Neuroticism and Non-Suicidal Self-Injury: A Serial Mediation Model of Sleep Disturbance and Cortisol Dysregulation

Neuroticism and Non-Suicidal Self-Injury: A Serial Mediation Model of Sleep Disturbance and Cortisol Dysregulation | 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 Article Neuroticism and Non-Suicidal Self-Injury: A Serial Mediation Model of Sleep Disturbance and Cortisol Dysregulation Xiang Zhang, Yijie Wang, Shaoxia Wang, Huarong He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8733092/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 Neuroticism is a robust personality predictor of non-suicidal self-injury (NSSI), a significant public health concern among youth. However, the specific psychobiological mechanisms linking this dispositional trait to self-injurious behavior remain unclear. Sleep disturbance and dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis are theoretically plausible mediators, but their sequential role has not been fully elucidated. Objective To test a serial mediation model in which sleep disturbance and cortisol dysregulation sequentially mediate the relationship between neuroticism and NSSI in Chinese youth. Method A cross-sectional survey was conducted with 302 adolescents and young adults. Participants completed self-report questionnaires assessing neuroticism, NSSI, sleep quality, and depressive symptoms. Salivary cortisol was collected over two days to calculate Cortisol Awakening Response (CAR) and Diurnal Cortisol Slope (DCS). Serial mediation analysis was performed, controlling for age, sex, education, and depressive symptoms. Results Neuroticism was positively correlated with NSSI, sleep disturbance, and a flatter DCS, and negatively with the CAR. Neuroticism had both a significant direct effect on NSSI and a significant indirect effect through the serial pathway of sleep disturbance and a blunted cortisol awakening response (Indirect Effect = 0.0008, 95% CI [0.0002, 0.0016]). A similar significant serial indirect effect was found for the diurnal cortisol slope model (Indirect Effect = 0.0007, 95% CI [0.0002, 0.0014]). Conclusions Neuroticism contributes to NSSI risk both directly and indirectly via a psychobiological cascade involving sleep disturbance and HPA axis dysregulation. These findings highlight sleep as a key mechanistic link and a promising target for intervention in high-neuroticism youth. Health sciences/Diseases Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Non-suicidal self-injury Neuroticism Sleep disturbance Cortisol HPA axis Figures Figure 1 Figure 2 Introduction Non-suicidal self-injury (NSSI), defined as the deliberate, self-inflicted destruction of body tissue without suicidal intent and for reasons not socially sanctioned , 1 is a significant and growing public health concern, particularly among adolescents and young adults . 2; 3 Recent large-scale meta-analyses confirm its high prevalence, with 12-month estimates in adolescent community samples ranging from 17% to over 22% 4; 5 . This behavior is frequently associated with a range of psychiatric morbidities, including mood and anxiety disorders 6 , and often functions as a maladaptive coping strategy for overwhelming emotional distress, a notion supported by meta-analytic evidence identifying affect regulation as its primary function 7 . Understanding the etiology of NSSI is crucial for effective intervention. A valuable framework for this endeavor is the stress-vulnerability model, which posits that inherent dispositions (vulnerabilities) interact with stressors to precipitate psychopathology 8 ; 9 . This model directs inquiry toward identifying key dispositional risk factors, such as personality traits, and the specific mechanisms through which their influence is expressed 10 . Among the most robust of these dispositional vulnerabilities for NSSI is neuroticism. Neuroticism, a personality trait characterized by emotional instability, negative affectivity, and heightened reactivity to stressors, has been consistently identified as a powerful predictor of NSSI. This association is supported by large-scale meta-analyses linking the trait to a spectrum of self-injurious and suicide-related behaviors 11 – 13 . Individuals with elevated neuroticism exhibit a greater propensity for self-injurious behaviors across diverse populations 14 ; 15 . This strong association is thought to be rooted in neuroticism’s role as a marker of stress sensitivity; those high in neuroticism not only perceive situations as more stressful but also experience more intense and prolonged negative emotional responses 16 . However, while the direct link between neuroticism and NSSI is well-established, the specific psychobiological pathways that translate this dispositional vulnerability into the act of self-injury remain underexplored. Given neuroticism’s profound impact on stress processing systems, its influence likely extends to fundamental physiological processes, such as sleep, warranting further investigation. Building on this premise, sleep disturbance emerges as a primary candidate for the initial step in this psychobiological cascade. The link between neuroticism and poor sleep is robust; individuals high in neuroticism frequently report difficulties with sleep onset and maintenance, a consequence of the heightened physiological arousal and cognitive rumination characteristic of the trait 17 . Critically, sleep disturbance is increasingly recognized as a potent risk factor for NSSI. Cross-sectional studies consistently find that poor sleep quality is associated with recent NSSI engagement in adolescents 18 ; 19 . While much of the evidence is cross-sectional, the consistency of this association, along with findings linking sleep disturbances to suicidal behaviors in meta-analyses 20 , strongly suggests its role as a critical intermediary. The mechanism linking sleep loss to self-injury is thought to involve impaired emotion regulation; sleep is vital for processing emotional experiences 21 , and its disruption can impair the brain's capacity to down-regulate negative affect, increasing the likelihood of resorting to NSSI for relief. This positions sleep disturbance as a key mechanistic bridge, translating the latent vulnerability of neuroticism into a state of acute emotional dysregulation that precedes self-injurious acts. The impact of sleep disturbance extends beyond psychological functioning to disrupt core neurobiological systems, most notably the hypothalamic-pituitary-adrenal (HPA) axis. Chronic sleep loss is a potent disruptor of HPA axis regulation, leading to altered circadian cortisol rhythms 22 . This physiological cascade is particularly relevant to NSSI, as a growing body of evidence indicates that individuals who self-injure exhibit significant HPA axis dysregulation. For instance, adolescents with NSSI have been shown to display altered cortisol patterns in the context of both childhood adversity and acute psychosocial stressors, often interpreted as a sign of HPA axis exhaustion or dysregulation from chronic stress 23 ; 24 . This attenuated stress reactivity can impair an individual's ability to mount an effective response to daily challenges, while cortisol dysregulation itself directly impacts neurotransmitter systems crucial for mood and impulse control 25 , exacerbating the emotion regulation deficits that precipitate NSSI. Building on this integrated theoretical framework, the present study aimed to empirically test a sequential psychobiological pathway linking neuroticism to NSSI. We hypothesized that the relationship between neuroticism (X) and NSSI (Y) is serially mediated by sleep disturbance (M1) and subsequent cortisol dysregulation (M2, indexed by Cortisol Awakening Response and Diurnal Cortisol Slope). Specifically, we predicted that higher neuroticism would be associated with greater sleep disturbance, which in turn would predict dysregulated cortisol patterns, ultimately contributing to a higher frequency of NSSI (Hypothesis: Neuroticism → Sleep Disturbance→Cortisol Dysregulation→NSSI). By examining this complete cascade in a single model, this study sought to move beyond prior research, which has largely focused on isolated components of this pathway. Clarifying this step-by-step mechanism is crucial for advancing our understanding of how a distal personality vulnerability translates into a proximal, harmful behavior. Furthermore, validating this model could provide a robust theoretical foundation for the development of novel, mechanism-based interventions—such as sleep-focused therapies or strategies aimed at normalizing HPA axis function—for adolescents and young adults with high neuroticism who are at an elevated risk for NSSI. Methods Study Design and Setting This cross-sectional study investigated the relationship between neuroticism, sleep disturbances, depressive symptoms, salivary cortisol, and NSSI in adolescents and young adults. Data collection occurred from March 2025 to June 2025 in Ningxia, China, with ethical approval from Medical Ethics Review Committee of Ningxia Medical University (Approval Number: 2025–3834). All methods were performed in accordance with the relevant guidelines and regulations. All participants provided written informed consent prior to participation, and parental consent was obtained for participants under the age of 18. Participants and Recruitment Participants were recruited from the general population, primarily university students, through a combination of online advertisements (WeChat, Sina Weibo), partnerships with local universities, and institutional channels. Eligible participants were aged 14–25 years, fluent in Mandarin Chinese, and able to complete study procedures. Exclusion criteria included the use of medications affecting cortisol levels, pregnancy or lactation, and a current or past diagnosis of severe psychiatric disorders (e.g., schizophrenia, bipolar disorder) in the last six months. A total of 350 participants were initially recruited, of whom 326 provided complete data. Following exclusions (24 due to incomplete questionnaires, 8 due to failing attention checks, and 6 due to contaminated cortisol samples), the final analytic sample comprised 302 participants (59.6% female, mean age 19.4 years, SD 2.6). Procedures Data were collected between March 2025 to June 2025. After providing informed consent, participants attended an initial briefing session either in-person or virtually, where they were given a study kit containing self-report questionnaires, salivette tubes for saliva collection, a sampling logbook, and insulated return bags. All materials were provided in Mandarin Chinese. Participants first completed a set of validated self-report measures via a secure online platform (WJX), assessing neuroticism, sleep quality, depressive symptoms, and NSSI behaviors. The questionnaires took approximately 25–30 minutes to complete, and participants were instructed to do so in a private, quiet setting. Following completion of the questionnaires, participants were trained on saliva collection procedures by research assistants, either in person or via video. They self-collected saliva over two consecutive weekdays at three time points: immediately upon waking, 30 minutes post-awakening, and 10:00 PM (before bedtime). Participants recorded exact awakening and sampling times in a logbook, and were instructed to refrain from eating, drinking (except water), brushing their teeth, smoking, or exercising for at least 30 minutes before each collection. To improve compliance, WeChat reminders were sent the evening before each collection day, and logbooks were reviewed to ensure adherence to the protocol. Samples were stored at 4°C immediately after collection and returned to the lab within 48 hours. All data were anonymized and securely stored. Furthermore, to minimize external stressors, data collection was scheduled outside of major academic periods, such as final exams, to avoid potential interference with participants' academic responsibilities. Measures Neuroticism Neuroticism was assessed using the Neuroticism subscale of the Revised NEO Personality Inventory (NEO-PI-R) 26 , a comprehensive instrument widely used for measuring personality traits across various populations. The NEO-PI-R consists of 240 items that evaluate five major personality domains: Neuroticism, Extraversion, Openness to Experience, Agreeableness, and Conscientiousness. Each domain is further divided into six facets, providing a detailed assessment of personality. The Neuroticism domain includes 48 items rated on a 5-point Likert scale from 0 (strongly disagree) to 4 (strongly agree), with total scores ranging from 0 to 192. Higher scores indicate greater levels of neuroticism, characterized by emotional instability and susceptibility to stress. The Chinese version of the NEO-PI-R has been validated in previous studies, demonstrating good internal consistency in both undergraduate and clinical samples, with Cronbach’s α coefficients of 0.91 and 0.93, respectively 27 . Sleep Disturbance The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep disturbance. This 19-item self-report measure evaluates seven domains of sleep quality: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, sleep medication use, and daytime dysfunction. The total score ranges from 0 to 21, with higher scores indicating poorer sleep quality. The Chinese version has demonstrated good internal consistency (Cronbach’s α = 0.82) and construct validity 28 . Depressive Symptoms Depressive symptoms were assessed using the Self-Rating Depression Scale (SDS), a widely used 20-item self-report measure assessing the severity of depressive symptoms. Higher scores indicate more severe depressive symptoms 29 . NSSI NSSI was assessed with a validated Chinese adolescent NSSI scale adapted for use in adolescents and young adults and administered with a 12-month recall period 30 . The instrument catalogs 18 self-injury behaviors spanning minor to more severe tissue damage (e.g., scratching the skin with sharp objects, re-opening wounds), and uses a two-part scoring system: (a) behavior frequency coded as 0, 1, 2–4, or ≥ 5; and (b) injury severity coded as none, mild, moderate, severe, or very severe. For each endorsed behavior, a behavior score is computed as frequency × severity; total NSSI severity is the sum across all behaviors, with higher totals indicating greater overall NSSI severity. Consistent with prior applications, any non-zero total score denotes the presence of NSSI for prevalence estimation in this study. Prior research in Chinese adolescent samples has demonstrated good internal consistency (Cronbach’s α typically 0.899) and acceptable construct validity (0.742) for this behavioral scoring framework. This study focused on the behavioral severity index; no separate NSSI-function questionnaire was administered. Salivary Cortisol Salivary cortisol was used to measure HPA axis activity. Participants collected three saliva samples per day at the following times: immediately upon waking, 30 minutes post-awakening, and at 10:00 PM. Cortisol concentrations were measured using a high-sensitivity enzyme immunoassay (Jianglai Biotechnology, Shanghai, China). Intra- and inter-assay coefficients of variation were kept below 10%. The cortisol awakening response (CAR) was calculated as the difference in cortisol levels between 30 minutes post-awakening and immediately upon waking. The diurnal cortisol slope (DCS) was computed as the slope across all three time points (waking, + 30 minutes, and bedtime). Statistical Analysis All analyses were performed in SPSS 24.0 Continuous variables are expressed as mean ± standard deviation (SD), and categorical variables as frequencies (n, %). The 12-month NSSI detection rate was defined as the proportion with a non-zero NSSI score; sex differences in detection rate were tested with a chi-square test. Spearman correlations were used to examine bivariate associations as several variables did not conform to a normal distribution. Serial mediation was tested with PROCESS Model 6 in two specifications: X = neuroticism; Y = NSSI; M1 = PSQI; M2 = CAR (Model 1) or DCS (Model 2). All models adjusted for age, sex (0 = female, 1 = male), years of education, and SDS scores. Indirect effects were derived from 5,000 bias-corrected bootstrap samples and deemed significant when the 95% confidence interval excluded zero; we report total, serial, and simple indirect effects, as well as the proportion mediated (total indirect ÷ total effect × 100%). Common-method bias was screened with Harman’s single-factor test. All items from each questionnaire were used as entries for exploratory factor analysis, and the results indicated that the first factor accounted for only 16.42% of the variance, which is below the critical standard of 40%. This suggests that there was no significant common method bias in this study. All tests were two-tailed, with an alpha level of 0.05. Results The final sample consisted of 302 participants. Descriptive statistics are reported in Table 1 . The detection rate of NSSI was 28.1% (n = 85). The detection rate differed by sex, with a higher rate in females (14.8%) than in males (10.7%), and the difference was statistically significant. Among participants, 4.02% reported only one method of self-injury, 15.90% had NSSI scores in the 2–10 range, and 5.54% had scores of 10 or higher. Table 1 Descriptive Statistics and Spearman Correlations Among Key Study Variables Variable M SD 1 2 3 4 5 6 7 7 8 1.Sex 1 2.Age 19.64 1.67 -0.017 1 3.Years of education 14.19 1.75 -0.019 0.962 ** 1 4.SDS 54.78 6.44 -0.130 ** 0.031 0.039 1 5.NSSI 1.89 2.65 -0.131 ** -0.025 -0.005 0.432 ** 1 6.Neuroticism 79.7 40.37 -0.138 ** -0.035 -0.006 0.493 ** 0.821 ** 1 6.PSQI 7.75 4.61 -0.109 ** 0.047 0.029 0.174 ** 0.332 ** 0.285 ** 1 7.CAR 93.38 47.27 0.119 ** -0.053 -0.048 -0.233 ** -0.345 ** -0.273 ** -0.436 ** 1 8.DCS -0.36 0.15 -0.09 0.008 -0.01 0.213 ** 0.389 ** 0.294 ** 0.296 ** -0.750 ** 1 *Note: *P < 0.05, **P < 0.01. SDS = Self-Rating Depression Scale; NSSI = Non-Suicidal Self-Injury; PSQI = Pittsburgh Sleep Quality Index;CAR = Cortisol Awakening Response;DCS=diurnal cortisol slope Relevance analysis Spearman correlations among neuroticism, NSSI, PSQI scores, CAR, and DCS are presented in Table 1 . Neuroticism was positively correlated with NSSI, PSQI, and DCS, and negatively correlated with CAR. NSSI showed the same pattern: positive correlations with neuroticism, PSQI, and DCS, and a negative correlation with CAR. PSQI was negatively correlated with CAR and positively correlated with DCS. Furthermore, sex (coded 0 = female, 1 = male) was negatively correlated with NSSI. Chain Mediation Analyses Based on the results of the correlation analysis, to examine the hypothesized indirect effects of neuroticism on NSSI, we conducted two separate chain mediation analyses using Model 6 of the SPSS PROCESS macro (v. 4.0; Hayes, 2017) with 5,000 bias-corrected bootstrap samples. In accordance with our a priori hypotheses, all analyses statistically controlled for participant age, sex, years of education, and depressive symptoms (SDS scores). Neuroticism was entered as the independent variable (X), NSSI as the dependent variable (Y), and PSQI scores as the first mediator (M1). The two models were tested separately for each HPA axis indicator as the second mediator (M2): Model 1 (M2 = CAR) (see Fig. 1 ) and Model 2 (M2 = DCS) (see Fig. 2 ). Model 1: Chain Mediation via Sleep Disturbance and CAR In the first model, neuroticism significantly predicted higher PSQI scores ( B = 0.031, 95% CI =[0.016,0.046]). PSQI, in turn, significantly predicted a blunted (lower) CAR ( B = -3.815, 95% CI =[-4.922,-2.709]). A blunted CAR significantly predicted higher NSSI frequency when controlling for all other variables ( B = -0.007, 95% CI =[-0.012,-0.002]).(Table 2 ) Table 2 Regression Analyses of the CAR Mediation Model (Model 1) Predictor variable Outcome variable: Sleep disturbance Outcome variable: CAR Outcome variable:NSSI B SE 95% CI P B SE 95% CI P B SE 95% CI P Sex -0.638 0.511 [-1.645,0.368] 0.213 3.635 4.96 [-6.126,13.396] 0.464 -0.175 0.21 [-0.588,0.238] 0.405 Age 1.071 0.536 [0.017,2.125] 0.046 -0.907 5.215 [-11.172,9.357] 0.862 -0.148 0.22 [-0.582,0.286] 0.504 Years of education -0.922 0.51 [-1.926,0.081] 0.071 -0.149 4.96 [-9.91,9.611] 0.976 0.145 0.21 [-0.267,0.558] 0.489 SDS 0.045 0.046 [-0.046,0.136] 0.329 -0.537 0.446 [-1.415,0.342] 0.23 -0.007 0.019 [-0.044,0.03] 0.703 Neuroticism 0.031 0.007 [0.016,0.046] < 0.001 -0.136 0.073 [-0.28,0.009] 0.066 0.042 0.003 [0.036,0.049] < 0.001 PSQI -3.815 0.562 [-4.922,-2.709] < 0.001 0.072 0.026 [0.022,0.123] 0.005 CAR -0.007 0.002 [-0.012,-0.002] 0.006 R 2 0.098 0.213 0.542 F 7.560** 13.324** 51.850** *Note: SDS = Self-Rating Depression Scale; NSSI = Non-Suicidal Self-Injury; PSQI = Pittsburgh Sleep Quality Index;CAR = Cortisol Awakening Respons The bias-corrected bootstrapping results showed a significant total indirect effect ( B = 0.004, 95% CI [0.0019, 0.0065]) and accounted for 8.64% of the total effect. Examining the specific indirect pathways, the hypothesized chain mediation (Lnd 3 : Neuroticism→PSQI→ CAR → NSSI) was significant ( B = 0.0008, 95% CI =[0.0002,0.0016]). Furthermore, the simple mediation through sleep disturbance alone (Lnd 1: Neuroticism→PSQI→NSSI) was also significant ( B = 0.0022, 95% CI =[0.0008,0.0041]). The direct effect of neuroticism on NSSI remained significant ( B = 0.0412, 95% CI =[0.0351,0.0473]). (Table 3 ) Table 3 Bootstrap Analysis of the CAR Mediation Model (Model 1) Indirect effect value Boot SE Boot LLCI Boot UICI Effect size Total indirect effect 0.004 0.0012 0.0019 0.0065 8.64% Lnd1: Neuroticism→PSQI→NSSI 0.0022 0.0008 0.0008 0.0041 4.75% Lnd2: Neuroticism→CAR→NSSI 0.0009 0.0006 -0.0001 0.0024 1.94% Lnd3: Neuroticism→PSQI→CAR→NSSI 0.0008 0.0004 0.0002 0.0016 1.73% Note: NSSI = Non-Suicidal Self-Injury; PSQI = Pittsburgh Sleep Quality Index; DCS=diurnal cortisol slope; CAR = Cortisol Awakening Respons Model 2: Chain Mediation via Sleep Disturbance and DCS In the second model, the pathway from neuroticism to sleep disturbance was identical ( B = 0.031, 95% CI =[0.016,0.046]). PSQI scores subsequently predicted a flatter (higher) diurnal cortisol slope ( B = 0.008, 95% CI =[0.004,0.012]). A flatter DCS, in turn, significantly predicted greater NSSI frequency ( B = 2.793, 95% CI =[1.372,4.214]). (Table 4 ) Table 4 Regression Analyses of the DCS Mediation Model (Model 2) Predictor variable Outcome variable: Sleep disturbance Outcome variableDCS Outcome variable:NSSI B SE 95% CI P B SE 95% CI P B SE 95% CI P Sex -0.638 0.511 [-1.645,0.368] 0.213 -0.004 0.017 [-0.037,0.029] 0.805 -0.188 0.207 [-0.596,0.22] 0.365 Age 1.071 0.536 [0.017,2.125] 0.046 0.026 0.018 [-0.008,0.061] 0.139 -0.214 0.219 [-0.645,0.216] 0.328 Years of education -0.922 0.51 [-1.926,0.081] 0.071 -0.026 0.017 [-0.059,0.007] 0.122 0.219 0.208 [-0.191,0.628] 0.294 SDS 0.045 0.046 [-0.046,0.136] 0.329 0.001 0.002 [-0.001,0.004] 0.329 -0.008 0.019 [-0.044,0.029] 0.681 Neuroticism 0.031 0.007 [0.016,0.046] < 0.001 0.001 < .0001 [0.0002,0.001] 0.003 0.041 0.003 [0.035,0.047] < 0.001 PSQI 0.008 0.002 [0.004,0.012] < 0.001 0.076 0.024 [0.029,0.124] 0.002 DCS 2.793 0.722 [1.372,4.214] < 0.001 R 2 0.098 0.136 0.553 F 7.560** 8.925** 54.164** Note: *P < 0.05, **P < 0.01. SDS = Self-Rating Depression Scale; NSSI = Non-Suicidal Self-Injury; PSQI = Pittsburgh Sleep Quality Index; DCS=diurnal cortisol slope The total indirect effect for this model was significant( B = 0.0051, 95% CI =[0.0028,0.008]). Crucially, the specified chain mediation (Neuroticism → PSQI → DCS → NSSI) was significant (Lnd 3: B = 0.0007, 95% CI =[0.0002,0.0014]). The simple mediation paths via sleep disturbance alone (Lnd 1; B = 0.0024, 95% CI [0.0008, 0.0043]) and via DCS alone (Lnd 2; B = 0.0021, 95% CI [0.0005, 0.0044]) were also significant. The direct effect of neuroticism on NSSI remained significant ( B = 0.041, 95% CI [0.0362, 0.0485]). (Table 5 ) Table 5 Bootstrap Analysis of the DCS Mediation Model (Model 2) Indirect effect value Boot SE Boot LLCI Boot UICI Effect size Total indirect effect 0.0051 0.0013 0.0028 0.008 11.02% Lnd1: Neuroticism→PSQI→NSSI 0.0024 0.0009 0.0008 0.0043 5.18% Lnd2: Neuroticism→DSC→NSSI 0.0021 0.001 0.0005 0.0044 4.54% Lnd3: Neuroticism→PSQI→DSC→NSSI 0.0007 0.0003 0.0002 0.0014 1.51% Note: NSSI = Non-Suicidal Self-Injury; PSQI = Pittsburgh Sleep Quality Index; DCS=diurnal cortisol slope Discussion The present study provides empirical support for an integrated psychobiological pathway linking neuroticism to NSSI in a community sample of Chinese adolescents and young adults. Our primary finding was the significant serial mediating role of sleep disturbance and subsequent cortisol dysregulation in this relationship. Specifically, our results supported a cascading process: higher neuroticism predicted greater sleep disturbance, which in turn was associated with altered HPA axis functioning, ultimately contributing to increased NSSI severity. Although the indirect effects through this specific chain were modest, their statistical significance, even after controlling for key covariates including depressive symptoms, suggests this is a valid and meaningful explanatory pathway within the complex etiology of NSSI. Our findings reaffirmed neuroticism as a robust dispositional vulnerability for NSSI. This aligns with a large body of literature demonstrating that the heightened stress reactivity and emotional instability inherent in neuroticism create a fertile ground for maladaptive coping behaviors 11 ; 12 ; 31 . The initial step of our model posits that this heightened vulnerability translates into concrete physiological disruption, specifically by impacting sleep. This is mechanistically plausible, as neuroticism’s core features—heightened physiological arousal and cognitive rumination—are known to directly contribute to difficulties with sleep onset and maintenance, a bidirectional relationship confirmed in large-scale studies 17 ; 32 . Thus, our model begins by grounding a personality trait in a tangible, disruptive biological process. Indeed, sleep disturbance emerged as a pivotal first-step mediator in our model. The observed association between neuroticism and poorer sleep quality is well-documented 17 . Our results build on this by situating sleep disturbance as a critical bridge to both physiological dysregulation and self-injurious behavior. This finding echoes recent research that emphasizes the importance of sleep as a key mediator connecting various adversities to NSSI. 33 ; 34 It also aligns with prospective evidence showing that adolescent sleep problems are linked to subsequent depressive symptoms through alterations in the CAR, thereby supporting the crucial role of sleep in HPA - related pathogenic cascades 35 . The next step in our cascade, the link from sleep disturbance to cortisol dysregulation, is also well-supported. Chronic insomnia and even acute sleep deprivation are known to dysregulate the HPA axis, often leading to flattened diurnal cortisol rhythms by sustaining sympathetic activation and reducing restorative sleep phases 22 ; 36 ; 37 . Our study advances this literature by demonstrating that this sleep-HPA link is an active component in the pathway to NSSI. Further downstream, our results implicated cortisol dysregulation as a key physiological link in this cascade. The findings of a blunted CAR and a flatter DCS in relation to higher NSSI severity are consistent with theories of chronic stress and HPA axis exhaustion 38 . While some studies have reported heightened cortisol reactivity in NSSI populations, particularly in response to anticipated stressors 23 , the blunted pattern observed here may reflect allostatic load in our non-clinical sample. By demonstrating that these cortisol patterns function as a sequential mediator, our study provides a tangible biological mechanism through which the effects of poor sleep are transduced into increased NSSI risk. This final step in the chain is mechanistically coherent: dysregulated cortisol patterns, such as a blunted CAR, reflect diminished capacity for anticipatory mobilization and recovery from daily stressors, which can erode emotion regulation capacities 13 . This makes NSSI more probable as an immediate, albeit maladaptive, means to downregulate acute distress, a function well-established in the experiential avoidance model of self-harm 24 ; 39 . Theoretically, these findings contribute to the stress-vulnerability framework by delineating a specific, multi-level pathway from a distal personality trait to a proximal clinical behavior. This supports a shift towards more complex, temporally-ordered models of NSSI etiology that integrate personality, behavior, and biology. Clinically, our findings highlight neuroticism as a valuable target for early screening, with sleep quality and HPA axis markers serving as potential indicators of risk progression. The significant mediating role of sleep is particularly promising for intervention. Given that sleep is often more amenable to change than ingrained personality traits, behavioral interventions such as Cognitive-Behavioral Therapy for Insomnia or strategies to improve sleep hygiene could be practical and effective preventive measures for high-neuroticism youth 18 . Our results provide a mechanistic rationale for why such interventions may be effective, suggesting they could disrupt the pathogenic cascade before it culminates in self-injury. Several limitations should be considered when interpreting these findings. First, the cross-sectional design precludes causal inferences and cannot rule out bidirectional relationships. Future research should employ longitudinal designs, such as ecological momentary assessment or multi-wave panel studies, to establish temporal precedence. Second, our reliance on self-report measures for NSSI and sleep may be subject to recall and social desirability biases. Objective measures, such as actigraphy for sleep and clinical interviews for NSSI, would strengthen future investigations. Third, salivary cortisol collection, despite adherence protocols, is susceptible to variability; incorporating long-term HPA axis markers like hair cortisol could provide a more stable index of chronic stress. Finally, our sample was composed predominantly of university students from a single region in China, which may limit the generalizability of our findings to other demographic, cultural, or clinical populations. Future studies should aim for more diverse and representative samples to test the robustness of this model. Conclusion In conclusion, this study provides initial cross-sectional evidence for a sequential pathway from neuroticism to NSSI via sleep disturbance and cortisol dysregulation. Although the effects are modest, they highlight a valid and mechanistically plausible route contributing to NSSI risk. These findings underscore the importance of integrating psychological and biological perspectives in the study of self-injury and suggest that targeting sleep may be a promising avenue for prevention and intervention in vulnerable youth. Declarations Competing interests The authors declare no competing interests. Consent for publication Not applicable. Author Contribution Xiang Zhang: Conceptualization, Methodology, Software, Validation, Formal analysis, Writing- Original draft preparation; Shaoxia Wang: Data curation, Resources,Investigation; Huarong He: Visualization, Investigation; Yijie Wang: Conceptualization, Methodology,Project administration, Funding acquisition. Acknowledgement We would like to express our gratitude to all the participants who volunteered for this study. Data Availability The datasets generated or analyzed during the current study are available from the corresponding author (Yijie Wang), upon reasonable request, but are not publicly available due to patient privacy restrictions. References Klonsky, E. D., Victor, S. E. & Saffer, B. Y. Nonsuicidal Self-Injury: What we Know, and What we Need to Know 565–568 (SAGE Publications Sage CA, 2014). Swannell, S. V., Martin, G. E., Page, A., Hasking, P. & St, J. N. Prevalence of Nonsuicidal Self-Injury in Nonclinical Samples: Systematic Review, Meta-Analysis and Meta-Regression. Suicide Life-Threat Behav. 44 , 273–303 (2014). Lim, K. et al. Global Lifetime and 12-Month Prevalence of Suicidal Behavior, Deliberate Self-Harm and Non-Suicidal Self-Injury in Children and Adolescents Between 1989 and 2018: A Meta-Analysis. Int. J. Environ. Res. Public Health . 16 , 4581 (2019). Xiao, Q., Song, X., Huang, L., Hou, D. & Huang, X. Global Prevalence and Characteristics of Non-Suicidal Self-Injury Between 2010 and 2021 Among a Non-Clinical Sample of Adolescents: A Meta-Analysis. Front. Psychiatry . 13 , 912441 (2022). Farkas, B. F., Takacs, Z. K., Kollarovics, N. & Balazs, J. The Prevalence of Self-Injury in Adolescence: A Systematic Review and Meta-Analysis. Eur. Child. Adolesc. Psych . 33 , 3439–3458 (2024). Klonsky, E. D. The Functions of Deliberate Self-Injury: A Review of the Evidence. Clin. Psychol. Rev. 27 , 226–239 (2007). Taylor, P. J. et al. A Meta-Analysis of the Prevalence of Different Functions of Non-Suicidal Self-Injury. J. Affect. Disord . 227 , 759–769 (2018). Hankin, B. L. & Abela, J. R. Nonsuicidal Self-Injury in Adolescence: Prospective Rates and Risk Factors in a 2½ Year Longitudinal Study. Psychiatry Res. 186 , 65–70 (2011). Ingram, R. E., Luxton, D. D., Vulnerability-Stress & Models Development of psychopathology: A vulnerability-stress perspective 32–46 (Sage Publications, Inc, 2005). Guerry, J. D. & Prinstein, M. J. Longitudinal Prediction of Adolescent Nonsuicidal Self-Injury: Examination of a Cognitive Vulnerability-Stress Model. J. Clin. Child. Adolesc. Psychol. 39 , 77–89 (2010). Mota, M., Ulguim, H. B., Jansen, K., Cardoso, T. A. & Souza, L. Are Big Five Personality Traits Associated to Suicidal Behaviour in Adolescents? A Systematic Review and Meta-Analysis. J. Affect. Disord . 347 , 115–123 (2024). Kotov, R., Gamez, W., Schmidt, F. & Watson, D. Linking Big Personality Traits to Anxiety, Depressive, and Substance Use Disorders: A Meta-Analysis. Psychol. Bull. 136 , 768–821 (2010). Wu, B. et al. Potential Mechanisms of Non-Suicidal Self-Injury (Nssi) in Major Depressive Disorder: A Systematic Review. Gen. Psychiat . 36 , e100946 (2023). Glenn, C. R. & Klonsky, E. D. Nonsuicidal Self-Injury Disorder: An Empirical Investigation in Adolescent Psychiatric Patients. J. Clin. Child. Adolesc. Psychol. 42 , 496–507 (2013). Claes, L., Houben, A., Vandereycken, W., Bijttebier, P. & Muehlenkamp, J. Brief Report: The Association Between Non-Suicidal Self-Injury, Self-Concept and Acquaintance with Self-Injurious Peers in a Sample of Adolescents. J. Adolesc. 33 , 775–778 (2010). Costa, P. T. & McCrae, R. R. Revised Neo Personality Inventory (Neo Pi-R) and Nep Five-Factor Inventory (Neo-Ffi): Professional Manual , Psychological Assessment Resources (Odessa, Fla. (P.O. Box 998, Odessa 33556), (1992). Alvaro, P. K., Roberts, R. M. & Harris, J. K. A Systematic Review Assessing Bidirectionality Between Sleep Disturbances, Anxiety, and Depression. Sleep 36 , 1059–1068 (2013). Bandel, S. L. & Brausch, A. M. Poor Sleep Associates with Recent Nonsuicidal Self-Injury Engagement in Adolescents. Behav. Sleep. Med. 18 , 81–90 (2020). Liang, J., Liu, Y., Zhang, T., Jia, F. & Hou, C. Sleep Characteristics and Non-Suicidal Self-Injury: Unveiling the Association in Depressed Adolescents. Bmc Psychiatry . 25 , 1027 (2025). Baldini, V. et al. Association Between Sleep Disturbances and Suicidal Behavior in Adolescents: A Systematic Review and Meta-Analysis. Front. Psychiatry . 15 , 1341686 (2024). Stickgold, R. & Walker, M. P. Sleep-Dependent Memory Consolidation and Reconsolidation. Sleep. Med. 8 , 331–343 (2007). Buckley, T. M. & Schatzberg, A. F. On the Interactions of the Hypothalamic-Pituitary-Adrenal (Hpa) Axis and Sleep: Normal Hpa Axis Activity and Circadian Rhythm, Exemplary Sleep Disorders. J. Clin. Endocrinol. Metab. 90 , 3106–3114 (2005). Reichl, C. et al. Hypothalamic-Pituitary-Adrenal Axis, Childhood Adversity and Adolescent Nonsuicidal Self-Injury. Psychoneuroendocrinology 74 , 203–211 (2016). Kaess, M. et al. Alterations in the Neuroendocrinological Stress Response to Acute Psychosocial Stress in Adolescents Engaging in Nonsuicidal Self-Injury. Psychoneuroendocrinology 37 , 157–161 (2012). Joëls, M. & Baram, T. Z. The Neuro-Symphony of Stress. Nat. Rev. Neurosci. 10 , 459–466 (2009). Costa, P. T. & McCrae, R. R. Revised Neo Personality Inventory (Neo Pi-R) and Nep Five-Factor Inventory (Neo-Ffi): Professional Manual , Psychological Assessment Resources (Odessa, Fla. (P.O. Box 998, Odessa 33556), (1992). Xi, C. et al. Psychometric Properties of the Chinese Version of the Neuroticism Subscale of the Neo-Pi. Front. Psychol. 9 , 1454 (2018). Tsai, P. S. et al. Psychometric Evaluation of the Chinese Version of the Pittsburgh Sleep Quality Index (Cpsqi) in Primary Insomnia and Control Subjects. Qual. Life Res. 14 , 1943–1952 (2005). Zung, W. W. A Self-Rating Depression Scale. Arch. Gen. Psychiatry . 12 , 63–70 (1965). Feng, Y. The Relation of Adolescents’ Self-Harm Behaviors, Individual Emotion Characteristics and Family Environment Factors. Central China Normal University (2008). Takahashi, M., Imahara, K., Miyamoto, Y., Myojo, K. & Yasuda, M. Association Between the Big Five Personality Traits and Suicide-Related Behaviors in Japanese Institutionalized Youths. PCN Rep. 3 , e186 (2024). Duggan, K. A., Friedman, H. S., McDevitt, E. A. & Mednick, S. C. Personality and Healthy Sleep: The Importance of Conscientiousness and Neuroticism. Plos One . 9 , e90628 (2014). Wang, H. et al. The Effect of Life Events On Nssi: The Chain Mediating Effect of Sleep Disturbances and Ples Among Chinese College Students. Front. Psychol. 15 , 1325436 (2024). Zheng, X., Chen, Y. & Zhu, J. Sleep Problems Mediate the Influence of Childhood Emotional Maltreatment On Adolescent Non-Suicidal Self-Injury: The Moderating Effect of Rumination. Child. Abuse Negl. 140 , 106161 (2023). Kuhlman, K. R. et al. Sleep Problems in Adolescence are Prospectively Linked to Later Depressive Symptoms Via the Cortisol Awakening Response. Dev. Psychopathol. 32 , 997–1006 (2020). Vargas, I. & Lopez-Duran, N. Investigating the Effect of Acute Sleep Deprivation On Hypothalamic-Pituitary-Adrenal-Axis Response to a Psychosocial Stressor. Psychoneuroendocrinology 79 , 1–8 (2017). McEwen, B. S. Sleep Deprivation as a Neurobiologic and Physiologic Stressor: Allostasis and Allostatic Load. Metab. -Clin Exp. 55 , S20–S23 (2006). Miller, G. E., Chen, E. & Zhou, E. S. If It Goes Up, Must It Come Down? Chronic Stress and the Hypothalamic-Pituitary-Adrenocortical Axis in Humans. Psychol. Bull. 133 , 25–45 (2007). Chapman, A. L., Gratz, K. L. & Brown, M. Z. Solving the Puzzle of Deliberate Self-Harm: The Experiential Avoidance Model. Behav. Res. Ther. 44 , 371–394 (2006). Additional Declarations No competing interests reported. Supplementary Files Table1.docx Table2.docx Table3.docx Table4.docx Table5.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8733092","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":588801688,"identity":"7bed040b-cabc-4063-a18f-d58f420b688f","order_by":0,"name":"Xiang Zhang","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Zhang","suffix":""},{"id":588801690,"identity":"174cabeb-3971-4437-9622-85de7254859d","order_by":1,"name":"Yijie Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYFCCA1Cah/nAgQ8/SNPClnhwZg9JtvHwGB/mYCNCocHBM4afC34dljPvOfPhMAMPgzy/2AECWg6cMZae2XfYWOZs74bDBRYMhjNnJxDScnaDNG/P7cQZ/LwbDs/gYUgwuE1Yy+bfEC08Dw7zsBGnZZs0zw+gFt4eBuK0SB44/82at+G/sQTPMQNgIEsQ9gvfjWPJt3n+pMlJ8CQ//vDhh408vzQBLQo3DjAwMLbB+RL4lYOAfH8DkPxDWOEoGAWjYBSMYAAAd8ROAAWKbU0AAAAASUVORK5CYII=","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yijie","middleName":"","lastName":"Wang","suffix":""},{"id":588801692,"identity":"8a4c236d-a88b-46c5-8fa9-406db953fa4f","order_by":2,"name":"Shaoxia Wang","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shaoxia","middleName":"","lastName":"Wang","suffix":""},{"id":588801693,"identity":"77183fc6-30cd-4f46-9b42-779f4c5e451d","order_by":3,"name":"Huarong He","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huarong","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2026-01-29 15:27:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8733092/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8733092/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102736016,"identity":"983f52c7-3343-4dfa-b93d-94e9346c0290","added_by":"auto","created_at":"2026-02-16 06:22:11","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":140359,"visible":true,"origin":"","legend":"\u003cp\u003eSerial Mediation Model with Sleep Disturbance and CAR\u003c/p\u003e\n\u003cp\u003eNote: SDS = Self-Rating Depression Scale; NSSI = Non-Suicidal Self-Injury; PSQI = Pittsburgh Sleep Quality Index; CAR = Cortisol Awakening Response; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8733092/v1/1558f89a498e8a8ffa027718.jpg"},{"id":102736012,"identity":"ea491e72-5437-43ed-b531-fb8b851508e6","added_by":"auto","created_at":"2026-02-16 06:22:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":140762,"visible":true,"origin":"","legend":"\u003cp\u003eSerial Mediation Model with Sleep Disturbance and CAR\u003c/p\u003e\n\u003cp\u003eNote: SDS = Self-Rating Depression Scale; NSSI = Non-Suicidal Self-Injury; PSQI = Pittsburgh Sleep Quality Index; CAR = Cortisol Awakening Response; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8733092/v1/f3446d424e70941f97355c4e.jpg"},{"id":107962745,"identity":"bbb0e972-e3b2-4342-9b7f-d124fed987b1","added_by":"auto","created_at":"2026-04-28 04:56:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":809030,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8733092/v1/74810fc8-a40e-4ffe-bb83-53d86c78efb0.pdf"},{"id":102736011,"identity":"419dae8e-6733-4805-98d5-1ecc5ae570bb","added_by":"auto","created_at":"2026-02-16 06:22:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16283,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8733092/v1/fcc65af68593fe81cde7d9ee.docx"},{"id":102736010,"identity":"6ca88afb-d769-4819-a6b0-12c66f3da35f","added_by":"auto","created_at":"2026-02-16 06:22:11","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16732,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8733092/v1/fc515034502adfe502cb8a40.docx"},{"id":102736014,"identity":"a3bf66a1-8926-4c7e-9d48-2156724f90d6","added_by":"auto","created_at":"2026-02-16 06:22:11","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":16496,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8733092/v1/d1933cc524d7ad47e311518a.docx"},{"id":102736015,"identity":"0c121779-1c3b-41f5-b2c7-62fa4e0841d1","added_by":"auto","created_at":"2026-02-16 06:22:11","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":12722,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-8733092/v1/1e8969cd515af20c497c8de9.docx"},{"id":102736013,"identity":"c73763e3-a273-4c18-9714-c377d599b791","added_by":"auto","created_at":"2026-02-16 06:22:11","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":12811,"visible":true,"origin":"","legend":"","description":"","filename":"Table5.docx","url":"https://assets-eu.researchsquare.com/files/rs-8733092/v1/052a5f334a0ec7dce21a010b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Neuroticism and Non-Suicidal Self-Injury: A Serial Mediation Model of Sleep Disturbance and Cortisol Dysregulation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNon-suicidal self-injury (NSSI), defined as the deliberate, self-inflicted destruction of body tissue without suicidal intent and for reasons not socially sanctioned ,\u003csup\u003e1\u003c/sup\u003e is a significant and growing public health concern, particularly among adolescents and young adults .\u003csup\u003e2; 3\u003c/sup\u003e Recent large-scale meta-analyses confirm its high prevalence, with 12-month estimates in adolescent community samples ranging from 17% to over 22% \u003csup\u003e4; 5\u003c/sup\u003e. This behavior is frequently associated with a range of psychiatric morbidities, including mood and anxiety disorders \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, and often functions as a maladaptive coping strategy for overwhelming emotional distress, a notion supported by meta-analytic evidence identifying affect regulation as its primary function \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Understanding the etiology of NSSI is crucial for effective intervention. A valuable framework for this endeavor is the stress-vulnerability model, which posits that inherent dispositions (vulnerabilities) interact with stressors to precipitate psychopathology \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e; 9\u003c/sup\u003e. This model directs inquiry toward identifying key dispositional risk factors, such as personality traits, and the specific mechanisms through which their influence is expressed\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Among the most robust of these dispositional vulnerabilities for NSSI is neuroticism.\u003c/p\u003e \u003cp\u003eNeuroticism, a personality trait characterized by emotional instability, negative affectivity, and heightened reactivity to stressors, has been consistently identified as a powerful predictor of NSSI. This association is supported by large-scale meta-analyses linking the trait to a spectrum of self-injurious and suicide-related behaviors \u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Individuals with elevated neuroticism exhibit a greater propensity for self-injurious behaviors across diverse populations\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e; 15\u003c/sup\u003e. This strong association is thought to be rooted in neuroticism\u0026rsquo;s role as a marker of stress sensitivity; those high in neuroticism not only perceive situations as more stressful but also experience more intense and prolonged negative emotional responses \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, while the direct link between neuroticism and NSSI is well-established, the specific psychobiological pathways that translate this dispositional vulnerability into the act of self-injury remain underexplored. Given neuroticism\u0026rsquo;s profound impact on stress processing systems, its influence likely extends to fundamental physiological processes, such as sleep, warranting further investigation.\u003c/p\u003e \u003cp\u003eBuilding on this premise, sleep disturbance emerges as a primary candidate for the initial step in this psychobiological cascade. The link between neuroticism and poor sleep is robust; individuals high in neuroticism frequently report difficulties with sleep onset and maintenance, a consequence of the heightened physiological arousal and cognitive rumination characteristic of the trait \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Critically, sleep disturbance is increasingly recognized as a potent risk factor for NSSI. Cross-sectional studies consistently find that poor sleep quality is associated with recent NSSI engagement in adolescents\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e; 19\u003c/sup\u003e. While much of the evidence is cross-sectional, the consistency of this association, along with findings linking sleep disturbances to suicidal behaviors in meta-analyses \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, strongly suggests its role as a critical intermediary. The mechanism linking sleep loss to self-injury is thought to involve impaired emotion regulation; sleep is vital for processing emotional experiences \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, and its disruption can impair the brain's capacity to down-regulate negative affect, increasing the likelihood of resorting to NSSI for relief. This positions sleep disturbance as a key mechanistic bridge, translating the latent vulnerability of neuroticism into a state of acute emotional dysregulation that precedes self-injurious acts.\u003c/p\u003e \u003cp\u003eThe impact of sleep disturbance extends beyond psychological functioning to disrupt core neurobiological systems, most notably the hypothalamic-pituitary-adrenal (HPA) axis. Chronic sleep loss is a potent disruptor of HPA axis regulation, leading to altered circadian cortisol rhythms \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This physiological cascade is particularly relevant to NSSI, as a growing body of evidence indicates that individuals who self-injure exhibit significant HPA axis dysregulation. For instance, adolescents with NSSI have been shown to display altered cortisol patterns in the context of both childhood adversity and acute psychosocial stressors, often interpreted as a sign of HPA axis exhaustion or dysregulation from chronic stress \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e; \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. This attenuated stress reactivity can impair an individual's ability to mount an effective response to daily challenges, while cortisol dysregulation itself directly impacts neurotransmitter systems crucial for mood and impulse control\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, exacerbating the emotion regulation deficits that precipitate NSSI.\u003c/p\u003e \u003cp\u003eBuilding on this integrated theoretical framework, the present study aimed to empirically test a sequential psychobiological pathway linking neuroticism to NSSI. We hypothesized that the relationship between neuroticism (X) and NSSI (Y) is serially mediated by sleep disturbance (M1) and subsequent cortisol dysregulation (M2, indexed by Cortisol Awakening Response and Diurnal Cortisol Slope). Specifically, we predicted that higher neuroticism would be associated with greater sleep disturbance, which in turn would predict dysregulated cortisol patterns, ultimately contributing to a higher frequency of NSSI (Hypothesis: Neuroticism \u0026rarr; Sleep Disturbance\u0026rarr;Cortisol Dysregulation\u0026rarr;NSSI). By examining this complete cascade in a single model, this study sought to move beyond prior research, which has largely focused on isolated components of this pathway. Clarifying this step-by-step mechanism is crucial for advancing our understanding of how a distal personality vulnerability translates into a proximal, harmful behavior. Furthermore, validating this model could provide a robust theoretical foundation for the development of novel, mechanism-based interventions\u0026mdash;such as sleep-focused therapies or strategies aimed at normalizing HPA axis function\u0026mdash;for adolescents and young adults with high neuroticism who are at an elevated risk for NSSI.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eThis cross-sectional study investigated the relationship between neuroticism, sleep disturbances, depressive symptoms, salivary cortisol, and NSSI in adolescents and young adults. Data collection occurred from March 2025 to June 2025 in Ningxia, China, with ethical approval from Medical Ethics Review Committee of Ningxia Medical University (Approval Number: 2025\u0026ndash;3834). All methods were performed in accordance with the relevant guidelines and regulations. All participants provided written informed consent prior to participation, and parental consent was obtained for participants under the age of 18.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants and Recruitment\u003c/h3\u003e\n\u003cp\u003eParticipants were recruited from the general population, primarily university students, through a combination of online advertisements (WeChat, Sina Weibo), partnerships with local universities, and institutional channels. Eligible participants were aged 14\u0026ndash;25 years, fluent in Mandarin Chinese, and able to complete study procedures. Exclusion criteria included the use of medications affecting cortisol levels, pregnancy or lactation, and a current or past diagnosis of severe psychiatric disorders (e.g., schizophrenia, bipolar disorder) in the last six months.\u003c/p\u003e \u003cp\u003eA total of 350 participants were initially recruited, of whom 326 provided complete data. Following exclusions (24 due to incomplete questionnaires, 8 due to failing attention checks, and 6 due to contaminated cortisol samples), the final analytic sample comprised 302 participants (59.6% female, mean age 19.4 years, SD 2.6).\u003c/p\u003e\n\u003ch3\u003eProcedures\u003c/h3\u003e\n\u003cp\u003eData were collected between March 2025 to June 2025. After providing informed consent, participants attended an initial briefing session either in-person or virtually, where they were given a study kit containing self-report questionnaires, salivette tubes for saliva collection, a sampling logbook, and insulated return bags. All materials were provided in Mandarin Chinese.\u003c/p\u003e \u003cp\u003eParticipants first completed a set of validated self-report measures via a secure online platform (WJX), assessing neuroticism, sleep quality, depressive symptoms, and NSSI behaviors. The questionnaires took approximately 25\u0026ndash;30 minutes to complete, and participants were instructed to do so in a private, quiet setting.\u003c/p\u003e \u003cp\u003e Following completion of the questionnaires, participants were trained on saliva collection procedures by research assistants, either in person or via video. They self-collected saliva over two consecutive weekdays at three time points: immediately upon waking, 30 minutes post-awakening, and 10:00 PM (before bedtime). Participants recorded exact awakening and sampling times in a logbook, and were instructed to refrain from eating, drinking (except water), brushing their teeth, smoking, or exercising for at least 30 minutes before each collection.\u003c/p\u003e \u003cp\u003eTo improve compliance, WeChat reminders were sent the evening before each collection day, and logbooks were reviewed to ensure adherence to the protocol. Samples were stored at 4\u0026deg;C immediately after collection and returned to the lab within 48 hours. All data were anonymized and securely stored. Furthermore, to minimize external stressors, data collection was scheduled outside of major academic periods, such as final exams, to avoid potential interference with participants' academic responsibilities.\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eNeuroticism\u003c/h2\u003e \u003cp\u003eNeuroticism was assessed using the Neuroticism subscale of the Revised NEO Personality Inventory (NEO-PI-R)\u003csup\u003e26\u003c/sup\u003e, a comprehensive instrument widely used for measuring personality traits across various populations. The NEO-PI-R consists of 240 items that evaluate five major personality domains: Neuroticism, Extraversion, Openness to Experience, Agreeableness, and Conscientiousness. Each domain is further divided into six facets, providing a detailed assessment of personality. The Neuroticism domain includes 48 items rated on a 5-point Likert scale from 0 (strongly disagree) to 4 (strongly agree), with total scores ranging from 0 to 192. Higher scores indicate greater levels of neuroticism, characterized by emotional instability and susceptibility to stress. The Chinese version of the NEO-PI-R has been validated in previous studies, demonstrating good internal consistency in both undergraduate and clinical samples, with Cronbach\u0026rsquo;s α coefficients of 0.91 and 0.93, respectively\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSleep Disturbance\u003c/h2\u003e \u003cp\u003eThe Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep disturbance. This 19-item self-report measure evaluates seven domains of sleep quality: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, sleep medication use, and daytime dysfunction. The total score ranges from 0 to 21, with higher scores indicating poorer sleep quality. The Chinese version has demonstrated good internal consistency (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.82) and construct validity \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDepressive Symptoms\u003c/h3\u003e\n\u003cp\u003eDepressive symptoms were assessed using the Self-Rating Depression Scale (SDS), a widely used 20-item self-report measure assessing the severity of depressive symptoms. Higher scores indicate more severe depressive symptoms\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eNSSI\u003c/h3\u003e\n\u003cp\u003eNSSI was assessed with a validated Chinese adolescent NSSI scale adapted for use in adolescents and young adults and administered with a 12-month recall period\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The instrument catalogs 18 self-injury behaviors spanning minor to more severe tissue damage (e.g., scratching the skin with sharp objects, re-opening wounds), and uses a two-part scoring system: (a) behavior frequency coded as 0, 1, 2\u0026ndash;4, or \u0026ge;\u0026thinsp;5; and (b) injury severity coded as none, mild, moderate, severe, or very severe. For each endorsed behavior, a behavior score is computed as frequency \u0026times; severity; total NSSI severity is the sum across all behaviors, with higher totals indicating greater overall NSSI severity. Consistent with prior applications, any non-zero total score denotes the presence of NSSI for prevalence estimation in this study. Prior research in Chinese adolescent samples has demonstrated good internal consistency (Cronbach\u0026rsquo;s α typically 0.899) and acceptable construct validity (0.742) for this behavioral scoring framework. This study focused on the behavioral severity index; no separate NSSI-function questionnaire was administered.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSalivary Cortisol\u003c/h2\u003e \u003cp\u003eSalivary cortisol was used to measure HPA axis activity. Participants collected three saliva samples per day at the following times: immediately upon waking, 30 minutes post-awakening, and at 10:00 PM. Cortisol concentrations were measured using a high-sensitivity enzyme immunoassay (Jianglai Biotechnology, Shanghai, China). Intra- and inter-assay coefficients of variation were kept below 10%. The cortisol awakening response (CAR) was calculated as the difference in cortisol levels between 30 minutes post-awakening and immediately upon waking. The diurnal cortisol slope (DCS) was computed as the slope across all three time points (waking, +\u0026thinsp;30 minutes, and bedtime).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll analyses were performed in SPSS 24.0 Continuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and categorical variables as frequencies (n, %). The 12-month NSSI detection rate was defined as the proportion with a non-zero NSSI score; sex differences in detection rate were tested with a chi-square test. Spearman correlations were used to examine bivariate associations as several variables did not conform to a normal distribution. Serial mediation was tested with PROCESS Model 6 in two specifications: X\u0026thinsp;=\u0026thinsp;neuroticism; Y\u0026thinsp;=\u0026thinsp;NSSI; M1\u0026thinsp;=\u0026thinsp;PSQI; M2\u0026thinsp;=\u0026thinsp;CAR (Model 1) or DCS (Model 2). All models adjusted for age, sex (0\u0026thinsp;=\u0026thinsp;female, 1\u0026thinsp;=\u0026thinsp;male), years of education, and SDS scores. Indirect effects were derived from 5,000 bias-corrected bootstrap samples and deemed significant when the 95% confidence interval excluded zero; we report total, serial, and simple indirect effects, as well as the proportion mediated (total indirect\u0026thinsp;\u0026divide;\u0026thinsp;total effect \u0026times; 100%). Common-method bias was screened with Harman\u0026rsquo;s single-factor test. All items from each questionnaire were used as entries for exploratory factor analysis, and the results indicated that the first factor accounted for only 16.42% of the variance, which is below the critical standard of 40%. This suggests that there was no significant common method bias in this study. All tests were two-tailed, with an alpha level of 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe final sample consisted of 302 participants. Descriptive statistics are reported in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The detection rate of NSSI was 28.1% (n\u0026thinsp;=\u0026thinsp;85). The detection rate differed by sex, with a higher rate in females (14.8%) than in males (10.7%), and the difference was statistically significant. Among participants, 4.02% reported only one method of self-injury, 15.90% had NSSI scores in the 2\u0026ndash;10 range, and 5.54% had scores of 10 or higher.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive Statistics and Spearman Correlations Among Key Study Variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"13\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.Years of education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.962\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.SDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.130\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.NSSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.131\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.432\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.Neuroticism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.138\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.493\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.821\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.PSQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.109\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.174\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.332\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.285\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.CAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.119\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.233\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.345\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.273\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.436\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.DCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.213\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.389\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.294\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.296\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.750\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003e\u003cem\u003e*Note: *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01. SDS\u0026thinsp;=\u0026thinsp;Self-Rating Depression Scale; NSSI\u0026thinsp;=\u0026thinsp;Non-Suicidal Self-Injury; PSQI\u0026thinsp;=\u0026thinsp;Pittsburgh Sleep Quality Index;CAR\u0026thinsp;=\u0026thinsp;Cortisol Awakening Response;DCS=diurnal cortisol slope\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eRelevance analysis\u003c/h2\u003e\n \u003cp\u003eSpearman correlations among neuroticism, NSSI, PSQI scores, CAR, and DCS are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Neuroticism was positively correlated with NSSI, PSQI, and DCS, and negatively correlated with CAR. NSSI showed the same pattern: positive correlations with neuroticism, PSQI, and DCS, and a negative correlation with CAR. PSQI was negatively correlated with CAR and positively correlated with DCS. Furthermore, sex (coded 0\u0026thinsp;=\u0026thinsp;female, 1\u0026thinsp;=\u0026thinsp;male) was negatively correlated with NSSI.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eChain Mediation Analyses\u003c/h2\u003e\n \u003cp\u003eBased on the results of the correlation analysis, to examine the hypothesized indirect effects of neuroticism on NSSI, we conducted two separate chain mediation analyses using Model 6 of the SPSS PROCESS macro (v. 4.0; Hayes, 2017) with 5,000 bias-corrected bootstrap samples.\u003c/p\u003e\n \u003cp\u003eIn accordance with our a priori hypotheses, all analyses statistically controlled for participant age, sex, years of education, and depressive symptoms (SDS scores). Neuroticism was entered as the independent variable (X), NSSI as the dependent variable (Y), and PSQI scores as the first mediator (M1). The two models were tested separately for each HPA axis indicator as the second mediator (M2): Model 1 (M2\u0026thinsp;=\u0026thinsp;CAR) (see Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) and Model 2 (M2\u0026thinsp;=\u0026thinsp;DCS) (see Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eModel 1: Chain Mediation via Sleep Disturbance and CAR\u003c/h2\u003e\n \u003cp\u003eIn the first model, neuroticism significantly predicted higher PSQI scores ( \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031, 95% CI =[0.016,0.046]). PSQI, in turn, significantly predicted a blunted (lower) CAR (\u003cem\u003eB\u003c/em\u003e = -3.815, 95% CI =[-4.922,-2.709]). A blunted CAR significantly predicted higher NSSI frequency when controlling for all other variables (\u003cem\u003eB\u003c/em\u003e = -0.007, 95% CI =[-0.012,-0.002]).(Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRegression Analyses of the CAR Mediation Model (Model 1)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"15\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePredictor variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eOutcome variable: Sleep disturbance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eOutcome variable: CAR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eOutcome variable:NSSI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-1.645,0.368]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-6.126,13.396]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.588,0.238]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.017,2.125]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-11.172,9.357]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.582,0.286]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears of education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-1.926,0.081]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-9.91,9.611]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.267,0.558]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.046,0.136]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-1.415,0.342]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.044,0.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.703\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeuroticism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.016,0.046]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.28,0.009]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.036,0.049]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-4.922,-2.709]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.022,0.123]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.012,-0.002]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e7.560**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e13.324**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e51.850**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"15\"\u003e\u003cem\u003e*Note: SDS\u0026thinsp;=\u0026thinsp;Self-Rating Depression Scale; NSSI\u0026thinsp;=\u0026thinsp;Non-Suicidal Self-Injury; PSQI\u0026thinsp;=\u0026thinsp;Pittsburgh Sleep Quality Index;CAR\u0026thinsp;=\u0026thinsp;Cortisol Awakening Respons\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe bias-corrected bootstrapping results showed a significant total indirect effect (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004, 95% CI [0.0019, 0.0065]) and accounted for 8.64% of the total effect. Examining the specific indirect pathways, the hypothesized chain mediation (Lnd 3 : Neuroticism\u0026rarr;PSQI\u0026rarr; CAR \u0026rarr; NSSI) was significant (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0008, 95% CI =[0.0002,0.0016]). Furthermore, the simple mediation through sleep disturbance alone (Lnd 1: Neuroticism\u0026rarr;PSQI\u0026rarr;NSSI) was also significant (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0022, 95% CI =[0.0008,0.0041]). The direct effect of neuroticism on NSSI remained significant (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0412, 95% CI =[0.0351,0.0473]). (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBootstrap Analysis of the CAR Mediation Model (Model 1)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndirect effect value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBoot SE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBoot LLCI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBoot UICI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect size\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal indirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.64%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnd1: Neuroticism\u0026rarr;PSQI\u0026rarr;NSSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.75%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnd2: Neuroticism\u0026rarr;CAR\u0026rarr;NSSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnd3: Neuroticism\u0026rarr;PSQI\u0026rarr;CAR\u0026rarr;NSSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.73%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cem\u003eNote: NSSI\u0026thinsp;=\u0026thinsp;Non-Suicidal Self-Injury; PSQI\u0026thinsp;=\u0026thinsp;Pittsburgh Sleep Quality Index; DCS=diurnal cortisol slope; CAR\u0026thinsp;=\u0026thinsp;Cortisol Awakening Respons\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eModel 2: Chain Mediation via Sleep Disturbance and DCS\u003c/h2\u003e\n \u003cp\u003eIn the second model, the pathway from neuroticism to sleep disturbance was identical ( \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031, 95% CI =[0.016,0.046]). PSQI scores subsequently predicted a flatter (higher) diurnal cortisol slope ( \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008, 95% CI =[0.004,0.012]). A flatter DCS, in turn, significantly predicted greater NSSI frequency ( \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.793, 95% CI =[1.372,4.214]). (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRegression Analyses of the DCS Mediation Model (Model 2)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"15\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePredictor variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eOutcome variable: Sleep disturbance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eOutcome variableDCS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eOutcome variable:NSSI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-1.645,0.368]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.037,0.029]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.596,0.22]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.365\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.017,2.125]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.008,0.061]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.645,0.216]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears of education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-1.926,0.081]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.059,0.007]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.191,0.628]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.046,0.136]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.001,0.004]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.044,0.029]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeuroticism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.016,0.046]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.0002,0.001]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.035,0.047]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.004,0.012]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.029,0.124]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.372,4.214]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e7.560**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e8.925**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e54.164**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"15\"\u003e\u003cem\u003eNote: *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01. SDS\u0026thinsp;=\u0026thinsp;Self-Rating Depression Scale; NSSI\u0026thinsp;=\u0026thinsp;Non-Suicidal Self-Injury; PSQI\u0026thinsp;=\u0026thinsp;Pittsburgh Sleep Quality Index; DCS=diurnal cortisol slope\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe total indirect effect for this model was significant( \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0051, 95% CI =[0.0028,0.008]). Crucially, the specified chain mediation (Neuroticism \u0026rarr; PSQI \u0026rarr; DCS \u0026rarr; NSSI) was significant (Lnd 3: \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0007, 95% CI =[0.0002,0.0014]). The simple mediation paths via sleep disturbance alone (Lnd 1; \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0024, 95% CI [0.0008, 0.0043]) and via DCS alone (Lnd 2; \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0021, 95% CI [0.0005, 0.0044]) were also significant. The direct effect of neuroticism on NSSI remained significant (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041, 95% CI [0.0362, 0.0485]). (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBootstrap Analysis of the DCS Mediation Model (Model 2)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndirect effect value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBoot SE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBoot LLCI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBoot UICI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect size\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal indirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.02%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnd1: Neuroticism\u0026rarr;PSQI\u0026rarr;NSSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.18%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnd2: Neuroticism\u0026rarr;DSC\u0026rarr;NSSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.54%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnd3: Neuroticism\u0026rarr;PSQI\u0026rarr;DSC\u0026rarr;NSSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cem\u003eNote: NSSI\u0026thinsp;=\u0026thinsp;Non-Suicidal Self-Injury; PSQI\u0026thinsp;=\u0026thinsp;Pittsburgh Sleep Quality Index; DCS=diurnal cortisol slope\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study provides empirical support for an integrated psychobiological pathway linking neuroticism to NSSI in a community sample of Chinese adolescents and young adults. Our primary finding was the significant serial mediating role of sleep disturbance and subsequent cortisol dysregulation in this relationship. Specifically, our results supported a cascading process: higher neuroticism predicted greater sleep disturbance, which in turn was associated with altered HPA axis functioning, ultimately contributing to increased NSSI severity. Although the indirect effects through this specific chain were modest, their statistical significance, even after controlling for key covariates including depressive symptoms, suggests this is a valid and meaningful explanatory pathway within the complex etiology of NSSI.\u003c/p\u003e \u003cp\u003eOur findings reaffirmed neuroticism as a robust dispositional vulnerability for NSSI. This aligns with a large body of literature demonstrating that the heightened stress reactivity and emotional instability inherent in neuroticism create a fertile ground for maladaptive coping behaviors \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e; \u003csup\u003e12\u003c/sup\u003e; \u003csup\u003e31\u003c/sup\u003e. The initial step of our model posits that this heightened vulnerability translates into concrete physiological disruption, specifically by impacting sleep. This is mechanistically plausible, as neuroticism\u0026rsquo;s core features\u0026mdash;heightened physiological arousal and cognitive rumination\u0026mdash;are known to directly contribute to difficulties with sleep onset and maintenance, a bidirectional relationship confirmed in large-scale studies\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e; \u003csup\u003e32\u003c/sup\u003e. Thus, our model begins by grounding a personality trait in a tangible, disruptive biological process.\u003c/p\u003e \u003cp\u003eIndeed, sleep disturbance emerged as a pivotal first-step mediator in our model. The observed association between neuroticism and poorer sleep quality is well-documented \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Our results build on this by situating sleep disturbance as a critical bridge to both physiological dysregulation and self-injurious behavior. This finding echoes recent research that emphasizes the importance of sleep as a key mediator connecting various adversities to NSSI. \u003csup\u003e33\u003c/sup\u003e; \u003csup\u003e34\u003c/sup\u003eIt also aligns with prospective evidence showing that adolescent sleep problems are linked to subsequent depressive symptoms through alterations in the CAR, thereby supporting the crucial role of sleep in HPA - related pathogenic cascades\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The next step in our cascade, the link from sleep disturbance to cortisol dysregulation, is also well-supported. Chronic insomnia and even acute sleep deprivation are known to dysregulate the HPA axis, often leading to flattened diurnal cortisol rhythms by sustaining sympathetic activation and reducing restorative sleep phases\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e; \u003csup\u003e36\u003c/sup\u003e; \u003csup\u003e37\u003c/sup\u003e. Our study advances this literature by demonstrating that this sleep-HPA link is an active component in the pathway to NSSI.\u003c/p\u003e \u003cp\u003eFurther downstream, our results implicated cortisol dysregulation as a key physiological link in this cascade. The findings of a blunted CAR and a flatter DCS in relation to higher NSSI severity are consistent with theories of chronic stress and HPA axis exhaustion \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. While some studies have reported heightened cortisol reactivity in NSSI populations, particularly in response to anticipated stressors\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, the blunted pattern observed here may reflect allostatic load in our non-clinical sample. By demonstrating that these cortisol patterns function as a sequential mediator, our study provides a tangible biological mechanism through which the effects of poor sleep are transduced into increased NSSI risk. This final step in the chain is mechanistically coherent: dysregulated cortisol patterns, such as a blunted CAR, reflect diminished capacity for anticipatory mobilization and recovery from daily stressors, which can erode emotion regulation capacities \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. This makes NSSI more probable as an immediate, albeit maladaptive, means to downregulate acute distress, a function well-established in the experiential avoidance model of self-harm \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e; \u003csup\u003e39\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTheoretically, these findings contribute to the stress-vulnerability framework by delineating a specific, multi-level pathway from a distal personality trait to a proximal clinical behavior. This supports a shift towards more complex, temporally-ordered models of NSSI etiology that integrate personality, behavior, and biology. Clinically, our findings highlight neuroticism as a valuable target for early screening, with sleep quality and HPA axis markers serving as potential indicators of risk progression. The significant mediating role of sleep is particularly promising for intervention. Given that sleep is often more amenable to change than ingrained personality traits, behavioral interventions such as Cognitive-Behavioral Therapy for Insomnia or strategies to improve sleep hygiene could be practical and effective preventive measures for high-neuroticism youth\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Our results provide a mechanistic rationale for why such interventions may be effective, suggesting they could disrupt the pathogenic cascade before it culminates in self-injury.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered when interpreting these findings. First, the cross-sectional design precludes causal inferences and cannot rule out bidirectional relationships. Future research should employ longitudinal designs, such as ecological momentary assessment or multi-wave panel studies, to establish temporal precedence. Second, our reliance on self-report measures for NSSI and sleep may be subject to recall and social desirability biases. Objective measures, such as actigraphy for sleep and clinical interviews for NSSI, would strengthen future investigations. Third, salivary cortisol collection, despite adherence protocols, is susceptible to variability; incorporating long-term HPA axis markers like hair cortisol could provide a more stable index of chronic stress. Finally, our sample was composed predominantly of university students from a single region in China, which may limit the generalizability of our findings to other demographic, cultural, or clinical populations. Future studies should aim for more diverse and representative samples to test the robustness of this model.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study provides initial cross-sectional evidence for a sequential pathway from neuroticism to NSSI via sleep disturbance and cortisol dysregulation. Although the effects are modest, they highlight a valid and mechanistically plausible route contributing to NSSI risk. These findings underscore the importance of integrating psychological and biological perspectives in the study of self-injury and suggest that targeting sleep may be a promising avenue for prevention and intervention in vulnerable youth.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXiang Zhang:\u0026nbsp;Conceptualization, Methodology, Software, Validation, Formal analysis, Writing- Original draft preparation;\u0026nbsp;Shaoxia Wang: Data curation, Resources,Investigation;\u0026nbsp;Huarong He: Visualization, Investigation;\u0026nbsp;Yijie Wang:\u0026nbsp;Conceptualization, Methodology,Project administration, Funding acquisition.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to express our gratitude to all the participants who volunteered for this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated or analyzed during the current study are available from the corresponding author (Yijie Wang), upon reasonable request, but are not publicly available due to patient privacy restrictions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKlonsky, E. 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Chronic Stress and the Hypothalamic-Pituitary-Adrenocortical Axis in Humans. \u003cem\u003ePsychol. Bull.\u003c/em\u003e \u003cb\u003e133\u003c/b\u003e, 25\u0026ndash;45 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChapman, A. L., Gratz, K. L. \u0026amp; Brown, M. Z. Solving the Puzzle of Deliberate Self-Harm: The Experiential Avoidance Model. \u003cem\u003eBehav. Res. Ther.\u003c/em\u003e \u003cb\u003e44\u003c/b\u003e, 371\u0026ndash;394 (2006).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Non-suicidal self-injury, Neuroticism, Sleep disturbance, Cortisol, HPA axis","lastPublishedDoi":"10.21203/rs.3.rs-8733092/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8733092/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNeuroticism is a robust personality predictor of non-suicidal self-injury (NSSI), a significant public health concern among youth. However, the specific psychobiological mechanisms linking this dispositional trait to self-injurious behavior remain unclear. Sleep disturbance and dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis are theoretically plausible mediators, but their sequential role has not been fully elucidated.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo test a serial mediation model in which sleep disturbance and cortisol dysregulation sequentially mediate the relationship between neuroticism and NSSI in Chinese youth.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eA cross-sectional survey was conducted with 302 adolescents and young adults. Participants completed self-report questionnaires assessing neuroticism, NSSI, sleep quality, and depressive symptoms. Salivary cortisol was collected over two days to calculate Cortisol Awakening Response (CAR) and Diurnal Cortisol Slope (DCS). Serial mediation analysis was performed, controlling for age, sex, education, and depressive symptoms.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eNeuroticism was positively correlated with NSSI, sleep disturbance, and a flatter DCS, and negatively with the CAR. Neuroticism had both a significant direct effect on NSSI and a significant indirect effect through the serial pathway of sleep disturbance and a blunted cortisol awakening response (Indirect Effect\u0026thinsp;=\u0026thinsp;0.0008, 95% CI [0.0002, 0.0016]). A similar significant serial indirect effect was found for the diurnal cortisol slope model (Indirect Effect\u0026thinsp;=\u0026thinsp;0.0007, 95% CI [0.0002, 0.0014]).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eNeuroticism contributes to NSSI risk both directly and indirectly via a psychobiological cascade involving sleep disturbance and HPA axis dysregulation. These findings highlight sleep as a key mechanistic link and a promising target for intervention in high-neuroticism youth.\u003c/p\u003e","manuscriptTitle":"Neuroticism and Non-Suicidal Self-Injury: A Serial Mediation Model of Sleep Disturbance and Cortisol Dysregulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 06:22:06","doi":"10.21203/rs.3.rs-8733092/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":"76d1145c-3e7c-4b9e-afbd-a2b0062c470b","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62640542,"name":"Health sciences/Diseases"},{"id":62640543,"name":"Biological sciences/Neuroscience"},{"id":62640544,"name":"Biological sciences/Psychology"},{"id":62640545,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-04-28T04:55:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 06:22:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8733092","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8733092","identity":"rs-8733092","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}