the Use of Cross-Sectional Designs. J Bus Psychol. 2019;34(2):125\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\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":"Adolescents, Sleep Quality, Depressive Symptoms, Coping Strategies, Self-Efficacy, Mediation, Moderation","lastPublishedDoi":"10.21203/rs.3.rs-5277627/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5277627/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAdolescence is a critical developmental stage characterized by emotional challenges and an increased vulnerability to depressive symptoms (DS). While poor sleep quality (PSQ) is known to correlate with DS, the roles of coping strategies (CS) and self-efficacy (SE) in this relationship remain underexplored.This study investigates the relationships between sleep quality (SQ), CS, SE and DS among adolescents, emphasizing the mediating role of CS and the moderating role of SE in the SQ-DS relationship.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUtilizing a cross-sectional design, data were collected from 1,132 junior high school students in China between January and June 2023.Participants completed self-report questionnaires assessing the Self-Rating Depression Scale (SDS), the Pittsburgh Sleep Quality Index (PSQI), the Simplified Coping Style Questionnaire (SCSQ), and the General Self-Efficacy Scale (GSES). Descriptive statistics, correlation analysis, regression analysis, and mediation-moderation analysis using PROCESS were conducted to examine variable relationships.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe findings revealed a significant positive relationship between PSQI and SDS (Beta\u0026thinsp;=\u0026thinsp;0.350, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that PSQ is associated with higher levels of DS. CS acted as a mediator; specifically, positive coping (SCSQ.AR) negatively predicted SDS (Beta = -0.432, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas negative coping (SCSQ.NC) positively predicted SDS (Beta\u0026thinsp;=\u0026thinsp;0.270, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). GSES did not significantly moderate the direct relationship between PSQI and SDS (B = -0.0076, p\u0026thinsp;=\u0026thinsp;0.5437), but it partially moderated the indirect effects through negative coping. Adolescents with lower SE were more prone to adopt negative coping strategies (NCS), which in turn exacerbated their DS.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePSQ is significantly associated with increased DS in adolescents, with NCS intensifying this relationship, especially among those with lower SE. Although enhancing SE alone may not significantly influence the direct impact of PSQ on DS, interventions that promote positive coping strategies (PCS) and reduce NCS, combined with efforts to enhance SE, could effectively alleviate DS. Future research should adopt a longitudinal approach to further elucidate these relationships and inform targeted mental health interventions for adolescents.\u003c/p\u003e","manuscriptTitle":"Impact of Sleep Quality on Depressive Symptoms in Adolescents: The Mediating Role of Coping Strategies and Limited Moderating Effect of Self-Efficacy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-30 10:01:04","doi":"10.21203/rs.3.rs-5277627/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":"7f119ae3-29ef-4bc4-ac6a-3c5fc25a7fef","owner":[],"postedDate":"October 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-17T04:23:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-30 10:01:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5277627","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5277627","identity":"rs-5277627","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}