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

doi:10.21203/rs.3.rs-5277627/v1

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Research Article

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

https://doi.org/10.21203/rs.3.rs-5277627/v1

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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

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.

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.

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.

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.

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.

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.

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

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.

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

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Background

Research Hypotheses

Methods

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The Relationship Between Sleep and Mental Health

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The Mediating Role of SE

The Moderating Role of SE

Broader implications for mental health interventions

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