Association between Sleep Quality, PTSD, and Depression Among Patients in Conflict-Affected Regions

Association between Sleep Quality, PTSD, and Depression Among Patients in Conflict-Affected Regions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between Sleep Quality, PTSD, and Depression Among Patients in Conflict-Affected Regions Mohammed Alhamood, Rose Hasan, Imad Saadeh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5825156/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: Psychiatric disorders, particularly post‐traumatic stress disorder (PTSD) and depression, posed a significant public health challenge in conflict-affected regions. Recurrent exposure to traumatic events and adverse socioeconomic conditions considerably increased their prevalence. Recent evidence has suggested that sleep quality was a key modifiable factor influencing neuropsychological outcomes. Aims and Objectives: This study aimed to evaluate the association between sleep quality and the severity of PTSD and depression among 1700 patients from conflict zones in Syria. We also investigated the impact of demographic and clinical variables on these relationships to inform the development of targeted, multidisciplinary mental health interventions. Methods: A cross-sectional design was employed, and data were collected from April 1 to October 1, 2024, at Tishreen Military Hospital and its affiliated centers. Participants (aged 18–79 years) with documented conflict exposure completed standardized face-to-face questionnaires. Sleep quality was assessed using an adapted Pittsburgh Sleep Quality Index (PSQI) categorized into Poor, Average, and Good. PTSD severity was measured via a modified PCL‑5 (0–10 scale; Cronbach’s α=0.89), and depressive symptoms via an adapted PHQ‑9 (0–10 scale; Cronbach’s α=0.85). Statistical analyses were performed using R 4.4.2 and included one-way ANOVA with post‑hoc Tukey’s HSD tests, Pearson correlation, binary logistic regression, k‑means clustering, and SEM. Assumptions of normality and homogeneity of variances were verified using Shapiro–Wilk and Levene’s tests, and missing data (≤5%) were addressed via multiple imputation. Results: Patients with poor sleep quality exhibited significantly higher PTSD scores compared to average and good sleepers (F(2,1697)=195.6, p<0.001; post‑hoc Tukey’s HSD, all pairwise p<0.001; partial η²≈0.187). A weak but significant positive correlation was observed between PTSD and depression (r=0.148, p<0.001). Logistic regression showed that demographic factors (age, gender, residency) did not significantly predict PTSD or depression prevalence. K‑means clustering identified three symptom subgroups based on score similarity and internal variance, which justified the selection of k‑means over other clustering approaches. SEM revealed that sleep quality significantly predicted PTSD, and PTSD significantly predicted depression; however, the indirect effect of sleep quality on depression via PTSD was not significant (indirect effect=–0.030, p=0.280). Conclusion: Findings underscored sleep quality’s critical role as a modifiable therapeutic target to mitigate PTSD severity among conflict-affected patients. Although a weak association between PTSD and depression was observed, sleep quality’s overall impact was robust. Integrating sleep-targeted interventions into multidisciplinary mental health care could benefit such populations. Future longitudinal studies using objective sleep assessments are warranted. Sleep Quality PTSD Depression Conflict-Affected Regions Mental Health Interventions Cross-Sectional Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Psychiatric disorders, particularly post-traumatic stress disorder (PTSD) and depression, were long recognized as major public health concerns in regions affected by conflict. Populations exposed to repeated traumatic events and chronic socioeconomic adversity exhibited markedly higher prevalence rates of these disorders compared to individuals in stable environments [ 1 , 2 ]. Global estimates suggested that PTSD prevalence in conflict zones reached 30–40%, highlighting the urgent need to identify modifiable factors that could attenuate these outcomes [ 3 ]. Recent research increasingly emphasized the critical role of sleep quality in neuropsychological health. Rather than being merely a secondary symptom, sleep disturbances were implicated in exacerbating PTSD and depressive symptoms [ 4 , 5 ]. However, most previous investigations focused on non‑conflict populations, leaving a significant gap in our understanding of how sleep quality influenced mental health in environments characterized by ongoing conflict and instability [ 6 , 7 ]. This study aimed to address this gap by examining the association between sleep quality and the severity of PTSD and depression in a sample of conflict‑affected Syrian patients, while also assessing the impact of key demographic and clinical variables on these relationships. Methods A cross-sectional study design was employed. Data were collected from April 1 to October 1, 2024, at Tishreen Military Hospital and its affiliated centers in conflict-affected areas of Syria [ 2 ]. The study targeted adults aged 18–79 years who had experienced war-related trauma—forced displacement, direct combat exposure, or loss of close family members due to conflict. Participants were required to have complete demographic and clinical data. Patients with severe psychiatric disorders unrelated to trauma (e.g., organic brain syndromes) or with significant cognitive impairments that hindered questionnaire completion were excluded. Sleep quality was assessed using an adapted Pittsburgh Sleep Quality Index (PSQI), simplified into three categories: Poor (1), Average (2), and Good (3) [ 8 ]. This adaptation was based on validations in similar conflict settings, and the instrument demonstrated good internal consistency (Cronbach’s α = 0.87). PTSD symptoms were measured with a modified PTSD Checklist for DSM‑5 (PCL‑5), recalibrated to a 0–10 scale while maintaining psychometric robustness (Cronbach’s α = 0.89) [ 9 ]. Depressive symptoms were evaluated using an adapted Patient Health Questionnaire‑9 (PHQ‑9) on a 0–10 scale, with established reliability in trauma-exposed populations (Cronbach’s α = 0.85) [ 10 ]. All instruments showed strong construct validity for neuropsychological outcomes in this context. Data were gathered via face-to-face questionnaires administered by trained research personnel. Quality assurance was ensured through regular monitoring and double data entry. All procedures adhered to the ethical standards of the Declaration of Helsinki and were approved by the Ethics Committee of Tishreen Military Hospital [ 11 ]. Informed consent was obtained from all participants; for anonymized data, individual consent was waived per national and institutional guidelines. Statistical Analysis Descriptive statistics—including means, standard deviations, and frequency distributions—were computed for all key variables. A one-way ANOVA was performed to compare PTSD scores across the three sleep quality categories, with post‑hoc Tukey’s HSD tests. Pearson correlation was employed to assess the relationship between PTSD and depression scores. A binary logistic regression was conducted to examine the independent effects of demographic factors (age, gender, residency) and sleep quality on the prevalence of PTSD and depression. Sleep quality was retained in the logistic‑regression model as a clinically modifiable exposure variable because it represents a key therapeutic target that may exert an independent effect on PTSD and depression beyond fixed demographic characteristics. K‑means clustering was applied to identify symptom‑based subgroups, and model assumptions of normality and homogeneity of variances were verified using Shapiro–Wilk and Levene’s tests, respectively [ 12 ]. Missing data (≤ 5%) were handled via multiple imputation [ 13 ]. In addition to these primary analyses, advanced methods—including ANCOVA to adjust for potential confounders and structural equation modeling (SEM) to examine direct and indirect effects among sleep quality, PTSD, and depression—were employed [ 14 , 15 ]. Diagnostic plots (residuals versus fitted values and Q–Q plots) were used to verify model assumptions; detailed results appear in the Supplementary Statistical Diagnostics Report [ 16 ]. All analyses were performed using R software (version 4.4.2) [ 17 ]. Additional diagnostic tests—checking for multicollinearity via the Variance Inflation Factor (VIF) and conducting sensitivity analyses—were carried out to ensure the robustness of the regression models. These tests confirmed that all model assumptions were met, and no further modifications were required. Symptom Severity Categorization Participants were categorized into three groups for both PTSD and depression—Mild (0–3), Moderate (4–7), and Severe (8–10)—based on score distributions in our sample and established cut‑off criteria from previous research. Results Demographic and Clinical Characteristics The final sample comprised 1,700 patients (50.65% male, 49.35% female) with a mean age of 49.1 ± 18.1 years. Chronic diseases were present in 60% of participants. Regarding sleep quality, 39% reported poor sleep, 40% average sleep, and 21% good sleep (Table 1). Relationship between Sleep Quality and PTSD Severity One‑way ANOVA revealed significant differences in PTSD scores across sleep quality categories (F(2, 1,697) = 195.6, p < 0.001; partial η² ≈ 0.187). Post‑hoc Tukey’s HSD tests showed that poor sleepers had higher PTSD scores than average or good sleepers (p < 0.001 for both comparisons), and average sleepers scored higher than good sleepers (p < 0.001) (Tables 2–3; Figure 1). Correlation Between PTSD and Depression Pearson correlation demonstrated a weak but significant positive association between PTSD and depression scores (r = 0.148, p < 0.001) (Table 4; Figure 2). Logistic Regression: Demographic Influences Binary logistic regression including age, gender, and residency did not identify significant predictors of PTSD or depression prevalence (all p 0.05) (Table 5). Prevalence . Regression coefficients and p‑values. K-Means Clustering Analysis K‑means clustering identified three symptom subgroups (Table 6; Figure 3): - Cluster 1: High PTSD (mean = 6.41), moderate depression (mean = 4.65) - Cluster 2: Low PTSD (mean = 3.48), low depression (mean = 3.42) - Cluster 3: Moderate PTSD (mean = 4.44), high depression (mean = 6.90) ANCOVA and Model Diagnostics ANCOVA adjusting for confounders confirmed homoscedasticity (Figure 4) and normality of residuals (Figure 5). Structural Equation Modeling (SEM) SEM indicated significant direct effects of sleep quality on PTSD and of PTSD on depression; however, the indirect effect of sleep quality on depression via PTSD was not significant (indirect effect = –0.030, p = 0.280; standardized effect ≈ –0.012). Discussion This study examined the association between sleep quality and PTSD and depression severity in 1,700 conflict-affected patients. Poor sleep quality was significantly associated with increased PTSD severity. The ANOVA results (F(2, 1,697) = 195.6, p < 0.001; partial η² ≈ 0.187) underscored the clinical importance of sleep disturbances in exacerbating PTSD symptoms, consistent with prior bidirectional findings between impaired sleep and PTSD [ 18 , 19 , 20 ]. A weak but significant positive correlation emerged between PTSD and depression scores, suggesting some neurobiological overlap [ 6 , 21 ]. However, the modest strength of this association implies that other factors may mediate their relationship. Logistic regression showed that demographic factors (age, gender, residency) did not significantly predict chronic disease prevalence, indicating the need to explore additional contributors [ 22 ]. K‑means clustering identified three distinct symptom subgroups, offering a basis for personalized therapeutic strategies. SEM demonstrated significant direct effects of sleep quality on PTSD and of PTSD on depression. Nevertheless, the indirect effect of sleep quality on depression via PTSD was not significant, likely due to the cross‑sectional design. Longitudinal studies are therefore warranted to clarify PTSD’s mediating role. Collectively, these findings highlight the necessity of integrating sleep‑focused interventions into multidisciplinary mental‑health programs for conflict‑affected populations. Despite these insights, several limitations must be acknowledged. The cross‑sectional design precluded causal inferences, and reliance on self‑reported sleep measures may have introduced bias. Furthermore, objective sleep assessments (e.g., polysomnography) and biological markers (e.g., cortisol, melatonin) were not included [ 5 , 7 , 23 ]. Future randomized controlled trials incorporating such measures are needed to evaluate the efficacy of sleep‑targeted interventions in this high‑risk population. Conclusion This study provided compelling evidence that sleep quality was a pivotal modifiable factor influencing PTSD severity among individuals in conflict‑affected regions. A weak positive correlation between PTSD and depression was observed; however, sleep quality emerged as the strongest predictor of PTSD severity in our analyses. These findings underscored the need to integrate sleep‑targeted interventions—such as Cognitive Behavioral Therapy for Insomnia (CBT‑I) and melatonin‑based pharmacotherapy—into multidisciplinary mental‑health care strategies for conflict‑exposed populations. Future studies employing longitudinal designs, objective sleep assessments (e.g., polysomnography), and biomarker assessments (e.g., cortisol, melatonin) are needed to further elucidate these relationships and to explore additional factors affecting chronic disease prevalence. Declarations Funding Declaration This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Conflict of Interest Statement: The authors declare that they have no conflicts of interest related to this study. Ethics Approval and Compliance with the Declaration of Helsinki: This study was approved by the Ethics Committee of Tishreen Military Hospital (Ethical Approval No.: 2024-001) and was conducted in accordance with the ethical standards of the Declaration of Helsinki. Informed Consent: Informed consent was obtained from all participants. For anonymized data, individual consent was waived according to national and institutional guidelines. Data Availability Statement: The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Human Subjects: Consent was obtained or waived for all participants as per ethical and institutional guidelines. Payment/Services Information: The authors declare that no financial or in-kind support was received for the submitted work. Financial Relationships: The authors declare no financial relationships with any organizations that might have an interest in the submitted work over the past three years. Consent to Publish Declaration: Written informed consent for participation was obtained from all study participants. As the data have been fully anonymized and no identifiable information is presented, a separate consent for publication is not applicable. Other Relationships: The authors confirm that they have no other relationships or activities that could appear to have influenced the submitted work. Acknowledgments The authors extend their deepest gratitude to the Military Medical Services Administration and to the administration of Tishreen Military Hospital—especially Dr. Moufid Darwich—for their invaluable support and guidance throughout this research. 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Tables Tables 1 to 6 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table16.docx ThedetailedRcodeusedforallanalyses.pdf SupplementaryStatisticalDiagnosticsReport.pdf 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5825156","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452592749,"identity":"9399f08f-752a-484f-a5b6-15b065c1b7dd","order_by":0,"name":"Mohammed Alhamood","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYDACCQYDEMXYIMF8AMSVIUULWwKIy0OKFh4wg7AW3dnNG5hu1GyT7Zfu+fzqRo0FDwP74aMb8Gkxu3OsgDnn2G3jmXPObrPOOQZ0GE9a2g28Wm7kGDDnsN1O3HAjd5txDhtQiwSPGRFa/oG05DwzzvlHrJbcNrAW5se5bURpSSs4nNsH9MuMNDPm3D4JHjbCfkne+Djn223Zfonkx59zvtXJ8bMfPoZXCwgcgNJsEmCSkHJkwPyBFNWjYBSMglEwcgAAJqNM/z2stJkAAAAASUVORK5CYII=","orcid":"","institution":"Tishreen Military Hospital","correspondingAuthor":true,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Alhamood","suffix":""},{"id":452592750,"identity":"bbe07c61-b8c1-4b91-9d09-41838ba00d30","order_by":1,"name":"Rose Hasan","email":"","orcid":"","institution":"Damascus university","correspondingAuthor":false,"prefix":"","firstName":"Rose","middleName":"","lastName":"Hasan","suffix":""},{"id":452592753,"identity":"f3fc715b-5181-408d-bfc2-2892207017ac","order_by":2,"name":"Imad Saadeh","email":"","orcid":"","institution":"Tishreen Military Hospital","correspondingAuthor":false,"prefix":"","firstName":"Imad","middleName":"","lastName":"Saadeh","suffix":""}],"badges":[],"createdAt":"2025-01-14 08:08:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5825156/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5825156/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82167630,"identity":"92039930-1775-4ade-ad62-dc5d626b2cee","added_by":"auto","created_at":"2025-05-07 09:23:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":93069,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBoxplot of depression scores (0–10) across sleep‑quality categories. \u003c/strong\u003eThe median is shown as a thick horizontal line; hinges represent the inter‑quartile range (IQR); whiskers extend to 1.5 × IQR; points denote outliers. Categories: Poor, Average, Good.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5825156/v1/aefbcec76c30b3e46a1320a5.jpg"},{"id":82166666,"identity":"2d65d1db-3142-443d-a860-33622e19a61b","added_by":"auto","created_at":"2025-05-07 09:15:00","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":282382,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot of post-traumatic stress disorder (PTSD) scores versus depression scores (0-10). The blue line represents the linear regression fit (r = 0.15; p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5825156/v1/44a62cee6406e2bcb9accfc1.jpg"},{"id":82169409,"identity":"8a7eaa0a-1498-4d0f-aeb5-961f6ae3c08c","added_by":"auto","created_at":"2025-05-07 09:39:00","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":300762,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatterplot of k‑means clusters for PTSD and depression scores (color‑coded).\u003c/strong\u003e Clusters were identified based on symptom severity: Cluster 1 (high PTSD, moderate depression), Cluster 2 (low PTSD, low depression), and Cluster 3 (moderate PTSD, high depression).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5825156/v1/d0ee85d4d33cb258ba688ada.jpg"},{"id":82166668,"identity":"d951a5b5-cf39-43c1-b009-86f6c3d3f5ab","added_by":"auto","created_at":"2025-05-07 09:15:00","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":113741,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResiduals versus fitted values plot from ANCOVA.\u003c/strong\u003eThe horizontal red line at zero indicates the expected mean of residuals; the scatter of residuals around this line demonstrates homoscedasticity across fitted values.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5825156/v1/7cfc9554c57850fd4be68e2e.jpg"},{"id":82167636,"identity":"d1856ec9-e456-43a3-9d81-9eee4bff0348","added_by":"auto","created_at":"2025-05-07 09:23:00","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":73888,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQ–Q plot of ANCOVA residuals.\u003c/strong\u003e The plot demonstrates that the residuals align closely with the theoretical normal distribution (red reference line), supporting the assumption of normality.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5825156/v1/6283d8eab0d35c03beb5db1b.jpg"},{"id":86216970,"identity":"1be53ec5-70e3-417b-8bfa-ab80c2faced4","added_by":"auto","created_at":"2025-07-08 06:17:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1589310,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5825156/v1/71ad13d4-7faf-4a0b-a5f9-877774b957a3.pdf"},{"id":82166671,"identity":"c666c6a7-8cad-46d8-b5c9-a6c253642a2e","added_by":"auto","created_at":"2025-05-07 09:15:00","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":715685,"visible":true,"origin":"","legend":"","description":"","filename":"Table16.docx","url":"https://assets-eu.researchsquare.com/files/rs-5825156/v1/6a78bb60e455f28f1fb0513e.docx"},{"id":82166692,"identity":"516b0b36-032f-4082-b319-f4ae76d8ffb7","added_by":"auto","created_at":"2025-05-07 09:15:00","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":485862,"visible":true,"origin":"","legend":"","description":"","filename":"ThedetailedRcodeusedforallanalyses.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5825156/v1/14b85f80620a1b6a63c9b71b.pdf"},{"id":82167643,"identity":"09349db3-4a3e-4f57-871e-4a517889782d","added_by":"auto","created_at":"2025-05-07 09:23:00","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":34374,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryStatisticalDiagnosticsReport.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5825156/v1/0cb5eee6f54b717cef7ad621.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Sleep Quality, PTSD, and Depression Among Patients in Conflict-Affected Regions","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePsychiatric disorders, particularly post-traumatic stress disorder (PTSD) and depression, were long recognized as major public health concerns in regions affected by conflict. Populations exposed to repeated traumatic events and chronic socioeconomic adversity exhibited markedly higher prevalence rates of these disorders compared to individuals in stable environments [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Global estimates suggested that PTSD prevalence in conflict zones reached 30\u0026ndash;40%, highlighting the urgent need to identify modifiable factors that could attenuate these outcomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent research increasingly emphasized the critical role of sleep quality in neuropsychological health. Rather than being merely a\u003c/p\u003e \u003cp\u003esecondary symptom, sleep disturbances were implicated in exacerbating PTSD and depressive symptoms [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, most previous investigations focused on non‑conflict populations, leaving a significant gap in our understanding of how sleep quality influenced mental health in environments characterized by ongoing conflict and instability [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aimed to address this gap by examining the association between sleep quality and the severity of PTSD and depression in a sample of conflict‑affected Syrian patients, while also assessing the impact of key demographic and clinical variables on these relationships.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eA cross-sectional study design was employed. Data were collected from April 1 to October 1, 2024, at Tishreen Military Hospital and its affiliated centers in conflict-affected areas of Syria [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The study targeted adults aged 18\u0026ndash;79 years who had experienced war-related trauma\u0026mdash;forced displacement, direct combat exposure, or loss of close family members due to conflict. Participants were required to have complete demographic and clinical data. Patients with severe psychiatric disorders unrelated to trauma (e.g., organic brain syndromes) or with significant cognitive impairments that hindered questionnaire completion were excluded.\u003c/p\u003e \u003cp\u003eSleep quality was assessed using an adapted Pittsburgh Sleep Quality Index (PSQI), simplified into three categories: Poor (1), Average (2), and Good (3) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This adaptation was based on\u003c/p\u003e \u003cp\u003evalidations in similar conflict settings, and the instrument demonstrated good internal consistency (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.87). PTSD symptoms were measured with a modified PTSD Checklist for DSM‑5 (PCL‑5), recalibrated to a 0\u0026ndash;10 scale while maintaining psychometric robustness (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.89) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Depressive symptoms were evaluated using an adapted Patient Health Questionnaire‑9 (PHQ‑9) on a 0\u0026ndash;10 scale, with established reliability in trauma-exposed populations (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.85) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. All instruments showed strong construct validity for neuropsychological outcomes in this context.\u003c/p\u003e \u003cp\u003eData were gathered via face-to-face questionnaires administered by trained research personnel. Quality assurance was ensured through regular monitoring and double data entry. All procedures adhered to the ethical standards of the Declaration of Helsinki and were approved by the Ethics Committee of Tishreen Military Hospital [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Informed consent was obtained from all participants; for anonymized data, individual consent was waived per national and institutional guidelines.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics\u0026mdash;including means, standard deviations, and frequency distributions\u0026mdash;were computed for all key variables. A one-way ANOVA was performed to compare PTSD scores across the three sleep quality categories, with post‑hoc Tukey\u0026rsquo;s HSD tests. Pearson correlation was employed to assess the relationship between PTSD and depression scores. A binary logistic regression was conducted to examine the independent effects of demographic factors (age, gender, residency) and sleep quality on the prevalence of PTSD and depression. Sleep quality was retained in the logistic‑regression model as a clinically modifiable exposure variable because it represents a key therapeutic target that may exert an independent effect on PTSD and depression beyond fixed demographic characteristics. K‑means clustering was applied to identify symptom‑based subgroups, and model assumptions of normality and homogeneity of variances were verified using Shapiro\u0026ndash;Wilk and Levene\u0026rsquo;s tests, respectively [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Missing data (\u0026le;\u0026thinsp;5%) were handled via multiple imputation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to these primary analyses, advanced methods\u0026mdash;including ANCOVA to adjust for potential confounders and structural equation modeling (SEM) to examine direct and indirect effects among sleep quality, PTSD, and depression\u0026mdash;were employed [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Diagnostic plots (residuals versus fitted values and Q\u0026ndash;Q plots) were used to verify model assumptions; detailed results appear in the Supplementary Statistical Diagnostics Report [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. All analyses were performed using R software (version 4.4.2) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditional diagnostic tests\u0026mdash;checking for multicollinearity via the Variance Inflation Factor (VIF) and conducting sensitivity analyses\u0026mdash;were carried out to ensure the robustness of the regression models. These tests confirmed that all model assumptions were met, and no further modifications were required.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSymptom Severity Categorization\u003c/h3\u003e\n\u003cp\u003eParticipants were categorized into three groups for both PTSD and depression\u0026mdash;Mild (0\u0026ndash;3), Moderate (4\u0026ndash;7), and Severe (8\u0026ndash;10)\u0026mdash;based on score distributions in our sample and established cut‑off criteria from previous research.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographic and Clinical Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final sample comprised 1,700 patients (50.65% male, 49.35% female) with a mean age of 49.1 \u0026plusmn; 18.1 years. Chronic diseases were present in 60% of participants. Regarding sleep quality, 39% reported poor sleep, 40% average sleep, and 21% good sleep (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between Sleep Quality and PTSD Severity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne‑way ANOVA revealed significant differences in PTSD scores across sleep quality categories (F(2, 1,697) = 195.6, p \u0026lt; 0.001; partial \u0026eta;\u0026sup2; \u0026asymp; 0.187). Post‑hoc Tukey\u0026rsquo;s HSD tests showed that poor sleepers had higher PTSD scores than average or good sleepers (p \u0026lt; 0.001 for both comparisons), and average sleepers scored higher than good sleepers (p \u0026lt; 0.001) (Tables 2\u0026ndash;3; Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation Between PTSD and Depression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePearson correlation demonstrated a weak but significant positive association between PTSD and depression scores (r = 0.148, p \u0026lt; 0.001) (Table 4; Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLogistic Regression: Demographic Influences\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBinary logistic regression including age, gender, and residency did not identify significant predictors of PTSD or depression prevalence (all p 0.05) (Table 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrevalence\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eRegression coefficients and p‑values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eK-Means Clustering Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eK‑means clustering identified three symptom subgroups (Table 6; Figure 3):\u003c/p\u003e\n\u003cp\u003e- Cluster 1: High PTSD (mean = 6.41), moderate depression (mean = 4.65)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Cluster 2: Low PTSD (mean = 3.48), low depression (mean = 3.42) \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Cluster 3: Moderate PTSD (mean = 4.44), high depression (mean = 6.90)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eANCOVA and Model Diagnostics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eANCOVA adjusting for confounders confirmed homoscedasticity (Figure 4) and normality of residuals (Figure 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural Equation Modeling (SEM)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSEM indicated significant direct effects of sleep quality on PTSD and of PTSD on depression; however, the indirect effect of sleep quality on depression via PTSD was not significant (indirect effect = \u0026ndash;0.030, p = 0.280; standardized effect \u0026asymp; \u0026ndash;0.012).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the association between sleep quality and PTSD and depression severity in 1,700 conflict-affected patients. Poor sleep quality was significantly associated with increased PTSD severity. The ANOVA results (F(2, 1,697)\u0026thinsp;=\u0026thinsp;195.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; partial η\u0026sup2; \u0026asymp; 0.187) underscored the clinical importance of sleep disturbances in exacerbating PTSD symptoms, consistent with prior bidirectional findings between impaired sleep and PTSD [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA weak but significant positive correlation emerged between PTSD and depression scores, suggesting some neurobiological overlap [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, the modest strength of this association implies that other factors may mediate their relationship. Logistic regression showed that demographic factors (age, gender, residency) did not significantly predict chronic disease prevalence, indicating the need to explore additional contributors [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eK‑means clustering identified three distinct symptom subgroups, offering a basis for personalized therapeutic strategies. SEM demonstrated significant direct effects of sleep quality on PTSD and of PTSD on depression. Nevertheless, the indirect effect of sleep quality on depression via PTSD was not significant, likely due to the cross‑sectional design. Longitudinal studies are\u003c/p\u003e \u003cp\u003etherefore warranted to clarify PTSD\u0026rsquo;s mediating role.\u003c/p\u003e \u003cp\u003eCollectively, these findings highlight the necessity of integrating sleep‑focused interventions into multidisciplinary mental‑health programs for conflict‑affected populations.\u003c/p\u003e \u003cp\u003eDespite these insights, several limitations must be acknowledged. The cross‑sectional design precluded causal inferences, and reliance on self‑reported sleep measures may have introduced bias. Furthermore, objective sleep assessments (e.g., polysomnography) and biological markers (e.g., cortisol, melatonin) were not included [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Future randomized controlled trials incorporating such measures are needed to evaluate the efficacy of sleep‑targeted interventions in this high‑risk population.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provided compelling evidence that sleep quality was a pivotal modifiable factor influencing PTSD severity among individuals in conflict‑affected regions. A weak positive correlation between PTSD and depression was observed; however, sleep quality emerged as the strongest predictor of PTSD severity in our analyses. These findings underscored the need to integrate sleep‑targeted interventions\u0026mdash;such as Cognitive Behavioral Therapy for Insomnia (CBT‑I) and melatonin‑based pharmacotherapy\u0026mdash;into multidisciplinary mental‑health care strategies for conflict‑exposed populations. Future studies employing longitudinal designs, objective sleep assessments (e.g., polysomnography), and biomarker assessments (e.g., cortisol, melatonin) are needed to further elucidate these relationships and to explore additional factors affecting chronic disease prevalence.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest related to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Compliance with the Declaration of Helsinki:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Tishreen Military Hospital (Ethical Approval No.: 2024-001) and was conducted in accordance with the ethical standards of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants. For anonymized data, individual consent was waived according to national and institutional guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Subjects:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent was obtained or waived for all participants as per ethical and institutional guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePayment/Services Information:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no financial or in-kind support was received for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial Relationships:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no financial relationships with any organizations that might have an interest in the submitted work over the past three years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish Declaration:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent for participation was obtained from all study participants. As the data have been fully anonymized and no identifiable information is presented, a separate consent for publication is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOther Relationships:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that they have no other relationships or activities that could appear to have influenced the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their deepest gratitude to the Military Medical Services Administration and to the administration of Tishreen Military Hospital\u0026mdash;especially Dr. Moufid Darwich\u0026mdash;for their invaluable support and guidance throughout this research. The authors also thank the reviewers for their insightful and constructive comments, and the journal\u0026rsquo;s editorial team for their meticulous guidance, which were instrumental in refining this work to meet the highest academic standards.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKessler, R.C., et al. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62(6):617\u0026thinsp;\u0026ndash;\u0026thinsp;27. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/archpsyc.62.6.617\u003c/span\u003e\u003cspan address=\"10.1001/archpsyc.62.6.617\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Erratum in: Arch Gen Psychiatry. 2005;62(7):709. Merikangas, Kathleen R [added]. 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PMID: 16075453.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePace-Schott, E.F., Germain, A. \u0026amp; Milad, M.R. Sleep and REM sleep disturbance in the pathophysiology of PTSD: the role of extinction memory. Biol Mood Anxiety Disord 5, 3 (2015). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13587-015-0018-9\u003c/span\u003e\u003cspan address=\"10.1186/s13587-015-0018-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 6 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Sleep Quality, PTSD, Depression, Conflict-Affected Regions, Mental Health Interventions, Cross-Sectional Analysis","lastPublishedDoi":"10.21203/rs.3.rs-5825156/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5825156/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePsychiatric disorders, particularly post‐traumatic stress disorder (PTSD) and depression, posed a significant public health challenge in conflict-affected regions. Recurrent exposure to traumatic events and adverse socioeconomic conditions considerably increased their prevalence. Recent evidence has suggested that sleep quality was a key modifiable factor influencing neuropsychological outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAims and Objectives:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to evaluate the association between sleep quality and the severity of PTSD and depression among 1700 patients from conflict zones in Syria. We also investigated the impact of demographic and clinical variables on these\u003c/p\u003e\n\u003cp\u003erelationships to inform the development of targeted, multidisciplinary mental health interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA cross-sectional design was employed, and data were collected from April 1 to October 1, 2024, at Tishreen Military Hospital and its affiliated centers. Participants (aged 18–79 years) with documented conflict exposure completed standardized face-to-face questionnaires. Sleep quality was assessed using an adapted Pittsburgh Sleep Quality Index (PSQI) categorized into Poor, Average, and Good. PTSD severity was measured via a modified PCL‑5 (0–10 scale; Cronbach’s α=0.89), and depressive symptoms via an adapted PHQ‑9 (0–10 scale; Cronbach’s α=0.85). Statistical analyses were performed using R 4.4.2 and included one-way ANOVA with post‑hoc Tukey’s HSD tests, Pearson correlation, binary logistic regression, k‑means clustering, and SEM. Assumptions of normality and homogeneity of variances were verified using Shapiro–Wilk and Levene’s tests, and missing data (≤5%) were addressed via multiple imputation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients with poor sleep quality exhibited significantly higher PTSD scores compared to average and good sleepers (F(2,1697)=195.6, p\u0026lt;0.001; post‑hoc Tukey’s HSD, all pairwise p\u0026lt;0.001; partial η²≈0.187). A weak but significant positive correlation was observed between PTSD and depression (r=0.148, p\u0026lt;0.001). Logistic regression showed that demographic factors (age, gender, residency) did not significantly predict PTSD or depression prevalence. K‑means clustering identified three symptom subgroups based on score similarity and internal variance, which justified the selection of k‑means over other clustering approaches. SEM revealed that sleep quality significantly\u003c/p\u003e\n\u003cp\u003epredicted PTSD, and PTSD significantly predicted depression; however, the indirect effect of sleep quality on depression via PTSD was not significant (indirect effect=–0.030, p=0.280).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFindings underscored sleep quality’s critical role as a modifiable therapeutic target to mitigate PTSD severity among conflict-affected patients. Although a weak association between PTSD and depression was observed, sleep quality’s overall impact was robust. Integrating sleep-targeted interventions into multidisciplinary mental health care could benefit such populations. Future longitudinal studies using objective sleep assessments are warranted.\u003c/p\u003e","manuscriptTitle":"Association between Sleep Quality, PTSD, and Depression Among Patients in Conflict-Affected Regions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 09:14:55","doi":"10.21203/rs.3.rs-5825156/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":"8061feef-6ee3-41b6-b89e-a9d74b131c09","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-08T06:08:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 09:14:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5825156","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5825156","identity":"rs-5825156","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}