Longitudinal associations between sleep duration and subjective well-being in middle-aged and the older Chinese adults: the mediating role of depressive symptoms

Longitudinal associations between sleep duration and subjective well-being in middle-aged and the older Chinese adults: the mediating role of depressive symptoms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Longitudinal associations between sleep duration and subjective well-being in middle-aged and the older Chinese adults: the mediating role of depressive symptoms Jieling Luo, Yuxuan Li, Liqing Wang, Yan Wang, Jie Shi, Xueting Zhu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7467157/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Evidence on how sleep duration affects subjective well-being and depressive symptoms in older Chinese adults remains limited. Methods Data from 10,706 participants aged 45 years and above in the China Health and Retirement Longitudinal Study (CHARLS) were analyzed. Descriptive statistics, restricted cubic spline models, and subgroup analyses were conducted. Mediation analysis with 1,000 bootstrap iterations assessed the mediating role of depression. Results Sleep duration was significantly associated with depression, self-rated health, life satisfaction, and life expectancy. Regression models indicated that approximately 6.5 hours of sleep was linked to the lowest depression risk and the highest well-being. Longer sleep was positively related to life satisfaction and self-rated health, though the effect plateaued beyond 6.5 hours. Gender subgroup analysis showed consistent patterns. Mediation analysis revealed that depressive symptoms partially mediated the association between sleep duration and subjective well-being. Conclusion Adequate sleep duration, particularly around 6.5 hours, is linked to lower depression risk and greater subjective well-being in Chinese middle-aged and older adults. Early screening for sleep and mental health issues in high-risk groups may help promote healthy aging. Health sciences/Diseases Health sciences/Health care Biological sciences/Psychology Social science/Psychology Health sciences/Risk factors Depression Sleep duration Subjective well-being Mediating effect Middle-aged and older people CHARLS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction The aging population has become a pressing challenge for both developed and developing countries worldwide( 1 ) By the end of 2023, China’s population aged 60 and above is expected to reach 297 million, or 21.1% of the total population( 2 , 3 ). Although there is no universally accepted definition of “successful aging” many researchers emphasize its subjective nature, advocating for the inclusion of older individuals’ self-perceptions in its definition( 4 ). The 14th Five-Year Plan for Healthy Aging calls for a comprehensive health service system for the elderly, with interventions addressing the various factors affecting their health. Therefore, it is crucial to prioritize not only fulfilling material needs and maintaining physical health, but also addressing older individuals’ spiritual needs, particularly enhancing their subjective well-being( 5 ). Subjective well-being refers to an individual’s overall emotional and cognitive evaluation of their life quality, based on their subjective experience of current life( 6 ). It is considered a key indicator of successful aging and plays a crucial role in addressing population aging. Subjective well-being, a key measure of quality of life in the elderly, has gained significant attention from the academic community( 7 ). Research has shown a strong relationship between subjective well-being and an individual’s cognitive ability and psychological state. Sato et al. ( 8 )used MRI to analyze brain regions related to subjective well-being, revealing a significant correlation between well-being and cognitive and psychological states. Recent studies in Western countries suggest that individuals with high subjective well-being and emotional vitality have a lower risk of developing diseases like cardiovascular conditions, hypertension, and stroke( 9 , 10 ). Recent studies have also shown a correlation between individuals’ subjective well-being and the economic status of their region of residence. Studies have found that developing countries tend to report lower levels of subjective well-being compared to developed countries( 11 ).Low subjective well-being can lead to serious consequences, such as deteriorating physical and mental health, frequent illnesses, and even suicide( 12 ). The evaluation of subjective well-being remains unstandardized, with existing assessment tools showing heterogeneity across studies. For instance, Fangfang Wen et al. employed the Life Satisfaction Scale by Huebner and the Positive and Negative Affect Scale by Zhang et al. to assess the subjective well-being of young people( 13 ). In contrast, Sunwoo et al. surveyed subjective well-being among elderly individuals in Europe using an assessment tool covering three dimensions: life satisfaction, well-being, and self-rated general health( 14 ). In a study of community-dwelling older adults, Chinese researchers Lanshuang Chen et al. used measures including life satisfaction, positive and negative affect, and depression (measured by the CES-D scale)( 15 ).To address the common limitation of small sample sizes, Yifan Zhou et al. used the China Health and Retirement Longitudinal Study(CHARLS) database to examine the relationship between visual impairment and subjective well-being in older adults. Their assessment included life satisfaction, subjective life expectancy, and self-rated health( 16 ). The present study will assess individuals' subjective well-being along three dimensions: life satisfaction, subjective life expectancy, and self-rated health score. This approach is informed by the findings of Zhou Yifan et al. Furthermore, depressive symptoms will be incorporated into the analysis as a significant outcome variable, thereby ensuring the comprehensive evaluation of the aforementioned dimensions. Sleep is crucial for both physical and mental well-being, with its duration and quality directly affecting the health of older adults( 17 ). Sleep disorders disrupt the body’s balance, triggering systemic responses that often lead to negative health outcomes. Sleep problems are common in the general population and are linked to several conditions, such as chronic diseases, cardiovascular disease, cognitive impairment, and an increased risk of cancer mortality. Chronic sleep problems are linked to an increased risk of both physical and mental illnesses, leading to overall health decline. Significant correlations have been observed between sleep quality( 18 ), duration( 19 ), and emotional states such as well-being, positive mood, and vitality. Sleep disorders can negatively affect well-being, whereas good well-being can promote better sleep quality. Sufficient and high-quality sleep significantly increases life satisfaction and self-rated health. In contrast, sleep deprivation or disorders are linked to reduced life satisfaction and shorter subjective life expectancy( 20 , 21 ). Depression is a common mental health issue that affects a significant proportion of middle-aged and older adults( 22 ). Research has shown a strong link between depressive symptoms and sleep disturbances( 23 ), with depression potentially contributing to sleep disorders such as insomnia and early waking. Additionally, studies have confirmed the relationship between subjective well-being and depression( 24 ). The biopsychosocial model highlights the interconnection of biological, psychological, and social factors that collectively influence an individual’s health. In this context( 25 ), sleep, as a key biological variable, influences social functioning and subjective well-being through depressive symptoms at the psychological level. Sleep disorders disrupt neurotransmitter balance and mood regulation, which can lead to depression and, in turn, reduce subjective well-being. Sleep-related issues at the biological level affect an individual’s evaluation of well-being at the social level through the mediating role of depressive symptoms. Overall, our study will identify the predictors of subjective well-being—such as life satisfaction, self-rated health, and subjective life expectancy—using longitudinal data. Additionally, it will examine the relationship between nighttime sleep duration, depressive symptoms, and subjective well-being using both linear and non-linear methods. The study proposes that depressive symptoms mediate the relationship between nighttime sleep duration and subjective well-being in middle-aged and older adults, as framed within the biopsychosocial model( 19 , 26 ). In conclusion, the study aims to provide a theoretical foundation and empirical evidence to clarify the factors influencing subjective well-being and explore potential intervention strategies for middle-aged and elderly populations in China. 2 Methods 2.1 Background of the study The data for this study were obtained from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey conducted by the National School of Development at Peking University. The CHARLS project, surveyed 150 counties, 450 communities (villages), and 12,073 households across 28 provinces (including autonomous regions and municipalities directly under the central government) using a probability proportional to size (PPS) sampling method. This method is considered representative of the entire population. By 2020, five waves of data collection had been completed: 2011, 2013, 2015, 2018, and 2020. Detailed descriptions of the CHARLS design and data collection procedures can be found in previous publications and on the official website( 27 , 28 ). 2.2 Study sample We used data from the CHARLS 2015 and 2018, a nationally representative longitudinal survey of Chinese individuals aged ≥ 45 years and their spouses. After identifying the study variables, the data were screened based on the following inclusion criterion: ( 1 ) age ≥ 45 years. The exclusion criterion was: ( 1 ) individuals with missing information on the primary study variables and covariates. A total of 8744 participants with missing data on general information and study variables were excluded from the analysis. The data were collated and analyzed, and 10706 respondents aged ≥ 45 years were included in the analysis (Fig. 1 ). 2.3 Measures 2.3.1 Measurement of depression Depression was assessed using the Chinese version of the Center for Epidemiological Survey Depression Scale (CES-D-10). The CES-D-10 employs a 4-point Likert scale with the following response options: “None,” “Rarely (1–2 days),” “Sometimes (3–4 days),” and “Always (5–7 days).” Two items were reverse-scored, while the remaining items were scored positively, with individual item scores ranging from 0 to 3. The total score ranges from 0 to 30, with higher scores indicating more severe depressive symptoms( 30 , 31 ). A score of ≥ 10 was used to identify depression. The CES-D-10 has been fully validated in the Chinese middle-aged and older adult population to demonstrate its satisfactory reliability and validity( 32 , 33 ) . 2.3.2 Measurement of sleep duration Sleep duration was assessed using a self-reported question asking participants about their average nightly sleep duration over the past month. Participants provided their responses in hours and minutes. Based on the participants’ answers, the sleep duration was divided into three categories: short sleep duration ( 9 h)( 34 , 35 ). 2.3.3 Measurement of self-assessed health Self-assessed health status was measured using the question, “How would you rate your health: very satisfied, satisfied, Somewhat satisfied, Less satisfied, or Very dissatisfied?” The five responses were coded as 1, 2, 3, 4, and 5, with higher scores indicating poorer health.The original questionnaire options were recoded as follows: 'Very satisfied', 'More satisfied' and 'Somewhat satisfied' were recoded as 'yes' (Satisfied); meanwhile, 'Less satisfied' and 'Very dissatisfied' were recoded as 'no' (Dissatisfied). 2.3.4 Measurement of subjective life expectancy Subjective life expectancy was assessed based on the respondents’ answers to the following question: “How likely do you think it is that you will be alive for the next 15 years or more? (‘Almost impossible,’ ‘unlikely,’ ‘maybe,’ ‘likely,’ ‘almost certain’)” The five responses were coded as 1, 2, 3, 4, and 5, with higher scores indicating greater subjective life expectancy( 16 ).The original questionnaire options were recoded as follows: 'likely' and 'almost certain' were recoded as 'yes' ; meanwhile, 'maybe' ,'Almost impossible' and 'unlikely' were recoded as 'no' . 2.3.5 Measurement of life satisfaction Life satisfaction was assessed based on the respondents’ answers to the following question: “Please consider your life in its totality. Would you rate your satisfaction as complete, very complete, somewhat complete, not too complete, or not at all complete?” The five responses were coded as 1, 2, 3, 4, and 5, with higher scores indicating lower levels of life satisfaction. The original questionnaire options were recoded as follows: 'Very satisfied', 'More satisfied' and 'Somewhat satisfied' were recoded as 'yes' (Satisfied); meanwhile, 'Less satisfied' and 'Very dissatisfied' were recoded as 'no' (Dissatisfied). 2.3.6 control variables The study included covariates such as sociodemographic factors, BMI, grip, health behaviors, comorbidity count, and sensory function. Sociodemographic factors encompassed age, education level (classified into four categories: illiterate, primary school, middle school, and high school or above), and marital status (married or unmarried). Health behaviors included social activity (Yes/No), smoking status (Yes/No) and alcohol consumption (Yes/No). Vision was assessed by asking participants about their regular use of corrective lenses and their ability to recognize distant objects, such as identifying a friend across the street, with glasses if applicable. Similarly, hearing was evaluated through questions about participants’ overall hearing status and the impact of hearing aids, if used. Participants rated their vision and hearing on a scale ranging from “excellent” to “poor.” Vision was categorized as “good” for ratings of “excellent,” “very good,” or “good”; “moderate” for “fair”; and “poor” for “poor.” The same classification criteria were applied to hearing( 36 ). For ease of statistical analysis, categorical variables were numerically encoded as follows, with the specific coding rules detailed in Table 1 . Table 1 Categorical variable assignment Categorical variable Variable assignment Age < 60 = 0,≥60 = 1 Gender Female = 0,Male = 1 Education Illiterate = 0,primary = 1,middle school = 2, high school + = 3 Marital Unmarried = 0, married = 1 Residence Rural = 0, Urban = 1 Smoking Yes = 1,No = 0 Drinking Yes = 1,No = 0 Sleep 9 = 2 Hearing Poor = 0,Fair = 1,Good = 2 Vision Poor = 0,Fair = 1,Good = 2 Social activity Yes = 1,No = 0 2.4 Statistical analyses Descriptive statistics and univariate analyses were conducted using IBM SPSS (version 22.0). Continuous variables were reported as mean ± standard deviation (Mean ± SD), and categorical variables were presented as frequencies and percentages. The chi-square test was used to assess differences in categorical variables between groups, while t test was employed for continuous variables. Data analyses were performed using R (version 4.4.2), with a significance level of α = 0.05. Pearson correlation analyses were conducted to examine the associations between subjective well-being, depression, grip strength, BMI, comorbidity count, and sleep duration. Multivariate linear regression with forward stepwise selection was used to identify potential influences on subjective well-being. Variables were selected and excluded based on thresholds set at αin = 0.05 and αout = 0.10, respectively. Sensitivity analyses primarily comprised linear regression analyses based on gender subgroups. The “forestplot” package in R was used to visualize the results of the regression analysis. Restricted cubic spline (RCS) regression models were used to explore the non-linear relationship between sleep duration, depression, and subjective well-being. To assess depression as a mediating variable, the following steps were taken: linear regression was used to explore the direct effect of sleep duration on subjective well-being, and depression was added to the regression model to analyze its mediating role between sleep duration and subjective well-being using the “mediation” package in R. The total, direct, and indirect effects in the model were validated using the bootstrap method with 1000 resampled iterations( 37 ). 3 Results 3.1 General characteristics of the participants This study involved 10,706 participants: 5,449 in the middle-aged group (45–59 years) and 5,257 in the elderly group (60 years and older). The age distribution was generally balanced. Regarding gender distribution, 5,520 participants (51.6%) were female, and 5,186 participants (48.4%) were male, reflecting a nearly equal gender ratio. Of the middle-aged and elderly participants, 5,484 (51.2%) reported insufficient sleep, 4,702 (43.9%) had normal sleep duration, and 514 (4.8%) reported sleeping more than 8 hours. All group differences were statistically significant, except for those related to education, smoking, drinking, and life satisfaction. Participant characteristics are presented in Table 2 . Table 2 Descriptive Statistics of Study Variables Across sleep duration Variables Categories Overall n(%) sleep duration n(%) χ² P Value 9 age <60 5449(50.9) 2650(48.6) 2557(46.9) 242(4.4) 39.506 < 0.001 ≥ 60 5257(49.1) 2834(53.9) 2151(40.9) 272(5.2) Gender Female 5520(51.6) 2952(53.5) 2305(41.8) 263(4.8) 24.09 < 0.001 Male 5186(48.4) 2532(48.8) 2403(46.3) 251(4.8) Education Illiterate 625(5.8) 323(51.7) 262(41.9) 40(6.4) 7.888 0.246 primary 1087(10.2) 542(49.9) 495(45.5) 50(4.6) middle 484(4.5) 236(48.8) 229(47.3) 19(3.9) high school+ 8510(79.5) 4383(51.5) 3722(43.7) 405(4.8) Marital Married 9551(89.2) 4814(50.4) 4291(4.9) 446(4.7) 33.060 < 0.001 Single 1155(10.8) 670(58.0) 417(4.1) 68(5.9) Residence Urban 1837(17.2) 1013(55.1) 789(43.0) 35(1.9) 45.914 < 0.001 Rural 8869(82.8) 4471(50.4) 3919(44.2) 479(5.4) Smoking Yes 4729(44.2) 2397(50.7) 2099(44.4) 233(4.9) 1.080 0.583 No 5977(55.8) 3087(51.6) 2609(43.7) 281(4.7) Drinking Yes 2967(27.7) 1513(51.0) 1326(44.7) 128(4.3) 2.548 0.280 No 7739(72.3) 3971(51.3) 3382(43.7) 386(5.0) Hearing Fair 5636(52.6) 2944(52.2) 2428(43.1) 264(4.7) 62.367 < 0.001 Good 3814(35.6) 1798(47.1) 1829(48.0) 187(4.9) poor 1256(11.7) 742(59.1) 451(359.0) 63(5.0) Vision Fair 6183(57.8) 3340(54.0) 2545(41.2) 298(4.8) 51.099 < 0.001 Good 4507(42.1) 2136(47.4) 2157(47.9) 214(4.7) poor 16(0.1) 8(50.0) 6(37.5) 2(12.5) Depression(2018) Yes 4111(38.4) 2400(58.4) 1514(36.8) 197(4.8) 144.248 < 0.001 NO 6595(61.6) 3084(46.8) 3194(48.4) 317(4.8) Social activity Yes 6139(57.3) 3162(51.5) 2725(44.4) 252(4.1) 15.310 < 0.001 NO 4567(42.7) 2322(50.8) 1983(43.4) 262(5.7) life satisfaction(2018) Yes 770(7.2) 371(48.2) 366(47.5) 33(4.3) 4.345 0.114 NO 9936(92.8) 5113(51.5) 4342(43.7) 481(4.8) self-assessed health(2018) Yes 7704(72.0) 3699(48.0) 3620(47.0) 385(5.0) 114.187 < 0.001 NO 3002(28.0) 1785(59.5) 1088(36.2) 129(4.3) subjective life expectancy(2018) Yes 2951(27.6) 1382(46.8) 1443(48.9) 126(4.3) 40.193 < 0.001 NO 7755(72.4) 4102(52.9) 3265(42.1) 388(5.0) 3.2 Correlation Between Sleep Duration, BMI, grip and Outcome Variables The Pearson correlation analysis revealed significant associations between sleep duration and several health-related factors among middle-aged and elderly individuals. Specifically, sleep duration was negatively correlated with depression (r = − 0.19, P < 0.001), self-rated health (r = − 0.12, P < 0.001), life satisfaction (r = − 0.10, P < 0.001), and positively correlated with life expectancy (r = 0.08, P < 0.001). Sleep duration was also negatively associated with comorbidity count (r = − 0.14, P < 0.01) and positively with grip strength (r = 0.11, P < 0.01). No significant correlation was found between BMI and self-rated health. Detailed results are presented in Table 3 . Table 3 Correlation Matrix of Sleep Duration, depression, and Outcome Variables Variables Mean(SD) 1 2 3 4 5 6 7 8 1.sleep duration(h) 6.39(1.90) 1 2.CES-D-10 8.62(6.49) -0.19* 1 3.life satisfaction 2.74(0.79) -0.10* 0.38* 1 4.subjective life expectancy 2.94(1.24) 0.08* -0.30* -0.17* 1 5.self-assessed health 3.06(0.94) -0.12* 0.39* 0.46* -0.29* 1 6. Grip 31.27(9.82) 0.11* -0.24* -0.06* 0.19* -0.12* 1 7. BMI 24.09(3.91) 0.04* -0.04* -0.04* 0.09* 0.01 0.09* 1 8. Comorbidity count 1.64(1.62) -0.14* 0.21* 0.09* -0.19* 0.27* -0.17* 0.11* 1 Note:*P < 0.001. 3.3 Results of the multivariate linear and nonlinear regression analysis examining the relationship between sleep duration and depression The study found a significant relationship between sleep duration and depressive symptoms, supported by both linear and nonlinear models. Linear regression analysis showed a significant negative correlation between sleep duration and depressive symptoms (P < 0.001), suggesting that longer sleep duration is linked to fewer depressive symptoms (Fig. 2 A). Nonlinear regression analysis, on the other hand, revealed significant nonlinear effects (P for non-linearity < 0.001). Specifically, depression risk decreased as nighttime sleep duration increased, reaching a minimum and stabilising around 6.5 hours of sleep. Detailed analysis results are presented in Fig. 5 A. Further analysis of gender-specific subgroups and regression analyses of the overall population revealed key factors influencing depression scores: co-morbidity count, sleep duration, social activity participation, grip strength, age, visual and auditory health, and marital status (see Figs. 2 A, 3 A, and 4 A). 3.4 Results of the multivariate linear and nonlinear regression analysis examining the relationship between sleep duration and subjective well-being The study found significant longitudinal causal relationships between sleep duration and subjective well-being, including self-rated health, life satisfaction, and subjective life expectancy, supported by both linear and nonlinear models. Linear regression analysis revealed significant negative correlations between sleep duration and self-rated health (P < 0.001), and life satisfaction (P < 0.001), suggesting that longer sleep duration is associated with higher self-rated health and greater life satisfaction (Figs. 2 B, 2 C). In contrast, sleep duration was positively correlated with subjective life expectancy (P < 0.001), indicating that longer sleep duration is associated with higher subjective life expectancy (Fig. 2 D). Nonlinear regression analysis further revealed significant nonlinear effects in all three relationships (P for non-linearity < 0.001). Specifically, the likelihood of being satisfied with self-rated health and life satisfaction increased with longer nighttime sleep, reaching its highest level around 6.5 hours and stabilizing thereafter(Figs. 5 ). Similarly, the likelihood of experiencing higher subjective life expectancy increased with longer sleep duration. Gender subgroup analysis and regression analyses of the overall population identified key predictors of these outcomes, including co-morbidity count, sleep duration, age, visual and auditory acuity, and marital status (Figs. 3 , 4 ). The above dose-response relationship was adjusted for following covariates: age, gender, education, marital status, residence, smoking, drinking ,Hearing,Vision,Social activity Comorbidity count, BMI, grip. 3.5 The results of regression and mediation analyses The control variables in the linear regression analysis included demographic characteristics such as age, education, residence, marital status, and lifestyle and health factors, including drinking, smoking, social activity, hearing, vision, BMI, grip strength, and comorbidity count. The covariate selection was validated by examining the variance inflation factor (VIF) and tolerance levels. The VIF values were all below 10, and tolerance levels were above 1, indicating no multicollinearity among the variables.The results from linear regression models 1, 4, and 7 (Table 4 ) confirmed this observation, showing a strong correlation between sleep duration and key health outcomes, including life satisfaction, self-rated health, and life expectancy among middle-aged and elderly individuals (β= -0.031, -0.036, 0.020, P < 0.001). In Models 3, 6, and 9, when depression was included as a mediator, sleep duration remained significantly associated with self-rated health, life satisfaction, and life expectancy (Table 4 ), suggesting that depression partially mediates the relationship between self-identity and sleep duration. When depression was included as a mediator in Models 3, 6, and 9, sleep duration maintained a significant association with self-rated health, life satisfaction, and life expectancy (Table 4 ). The findings indicate that depression partially mediates the relationship between self-identity and sleep duration. Bootstrap analysis revealed that depressive tendencies partially mediate the effects of sleep duration on life satisfaction, self-rated health, and life expectancy, as detailed in Table 5 . Table 4 Linear regression analysis of the mediating effects of depression on sleep duration and subjective life expectancy Variables life satisfaction self-assessed health subjective life expectancy Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 β t β t β t β t β t β t β t β t β t Sleep duration -0.031*** -7.595 -0.463 *** -14.898 -0.010* -2.526 -0.036*** -7.752 -0.463 *** -14.898 -0.012** -2.955 0.020** 3.263 -0.463 *** -14.898 0.000 0.054 CES-D-10 0.045** 38.541 0.048** 36.299 -0.043*** -23.604 R 2 0.034 0.146 0.152 0.104 0.146 0.202 0.113 0.146 0.156 F 29.2 140.3 137.0 95.14 140.3 193.3 104.5 140.3 141.9 Note:The table shows the results of linear regression models assessing the mediating effects of depression on the relationship between sleep duration and different outcomes (life satisfaction, self-assessed health, and subjective life expectancy).β = standardized regression coefficient. t = t-statistic. p = significance level. R² = proportion of variance explained by the model. F = overall model significance. *P < 0.05; **P < 0.01; ***P < 0.001. Table 5 Mediation Analysis Results of Dependent Variables Dependent variable Effect P 95%CI LICI ULCI Life satisfaction Total effect -0.031 < 0.001 -0.040 -0.02 Direct effect -0.010 0.014 -0.018 0.00 Indirect effect -0.021 < 0.001 -0.024 -0.02 Self-assessed health Total effect -0.037 < 0.001 -0.046 -0.03 Direct effect -0.010 0.006 -0.022 0.00 Indirect effect -0.027 < 0.001 -0.026 -0.02 Subjective life expectancy Total effect 0.020 < 0.001 0.008 0.03 Direct effect 0.000 0.99 -0.011 0.01 Indirect effect 0.020 < 0.001 0.017 0.02 CI: Confidence Interval;LLCI: Lower Limit Confidence Interval;ULCI: Upper Limit Confidence Interval. 4 Discussion This study used data from the 2015 China Health and Retirement Longitudinal Study (CHARLS) to analyze the current state of nighttime sleep in middle-aged and elderly populations in China. The study found that in 2015, 51.0% of individuals experienced inadequate nighttime sleep, significantly higher than the 5.1% prevalence of long sleep duration. This suggests that inadequate sleep duration has become the primary sleep issue among middle-aged and elderly populations in China. This figure is notably higher than the 48.3% reported by Chen et al. ( 38 ) using the 2011 CHARLS data, indicating a possible link to China’s economic and social development and increasing life stress. This finding indicates that the problem of insufficient sleep has become more pronounced in recent years. A longitudinal study has shown that individuals with chronic sleep deprivation are more likely to develop cardiomyopathy and vascular metabolic diseases( 39 ). Given the high prevalence and serious health consequences of sleep deprivation, public health agencies should implement more effective measures to screen for and manage sleep disorders.Regarding gender differences, this study found that middle-aged and elderly women had a significantly higher prevalence of insufficient sleep than men, likely due to the greater family and caregiving responsibilities that shorten their nighttime sleep. Jiang et al. also found that individuals with insufficient nighttime sleep were more likely to be female, older, and suffer from more chronic illnesses( 35 ), consistent with this study’s findings, highlighting the need for special attention to these individuals. Correlation analysis revealed a significant association between sleep duration, grip strength, and BMI. Grip strength serves as a validated marker of musculoskeletal health and functional status in middle-aged and older adults, and has been consistently linked to sleep quality in previous studies ( 40 ). Inadequate sleep contributes to muscle atrophy and dysregulation of metabolic and appetite-related pathways, leading to both decreased grip strength and increased adiposity. Conversely, obesity and metabolic disturbances impair sleep quality and duration, while reduced muscle strength limits physical activity and disrupts circadian homeostasis, further exacerbating sleep impairment( 41 ).Therefore, greater attention should be directed toward the nutritional and metabolic health of individuals with insufficient sleep duration, with particular emphasis on monitoring BMI and grip strength. Early screening and intervention targeting high-risk groups—such as those with obesity or sarcopenia—are essential to prevent the onset of physiological and psychological chain reactions. This study examined the relationship between nighttime sleep duration, depression, and subjective well-being in middle-aged and older adults. The findings showed that baseline sleep duration significantly affected depression levels and subjective well-being, consistent with the results reported by Zhu et al.( 42 ) The study’s longitudinal design allowed for a better inference of causal relationships. Subgroup sensitivity analyses yielded results consistent with the main findings, further supporting their robustness. Adequate sleep helps regulate emotions and stabilize mood, reducing anxiety and depression( 43 ). In turn, this can enhance subjective well-being. However, there is currently no standardized quantitative definition of sleep deprivation. Some scholars define sleep deprivation as ≤ 6 hours of nighttime sleep( 44 , 45 ), whereas the National Sleep Foundation (NSF) recommends a threshold of < 7 hours( 34 ).This study adopted a threshold of less than 7 hours of sleep per night, consistent with the definition recommended by the National Sleep Foundation (NSF). Preliminary analyses investigated the relationships between sleep duration, depressive symptoms, and subjective well-being.This standard has also been employed by Chinese scholar Bingxin Jiang et al. in their research( 35 ). To better capture the trend in perceived well-being across varying sleep durations, sleep duration was treated as a continuous variable to examine its dose–response relationship with subjective well-being. The findings indicated that when nightly sleep duration reached approximately 6.5 hours, individuals exhibited a lower risk of depression and higher levels of subjective well-being.Beyond this threshold, the association between sleep duration and these outcomes plateaued, indicating that the optimal minimum sleep duration is approximately 6.5 hours. Conversely, sleep durations exceeding this threshold may be linked to an increased risk of sleep apnea among middle-aged and older adults. Therefore, it is essential for this population to maintain an appropriate sleep duration—neither too short nor too long—in order to support both psychological and physiological homeostasis( 46 ). This study found that increased sleep duration was linked to improved self-rated health and life satisfaction, with depression acting as a partial mediator. Ouyang identified a bidirectional relationship between depression and sleep duration( 47 ), noting that depression is often associated with abnormal neurotransmitter levels (5-hydroxytryptamine and dopamine)( 48 ), both of which regulate sleep( 49 ). Low levels of 5-hydroxytryptamine are associated with poor sleep quality, including insomnia and early awakening. Sleep deprivation further exacerbates neurotransmitter imbalances, intensifying depressive symptoms. These depressive symptoms, in turn, directly reduce patients’ levels of satisfaction( 24 ). This evidence supports the mediating role of depression in the relationship between sleep quality and patient satisfaction, indicating that improving sleep quality and alleviating depressive symptoms can effectively enhance overall patient satisfaction. Subjective life expectancy (SLE) refers to an individual’s psychological estimate of their remaining years of life. This estimate is shaped by an individual’s judgment and perception of their remaining lifespan. A Korean Longitudinal Study of Aging (KLoSA) survey showed that subjective life expectancy is associated with physical health and perceptions of aging, disease, and death( 50 ).The study identified a significant link between sleep duration and subjective life expectancy among Chinese adults aged 45 and older. Sleep, a basic physiological need, is vital for maintaining optimal health. Adequate sleep is key to restoring physical strength and strengthening immunity. Additionally, adequate sleep supports mental health, which may shape an individual’s expectations of future longevity( 51 ). This supports the idea that the impact of sleep duration on subjective life expectancy is partly mediated by depressive mood. Longer sleep duration reduces depressive symptoms, improving well-being and life satisfaction. In summary, this study explored the relationship between sleep duration, depression, and subjective well-being in middle-aged and elderly individuals using both linear and nonlinear approaches. The nonlinear analysis found that when sleep duration reached 6.5 hours, the risk of depression was lower, and subjective well-being levels were higher. Middle-aged and elderly individuals should ensure at least 6.5 hours of sleep to maintain their subjective well-being. Linear regression analysis indicated that early screening should focus on individuals at risk, including the elderly, unmarried individuals, those with comorbidities, visual and hearing impairments, sleep deprivation, weak grip strength, and obesity. Emphasis should be placed on external support and the establishment of multi-dimensional social networks to reduce depression levels and improve subjective well-being. Furthermore, this study suggests that good sleep is closely linked to subjective well-being (including life satisfaction, self-rated health, and life expectancy) among Chinese middle-aged and elderly individuals, with depressive tendencies partially mediating this relationship. Sleep deprivation directly impacts emotional and psychological well-being, further reducing subjective well-being by worsening depressive symptoms. However, effective sleep management strategies for middle-aged and elderly populations are noticeably lacking worldwide, and most individuals with sleep problems lack adequate interventions. In the future, attention should be given to sleep-related issues, and policies should include widespread education on sleep health to foster accurate knowledge of sleep and raise awareness of sleep management. 5 limitations This research utilized data from the China Health and Retirement Longitudinal Study (CHARLS), drawing on a sample that comprehensively represents the country’s demographic landscape. The possible limitations of this study are as follows:Firstly, this study only assessed sleep duration and did not fully capture individuals’ overall sleep quality. However, sleep encompasses multiple dimensions beyond duration, such as the frequency of sleep interruptions. Future research should consider integrating both subjective and objective sleep data to provide a more comprehensive assessment of sleep quality.Secondly, while CHARLS offers a nationally representative sample of Chinese middle-aged and elderly populations, certain subgroups—such as individuals living in remote areas or underrepresented ethnic minorities—may not be fully represented, which could limit the generalizability of the findings. 6 Conclusion This study identified a significant relationship between sleep duration, depression, and subjective well-being in Chinese middle-aged and elderly individuals. A sleep duration of at least 6.5 hours was associated with lower depression risk and higher subjective well-being. Early screening should target high-risk groups, including those with comorbidities, sleep deprivation, or weak grip strength. The findings highlight the mediating role of depression in the sleep–well-being link and underscore the need for effective, non-pharmacological sleep interventions. Future policies should promote sleep health education to improve public awareness and support psychological well-being in aging populations. Declarations Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The datasets supporting the conclusions of this article are available publicly. http://charls.pku.edu.cn/pages/data/111/en.html Clinical trial number Not applicable Author contributions JL: Conceptualization, Data curation, Software, Visualization, Writing – original draft, YL: Writing – original draft, Formal analysis, Investigation, Methodology, Supervision, Validation. LW: Conceptualization, Software, Validation, Visualization. YW: Data curation, Formal analysis, Methodology, Project administration, Writing – original draft. JS: Investigation, Methodology, Writing – original draft. XZ: Conceptualization, Data curation, Writing – original draft, Formal analysis.GM: Funding acquisition, Project administration, Supervision, Validation, Writing – review & editing. Funding The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article. Acknowledgments We are grateful to all participants enrolled in the CHARLS and its members. We are grateful to CHARLS for providing us with these data. We appreciate all the co-researchers, reviewers, and editors. Ethics approval and consent to participate According to Article 32 (1) and (2) of the Measures for Ethical Review of Life Science and Medical Research Involving Human Beings issued on February 18, 2023, ethical review may be waived if (a) research is conducted using legally obtained public data or data generated by observation and not interfering with public behavior; and (b) research is conducted using anonymized information data. Patient and Public Involvement Patients and members of the public were not involved in the design, conduct, reporting, or dissemination plans of this research. This study is a secondary analysis of publicly available CHARLS data. As such, there was no direct patient or public involvement at any stage of the research process. References Christensen K, Doblhammer G, Rau R, Vaupel JW. Ageing Populations: The Challenges Ahead. Lancet (2009) 374(9696):1196-208. doi: 10.1016/s0140-6736(09)61460-4. 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10:48:48","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":165140,"visible":true,"origin":"","legend":"","description":"","filename":"256a5d5ee376488099ae13b9befb84441structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7467157/v1/a1faba3fb94b62328cc647db.xml"},{"id":97433469,"identity":"dc8e4d04-84a5-4f86-8157-56d841ea54d1","added_by":"auto","created_at":"2025-12-04 10:48:48","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":177519,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7467157/v1/3f66f1f701cf9fc1684d3d46.html"},{"id":97433451,"identity":"1f8fe3c7-a1d1-40ce-9d9e-f27b93868388","added_by":"auto","created_at":"2025-12-04 10:48:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":303792,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSample flowchart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7467157/v1/f03ea2189ade5466b6b61100.png"},{"id":97666823,"identity":"3d8c4752-bdcb-4a4a-a0aa-39d5c379704d","added_by":"auto","created_at":"2025-12-08 09:22:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":867204,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegression Analysis of Depression and Subjective Well-being Among Middle-aged and Elderly Chinese Individuals\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7467157/v1/a2cfb7554441d58bc2aac611.png"},{"id":97667311,"identity":"41e696ff-f53b-464b-9cf1-73f733a4cf93","added_by":"auto","created_at":"2025-12-08 09:23:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":904513,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegression Analysis of Depression and Subjective Well-being Among Middle-aged and Elderly Chinese Individuals（Male）\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7467157/v1/7e479929ee25b325934cec78.png"},{"id":97433454,"identity":"a202d53b-d8e2-4d84-8d15-d2c650fc021f","added_by":"auto","created_at":"2025-12-04 10:48:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":976279,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegression Analysis of Depression and Subjective Well-being Among Middle-aged and Elderly Chinese Individuals（Female）\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7467157/v1/3933b14040d9b296c4174a26.png"},{"id":97433455,"identity":"8399f2f2-bc6d-406b-96f4-652917e5fec3","added_by":"auto","created_at":"2025-12-04 10:48:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":794433,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDose-response curve of sleep duration and subjective well-being using an RCS regression model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe above dose-response relationship was adjusted for following covariates: age, gender, education, marital status, residence, smoking , drinking ,Hearing,Vision,Social activity Comorbidity count, BMI, grip.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7467157/v1/04bca66b43abbc8d6f6f9370.png"},{"id":97677542,"identity":"910d029e-268c-482a-bdb0-643772ba2ff6","added_by":"auto","created_at":"2025-12-08 09:53:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6145798,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7467157/v1/7b53d5c6-9ea2-4140-98d8-18592c17b169.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Longitudinal associations between sleep duration and subjective well-being in middle-aged and the older Chinese adults: the mediating role of depressive symptoms","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe aging population has become a pressing challenge for both developed and developing countries worldwide(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) By the end of 2023, China\u0026rsquo;s population aged 60 and above is expected to reach 297\u0026nbsp;million, or 21.1% of the total population(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Although there is no universally accepted definition of \u0026ldquo;successful aging\u0026rdquo; many researchers emphasize its subjective nature, advocating for the inclusion of older individuals\u0026rsquo; self-perceptions in its definition(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The 14th Five-Year Plan for Healthy Aging calls for a comprehensive health service system for the elderly, with interventions addressing the various factors affecting their health. Therefore, it is crucial to prioritize not only fulfilling material needs and maintaining physical health, but also addressing older individuals\u0026rsquo; spiritual needs, particularly enhancing their subjective well-being(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSubjective well-being refers to an individual\u0026rsquo;s overall emotional and cognitive evaluation of their life quality, based on their subjective experience of current life(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). It is considered a key indicator of successful aging and plays a crucial role in addressing population aging. Subjective well-being, a key measure of quality of life in the elderly, has gained significant attention from the academic community(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Research has shown a strong relationship between subjective well-being and an individual\u0026rsquo;s cognitive ability and psychological state. Sato et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)used MRI to analyze brain regions related to subjective well-being, revealing a significant correlation between well-being and cognitive and psychological states. Recent studies in Western countries suggest that individuals with high subjective well-being and emotional vitality have a lower risk of developing diseases like cardiovascular conditions, hypertension, and stroke(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Recent studies have also shown a correlation between individuals\u0026rsquo; subjective well-being and the economic status of their region of residence. Studies have found that developing countries tend to report lower levels of subjective well-being compared to developed countries(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).Low subjective well-being can lead to serious consequences, such as deteriorating physical and mental health, frequent illnesses, and even suicide(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe evaluation of subjective well-being remains unstandardized, with existing assessment tools showing heterogeneity across studies. For instance, Fangfang Wen et al. employed the Life Satisfaction Scale by Huebner and the Positive and Negative Affect Scale by Zhang et al. to assess the subjective well-being of young people(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In contrast, Sunwoo et al. surveyed subjective well-being among elderly individuals in Europe using an assessment tool covering three dimensions: life satisfaction, well-being, and self-rated general health(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). In a study of community-dwelling older adults, Chinese researchers Lanshuang Chen et al. used measures including life satisfaction, positive and negative affect, and depression (measured by the CES-D scale)(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).To address the common limitation of small sample sizes, Yifan Zhou et al. used the China Health and Retirement Longitudinal Study(CHARLS) database to examine the relationship between visual impairment and subjective well-being in older adults. Their assessment included life satisfaction, subjective life expectancy, and self-rated health(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The present study will assess individuals' subjective well-being along three dimensions: life satisfaction, subjective life expectancy, and self-rated health score. This approach is informed by the findings of Zhou Yifan et al. Furthermore, depressive symptoms will be incorporated into the analysis as a significant outcome variable, thereby ensuring the comprehensive evaluation of the aforementioned dimensions.\u003c/p\u003e\u003cp\u003eSleep is crucial for both physical and mental well-being, with its duration and quality directly affecting the health of older adults(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Sleep disorders disrupt the body\u0026rsquo;s balance, triggering systemic responses that often lead to negative health outcomes. Sleep problems are common in the general population and are linked to several conditions, such as chronic diseases, cardiovascular disease, cognitive impairment, and an increased risk of cancer mortality. Chronic sleep problems are linked to an increased risk of both physical and mental illnesses, leading to overall health decline. Significant correlations have been observed between sleep quality(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), duration(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), and emotional states such as well-being, positive mood, and vitality. Sleep disorders can negatively affect well-being, whereas good well-being can promote better sleep quality. Sufficient and high-quality sleep significantly increases life satisfaction and self-rated health. In contrast, sleep deprivation or disorders are linked to reduced life satisfaction and shorter subjective life expectancy(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDepression is a common mental health issue that affects a significant proportion of middle-aged and older adults(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Research has shown a strong link between depressive symptoms and sleep disturbances(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), with depression potentially contributing to sleep disorders such as insomnia and early waking. Additionally, studies have confirmed the relationship between subjective well-being and depression(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The biopsychosocial model highlights the interconnection of biological, psychological, and social factors that collectively influence an individual\u0026rsquo;s health. In this context(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), sleep, as a key biological variable, influences social functioning and subjective well-being through depressive symptoms at the psychological level. Sleep disorders disrupt neurotransmitter balance and mood regulation, which can lead to depression and, in turn, reduce subjective well-being. Sleep-related issues at the biological level affect an individual\u0026rsquo;s evaluation of well-being at the social level through the mediating role of depressive symptoms.\u003c/p\u003e\u003cp\u003eOverall, our study will identify the predictors of subjective well-being\u0026mdash;such as life satisfaction, self-rated health, and subjective life expectancy\u0026mdash;using longitudinal data. Additionally, it will examine the relationship between nighttime sleep duration, depressive symptoms, and subjective well-being using both linear and non-linear methods. The study proposes that depressive symptoms mediate the relationship between nighttime sleep duration and subjective well-being in middle-aged and older adults, as framed within the biopsychosocial model(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In conclusion, the study aims to provide a theoretical foundation and empirical evidence to clarify the factors influencing subjective well-being and explore potential intervention strategies for middle-aged and elderly populations in China.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Background of the study\u003c/h2\u003e\u003cp\u003eThe data for this study were obtained from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey conducted by the National School of Development at Peking University. The CHARLS project, surveyed 150 counties, 450 communities (villages), and 12,073 households across 28 provinces (including autonomous regions and municipalities directly under the central government) using a probability proportional to size (PPS) sampling method. This method is considered representative of the entire population. By 2020, five waves of data collection had been completed: 2011, 2013, 2015, 2018, and 2020. Detailed descriptions of the CHARLS design and data collection procedures can be found in previous publications and on the official website(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Study sample\u003c/h2\u003e\u003cp\u003eWe used data from the CHARLS 2015 and 2018, a nationally representative longitudinal survey of Chinese individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years and their spouses. After identifying the study variables, the data were screened based on the following inclusion criterion: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) age\u0026thinsp;\u0026ge;\u0026thinsp;45 years. The exclusion criterion was: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) individuals with missing information on the primary study variables and covariates. A total of 8744 participants with missing data on general information and study variables were excluded from the analysis. The data were collated and analyzed, and 10706 respondents aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years were included in the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Measures\u003c/h2\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1 Measurement of depression\u003c/h2\u003e\u003cp\u003eDepression was assessed using the Chinese version of the Center for Epidemiological Survey Depression Scale (CES-D-10). The CES-D-10 employs a 4-point Likert scale with the following response options: \u0026ldquo;None,\u0026rdquo; \u0026ldquo;Rarely (1\u0026ndash;2 days),\u0026rdquo; \u0026ldquo;Sometimes (3\u0026ndash;4 days),\u0026rdquo; and \u0026ldquo;Always (5\u0026ndash;7 days).\u0026rdquo; Two items were reverse-scored, while the remaining items were scored positively, with individual item scores ranging from 0 to 3. The total score ranges from 0 to 30, with higher scores indicating more severe depressive symptoms(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). A score of \u0026ge;\u0026thinsp;10 was used to identify depression. The CES-D-10 has been fully validated in the Chinese middle-aged and older adult population to demonstrate its satisfactory reliability and validity(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) .\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2 Measurement of sleep duration\u003c/h2\u003e\u003cp\u003eSleep duration was assessed using a self-reported question asking participants about their average nightly sleep duration over the past month. Participants provided their responses in hours and minutes. Based on the participants\u0026rsquo; answers, the sleep duration was divided into three categories: short sleep duration (\u0026lt;\u0026thinsp;7 h), medium sleep duration (7\u0026thinsp;~\u0026thinsp;9 h), and long sleep duration (\u0026gt;\u0026thinsp;9 h)(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.3.3 Measurement of self-assessed health\u003c/h2\u003e\u003cp\u003eSelf-assessed health status was measured using the question, \u0026ldquo;How would you rate your health: very satisfied, satisfied, Somewhat satisfied, Less satisfied, or Very dissatisfied?\u0026rdquo; The five responses were coded as 1, 2, 3, 4, and 5, with higher scores indicating poorer health.The original questionnaire options were recoded as follows: 'Very satisfied', 'More satisfied' and 'Somewhat satisfied' were recoded as 'yes' (Satisfied); meanwhile, 'Less satisfied' and 'Very dissatisfied' were recoded as 'no' (Dissatisfied).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.3.4 Measurement of subjective life expectancy\u003c/h2\u003e\u003cp\u003eSubjective life expectancy was assessed based on the respondents\u0026rsquo; answers to the following question: \u0026ldquo;How likely do you think it is that you will be alive for the next 15 years or more? (\u0026lsquo;Almost impossible,\u0026rsquo; \u0026lsquo;unlikely,\u0026rsquo; \u0026lsquo;maybe,\u0026rsquo; \u0026lsquo;likely,\u0026rsquo; \u0026lsquo;almost certain\u0026rsquo;)\u0026rdquo; The five responses were coded as 1, 2, 3, 4, and 5, with higher scores indicating greater subjective life expectancy(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).The original questionnaire options were recoded as follows: 'likely' and 'almost certain' were recoded as 'yes' ; meanwhile, 'maybe' ,'Almost impossible' and 'unlikely' were recoded as 'no' .\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.3.5 Measurement of life satisfaction\u003c/h2\u003e\u003cp\u003eLife satisfaction was assessed based on the respondents\u0026rsquo; answers to the following question: \u0026ldquo;Please consider your life in its totality. Would you rate your satisfaction as complete, very complete, somewhat complete, not too complete, or not at all complete?\u0026rdquo; The five responses were coded as 1, 2, 3, 4, and 5, with higher scores indicating lower levels of life satisfaction. The original questionnaire options were recoded as follows: 'Very satisfied', 'More satisfied' and 'Somewhat satisfied' were recoded as 'yes' (Satisfied); meanwhile, 'Less satisfied' and 'Very dissatisfied' were recoded as 'no' (Dissatisfied).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.3.6 control variables\u003c/h2\u003e\u003cp\u003eThe study included covariates such as sociodemographic factors, BMI, grip, health behaviors, comorbidity count, and sensory function. Sociodemographic factors encompassed age, education level (classified into four categories: illiterate, primary school, middle school, and high school or above), and marital status (married or unmarried). Health behaviors included social activity (Yes/No), smoking status (Yes/No) and alcohol consumption (Yes/No). Vision was assessed by asking participants about their regular use of corrective lenses and their ability to recognize distant objects, such as identifying a friend across the street, with glasses if applicable. Similarly, hearing was evaluated through questions about participants\u0026rsquo; overall hearing status and the impact of hearing aids, if used. Participants rated their vision and hearing on a scale ranging from \u0026ldquo;excellent\u0026rdquo; to \u0026ldquo;poor.\u0026rdquo; Vision was categorized as \u0026ldquo;good\u0026rdquo; for ratings of \u0026ldquo;excellent,\u0026rdquo; \u0026ldquo;very good,\u0026rdquo; or \u0026ldquo;good\u0026rdquo;; \u0026ldquo;moderate\u0026rdquo; for \u0026ldquo;fair\u0026rdquo;; and \u0026ldquo;poor\u0026rdquo; for \u0026ldquo;poor.\u0026rdquo; The same classification criteria were applied to hearing(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). For ease of statistical analysis, categorical variables were numerically encoded as follows, with the specific coding rules detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCategorical variable assignment\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategorical variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable assignment\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;60\u0026thinsp;=\u0026thinsp;0,\u0026ge;60\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u0026thinsp;=\u0026thinsp;0,Male\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIlliterate\u0026thinsp;=\u0026thinsp;0,primary\u0026thinsp;=\u0026thinsp;1,middle school\u0026thinsp;=\u0026thinsp;2, high school\u0026thinsp;+\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnmarried\u0026thinsp;=\u0026thinsp;0, married\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural\u0026thinsp;=\u0026thinsp;0, Urban\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1,No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1,No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;7\u0026thinsp;=\u0026thinsp;0, 7\u0026thinsp;~\u0026thinsp;9\u0026thinsp;=\u0026thinsp;1,\u0026gt;9\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHearing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoor\u0026thinsp;=\u0026thinsp;0,Fair\u0026thinsp;=\u0026thinsp;1,Good\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVision\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoor\u0026thinsp;=\u0026thinsp;0,Fair\u0026thinsp;=\u0026thinsp;1,Good\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1,No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Statistical analyses\u003c/h2\u003e\u003cp\u003eDescriptive statistics and univariate analyses were conducted using IBM SPSS (version 22.0). Continuous variables were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), and categorical variables were presented as frequencies and percentages. The chi-square test was used to assess differences in categorical variables between groups, while t test was employed for continuous variables.\u003c/p\u003e\u003cp\u003eData analyses were performed using R (version 4.4.2), with a significance level of α\u0026thinsp;=\u0026thinsp;0.05. Pearson correlation analyses were conducted to examine the associations between subjective well-being, depression, grip strength, BMI, comorbidity count, and sleep duration. Multivariate linear regression with forward stepwise selection was used to identify potential influences on subjective well-being. Variables were selected and excluded based on thresholds set at αin\u0026thinsp;=\u0026thinsp;0.05 and αout\u0026thinsp;=\u0026thinsp;0.10, respectively. Sensitivity analyses primarily comprised linear regression analyses based on gender subgroups. The \u0026ldquo;forestplot\u0026rdquo; package in R was used to visualize the results of the regression analysis. Restricted cubic spline (RCS) regression models were used to explore the non-linear relationship between sleep duration, depression, and subjective well-being. To assess depression as a mediating variable, the following steps were taken: linear regression was used to explore the direct effect of sleep duration on subjective well-being, and depression was added to the regression model to analyze its mediating role between sleep duration and subjective well-being using the \u0026ldquo;mediation\u0026rdquo; package in R. The total, direct, and indirect effects in the model were validated using the bootstrap method with 1000 resampled iterations(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 General characteristics of the participants\u003c/h2\u003e\n \u003cp\u003eThis study involved 10,706 participants: 5,449 in the middle-aged group (45\u0026ndash;59 years) and 5,257 in the elderly group (60 years and older). The age distribution was generally balanced. Regarding gender distribution, 5,520 participants (51.6%) were female, and 5,186 participants (48.4%) were male, reflecting a nearly equal gender ratio. Of the middle-aged and elderly participants, 5,484 (51.2%) reported insufficient sleep, 4,702 (43.9%) had normal sleep duration, and 514 (4.8%) reported sleeping more than 8 hours. All group differences were statistically significant, except for those related to education, smoking, drinking, and life satisfaction. Participant characteristics are presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive Statistics of Study Variables Across sleep duration\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCategories\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOverall n(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003esleep duration n(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP Value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;7\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e7\u0026thinsp;~\u0026thinsp;9\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;9\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5449(50.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2650(48.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2557(46.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e242(4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5257(49.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2834(53.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2151(40.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e272(5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5520(51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2952(53.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2305(41.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e263(4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5186(48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2532(48.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2403(46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e251(4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIlliterate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e625(5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e323(51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e262(41.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40(6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eprimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1087(10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e542(49.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e495(45.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50(4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e484(4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e236(48.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e229(47.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19(3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehigh school+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8510(79.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4383(51.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3722(43.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e405(4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9551(89.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4814(50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4291(4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e446(4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1155(10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e670(58.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e417(4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68(5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1837(17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1013(55.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e789(43.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35(1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8869(82.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4471(50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3919(44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e479(5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4729(44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2397(50.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2099(44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e233(4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5977(55.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3087(51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2609(43.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e281(4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2967(27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1513(51.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1326(44.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e128(4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7739(72.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3971(51.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3382(43.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e386(5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHearing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFair\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5636(52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2944(52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2428(43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e264(4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3814(35.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1798(47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1829(48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e187(4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1256(11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e742(59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e451(359.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63(5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFair\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6183(57.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3340(54.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2545(41.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e298(4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4507(42.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2136(47.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2157(47.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e214(4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8(50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6(37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression(2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4111(38.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2400(58.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1514(36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e197(4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e144.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6595(61.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3084(46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3194(48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e317(4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6139(57.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3162(51.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2725(44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e252(4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4567(42.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2322(50.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1983(43.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e262(5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elife satisfaction(2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e770(7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e371(48.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e366(47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33(4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9936(92.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5113(51.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4342(43.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e481(4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eself-assessed health(2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7704(72.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3699(48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3620(47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e385(5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e114.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3002(28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1785(59.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1088(36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e129(4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esubjective life expectancy(2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2951(27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1382(46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1443(48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e126(4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7755(72.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4102(52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3265(42.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e388(5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Correlation Between Sleep Duration, BMI, grip and Outcome Variables\u003c/h2\u003e\n \u003cp\u003eThe Pearson correlation analysis revealed significant associations between sleep duration and several health-related factors among middle-aged and elderly individuals. Specifically, sleep duration was negatively correlated with depression (r = \u0026minus;\u0026thinsp;0.19, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), self-rated health (r = \u0026minus;\u0026thinsp;0.12, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), life satisfaction (r = \u0026minus;\u0026thinsp;0.10, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and positively correlated with life expectancy (r\u0026thinsp;=\u0026thinsp;0.08, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Sleep duration was also negatively associated with comorbidity count (r = \u0026minus;\u0026thinsp;0.14, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and positively with grip strength (r\u0026thinsp;=\u0026thinsp;0.11, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). No significant correlation was found between BMI and self-rated health. Detailed results are presented in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation Matrix of Sleep Duration, depression, and Outcome Variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean(SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.sleep duration(h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.39(1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.CES-D-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.62(6.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.19*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.life satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.74(0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.10*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.subjective life expectancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.94(1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.30*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.17*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.self-assessed health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.06(0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.12*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.29*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6. Grip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.27(9.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.24*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.12*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7. BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.09(3.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8. Comorbidity count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.64(1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.14*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.19*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.17*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003eNote:*P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003ch2\u003e\u003cstrong\u003e3.3 Results of the multivariate linear and nonlinear regression analysis examining the relationship between sleep duration and depression\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eThe study found a significant relationship between sleep duration and depressive symptoms, supported by both linear and nonlinear models. Linear regression analysis showed a significant negative correlation between sleep duration and depressive symptoms (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that longer sleep duration is linked to fewer depressive symptoms (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). Nonlinear regression analysis, on the other hand, revealed significant nonlinear effects (P for non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Specifically, depression risk decreased as nighttime sleep duration increased, reaching a minimum and stabilising around 6.5 hours of sleep. Detailed analysis results are presented in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA. Further analysis of gender-specific subgroups and regression analyses of the overall population revealed key factors influencing depression scores: co-morbidity count, sleep duration, social activity participation, grip strength, age, visual and auditory health, and marital status (see Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e\n \u003ch2\u003e\u003cstrong\u003e3.4 Results of the multivariate linear and nonlinear regression analysis examining the relationship between sleep duration and subjective well-being\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eThe study found significant longitudinal causal relationships between sleep duration and subjective well-being, including self-rated health, life satisfaction, and subjective life expectancy, supported by both linear and nonlinear models. Linear regression analysis revealed significant negative correlations between sleep duration and self-rated health (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and life satisfaction (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that longer sleep duration is associated with higher self-rated health and greater life satisfaction (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). In contrast, sleep duration was positively correlated with subjective life expectancy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that longer sleep duration is associated with higher subjective life expectancy (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). Nonlinear regression analysis further revealed significant nonlinear effects in all three relationships (P for non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Specifically, the likelihood of being satisfied with self-rated health and life satisfaction increased with longer nighttime sleep, reaching its highest level around 6.5 hours and stabilizing thereafter(Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Similarly, the likelihood of experiencing higher subjective life expectancy increased with longer sleep duration. Gender subgroup analysis and regression analyses of the overall population identified key predictors of these outcomes, including co-morbidity count, sleep duration, age, visual and auditory acuity, and marital status (Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe above dose-response relationship was adjusted for following covariates: age, gender, education, marital status, residence, smoking, drinking ,Hearing,Vision,Social activity Comorbidity count, BMI, grip.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 The results of regression and mediation analyses\u003c/h2\u003e\n \u003cp\u003eThe control variables in the linear regression analysis included demographic characteristics such as age, education, residence, marital status, and lifestyle and health factors, including drinking, smoking, social activity, hearing, vision, BMI, grip strength, and comorbidity count. The covariate selection was validated by examining the variance inflation factor (VIF) and tolerance levels. The VIF values were all below 10, and tolerance levels were above 1, indicating no multicollinearity among the variables.The results from linear regression models 1, 4, and 7 (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) confirmed this observation, showing a strong correlation between sleep duration and key health outcomes, including life satisfaction, self-rated health, and life expectancy among middle-aged and elderly individuals (\u0026beta;= -0.031, -0.036, 0.020, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In Models 3, 6, and 9, when depression was included as a mediator, sleep duration remained significantly associated with self-rated health, life satisfaction, and life expectancy (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), suggesting that depression partially mediates the relationship between self-identity and sleep duration. When depression was included as a mediator in Models 3, 6, and 9, sleep duration maintained a significant association with self-rated health, life satisfaction, and life expectancy (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The findings indicate that depression partially mediates the relationship between self-identity and sleep duration. Bootstrap analysis revealed that depressive tendencies partially mediate the effects of sleep duration on life satisfaction, self-rated health, and life expectancy, as detailed in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eLinear regression analysis of the mediating effects of depression on sleep duration and subjective life expectancy\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003elife satisfaction\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eself-assessed health\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003esubjective life expectancy\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 6\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 7\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 9\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.031***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.463\u003c/p\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-14.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.010*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.036***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.463\u003c/p\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-14.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.012**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.463\u003c/p\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-14.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCES-D-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.045**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.048**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.043***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-23.604\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e137.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e193.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eNote:The table shows the results of linear regression models assessing the mediating effects of depression on the relationship between sleep duration and different outcomes (life satisfaction, self-assessed health, and subjective life expectancy).\u0026beta;\u0026thinsp;=\u0026thinsp;standardized regression coefficient. t\u0026thinsp;=\u0026thinsp;t-statistic. p\u0026thinsp;=\u0026thinsp;significance level. R\u0026sup2; = proportion of variance explained by the model. F\u0026thinsp;=\u0026thinsp;overall model significance. *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab5\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMediation Analysis Results of Dependent Variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDependent variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLICI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eULCI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLife satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-assessed health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubjective life expectancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eCI: Confidence Interval;LLCI: Lower Limit Confidence Interval;ULCI: Upper Limit Confidence Interval.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study used data from the 2015 China Health and Retirement Longitudinal Study (CHARLS) to analyze the current state of nighttime sleep in middle-aged and elderly populations in China. The study found that in 2015, 51.0% of individuals experienced inadequate nighttime sleep, significantly higher than the 5.1% prevalence of long sleep duration. This suggests that inadequate sleep duration has become the primary sleep issue among middle-aged and elderly populations in China. This figure is notably higher than the 48.3% reported by Chen et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) using the 2011 CHARLS data, indicating a possible link to China\u0026rsquo;s economic and social development and increasing life stress. This finding indicates that the problem of insufficient sleep has become more pronounced in recent years. A longitudinal study has shown that individuals with chronic sleep deprivation are more likely to develop cardiomyopathy and vascular metabolic diseases(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Given the high prevalence and serious health consequences of sleep deprivation, public health agencies should implement more effective measures to screen for and manage sleep disorders.Regarding gender differences, this study found that middle-aged and elderly women had a significantly higher prevalence of insufficient sleep than men, likely due to the greater family and caregiving responsibilities that shorten their nighttime sleep. Jiang et al. also found that individuals with insufficient nighttime sleep were more likely to be female, older, and suffer from more chronic illnesses(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), consistent with this study\u0026rsquo;s findings, highlighting the need for special attention to these individuals. Correlation analysis revealed a significant association between sleep duration, grip strength, and BMI. Grip strength serves as a validated marker of musculoskeletal health and functional status in middle-aged and older adults, and has been consistently linked to sleep quality in previous studies (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Inadequate sleep contributes to muscle atrophy and dysregulation of metabolic and appetite-related pathways, leading to both decreased grip strength and increased adiposity. Conversely, obesity and metabolic disturbances impair sleep quality and duration, while reduced muscle strength limits physical activity and disrupts circadian homeostasis, further exacerbating sleep impairment(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).Therefore, greater attention should be directed toward the nutritional and metabolic health of individuals with insufficient sleep duration, with particular emphasis on monitoring BMI and grip strength. Early screening and intervention targeting high-risk groups\u0026mdash;such as those with obesity or sarcopenia\u0026mdash;are essential to prevent the onset of physiological and psychological chain reactions.\u003c/p\u003e\u003cp\u003eThis study examined the relationship between nighttime sleep duration, depression, and subjective well-being in middle-aged and older adults. The findings showed that baseline sleep duration significantly affected depression levels and subjective well-being, consistent with the results reported by Zhu et al.(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe study\u0026rsquo;s longitudinal design allowed for a better inference of causal relationships. Subgroup sensitivity analyses yielded results consistent with the main findings, further supporting their robustness. Adequate sleep helps regulate emotions and stabilize mood, reducing anxiety and depression(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). In turn, this can enhance subjective well-being. However, there is currently no standardized quantitative definition of sleep deprivation. Some scholars define sleep deprivation as \u0026le;\u0026thinsp;6 hours of nighttime sleep(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), whereas the National Sleep Foundation (NSF) recommends a threshold of \u0026lt;\u0026thinsp;7 hours(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).This study adopted a threshold of less than 7 hours of sleep per night, consistent with the definition recommended by the National Sleep Foundation (NSF). Preliminary analyses investigated the relationships between sleep duration, depressive symptoms, and subjective well-being.This standard has also been employed by Chinese scholar Bingxin Jiang et al. in their research(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). To better capture the trend in perceived well-being across varying sleep durations, sleep duration was treated as a continuous variable to examine its dose\u0026ndash;response relationship with subjective well-being. The findings indicated that when nightly sleep duration reached approximately 6.5 hours, individuals exhibited a lower risk of depression and higher levels of subjective well-being.Beyond this threshold, the association between sleep duration and these outcomes plateaued, indicating that the optimal minimum sleep duration is approximately 6.5 hours. Conversely, sleep durations exceeding this threshold may be linked to an increased risk of sleep apnea among middle-aged and older adults. Therefore, it is essential for this population to maintain an appropriate sleep duration\u0026mdash;neither too short nor too long\u0026mdash;in order to support both psychological and physiological homeostasis(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study found that increased sleep duration was linked to improved self-rated health and life satisfaction, with depression acting as a partial mediator. Ouyang identified a bidirectional relationship between depression and sleep duration(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e), noting that depression is often associated with abnormal neurotransmitter levels (5-hydroxytryptamine and dopamine)(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), both of which regulate sleep(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Low levels of 5-hydroxytryptamine are associated with poor sleep quality, including insomnia and early awakening. Sleep deprivation further exacerbates neurotransmitter imbalances, intensifying depressive symptoms. These depressive symptoms, in turn, directly reduce patients\u0026rsquo; levels of satisfaction(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). This evidence supports the mediating role of depression in the relationship between sleep quality and patient satisfaction, indicating that improving sleep quality and alleviating depressive symptoms can effectively enhance overall patient satisfaction. Subjective life expectancy (SLE) refers to an individual\u0026rsquo;s psychological estimate of their remaining years of life. This estimate is shaped by an individual\u0026rsquo;s judgment and perception of their remaining lifespan. A Korean Longitudinal Study of Aging (KLoSA) survey showed that subjective life expectancy is associated with physical health and perceptions of aging, disease, and death(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).The study identified a significant link between sleep duration and subjective life expectancy among Chinese adults aged 45 and older. Sleep, a basic physiological need, is vital for maintaining optimal health. Adequate sleep is key to restoring physical strength and strengthening immunity. Additionally, adequate sleep supports mental health, which may shape an individual\u0026rsquo;s expectations of future longevity(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). This supports the idea that the impact of sleep duration on subjective life expectancy is partly mediated by depressive mood. Longer sleep duration reduces depressive symptoms, improving well-being and life satisfaction.\u003c/p\u003e\u003cp\u003eIn summary, this study explored the relationship between sleep duration, depression, and subjective well-being in middle-aged and elderly individuals using both linear and nonlinear approaches. The nonlinear analysis found that when sleep duration reached 6.5 hours, the risk of depression was lower, and subjective well-being levels were higher. Middle-aged and elderly individuals should ensure at least 6.5 hours of sleep to maintain their subjective well-being. Linear regression analysis indicated that early screening should focus on individuals at risk, including the elderly, unmarried individuals, those with comorbidities, visual and hearing impairments, sleep deprivation, weak grip strength, and obesity. Emphasis should be placed on external support and the establishment of multi-dimensional social networks to reduce depression levels and improve subjective well-being. Furthermore, this study suggests that good sleep is closely linked to subjective well-being (including life satisfaction, self-rated health, and life expectancy) among Chinese middle-aged and elderly individuals, with depressive tendencies partially mediating this relationship. Sleep deprivation directly impacts emotional and psychological well-being, further reducing subjective well-being by worsening depressive symptoms. However, effective sleep management strategies for middle-aged and elderly populations are noticeably lacking worldwide, and most individuals with sleep problems lack adequate interventions. In the future, attention should be given to sleep-related issues, and policies should include widespread education on sleep health to foster accurate knowledge of sleep and raise awareness of sleep management.\u003c/p\u003e"},{"header":"5 limitations","content":"\u003cp\u003eThis research utilized data from the China Health and Retirement Longitudinal Study (CHARLS), drawing on a sample that comprehensively represents the country\u0026rsquo;s demographic landscape. The possible limitations of this study are as follows:Firstly, this study only assessed sleep duration and did not fully capture individuals\u0026rsquo; overall sleep quality. However, sleep encompasses multiple dimensions beyond duration, such as the frequency of sleep interruptions. Future research should consider integrating both subjective and objective sleep data to provide a more comprehensive assessment of sleep quality.Secondly, while CHARLS offers a nationally representative sample of Chinese middle-aged and elderly populations, certain subgroups\u0026mdash;such as individuals living in remote areas or underrepresented ethnic minorities\u0026mdash;may not be fully represented, which could limit the generalizability of the findings.\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eThis study identified a significant relationship between sleep duration, depression, and subjective well-being in Chinese middle-aged and elderly individuals. A sleep duration of at least 6.5 hours was associated with lower depression risk and higher subjective well-being. Early screening should target high-risk groups, including those with comorbidities, sleep deprivation, or weak grip strength. The findings highlight the mediating role of depression in the sleep\u0026ndash;well-being link and underscore the need for effective, non-pharmacological sleep interventions. Future policies should promote sleep health education to improve public awareness and support psychological well-being in aging populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The datasets supporting the conclusions of this article are available publicly.\u003c/p\u003e\n \u003cp\u003ehttp://charls.pku.edu.cn/pages/data/111/en.html\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNot applicable\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eJL: Conceptualization, Data curation, Software, Visualization, Writing \u0026ndash; original draft, YL: Writing \u0026ndash; original draft, Formal analysis, Investigation, Methodology, Supervision, Validation. LW: Conceptualization, Software, Validation, Visualization. YW: Data curation, Formal analysis, Methodology, Project administration, Writing \u0026ndash; original draft. JS: Investigation, Methodology, Writing \u0026ndash; original draft. XZ: Conceptualization, Data curation, Writing \u0026ndash; original draft, Formal analysis.GM: Funding acquisition, Project administration, Supervision, Validation, Writing \u0026ndash; review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWe are grateful to all participants enrolled in the CHARLS and its members. We are grateful to CHARLS for providing us with these data. We appreciate all the co-researchers, reviewers, and editors.\u003c/p\u003e\n \u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n \u003cp\u003eAccording to Article 32 (1) and (2) of the Measures for Ethical Review of Life Science and Medical Research Involving Human Beings issued on February 18, 2023, ethical review may be waived if (a) research is conducted using legally obtained public data or data generated by observation and not interfering with public behavior; and (b) research is conducted using anonymized information data.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePatient and Public Involvement\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePatients and members of the public were not involved in the design, conduct, reporting, or dissemination plans of this research. This study is a secondary analysis of publicly available CHARLS data. As such, there was no direct patient or public involvement at any stage of the research process.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003cbr\u003e\u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChristensen K, Doblhammer G, Rau R, Vaupel JW. Ageing Populations: The Challenges Ahead. \u003cem\u003eLancet\u003c/em\u003e (2009) 374(9696):1196-208. doi: 10.1016/s0140-6736(09)61460-4.\u003c/li\u003e\n\u003cli\u003eAgeing and health in China 2023. https://www.who.int/china/health-topics/ageing. [Accessed 23 Dec 2024].\u003c/li\u003e\n\u003cli\u003eNational Health Commission of the People\u0026rsquo;s Republic of China Healthy China action.http://www.nhc.gov.cn/guihuaxxs/s3585u/201907/e9275fb95d5b4295be8308415d4cd1b2.shtml.[Accessed 23 Dec 2024].\u003c/li\u003e\n\u003cli\u003eTeater B, Chonody JM. How Do Older Adults Define Successful Aging? 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Epub 20221023. doi: 10.1016/j.ssmph.2022.101271. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Depression, Sleep duration, Subjective well-being, Mediating effect, Middle-aged and older people, CHARLS","lastPublishedDoi":"10.21203/rs.3.rs-7467157/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7467157/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eEvidence on how sleep duration affects subjective well-being and depressive symptoms in older Chinese adults remains limited.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eData from 10,706 participants aged 45 years and above in the China Health and Retirement Longitudinal Study (CHARLS) were analyzed. Descriptive statistics, restricted cubic spline models, and subgroup analyses were conducted. Mediation analysis with 1,000 bootstrap iterations assessed the mediating role of depression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSleep duration was significantly associated with depression, self-rated health, life satisfaction, and life expectancy. Regression models indicated that approximately 6.5 hours of sleep was linked to the lowest depression risk and the highest well-being. Longer sleep was positively related to life satisfaction and self-rated health, though the effect plateaued beyond 6.5 hours. Gender subgroup analysis showed consistent patterns. Mediation analysis revealed that depressive symptoms partially mediated the association between sleep duration and subjective well-being.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eAdequate sleep duration, particularly around 6.5 hours, is linked to lower depression risk and greater subjective well-being in Chinese middle-aged and older adults. Early screening for sleep and mental health issues in high-risk groups may help promote healthy aging.\u003c/p\u003e","manuscriptTitle":"Longitudinal associations between sleep duration and subjective well-being in middle-aged and the older Chinese adults: the mediating role of depressive symptoms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-04 10:48:43","doi":"10.21203/rs.3.rs-7467157/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-29T12:36:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-21T20:02:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63322586430993794972807106215306352131","date":"2026-01-11T14:59:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214003004698376928412279968635552698176","date":"2026-01-04T23:54:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-03T12:19:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"130774872855425262030310092705012696976","date":"2026-01-02T23:55:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-01T10:47:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-29T11:01:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-28T06:19:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-27T12:44:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-27T02:40:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"71e7e552-1276-4e14-ba8b-28c523a684c6","owner":[],"postedDate":"December 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59040130,"name":"Health sciences/Diseases"},{"id":59040131,"name":"Health sciences/Health care"},{"id":59040132,"name":"Biological sciences/Psychology"},{"id":59040133,"name":"Social science/Psychology"},{"id":59040134,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-18T06:40:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-04 10:48:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7467157","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7467157","identity":"rs-7467157","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}