Associations between sleep quality, fatigue, social isolation, and depressive symptoms in patients with heart failure: a parallel mediation analysis

Associations between sleep quality, fatigue, social isolation, and depressive symptoms in patients with heart failure: a parallel mediation analysis | 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 Associations between sleep quality, fatigue, social isolation, and depressive symptoms in patients with heart failure: a parallel mediation analysis Aiping Zhang, Yuxuan Wang, Baizan Tang, Yuwei Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4520177/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Depression is a common comorbidity in patients with heart failure (HF) which could lead to increased mortality and morbidity. Meanwhile, a majority of patients with HF suffer from poor sleep quality which has negative impacts of patients’ physical, social, and mental health, leading to a risk of fatigue, social isolation, and depressive symptoms. However, the interrelationships among the four factors remain unclear in the literature. This study aimed to assess the rate of depressive symptoms and the interrelationships among sleep quality, fatigue, social isolation, and depressive symptoms in patients with HF in China. Methods This cross-sectional study was conducted at a general hospital in China. A convenience sample of 300 patients with HF was recruited from January to March 2024. Self-reported instruments were used to measure sleep quality, fatigue, social isolation, and depressive symptoms. Descriptive, Pearson correlation, and parallel mediation analyses were conducted via SPSS 26.0. Results Results showed that 72.3% of the participants had moderate to severe depressive symptoms. Fatigue and social isolation performed parallel mediation effects on the relationship between sleep quality and depressive symptoms. Conclusion These findings highlight the prevention and management of depressive symptoms in patients with HF. Future studies are needed to design and evaluate the effectiveness of interventions that incorporate multiple components on improving sleep quality, social isolation, and fatigue in patients with HF. sleep quality fatigue social isolation depressive symptoms mediation Figures Figure 1 Figure 2 BACKGROUND Heart failure (HF) is a condition where symptoms and/or signs are caused by a cardiac abnormality, confirmed by high levels of natriuretic peptides and/or evidence of congestion in the lungs or throughout the body [1]. The symptoms of HF typically include shortness of breath, fatigue, swelling, and heart palpitations, which bring a significant impact on individuals’ physical and mental health. According to a recent systematic review, the global prevalence of HF in general adults ranged from 1–3% [1]. By 2030, it is estimated that the number of individuals diagnosed with HF will increase by 46% (or over 8 million people) compared to the current number [2]. Depression is a frequently reported comorbidity among patients with HF, which could result in an increased mortality and morbidity [3]. A recent systematic review on depression on patients with HF found a global depression prevalence of 28.1% for moderate to severe symptoms, with a range from 11.3–50.0% [4]. Emerging evidence underscores poor sleep quality, fatigue, and social isolation as significant correlates of depressive symptoms in patients with HF [3, 5–7]. Due to HF-related symptoms such as nocturnal dyspnea and palpitation, most of patients with HF reported poor sleep quality, with a proportion ranging from 70–90% [5, 6]. Poor sleep quality usually has negative impacts on patients’ health and well-being, in terms of physical, social, and mental aspects [6]. Specifically, patients with HF who have poor sleep quality are more likely to report fatigue, characterized by a persistent and widespread feeling of physical and psychological exhaustion that cannot be alleviated by typical rest or recuperation methods [8]. Meanwhile, poor sleep quality is associated with a risk of social isolation, marked by a lack of meaningful social networks [5, 6]. Importantly, the mental health outcomes of poor sleep quality are profound, as it serves as a predictive factor for both clinical and subclinical depression among patients with HF [9]. As mentioned earlier, previous studies found that poor sleep quality, fatigue, and social isolation were associated with depressive symptoms in patients with HF [3, 5–7]. The above empirical findings indicate that there may be mediating effects of fatigue and social isolation between sleep quality and depressive symptoms in patients with HF. However, this has not been explored in this literature. Considering the negative consequences of poor sleep quality and depressive symptoms, understanding the interrelationships among sleep quality, fatigue, social isolation, and depressive symptoms in patients with HF is important. This work could provide insights for understanding the multifaceted nature of depressive symptoms in patients with HF and informing targeted interventions aimed at mitigating their adverse impact on patient outcomes and well-being. Therefore, this study aimed to assess the rate of the depressive symptoms and the mediating effects of fatigue and social isolation between sleep quality and depressive symptoms, a hypothesized model (Fig. 1) was informed by the pathophysiological interplay between depression and heart failure by Sbolli et al. and the concept analysis of sleep quality by Nelson et al. [3, 10]. Specifically, Sbolli et al. summarized that social factors (such as social isolation) and physical symptoms (such as poor sleep quality and fatigue) could contribute to depression in patients with HF [3]. According to Nelson et al., poor sleep quality could lead to negative consequences such as fatigue and strained social relationships [10]. In hence, this study hypothesized that sleep quality, fatigue, and social isolation would be associated with depressive symptoms and the association between sleep quality and depressive symptoms would be separately mediated by fatigue and social isolation. METHODS Study design and sample size The cross-sectional study included patients with HF at the cardiology department of the University (Blinded for review) between January and March 2024. A convenience sampling method was used to recruit eligible participants. The inclusion criteria of the participants in this study were: Patients with a confirmed diagnosis of chronic HF by physicians and being at least 18 years of age. Patients who had a diagnosis of neurocognitive disorders (e. g, dementia) or a diagnosis of mental disorders were excluded. Bujang, Sa’at, and Bakar recommended a sample of 300 subjects or more to generate a close approximation of estimates for multiple linear regression in cross-sectional studies [11]. This study finally collected 300 valid questionnaires which met the minimum sample size requirement. Procedure Before data collection, nurse coordinators accepted training to screen and recruit eligible patients from the cardiology department. The training regarding consistent guidance on filling the questionnaire for participants was also conducted. After explaining the aims of the study, patients who were willing to participate in this study were asked to fill in the questionnaire in a separate room at the department. A nurse coordinator was responsible for gathering and reviewing the questionnaire, as well as assisting participants with clarifying any unfamiliar items in the survey upon request. Measures Socio-demographic and clinical information Socio-demographic and clinical characteristics of participants include age, sex, education background, NYHA functional class, and the duration of illness. The Pittsburgh Sleep Quality Index (PSQI) PQSI was used to measure sleep quality. It has 19 items and covers seven dimensions including subjective sleep quality, sleep latency, sleep duration, sleep disturbance, sleep efficiency, hypnotic drug use, and daytime dysfunction [12]. According to a 4-Likert rating scale, each dimension is scored from 0 to 3, with the total score ranging from 0 to 21. The higher the score, the worse the sleep quality of patients. The Chinese version of PSQI had an overall Cronbach coefficient of 0.82–0.83 and a test-retest reliability of 0.77–0.85 [13]. The Cronbach’s alpha coefficient for the total scale was 0.70 in this study. The 13-item Beck Depression Inventory (BDI-13) BDI-13 was applied to assess depressive symptoms. It has 13 items to evaluate respondents’ feelings on depressive symptoms in the last week [14]. Each item has four options, with rating scores ranging from 0 to 3. A total score ranges from 0 to 39, with a higher score indicating more depressive symptoms. The BDI-13 total score was used to categorize respondents into four groups based on their level of depressive symptoms: no depressive symptoms (score of 0–4), mild symptoms (score of 5–7), moderate symptoms (score of 8–15), and severe depressive symptoms (score of 16–39) [14]. The Chinese version of BDI-13 has been applied to different populations in China, with an overall Cronbach coefficient ranged from 0.85 to 0.88 [15]. The Cronbach’s alpha coefficient for the total scale was 0.82 in this study. Multidimensional Fatigue Inventory (MFI) MFI was used to measure fatigue. It has 20 items and covering five dimensions, including general fatigue, physical fatigue, reduced activity, reduced motivation, and mental fatigue [16]. Based on a 5-Likert rating scale, each item was rated from 1 (strongly agree) to 5 (strongly disagree). A total score ranges from 20 to 100, with a higher total score indicating a higher level of fatigue. The Chinese version of MFI had a good reliability and validity [17]. The Cronbach coefficient of MFI in Chinese general population was 0.92, and the Cronbach coefficients of general fatigue, physical fatigue, mental fatigue, reduced motivation, and reduced activity were 0.89, 0.89, 0.73, 0.83, and 0.80, respectively, indicating good internal consistency [17]. The Cronbach’s alpha coefficient for the total scale was 0.79 in this study. The Lubben Social Network Scale (LSNS) LSNS was used to measure social isolation. It has six items asking the size of active and intimate networks of family and friends with whom they could talk or call for help [18]. Each item is rated from 0 (none) to 5 (nine or more). The total score of LSNS ranges from 0 to 30, with a lower score indicating a smaller size of social networks and a higher risk of social isolation. The Chinese LSNS has shown satisfactory validity and reliability in the Chinese population, with an overall Cronbach coefficient of 0.83 [19]. The Cronbach’s alpha coefficient for the total scale was 0.77 in this study. Ethical consideration This study obtained ethical approval (2024LL-21) from the University (Blinded for review). Participants were informed that taking part in the study would not cause any harm to them, and their personal information would remain confidential. Additionally, they were informed that they could withdraw from the study at any point before data analysis without any negative consequences such as affecting their appointments with physicians or treatment. All participants provided informed consent before questionnaire completion. Data analysis All analyses were conducted via the SPSS 26.0. The distributions of the total scores of PQSI, BDI-13, MFI, and LSNS were checked by according to the absolute values of skewness and kurtosis. Since their absolute values of skewness were less than 2 and that of kurtosis less than 7, there were no severe violations of normality distribution [20]. Characteristics of patients with HF were described as frequencies and percentages for categorical variables and mean (SD) for continuous variables. To assess the rate of depressive symptoms of the participants, four categories of the total score of BDI-13 were used, including no depressive symptoms (0–4), mild depressive symptoms (5–7), moderate depressive symptoms (8–15), and severe depressive symptoms (score of 16–39) [14]. Pearson correlation analysis was conducted to explore the correlations among the total scores of PQSI, BDI-13, MFI, and LSNS. A parallel mediation model with fatigue (mediator 1) and social isolation (mediator 2) was conducted to test the relationships among sleep quality, fatigue, social isolation, and depressive symptoms (Fig. 1) (PROCESS SPSS macro, model 4).[21] Except the total (c) and direct effects (c’) of the sleep quality on the depressive symptoms, two mediating pathways were assessed: (1) sleep quality-fatigue-depressive symptoms (a1b1) and (2) sleep quality-social isolation-depressive symptoms (a2b2). A 10,000-sample bootstrap procedure was used to estimate 95% bias corrected confidence intervals [21]. If the bias corrected confidence intervals do not contain zero, it indicates significant mediating effects at p < 0.05. The effects of socio-demographic and clinical information (age, sex, education background, NYHA functional class, and duration of illness) were controlled in the parallel mediation model. RESULTS Descriptive statistics A total of 310 eligible patients were invited to participate in this study, of whom ten refused due to a lack of interest. The response rate was 97% and an analytic sample consisted of 300 participants. The socio-demographic and clinical characteristics of participants are shown in Table 1. The mean age of the participants was 64.95 (SD = 12.89) years old. More than half of participants were female (53.7%, n = 161). Most of the participants completed middle or high school (75.7%, n = 227). Most of the participants (88.7%) had NYHA Ⅱ ~ Ⅳ functional class levels. The average duration of the HF in the participants was 8.56 years (SD = 5.45). Based on the scores of the BDI-13, 18.7% of the participants were not depressed (n = 56), 9% reported mild depressive symptoms (n = 27), 35.3% reported moderate depressive symptoms (n = 106), and 37% reported severe depressive symptoms (n = 111). Table 1 Descriptive statistics for sociodemographic and clinical characteristics of participants (n = 300) Characteristics Mean ± SD /n (%) Age 64.95 ± 12.89 Sex Female 161 (53.7) Male 139 (46.3) Education background Primary school or below 38 (12.7) Middle or high school 227 (75.7) College or above 35 (11.7) NYHA functional class Ⅰ 34 (11.3) Ⅱ 118 (39.3) Ⅲ 134 (44.7) Ⅳ 14 (4.7) Duration of illness 8.56 ± 5.45 Note: * p < 0.05 Correlations between sleep quality, fatigue, social isolation, and depressive symptoms Descriptive statistics and correlations of the measures used in the study are presented in Table 2. More depressive symptoms were related to poorer sleep quality (r = 0.488, p < 0.001) and a higher level of fatigue (r = 0.315, p < 0.001). Meanwhile, more depressive symptoms were related to a higher risk of social isolation (r = -0.250, p < 0.001). Table 2 Descriptive statistics and correlations of depressive symptoms, sleep quality, fatigue, and social isolation (n = 300) Mean ± SD BDI-13 PSQI MFI LSNS BDI-13 12.40 ± 7.25 1 PSQI 7.43 ± 3.46 0.488 ** 1 MFI 60.01 ± 7.95 0.315 ** 0.307 ** 1 LSNS 11.12 ± 5.06 -0.250 ** -0.141 ** -0.209 ** 1 Abbreviations: BDI-13: The 13-item Beck Depression Inventory; PSQI: The Pittsburgh Sleep Quality Index; MFI: Multidimensional Fatigue Inventory; LSNS: The Lubben Social Network Scale. ** p < 0.001 Parallel mediation analysis Figure 2 describes all the paths for the parallel mediation model and the coefficients are displayed in Tables 3 and 4. The total effect (Bc = 1.00, SE = 0.11, t = 9.51, p < 0.001) and the direct effect (Bc’ = 0.87, SE = 0.11, t = 8.13, p < 0.001) of sleep quality on depressive symptoms were found to be significant. After accounting for all the socio-demographic and clinical variables, the indirect pathways from sleep quality to depressive symptoms though fatigue [path a1b1: point estimate = 0.088, SE = 0.040; Bias corrected convince interval (0.017, 1.173)] or social isolation [path a2b2: point estimate = 0.046, SE = 0.028; Bias corrected convince interval (0.002, 0.108)] turned out to be significant. These results were in line with the hypotheses that sleep quality, fatigue, and social isolation were related to depressive symptoms and fatigue and social isolation parallelly mediated the relationship between sleep quality and depressive symptoms. Figure 2 The parallel mediating effects of fatigue and social isolation between sleep quality and depressive symptoms. Note: c = total effect, c’ = direct effect Table 3 Results of the regression analyses testing the parallel mediating effects of fatigue and social isolation in the relationship between sleep quality and depressive symptoms (n = 300) Predictors Direct effect (SE) Total effect (SE) LSNS MFI BDI-13 BDI-13 Constant 11.111 (2.697) ** 47.424 (4.080) ** -3.213 (4.170) 0.255 (3.407) PSQI -0.188 (0.083) * 0.674 (0.126) ** 0.871 (0.107) ** 1.004 (0.106) ** LSNS -0.242 (0.072) ** MFI 0.130 (0.048) ** Age -0.011 (0.028) -0.001 (0.042) -0.004 (0.034) ** -0.001 (0.035) Sex -0.319 (0.587) -0.206 (0.889) 0.919 (0.718) 0.969 (0.742) Education background 1.664 (0.622) ** 1.838 (0.941) 0.783 (0.777) 0.617 (0.787) NYHA -0.596 (0.486) 1.946 (0.735) ** -0.299 (0.601) 0.099 (0.614) Duration of illness 0.093 (0.071) -0.047 (0.107) 0.239 (0.087) ** 0.211 (0.090) * R 2 0.060 ** 0.127 ** 0.321 ** 0.268 ** * p < 0.05, ** p < 0.001 Note: Unstandardized regression coefficients (beta) with standard errors (SEs) in parentheses are presented. Abbreviation: BDI-13: The 13-item Beck Depression Inventory; PSQI: The Pittsburgh Sleep Quality Index; MFI: Multidimensional Fatigue Inventory; LSNS: The Lubben Social Network Scale Table 4 Bootstrapped point estimates with standard errors and 95% bias corrected confidence intervals for all indirect effects between sleep quality and depressive symptoms (Path) Point estimate SE Bootstrapping 95% bias corrected confidence interval Lower Upper Indirect effects Model 1: Via MFI (a1b1) 0.0876 0.0403 0.0171 1.1737 Model 2: Via LSNS (a2b2) 0.0455 0.0276 0.0023 0.1079 Total indirect effect 0.1332 0.0470 0.0511 0.2355 Pairwise contrasts Model 1 versus Model 2 -0.0421 0.0506 -0.1414 0.0564 Abbreviations: MFI: Multidimensional Fatigue Inventory; LSNS: The Lubben Social Network Scale; SE: standard error. DISCUSSION This study aimed to explore the rate of depressive symptoms in patients with HF in China and mediating roles of fatigue and social isolation in the relationship between sleep quality and depressive symptoms in this population. Results found that 72.3% of the participants had moderate to severe depressive symptoms. The parallel mediation analysis found that sleep quality, fatigue, and social isolation were significantly related to depressive symptoms. Meanwhile, fatigue and social isolation were independent mediators between sleep quality and depressive symptoms. These findings contribute to the existing evidence on the understanding of depressive symptoms in patients with HF in China and the pathways underlying the association of sleep quality with depressive symptoms in this population. The rate of depressive symptoms was high in our study, with 72.3% of participants with moderate to severe depressive symptoms. This proportion is consistent with the range reported in the meta-analysis on the prevalence of depressive symptoms in patients with HF in China (13%-82%) but higher than the global level (11.3%-51.0%) [4, 22]. The higher rate of depressive symptoms in this study could be attributed to the characteristics of our sample and the methodology employed in assessing depressive symptoms. First, unlike global studies, which may include a more heterogeneous population and utilize different assessment tools, this study focused on a specific region in China. Compared with Western countries, Eastern countries such as China may have fewer rehabilitation programs for patients with HF to prevent or reduce depressive symptoms [4]. This might lead to a higher concentration of depressive symptoms in our sample. Second, a higher NYHA functional class level may be related to a high risk of depressive symptoms [23]. Nearly half of the participants in this study had NYHA 3~4 functional class levels, which is a higher proportion than that in the global meta-analysis (36%), suggesting a potentially greater burden of illness in our sample. The high rate of moderate to severe depressive symptoms in the present study suggests that healthcare providers and clinicians should identify and prevent depressive symptoms in the management of patients with HF. A key finding is that sleep quality was related to depressive symptoms and this relationship was mediated by fatigue. In other words, participants with poor sleep quality may have a higher level of fatigue, which further leads to more depressive symptoms. This may be because poor sleep quality could lead to tiredness and fatigue, which may aggravate the symptoms of fatigue in patients with HF [24]. The long-term fatigue would impair individuals’ active cognitive processes such as coping and problem-solving [24]. As a result, decreased coping and problem-solving abilities may lead to individuals being more prone to negative feelings and thus a higher risk of depressive symptoms. The mediating role of fatigue was also found in the relationship between sleep quality and quality of life in patients with HF [25], which supports our finding. Another key finding is that social isolation mediated the relationship between sleep quality and depressive symptoms. That is to say, participants who had poor sleep quality were more likely to be socially isolated, which further resulted in more depressive symptoms. The possible reason may be that poor sleep quality could make certain parts of the brain more sensitive to social distances, such as avoiding people, but also impair other areas that promote prosocial behavior, such as understanding someone else’s intentions [26]. Therefore, poor sleep quality in patients with HF may increase their susceptibility to social isolation. Further, patients experiencing social isolation may attend more to negative information and interpret it negatively which could make them prone to depressive symptoms [27]. Implications The findings indicate a high proportion of patients with HF may experience depressive symptoms. While current HF guidelines suggest screening for depression among HF patients, the depressive symptoms in patients with HF are usually unrecognized [3]. Our study suggests that healthcare providers should be aware that the depressive symptoms are consequences of multiple factors. Further, they should provide patients and their families with such knowledge about the interrelationships between sleep quality, fatigue, social isolation, and depressive symptoms, which can raise awareness and encourage proactive self-management strategies. Meanwhile, healthcare providers should monitor and intervene in patients’ sleep quality, level of fatigue, and social isolation, which may comprehensively prevent or reduce depressive symptoms. This study found that sleep quality was a significant correlate of depressive symptoms which may provide information for potential interventions. Existing evidence confirmed the benefits of cognitive behavior therapy for insomnia (CBTi) in improving sleep quality and mental health outcomes in people with insomnia [28]. Researchers from the USA also found that CBTi had a statistically significant effect on improving sleep quality, fatigue, and depression among patients with HF [29]. To the best of our knowledge, although C. Zhang et al. [30] have recently culturally adapted CBTi in patients with insomnia in the Chinese context and found a smartphone-based CBTi improved insomnia severity compared with sleep education, the benefits of CBTi on improving the sleep quality among patients with HF have not been tested in the Chinese context. Future research is needed to test if CBTi could improve sleep quality, fatigue, and depressive symptoms among patients with HF in China. Furthermore, the mediating roles of fatigue and social isolation suggest that adopting a multifaceted and integrated interventions on fatigue (e.g., through behavioral activation) and social isolation (e.g., support groups) may provide an indirect pathway to prevent or reduce depressive symptoms in clinical practice. Healthcare professionals may need to collaborate closely with other professionals such as psychologists, social workers, physiotherapists, etc., for comprehensive management for patients with HF. For example, group-delivered CBTi with components such as increasing social interactions may be considered. This may provide patients with HF a safe and comfortable social environment to share their experiences and concerns in the treatment and self-management. Multidisciplinary effort will be important when healthcare professionals design and evaluate the effectiveness of such comprehensive interventions in patients with HF. Limitations The present study was designed as a cross-sectional study, which cannot infer the causal relationship among sleep quality, fatigue, social isolation, and depressive symptoms. Longitudinal studies are needed to clarify the causal relationships among these variables. Meanwhile, this study applied a convenient sampling of hospitalized patients with HF from a general hospital. Therefore, it is challenging to ensure the complete generalizability to patients with HF in the broader community or other geographical locations. Future studies may extend the scope of investigation and include a cross-cultural perspective in exploring the interrelationships among these variables among patients with HF. CONCLUSIONS This study explored the rate of depressive symptoms and the mediating roles of social isolation and fatigue in the relationship between sleep quality and depressive symptoms in patients with HF in China. Results showed that 72.3% of participants with moderate to severe depressive symptoms. Sleep quality, social isolation, and fatigue were significant correlates of depressive symptoms in patients with HF, with social isolation and fatigue performing parallel mediations in the relationship between sleep quality and depressive symptoms. Despite the limitations inherent in the cross-sectional design, these results offer insights for clinical practice, suggesting that interventions targeting improvements in sleep quality, social isolation, and fatigue may be beneficial in mitigating or preventing depressive symptoms in patients with HF. Abbreviations HF Heart failure NYHA New York Heart Association PSQI The Pittsburgh Sleep Quality Index BDI-13 The 13-item Beck Depression Inventory MFI Multidimensional Fatigue Inventory LSNS The Lubben Social Network Scale CBTi cognitive behavior therapy for insomnia Declarations Ethics approval and consent to participate The research ethics committee of the Third Affiliated Hospital of Qiqihar Medical University approved the study (Approval Number: 2024LL-21). All participants provided informed consent before questionnaire completion. Consent for publication Not applicable Availability of data and materials The research data is confidential to protect study participant privacy. Competing interests The authors declare no competing interests. Funding This study was supported by the Qiqihar Science and Technology Planning Project. The grant number will be updated soon. Authors’ contributions A.Z. contributed to the investigation, methodology, formal analysis, funding acquisition, and writing - original draft. Y.W. contributed to the formal analysis and writing - original draft. B.T. contributed to the project administration, supervision, and writing - review & editing. Y.W. contributed to the conceptualization, project administration, supervision, and writing - review & editing . Acknowledgements We thank the staffs of the hospital for their cooperation and all the participants who actively participated in the study. References Savarese G, Becher PM, Lund LH, Seferovic P, Rosano GM, Coats AJ: Global burden of heart failure: a comprehensive and updated review of epidemiology . Cardiovascular research 2022, 118 (17):3272–3287. Tsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, Boehme AK, Buxton AE, Carson AP, Commodore-Mensah Y: Heart disease and stroke statistics—2022 update: a report from the American Heart Association . Circulation 2022, 145 (8):e153-e639. Sbolli M, Fiuzat M, Cani D, O'Connor CM: Depression and heart failure: the lonely comorbidity . European journal of heart failure 2020, 22 (11):2007–2017. Moradi M, Doostkami M, Behnamfar N, Rafiemanesh H, Behzadmehr R: Global prevalence of depression among heart failure patients: a systematic review and meta-analysis . Current problems in cardiology 2022, 47 (6):100848. Lainsamputty F, Chen H-M: The correlation between fatigue and sleep quality among patients with heart failure . NurseLine Journal 2018, 3 (2):100–114. Zheng T: Sleep disturbance in heart failure: A concept analysis . In: Nursing forum: 2021 : Wiley Online Library; 2021: 710–716. Sheffler JL, Schmiege SJ, Sussman J, Bekelman DB: A longitudinal analysis of the relationships between depression, fatigue, and pain in patients with heart failure . Aging & mental health 2021, 25 (12):2272–2278. Pavlovic NV, Gilotra NA, Lee CS, Ndumele C, Mammos D, Dennisonhimmelfarb C, AbshireSaylor M: Fatigue in persons with heart failure: a systematic literature review and meta-synthesis using the biopsychosocial model of health . Journal of cardiac failure 2022, 28 (2):283–315. Norra C, Kummer J, Boecker M, Skobel E, Schauerte P, Wirtz M, Gauggel S, Forkmann T: Poor sleep quality is associated with depressive symptoms in patients with heart disease . International journal of behavioral medicine 2012, 19 :526–534. Nelson KL, Davis JE, Corbett CF: Sleep quality: An evolutionary concept analysis . In: Nursing forum: 2022 : Wiley Online Library; 2022: 144–151. Bujang MA, Sa’at N, Bakar TMITA: Determination of minimum sample size requirement for multiple linear regression and analysis of covariance based on experimental and non-experimental studies . Epidemiology, biostatistics, and public health 2017, 14 (3). Buysse DJ, Reynolds III CF, Monk TH, Berman SR, Kupfer DJ: The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research . Psychiatry research 1989, 28 (2):193–213. Tsai P-S, Wang S-Y, Wang M-Y, Su C-T, Yang T-T, Huang C-J, Fang S-C: Psychometric evaluation of the Chinese version of the Pittsburgh Sleep Quality Index (CPSQI) in primary insomnia and control subjects . Quality of Life Research 2005, 14 :1943–1952. Beck AT, Rial WY, Rickels K: Short form of depression inventory: cross-validation . Psychological reports 1974, 34 (3_suppl):1184–1186. Wang C, Wang D: Validity and reliability of Chinese version of 28-item Substance Use Risk Profile Scale in Chinese adolescents and young adults . Current Psychology 2021, 40 :4507–4515. Smets E, Garssen B, Bonke Bd, De Haes J: The Multidimensional Fatigue Inventory (MFI) psychometric qualities of an instrument to assess fatigue . Journal of psychosomatic research 1995, 39 (3):315–325. Chuang L-L, Chuang Y-F, Hsu M-J, Huang Y-Z, Wong AM, Chang Y-J: Validity and reliability of the traditional Chinese version of the multidimensional fatigue inventory in general population . PLoS One 2018, 13 (5):e0189850. Lubben J, Blozik E, Gillmann G, Iliffe S, von Renteln Kruse W, Beck JC, Stuck AE: Performance of an abbreviated version of the Lubben Social Network Scale among three European community-dwelling older adult populations . The Gerontologist 2006, 46 (4):503–513. Chang Q, Sha F, Chan CH, Yip PS: Validation of an abbreviated version of the Lubben Social Network Scale (“LSNS-6”) and its associations with suicidality among older adults in China . PloS one 2018, 13 (8):e0201612. Finney SJ, DiStefano C: Non-normal and categorical data in structural equation modeling . Structural equation modeling: A second course 2006, 10 (6):269–314. Hayes AF: PROCESS: A versatile computational tool for observed variable mediation, moderation, and conditional process modeling . In.: University of Kansas, KS; 2012. Lin X-x, Gao B-B, Huang J-y: Prevalence of depressive symptoms in patients with Heart Failure in China: a meta-analysis of comparative studies and epidemiological surveys . Journal of Affective Disorders 2020, 274 :774–783. Botto J, Martins S, Moreira E, Cardoso JS, Fernandes L: The magnitude of depression in heart failure patients and its association with NYHA class . European Psychiatry 2021, 64 (S1):S342-S342. Chattu VK, Manzar MD, Kumary S, Burman D, Spence DW, Pandi-Perumal SR: The global problem of insufficient sleep and its serious public health implications . In: Healthcare: 2018 : MDPI; 2018: 1. Zhang J, Chai X, Ye Y, Zhao Q, Fan X: Association between sleep and quality of life in heart failure patient–caregiver dyads and mediation of fatigue: An actor–partner interdependence mediation model . Journal of Advanced Nursing 2022, 78 (8):2436–2447. Ben Simon E, Walker MP: Sleep loss causes social withdrawal and loneliness . Nature communications 2018, 9 (1):3146. Gable SL, Bedrov A: Social isolation and social support in good times and bad times . Current Opinion in Psychology 2022, 44 :89–93. 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 medicine reviews 2021, 60 :101556. Redeker NS, Conley S, Anderson G, Cline J, Andrews L, Mohsenin V, Jacoby D, Jeon S: Effects of cognitive behavioral therapy for insomnia on sleep, symptoms, stress, and autonomic function among patients with heart failure . Behavioral sleep medicine 2018. Zhang C, Liu Y, Guo X, Liu Y, Shen Y, Ma J: Digital cognitive behavioral therapy for insomnia using a smartphone application in China: a pilot randomized clinical trial . JAMA Network Open 2023, 6 (3):e234866-e234866. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4520177","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":310305756,"identity":"b9b8312c-4478-44f9-9f79-6912e58fa555","order_by":0,"name":"Aiping Zhang","email":"","orcid":"","institution":"The Third Affiliated Hospital of Qiqihar Medical University","correspondingAuthor":false,"prefix":"","firstName":"Aiping","middleName":"","lastName":"Zhang","suffix":""},{"id":310305758,"identity":"c21afed5-53b1-4bc4-be99-1047e3795f92","order_by":1,"name":"Yuxuan Wang","email":"","orcid":"","institution":"Qiqihar Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuxuan","middleName":"","lastName":"Wang","suffix":""},{"id":310305759,"identity":"3dc699e5-006a-41f6-99a6-fabef7b0eeaa","order_by":2,"name":"Baizan Tang","email":"","orcid":"","institution":"The Third Affiliated Hospital of Qiqihar Medical University","correspondingAuthor":false,"prefix":"","firstName":"Baizan","middleName":"","lastName":"Tang","suffix":""},{"id":310305762,"identity":"6e857f9e-f2f0-4a7f-9981-9168f12d6bf8","order_by":3,"name":"Yuwei Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACeWb+BwaJf2zk+NkbiNRi2N7DUPCxIc1YsucAsdacOcPwcWbD4cQNNxKI1ME4I/fgZt4dh40Nbj7eeIOhxiaaoBZ2ibxkY94z6XKSt9OKLRiOpeU2ELYlwcyYh83amO92jpkEY8NhwloYbiSY/+ZhY05suHmGWC1nzhgYzmxzTpxwg4dILYbtbQkGH86AAhnolwRi/CLPzHzAIKECFJWHN974UGNDhMOQgIFEAinKIVpI1TEKRsEoGAUjAwAAzM1EJUplj1UAAAAASUVORK5CYII=","orcid":"","institution":"Jiaxing University","correspondingAuthor":true,"prefix":"","firstName":"Yuwei","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-06-03 07:59:53","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4520177/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4520177/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58747062,"identity":"ab3b6268-aaff-4007-9db9-4d534f9f036c","added_by":"auto","created_at":"2024-06-20 15:12:48","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86065,"visible":true,"origin":"","legend":"\u003cp\u003eThe hypothesized parallel mediating effects of fatigue and social isolation between sleep quality and depressive symptoms.\u003c/p\u003e\n\u003cp\u003eNote: c = total effect, c’ = direct effect\u003c/p\u003e","description":"","filename":"floatimage19.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4520177/v1/fe6c29f76868820656ff4913.jpeg"},{"id":58747061,"identity":"fdd2adb2-9c89-4879-9ced-0f9a7383f9d1","added_by":"auto","created_at":"2024-06-20 15:12:47","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":149532,"visible":true,"origin":"","legend":"\u003cp\u003eThe parallel mediating effects of fatigue and social isolation between sleep quality and depressive symptoms.\u003c/p\u003e\n\u003cp\u003eNote: c = total effect, c’ = direct effect\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4520177/v1/8ac114e000577c31022ca11b.jpeg"},{"id":69770482,"identity":"4d6f3a63-cd32-4963-bb89-852ae2b97ff1","added_by":"auto","created_at":"2024-11-25 06:32:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1544321,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4520177/v1/943225d1-ae55-41e9-bce0-c44b6238af38.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations between sleep quality, fatigue, social isolation, and depressive symptoms in patients with heart failure: a parallel mediation analysis","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eHeart failure (HF) is a condition where symptoms and/or signs are caused by a cardiac abnormality, confirmed by high levels of natriuretic peptides and/or evidence of congestion in the lungs or throughout the body [1]. The symptoms of HF typically include shortness of breath, fatigue, swelling, and heart palpitations, which bring a significant impact on individuals\u0026rsquo; physical and mental health. According to a recent systematic review, the global prevalence of HF in general adults ranged from 1\u0026ndash;3% [1]. By 2030, it is estimated that the number of individuals diagnosed with HF will increase by 46% (or over 8\u0026nbsp;million people) compared to the current number [2].\u003c/p\u003e \u003cp\u003eDepression is a frequently reported comorbidity among patients with HF, which could result in an increased mortality and morbidity [3]. A recent systematic review on depression on patients with HF found a global depression prevalence of 28.1% for moderate to severe symptoms, with a range from 11.3\u0026ndash;50.0% [4]. Emerging evidence underscores poor sleep quality, fatigue, and social isolation as significant correlates of depressive symptoms in patients with HF [3, 5\u0026ndash;7].\u003c/p\u003e \u003cp\u003eDue to HF-related symptoms such as nocturnal dyspnea and palpitation, most of patients with HF reported poor sleep quality, with a proportion ranging from 70\u0026ndash;90% [5, 6]. Poor sleep quality usually has negative impacts on patients\u0026rsquo; health and well-being, in terms of physical, social, and mental aspects [6]. Specifically, patients with HF who have poor sleep quality are more likely to report fatigue, characterized by a persistent and widespread feeling of physical and psychological exhaustion that cannot be alleviated by typical rest or recuperation methods [8]. Meanwhile, poor sleep quality is associated with a risk of social isolation, marked by a lack of meaningful social networks [5, 6]. Importantly, the mental health outcomes of poor sleep quality are profound, as it serves as a predictive factor for both clinical and subclinical depression among patients with HF [9].\u003c/p\u003e \u003cp\u003eAs mentioned earlier, previous studies found that poor sleep quality, fatigue, and social isolation were associated with depressive symptoms in patients with HF [3, 5\u0026ndash;7]. The above empirical findings indicate that there may be mediating effects of fatigue and social isolation between sleep quality and depressive symptoms in patients with HF. However, this has not been explored in this literature. Considering the negative consequences of poor sleep quality and depressive symptoms, understanding the interrelationships among sleep quality, fatigue, social isolation, and depressive symptoms in patients with HF is important. This work could provide insights for understanding the multifaceted nature of depressive symptoms in patients with HF and informing targeted interventions aimed at mitigating their adverse impact on patient outcomes and well-being.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to assess the rate of the depressive symptoms and the mediating effects of fatigue and social isolation between sleep quality and depressive symptoms, a hypothesized model (Fig.\u0026nbsp;1) was informed by the pathophysiological interplay between depression and heart failure by Sbolli et al. and the concept analysis of sleep quality by Nelson et al. [3, 10]. Specifically, Sbolli et al. summarized that social factors (such as social isolation) and physical symptoms (such as poor sleep quality and fatigue) could contribute to depression in patients with HF [3]. According to Nelson et al., poor sleep quality could lead to negative consequences such as fatigue and strained social relationships [10]. In hence, this study hypothesized that sleep quality, fatigue, and social isolation would be associated with depressive symptoms and the association between sleep quality and depressive symptoms would be separately mediated by fatigue and social isolation.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and sample size\u003c/h2\u003e \u003cp\u003e The cross-sectional study included patients with HF at the cardiology department of the University (Blinded for review) between January and March 2024. A convenience sampling method was used to recruit eligible participants. The inclusion criteria of the participants in this study were: Patients with a confirmed diagnosis of chronic HF by physicians and being at least 18 years of age. Patients who had a diagnosis of neurocognitive disorders (e. g, dementia) or a diagnosis of mental disorders were excluded. Bujang, Sa\u0026rsquo;at, and Bakar recommended a sample of 300 subjects or more to generate a close approximation of estimates for multiple linear regression in cross-sectional studies [11]. This study finally collected 300 valid questionnaires which met the minimum sample size requirement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eProcedure\u003c/h2\u003e \u003cp\u003eBefore data collection, nurse coordinators accepted training to screen and recruit eligible patients from the cardiology department. The training regarding consistent guidance on filling the questionnaire for participants was also conducted. After explaining the aims of the study, patients who were willing to participate in this study were asked to fill in the questionnaire in a separate room at the department. A nurse coordinator was responsible for gathering and reviewing the questionnaire, as well as assisting participants with clarifying any unfamiliar items in the survey upon request.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eSocio-demographic and clinical information\u003c/h2\u003e \u003cp\u003eSocio-demographic and clinical characteristics of participants include age, sex, education background, NYHA functional class, and the duration of illness.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eThe Pittsburgh Sleep Quality Index (PSQI)\u003c/h2\u003e \u003cp\u003ePQSI was used to measure sleep quality. It has 19 items and covers seven dimensions including subjective sleep quality, sleep latency, sleep duration, sleep disturbance, sleep efficiency, hypnotic drug use, and daytime dysfunction [12]. According to a 4-Likert rating scale, each dimension is scored from 0 to 3, with the total score ranging from 0 to 21. The higher the score, the worse the sleep quality of patients. The Chinese version of PSQI had an overall Cronbach coefficient of 0.82\u0026ndash;0.83 and a test-retest reliability of 0.77\u0026ndash;0.85 [13]. The Cronbach\u0026rsquo;s alpha coefficient for the total scale was 0.70 in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eThe 13-item Beck Depression Inventory (BDI-13)\u003c/h2\u003e \u003cp\u003eBDI-13 was applied to assess depressive symptoms. It has 13 items to evaluate respondents\u0026rsquo; feelings on depressive symptoms in the last week [14]. Each item has four options, with rating scores ranging from 0 to 3. A total score ranges from 0 to 39, with a higher score indicating more depressive symptoms. The BDI-13 total score was used to categorize respondents into four groups based on their level of depressive symptoms: no depressive symptoms (score of 0\u0026ndash;4), mild symptoms (score of 5\u0026ndash;7), moderate symptoms (score of 8\u0026ndash;15), and severe depressive symptoms (score of 16\u0026ndash;39) [14]. The Chinese version of BDI-13 has been applied to different populations in China, with an overall Cronbach coefficient ranged from 0.85 to 0.88 [15]. The Cronbach\u0026rsquo;s alpha coefficient for the total scale was 0.82 in this study.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eMultidimensional Fatigue Inventory (MFI)\u003c/h2\u003e \u003cp\u003eMFI was used to measure fatigue. It has 20 items and covering five dimensions, including general fatigue, physical fatigue, reduced activity, reduced motivation, and mental fatigue [16]. Based on a 5-Likert rating scale, each item was rated from 1 (strongly agree) to 5 (strongly disagree). A total score ranges from 20 to 100, with a higher total score indicating a higher level of fatigue. The Chinese version of MFI had a good reliability and validity [17]. The Cronbach coefficient of MFI in Chinese general population was 0.92, and the Cronbach coefficients of general fatigue, physical fatigue, mental fatigue, reduced motivation, and reduced activity were 0.89, 0.89, 0.73, 0.83, and 0.80, respectively, indicating good internal consistency [17]. The Cronbach\u0026rsquo;s alpha coefficient for the total scale was 0.79 in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eThe Lubben Social Network Scale (LSNS)\u003c/h2\u003e \u003cp\u003eLSNS was used to measure social isolation. It has six items asking the size of active and intimate networks of family and friends with whom they could talk or call for help [18]. Each item is rated from 0 (none) to 5 (nine or more). The total score of LSNS ranges from 0 to 30, with a lower score indicating a smaller size of social networks and a higher risk of social isolation. The Chinese LSNS has shown satisfactory validity and reliability in the Chinese population, with an overall Cronbach coefficient of 0.83 [19]. The Cronbach\u0026rsquo;s alpha coefficient for the total scale was 0.77 in this study.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEthical consideration\u003c/h2\u003e \u003cp\u003eThis study obtained ethical approval (2024LL-21) from the University (Blinded for review). Participants were informed that taking part in the study would not cause any harm to them, and their personal information would remain confidential. Additionally, they were informed that they could withdraw from the study at any point before data analysis without any negative consequences such as affecting their appointments with physicians or treatment. All participants provided informed consent before questionnaire completion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eAll analyses were conducted via the SPSS 26.0. The distributions of the total scores of PQSI, BDI-13, MFI, and LSNS were checked by according to the absolute values of skewness and kurtosis. Since their absolute values of skewness were less than 2 and that of kurtosis less than 7, there were no severe violations of normality distribution [20]. Characteristics of patients with HF were described as frequencies and percentages for categorical variables and mean (SD) for continuous variables. To assess the rate of depressive symptoms of the participants, four categories of the total score of BDI-13 were used, including no depressive symptoms (0\u0026ndash;4), mild depressive symptoms (5\u0026ndash;7), moderate depressive symptoms (8\u0026ndash;15), and severe depressive symptoms (score of 16\u0026ndash;39) [14]. Pearson correlation analysis was conducted to explore the correlations among the total scores of PQSI, BDI-13, MFI, and LSNS.\u003c/p\u003e \u003cp\u003eA parallel mediation model with fatigue (mediator 1) and social isolation (mediator 2) was conducted to test the relationships among sleep quality, fatigue, social isolation, and depressive symptoms (Fig.\u0026nbsp;1) (PROCESS SPSS macro, model 4).[21] Except the total (c) and direct effects (c\u0026rsquo;) of the sleep quality on the depressive symptoms, two mediating pathways were assessed: (1) sleep quality-fatigue-depressive symptoms (a1b1) and (2) sleep quality-social isolation-depressive symptoms (a2b2). A 10,000-sample bootstrap procedure was used to estimate 95% bias corrected confidence intervals [21]. If the bias corrected confidence intervals do not contain zero, it indicates significant mediating effects at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The effects of socio-demographic and clinical information (age, sex, education background, NYHA functional class, and duration of illness) were controlled in the parallel mediation model.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eDescriptive statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 310 eligible patients were invited to participate in this study, of whom ten refused due to a lack of interest. The response rate was 97% and an analytic sample consisted of 300 participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe socio-demographic and clinical characteristics of participants are shown in Table 1. The mean age of the participants was 64.95 (SD = 12.89) years old. More than half of participants were female (53.7%, n = 161). Most of the participants completed middle or high school (75.7%, n = 227). Most of the participants (88.7%) had NYHA Ⅱ ~ Ⅳ functional class levels. The average duration of the HF in the participants was 8.56 years (SD = 5.45). Based on the scores of the BDI-13, 18.7% of the participants were not depressed (n = 56), 9% reported mild depressive symptoms (n = 27), 35.3% reported moderate depressive symptoms (n = 106), and 37% reported severe depressive symptoms (n = 111).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eDescriptive statistics for sociodemographic and clinical characteristics of participants (n = 300)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"293\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"64.50511945392492%\" valign=\"top\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.49488054607509%\" valign=\"top\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD /n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"64.50511945392492%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.49488054607509%\" valign=\"top\"\u003e\n \u003cp\u003e64.95 \u0026plusmn; 12.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"64.50511945392492%\" valign=\"top\"\u003e\n \u003cp\u003eSex\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.49488054607509%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"64.50511945392492%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.49488054607509%\" valign=\"top\"\u003e\n \u003cp\u003e161 (53.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"64.50511945392492%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.49488054607509%\" valign=\"top\"\u003e\n \u003cp\u003e139 (46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"64.50511945392492%\" valign=\"top\"\u003e\n \u003cp\u003eEducation background\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.49488054607509%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"64.50511945392492%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary school or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.49488054607509%\" valign=\"top\"\u003e\n \u003cp\u003e38 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"64.50511945392492%\" valign=\"top\"\u003e\n \u003cp\u003eMiddle or high school\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.49488054607509%\" valign=\"top\"\u003e\n \u003cp\u003e227 (75.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"64.50511945392492%\" valign=\"top\"\u003e\n \u003cp\u003eCollege or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.49488054607509%\" valign=\"top\"\u003e\n \u003cp\u003e35 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"64.50511945392492%\" valign=\"top\"\u003e\n \u003cp\u003eNYHA functional class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.49488054607509%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"64.50511945392492%\" valign=\"top\"\u003e\n \u003cp\u003eⅠ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.49488054607509%\" valign=\"top\"\u003e\n \u003cp\u003e34 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"64.50511945392492%\" valign=\"top\"\u003e\n \u003cp\u003eⅡ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.49488054607509%\" valign=\"top\"\u003e\n \u003cp\u003e118 (39.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"64.50511945392492%\" valign=\"top\"\u003e\n \u003cp\u003eⅢ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.49488054607509%\" valign=\"top\"\u003e\n \u003cp\u003e134 (44.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"64.50511945392492%\" valign=\"top\"\u003e\n \u003cp\u003eⅣ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.49488054607509%\" valign=\"top\"\u003e\n \u003cp\u003e14 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"64.50511945392492%\" valign=\"top\"\u003e\n \u003cp\u003eDuration of illness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.49488054607509%\" valign=\"top\"\u003e\n \u003cp\u003e8.56 \u0026plusmn; 5.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelations between sleep quality, fatigue, social isolation, and depressive symptoms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics and correlations of the measures used in the study are presented in Table 2. More depressive symptoms were related to poorer sleep quality (r = 0.488, p \u0026lt; 0.001) and a higher level of fatigue (r = 0.315, p \u0026lt; 0.001). Meanwhile, more depressive symptoms were related to a higher risk of social isolation (r = -0.250, p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Descriptive statistics and correlations of depressive symptoms, sleep quality, fatigue, and social isolation (n = 300)\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.083182640144667%\" valign=\"top\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.636528028933093%\" valign=\"top\"\u003e\n \u003cp\u003eBDI-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.636528028933093%\" valign=\"top\"\u003e\n \u003cp\u003ePSQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.636528028933093%\" valign=\"top\"\u003e\n \u003cp\u003eMFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.636528028933093%\" valign=\"top\"\u003e\n \u003cp\u003eLSNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003eBDI-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.083182640144667%\" valign=\"top\"\u003e\n \u003cp\u003e12.40 \u0026plusmn; 7.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.636528028933093%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.636528028933093%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.636528028933093%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.636528028933093%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003ePSQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.083182640144667%\" valign=\"top\"\u003e\n \u003cp\u003e7.43 \u0026plusmn; 3.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.636528028933093%\" valign=\"top\"\u003e\n \u003cp\u003e0.488\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.636528028933093%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.636528028933093%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.636528028933093%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003eMFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.083182640144667%\" valign=\"top\"\u003e\n \u003cp\u003e60.01 \u0026plusmn; 7.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.636528028933093%\" valign=\"top\"\u003e\n \u003cp\u003e0.315\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.636528028933093%\" valign=\"top\"\u003e\n \u003cp\u003e0.307\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.636528028933093%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.636528028933093%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003eLSNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.083182640144667%\" valign=\"top\"\u003e\n \u003cp\u003e11.12 \u0026plusmn; 5.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.636528028933093%\" valign=\"top\"\u003e\n \u003cp\u003e-0.250\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.636528028933093%\" valign=\"top\"\u003e\n \u003cp\u003e-0.141\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.636528028933093%\" valign=\"top\"\u003e\n \u003cp\u003e-0.209\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.636528028933093%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: BDI-13: The 13-item Beck Depression Inventory; PSQI: The Pittsburgh Sleep Quality Index; MFI: Multidimensional Fatigue Inventory;\u0026nbsp;LSNS:\u0026nbsp;The Lubben Social Network Scale. **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParallel mediation analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 2 describes all the paths for the parallel mediation model and the coefficients are displayed in Tables 3 and 4. The total effect (Bc = 1.00, SE = 0.11, t = 9.51, p \u0026lt; 0.001) and the direct effect (Bc\u0026rsquo; = 0.87, SE = 0.11, t = 8.13, p \u0026lt; 0.001) of sleep quality on depressive symptoms were found to be significant. After accounting for all the socio-demographic and clinical variables, the indirect pathways from sleep quality to depressive symptoms though fatigue [path a1b1: point estimate = 0.088, SE = 0.040; Bias corrected convince interval (0.017, 1.173)] or social isolation [path a2b2: point estimate = 0.046, SE = 0.028; Bias corrected convince interval (0.002, 0.108)] turned out to be significant. These results were in line with the hypotheses that sleep quality, fatigue, and social isolation were related to depressive symptoms and fatigue and social isolation parallelly mediated the relationship between sleep quality and depressive symptoms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2\u003c/strong\u003e The parallel mediating effects of fatigue and social isolation between sleep quality and depressive symptoms.\u003c/p\u003e\n\u003cp\u003eNote: c = total effect, c\u0026rsquo; = direct effect\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eResults of the regression analyses testing the parallel mediating effects of fatigue and social isolation in the relationship between sleep quality and depressive symptoms (n = 300)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.702479338842975%\" valign=\"top\"\u003e\n \u003cp\u003ePredictors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64.13223140495867%\" colspan=\"3\"\u003e\n \u003cp\u003eDirect effect (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.165289256198346%\"\u003e\n \u003cp\u003eTotal effect (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.728476821192054%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.85430463576159%\" valign=\"top\"\u003e\n \u003cp\u003eLSNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.85430463576159%\" valign=\"top\"\u003e\n \u003cp\u003eMFI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.364238410596027%\" valign=\"top\"\u003e\n \u003cp\u003eBDI-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.198675496688743%\" valign=\"top\"\u003e\n \u003cp\u003eBDI-13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.728476821192054%\" valign=\"top\"\u003e\n \u003cp\u003eConstant\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.85430463576159%\" valign=\"top\"\u003e\n \u003cp\u003e11.111 (2.697)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.85430463576159%\" valign=\"top\"\u003e\n \u003cp\u003e47.424 (4.080)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.364238410596027%\" valign=\"top\"\u003e\n \u003cp\u003e-3.213 (4.170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.198675496688743%\" valign=\"top\"\u003e\n \u003cp\u003e0.255 (3.407)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.728476821192054%\" valign=\"top\"\u003e\n \u003cp\u003ePSQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.85430463576159%\" valign=\"top\"\u003e\n \u003cp\u003e-0.188 (0.083)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.85430463576159%\" valign=\"top\"\u003e\n \u003cp\u003e0.674 (0.126)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.364238410596027%\" valign=\"top\"\u003e\n \u003cp\u003e0.871 (0.107)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.198675496688743%\" valign=\"top\"\u003e\n \u003cp\u003e1.004 (0.106)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.728476821192054%\" valign=\"top\"\u003e\n \u003cp\u003eLSNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.85430463576159%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.85430463576159%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.364238410596027%\" valign=\"top\"\u003e\n \u003cp\u003e-0.242 (0.072)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.198675496688743%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.728476821192054%\" valign=\"top\"\u003e\n \u003cp\u003eMFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.85430463576159%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.85430463576159%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.364238410596027%\" valign=\"top\"\u003e\n \u003cp\u003e0.130 (0.048)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.198675496688743%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.728476821192054%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.85430463576159%\" valign=\"top\"\u003e\n \u003cp\u003e-0.011 (0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.85430463576159%\" valign=\"top\"\u003e\n \u003cp\u003e-0.001 (0.042)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.364238410596027%\" valign=\"top\"\u003e\n \u003cp\u003e-0.004 (0.034)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.198675496688743%\" valign=\"top\"\u003e\n \u003cp\u003e-0.001 (0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.728476821192054%\" valign=\"top\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.85430463576159%\" valign=\"top\"\u003e\n \u003cp\u003e-0.319 (0.587)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.85430463576159%\" valign=\"top\"\u003e\n \u003cp\u003e-0.206 (0.889)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.364238410596027%\" valign=\"top\"\u003e\n \u003cp\u003e0.919 (0.718)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.198675496688743%\" valign=\"top\"\u003e\n \u003cp\u003e0.969 (0.742)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.728476821192054%\" valign=\"top\"\u003e\n \u003cp\u003eEducation background\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.85430463576159%\" valign=\"top\"\u003e\n \u003cp\u003e1.664 (0.622)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.85430463576159%\" valign=\"top\"\u003e\n \u003cp\u003e1.838 (0.941)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.364238410596027%\" valign=\"top\"\u003e\n \u003cp\u003e0.783 (0.777)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.198675496688743%\" valign=\"top\"\u003e\n \u003cp\u003e0.617 (0.787)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.728476821192054%\" valign=\"top\"\u003e\n \u003cp\u003eNYHA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.85430463576159%\" valign=\"top\"\u003e\n \u003cp\u003e-0.596 (0.486)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.85430463576159%\" valign=\"top\"\u003e\n \u003cp\u003e1.946 (0.735)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.364238410596027%\" valign=\"top\"\u003e\n \u003cp\u003e-0.299 (0.601)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.198675496688743%\" valign=\"top\"\u003e\n \u003cp\u003e0.099 (0.614)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.728476821192054%\" valign=\"top\"\u003e\n \u003cp\u003eDuration of illness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.85430463576159%\" valign=\"top\"\u003e\n \u003cp\u003e0.093 (0.071)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.85430463576159%\" valign=\"top\"\u003e\n \u003cp\u003e-0.047 (0.107)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.364238410596027%\" valign=\"top\"\u003e\n \u003cp\u003e0.239 (0.087)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.198675496688743%\" valign=\"top\"\u003e\n \u003cp\u003e0.211 (0.090)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.728476821192054%\" valign=\"top\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.85430463576159%\" valign=\"top\"\u003e\n \u003cp\u003e0.060\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.85430463576159%\" valign=\"top\"\u003e\n \u003cp\u003e0.127\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.364238410596027%\" valign=\"top\"\u003e\n \u003cp\u003e0.321\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.198675496688743%\" valign=\"top\"\u003e\n \u003cp\u003e0.268\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e\n\u003cp\u003eNote: Unstandardized regression coefficients (beta) with standard errors (SEs) in parentheses are presented. Abbreviation:\u0026nbsp;BDI-13: The 13-item Beck Depression Inventory; PSQI: The Pittsburgh Sleep Quality Index; MFI: Multidimensional Fatigue Inventory; LSNS: The Lubben Social Network Scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003eBootstrapped point estimates with standard errors and 95% bias corrected confidence intervals for all indirect effects between sleep quality and depressive symptoms\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"553\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.177215189873415%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\" valign=\"top\"\u003e\n \u003cp\u003e(Path)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.743218806509946%\" valign=\"top\"\u003e\n \u003cp\u003ePoint estimate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\" valign=\"top\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.209764918625677%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eBootstrapping 95% bias corrected confidence interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.177215189873415%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.743218806509946%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003eLower\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.839059674502712%\" valign=\"top\"\u003e\n \u003cp\u003eUpper\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.177215189873415%\" valign=\"top\"\u003e\n \u003cp\u003eIndirect effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.743218806509946%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.839059674502712%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.177215189873415%\" valign=\"top\"\u003e\n \u003cp\u003eModel 1: Via MFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\" valign=\"top\"\u003e\n \u003cp\u003e(a1b1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.743218806509946%\" valign=\"top\"\u003e\n \u003cp\u003e0.0876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\" valign=\"top\"\u003e\n \u003cp\u003e0.0403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003e0.0171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.839059674502712%\" valign=\"top\"\u003e\n \u003cp\u003e1.1737\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.177215189873415%\" valign=\"top\"\u003e\n \u003cp\u003eModel 2: Via LSNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\" valign=\"top\"\u003e\n \u003cp\u003e(a2b2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.743218806509946%\" valign=\"top\"\u003e\n \u003cp\u003e0.0455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\" valign=\"top\"\u003e\n \u003cp\u003e0.0276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003e0.0023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.839059674502712%\" valign=\"top\"\u003e\n \u003cp\u003e0.1079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.177215189873415%\" valign=\"top\"\u003e\n \u003cp\u003eTotal indirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.743218806509946%\" valign=\"top\"\u003e\n \u003cp\u003e0.1332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\" valign=\"top\"\u003e\n \u003cp\u003e0.0470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003e0.0511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.839059674502712%\" valign=\"top\"\u003e\n \u003cp\u003e0.2355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.177215189873415%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.743218806509946%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.839059674502712%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.177215189873415%\" valign=\"top\"\u003e\n \u003cp\u003ePairwise contrasts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.743218806509946%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.839059674502712%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.177215189873415%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Model 1 versus Model 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.743218806509946%\" valign=\"top\"\u003e\n \u003cp\u003e-0.0421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.934900542495479%\" valign=\"top\"\u003e\n \u003cp\u003e0.0506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003e-0.1414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.839059674502712%\" valign=\"top\"\u003e\n \u003cp\u003e0.0564\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: MFI: Multidimensional Fatigue Inventory; LSNS: The Lubben Social Network Scale; SE: standard error.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study aimed to explore the rate of depressive symptoms in patients with HF in China and mediating roles of fatigue and social isolation in the relationship between sleep quality and depressive symptoms in this population. Results found that 72.3% of the participants had moderate to severe depressive symptoms. The parallel mediation analysis found that sleep quality, fatigue, and social isolation were significantly related to depressive symptoms. Meanwhile, fatigue and social isolation were independent mediators between sleep quality and depressive symptoms. These findings contribute to the existing evidence on the understanding of depressive symptoms in patients with HF in China and the pathways underlying the association of sleep quality with depressive symptoms in this population.\u003c/p\u003e\n\u003cp\u003eThe rate of depressive symptoms was high in our study, with 72.3% of participants with moderate to severe depressive symptoms. This proportion is consistent with the range reported in the meta-analysis on the prevalence of depressive symptoms in patients with HF in China (13%-82%) but higher than the global level (11.3%-51.0%) [4, 22]. The higher rate of depressive symptoms in this study could be attributed to the characteristics of our sample and the methodology employed in assessing depressive symptoms. First, unlike global studies, which may include a more heterogeneous population and utilize different assessment tools, this study focused on a specific region in China. Compared with Western countries, Eastern countries such as China may have fewer rehabilitation programs for patients with HF to prevent or reduce depressive symptoms [4]. This might lead to a higher concentration of depressive symptoms in our sample. Second, a higher NYHA functional class level may be related to a high risk of depressive symptoms [23]. Nearly half of the participants in this study had NYHA 3~4 functional class levels, which is a higher proportion than that in the global meta-analysis (36%), suggesting a potentially greater burden of illness in our sample. The high rate of moderate to severe depressive symptoms in the present study suggests that healthcare providers and clinicians should identify and prevent depressive symptoms in the management of patients with HF.\u003c/p\u003e\n\u003cp\u003eA key finding is that sleep quality was related to depressive symptoms and this relationship was mediated by fatigue. In other words, participants with poor sleep quality may have a higher level of fatigue, which further leads to more depressive symptoms. This may be because poor sleep quality could lead to tiredness and fatigue, which may aggravate the symptoms of fatigue in patients with HF [24]. The long-term fatigue would impair individuals\u0026rsquo; active cognitive processes such as coping and problem-solving [24]. As a result, decreased coping and problem-solving abilities may lead to individuals being more prone to negative feelings and thus a higher risk of depressive symptoms. The mediating role of fatigue was also found in the relationship between sleep quality and quality of life in patients with HF [25], which supports our finding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnother key finding is that social isolation mediated the relationship between sleep quality and depressive symptoms. That is to say, participants who had poor sleep quality were more likely to be socially isolated, which further resulted in more depressive symptoms. The possible reason may be that poor sleep quality could make certain parts of the brain more sensitive to social distances, such as avoiding people, but also impair other areas that promote prosocial behavior, such as understanding someone else\u0026rsquo;s intentions [26]. Therefore, poor sleep quality in patients with HF may increase their susceptibility to social isolation. Further, patients experiencing social isolation may attend more to negative information and interpret it negatively which could make them prone to depressive symptoms [27].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings indicate a high proportion of patients with HF may experience depressive symptoms. While current HF guidelines suggest screening for depression among HF patients, the depressive symptoms in patients with HF are usually unrecognized [3]. Our study suggests that healthcare providers should be aware that the depressive symptoms are consequences of multiple factors. Further, they should provide patients and their families with such knowledge about the interrelationships between sleep quality, fatigue, social isolation, and depressive symptoms, which can raise awareness and encourage proactive self-management strategies. Meanwhile, healthcare providers should monitor and intervene in patients\u0026rsquo; sleep quality, level of fatigue, and social isolation, which may comprehensively prevent or reduce depressive symptoms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study found that sleep quality was a significant correlate of depressive symptoms which may provide information for potential interventions. Existing evidence confirmed the benefits of cognitive behavior therapy for insomnia (CBTi) in improving sleep quality and mental health outcomes in people with insomnia [28]. Researchers from the USA also found that CBTi had a statistically significant effect on improving sleep quality, fatigue, and depression among patients with HF [29]. To the best of our knowledge, although C. Zhang et al. [30] have recently culturally adapted CBTi in patients with insomnia in the Chinese context and found a smartphone-based CBTi improved insomnia severity compared with sleep education, the benefits of CBTi on improving the sleep quality among patients with HF have not been tested in the Chinese context. Future research is needed to test if CBTi could improve sleep quality, fatigue, and depressive symptoms among patients with HF in China.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, the mediating roles of fatigue and social isolation suggest that adopting a multifaceted and integrated interventions on fatigue (e.g., through behavioral activation) and social isolation (e.g., support groups) may provide an indirect pathway to prevent or reduce depressive symptoms in clinical practice. Healthcare professionals may need to collaborate closely with other professionals such as psychologists, social workers, physiotherapists, etc., for comprehensive management for patients with HF. For example, group-delivered CBTi with components such as increasing social interactions may be considered. This may provide patients with HF a safe and comfortable social environment to share their experiences and concerns in the treatment and self-management. Multidisciplinary effort will be important when healthcare professionals design and evaluate the effectiveness of such comprehensive interventions in patients with HF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was designed as a cross-sectional study, which cannot infer the causal relationship among sleep quality, fatigue, social isolation, and depressive symptoms. Longitudinal studies are needed to clarify the causal relationships among these variables. Meanwhile, this study applied a convenient sampling of hospitalized patients with HF from a general hospital. Therefore, it is challenging to ensure the complete generalizability to patients with HF in the broader community or other geographical locations. Future studies may extend the scope of investigation and include a cross-cultural perspective in exploring the interrelationships among these variables among patients with HF.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study explored the rate of depressive symptoms and the mediating roles of social isolation and fatigue in the relationship between sleep quality and depressive symptoms in patients with HF in China. Results showed that 72.3% of participants with moderate to severe depressive symptoms. Sleep quality, social isolation, and fatigue were significant correlates of depressive symptoms in patients with HF, with social isolation and fatigue performing parallel mediations in the relationship between sleep quality and depressive symptoms. Despite the limitations inherent in the cross-sectional design, these results offer insights for clinical practice, suggesting that interventions targeting improvements in sleep quality, social isolation, and fatigue may be beneficial in mitigating or preventing depressive symptoms in patients with HF. \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHeart failure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNYHA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNew York Heart Association\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSQI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Pittsburgh Sleep Quality Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBDI-13\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe 13-item Beck Depression Inventory\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultidimensional Fatigue Inventory\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLSNS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Lubben Social Network Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCBTi\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecognitive behavior therapy for insomnia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research ethics committee of the Third Affiliated Hospital of Qiqihar Medical University approved the study (Approval Number: 2024LL-21).\u0026nbsp;All participants provided informed consent before questionnaire completion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research data is confidential\u0026nbsp;to protect study participant privacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Qiqihar Science and Technology Planning Project. The grant number will be updated soon.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.Z. contributed to the investigation, methodology, formal analysis, funding acquisition, and writing - original draft. Y.W. contributed to the formal analysis and writing - original draft. B.T. contributed to the project administration, supervision, and writing - review \u0026amp; editing. Y.W. contributed to the conceptualization, project administration, supervision, and writing - review \u0026amp; editing\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the staffs of the hospital for their cooperation and all the participants who actively participated in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eSavarese G, Becher PM, Lund LH, Seferovic P, Rosano GM, Coats AJ: \u003cstrong\u003eGlobal burden of heart failure: a comprehensive and updated review of epidemiology\u003c/strong\u003e. \u003cem\u003eCardiovascular research\u003c/em\u003e 2022, \u003cstrong\u003e118\u003c/strong\u003e(17):3272\u0026ndash;3287.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eTsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, Boehme AK, Buxton AE, Carson AP, Commodore-Mensah Y: \u003cstrong\u003eHeart disease and stroke statistics\u0026mdash;2022 update: a report from the American Heart Association\u003c/strong\u003e. \u003cem\u003eCirculation\u003c/em\u003e 2022, \u003cstrong\u003e145\u003c/strong\u003e(8):e153-e639.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSbolli M, Fiuzat M, Cani D, O\u0026apos;Connor CM: \u003cstrong\u003eDepression and heart failure: the lonely comorbidity\u003c/strong\u003e. \u003cem\u003eEuropean journal of heart failure\u003c/em\u003e 2020, \u003cstrong\u003e22\u003c/strong\u003e(11):2007\u0026ndash;2017.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMoradi M, Doostkami M, Behnamfar N, Rafiemanesh H, Behzadmehr R: \u003cstrong\u003eGlobal prevalence of depression among heart failure patients: a systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eCurrent problems in cardiology\u003c/em\u003e 2022, \u003cstrong\u003e47\u003c/strong\u003e(6):100848.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLainsamputty F, Chen H-M: \u003cstrong\u003eThe correlation between fatigue and sleep quality among patients with heart failure\u003c/strong\u003e. \u003cem\u003eNurseLine Journal\u003c/em\u003e 2018, \u003cstrong\u003e3\u003c/strong\u003e(2):100\u0026ndash;114.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eZheng T: \u003cstrong\u003eSleep disturbance in heart failure: A concept analysis\u003c/strong\u003e. In: \u003cem\u003eNursing forum: 2021\u003c/em\u003e: Wiley Online Library; 2021: 710\u0026ndash;716.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSheffler JL, Schmiege SJ, Sussman J, Bekelman DB: \u003cstrong\u003eA longitudinal analysis of the relationships between depression, fatigue, and pain in patients with heart failure\u003c/strong\u003e. \u003cem\u003eAging \u0026amp; mental health\u003c/em\u003e 2021, \u003cstrong\u003e25\u003c/strong\u003e(12):2272\u0026ndash;2278.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003ePavlovic NV, Gilotra NA, Lee CS, Ndumele C, Mammos D, Dennisonhimmelfarb C, AbshireSaylor M: \u003cstrong\u003eFatigue in persons with heart failure: a systematic literature review and meta-synthesis using the biopsychosocial model of health\u003c/strong\u003e. \u003cem\u003eJournal of cardiac failure\u003c/em\u003e 2022, \u003cstrong\u003e28\u003c/strong\u003e(2):283\u0026ndash;315.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eNorra C, Kummer J, Boecker M, Skobel E, Schauerte P, Wirtz M, Gauggel S, Forkmann T: \u003cstrong\u003ePoor sleep quality is associated with depressive symptoms in patients with heart disease\u003c/strong\u003e. \u003cem\u003eInternational journal of behavioral medicine\u003c/em\u003e 2012, \u003cstrong\u003e19\u003c/strong\u003e:526\u0026ndash;534.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eNelson KL, Davis JE, Corbett CF: \u003cstrong\u003eSleep quality: An evolutionary concept analysis\u003c/strong\u003e. In: \u003cem\u003eNursing forum: 2022\u003c/em\u003e: Wiley Online Library; 2022: 144\u0026ndash;151.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBujang MA, Sa\u0026rsquo;at N, Bakar TMITA: \u003cstrong\u003eDetermination of minimum sample size requirement for multiple linear regression and analysis of covariance based on experimental and non-experimental studies\u003c/strong\u003e. \u003cem\u003eEpidemiology, biostatistics, and public health\u003c/em\u003e 2017, \u003cstrong\u003e14\u003c/strong\u003e(3).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBuysse DJ, Reynolds III CF, Monk TH, Berman SR, Kupfer DJ: \u003cstrong\u003eThe Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research\u003c/strong\u003e. \u003cem\u003ePsychiatry research\u003c/em\u003e 1989, \u003cstrong\u003e28\u003c/strong\u003e(2):193\u0026ndash;213.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eTsai P-S, Wang S-Y, Wang M-Y, Su C-T, Yang T-T, Huang C-J, Fang S-C: \u003cstrong\u003ePsychometric evaluation of the Chinese version of the Pittsburgh Sleep Quality Index (CPSQI) in primary insomnia and control subjects\u003c/strong\u003e. \u003cem\u003eQuality of Life Research\u003c/em\u003e 2005, \u003cstrong\u003e14\u003c/strong\u003e:1943\u0026ndash;1952.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBeck AT, Rial WY, Rickels K: \u003cstrong\u003eShort form of depression inventory: cross-validation\u003c/strong\u003e. \u003cem\u003ePsychological reports\u003c/em\u003e 1974, \u003cstrong\u003e34\u003c/strong\u003e(3_suppl):1184\u0026ndash;1186.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eWang C, Wang D: \u003cstrong\u003eValidity and reliability of Chinese version of 28-item Substance Use Risk Profile Scale in Chinese adolescents and young adults\u003c/strong\u003e. \u003cem\u003eCurrent Psychology\u003c/em\u003e 2021, \u003cstrong\u003e40\u003c/strong\u003e:4507\u0026ndash;4515.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSmets E, Garssen B, Bonke Bd, De Haes J: \u003cstrong\u003eThe Multidimensional Fatigue Inventory (MFI) psychometric qualities of an instrument to assess fatigue\u003c/strong\u003e. \u003cem\u003eJournal of psychosomatic research\u003c/em\u003e 1995, \u003cstrong\u003e39\u003c/strong\u003e(3):315\u0026ndash;325.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eChuang L-L, Chuang Y-F, Hsu M-J, Huang Y-Z, Wong AM, Chang Y-J: \u003cstrong\u003eValidity and reliability of the traditional Chinese version of the multidimensional fatigue inventory in general population\u003c/strong\u003e. \u003cem\u003ePLoS One\u003c/em\u003e 2018, \u003cstrong\u003e13\u003c/strong\u003e(5):e0189850.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLubben J, Blozik E, Gillmann G, Iliffe S, von Renteln Kruse W, Beck JC, Stuck AE: \u003cstrong\u003ePerformance of an abbreviated version of the Lubben Social Network Scale among three European community-dwelling older adult populations\u003c/strong\u003e. \u003cem\u003eThe Gerontologist\u003c/em\u003e 2006, \u003cstrong\u003e46\u003c/strong\u003e(4):503\u0026ndash;513.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eChang Q, Sha F, Chan CH, Yip PS: \u003cstrong\u003eValidation of an abbreviated version of the Lubben Social Network Scale (\u0026ldquo;LSNS-6\u0026rdquo;) and its associations with suicidality among older adults in China\u003c/strong\u003e. \u003cem\u003ePloS one\u003c/em\u003e 2018, \u003cstrong\u003e13\u003c/strong\u003e(8):e0201612.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eFinney SJ, DiStefano C: \u003cstrong\u003eNon-normal and categorical data in structural equation modeling\u003c/strong\u003e. \u003cem\u003eStructural equation modeling: A second course\u003c/em\u003e 2006, \u003cstrong\u003e10\u003c/strong\u003e(6):269\u0026ndash;314.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHayes AF: \u003cstrong\u003ePROCESS: A versatile computational tool for observed variable mediation, moderation, and conditional process modeling\u003c/strong\u003e. In.: University of Kansas, KS; 2012.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLin X-x, Gao B-B, Huang J-y: \u003cstrong\u003ePrevalence of depressive symptoms in patients with Heart Failure in China: a meta-analysis of comparative studies and epidemiological surveys\u003c/strong\u003e. \u003cem\u003eJournal of Affective Disorders\u003c/em\u003e 2020, \u003cstrong\u003e274\u003c/strong\u003e:774\u0026ndash;783.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBotto J, Martins S, Moreira E, Cardoso JS, Fernandes L: \u003cstrong\u003eThe magnitude of depression in heart failure patients and its association with NYHA class\u003c/strong\u003e. \u003cem\u003eEuropean Psychiatry\u003c/em\u003e 2021, \u003cstrong\u003e64\u003c/strong\u003e(S1):S342-S342.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eChattu VK, Manzar MD, Kumary S, Burman D, Spence DW, Pandi-Perumal SR: \u003cstrong\u003eThe global problem of insufficient sleep and its serious public health implications\u003c/strong\u003e. In: \u003cem\u003eHealthcare: 2018\u003c/em\u003e: MDPI; 2018: 1.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eZhang J, Chai X, Ye Y, Zhao Q, Fan X: \u003cstrong\u003eAssociation between sleep and quality of life in heart failure patient\u0026ndash;caregiver dyads and mediation of fatigue: An actor\u0026ndash;partner interdependence mediation model\u003c/strong\u003e. \u003cem\u003eJournal of Advanced Nursing\u003c/em\u003e 2022, \u003cstrong\u003e78\u003c/strong\u003e(8):2436\u0026ndash;2447.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBen Simon E, Walker MP: \u003cstrong\u003eSleep loss causes social withdrawal and loneliness\u003c/strong\u003e. \u003cem\u003eNature communications\u003c/em\u003e 2018, \u003cstrong\u003e9\u003c/strong\u003e(1):3146.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGable SL, Bedrov A: \u003cstrong\u003eSocial isolation and social support in good times and bad times\u003c/strong\u003e. \u003cem\u003eCurrent Opinion in Psychology\u003c/em\u003e 2022, \u003cstrong\u003e44\u003c/strong\u003e:89\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eScott AJ, Webb TL, Martyn-St James M, Rowse G, Weich S: \u003cstrong\u003eImproving sleep quality leads to better mental health: A meta-analysis of randomised controlled trials\u003c/strong\u003e. \u003cem\u003eSleep medicine reviews\u003c/em\u003e 2021, \u003cstrong\u003e60\u003c/strong\u003e:101556.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRedeker NS, Conley S, Anderson G, Cline J, Andrews L, Mohsenin V, Jacoby D, Jeon S: \u003cstrong\u003eEffects of cognitive behavioral therapy for insomnia on sleep, symptoms, stress, and autonomic function among patients with heart failure\u003c/strong\u003e. \u003cem\u003eBehavioral sleep medicine\u003c/em\u003e 2018.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eZhang C, Liu Y, Guo X, Liu Y, Shen Y, Ma J: \u003cstrong\u003eDigital cognitive behavioral therapy for insomnia using a smartphone application in China: a pilot randomized clinical trial\u003c/strong\u003e. \u003cem\u003eJAMA Network Open\u003c/em\u003e 2023, \u003cstrong\u003e6\u003c/strong\u003e(3):e234866-e234866.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"sleep quality, fatigue, social isolation, depressive symptoms, mediation","lastPublishedDoi":"10.21203/rs.3.rs-4520177/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4520177/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDepression is a common comorbidity in patients with heart failure (HF) which could lead to increased mortality and morbidity. Meanwhile, a majority of patients with HF suffer from poor sleep quality which has negative impacts of patients\u0026rsquo; physical, social, and mental health, leading to a risk of fatigue, social isolation, and depressive symptoms. However, the interrelationships among the four factors remain unclear in the literature. This study aimed to assess the rate of depressive symptoms and the interrelationships among sleep quality, fatigue, social isolation, and depressive symptoms in patients with HF in China.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was conducted at a general hospital in China. A convenience sample of 300 patients with HF was recruited from January to March 2024. Self-reported instruments were used to measure sleep quality, fatigue, social isolation, and depressive symptoms. Descriptive, Pearson correlation, and parallel mediation analyses were conducted via SPSS 26.0.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eResults showed that 72.3% of the participants had moderate to severe depressive symptoms. Fatigue and social isolation performed parallel mediation effects on the relationship between sleep quality and depressive symptoms.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings highlight the prevention and management of depressive symptoms in patients with HF. Future studies are needed to design and evaluate the effectiveness of interventions that incorporate multiple components on improving sleep quality, social isolation, and fatigue in patients with HF.\u003c/p\u003e","manuscriptTitle":"Associations between sleep quality, fatigue, social isolation, and depressive symptoms in patients with heart failure: a parallel mediation analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-20 15:12:43","doi":"10.21203/rs.3.rs-4520177/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e0a19ffa-916c-4890-9702-9c81336ab89c","owner":[],"postedDate":"June 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-25T06:24:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-20 15:12:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4520177","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4520177","identity":"rs-4520177","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}