Depression severity and neuropsychiatric symptoms among late-life depression patients in nursing homes: A moderated mediation model of sleep quality and resilience

Depression severity and neuropsychiatric symptoms among late-life depression patients in nursing homes: A moderated mediation model of sleep quality and resilience | 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 Depression severity and neuropsychiatric symptoms among late-life depression patients in nursing homes: A moderated mediation model of sleep quality and resilience Ziping Zhu, Yuanjiao Yan, Danting Chen, Yanhong Shi, Chenshan Huang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4697569/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Nov, 2025 Read the published version in BMC Psychiatry → Version 1 posted 10 You are reading this latest preprint version Abstract Background Depression severity significantly influences neuropsychiatric symptoms (NPS), yet the underlying mediating and moderating mechanisms of this relationship remain insufficiently explored. Methods We employed cluster sampling to select 414 LLD patients from 42 nursing homes across nine cities in Fujian Province, China. Mediation and moderation analyses were conducted using the PROCESS macro model to determine the interactions between depression severity, sleep quality, resilience, and NPS. Results The findings indicate that NPS prevalence among LLD patients in nursing homes is substantial. Sleep quality partially mediated the relationship between depression severity and NPS. Additionally, resilience moderated both the direct and indirect effects within the mediation model, highlighting its significant role in mitigating the impact of depression severity on NPS. Conclusion The results underscore the importance of targeting sleep quality and resilience in clinical interventions for LLD patients in nursing homes. Enhancing sleep quality and resilience could potentially disrupt the link between depression severity and NPS, thereby improving patient outcomes. Depression severity Neuropsychiatric symptoms Sleep quality Resilience Moderated mediation model Late-life depression patients in nursing homes Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Late-life depression (LLD) is a depressive disorder that occurs in older adults aged 60 years and older, and is closely related to co-morbidities, high mortality rates, and high family and social burdens[1, 2]. As such, it is considered an important public health issue in the process of population ageing. The prevalence of LLD in nursing homes reaches 36.8%[3], which is higher than the community prevalence rate (11.3%) [4] and the overall prevalence (20.0%)[5]. According to China's Ministry of Civil Affairs, by the end of 2021, the elderly population residing in nursing homes accounted for approximately 1.07% of the country's total elderly population over the age of 60, a Figureure that is rising as China's society ages[6]. Thus, academics should not ignore the mental health issues of older people in institutions. Unlike depression in young adults, LLD patients have more neuropsychiatric symptoms (NPS)[7]. The remission rate of LLD after first-line antidepressant treatment is about 50%, and failure to remit leads to persistent NPS[8]. NPS is non-cognitive, behavior or psychiatric symptoms such as abnormal motor behavior, irritability, anxiety and hallucinations[9], with a prevalence of 17.0% in older adults [10]. Previous studies have shown that LLD patients with comorbid NPS always have a poorer clinical prognosis, such as increased cerebrovascular risk[11], all-cause mortality risk[12], dementia risk[13]. However, the number of studies that have comprehensively assessed NPS in LLD patients is limited to date. Therefore, it is important to identify the factors of NPS and explore the potential mechanisms by which these factors contribute to NPS. Depression severity has been identified as a noteworthy risk factor for NPS[14, 15]. A meta-analysis showed that higher baseline depression severity is the most important clinical variable associated with poor response to LLD treatment and is common across the lifespan of depression[16]. Empirical finding provides evidence for a positive predictive relationship between depression severity and NPS in older adults[17]. However, the exact mechanism of this relationship has not been fully clarified. Although structural and functional changes in the brain triggered by depression may play a role[18], these mechanisms need to be further explored. Research suggests a bidirectional relationship between sleep quality and mental health: sleep disorders can cause or exacerbate depressive symptoms, and depression can interfere with sleep quality[19]. Sleep is a variable lifestyle habit, and sleep quality is particularly important for maintaining health-related quality of life[20]. Poor sleep quality is both the most prominent symptom and risk factor for LLD patients[21, 22]. A previous study reported that sleep quality was negatively associated with depression severity and mediated the effect of depression severity on quality of life[23]. In addition, both cross-sectional and longitudinal studies have suggested that poor sleep quality and NPS may be positively associated[24, 25]. Thus, sleep quality may mediate the link between depression severity and NPS. Few studies have explored the complex relationship between sleep quality, depression severity, and NPS in population-based samples of LLD patients in nursing homes. Resilience is another focus of NPS protective factor research. Many studies have shown that resilience and NPS show a negative correlation [26, 27]. In LLD patients, resilience has been shown to be negatively associated with depression and positively associated with quality of life[28]. Resilience is a dynamic process, and has been defined as the ability of an individual to adapt and recover in the face of stress and adversity[29]. A previous study showed that resilience moderated the relationship between s depression severity and loneliness[30]. Furthermore, Stress-coping theory emphasizes that resilience is an effective coping mechanism that can help individuals better adapt to and manage depression severity[31]. However, there is a paucity of research on resilience as a direct or indirect buffer against the negative effects of depression severity on NPS. In conclusion, depression severity, sleep quality, and resilience all play important roles in NPS, but the possible effects of these mechanisms on NPS are unclear in LLD patients in nursing homes. Therefore, the aim of the present study was to assess the prevalence of NPS, to explore whether sleep quality mediates the association between depression severity and NPS, and to evaluate a moderated mediation model. In the moderated mediation model, we hypothesized that sleep quality may mediate the association between depression severity and NPS among LLD patients in nursing home. In addition, resilience may play a moderating role in the direct (path a: depression severity - NPS) and/or indirect effects of depression severity on NPS (path b: sleep quality - NPS), respectively. Materials and Methods Participants and ethical statement We conducted a cross-sectional questionnaire survey in nine cities in Fujian Province, China. A cluster sampling method was used to include LLD patients from 42 nursing homes who met the study criteria. Inclusion criteria for LLD patients in this study were 1) age at first onset ≥ 60 years, 2) meeting the criteria for depression in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), 3) residing in a nursing home for more than 2 months. The exclusion criteria were: 1) patients with severe neurological diseases or traumatic brain injury, 2) patients with a history of alcoholism or drug dependence, 3) patients with language disorders or those who were unable to communicate normally for any reason. The Ethics Committee of Fujian Medical University approved all study methods (approval no. 2023 − 177). All participants were informed of the purpose of the study and signed an informed consent form. The study began in June 2023 and ended in October 2023, as shown in Figure. 1 . 419 questionnaires were sent out through screening, of which 414 respondents effectively completed the survey, with a valid completion rate of 98.81%. Measures Sociodemographic characteristics Study data included age, gender, marital status, educational level, previous residence, economic status (pension), chronic condition, history of psychotropic medication use, smartphone use, intellectual activity and volunteer activity. Marital status was divided into married and unmarried (including single, divorced and widowed). Educational was classified into as primary school or under, middle or high school and college or above. Previous residence was categorized as city, town and rural. Economic status was classified as 5000 RMB/month. Intellectual activities and voluntary activities are classified as Never, Seldom (1–2 times per week), Often (3–4 times per week) and Usually (5–7 times per week). Neuropsychiatric symptoms The use of the Mild Behavioral Impairment Checklist (MBI-C) allows for the assessment of a patient's NPS. The scale consists of 34 questions reflecting five diagnostic domains: lack of motivation, affective incoherence, impulse control disorders, social withdrawal, and perceptual or thinking abnormalities [ 32 ]. The total score ranges from 0 to 102, with higher scores indicating greater severity of NPS. The scale has been shown to have good reliability and validity in Chinese populations [ 33 ]. In this study, the Cronbach's alpha of the MBI-C was 0. 882. Depression severity The Geriatric Depression Scale (GDS-15) is a simplified version of the GDS-30 used to assess the severity of depression [ 34 ]. Each item requires a dichotomous response (yes or no) and is scored as 1 or 0. The total score ranges from 0–15, with higher scores indicating more severe depression. The GDS-15 has been validated in a Chinese population [ 35 ]. In this study, the Cronbach's alpha of the GDS-15 was 0.706. Sleep quality The Pittsburgh Sleep Quality Index (PSQI) was used to assess patients' sleep quality over the past 1 month. The scale consists of 19 entries with 7 components (subjective sleep quality, time to fall asleep, sleep duration, sleep efficiency, sleep disorders, hypnotic medications, and daytime functioning). The total score ranges from 0–21, with the higher the total score, the poorer the sleep quality [ 36 ]. Studies have shown that the PSQI has demonstrated good reliability in Chinese populations [ 37 ]. In this study, the Cronbach's alpha of the PSQI was 0.713. Resilience Using the Connor-Davidson Resilience Scale (CD-RISC-10) [ 38 ] is a short version of the CD-RISC [ 39 ]. It consists of 10 items that are used to provide a quick assessment of an individual's level of resilience. Total scores range from 0 to 40, with higher scores indicating greater resilience. CD-RISC-10 has shown good reliability in studies in China [ 40 ]. In our study, the Cronbach's alpha of the CD-RISC-10 was 0.948. Statistical analyses In this study, means and standard deviations were used to describe continuous variables and proportions and frequencies were used to represent categorical variables. The t-test was used to test the differences between continuous variables. The chi-square test was used to test differences between categorical variables. Correlations between depression severity, sleep quality, resilience, and NPS of LLD patients in nursing homes were then analysed using Pearson correlation. Finally, mediation and moderated mediation models were analysed using the PROCESS macro [ 41 ], including Model 4 (analyzing mediation) and Model 15 (analyzing direct or indirect pathways mediated by a variable). In this study, 5000 Bootstrapping sessions were used to calculate 95% confidence intervals (CIs) for the indirect effects of potential mediators. All analyses were conducted in SPSS 27 and used a p-value of less than 0.05 as the standard severity of significance (two-sided test). In addition, all models controlled for covariates (age, gender) and were standardized for all study variables. Results Characteristics of the participants As shown in Table 1 , a total of 414 LLD patients in nursing homes were selected for this study, with a mean age of 78.85 ± 9.00 years and a prevalence of NPS of 33.33% (MBI-C > 7), of whom 39 (28.26%) were male and 99 (71.74%) were female. This shows that female LLD patients were more likely to have NPS. Furthermore, univariate analysis showed that sex, marital status, previous residence, smartphone use, intellectual activity, p < 0.05). Table 1 Comparison of the prevalence of NPS in LLD patients with different demographic characteristics (n = 414). Variables Total (n = 414) MBI (n = 138) Non MBI (n = 276) χ 2 /t p Age (years) 78.85 ± 9.00 79.75 ± 8.60 78.40 ± 9.18 1.438 0.151 Gender 5.053 0.029 Male 148 (35.75) 39 (28.26) 109 (39.49) Female 266 (64.25) 99 (71.74) 167 (60.51) Marital status 10.680 0.001 Married 147 (35.50) 34 (24.64) 113 (40.94) Unmarried 267 (64.50) 104 (75.36) 163 (59.06) Educational level 0.305 0.859 Primary school or under 180 (43.50) 62 (44.93) 118 (42.75) Middle or high school 172 (41.50) 57 (41.30) 115 (41.67) College or above 62 (15.00) 19 (13.77) 43 (15.58) Previous residence 6.260 0.042 City 236 (57.00) 77 (55.80) 159 (57.61) Town 81 (19.57) 20 (14.49) 61 (22.10) Rural 97 (23.43) 41 (29.71) 56 (20.29) Economic status 3.847 0.278 5000RMB/month 87 (21.01) 28 (20.29) 59 (21.38) Chronic condition 1.614 0.219 Yes 283(68.36) 100 (72.46) 183 (66.30) No 131 (31.64) 38 (27.54) 93 (33.70) History of psychotropic medication use 0.956 0.367 Yes 128 (30.92) 47 (34.06) 81 (29.35) No 286 (69.08) 91 (65.94) 195 (70.65) Smartphone use 6.980 0.006 Yes 212 (51.21) 58 (42.03) 154 (55.80) No 202 (48.79) 80 (57.97) 122 (44.20) Intellectual activities 18.025 < 0.001 Never 204 (49.28) 88 (63.77) 116 (42.03) Seldom 111 (26.81) 24 (17.39) 87 (31.52) Often 45 (10.87) 11 (7.97) 34 (12.32) Usually 54 (13.04) 15 (10.87) 39 (14.13) Volunteering activities 2.999 0.397 Never 206 (49.76) 67 (48.55) 139 (50.36) Seldom 131 (31.64) 45 (32.61) 86 (31.16) Often 43 (10.39) 18 (13.04) 25 (9.06) Usually 34 (8.21) 8 (5.80) 26 (9.42) Note : Continuous variables are expressed as mean ± SD and analysed by t test; categorical variables are expressed as n (%) and analysed by chi-square tests. Bivariate correlations among all variables As shown in Table 2 , the result displays the correlation matrix for the main study variables, providing the means, standard deviations and correlations between the variables studied. Notably, both GDS-15 scores and MBI - C scores were positively correlated with PSQI scores (for GDS-15 scores, r = 0.257, p < 0.001; for MBI - C scores, r = 0.363, p < 0.001) and resilience (for GDS-15 scores, r = − 0.445, p < 0.001; for MBI - C scores, r = − 0.505, p < 0.001). There was a significant positive correlation between GDS-15 scores and MBI-C scores ( r = 0.427, p < 0.001), and a negative correlation between CD - RISC scores and PSQI scores ( r = -0.331, p < 0.01). The above results suggest that the mediation model between these four variables is reasonable. Table 2 Bivariate correlations between depression severity, sleep quality, resilience and NPS (n = 414). Variable M ± SD 1 2 3 4 1GDS-15 7.70 ± 2.62 1 2PSQI 10.41 ± 4.86 0.257 ** 1 3CD-RISC 20.24 ± 8.41 -0.445 ** -0.331 ** 1 4MBI-C 6.22 ± 7.67 0.427 ** 0.363 ** -0.505 ** 1 Note: ** p < 0.01 Mediation analyses As shown in Table 3 , regression analyses for Model 1 showed that depression severity was a significant positive predictor of poor sleep quality ( β = 0.256, p < 0.001). Regression analyses for Model 2 showed that depression severity was a significant positive predictor of NPS ( β = 0.362, p < 0.001), and poor sleep quality was a significant positive predictor of NPS ( β = 0.265, p < 0.001). Table 3 Regression analyses for mediated moderation analysis (n = 414). Variable Poor Sleep Quality MBI Model 1 Model 2 Model 3 Model 4 Depression severity 0.256 *** 0.362 *** 0.232 *** 0.179 *** Sleep Quality 0.265 *** 0.190 *** 0.179 *** Resilience -0.332 *** -0.312 *** Depression severity * Resilience -0.102 ** Sleep quality * Resilience -0.096 * R 2 0.086 0.262 0.343 0.372 ΔR 2 0.086 0.262 0.343 0.029 F 12.811 *** 36.215 *** 42.635 *** 34.424 *** Note : Controlled for age and sex ⁎⁎⁎ p < 0.001, ⁎⁎ p < 0.01, * p < 0.05 According to Table 4 , we analysed the mediated effects of sleep quality on the relationship between depression severity and NPS using PROCESS model 4, which showed that the 95% CIs for all paths did not contain 0, indicating that these paths were significant. Specifically, the indirect effect of poor sleep quality on the relationship between depression severity and NPS (B = 0.068, 95% CI = [0.037, 0.102]) was significant, suggesting a mediating effect of poor sleep quality. Table 4 Table of effects analyses (n = 414). pathway PROCESS model 4 B SE LLCI ULCI intermediary effect a*b 0.068 0.016 0.037 0.102 Direct effect c , 0.362 0.044 0.275 0.448 total effect a*b + c , 0.430 0.044 0.343 0.517 Note : a: depression severity - sleep quality; b: sleep quality - NPS; c , : depression severity - NPS. Moderated analysis of moderation As shown in Table 3 , according to our hypotheses, resilience may play a moderating role between depression severity and NPS, or between poor sleep quality and NPS, respectively. The results of the mediation analyses showed that the interaction term between depression severity and resilience was a significant predictor of NPS ( β = -0.102, p = 0.005); the interaction term between poor sleep quality and resilience was a significant predictor of NPS ( β = -0.096, p = 0.016). Further analyses using PROCSS model 15 and the results of the simple slope test (see Table 5 and Figure. 2) further indicated that depression severity was a significant positive predictor of NPS in the low resilience level ( β = 0.281, p < 0.001), whereas depression was not a predictor of NPS in the high resilience level ( β = 0.077, p = 0.220). Table 5 Simple slopes of depression's effect on NPS at different values of resilience. Resilience Effect se t p LLCI ULCI Low resilience (M - SD) 0.281 0.054 5.194 < 0.001 0.175 0.387 Medium resilience (M) 0.179 0.046 3.883 < 0.001 0.088 0.270 High resilience (M + SD) 0.077 0.063 1.229 0.220 -0.046 0.201 The results of the simple slope test (see Table 6 and Figure. 3) further indicated that poor sleep quality was a significant positive predictor of NPS severity in the low resilience level ( β = 0.275, p < 0.001), whereas poor sleep quality was not predictive of NPS in the high resilience level ( β = 0.083, p = 0.159). Thus, resilience also significantly attenuated the negative effect of poor sleep quality on NPS. Table 6 Simple slopes of the effect of poor sleep quality on the severity of NPS for different values of resilience. Resilience Effect se t p LLCI ULCI Low resilience (M - SD) 0.275 0.058 4.753 0.000 0.161 0.389 Medium resilience (M) 0.179 0.043 4.211 0.000 0.095 0.262 High resilience (M + SD) 0.083 0.059 1.410 0.159 -0.033 0.198 Mediating pathway model Figure.4 illustrates the mediated pathway model for regulation. The pathway coefficients show that all relationships in the model are significantly positive and negative. The direct effect of depression severity on NPS remained significant after poor sleep quality was used as a mediator and resilience as a moderator. Thus, the associations between depression severity and NPS in patients in nursing homes are partially mediated by poor sleep quality, and these associations are in turn moderated by resilience. Discussion This study investigated the prevalence of NPS among LLD patients in nursing homes and examined the relationship between depression severity, sleep quality, resilience, and NPS to provide a theoretical basis for future research to reduce the incidence of NPS. The findings showed that the relationship between depression severity and NPS in LLD patients was partially mediated by sleep quality, suggesting that depression severity may have both direct and indirect effects on NPS. In addition, the findings support that higher levels of resilience in nursing home patients with LLD may buffer the adverse effects of depression severity and sleep quality on NPS. These findings have important clinical implications and suggest that enhancing sleep quality and resilience in LLD patients is a key measure for reducing NPS in nursing homes. Not only do they theoretically enrich the understanding of the mental health of elderly depressed patients in nursing homes, but they also provide specific intervention pathways to improve the mental health status of this population in practice, which can effectively enhance the quality of life and well-being of the elderly. In the present study, the prevalence of NPS among LLD patients in nursing homes was 33.33%, which was significantly higher than that(3.50%)of the elderly in psychiatric outpatient clinics [ 42 ] and those (10.00–15.00%) in the community [ 43 ]. Differences in study sites may explain some of the differences. The mean score for depression severity in this study was (7.70 ± 2.62), which was higher than in a Chinese study of community and hospital LLD patients (7.07 ± 3.33) [ 44 ]. Similarly, the sleep quality score of nursing home LLD patients (10.41 ± 4.86) was higher than that of Yunnan hospital LLD patients in China (6.88 ± 2.45) [ 45 ]. In contrast, the resilience score of LLD patients in nursing homes (20.24 ± 8.41) was higher than that of empty nesters in Huzhou, China (12.44 ± 3.63) [ 46 ]. A plausible explanation is that LLD patients living in nursing homes are more likely to face a variety of psychological burdens and have poorer sleep quality, which leads to an increased level of resilience [ 47 , 48 ]. Our study demonstrated a significant positive correlation between depression severity and NPS in LLD patients in nursing homes, which is consistent with previous study [ 49 – 51 ]. In our study, LLD patients in nursing homes moving from a familiar home environment to a new, unfamiliar environment tended to show higher depression severity, affecting the expression of NPS [ 3 ]. Neurophysiological studies suggest that depression severity may influence the NPS by affecting neurotransmitter systems, inflammatory responses, and functional brain connectivity [ 52 ]. Although this association has been demonstrated in several studies, the specific neural mechanisms are unclear. Previous studies have demonstrated that better sleep quality and higher resilience level are protective factors for NPS in LLD patients [ 25 , 53 ]. The protective effect of these factors was similarly demonstrated in our study. The present study found that sleep quality mediated the relationship between depression severity and NPS to some extent, which may reveal a potential mechanism by which depression severity indirectly affects NPS. Previous studies have demonstrated that sleep quality has a mediating role in mediating the association between depressive symptoms and cognitive decline [ 54 ]. In our study, depression severity was directly related to NPS, depression severity was negatively related to sleep quality, and sleep quality was negatively related to NPS. Better sleep quality may help to reduce the association between depression severity and NPS[ 55 , 56 ]. The results of a recent meta-analysis showed that greater improvements in sleep quality led to greater improvements in mental health, but there were differences in the effectiveness of interventions to improve sleep quality for different psychological problems [ 57 ]. Therefore, further research is necessary to improve understanding of the effectiveness of these interventions. In conclusion, interventions and management of sleep quality problems should be emphasized in the treatment and care of LLD patients. According to our moderator-mediator analyses, resilience moderated the relationship between depression severity and NPS, as well as the second half of the mediated effect of sleep quality. This highlights the research and clinical relevance of resilience for understanding the impact of NPS, namely that resilience can reduce NPS to some extent. Resilience is a dynamic moderating process, and the experience of adversity modifies an individual's response to stress, which can be altered and learnt throughout the lifespan [ 58 , 59 ]. Our study focused on NPS in LLD patients in nursing homes and found that they had higher severity of depression and poor sleep quality, but the effects of depression severity and sleep quality on NPS diminished with increasing resilience. Therefore, increasing resilience can be a key goal in improving NPS in patients with LLD [ 60 , 61 ]. Targeted psychological interventions, supportive therapy, and rehabilitation programs can help patients improve their resilience, thereby reducing depressive symptoms and improving sleep quality. Future research and clinical practice should further explore and validate the effectiveness of different interventions. Limitations This study has some limitations in interpreting the results. Firstly, the cross-sectional data makes it challenging to make causal inferences between the identified variables and NPS. It does not allow for analysing causal relationships between variables. In the future, further longitudinal studies are needed to investigate causal relationships between depression severity and NPS, as well as more accurate mediating relationships. Secondly, the data in the study usually rely on self-reports or subjective evaluations by patients or subjects, which may be subjectively biased, and therefore in-depth interviews and behavior observations should be conducted. Thirdly, although we controlled for some possible confounding variables (e.g., age, gender), other uncontrolled variables (e.g., social support and functional limitations, etc.) may still have an impact on the results. Further future research needs to incorporate additional variables to gain a more complete understanding of this issue. Finally, this study was conducted only in nursing homes, and the limitations of the study site may limit the generalizability of the results. Therefore, future studies should be conducted in different settings and venues, including community and home care settings, to validate the generalizability of the results and to explore the potential impact of different settings on the study variables. Conclusion In conclusion, this is the first investigation of the relationship between depression severity and NPS among LLD patients in nursing homes using a moderated mediation model. In the present study, the prevalence of NPS in LLD patients was 33.33%, and sleep quality moderated to some extent the relationship between depression severity and NPS. Resilience moderated the direct effect of depression severity on NPS, as well as the latter part of the mediating effect of sleep quality. It may be important to design interventions for LLD patients in nursing homes, especially those with poor sleep quality and low level of resilience, that result in improved sleep quality and low level of resilience in LLD patients in order to reduce the expression of NPS.This not only provides valuable guidance for optimizing care strategies in nursing homes, but also offers practical directions for interventions in clinical practice to further safeguard the mental health and overall quality of life of elderly patients. Declarations Acknowledgment We acknowledge all the participants and assisting researchers. Author contributions Conception and design of study: Z.Z. and Y.Y.; data collection: D.C., Y.S., and C.H.; data analysis and interpretation: Z.Z. and R.L.; manuscript preparation: Z.Z.; critical review of the manuscript: H.L. and R.L. Funding This study was funded by the National Natural Science Foundation of China (72104050). Data availability All data generated or analysed during this study are included in this published article. Ethics approval and consent to participate Patients were briefed on the anonymous and confidential nature of the survey, its purpose, and significance, and signed informed consent forms. This study obtained approval from the Ethics Committee of the Fujian Medical University (Approval No. 2023-177). Consent for publication We consent for publication. Competing interests The authors declare no competing interests. References Alexopoulos GS. Mechanisms and treatment of late-life depression. Transl Psychiatry. 2019;9:188. Malhi GS, Mann JJ. Depression. Lancet. 2018;392:2299–312. Tang T, Jiang J, Tang X. Prevalence of depression among older adults living in care homes in China: A systematic review and meta-analysis. Int J Nurs Stud. 2022;125:104114. Chen S, Conwell Y, Vanorden K, Lu N, Fang Y, Ma Y, et al. 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Laird KT, Lavretsky H, Paholpak P, Vlasova RM, Roman M, St Cyr N, et al. Clinical correlates of resilience factors in geriatric depression. Int Psychogeriatr. 2019;31:193–202. Waugh CE, Koster EHW. A resilience framework for promoting stable remission from depression. Clin Psychol Rev. 2015;41:49–60. Zhao X, Zhang D, Wu M, Yang Y, Xie H, Li Y, et al. Loneliness and depression symptoms among the elderly in nursing homes: A moderated mediation model of resilience and social support. Psychiatry Res. 2018;268:143–51. Monsen RB, Floyd RL, Brookman JC. Stress-coping-adaptation: concepts for nursing. Nurs Forum. 1992;27:27–32. Ismail Z, Agüera-Ortiz L, Brodaty H, Cieslak A, Cummings J, Fischer CE, et al. The Mild Behavioral Impairment Checklist (MBI-C): A Rating Scale for Neuropsychiatric Symptoms in Pre-Dementia Populations. J Alzheimers Dis. 2017;56:929–38. Cui Y, Dai S, Miao Z, Zhong Y, Liu Y, Liu L, et al. Reliability and Validity of the Chinese Version of the Mild Behavioral Impairment Checklist for Screening for Alzheimer’s Disease. J Alzheimers Dis. 2019;70:747–56. Yesavage JA, Brink TL, Rose TL, Lum O, Huang V, Adey M, et al. Development and validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res. 1982;17:37–49. Zhang C, Zhang H, Zhao M, Chen C, Li Z, Liu D, et al. Psychometric properties and modification of the 15-item geriatric depression scale among Chinese oldest-old and centenarians: a mixed-methods study. BMC Geriatr. 2022;22:144. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28:193–213. Zhang C, Xiao S, Lin H, Shi L, Zheng X, Xue Y, et al. The association between sleep quality and psychological distress among older Chinese adults: a moderated mediation model. BMC Geriatr. 2022;22:35. Campbell-Sills L, Stein MB. Psychometric analysis and refinement of the Connor-davidson Resilience Scale (CD-RISC): Validation of a 10-item measure of resilience. J Trauma Stress. 2007;20:1019–28. Connor KM, Davidson JRT. Development of a new resilience scale: the Connor-Davidson Resilience Scale (CD-RISC). Depress Anxiety. 2003;18:76–82. Cheng C, Dong D, He J, Zhong X, Yao S. Psychometric properties of the 10-item Connor-Davidson Resilience Scale (CD-RISC-10) in Chinese undergraduates and depressive patients. J Affect Disord. 2020;261:211–20. Hayes AF, Preacher KJ. Statistical mediation analysis with a multicategorical independent variable. Br J Math Stat Psychol. 2014;67:451–70. Matsuoka T, Ismail Z, Narumoto J. Prevalence of Mild Behavioral Impairment and Risk of Dementia in a Psychiatric Outpatient Clinic. J Alzheimers Dis. 2019;70:505–13. Creese B, Ismail Z. Mild behavioral impairment: measurement and clinical correlates of a novel marker of preclinical Alzheimer’s disease. Alzheimers Res Ther. 2022;14:2. Xie Z, Lv X, Hu Y, Ma W, Xie H, Lin K, et al. Development and validation of the geriatric depression inventory in Chinese culture. Int Psychogeriatr. 2015;27:1505–11. Shao H, Li N, Chen M, Zhang J, Chen H, Zhao M, et al. A voxel-based morphometry investigation of brain structure variations in late-life depression with insomnia. Front Psychiatry. 2023;14:1201256. Song L, Wang Y, Zhang Q, Yin J, Gan W, Shang S, et al. The mediating effect of resilience on mental health literacy and positive coping style among Chinese empty nesters: A cross-sectional study. Front Psychol. 2023;14:1093446. Borges C, Ellis JG, Ruivo Marques D. The Role of Sleep Effort as a Mediator Between Anxiety and Depression. Psychol Rep. 2023;:332941221149181. Song J, Yang L, Han M, Wu Y. Study on the Mental Health of the Elderly under Different Pension Models. J Healthc Eng. 2022;2022:2367406. Ferraro PM, Gervino E, De Maria E, Meo G, Ponzano M, Pardini M, et al. Mild behavioral impairment as a potential marker of predementia risk states in motor neuron diseases. Eur J Neurol. 2023;30:47–56. Koutsouleris N, Kahn RS, Chekroud AM, Leucht S, Falkai P, Wobrock T, et al. Multisite prediction of 4-week and 52-week treatment outcomes in patients with first-episode psychosis: a machine learning approach. Lancet Psychiatry. 2016;3:935–46. Yang A-N, Wang X-L, Rui H-R, Luo H, Pang M, Dou X-M. Neuropsychiatric Symptoms and Risk Factors in Mild Cognitive Impairment: A Cohort Investigation of Elderly Patients. J Nutr Health Aging. 2020;24:237–41. Belleau EL, Treadway MT, Pizzagalli DA. The Impact of Stress and Major Depressive Disorder on Hippocampal and Medial Prefrontal Cortex Morphology. Biol Psychiatry. 2019;85:443–53. Antonucci LA, Pergola G, Rampino A, Rocca P, Rossi A, Amore M, et al. Clinical and psychological factors associated with resilience in patients with schizophrenia: data from the Italian network for research on psychoses using machine learning. Psychol Med. 2023;53:5717–28. Liu X, Xia X, Hu F, Hao Q, Hou L, Sun X, et al. The mediation role of sleep quality in the relationship between cognitive decline and depression. BMC Geriatr. 2022;22:178. Chunnan L, Shaomei S, Wannian L. The association between sleep and depressive symptoms in US adults: data from the NHANES (2007-2014). Epidemiol Psychiatr Sci. 2022;31:e63. Sullivan EC, James E, Henderson L-M, McCall C, Cairney SA. The influence of emotion regulation strategies and sleep quality on depression and anxiety. Cortex. 2023;166:286–305. Scott AJ, Webb TL, Martyn-St James M, Rowse G, Weich S. Improving sleep quality leads to better mental health: A meta-analysis of randomised controlled trials. Sleep Med Rev. 2021;60:101556. Mesman E, Vreeker A, Hillegers M. Resilience and mental health in children and adolescents: an update of the recent literature and future directions. Curr Opin Psychiatry. 2021;34:586–92. van Kessel G. The ability of older people to overcome adversity: a review of the resilience concept. Geriatr Nurs. 2013;34:122–7. Fontes AP, Neri AL. Resilience in aging: literature review. Cien Saude Colet. 2015;20:1475–95. Fuster V. The power of resilience. J Am Coll Cardiol. 2014;64:840–2. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Nov, 2025 Read the published version in BMC Psychiatry → Version 1 posted Editorial decision: Revision requested 15 Jul, 2025 Reviews received at journal 24 Jun, 2025 Reviewers agreed at journal 29 May, 2025 Reviews received at journal 26 Dec, 2024 Reviewers agreed at journal 21 Nov, 2024 Reviewers invited by journal 26 Aug, 2024 Editor invited by journal 01 Aug, 2024 Editor assigned by journal 10 Jul, 2024 Submission checks completed at journal 09 Jul, 2024 First submitted to journal 06 Jul, 2024 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. 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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-4697569","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":334919090,"identity":"74f48b10-efd9-4a56-b91d-12f10a7d71e4","order_by":0,"name":"Ziping Zhu","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ziping","middleName":"","lastName":"Zhu","suffix":""},{"id":334919091,"identity":"01b19103-d456-44ef-a6b1-baad5d3d30c5","order_by":1,"name":"Yuanjiao Yan","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuanjiao","middleName":"","lastName":"Yan","suffix":""},{"id":334919092,"identity":"842673c4-02df-48c7-99d2-b41af70175a1","order_by":2,"name":"Danting Chen","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Danting","middleName":"","lastName":"Chen","suffix":""},{"id":334919093,"identity":"29d5d6e8-c26b-40f2-ae91-26995c5fb4d9","order_by":3,"name":"Yanhong Shi","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanhong","middleName":"","lastName":"Shi","suffix":""},{"id":334919094,"identity":"2dd4cce4-63c3-4d78-887f-a4b64c49a6d3","order_by":4,"name":"Chenshan Huang","email":"","orcid":"","institution":"Fujian Medical 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selection\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4697569/v1/7022104faca00c7fcef6c250.png"},{"id":62157267,"identity":"98894598-0618-4ce9-a9ad-0ffb8a75d2bd","added_by":"auto","created_at":"2024-08-09 21:16:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":16679,"visible":true,"origin":"","legend":"\u003cp\u003eModerate effects of resilience on depression severity and NPS.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4697569/v1/d78c74b4288ddc86f2bc8de5.png"},{"id":62156204,"identity":"31d115eb-1d89-4273-a668-dc82de583260","added_by":"auto","created_at":"2024-08-09 21:08:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":16927,"visible":true,"origin":"","legend":"\u003cp\u003eResilience moderates the relationship between the sleep quality and of NPS.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4697569/v1/ec6c1d81fb8ad2c8c0f2d6ac.png"},{"id":62157266,"identity":"6fe855b3-a58a-4040-a2e4-4116cb5b2fa4","added_by":"auto","created_at":"2024-08-09 21:16:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":37119,"visible":true,"origin":"","legend":"\u003cp\u003eThe final moderated mediation model (\u003csup\u003e⁎⁎⁎\u003c/sup\u003e\u003cem\u003ep \u0026lt; \u003c/em\u003e0.001, \u003csup\u003e⁎⁎\u003c/sup\u003e\u003cem\u003e p \u003c/em\u003e\u0026lt; 0.01, \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4697569/v1/96b2ae8d18fcbfc78b091f9d.png"},{"id":96105310,"identity":"b1dc6e23-d364-401b-885e-c33a5822f462","added_by":"auto","created_at":"2025-11-17 16:11:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1169666,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4697569/v1/54aaf161-3b3d-44e5-a629-4eab6c76d992.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Depression severity and neuropsychiatric symptoms among late-life depression patients in nursing homes: A moderated mediation model of sleep quality and resilience","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLate-life depression (LLD) is a depressive disorder that occurs in older adults aged 60 years and older, and is closely related to co-morbidities, high mortality rates, and high family and social burdens[1, 2]. As such, it is considered an important public health issue in the process of population ageing. The prevalence of LLD in nursing homes reaches 36.8%[3], which is higher than the community prevalence rate (11.3%) [4] and the overall prevalence (20.0%)[5]. According to China\u0026apos;s Ministry of Civil Affairs, by the end of 2021, the elderly population residing in nursing homes accounted for approximately 1.07% of the country\u0026apos;s total elderly population over the age of 60, a Figureure that is rising as China\u0026apos;s society ages[6]. Thus, academics should not ignore the mental health issues of older people in institutions.\u003c/p\u003e\n\u003cp\u003eUnlike depression in young adults, LLD patients have more neuropsychiatric symptoms (NPS)[7]. The remission rate of LLD after first-line antidepressant treatment is about 50%, and failure to remit leads to persistent NPS[8]. NPS is non-cognitive, behavior or psychiatric symptoms such as abnormal motor behavior, irritability, anxiety and hallucinations[9], with a prevalence of 17.0% in older adults [10]. Previous studies have shown that LLD patients with comorbid NPS always have a poorer clinical prognosis, such as increased cerebrovascular risk[11], all-cause mortality risk[12], dementia risk[13]. However, the number of studies that have comprehensively assessed NPS in LLD patients is limited to date. Therefore, it is important to identify the factors of NPS and explore the potential mechanisms by which these factors contribute to NPS.\u003c/p\u003e\n\u003cp\u003eDepression severity has been identified as a noteworthy risk factor for NPS[14, 15]. A meta-analysis showed that higher baseline depression severity is the most important clinical variable associated with poor response to LLD treatment and is common across the lifespan of depression[16]. Empirical finding provides evidence for a positive predictive relationship between depression severity and NPS in older adults[17]. However, the exact mechanism of this relationship has not been fully clarified. Although structural and functional changes in the brain triggered by depression may play a role[18], these mechanisms need to be further explored.\u003c/p\u003e\n\u003cp\u003eResearch suggests a bidirectional relationship between sleep quality and mental health: sleep disorders can cause or exacerbate depressive symptoms, and depression can interfere with sleep quality[19]. Sleep is a variable lifestyle habit, and sleep quality is particularly important for maintaining health-related quality of life[20]. Poor sleep quality is both the most prominent symptom and risk factor for LLD patients[21, 22]. A previous study reported that sleep quality was negatively associated with depression severity and mediated the effect of depression severity on quality of life[23]. In addition, both cross-sectional and longitudinal studies have suggested that poor sleep quality and NPS may be positively associated[24, 25]. Thus, sleep quality may mediate the link between depression severity and NPS. Few studies have explored the complex relationship between sleep quality, depression severity, and NPS in population-based samples of LLD patients in nursing homes.\u003c/p\u003e\n\u003cp\u003eResilience is another focus of NPS protective factor research. Many studies have shown that resilience and NPS show a negative correlation [26, 27]. In LLD patients, resilience has been shown to be negatively associated with depression and positively associated with quality of life[28]. Resilience is a dynamic process, and has been defined as the ability of an individual to adapt and recover in the face of stress and adversity[29]. A previous study showed that resilience moderated the relationship between s depression severity and loneliness[30]. Furthermore, Stress-coping theory emphasizes that resilience is an effective coping mechanism that can help individuals better adapt to and manage depression severity[31]. However, there is a paucity of research on resilience as a direct or indirect buffer against the negative effects of depression severity on NPS.\u003c/p\u003e\n\u003cp\u003eIn conclusion, depression severity, sleep quality, and resilience all play important roles in NPS, but the possible effects of these mechanisms on NPS are unclear in LLD patients in nursing homes. Therefore, the aim of the present study was to assess the prevalence of NPS, to explore whether sleep quality mediates the association between depression severity and NPS, and to evaluate a moderated mediation model. In the moderated mediation model, we hypothesized that sleep quality may mediate the association between depression severity and NPS among LLD patients in nursing home. In addition, resilience may play a moderating role in the direct (path a: depression severity - NPS) and/or indirect effects of depression severity on NPS (path b: sleep quality - NPS), respectively.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and ethical statement\u003c/h2\u003e \u003cp\u003eWe conducted a cross-sectional questionnaire survey in nine cities in Fujian Province, China. A cluster sampling method was used to include LLD patients from 42 nursing homes who met the study criteria. Inclusion criteria for LLD patients in this study were 1) age at first onset\u0026thinsp;\u0026ge;\u0026thinsp;60 years, 2) meeting the criteria for depression in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), 3) residing in a nursing home for more than 2 months. The exclusion criteria were: 1) patients with severe neurological diseases or traumatic brain injury, 2) patients with a history of alcoholism or drug dependence, 3) patients with language disorders or those who were unable to communicate normally for any reason.\u003c/p\u003e \u003cp\u003e The Ethics Committee of Fujian Medical University approved all study methods (approval no. 2023\u0026thinsp;\u0026minus;\u0026thinsp;177). All participants were informed of the purpose of the study and signed an informed consent form. The study began in June 2023 and ended in October 2023, as shown in \u003cb\u003eFigure. 1\u003c/b\u003e. 419 questionnaires were sent out through screening, of which 414 respondents effectively completed the survey, with a valid completion rate of 98.81%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eSociodemographic characteristics\u003c/h2\u003e \u003cp\u003eStudy data included age, gender, marital status, educational level, previous residence, economic status (pension), chronic condition, history of psychotropic medication use, smartphone use, intellectual activity and volunteer activity. Marital status was divided into married and unmarried (including single, divorced and widowed). Educational was classified into as primary school or under, middle or high school and college or above. Previous residence was categorized as city, town and rural. Economic status was classified as \u0026lt;\u0026thinsp;1000 RMB/month, 1000\u0026ndash;3000 RMB/month, 3001\u0026ndash;5000 RMB/month and \u0026gt;\u0026thinsp;5000 RMB/month. Intellectual activities and voluntary activities are classified as Never, Seldom (1\u0026ndash;2 times per week), Often (3\u0026ndash;4 times per week) and Usually (5\u0026ndash;7 times per week).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eNeuropsychiatric symptoms\u003c/h2\u003e \u003cp\u003eThe use of the Mild Behavioral Impairment Checklist (MBI-C) allows for the assessment of a patient's NPS. The scale consists of 34 questions reflecting five diagnostic domains: lack of motivation, affective incoherence, impulse control disorders, social withdrawal, and perceptual or thinking abnormalities [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The total score ranges from 0 to 102, with higher scores indicating greater severity of NPS. The scale has been shown to have good reliability and validity in Chinese populations [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In this study, the Cronbach's alpha of the MBI-C was 0. 882.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eDepression severity\u003c/h2\u003e \u003cp\u003eThe Geriatric Depression Scale (GDS-15) is a simplified version of the GDS-30 used to assess the severity of depression [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Each item requires a dichotomous response (yes or no) and is scored as 1 or 0. The total score ranges from 0\u0026ndash;15, with higher scores indicating more severe depression. The GDS-15 has been validated in a Chinese population [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In this study, the Cronbach's alpha of the GDS-15 was 0.706.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eSleep quality\u003c/h2\u003e \u003cp\u003eThe Pittsburgh Sleep Quality Index (PSQI) was used to assess patients' sleep quality over the past 1 month. The scale consists of 19 entries with 7 components (subjective sleep quality, time to fall asleep, sleep duration, sleep efficiency, sleep disorders, hypnotic medications, and daytime functioning). The total score ranges from 0\u0026ndash;21, with the higher the total score, the poorer the sleep quality [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Studies have shown that the PSQI has demonstrated good reliability in Chinese populations [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In this study, the Cronbach's alpha of the PSQI was 0.713.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eResilience\u003c/h2\u003e \u003cp\u003eUsing the Connor-Davidson Resilience Scale (CD-RISC-10) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] is a short version of the CD-RISC [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. It consists of 10 items that are used to provide a quick assessment of an individual's level of resilience. Total scores range from 0 to 40, with higher scores indicating greater resilience. CD-RISC-10 has shown good reliability in studies in China [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In our study, the Cronbach's alpha of the CD-RISC-10 was 0.948.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eIn this study, means and standard deviations were used to describe continuous variables and proportions and frequencies were used to represent categorical variables. The t-test was used to test the differences between continuous variables. The chi-square test was used to test differences between categorical variables. Correlations between depression severity, sleep quality, resilience, and NPS of LLD patients in nursing homes were then analysed using Pearson correlation. Finally, mediation and moderated mediation models were analysed using the PROCESS macro [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], including Model 4 (analyzing mediation) and Model 15 (analyzing direct or indirect pathways mediated by a variable). In this study, 5000 Bootstrapping sessions were used to calculate 95% confidence intervals (CIs) for the indirect effects of potential mediators. All analyses were conducted in SPSS 27 and used a p-value of less than 0.05 as the standard severity of significance (two-sided test). In addition, all models controlled for covariates (age, gender) and were standardized for all study variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the participants\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a total of 414 LLD patients in nursing homes were selected for this study, with a mean age of 78.85\u0026thinsp;\u0026plusmn;\u0026thinsp;9.00 years and a prevalence of NPS of 33.33% (MBI-C\u0026thinsp;\u0026gt;\u0026thinsp;7), of whom 39 (28.26%) were male and 99 (71.74%) were female. This shows that female LLD patients were more likely to have NPS. Furthermore, univariate analysis showed that sex, marital status, previous residence, smartphone use, intellectual activity, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the prevalence of NPS in LLD patients with different demographic characteristics (n\u0026thinsp;=\u0026thinsp;414).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;414)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMBI\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;138)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon MBI\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;276)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e/t\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.85\u0026thinsp;\u0026plusmn;\u0026thinsp;9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.75\u0026thinsp;\u0026plusmn;\u0026thinsp;8.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.40\u0026thinsp;\u0026plusmn;\u0026thinsp;9.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148 (35.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (28.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (39.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e266 (64.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (71.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167 (60.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147 (35.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (24.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113 (40.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e267 (64.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (75.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163 (59.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school or under\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180 (43.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (44.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e118 (42.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle or high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172 (41.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (41.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115 (41.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (15.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (13.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (15.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e236 (57.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (55.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e159 (57.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (19.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (14.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (22.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97 (23.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (29.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (20.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;1000RMB/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98 (23.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (28.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (21.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000-3000RMB/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94 (22.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (18.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3001-5000RMB/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135 (32.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (33.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (32.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;5000RMB/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87 (21.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (20.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (21.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e283(68.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (72.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e183 (66.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131 (31.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (27.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93 (33.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of psychotropic medication use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128 (30.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (34.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (29.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e286 (69.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (65.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e195 (70.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmartphone use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212 (51.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (42.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e154 (55.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202 (48.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (57.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122 (44.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntellectual activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e204 (49.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (63.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116 (42.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeldom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111 (26.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (17.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87 (31.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (10.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (7.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (12.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsually\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (13.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (10.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (14.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolunteering activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206 (49.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (48.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139 (50.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeldom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131 (31.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (32.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (31.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (10.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (13.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (9.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsually\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (8.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (5.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (9.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote\u003c/em\u003e: Continuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and analysed by t test; categorical variables are expressed as n (%) and analysed by chi-square tests.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBivariate correlations among all variables\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the result displays the correlation matrix for the main study variables, providing the means, standard deviations and correlations between the variables studied. Notably, both GDS-15 scores and MBI - C scores were positively correlated with PSQI scores (for GDS-15 scores, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.257, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; for MBI - C scores, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.363, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and resilience (for GDS-15 scores, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.445, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; for MBI - C scores, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.505, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There was a significant positive correlation between GDS-15 scores and MBI-C scores (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.427, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a negative correlation between CD - RISC scores and PSQI scores (\u003cem\u003er\u003c/em\u003e = -0.331, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The above results suggest that the mediation model between these four variables is reasonable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate correlations between depression severity, sleep quality, resilience and NPS (n\u0026thinsp;=\u0026thinsp;414).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1GDS-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.70\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2PSQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e10.41\u0026thinsp;\u0026plusmn;\u0026thinsp;4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.257\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3CD-RISC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e20.24\u0026thinsp;\u0026plusmn;\u0026thinsp;8.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.445\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.331\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4MBI-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.22\u0026thinsp;\u0026plusmn;\u0026thinsp;7.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.427\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.363\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.505\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMediation analyses\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, regression analyses for Model 1 showed that depression severity was a significant positive predictor of poor sleep quality (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.256, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Regression analyses for Model 2 showed that depression severity was a significant positive predictor of NPS (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.362, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and poor sleep quality was a significant positive predictor of NPS (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.265, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression analyses for mediated moderation analysis (n\u0026thinsp;=\u0026thinsp;414).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePoor Sleep Quality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eMBI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression severity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.256\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.362\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.232\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.179\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.265\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.190\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.179\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResilience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.332\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.312\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression severity\u003c/p\u003e \u003cp\u003e* Resilience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.102\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep quality\u003c/p\u003e \u003cp\u003e* Resilience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.096\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.811\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.215\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.635\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.424\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote\u003c/em\u003e: Controlled for age and sex\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e⁎⁎⁎\u003c/sup\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001, \u003csup\u003e⁎⁎\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, we analysed the mediated effects of sleep quality on the relationship between depression severity and NPS using PROCESS model 4, which showed that the 95% CIs for all paths did not contain 0, indicating that these paths were significant. Specifically, the indirect effect of poor sleep quality on the relationship between depression severity and NPS (B\u0026thinsp;=\u0026thinsp;0.068, 95% CI = [0.037, 0.102]) was significant, suggesting a mediating effect of poor sleep quality.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTable of effects analyses (n\u0026thinsp;=\u0026thinsp;414).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003epathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003ePROCESS model 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLLCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eULCI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eintermediary effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ea*b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ec\u003csup\u003e,\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etotal effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ea*b\u0026thinsp;+\u0026thinsp;c\u003csup\u003e,\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote\u003c/em\u003e: a: depression severity - sleep quality; b: sleep quality - NPS; c\u003csup\u003e,\u003c/sup\u003e : depression severity - NPS.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eModerated analysis of moderation\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, according to our hypotheses, resilience may play a moderating role between depression severity and NPS, or between poor sleep quality and NPS, respectively. The results of the mediation analyses showed that the interaction term between depression severity and resilience was a significant predictor of NPS (\u003cem\u003eβ\u003c/em\u003e = -0.102, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005); the interaction term between poor sleep quality and resilience was a significant predictor of NPS (\u003cem\u003eβ\u003c/em\u003e = -0.096, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016).\u003c/p\u003e \u003cp\u003eFurther analyses using PROCSS model 15 and the results of the simple slope test (see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Figure. 2) further indicated that depression severity was a significant positive predictor of NPS in the low resilience level (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.281, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas depression was not a predictor of NPS in the high resilience level (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.077, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.220).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSimple slopes of depression's effect on NPS at different values of resilience.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResilience\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ese\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLLCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eULCI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow resilience (M - SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium resilience (M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh resilience (M\u0026thinsp;+\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results of the simple slope test (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Figure. 3) further indicated that poor sleep quality was a significant positive predictor of NPS severity in the low resilience level (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.275, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas poor sleep quality was not predictive of NPS in the high resilience level (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.083, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.159). Thus, resilience also significantly attenuated the negative effect of poor sleep quality on NPS.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSimple slopes of the effect of poor sleep quality on the severity of NPS for different values of resilience.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResilience\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ese\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLLCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eULCI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow resilience (M - SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium resilience (M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh resilience (M\u0026thinsp;+\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMediating pathway model\u003c/h2\u003e \u003cp\u003eFigure.4 illustrates the mediated pathway model for regulation. The pathway coefficients show that all relationships in the model are significantly positive and negative. The direct effect of depression severity on NPS remained significant after poor sleep quality was used as a mediator and resilience as a moderator. Thus, the associations between depression severity and NPS in patients in nursing homes are partially mediated by poor sleep quality, and these associations are in turn moderated by resilience.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the prevalence of NPS among LLD patients in nursing homes and examined the relationship between depression severity, sleep quality, resilience, and NPS to provide a theoretical basis for future research to reduce the incidence of NPS. The findings showed that the relationship between depression severity and NPS in LLD patients was partially mediated by sleep quality, suggesting that depression severity may have both direct and indirect effects on NPS. In addition, the findings support that higher levels of resilience in nursing home patients with LLD may buffer the adverse effects of depression severity and sleep quality on NPS. These findings have important clinical implications and suggest that enhancing sleep quality and resilience in LLD patients is a key measure for reducing NPS in nursing homes. Not only do they theoretically enrich the understanding of the mental health of elderly depressed patients in nursing homes, but they also provide specific intervention pathways to improve the mental health status of this population in practice, which can effectively enhance the quality of life and well-being of the elderly.\u003c/p\u003e \u003cp\u003eIn the present study, the prevalence of NPS among LLD patients in nursing homes was 33.33%, which was significantly higher than that(3.50%)of the elderly in psychiatric outpatient clinics [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and those (10.00\u0026ndash;15.00%) in the community [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Differences in study sites may explain some of the differences. The mean score for depression severity in this study was (7.70\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62), which was higher than in a Chinese study of community and hospital LLD patients (7.07\u0026thinsp;\u0026plusmn;\u0026thinsp;3.33) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Similarly, the sleep quality score of nursing home LLD patients (10.41\u0026thinsp;\u0026plusmn;\u0026thinsp;4.86) was higher than that of Yunnan hospital LLD patients in China (6.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In contrast, the resilience score of LLD patients in nursing homes (20.24\u0026thinsp;\u0026plusmn;\u0026thinsp;8.41) was higher than that of empty nesters in Huzhou, China (12.44\u0026thinsp;\u0026plusmn;\u0026thinsp;3.63) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. A plausible explanation is that LLD patients living in nursing homes are more likely to face a variety of psychological burdens and have poorer sleep quality, which leads to an increased level of resilience [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study demonstrated a significant positive correlation between depression severity and NPS in LLD patients in nursing homes, which is consistent with previous study [\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In our study, LLD patients in nursing homes moving from a familiar home environment to a new, unfamiliar environment tended to show higher depression severity, affecting the expression of NPS [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Neurophysiological studies suggest that depression severity may influence the NPS by affecting neurotransmitter systems, inflammatory responses, and functional brain connectivity [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Although this association has been demonstrated in several studies, the specific neural mechanisms are unclear. Previous studies have demonstrated that better sleep quality and higher resilience level are protective factors for NPS in LLD patients [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The protective effect of these factors was similarly demonstrated in our study.\u003c/p\u003e \u003cp\u003eThe present study found that sleep quality mediated the relationship between depression severity and NPS to some extent, which may reveal a potential mechanism by which depression severity indirectly affects NPS. Previous studies have demonstrated that sleep quality has a mediating role in mediating the association between depressive symptoms and cognitive decline [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In our study, depression severity was directly related to NPS, depression severity was negatively related to sleep quality, and sleep quality was negatively related to NPS. Better sleep quality may help to reduce the association between depression severity and NPS[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The results of a recent meta-analysis showed that greater improvements in sleep quality led to greater improvements in mental health, but there were differences in the effectiveness of interventions to improve sleep quality for different psychological problems [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Therefore, further research is necessary to improve understanding of the effectiveness of these interventions. In conclusion, interventions and management of sleep quality problems should be emphasized in the treatment and care of LLD patients.\u003c/p\u003e \u003cp\u003eAccording to our moderator-mediator analyses, resilience moderated the relationship between depression severity and NPS, as well as the second half of the mediated effect of sleep quality. This highlights the research and clinical relevance of resilience for understanding the impact of NPS, namely that resilience can reduce NPS to some extent. Resilience is a dynamic moderating process, and the experience of adversity modifies an individual's response to stress, which can be altered and learnt throughout the lifespan [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Our study focused on NPS in LLD patients in nursing homes and found that they had higher severity of depression and poor sleep quality, but the effects of depression severity and sleep quality on NPS diminished with increasing resilience. Therefore, increasing resilience can be a key goal in improving NPS in patients with LLD [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Targeted psychological interventions, supportive therapy, and rehabilitation programs can help patients improve their resilience, thereby reducing depressive symptoms and improving sleep quality. Future research and clinical practice should further explore and validate the effectiveness of different interventions.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has some limitations in interpreting the results. Firstly, the cross-sectional data makes it challenging to make causal inferences between the identified variables and NPS. It does not allow for analysing causal relationships between variables. In the future, further longitudinal studies are needed to investigate causal relationships between depression severity and NPS, as well as more accurate mediating relationships. Secondly, the data in the study usually rely on self-reports or subjective evaluations by patients or subjects, which may be subjectively biased, and therefore in-depth interviews and behavior observations should be conducted. Thirdly, although we controlled for some possible confounding variables (e.g., age, gender), other uncontrolled variables (e.g., social support and functional limitations, etc.) may still have an impact on the results. Further future research needs to incorporate additional variables to gain a more complete understanding of this issue. Finally, this study was conducted only in nursing homes, and the limitations of the study site may limit the generalizability of the results. Therefore, future studies should be conducted in different settings and venues, including community and home care settings, to validate the generalizability of the results and to explore the potential impact of different settings on the study variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this is the first investigation of the relationship between depression severity and NPS among LLD patients in nursing homes using a moderated mediation model. In the present study, the prevalence of NPS in LLD patients was 33.33%, and sleep quality moderated to some extent the relationship between depression severity and NPS. Resilience moderated the direct effect of depression severity on NPS, as well as the latter part of the mediating effect of sleep quality. It may be important to design interventions for LLD patients in nursing homes, especially those with poor sleep quality and low level of resilience, that result in improved sleep quality and low level of resilience in LLD patients in order to reduce the expression of NPS.This not only provides valuable guidance for optimizing care strategies in nursing homes, but also offers practical directions for interventions in clinical practice to further safeguard the mental health and overall quality of life of elderly patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch1\u003eAcknowledgment\u003c/h1\u003e\n\u003cp\u003eWe acknowledge all the participants and assisting researchers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design of study: Z.Z. and Y.Y.; data collection: D.C., Y.S., and C.H.; data analysis and interpretation: Z.Z. and R.L.; manuscript preparation: Z.Z.; critical review of the manuscript: H.L. and R.L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the National Natural Science Foundation of China (72104050).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate Patients were briefed on the anonymous and confidential nature of the survey, its purpose, and significance, and signed informed consent forms. This study obtained approval from the Ethics Committee of the Fujian Medical University (Approval No. 2023-177).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlexopoulos GS. Mechanisms and treatment of late-life depression. Transl Psychiatry. 2019;9:188.\u003c/li\u003e\n\u003cli\u003eMalhi GS, Mann JJ. Depression. Lancet. 2018;392:2299\u0026ndash;312.\u003c/li\u003e\n\u003cli\u003eTang T, Jiang J, Tang X. Prevalence of depression among older adults living in care homes in China: A systematic review and meta-analysis. 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J Am Coll Cardiol. 2014;64:840\u0026ndash;2.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Depression severity, Neuropsychiatric symptoms, Sleep quality, Resilience, Moderated mediation model, Late-life depression patients in nursing homes","lastPublishedDoi":"10.21203/rs.3.rs-4697569/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4697569/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground\u003c/b\u003e Depression severity significantly influences neuropsychiatric symptoms (NPS), yet the underlying mediating and moderating mechanisms of this relationship remain insufficiently explored.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e We employed cluster sampling to select 414 LLD patients from 42 nursing homes across nine cities in Fujian Province, China. Mediation and moderation analyses were conducted using the PROCESS macro model to determine the interactions between depression severity, sleep quality, resilience, and NPS.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e The findings indicate that NPS prevalence among LLD patients in nursing homes is substantial. Sleep quality partially mediated the relationship between depression severity and NPS. Additionally, resilience moderated both the direct and indirect effects within the mediation model, highlighting its significant role in mitigating the impact of depression severity on NPS.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusion\u003c/b\u003e The results underscore the importance of targeting sleep quality and resilience in clinical interventions for LLD patients in nursing homes. Enhancing sleep quality and resilience could potentially disrupt the link between depression severity and NPS, thereby improving patient outcomes.\u003c/p\u003e","manuscriptTitle":"Depression severity and neuropsychiatric symptoms among late-life depression patients in nursing homes: A moderated mediation model of sleep quality and resilience","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 21:08:03","doi":"10.21203/rs.3.rs-4697569/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-15T08:38:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-24T13:14:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199964651443836338199062019766340326117","date":"2025-05-29T22:27:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-26T21:34:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224722306345450307394955703488034675601","date":"2024-11-21T18:34:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-27T02:38:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-01T19:57:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-10T04:36:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-10T03:37:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2024-07-06T16:04:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3faebbc0-9838-494f-9f65-9c6f851435b7","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:06:41+00:00","versionOfRecord":{"articleIdentity":"rs-4697569","link":"https://doi.org/10.1186/s12888-025-07571-z","journal":{"identity":"bmc-psychiatry","isVorOnly":false,"title":"BMC Psychiatry"},"publishedOn":"2025-11-15 15:57:32","publishedOnDateReadable":"November 15th, 2025"},"versionCreatedAt":"2024-08-09 21:08:03","video":"","vorDoi":"10.1186/s12888-025-07571-z","vorDoiUrl":"https://doi.org/10.1186/s12888-025-07571-z","workflowStages":[]},"version":"v1","identity":"rs-4697569","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4697569","identity":"rs-4697569","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}