The Relationship Between Multimorbidity and Depressive symptoms among Chinese Older Adults—The Mediating Role of Activities of Daily Living and Sleep Quality

The Relationship Between Multimorbidity and Depressive symptoms among Chinese Older Adults—The Mediating Role of Activities of Daily Living and Sleep Quality | 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 The Relationship Between Multimorbidity and Depressive symptoms among Chinese Older Adults—The Mediating Role of Activities of Daily Living and Sleep Quality Chenglu Li, Chunxiao Long, Fangjing Wu, Haiyang Wu, Yuqian Li, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6443491/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 Objective: This study aimed to explore the underlying mechanisms between multimorbidity and depressive symptoms among the older adults, and further investigate the multiple mediating effects of activities of daily living (ADL) and sleep quality. Methods: A total of 7290 older adults from the China Health and Retirement Longitudinal Study database completed questionnaires on multimorbidity, depressive symptoms, activities of daily living and sleep quality. We used a series of mediation models proposed by Hayes to study the relationship between variables. Results: Multimorbidity was positively correlated with ADL (r = 0.118, P < 0.001), and depressive symptoms levels (r = 0.114, P < 0.001), respectively. Multimorbidity was negatively correlated with sleep quality (r = -0.108, P <0.001). Multimorbidity was significantly associated with depressive symptoms ( P <0.001), with a direct effect of 1.563 and indirect effects through ADL (0.175) and sleep quality (0.676). A serial mediation effect was also observed (0.048). Conclusion: These findings offer crucial insights into the complexities of psychological well-being in aging populations and provide actionable strategies for enhancing mental health care tailored to this vulnerable demographic. The empirical evidence presented in this study provides support for improving the mental health status of elders population. Multimorbidity Depressive symptoms Activities of daily living Sleep quality Multiple mediating effects Figures Figure 1 Figure 2 Figure 3 Background Chronic diseases represent a significant global public health challenge, encompassing a wide range of conditions such as cardiovascular diseases, chronic respiratory diseases, cancer, obesity, diabetes, kidney disease, and liver disease[ 1 ]. Compared to single chronic conditions, the prevalence of individuals with multimorbidity is estimated to be around one-third of the global adult population[ 2 ]. Multimorbidity is commonly defined as the coexistence of two or more chronic diseases in an individual[ 3 ]. According to projections by the American Medical Association, it is anticipated that by 2030, there will be approximately 37 million new cases of multimorbidity worldwide [ 4 ]. Research indicates that the prevalence of multimorbidity sharply increases with age, with around 70% of individuals expected to have two or more chronic diseases by the age of 65 [ 5 ]. In China, multimorbidity is a major contributor to health burden and health outcome disparities, with a prevalence of nearly 50% among older adults [ 6 ]. This condition further leads to functional impairments in elders, increased treatment burden, decreased health-related quality of life, heightened healthcare utilization, and an elevated risk of mortality [ 7 ]. Therefore, identifying the presence and patterns of multimorbidity in individuals or populations is of significant importance for clinicians, researchers, and health policy makers [ 8 ]. Depressive symptoms is a chronic mental disorder that has gradually emerged as a significant public health challenge due to its high prevalence and the risks it poses for disability and suicide [ 9 ]. According to the World Health Organization, depressive symptoms accounts for 10% of the global burden of non-fatal diseases[ 10 ]. In China, there are over 54 million individuals with depressive disorders, representing approximately 17% of the global burden of mental disorders. The lifetime prevalence of depressive symptoms among adults in China is as high as 6.8% [ 11 ]. With the aging population, depressive symptoms is prevalent among middle-aged and older adults in China [ 12 ], with a significant proportion of individuals aged 45 and above experiencing depressive symptoms [ 13 ]. However, there is a substantial gap between mental health needs and the availability of mental health professionals in China, with fewer than 8.75 mental health workers per 100,000 people, highlighting a significant disparity between mental health demand and supply [ 14 ]. Numerous studies have shown that activities of daily living (ADL) and sleep quality are crucial factors influencing the health of older adults[ 15 – 17 ]. Not only is preventing multiple chronic conditions and depressive symptoms vital for enhancing theove the physical and mental health of these individuals, but it also significantly improves their quality of life. Multimorbidity, or the co-existence of multiple chronic conditions, frequently has a detrimental effect on the ADL and sleep quality of older adults, increasing the risk of dysfunction by 16% for each additional disease[ 18 ]. Furthermore, both ADL and sleep quality are widely regarded as essential factors that can significantly influence depressive symptoms levels in older adults[ 19 ]. This study aimed to investigate the association between multimorbidity and depressive symptoms among older adults, specifically examining the multiple mediating effects of activities of daily living and sleep quality. To explore the this relationship and its influencing factors, so as to provide specific evidence and feasible intervention measures for improving the physical and mental health of older adults. This endeavor not only contributes to a deeper understanding of the health challenges faced by this population but also lays a scientific foundation for developing targeted health promotion strategies. Literature Review Currently, researchers are increasingly focusing on the relationship between multimorbidity and its comorbidities, with growing attention on comorbidities such as depressive symptoms [20]. Numerous studies have shown a higher prevalence of depressive symptoms among individuals with multimorbidity [21]. Chronic diseases are characterized by long disease courses, high treatment costs, complex complications, and poor prognoses. Multimorbidity, with its added complexity, imposes greater economic, medical, and psychological stress on patients compared to single chronic diseases, making individuals more susceptible to developing negative emotions and mental disorders like depressive symptoms [22] . Furthermore, some scholars have suggested that the increasing number of chronic conditions in multimorbidity is directly proportional to the rising detection rates of depressive symptoms. In other words, individuals with two or more chronic diseases in multimorbidity have a higher prevalence of depressive symptoms [23]. This conclusion implies that multimorbidity may be a contributing factor to the development of depressive symptoms. Researchers have extensively studied this phenomenon, highlighting the widespread occurrence of comorbid depressive symptoms among individuals with multimorbidity in multiple countries globally [24]. It is evident that the multimorbidity of multimorbidity and depressive symptoms is a prevalent public health issue on a global scale. While it is established that older adults with multimorbidity are more prone to psychiatric issues, including depressive symptoms, the underlying mechanism remains unclear[25]. Studies have identified key factors influencing depressive symptoms, including education level, economic status, sleep quality, multimorbidity, and functional disorders. Conversely, factors such as family interaction, intergenerational financial support, the intensity of social activities, and life satisfaction have been shown to offer protection against depressive symptoms. Thus, this study aimed to specifically delve into the potential mechanism of depressive symptoms in older adults with multiple disorders through the lens of a mediating effect model. The limitation of ADL caused by multimorbidity may be a risk factor for depressive symptoms. A study showed that multimorbidity can affect the daily function of the elderly to varying degrees, including motor function, perceptual function, and speech function[26]. Previous studies have shown that limitations in physical functioning increase psychological stress in older adults, leading to negative emotions and significantly increasing the likelihood of depressive symptoms[27–29]. Thus, ADL may play a mediating role in the occurrence of multimorbidity and depressive symptoms. However, further exploration of the relationship between these three factors is currently limited. Sleep quality is also an important factor affecting multimorbidity and depressive symptoms. A study found that good sleep quality has a positive effect on improving the physical health of individuals with multimornis[30]. In addition, good sleep quality can enhance the self-efficacy of patients and improve their mental health. In other words, good sleep quality promotes the physical and mental health of patients with comorbidities. Xu further proposed that sleep quality plays a multiple mediating role in the relationship between chronic diseases and depressive symptoms by enhancing the psychological resilience of middle-aged and elderly people hospitalized for chronic diseases [31]. In addition, urthermore, an Indian study has demonstrated a notable correlation between multimorbidity and depressive symptoms, suggesting that this association may be mediated by functional and behavioral health factors[32]. Functional health encompasses aspects such as self-rated health, poor sleep quality, difficulties in Activities of Daily Living, Instrumental Activities of Daily Living disorders, pain, and behavioral health factors like physical inactivity. This research robustly supports the role of ADL and sleep quality as factors influencing multimorbidity and depressive symptoms in India. This study aimed to further validate the applicability of these findings in China, another developing country context. There is also an association between sleep quality and ADL, with studies showing that women with poor sleep quality walk slower and have poorer grip strength[33]. In summary, while research has explored the individual relationships between depressive symptoms, multimorbidity, sleep quality, and ADL, there remains a scarcity of robust evidence consolidating the associations among these four factors. Consequently, further investigations with broader sample sizes are imperative to strengthen our understanding of these interconnections. Starting from the inadequacy of current research and the pressing public health needs in China, this study explores the relationship between multimorbidity and depressive symptoms among Chinese older adults. In particular, it investigates the mediating roles of ADL and sleep quality in this association. The conceptual basis of this study is grounded in the Disablement Process Model, which posits that chronic illnesses influence psychological well-being primarily through progressive physical limitations. In addition, the Biopsychosocial Model suggests that behavioral (e.g., sleep) and physiological mechanisms (e.g., ADL limitations) serve as critical intermediaries linking physical disease and mental health outcomes. These two theoretical models jointly support the rationale for examining a serial mediation pathway from multimorbidity to depressive symptoms via ADL and sleep quality. The findings are expected to contribute to addressing the current research gap in understanding how multimorbidity may lead to depressive symptoms, while also offering a scientific basis for clinical prevention and public health intervention strategies tailored to aging populations in China.Therefore, we propose the following four hypotheses (see Fig 1.): Hypothesis 1: There is a correlation between multimorbidity and depressive symptoms(total effect, c). Hypothesis 2: Multimorbidity has a specific indirect effect on depressive symptoms through ADL (indirect effect a 1 b 1 ). Hypothesis 3: Multimorbidity indirectly affects depressive symptoms through sleep quality (indirect effect a 2 b 2 ). Hypothesis 4: Multimorbidity has a series of multiple mediating effects on depressive symptoms, first through ADL, and then through sleep quality (serial indirect effect a 1 d 12 b 2 ). Methods Study population This study utilized the China Health and Retirement Longitudinal Study (CHARLS), a large-scale longitudinal study that involves face-to-face interviews with a nationally representative sample of Chinese residents aged 45 and above[34]. The study collected high-quality data using structured questionnaires and a multi-stage, stratified, and probability-proportional-to-size sampling method [35]. In this research, we utilized the latest CHARLS data from 2020, which included 19,395 respondents. To investigate the relationship between multimorbidity and depressive symptoms, we included samples based on the following criteria: (1) no missing values for the respondent selection variables(multimorbidity, ADL, sleep quality, depression symstoms), (2) no missing covariates (age, gender, location, education level, health insurance, marital status, alcohol consumption, and smoking status). Ultimately, our final analytical sample consisted of 7,290 respondents aged 60 and above. Variables Multimorbidity Multimorbidity is typically defined as the coexistence of two or more chronic diseases in the same individual [36]. Fifteen self-reported chronic physical health conditions were used to measure multimorbidity, including hypertension, dyslipidemia, diabetes, cancer, chronic lung disease, liver disease, heart disease, stroke, kidney disease, digestive system diseases, mental illness, memory-related diseases, Parkinson's disease, arthritis, and asthma. The number of non-communicable diseases for each participant was summed to identify individuals with multimorbidity in the sample (37]. This is a categorical variable with options "Yes" (having two or more chronic diseases) or "No" (having none or only one chronic disease). Depressive symptoms The assessment of depressive symptoms in CHARLS was conducted using the 10-item short form of the Center for Epidemiologic Studies Depression Scale (CESD-10) [38]. Frequency responses were categorized into four groups: rarely or none of the time (<1 day, scored as 1); some or a little of the time (1-2 days, scored as 2); occasionally or a moderate amount of the time (3-4 days, scored as 3); most or all of the time (5-7 days, scored as 4). The sleep quality item of the CES-D-10 was not included in this study. The total score ranges from 9 to 36, with higher scores indicating more severe depressive symptoms [39]. The Cronbach's α for the CES-D-9 was 0.706. Activities of daily living (ADL) In the CHARLS survey, respondents were asked about the difficulty level of six activities of daily living using the Katz Index of Independence in ADL[40]. These activities include dressing, bathing, eating, transferring (moving in and out of bed), toileting, and controlling urination and bowel movements (continence). All respondents were required to choose one of four options for each activity: “no difficulty at all”; “some difficulty but still able to do it”; “difficulty and needing help”; “unable to do it”. In this study, we assign a value of 0 to the options “No difficulty at all” and “some difficulty but still able to do it”, and assign a value of 1 to the options “difficulty and needing help” and “unable to do it”. In ADL disability data, missing values are handled by direct deletion. A higher total score indicates more severe impairment in individual activities of daily living[41]. The Cronbach's α coefficient for the ADL scale is 0.782. Sleep quality One item from the Center for Epidemiologic Studies Depression Scale (CES-D) is used to assess subjective sleep quality: "My sleep quality is not good." Responses are categorized into four frequency options: rarely or none of the time (<1 day); some or a little of the time (1-2 days); occasionally or about half of the time (3-4 days); most or all of the time (5-7 days). Demographic characteristics Based on the combing of previous studies, we set other factors affecting multimorbidity and depressive symptoms as covariates. In this study, the following variables were included as covariates: age, gender, marital status (married/cohabiting, single, and other), residence (rural, urban, semi-urban, and other), education level (illiterate, primary school, middle school and above), health insurance (UEBMI, Urban Employee Basic Medical Insurance; URBMI, Urban Resident Basic Medical Insurance; NCMS, New Cooperative Medical Scheme for Rural Residents; and other insurances), former smokers (yes, no), and former drinkers (yes, no). Statistical analysis We conducted all statistical analyses using R version 4.2.0. Firstly, this study employed Kruskal-Wallis tests to analyze differences in depressive symptoms across different sociodemographic characteristics. Chi-square tests were used to compare characteristic differences among different subgroups of depressive symptoms[43]. Secondly, Pearson correlation was conducted to examine bivariate relations between multimorbidity, depressive symptoms, ADL, and sleep quality. This study utilized the PROCESS model developed by Hayes to explore the mediating role of ADL and sleep quality [44]. The serial mediation model felt multiple regulations that give back coefficients for each variable, verified immediation effect by comparing the past coefficients represented by regression coefficients. In this study, the independent variable, dependent variable, and two mediating variables are multimorbidity (X), depressive symptoms (Y), ADL (M1), and sleep quality (M2). In the analysis of mediation effects, the total effect c is decomposed into the direct effect c’ and the indirect effect transmitted through the mediator variable, which can be specifically expressed as .To assess effect sizes and obtain 95% confidence intervals, we performed the bootstrap method with 5000 iterations to evaluate direct, indirect, and total effects [45]. A path was considered significant at the 0.05 level if the 95% confidence interval did not span zero [46].. Results Descriptive statistics In 2020, a total of 7290 participants aged 60 and above were included in our final sample (67.04% aged between 60 and 69; 27.94% between 70 and 79; 5.02% over 80), with 3700 males (50.75%) and 3590 females (49.25%). Among the participants, 64.68% resided in rural areas, 70.4% had lower education levels (primary school and below), 80.48% were married or partnered, and 55.51% were covered by the New Cooperative Medical Scheme. As shown in Table 1, we utilized Kruskal-Wallis tests to investigate the sociodemographic characteristics of participants and lifestyle factors related to depressive symptoms levels. There were significant differences in the depressive symptoms among gender, marital status, current place of residence, education level, type of health insurance, smoking status, and alcohol consumption. ( P < 0.001). Table 1 . Socio-demographic characteristics and lifestyle factors of participants associated to depressive symptoms of older adults in China, 2020 (N=7290) Variables N(%) Depressive symptoms χ2 P -value (M±SD) Gender 230.103 <0.001 Male 3700 (50.75) 16.055 ± 5.483 Female 3590 (49.25) 18.084 ± 6.193 Age 69.228 0.079 60–69 4887 (67.04) 16.908 ± 5.923 70–79 2037 (27.94) 17.496 ± 6.014 80- 366 (5.02) 16.544 ± 5.413 Marital status 139.852 <0.001 Married/partnered 5867 (80.48) 16.693 ± 5.778 Single 1423 (19.52) 18.543 ± 6.310 Residence 377.591 <0.001 City/Town Center Area 1816 (24.91) 15.290 ± 5.422 Urban-Rural/Township Integration Zone 754 (10.34) 16.394 ± 5.674 Village 4715 (64.68) 17.840 ± 6.001 Special Area 5 (0.07) 16.000 ± 4.848 Education 477.671 <0.001 Illiterate 1913 (26.24) 18.674 ± 6.071 Primary school 3219 (44.16) 17.303 ± 5.885 Secondary school and above 2158 (29.60) 15.247 ± 5.368 Insurance 542.013 <0.001 UEBMI 1578 (21.65) 14.642 ± 5.118 URBMI 299 (4.10) 15.652 ± 5.389 NRCMS 4047 (55.51) 17.826 ± 5.964 others 1366 (18.74) 17.862 ± 6.017 Ex-smoker 113.753 <0.001 no 3927 (53.87) 17.636 ± 6.162 yes 3363 (46.13) 16.375 ± 5.573 Ex-drinker 164.367 <0.001 no 2529 (34.69) 15.978 ± 5.477 yes 4761 (65.31) 17.626 ± 6.081 Correlation analysis among various variables The correlation matrix for the key study variables is presented in Table 2. Multimorbidity was positively correlated with ADL (r = 0.118, P < 0.001), and depression levels (r = 0.114, P < 0.001), respectively. Multimorbidity was negatively correlated with sleep quality (r = -0.108, P < 0.001). ADL was negatively correlated with sleep quality (r = -0.108, P < 0.001), and was significantly positively correlated with depressive symptoms levels (r = 0.145, P < 0.001). Table 2. Descriptive statistics and correlations among the key variables (n = 7290). M±SD Multimorbidity ADL Sleep quality Depressive symptoms Multimorbidity 0.149±0.357 1 ADL 2.901±1.211 0.118*** 1 Sleep quality 1.059±0.236 -0.108*** -0.108*** 1 Depressive symptoms 17.05±5.930 0.114*** 0.145*** -0.340*** 1 Note: ADL: Activities of Daily Living; * P <0 .05, ** P < 0.01, *** P < 0.001. Multiple mediation analyses This study employed multiple mediation analysis to explore the mediating roles of ADL and sleep quality in the relationship between multimorbidity and depressive symptoms. Control variables included gender, age, marital status, current residence, education level, health insurance, smoking status, and drinking status. The analysis (Table 3) revealed that multimorbidity was positively associated with ADL (B=0.125, P < 0.001) and depressive symptoms (B=2.462, P < 0.001), and negatively associated with sleep quality (B=-0.353, P < 0.001). ADL were negatively related to sleep quality (d 12 =-0.200, P < 0.001) and positively related to depressive symptoms (B=1.396, P < 0.001). Sleep quality was negatively associated with depressive symptoms (B=-1.917, P < 0.001). Table 3. Regression coefficients in the serial mediation analysis (N=7290). Depressive symptoms ADL Sleep quality Depressive symptoms (Intercept) 17.054 *** 0.109 *** 2.923 *** 17.054 *** Gender 1.303 *** 0.027 -0.459 *** 0.375 * Residency 0.757 *** 0.003 -0.036 0.684 *** Education -0.723 *** -0.001 0.015 -0.693 *** Smoking 0.292 0.032 -0.055 0.131 Drinking -0.748 *** -0.061 *** 0.034 -0.575 *** Marriage 0.332 *** 0.001 -0.037 *** 0.261 *** Social insurance 0.508 *** 0.005 -0.022 0.458 *** Age -0.009 0.007 *** 0.004 -0.013 Multimorbidity 2.462 *** 0.125 *** -0.353 *** 1.563 *** ADL -0.200 *** 1.396 *** Sleep quality -1.917 *** R 2 0.118 0.018 0.063 0.287 Adj. R 2 0.117 0.016 0.062 0.286 Note: ADL: Activities of Daily Living; * P <0 .05, ** P < 0.01, *** P < 0.001. The mediation path model is depicted in Fig 2. Path coefficients demonstrate that all relationships in the model exhibit significant positive and negative associations. By incorporating ADL and sleep quality as two mediating variables into the model, the direct effect of multimorbidity on depressive symptoms remains significant. Therefore, some of the relationships between multimorbidity and depressive symptoms are mediated through ADL and sleep quality. Bootstrap test of mediators Table 4 presents the total, direct, and indirect effects, with the 95% CIs of all path coefficients excluding 0. As shown in Table 4, multimorbidity is associated with depressive symptoms through three mediating pathways: (1) the independent mediating effect of activities of daily living (effect = 0.175; 95%CI = [0.111, 0.246]), (2) the independent mediating effect of sleep quality (effect = 0.676; 95%CI = [0.527, 0.825]), (3) and the serial mediating effect of activities of daily living and sleep quality (effect = 0.048; 95%CI = [0.029, 0.069]). The effect of multimorbidity on depressive symptoms remains significant in the model even after incorporating these two mediating variables. Table 4. Direct and indirect effects of multimorbidity (X) on depressive symptoms (Y) symptoms through ADL (M1) and sleep quality (M2) (n=7290) 。 Path Estimate Bootstrap SE Beta Bootstrap LLCI* Bootstrap ULCI* Total effect X→Y 2.462 0.191 0.148*** 2.078 2.826 Direct effect X→Y 1.563 0.173 0.094*** 1.219 1.901 Total indirect effect X→Y 0.899 0.086 0.054*** 0.731 1.069 Specific indirect effects X→M1→Y 0.175 0.034 0.011*** 0.111 0.246 X→M2→Y 0.676 0.076 0.041*** 0.527 0.825 X→M1→M2→Y 0.048 0.010 0.003*** 0.029 0.069 CI - confidence interval, LLCI - lower limit of confidence interval, SE - standard error, ULCI - upper limit of confidence interval*95% confidence interval.*** P <0.001. Discussion In this nationwide study, we delved into the relationship between multimorbidity and depressive symptoms. The findings were consistent with our initial hypotheses, revealing significant associations among multimorbidity, ADL, sleep quality, and depressive symptoms. Our research indicates that ADL and sleep quality each serve as mediating variables in the pathways linking multimorbidity to depressive symptoms. Furthermore, employing a chain mediation model, we elucidated the serial mediation effects of ADL and sleep quality on the relationship between multimorbidity and depressive symptoms. Specifically, multimorbidity often effect ADL, subsequently influencing sleep quality, and ultimately precipitating depressive symptoms. This underscores the importance of considering both ADL and sleep quality, along with their interplay, when examining the relationship between multimorbidity and depressive symptoms. The results indicate a positive correlation between multimorbidity and depressive symptoms, thus validating hypothesis 1 . Specifically, it was found that the greater the number of chronic illnesses a patient has, the higher their risk of depressive symptoms. This aligns with previous research findings; for instance, a multinational study by Haiyang Yu et al. demonstrated that individuals with multimorbidity often face elevated risks of depressive symptoms, whether in China or the United States[ 47 ]. Some studies also found that individuals with five or more chronic illnesses are more prone to depressive symptoms compared to those without any chronic physical conditions[ 48 , 49 ]. This could be attributed to the fact that individuals with multimorbidity often experience higher levels of disability, pain, cognitive impairments, and lower quality of life, which may precipitate depressive symptoms[ 50 ]. Therefore, this study further investigates the underlying mechanisms through which multimorbidity effect depressive symptoms. Firstly, there is the indirect mediating effect of ADL in the effect of multimorbidity on depressive symptoms. Model testing indicates that multimorbidity can indirectly increase the risk of depressive symptoms by increasing ADL disorder thus confirming hypothesis 2 . Multimorbidity affect ADL, a conclusion supported by various studies which demonstrate that the greater the number of chronic illnesses among middle-aged and older adults, the more limited their ADL becomes[ 51 – 54 ]. Research has shown that patients with chronic diseases such as diabetes have significantly weaker skeletal muscle strength compared to healthy individuals, thereby restricting the ADL of middle-aged and older adults[ 55 ]. Additionally, studies have indicated that multimorbidity influence ADL by triggering depressive symptoms[ 56 ]. Furthermore, both our study and previous research highlight the significant effect of ADL on depressive symptoms; limitations in ADL may increase the risk of depressive symptoms among middle-aged and older adults and their spouses [ 57 , 58 ].Considering the collective findings, it can be inferred that physical impairments resulting from various chronic illnesses affect ADL, restricting their social participation and interaction, making it challenging to maintain normal social relationships, thus effecting their psychological well-being. This insight suggests that when attempting to mitigate the negative effect of multimorbidity on depressive symptoms, attention should be paid to patients' daily life abilities. Strengthening multimorbidity rehabilitation treatment and enhancing social support can also alleviate this negative effect[ 59 ]. What’s more, our study indicates that multimorbidity also influence depressive symptoms by affecting sleep quality, thus validating hypothesis 3 . Preliminary evidence suggests that suffering from various chronic illnesses can effect patients' sleep quality, with individuals affected by multimorbidity facing a significantly increased risk of declining sleep quality in the future[ 60 ]. Additionally, multiple studies have shown a close relationship between sleep quality and depressive symptoms [ 61 , 62 ], with research indicating a strong correlation between disrupted melatonin secretion and depressive moods[ 63 ]. This mediating effect may stem from physical pain caused by multimorbidity, leading to sleep problems such as insomnia and vivid dreams. Poorer sleep quality can increase psychological stress in patients, potentially triggering or exacerbating depressive symptoms. Therefore, when discussing the mechanisms through which multimorbidity affect depressive symptoms in middle-aged and older adults, it is crucial to consider the role of sleep quality in this process. Our research reveals that the relationship between multimorbidity and depressive symptoms is mediated by the sequential intermediary roles of ADL and sleep quality. Specifically, multimorbidity can be influenced separately by ADL and sleep quality, and can also lead to depressive symptoms through the influence of ADL on sleep quality. Therefore, hypothesis 4 is verifiable. One study uncovered that the association between multimorbidity and depressive symptoms is mediated by the frequency of bodily pain and sleep duration[ 64 ]. However, this study did not link multimorbidity with ADL. Moreover, there is substantial evidence indicating a correlation between ADL and sleep quality[ 65 , 66 ]. Yet, to our knowledge, there is currently few research on how these two factors mediate the sequential intermediary effects of multimorbidity on depressive symptoms. It provides insights for chronic disease management organizations and personnel, suggesting that by improving the ADL and sleep quality of individuals with multimorbidity, their risk of depressive symptoms can be effectively alleviated. In summary, the findings of this study clearly delineate the mediating pathways between multimorbidity and depressive symptoms. Multimorbidity can increase the risk of depressive symptoms through the mediating effect of ADL and sleep quality, respectively, and it can also increase the risk of depressive symptoms through the chain effect of ADL and sleep quality. Based on this, we suggest that the relevant government departments should strengthen the screening and management of multimorbidity, and at the same time, older adults should pay attention to sleep quality and keep exercise to improve their daily activities, so as to reduce the risk of depressive symptoms. To some extent, the results of this study may be generalizable beyond the Chinese population, as the multimorbidity discussed in the study are not exclusive to this demographic. However, the universality of these findings is constrained by cultural differences, social norms, and economic conditions. It is recommended that future research endeavors undertake multinational and cross-cultural studies to enhance the generalizability of the findings. Strengths and limitations This study reveals the intrinsic mechanism of multimorbidity and depressive symptoms from a new perspective, and reflects the mediating effect and chain mediating effect of ADL and sleep quality. It can provide new ideas for relevant decision-making departments to improve the mental health of older adults, and can also effectively contributes to advancing the research landscape in this domain. This study has some limitations in interpreting the results. Firstly, cross-sectional data make it challenging to establish causal relationships between variables. To clarify these findings, longitudinal studies are warranted in the future. Additionally, existing research has consistently demonstrated a close association between multimorbidity and depressive symptoms. However, their indirect effects are too intricate to be solely explained by mediators such as ADL and sleep quality. To gain a more comprehensive and in-depth understanding of the underlying mechanisms, future research should incorporate a broader range of variables into the model. Conclusions Multimorbidity might impair mental health in Chinese older adults through ADL and sleep quality. These findings offer crucial insights into the complexities of psychological well-being in aging populations and provide actionable strategies for enhancing mental health care tailored to this vulnerable demographic. The empirical evidence presented in this study provides substantial support for improving the mental health status of older adults. It is important to focus on the diversity of the middle-aged and older adults. In the field of public health, it is also important to develop effective and sustainable interventions to alleviate the burden of multimorbidity and reduce the level of depressive symptoms among older adults. Abbreviations CHARLS China Health and Retirement Longitudinal Study ADL Activities of daily living CESD-10 the 10-item short form of the Center for Epidemiologic Studies Depression Scale CES-D the Center for Epidemiologic Studies Depression Scale UEBMI Urban Employee Basic Medical Insurance URBMI Urban Resident Basic Medical Insurance NCMS New Cooperative Medical Scheme for Rural Residents Declarations Acknowledgements We are deeply grateful to the China Health and Retirement Longitudinal Study (CHARLS) team for providing access to the invaluable data that underpins our research. Special recognition goes to the participants of the CHARLS for their willingness to share their experiences and data, which are crucial for our study on multimorbidity and depressive symptoms among older adults. Authors’ contribution Chenglu Li was responsible for compiling and presenting the results section, ensuring accurate presentation of the data. Chunxiao Long provided a profound interpretation of the research results and placed them within the context of existing literature. Fangjing Wu and Haiyang Wu applied appropriate statistical methods to interpret the data, ensuring robust and reliable results. Yuqian Li was responsible for researching and writing the background section, conducting a comprehensive review of relevant literature. Lei Shi, Yangdong Fan, and Meifang Zhou provided valuable feedback on this draft and made revisions to the draft. All authors have read and approved the final manuscript. Funding This work was supported by National Natural Science Foundation of China (72104098), Guangdong Basic and Applied Basic Research Foundation (2023A1515010902), Open Fund of Key Research Baseof Philosophy and Social Science of Higher Education in Guangdong Province-Local Government Development Research Institute of Shantou University(07423002-07), Key Laboratory of Philosophy and Social Sciences of Colleges and Universities in Guangdong Province (2023WSYS005), High level Talent Funding Project of Guangzhou Medical University (06-445-1157). The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Ethical approval All rounds of CHARLS investigations have been approved by the Biomedical Ethics Committee of Peking University. 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Yang Z, Sun F, Zhao L, Hu T, Lin X, Guo Y. Self-efficacy and well-being in the association between caregiver burden and sleep quality among caregivers of elderly patients having multiple chronic conditions in rural China: a serial multiple mediation analysis. BMC Nurs. 2023 Nov 13;22(1):424. Xu J, Zhang L, Sun H, Gao Z, Wang M, Hu M, et al. Psychological resilience and quality of life among middle-aged and older adults hospitalized with chronic diseases: multiple mediating effects through sleep quality and depression. BMC Geriatr. 2023 Nov 17;23(1):752. Ansari S, Anand A, Hossain B. Multimorbidity and depression among older adults in India: Mediating role of functional and behavioural health. PLOS ONE. 2022 Jun 7;17(6):e0269646. Goldman SE, Stone KL, Ancoli-Israel S, Blackwell T, Ewing SK, Boudreau R, et al. Poor Sleep is Associated with Poorer Physical Performance and Greater Functional Limitations in Older Women. Sleep. 2007 Oct;30(10):1317–24. CHARLS [Internet]. [cited 2024 Apr 9]. 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J Affect Disord. 2023 Mar 15;325:640–6. Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW. STUDIES OF ILLNESS IN THE AGED. THE INDEX OF ADL: A STANDARDIZED MEASURE OF BIOLOGICAL AND PSYCHOSOCIAL FUNCTION. JAMA. 1963 Sep 21;185:914–9. Liu Q, Pan H, Wu Y. Migration Status, Internet Use, and Social Participation among Middle-Aged and Older Adults in China: Consequences for Depression. Int J Environ Res Public Health. 2020 Aug;17(16):6007. Li SH, Lloyd AR, Graham BM. Subjective sleep quality and characteristics across the menstrual cycle in women with and without Generalized Anxiety Disorder. J Psychosom Res. 2021 Sep;148:110570. Yang Q, Jia J. Association of intergenerational relationships with cognitive impairment among Chinese adults 80 years of age or older: prospective cohort study. BMC Geriatr. 2022 Nov 7;22(1):838. Hayes A. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. In 2013 [cited 2024 Apr 2]. Available from: https://www.semanticscholar.org/paper/Introduction-to-Mediation%2C-Moderation%2C-and-Process-Hayes/0316f2bc77021bcf6499374213ffec0a95926dd9 Xian G, Chai Y, Gong Y, He W, Ma C, Zhang X, et al. The relationship between healthy lifestyles and cognitive function in Chinese older adults: the mediating effect of depressive symptoms. BMC Geriatr. 2024 Mar 28;24(1):299. Sobel ME. Asymptotic Confidence Intervals for Indirect Effects in Structural Equation Models. Sociol Methodol. 1982;13:290–312. Yu H, Zhang Y, Hu M, Xiang B, Wang S, Wang Q. Inter- and intrapopulation differences in the association between physical multimorbidity and depressive symptoms. J Affect Disord. 2024 Jun 1;354:434–42. Read JR, Sharpe L, Modini M, Dear BF. Multimorbidity and depression: A systematic review and meta-analysis. J Affect Disord. 2017 Oct 15;221:36–46. Cheng C, Du Y, Bai J. Physical multimorbidity and psychological distress among Chinese older adults: Findings from Chinese Longitudinal Healthy Longevity Survey. Asian J Psychiatry. 2022 Apr 1;70:103022. Sinaei F, Zendehdel K, Adili M, Ardestani A, Montazeri A, Mohagheghi MA. Association Between Breast Reconstruction Surgery and Quality of Life in Iranian Breast Cancer Patients. Acta Med Iran. 2017 Jan;55(1):35–41. Wang LY, Feng M, Hu XY, Tang ML. Association of daily health behavior and activity of daily living in older adults in China. Sci Rep. 2023 Nov 9;13:19484. W Q, V K, L D, Rs S, R M, P R, et al. Coordinated Care Experiences Among Middle-Aged and Older Adults with Multiple Chronic Conditions: Characteristics, Correlates, and Consequences for Health and Healthcare Utilization. The Gerontologist [Internet]. 2024 Feb 28 [cited 2024 Apr 8]; Available from: https://pubmed.ncbi.nlm.nih.gov/38416875/ Li H, Wang A, Gao Q, Wang X, Luo Y, Yang X, et al. Prevalence of somatic-mental multimorbidity and its prospective association with disability among older adults in China. Aging. 2020 Apr 25;12(8):7218–31. Chen Y, Ji H, Shen Y, Liu D. Chronic disease and multimorbidity in the Chinese older adults’ population and their impact on daily living ability: a cross-sectional study of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Arch Public Health. 2024 Feb 1;82:17. Park SW, Goodpaster BH, Strotmeyer ES, Kuller LH, Broudeau R, Kammerer C, et al. Accelerated Loss of Skeletal Muscle Strength in Older Adults With Type 2 Diabetes: The Health, Aging, and Body Composition Study. Diabetes Care. 2007 Jun 1;30(6):1507–12. Peng S, Wang S, Feng XL. Multimorbidity, depressive symptoms and disability in activities of daily living amongst middle-aged and older Chinese: Evidence from the China Health and Retirement Longitudinal Study. J Affect Disord. 2021 Dec 1;295:703–10. Xiao S, Shi L, Xue Y, Zheng X, Zhang J, Chang J, et al. The relationship between activities of daily living and psychological distress among Chinese older adults: A serial multiple mediation model. J Affect Disord. 2022 Mar 1;300:462–8. Liu H, Ma Y, Lin L, Sun Z, Li Z, Jiang X. Association between activities of daily living and depressive symptoms among older adults in China: evidence from the CHARLS. Front Public Health. 2023 Nov 16;11:1249208. Tian G, Li R, Cui Y, Zhou T, Shi Y, Yang W, et al. Association between disability, social support and depressive symptoms in Chinese older adults: A national study. Front Public Health [Internet]. 2022 Aug 19 [cited 2024 Apr 9];10. Available from: https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.980465/full Wang X, Wang R, Zhang D. Bidirectional associations between sleep quality/duration and multimorbidity in middle-aged and older people Chinese adults: a longitudinal study. BMC Public Health. 2024 Mar 5;24(1):708. Cantürk RGY, Cömert IT, Uğraş S, Akcan G, Salkım Dİ, Tutkun E, et al. Relationship Between REM Sleep Behavior Disorder and Depression and Anxiety and Night Eating Syndrome. 2020 [cited 2024 Apr 10]; Available from: http://acikerisim.fsm.edu.tr/xmlui/handle/11352/3770 Zhang C, Xiao S, Lin H, Shi L, Zheng X, Xue Y, et al. The association between sleep quality and psychological distress among older Chinese adults: a moderated mediation model. BMC Geriatr. 2022 Jan 10;22(1):35. Pandi-Perumal SR, Moscovitch A, Srinivasan V, Spence DW, Cardinali DP, Brown GM. Bidirectional communication between sleep and circadian rhythms and its implications for depression: lessons from agomelatine. Prog Neurobiol. 2009 Aug;88(4):264–71. Ye X, Wang X. Associations of multimorbidity with body pain, sleep duration, and depression among middle-aged and older adults in China. Health Qual Life Outcomes. 2024 Feb 27;22(1):23. Yang S, Wang S, Liu G, Li R, Li X, Chen S, et al. The relationship between sleep status and activity of daily living: based on China Hainan centenarians cohort study. BMC Geriatr. 2023 Dec 4;23(1):796. Wang Z, Ni X, Gao D, Fang S, Huang X, Jiang M, et al. The relationship between sleep duration and activities of daily living (ADL) disability in the Chinese oldest-old: A cross-sectional study. PeerJ. 2023 Feb 14;11:e14856. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6443491","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":476404356,"identity":"8b1144a2-b599-4420-8e76-8898b5a5bc47","order_by":0,"name":"Chenglu Li","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chenglu","middleName":"","lastName":"Li","suffix":""},{"id":476404358,"identity":"04ee07f0-ca12-4c39-9cb3-06fc8bf6c059","order_by":1,"name":"Chunxiao Long","email":"","orcid":"","institution":"Guangzhou Medical 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07:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6443491/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6443491/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85478452,"identity":"da8f8759-9383-48fc-a785-68378ae5880f","added_by":"auto","created_at":"2025-06-26 10:30:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":30910,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHypothetical series of mediating models linking multimorbidity with depressive symptoms levels through daily functional activities and sleep quality as mediators.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote: a\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e, a\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e, b\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e, b\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e, c, c′, and\u0026nbsp;d\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e represent path coefficients.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6443491/v1/09a70c952e167e426b8a01b8.png"},{"id":85478453,"identity":"0411ac07-b40d-47e3-8d49-c52f5ae7cfe6","added_by":"auto","created_at":"2025-06-26 10:30:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":110411,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant selection.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6443491/v1/876b70d2862c77a98c6dabd7.png"},{"id":85478455,"identity":"8b3edaad-cf8b-4e64-81bb-56925d697690","added_by":"auto","created_at":"2025-06-26 10:30:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":39079,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA multiple mediation model of the association between activities of multimorbidity and depressive symptoms through ADL and sleep quality. Path coefficients are shown.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote: *\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e\u0026lt; 0.05, **\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eP \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e\u0026lt; 0.01, ***\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eP \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e\u0026lt; 0.001.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6443491/v1/f67999c6649aef546fab8c7f.png"},{"id":102395175,"identity":"0d147088-37d3-481b-b075-29f8dde8db60","added_by":"auto","created_at":"2026-02-11 09:27:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1312013,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6443491/v1/86631d0d-4b5f-45c1-9e3a-08f9283f2a8a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Relationship Between Multimorbidity and Depressive symptoms among Chinese Older Adults—The Mediating Role of Activities of Daily Living and Sleep Quality","fulltext":[{"header":"Background","content":"\u003cp\u003eChronic diseases represent a significant global public health challenge, encompassing a wide range of conditions such as cardiovascular diseases, chronic respiratory diseases, cancer, obesity, diabetes, kidney disease, and liver disease[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Compared to single chronic conditions, the prevalence of individuals with multimorbidity is estimated to be around one-third of the global adult population[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Multimorbidity is commonly defined as the coexistence of two or more chronic diseases in an individual[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. According to projections by the American Medical Association, it is anticipated that by 2030, there will be approximately 37\u0026nbsp;million new cases of multimorbidity worldwide [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResearch indicates that the prevalence of multimorbidity sharply increases with age, with around 70% of individuals expected to have two or more chronic diseases by the age of 65 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In China, multimorbidity is a major contributor to health burden and health outcome disparities, with a prevalence of nearly 50% among older adults [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This condition further leads to functional impairments in elders, increased treatment burden, decreased health-related quality of life, heightened healthcare utilization, and an elevated risk of mortality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, identifying the presence and patterns of multimorbidity in individuals or populations is of significant importance for clinicians, researchers, and health policy makers [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDepressive symptoms is a chronic mental disorder that has gradually emerged as a significant public health challenge due to its high prevalence and the risks it poses for disability and suicide [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. According to the World Health Organization, depressive symptoms accounts for 10% of the global burden of non-fatal diseases[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In China, there are over 54\u0026nbsp;million individuals with depressive disorders, representing approximately 17% of the global burden of mental disorders. The lifetime prevalence of depressive symptoms among adults in China is as high as 6.8% [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. With the aging population, depressive symptoms is prevalent among middle-aged and older adults in China [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], with a significant proportion of individuals aged 45 and above experiencing depressive symptoms [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, there is a substantial gap between mental health needs and the availability of mental health professionals in China, with fewer than 8.75 mental health workers per 100,000 people, highlighting a significant disparity between mental health demand and supply [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNumerous studies have shown that activities of daily living (ADL) and sleep quality are crucial factors influencing the health of older adults[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Not only is preventing multiple chronic conditions and depressive symptoms vital for enhancing theove the physical and mental health of these individuals, but it also significantly improves their quality of life. Multimorbidity, or the co-existence of multiple chronic conditions, frequently has a detrimental effect on the ADL and sleep quality of older adults, increasing the risk of dysfunction by 16% for each additional disease[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Furthermore, both ADL and sleep quality are widely regarded as essential factors that can significantly influence depressive symptoms levels in older adults[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aimed to investigate the association between multimorbidity and depressive symptoms among older adults, specifically examining the multiple mediating effects of activities of daily living and sleep quality. To explore the this relationship and its influencing factors, so as to provide specific evidence and feasible intervention measures for improving the physical and mental health of older adults. This endeavor not only contributes to a deeper understanding of the health challenges faced by this population but also lays a scientific foundation for developing targeted health promotion strategies.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eCurrently, researchers are increasingly focusing on the relationship between multimorbidity and its comorbidities, with growing attention on comorbidities such as depressive symptoms [20]. Numerous studies have shown a higher prevalence of depressive symptoms among individuals with multimorbidity [21]. Chronic diseases are characterized by long disease courses, high treatment costs, complex complications, and poor prognoses. Multimorbidity, with its added complexity, imposes greater economic, medical, and psychological stress on patients compared to single chronic diseases, making individuals more susceptible to developing negative emotions and mental disorders like depressive symptoms [22] .\u003c/p\u003e\n\u003cp\u003eFurthermore, some scholars have suggested that the increasing number of chronic conditions in multimorbidity is directly proportional to the rising detection rates of depressive symptoms. In other words, individuals with two or more chronic diseases in multimorbidity have a higher prevalence of depressive symptoms [23]. This conclusion implies that multimorbidity may be a contributing factor to the development of depressive symptoms. Researchers have extensively studied this phenomenon, highlighting the widespread occurrence of comorbid depressive symptoms among individuals with multimorbidity in multiple countries globally [24]. It is evident that the multimorbidity of multimorbidity and depressive symptoms is a prevalent public health issue on a global scale.\u003c/p\u003e\n\u003cp\u003eWhile it is established that older adults with multimorbidity are more prone to psychiatric issues, including depressive symptoms, the underlying mechanism remains unclear[25]. Studies have identified key factors influencing depressive symptoms, including education level, economic status, sleep quality, multimorbidity, and functional disorders. Conversely, factors such as family interaction, intergenerational financial support, the intensity of social activities, and life satisfaction have been shown to offer protection against depressive symptoms. Thus, this study aimed to specifically delve into the potential mechanism of depressive symptoms in older adults with multiple disorders through the lens of a mediating effect model.\u003c/p\u003e\n\u003cp\u003eThe limitation of ADL caused by multimorbidity may be a risk factor for depressive symptoms. A study showed that multimorbidity can affect the daily function of the elderly to varying degrees, including motor function, perceptual function, and speech function[26]. Previous studies have shown that limitations in physical functioning increase psychological stress in older adults, leading to negative emotions and significantly increasing the likelihood of depressive symptoms[27–29]. Thus, ADL may play a mediating role in the occurrence of multimorbidity and depressive symptoms. However, further exploration of the relationship between these three factors is currently limited.\u003c/p\u003e\n\u003cp\u003eSleep quality is also an important factor affecting multimorbidity and depressive symptoms. A study found that good sleep quality has a positive effect on improving the physical health of individuals with multimornis[30]. In addition, good sleep quality can enhance the self-efficacy of patients and improve their mental health. In other words, good sleep quality promotes the physical and mental health of patients with comorbidities. Xu further proposed that sleep quality plays a multiple mediating role in the relationship between chronic diseases and depressive symptoms by enhancing the psychological resilience of middle-aged and elderly people hospitalized for chronic diseases [31].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, urthermore, an Indian study has demonstrated a notable correlation between multimorbidity and depressive symptoms, suggesting that this association may be mediated by functional and behavioral health factors[32]. Functional health encompasses aspects such as self-rated health, poor sleep quality, difficulties in Activities of Daily Living, Instrumental Activities of Daily Living disorders, pain, and behavioral health factors like physical inactivity. This research robustly supports the role of ADL and sleep quality as factors influencing multimorbidity and depressive symptoms in India. This study aimed to further validate the applicability of these findings in China, another developing country context.\u003c/p\u003e\n\u003cp\u003eThere is also an association between sleep quality and ADL, with studies showing that women with poor sleep quality walk slower and have poorer grip strength[33]. In summary, while research has explored the individual relationships between depressive symptoms, multimorbidity, sleep quality, and ADL, there remains a scarcity of robust evidence consolidating the associations among these four factors. Consequently, further investigations with broader sample sizes are imperative to strengthen our understanding of these interconnections.\u003c/p\u003e\n\u003cp\u003eStarting from the inadequacy of current research and the pressing public health needs in China, this study explores the relationship between multimorbidity and depressive symptoms among Chinese older adults. In particular, it investigates the mediating roles of ADL and sleep quality in this association. The conceptual basis of this study is grounded in the Disablement Process Model, which posits that chronic illnesses influence psychological well-being primarily through progressive physical limitations. In addition, the Biopsychosocial Model suggests that behavioral (e.g., sleep) and physiological mechanisms (e.g., ADL limitations) serve as critical intermediaries linking physical disease and mental health outcomes. These two theoretical models jointly support the rationale for examining a serial mediation pathway from multimorbidity to depressive symptoms via ADL and sleep quality.\u003c/p\u003e\n\u003cp\u003eThe findings are expected to contribute to addressing the current research gap in understanding how multimorbidity may lead to depressive symptoms, while also offering a scientific basis for clinical prevention and public health intervention strategies tailored to aging populations in China.Therefore, we propose the following four hypotheses (see Fig 1.):\u003c/p\u003e\n\u003cp\u003eHypothesis 1: There is a correlation between multimorbidity and depressive symptoms(total effect, c).\u003c/p\u003e\n\u003cp\u003eHypothesis 2: Multimorbidity has a specific indirect effect on depressive symptoms through ADL (indirect effect a\u003csub\u003e1\u003c/sub\u003eb\u003csub\u003e1\u003c/sub\u003e).\u003c/p\u003e\n\u003cp\u003eHypothesis 3: Multimorbidity indirectly affects depressive symptoms through sleep quality (indirect effect a\u003csub\u003e2\u003c/sub\u003eb\u003csub\u003e2\u003c/sub\u003e).\u003c/p\u003e\n\u003cp\u003eHypothesis 4: Multimorbidity has a series of multiple mediating effects on depressive symptoms, first through ADL, and then through sleep quality (serial indirect effect a\u003csub\u003e1\u003c/sub\u003ed\u003csub\u003e12\u003c/sub\u003eb\u003csub\u003e2\u003c/sub\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized the China Health and Retirement Longitudinal Study (CHARLS), a large-scale longitudinal study that involves face-to-face interviews with a nationally representative sample of Chinese residents aged 45 and above[34]. The study collected high-quality data using structured questionnaires and a multi-stage, stratified, and probability-proportional-to-size sampling method [35]. In this research, we utilized the latest CHARLS data from 2020, which included 19,395 respondents. To investigate the relationship between multimorbidity and depressive symptoms, we included samples based on the following criteria: (1) no missing values for the respondent selection variables(multimorbidity, ADL, sleep quality, depression symstoms), (2) no missing covariates (age, gender, location, education level, health insurance, marital status, alcohol consumption, and smoking status). Ultimately, our final analytical sample consisted of 7,290 respondents aged 60 and above.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultimorbidity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultimorbidity is typically defined as the coexistence of two or more chronic diseases in the same individual [36]. Fifteen self-reported chronic physical health conditions were used to measure multimorbidity, including hypertension, dyslipidemia, diabetes, cancer, chronic lung disease, liver disease, heart disease, stroke, kidney disease, digestive system diseases, mental illness, memory-related diseases, Parkinson\u0026apos;s disease, arthritis, and asthma. The number of non-communicable diseases for each participant was summed to identify individuals with multimorbidity in the sample (37]. This is a categorical variable with options \u0026quot;Yes\u0026quot; (having two or more chronic diseases) or \u0026quot;No\u0026quot; (having none or only one chronic disease).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepressive symptoms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe assessment of depressive symptoms in CHARLS was conducted using the 10-item short form of the Center for Epidemiologic Studies Depression Scale (CESD-10) [38]. Frequency responses were categorized into four groups: rarely or none of the time (\u0026lt;1 day, scored as 1); some or a little of the time (1-2 days, scored as 2); occasionally or a moderate amount of the time (3-4 days, scored as 3); most or all of the time (5-7 days, scored as 4). The sleep quality item of the CES-D-10 was not included in this study. The total score ranges from 9 to 36, with higher scores indicating more severe depressive symptoms [39]. The Cronbach\u0026apos;s \u0026alpha; for the CES-D-9 was 0.706.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eActivities of daily living (ADL)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the CHARLS survey, respondents were asked about the difficulty level of six activities of daily living using the Katz Index of Independence in ADL[40]. These activities include dressing, bathing, eating, transferring (moving in and out of bed), toileting, and controlling urination and bowel movements (continence). All respondents were required to choose one of four options for each activity: \u0026ldquo;no difficulty at all\u0026rdquo;; \u0026ldquo;some difficulty but still able to do it\u0026rdquo;; \u0026ldquo;difficulty and needing help\u0026rdquo;; \u0026ldquo;unable to do it\u0026rdquo;. In this study, we assign a value of 0 to the options \u0026ldquo;No difficulty at all\u0026rdquo; and \u0026ldquo;some difficulty but still able to do it\u0026rdquo;, and assign a value of 1 to the options \u0026ldquo;difficulty and needing help\u0026rdquo; and \u0026ldquo;unable to do it\u0026rdquo;. In ADL disability data, missing values are handled by direct deletion. A higher total score indicates more severe impairment in individual activities of daily living[41]. The Cronbach\u0026apos;s \u0026alpha; coefficient for the ADL scale is 0.782.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSleep quality\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne item from the Center for Epidemiologic Studies Depression Scale (CES-D) is used to assess subjective sleep quality: \u0026quot;My sleep quality is not good.\u0026quot; Responses are categorized into four frequency options: rarely or none of the time (\u0026lt;1 day); some or a little of the time (1-2 days); occasionally or about half of the time (3-4 days); most or all of the time (5-7 days).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the combing of previous studies, we set other factors affecting multimorbidity and depressive symptoms as covariates. In this study, the following variables were included as covariates: age, gender, marital status (married/cohabiting, single, and other), residence (rural, urban, semi-urban, and other), education level (illiterate, primary school, middle school and above), health insurance (UEBMI, Urban Employee Basic Medical Insurance; URBMI, Urban Resident Basic Medical Insurance; NCMS, New Cooperative Medical Scheme for Rural Residents; and other insurances), former smokers (yes, no), and former drinkers (yes, no).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted all statistical analyses using R version 4.2.0. Firstly, this study employed Kruskal-Wallis tests to analyze differences in depressive symptoms across different sociodemographic characteristics. Chi-square tests were used to compare characteristic differences among different subgroups of depressive symptoms[43]. Secondly, Pearson correlation was conducted to examine bivariate relations between multimorbidity, depressive symptoms, ADL, and sleep quality. This study utilized the PROCESS model developed by Hayes to explore the mediating role of ADL and sleep quality [44]. The serial mediation model felt multiple regulations that give back coefficients for each variable, verified immediation effect by comparing the past coefficients represented by regression coefficients. In this study, the independent variable, dependent variable, and two mediating variables are multimorbidity (X), depressive symptoms (Y), ADL (M1), and sleep quality (M2). In the analysis of mediation effects, the total effect c is decomposed into the direct effect c\u0026rsquo; and the indirect effect transmitted through the mediator variable, which can be specifically expressed as \u003cimg src=\"data:image/png;base64,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\"\u003e.To assess effect sizes and obtain 95% confidence intervals, we performed the bootstrap method with 5000 iterations to evaluate direct, indirect, and total effects [45]. A path was considered significant at the 0.05 level if the 95% confidence interval did not span zero [46]..\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDescriptive statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn 2020, a total of 7290 participants aged 60 and above were included in our final sample (67.04% aged between 60 and 69; 27.94% between 70 and 79; 5.02% over 80), with 3700 males (50.75%) and 3590 females (49.25%). Among the participants, 64.68% resided in rural areas, 70.4% had lower education levels (primary school and below), 80.48% were married or partnered, and 55.51% were covered by the New Cooperative Medical Scheme. As shown in Table 1, we utilized Kruskal-Wallis tests to investigate the sociodemographic characteristics of participants and lifestyle factors related to depressive symptoms levels. There were significant differences in the depressive symptoms among gender, marital status, current place of residence, education level, type of health insurance, smoking status, and alcohol consumption. (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSocio-demographic characteristics\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eand lifestyle factors of participants associated to depressive symptoms\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eof older\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eadults\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ein China, 2020\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(N=7290)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eN(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDepressive symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026chi;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e(M\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eGender\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e230.103\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e3700 (50.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e16.055 \u0026plusmn; 5.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e3590 (49.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e18.084 \u0026plusmn; 6.193\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e69.228\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.079\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e60\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e4887 (67.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e16.908 \u0026plusmn; 5.923\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e70\u0026ndash;79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e2037 (27.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e17.496 \u0026plusmn; 6.014\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e80-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e366 (5.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e16.544 \u0026plusmn; 5.413\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e139.852\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eMarried/partnered\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e5867 (80.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e16.693 \u0026plusmn; 5.778\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1423 (19.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e18.543 \u0026plusmn; 6.310\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e377.591\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eCity/Town Center Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1816 (24.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e15.290 \u0026plusmn; 5.422\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eUrban-Rural/Township Integration Zone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e754 (10.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e16.394 \u0026plusmn; 5.674\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eVillage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e4715 (64.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e17.840 \u0026plusmn; 6.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eSpecial Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e5 (0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e16.000 \u0026plusmn; 4.848\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e477.671\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eIlliterate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1913 (26.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e18.674 \u0026plusmn; 6.071\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003ePrimary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e3219 (44.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e17.303 \u0026plusmn; 5.885\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eSecondary school and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2158 (29.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e15.247 \u0026plusmn; 5.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eInsurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e542.013\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eUEBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1578 (21.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e14.642 \u0026plusmn; 5.118\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eURBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e299 (4.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e15.652 \u0026plusmn; 5.389 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eNRCMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e4047 (55.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e17.826 \u0026plusmn; 5.964\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eothers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1366 (18.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e17.862 \u0026plusmn; 6.017\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eEx-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e113.753\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e3927 (53.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e17.636 \u0026plusmn; 6.162 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e3363 (46.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e16.375 \u0026plusmn; 5.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eEx-drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e164.367\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2529 (34.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e15.978 \u0026plusmn; 5.477\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e4761 (65.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e17.626 \u0026plusmn; 6.081\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation analysis among various\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003evariables\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe correlation matrix for the key study variables is presented in Table 2. Multimorbidity was positively correlated with ADL (r = 0.118, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001), and depression levels (r = 0.114, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), respectively. Multimorbidity was negatively correlated with sleep quality (r = -0.108, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001). ADL was negatively correlated with sleep quality (r = -0.108, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), and was significantly positively correlated with depressive symptoms levels (r = 0.145, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Descriptive statistics and correlations among the key variables (n = 7290).\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eM\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eMultimorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eADL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eSleep quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eDepressive symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eMultimorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.149\u0026plusmn;0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eADL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2.901\u0026plusmn;1.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.118***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eSleep quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.059\u0026plusmn;0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-0.108***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.108***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eDepressive symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e17.05\u0026plusmn;5.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.114***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.145***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-0.340***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\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\u003eNote: ADL: Activities of Daily Living; *\u003cem\u003e\u0026nbsp;P\u003c/em\u003e \u0026lt;0 .05, **\u003cem\u003e\u0026nbsp;P\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultiple mediation analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed multiple mediation analysis to explore the mediating roles of ADL and sleep quality in the relationship between multimorbidity and depressive symptoms. Control variables included gender, age, marital status, current residence, education level, health insurance, smoking status, and drinking status. The analysis (Table 3) revealed that multimorbidity was positively associated with ADL (B=0.125, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001) and depressive symptoms\u0026nbsp;(B=2.462, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001), and negatively associated with sleep quality (B=-0.353,\u003cem\u003e\u0026nbsp;P\u003c/em\u003e\u0026lt; 0.001). ADL were negatively related to sleep quality (d\u003csub\u003e12\u003c/sub\u003e=-0.200, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001) and positively related to depressive symptoms\u0026nbsp;(B=1.396, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001). Sleep quality was negatively associated with depressive symptoms\u0026nbsp;(B=-1.917, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Regression coefficients in the serial mediation analysis (N=7290).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003eDepressive\u0026nbsp;symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eADL\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSleep quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eDepressive\u0026nbsp;symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e17.054 *** \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.109 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.923 ***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e17.054 *** \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eGender\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.303 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.459 *** \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.375 *\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eResidency\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.757 ***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.003 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.684 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e-0.723 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e-0.693 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eSmoking\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.292 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.032\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.131 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eDrinking\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e-0.748 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-0.061 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.034 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e-0.575 ***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eMarriage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.332 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.037 *** \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.261 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eSocial insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.508 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.022\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.458 ***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.007 ***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eMultimorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e2.462 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.125 ***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.353 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.563 ***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eADL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.200 ***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.396 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eSleep quality\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e-1.917 ***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.117\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.062\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: ADL: Activities of Daily Living; * \u003cem\u003eP\u003c/em\u003e \u0026lt;0 .05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003eThe mediation path model is depicted in Fig 2. Path coefficients demonstrate that all relationships in the model exhibit significant positive and negative associations. By incorporating ADL and sleep quality as two mediating variables into the model, the direct effect of multimorbidity on depressive symptoms remains significant. Therefore, some of the relationships between multimorbidity and depressive symptoms are mediated through ADL and sleep quality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBootstrap test of mediators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 4 presents the total, direct, and indirect effects, with the 95% CIs of all path coefficients excluding 0. As shown in Table 4, multimorbidity is associated with depressive symptoms through three mediating pathways: (1) the independent mediating effect of activities of daily living (effect = 0.175; 95%CI = [0.111, 0.246]), (2) the independent mediating effect of sleep quality (effect = 0.676; 95%CI = [0.527, 0.825]), (3) and the serial mediating effect of activities of daily living and sleep quality (effect = 0.048; 95%CI = [0.029, 0.069]). The effect of multimorbidity on depressive symptoms remains significant in the model even after incorporating these two mediating variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Direct and indirect effects of multimorbidity (X) on depressive symptoms (Y) symptoms through ADL (M1) and sleep quality (M2) (n=7290)\u003c/strong\u003e\u003cstrong\u003e。\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003ePath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eBootstrap SE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eBootstrap LLCI*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBootstrap ULCI*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTotal effect X\u0026rarr;Y\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e2.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.148***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e2.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eDirect effect X\u0026rarr;Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.094***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.901\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTotal indirect effect X\u0026rarr;Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.054***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eSpecific indirect effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eX\u0026rarr;M1\u0026rarr;Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.011***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eX\u0026rarr;M2\u0026rarr;Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.041***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eX\u0026rarr;M1\u0026rarr;M2\u0026rarr;Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.003***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCI - confidence interval, LLCI - lower limit of confidence interval, SE - standard error, ULCI - upper limit of confidence interval*95% confidence interval.***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this nationwide study, we delved into the relationship between multimorbidity and depressive symptoms. The findings were consistent with our initial hypotheses, revealing significant associations among multimorbidity, ADL, sleep quality, and depressive symptoms. Our research indicates that ADL and sleep quality each serve as mediating variables in the pathways linking multimorbidity to depressive symptoms. Furthermore, employing a chain mediation model, we elucidated the serial mediation effects of ADL and sleep quality on the relationship between multimorbidity and depressive symptoms. Specifically, multimorbidity often effect ADL, subsequently influencing sleep quality, and ultimately precipitating depressive symptoms. This underscores the importance of considering both ADL and sleep quality, along with their interplay, when examining the relationship between multimorbidity and depressive symptoms.\u003c/p\u003e \u003cp\u003eThe results indicate a positive correlation between multimorbidity and depressive symptoms, thus validating hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Specifically, it was found that the greater the number of chronic illnesses a patient has, the higher their risk of depressive symptoms. This aligns with previous research findings; for instance, a multinational study by Haiyang Yu et al. demonstrated that individuals with multimorbidity often face elevated risks of depressive symptoms, whether in China or the United States[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Some studies also found that individuals with five or more chronic illnesses are more prone to depressive symptoms compared to those without any chronic physical conditions[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. This could be attributed to the fact that individuals with multimorbidity often experience higher levels of disability, pain, cognitive impairments, and lower quality of life, which may precipitate depressive symptoms[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Therefore, this study further investigates the underlying mechanisms through which multimorbidity effect depressive symptoms.\u003c/p\u003e \u003cp\u003eFirstly, there is the indirect mediating effect of ADL in the effect of multimorbidity on depressive symptoms. Model testing indicates that multimorbidity can indirectly increase the risk of depressive symptoms by increasing ADL disorder thus confirming hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Multimorbidity affect ADL, a conclusion supported by various studies which demonstrate that the greater the number of chronic illnesses among middle-aged and older adults, the more limited their ADL becomes[\u003cspan additionalcitationids=\"CR52 CR53\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Research has shown that patients with chronic diseases such as diabetes have significantly weaker skeletal muscle strength compared to healthy individuals, thereby restricting the ADL of middle-aged and older adults[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Additionally, studies have indicated that multimorbidity influence ADL by triggering depressive symptoms[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Furthermore, both our study and previous research highlight the significant effect of ADL on depressive symptoms; limitations in ADL may increase the risk of depressive symptoms among middle-aged and older adults and their spouses [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].Considering the collective findings, it can be inferred that physical impairments resulting from various chronic illnesses affect ADL, restricting their social participation and interaction, making it challenging to maintain normal social relationships, thus effecting their psychological well-being. This insight suggests that when attempting to mitigate the negative effect of multimorbidity on depressive symptoms, attention should be paid to patients' daily life abilities. Strengthening multimorbidity rehabilitation treatment and enhancing social support can also alleviate this negative effect[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhat\u0026rsquo;s more, our study indicates that multimorbidity also influence depressive symptoms by affecting sleep quality, thus validating hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Preliminary evidence suggests that suffering from various chronic illnesses can effect patients' sleep quality, with individuals affected by multimorbidity facing a significantly increased risk of declining sleep quality in the future[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Additionally, multiple studies have shown a close relationship between sleep quality and depressive symptoms [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], with research indicating a strong correlation between disrupted melatonin secretion and depressive moods[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. This mediating effect may stem from physical pain caused by multimorbidity, leading to sleep problems such as insomnia and vivid dreams. Poorer sleep quality can increase psychological stress in patients, potentially triggering or exacerbating depressive symptoms. Therefore, when discussing the mechanisms through which multimorbidity affect depressive symptoms in middle-aged and older adults, it is crucial to consider the role of sleep quality in this process.\u003c/p\u003e \u003cp\u003eOur research reveals that the relationship between multimorbidity and depressive symptoms is mediated by the sequential intermediary roles of ADL and sleep quality. Specifically, multimorbidity can be influenced separately by ADL and sleep quality, and can also lead to depressive symptoms through the influence of ADL on sleep quality. Therefore, hypothesis \u003cspan refid=\"FPar4\" class=\"InternalRef\"\u003e4\u003c/span\u003e is verifiable. One study uncovered that the association between multimorbidity and depressive symptoms is mediated by the frequency of bodily pain and sleep duration[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. However, this study did not link multimorbidity with ADL. Moreover, there is substantial evidence indicating a correlation between ADL and sleep quality[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Yet, to our knowledge, there is currently few research on how these two factors mediate the sequential intermediary effects of multimorbidity on depressive symptoms. It provides insights for chronic disease management organizations and personnel, suggesting that by improving the ADL and sleep quality of individuals with multimorbidity, their risk of depressive symptoms can be effectively alleviated.\u003c/p\u003e \u003cp\u003eIn summary, the findings of this study clearly delineate the mediating pathways between multimorbidity and depressive symptoms. Multimorbidity can increase the risk of depressive symptoms through the mediating effect of ADL and sleep quality, respectively, and it can also increase the risk of depressive symptoms through the chain effect of ADL and sleep quality. Based on this, we suggest that the relevant government departments should strengthen the screening and management of multimorbidity, and at the same time, older adults should pay attention to sleep quality and keep exercise to improve their daily activities, so as to reduce the risk of depressive symptoms. To some extent, the results of this study may be generalizable beyond the Chinese population, as the multimorbidity discussed in the study are not exclusive to this demographic. However, the universality of these findings is constrained by cultural differences, social norms, and economic conditions. It is recommended that future research endeavors undertake multinational and cross-cultural studies to enhance the generalizability of the findings.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis study reveals the intrinsic mechanism of multimorbidity and depressive symptoms from a new perspective, and reflects the mediating effect and chain mediating effect of ADL and sleep quality. It can provide new ideas for relevant decision-making departments to improve the mental health of older adults, and can also effectively contributes to advancing the research landscape in this domain.\u003c/p\u003e \u003cp\u003eThis study has some limitations in interpreting the results. Firstly, cross-sectional data make it challenging to establish causal relationships between variables. To clarify these findings, longitudinal studies are warranted in the future. Additionally, existing research has consistently demonstrated a close association between multimorbidity and depressive symptoms. However, their indirect effects are too intricate to be solely explained by mediators such as ADL and sleep quality. To gain a more comprehensive and in-depth understanding of the underlying mechanisms, future research should incorporate a broader range of variables into the model.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eMultimorbidity might impair mental health in Chinese older adults through ADL and sleep quality. These findings offer crucial insights into the complexities of psychological well-being in aging populations and provide actionable strategies for enhancing mental health care tailored to this vulnerable demographic. The empirical evidence presented in this study provides substantial support for improving the mental health status of older adults. It is important to focus on the diversity of the middle-aged and older adults. In the field of public health, it is also important to develop effective and sustainable interventions to alleviate the burden of multimorbidity and reduce the level of depressive symptoms among older adults.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHARLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChina Health and Retirement Longitudinal Study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eActivities of daily living\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCESD-10\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe 10-item short form of the Center for Epidemiologic Studies Depression Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCES-D\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe Center for Epidemiologic Studies Depression Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUEBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUrban Employee Basic Medical Insurance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eURBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUrban Resident Basic Medical Insurance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNew Cooperative Medical Scheme for Rural Residents\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are deeply grateful to the China Health and Retirement Longitudinal Study (CHARLS) team for providing access to the invaluable data that underpins our research. Special recognition goes to the participants of the CHARLS for their willingness to share their experiences and data, which are crucial for our study on multimorbidity and depressive symptoms among older adults.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChenglu Li was responsible for compiling and presenting the results section, ensuring accurate presentation of the data. Chunxiao Long provided a profound interpretation of the research results and placed them within the context of existing literature. Fangjing Wu and Haiyang Wu applied appropriate statistical methods to interpret the data, ensuring robust and reliable results. Yuqian Li was responsible for researching and writing the background section, conducting a comprehensive review of relevant literature. Lei Shi, Yangdong Fan, and Meifang Zhou provided valuable feedback on this draft and made revisions to the draft. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Natural Science Foundation of China (72104098), Guangdong Basic and Applied Basic Research Foundation (2023A1515010902), Open Fund of Key Research Baseof Philosophy and Social Science of Higher Education in Guangdong Province-Local Government Development Research Institute of Shantou University(07423002-07), Key Laboratory of Philosophy and Social Sciences of Colleges and Universities in Guangdong Province (2023WSYS005), High level Talent Funding Project of Guangzhou Medical University (06-445-1157). The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll rounds of CHARLS investigations have been approved by the Biomedical Ethics Committee of Peking University. Institutional Review Board approval number for the main household survey, including anthropometrics, is IRB00001052-11015.\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"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBergman P, Brighenti S. Targeted Nutrition in Chronic Disease. Nutrients. 2020 Jun 5;12(6):1682.\u003c/li\u003e\n \u003cli\u003eHu J, Zheng X, Shi G, Guo L. 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PeerJ. 2023 Feb 14;11:e14856.\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Multimorbidity, Depressive symptoms, Activities of daily living, Sleep quality, Multiple mediating effects ","lastPublishedDoi":"10.21203/rs.3.rs-6443491/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6443491/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThis study aimed to explore the underlying mechanisms between multimorbidity and depressive symptoms among the older adults, and further investigate the multiple mediating effects of activities of daily living (ADL) and sleep quality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A total of 7290 older adults from the China Health and Retirement Longitudinal Study database completed questionnaires on multimorbidity, depressive symptoms, activities of daily living and sleep quality. We used a series of mediation models proposed by Hayes to study the relationship between variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eMultimorbidity was positively correlated with ADL (r = 0.118, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001), and depressive symptoms levels (r = 0.114, \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001), respectively. Multimorbidity was negatively correlated with sleep quality (r = -0.108, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Multimorbidity was significantly associated with depressive symptoms (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), with a direct effect of 1.563 and indirect effects through ADL (0.175) and sleep quality (0.676). A serial mediation effect was also observed (0.048).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThese findings offer crucial insights into the complexities of psychological well-being in aging populations and provide actionable strategies for enhancing mental health care tailored to this vulnerable demographic. The empirical evidence presented in this study provides support for improving the mental health status of elders population.\u003c/p\u003e","manuscriptTitle":"The Relationship Between Multimorbidity and Depressive symptoms among Chinese Older Adults—The Mediating Role of Activities of Daily Living and Sleep Quality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-26 10:29:59","doi":"10.21203/rs.3.rs-6443491/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":"d9b3a94f-16f8-42a1-8248-31da2df1b9fd","owner":[],"postedDate":"June 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T09:26:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-26 10:29:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6443491","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6443491","identity":"rs-6443491","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}