Relationship between Multidimensional Sleep Health and Cognitive Function in Older Adults in Nursing Homes: A Cross-Sectional Study

Relationship between Multidimensional Sleep Health and Cognitive Function in Older Adults in Nursing Homes: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Relationship between Multidimensional Sleep Health and Cognitive Function in Older Adults in Nursing Homes: A Cross-Sectional Study Yang Li, Xinxiu Dong, Hui Hu, Xiaotong Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8801555/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background As an increasingly prevalent geriatric health issue, mild cognitive impairment (MCI) is a critical transitional stage preceding dementia, with a high risk of progression to dementia but also substantial plasticity for intervention. Sleep problems are prevalent among elderly people in nursing homes and can increase the risk of cognitive impairment in the elderly. Although existing studies have explored the association between sleep problems and cognitive function.However, most previous studies have focused on single sleep characteristics rather than comprehensive sleep health. Methods A cross-sectional study was conducted among 416 older adults aged ≥ 60 years from 3 nursing homes in Wuhan. The Sleep Health Composite Score (Sleep HCS) based on the SATED model (Satisfaction/quality, Alertness, Timing, Efficiency, Duration) was used to assess multidimensional sleep health, while cognitive function was evaluated by using the Montreal Cognitive Assessment Basic Version (MoCA-B). Correlation analysis and binary logistic regression were performed to explore the associations between sleep health and MCI. Results Among participants, 47.35% were diagnosed with MCI, and 70.7% had sleep problems in at least two domains. Sleep HCS was positively correlated with MoCA-B scores (r = 0.141, P < 0.01) and served as an independent protective factor for MCI (adjusted OR = 0.493, 95% CI: 0.366–0.665, P < 0.001). Specifically, poor sleep efficiency (adjusted OR = 1.969, 95% CI: 1.102–3.521, P = 0.022) and abnormal sleep midpoint (adjusted OR = 1.955, 95% CI: 1.142–3.349, P = 0.015) were significantly associated with an elevated risk of MCI. Conclusion Multidimensional sleep health, particularly sleep efficiency and sleep midpoint, is closely associated with the risk of MCI in nursing home-dwelling older adults. The findings highlight the importance of comprehensive sleep health assessment and targeted sleep interventions to delay cognitive decline in this population. Cognitive function Sleep health Older adults Background Cognitive impairment refers to a group of syndromes characterized by core symptoms of acquired cognitive damage, which can lead to reduced ability in daily life and work, and may or may not be accompanied by psychobehavioral abnormalities. According to the severity of the condition, it can be classified into subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia[ 1 ].Among these, Alzheimer's disease (AD) is the most common type of dementia.MCI is a transitional stage between the cognitive changes associated with normal aging and clinical dementia, and it constitutes a pathological condition. Individuals with MCI exhibit certain impairments in cognitive domains including memory, language, attention, visuospatial function, executive function, and orientation, while maintaining intact daily living abilities[ 2 ].Epidemiological data show that the prevalence rate of dementia among people aged 60 years and above in China is 6.0%, and that of MCI is 15.5%[ 3 ]. It is estimated that 15%~28% of elderly individuals with MCI progress to dementia within one year, and more than 70% do so within 5–10 years[ 4 ]. However, another study[ 5 ] has indicated that approximately 44% of elderly individuals with MCI can revert to normal cognitive function after one year of cognitive management. Thus,MCI stages represent the preclinical phases of dementia, characterized by bidirectional transformability and plasticity, and serve as the "golden window period" for the early intervention, prevention and treatment of dementia. As a basic physiological need of humans, sleep is closely related to physical and mental health[ 6 ].Adequate sleep not only maintains physical vitality but also plays a crucial role in the regulation of cognitive function, memory consolidation, and emotional regulation[ 7 ].Quality sleep facilitates the clearance of intracerebral metabolites, supports brain function repair, and helps maintain homeostasis[ 8 ]. In recent years, sleep has been identified as a modifiable risk factor for MCI[ 9 ]. Furthermore, abnormal changes in sleep patterns are expected to serve as potentially valuable signals for the early identification of MCI[ 10 ].Data show that globally, 30%-40% of elderly individuals suffer from sleep disorders, and 88% of those aged 65 years and above experience sleep problems[ 11 ]. A meta-analysis[ 12 ] has revealed that the prevalence of sleep disorders among elderly individuals in China is 46%. Elderly people with sleep disorders have a 1.39 times higher risk of developing MCI than those with normal sleep[ 13 ], and this risk will further increase the likelihood of progressing to dementia[ 14 ]. Compared with healthy elderly individuals, patients with MCI and AD have poorer sleep quality, often accompanied by various sleep disorder manifestations, such as shortened total sleep time, increased number of nocturnal awakenings, decreased sleep efficiency, exacerbated sleep fragmentation, and sleep-related daytime dysfunction[ 15 ].Especially in nursinghomes, residents showed significantly poorer sleep compared to community-dwelling older adults[ 16 ], caused by factors such as environmental noise, nocturnal care practices and social disengagement.In summary, both cognitive decline and sleep problems are common geriatric health issues during aging. On the one hand, the incidence of sleep problems is relatively high among elderly patients with MCI; on the other hand, sleep problems can increase the risk of cognitive decline and accelerate the progression of MCI to dementia. Relevant clinical guidelines[ 17 ]have explicitly identified sleep as a modifiable risk factor for cognitive impairment and incorporated sleep management into the primary prevention system for cognitive impairment. Early screening, systematic assessment, and scientific management of sleep health in the elderly living in nursing homes can serve as a reasonable, effective, and easily scalable intervention strategy to delay the progression of cognitive decline. However, previous studies have some limitations: most studies have used a single scale or have focused on the association between individual sleep characteristics (such as sleep quality and sleep duration) and cognitive outcomes[ 18 ], overlooking the multidimensional nature of sleep.In 2014, Buysse[ 18 ] first proposed the concept of "sleep health", defining it as a multiple dimensions of sleep that adapts to individual, social, and environmental needs and promotes physical and mental health. Meanwhile, he also proposed the SATED Scale (Satisfaction/quality, Alertness, Timing, Efficiency, and Duration Scale) to assess five sleep dimensions: satisfaction, alertness, sleep timing, sleep efficiency, and sleep duration[ 19 , 20 ].To date, existing studies have explored the correlation between sleep health and metabolic diseases as well as mental health outcomes[ 19 ], but few have focused on the association between sleep health and cognitive function. Cognitive decline and sleep problems associated with aging not only seriously affect the quality of life of patients and their caregivers but also impose a heavy economic burden, psychological stress, and physical health impairments, making them a pressing major public health issue in contemporary society. Therefore, from a multidimensional perspective of sleep health, this study systematically investigates the correlation between sleep health and cognitive function in older adults, aiming to provide new insights and evidence for the early prevention and control of cognitive impairment. Methods Population This study recruited participants from 3 nursing home in Wuhan using a convenience sampling method. The sample size was calculated using the formula of [Z²P(1-P)]/d², with a significance level set at 0.05 and CI of 95% [ 21 ].The prevalence of MCI among older adults in Chinese nursing home was set at 0.54[ 22 ] with a relative precision of 0.05. After accounting for a 10% invalid sample size, the final calculated sample size was 425 participants. Inclusion criteria: (1) Age > 60 years; (2) Length of residence in the nursing home ≥ 6 months; (3) Possessing normal communication and comprehension abilities, and being able to complete the questionnaire.Exclusion criteria:(1) presence dementia such as Alzheimer’s disease, vascular dementia, and other neurological diseases that can cause brain dysfunction, which diagnosed by a medical institution; (2) Those with severe visual and hearing impairment; (3) Those with comorbid psychiatric or neurological disorders that result in the inability to communicate normally. Prior to the interviews, all participants provided informed consent. The study was approved by the Ethics Committee of Jianghan University (Approved No. of ethic committee: JHDXKJLL2025-261). Sleep health assessment Sleep Health Composite Score(sleep HCS) was calculated using the SATED model sleep dimensions[ 18 ],which includes sleep satisfaction/quality,aterness, sleep timing, sleep efficiency, sleep duration.Each domain was measured using a corresponding subjective sleep rating scale and was dichotomized (1 = good, 0 = poor) with established cutoff points (Table 1 ) which were validated in previous Literature[ 23 – 27 ]. A sum of scores across the five domains was used as the composite sleep health score (Range = 0–5),A higher score indicates a higher level of sleep health[ 28 ]. Cognitive function assessment Montreal Cognitive Assessment (MoCA) Test is a widely used screening assessment tool for cognitive function of older adults. Studies have shown that the MoCA test has high sensitivity (80–100%) [ 29 ]and specificity (50–76%) in identifying MCI, and it is more accurate than the Mini-Mental State Examination Scale in distinguishing between normal and MCI (Grade A recommendation)[ 30 ].The Montreal Cognitive Assessment Basic Version (MoCA-B) was used to evaluate the overall cognitive function of the elderly based on the expert consensus on neuropsychological assessment[ 30 ]. The Chinese version of MoCA-B is a revised edition tailored for older adults with low educational attainment[ 31 ], and shows no ceiling effect among older adults with high educational attainment. MoCA-B consists of 8 cognitive domains: executive function, language, orientation, calculation, conceptual thinking, memory, visual perception, and attention( www.mocatest.org , visit Basic section).MoCA-B has a score of 30, and a higher score indicates better cognitive function. The cut-off values for diagnosing MCI were stratified by educational level as follows: participants with ≤ 6 years of education and a score ≤ 19; those with 7–12 years of education and a score ≤ 22; and those with > 12 years of education and a score ≤ 24 were classified as having cognitive impairment.The Cronbach's α of the MoCA-B scale was 0.807, and the optimal sensitivity and specificity could be obtained in all education groups by using the above cut-off score[ 32 ]. Participants with MoCA-B score ≤ the critical value received phase II cognitive testing, including Activities of Daily Living Scale (ADL) and Clinical Dementia Rating (CDR). MCI was diagnosed according to Petersen criteria[ 33 ] and relevant guidelines and expert consensus[ 34 ] as follows: (1) complaints of memory loss; (2)MoCA-B score less than the critical value; (3) normal or very slight impairment in activities of daily living, such as ADL score < 16; (4) Clinical Dementia Rating (CDR) = 0.5. Covariates Potential covariates include socio-demographics (e.g.,gender,age, education, marital status, residential status,monthly income), life style factors (e.g., smorking,coffee, drink,tea,physical exercise,social activities), and sleep-related variables(e.g., number of chronic diseasess, mental health).Mental health status was assessed using the Kessler Psychological Distress Scale (K6), which comprises six psychological symptoms[ 32 ]:“nervous”, “depressed”, “restless”, “hopeless”, “having difficulty doing everything”, and “feeling worthless”. Each symptom was rated on a 5-point Likert scale. The total score of the scale ranged from 0 to 24, with a cut-off value of 12. A score higher than 12 indicated a high risk of mental disorder.All other variables measured using a survey designed by the research team. Statistical analysis Missing data were complemented using multiple interpolation.Data were presented as mean (standard deviation,SD) or frequency (n) and proportion (%). Before statistical analysis,data were tested for independence, normality, and homogeneity of variance. Independent-samples t-test was used for continuous variables, and the χ² test was adopted for categorical variables to compare the measurement data between the two groups.Correlation analysis was performed to examine the univariate associations between the sleep HCS and its domains, as well as cognitive function-related indicators. For continuous variables conforming to a normal distribution, Pearson correlation analysis was adopted; for dichotomous variables, point-biserial correlation analysis was employed.In the multivariate analysis, Table 1 Sleep health dimensions and cut points used to construct sleep health composite score Dimension Definition Assessment/Item Cut point Sleep satisfaction /quality[ 23 ] Sleep satisfaction refers to the evaluation of an individual's overall satisfaction with their own sleep status. Self-rated sleep quality score (from PSQI Item 6) 1:Item 6 of the PSQI had a score of 0 or 1. 0:Item 6 of the PSQI had a score of 2 or 3. Aterness[ 24 ] Alertness refers to the ability to maintain wakefulness during the daytime. Using the Epworth Sleepiness Scale (ESS) 1:ESS10 Sleep timing [ 25 ] Sleep timing refers to the consistent bedtime and wake time within a 24-hour period, and serves as a marker of circadian phase. It was expressed as the sleep midpoint, calculated by the formula: Bedtime + (Wake-up Time − Bedtime) / 2 1: 2:00–4:00 am 0: 4:00 am Sleep efficiency [ 26 ] Sleep efficiency is defined as the ease of falling asleep and returning to sleep. Three items from the PSQI (Items 1, 3, and 4) were used to calculate sleep efficiency. 1:>85% 0:<85% Sleep duration[ 27 ] Sleep duration generally refers to the total accumulated sleep within a 24-hour period, including nocturnal sleep and daytime napping. PSQI(Items 4):How many hours do you actually sleep each night? 1:7−9h 0:9h PSQI:Pittsburgh sleep quality index; binary logistic regression analyses were used to determine the association between sleep health and MCI, and two models were developed.The unadjusted model no covariate was adjusted. The adjusted model further incorporated covariates with P < 0.05 in the univariate analysis, including sex, age, marital status, educational level, monthly income, physical exercise, smoking, drinking and mental health.A two-tailed p < 0.05 was considered statistically significant.All data analyses were performed using IBM SPSS software (version 27.0). Results Characteristics of the participants This study conducted face-to-face questionnaire surveys among 430 participants from 3 nursing homes in Wuhan from March to August 2025.After excluding invalid questionnaires, a total of 416 participants were finally included, with a questionnaire recovery rate of 96.74%.Table 2 shows the characteristics of the participants (N = 416). Their mean age was 70.55 years (SD = 7.58). The mean body mass index(BMI) was 23.36 kg/m² (SD = 2.97). According to the diagnostic criteria of MCI based on the MoCA-B, 197 participants (47.35%) were classified as MCI. Table 2 also shows the between-group comparisons of demographic variables between the MCI and non-MCI (NC)groups. Overall, age, gender, marital status, educational level, monthly income, frequency of physical activity, drinking and mental health were significantly associated with the risk of MCI (p < 0.05). Table 2 Participant characteristics and group comparison for MCI (N = 416) Variables Total( N = 416) MCI P No( n = 219) Yes( n = 197) Age(year) 70.5(7.6) 68.7(6.3) 72.5(8.3) <0.001 BMI(kg/m 2 ) 23.4(2.9) 23.4(2.8) 23.3(3.1) 0.524 Gender Male 171(41.1%) 105(47.9%) 66(33.5%) 0.003 Female 245(58.9%) 114(52.1%) 131(66.5%) Marital status Yes 237(57.0%) 139(63.5%) 98(49.7%) 0.005 Single/divorce/separation 179(43.0%) 80(36.5%) 99(50.3%) Residential status Single room 23(5.5%) 15(6.8%) 8(4.1%) 0.443 Double room 343(82.5%) 179(81.7%) 164(83.2%) Triple room 50(12.0%) 25(11.4%) 25(12.7%) Education leve Illiteracy/Primary school 36(8.7%) 11(5.0%) 25(12.7%) <0.001 Junior high school 105(25.2%) 52(23.7%) 53(26.9%) High school 169(40.6%) 85(38.8%) 84(42.6%) University or above 106(25.5%) 71(32.4%) 35(17.8%) Monthly income (RMB) < 2000 146(35.1%) 56(25.6%) 90(45.7%) 4000 116(27.9%) 83(37.9%) 33(16.8%) Physical exercise Never 120(28.8%) 58(26.5%) 62(31.5%) 0.046 1-3times/week 242(58.2%) 125(57.1%) 117(59.4%) > 3times/week 54(13.0%) 36(16.4%) 18(9.1%) Social activities Never 73(17.5%) 35(16.0%) 38(19.3%) 0.05 1-3times/week 282(67.8%) 144(65.8%) 138(70.1%) > 3times/week 61(14.7%) 40(18.3%) 21(10.7%) Smorking Yes 146(35.1%) 80(36.5%) 66(33.5%) 0.518 No 270(64.9%) 139(63.5%) 131(66.5%) Drinking Yes 213(51.2%) 92(42.0%) 111(56.3%) 0.003 No 203(48.8%) 127(58.0%) 86(43.7%) Number of chronic diseases 0 89(21.4%) 49(22.4%) 40(20.3%) 0.776 1–2 264(63.5%) 139(63.5%) 125(63.5%) > 3 63(15.1%) 31(14.2%) 32(16.2%) Kessler Psychological Distress Scale Score 5.7(3.7) 4.8(3.5) 6.8(3.7) <0.001 Notes: BMI=Body Mass Index; data were presented as mean (SD) or n (%) Sleep health of the participants Table 3 presents the sleep health characteristics of the participants. Based on the composite sleep health score, 1%, 16.1%, 25.2%, 28.4%, 20.9%, and 8.4% of participants achieved scores of 0, 1, 2, 3, 4, and 5, respectively. A total of 70.7% of participants had sleep problems in at least two domains. The mean sleep duration was 398.42 minutes (SD = 79.17), the mean ESS score was 5.35 (SD = 4.24), the mean sleep efficiency was 76.44%, and the mean sleep midpoint was 01:56. Table 4 shows the distribution of participants across each domain of sleep health, as well as the correlation analysis between these domains and the PSQI score.With regard to individual sleep domains, 204 participants (49.0%) had an earlier or later sleep midpoint; 146 (35.1%) had poor sleep quality;265 (63.7%) had low sleep efficiency;65 (15.6%) had excessive daytime sleepiness; and 214 (51.4%) had short sleep duration ( 9 hours).The sleep HCS, sleep quality, sleep efficiency, and sleep duration was correlated with the PSQI score. Among these, the composite sleep health score showed the strongest correlation with the PSQI score ( r = -0.644), whereas sleep midpoint and alterness was not correlated with the PSQI score. Table 3 Sleep health and cognitive function of the participants (N = 416) Variables N(%)/Mean(SD) Range Sleep health composite score 2.77(1.21) 0–5 0 4(1.0%) 1 67(16.1%) 2 105(25.2%) 3 118(28.4%) 4 87(20.9%) 5 35(8.4%) Individual sleep dimension Sleep quality 1.37(0.64) 0–3 Sleep duration (min) 398.42(79.17) 240–600 Aterness(ESS score) 5.35(4.24) 0–20 Sleep efficiency (%) 76.44(13.50) 35–98 Sleep midpoint(am) 1:56(0:46) 0:00–5:01 Table 4 Correlations between Sleep Health and global Pittsburgh Sleep Quality Index (PSQI) score Variables N(%)/Mean(SD) PSQI[Mean(SD)] Correlation analysis Sleep midpoint a r pb = -0.059; p = 0.233 Goog 212(51.0%) 7.61(3.67) Poor 204(49.0%) 8.03(3.59) Sleep quality a r pb = -0.678; p < 0.001 Goog 270(64.9%) 6.01(2.38) Poor 146(35.1%) 11.17(3.14) Sleep efficiency a r pb = -0.482; p < 0.001 Goog 151(36.3%) 5.50(2.62) Poor 265(63.7%) 9.14(3.47) Alertness a r pb =-−0.028; p = 0.564 Goog 351(84.4%) 7.77(3.72) Poor 65(15.6%) 8.06(3.17) Sleep duration c r s =-−0.475; p < 0.001 Average 170(40.9%) 6.15(2.63) Short 214(51.4%) 9.50(3.63) long 32(7.7%) 5.43(2.81) Sleep HCS b 2.77(1.21) 7.82(3.63) r= -0.644; p < 0.001 Notes: a:Point-biserial correlation analysis was used. b:Person correlation analysis was used. c:Spearman's rank-order correlation analysis was used. Association Between Sleep Health and Cognitive Function Table 5 shows the correlations between the sleep health and the MoCA-B score .The sleep HCS was positively correlated with the MoCA-B score (r = 0.141), and was also correlated with executive function, calculation, memory, and attention.The MoCA-B score was correlated with sleep efficiency, sleep midpoint, and sleep quality, whereas its correlation with sleep duration and alterness was not statistically significant. To further investigate the effects of sleep health and its individual domains on cognitive function, we entered sleep variables that showed significant associations in the univariate analysis into binary logistic regression models (Table 6 ).In the unadjusted model, sleep efficiency (OR = 1.766, 95% CI: 1.079–2.890) and the sleep HCS (OR = 0.499, 95% CI: 0.389–0.640) were associated with the risk of MCI.After adjustment for covariates, sleep midpoint (OR = 1.955, 95% CI: 1.142–3.349), sleep efficiency (OR = 1.969, 95% CI: 1.102–3.521), and the sleep HCS (OR = 0.493, 95% CI: 0.366–0.665) emerged as significant predictors of MCI. Table 5 The correlations between sleep health and cognitive function( r ) MoCA-B executive function Orientation calculation Abstracting Memory Visuospatial function Language skills Attention Sleep duration a 0.002 0.097* 0.024 0.045 -0.011 0.054 -0.063 -0.104* 0.001 Sleep efficiency a 0.110* 0.002 0.117* 0.143** 0.127** 0.080 0.023 -0.028 0.098* Sleep midpoint a 0.119* 0.177** 0.071 0.034 0.066 0.077 0.098* 0.067 0.071 Alertness a -0.016 -0.057 -0.079 0.087 -0.021 0.016 0.046 -0.050 -0.059 Sleep quality a 0.134** 0.097* 0.088 0.102* -0.067 0.080 0.127** 0.043 0.155** Sleep HCS b 0.141** 0.134** 0.096 0.155** 0.071 0.122* 0.087 -0.024 0.112* Notes:*p < 0.05, **p < 0.01;a:Point-biserial correlation analysis was used. b:Person correlation analysis was used. Table 6 Logistic regression models using each dimension of sleep health predicting MCI(N = 416) Variables Unadjusted model OR(95%CI) P Adjusted model OR(95%CI) P Sleep midpoint a 1.483(0.933 ~ 2.357) 0.096 1.955(1.142 ~ 3.349) 0.015 Sleep quality b 1.186(0.733 ~ 1.919) 0.486 1.409(0.767 ~ 2.591) 0.269 Sleep efficiency c 1.766(1.079 ~ 2.890) 0.024 1.969(1.102 ~ 3.521) 0.022 Sleep HCS 0.499(0.389 ~ 0.640) < 0.001 0.493(0.366 ~ 0.665) < 0.001 Notes: a Reference group=Good sleep midpoint; b Reference group=Good sleep quality; c Reference group=Good sleep efficiency; OR=Odds ratio; 95%CI = 95% Confidence Interval; adjusted for age, gender,marital, education, income, physical exercise, drinking ,mental health in model. Discussion This cross-sectional study recruited older adults in nursing homes as participants, and explored the association between sleep health and cognitive function. The results revealed that 70.7% of participants experienced sleep health problems in at least two domains, with abnormal sleep efficiency being the most prevalent (63.7%). The mean sleep efficiency among participants was 76.44%, which was considerably lower than the clinical cut-off value of 85%.Our study shows that a higher sleep HCS was associated with a lower risk of MCI among older adults in nursing homes (OR = 0.499, 95% CI: 0.389–0.640). After adjusting for confounding covariates, the sleep HCS remained an independent protective factor for cognitive function. Specifically, the risk of incident MCI was reduced by approximately 50% with each increment in a healthy sleep domain (OR = 0.493, 95% CI: 0.366–0.665).Notably, In contrast to previous studies, sleep duration and sleep quality—widely investigated indicators in sleep research—showed no significant association with the risk of incident MCI, whereas sleep efficiency and sleep midpoint were associated with MCI risk. These findings are consistent with those study by Kristin[33]. Using the SATED model, this study examined the relationship between multiple sleep health domains and cognitive function, providing a more comprehensive depiction of sleep health than single-dimensional indicators.To verify the validity of the Sleep HCS, this study examined the correlation between the Sleep HCS and PSQI scores, revealing a strong negative correlation( r = − 0.644, p < 0.001). Scores from four domains, namely sleep quality, sleep efficiency, alertness and sleep duration, were all significantly correlated with the PSQI score, indicating a high degree of consistency between the Sleep HCS and the PSQI.This composite score can not only comprehensively and objectively reflect sleep health status, but also identify the specific weak domains of sleep health at the individual level, thereby providing an important evidence base for the development of subsequent targeted sleep interventions and health management for the elderly population. As a core component of overall sleep health, sleep efficiency may serve as a key driver of the cross-sectional association between sleep health and cognitive function. This study demonstrated that low sleep efficiency (< 85%) was a risk factor for incident MCI. After adjustment for potential confounding variables, participants with low sleep efficiency had 1.9 times the risk of developing MCI compared with those with high sleep efficiency(OR = 1.969, 95% CI: 1.102–3.521). Long sleep latency (≥ 30 min), Wake After Sleep Onset(WASO), and sleep fragmentation (SF) are all core indicators that reflect sleep efficiency.Numerous studies have confirmed that long sleep latency[33–35], long WASO[36, 37], and sleep fragmentation[38–40] are all significantly positively correlated with cognitive decline. In addition to its adverse impact on cognitive function, low sleep efficiency (< 85%) is also significantly associated with impairments in memory and verbal function.This may be related to the increased Aβ deposition in the frontal lobe and/or precuneus caused by both low sleep efficiency[41] and sleep fragmentation[42]. Low sleep efficiency is associated with white matter volume atrophy. Furthermore, sleep fragmentation can suppress the function of neuronal pathways, particularly the gamma-aminobutyric acid (GABA) and cyclic adenosine monophosphate (cAMP) pathways, thereby impairing synaptic plasticity [43]. Early impairment in synaptic plasticity may be a primary contributor to the development of memory deficits.One laboratory-based study also found that the influence of sleep continuity on cognitive function is independent of sleep duration. That is, even with sufficient sleep duration, disrupted sleep continuity can still exert adverse effects on cognitive function.Poor sleep efficiency is an extremely prevalent sleep problem among older adults residing in nursing homes. A study of older women[44] demonstrated that compared with older adults with higher sleep efficiency, those with poor sleep efficiency had a significantly elevated risk of nursing home admission (OR = 3.25, 95% CI 1.35–7.82), with their odds of being admitted to a nursing home being more than three times higher than the former. These findings indicate that poor sleep efficiency is not only an important factor driving older adults to enter nursing homes, but also one of the most prominent sleep problems among residents of such facilities. In the SATED model, sleep timing is assessed using the sleep midpoint, which is calculated as:sleep midpoint = bedtime + (wake time − bedtime) / 2.Accordingly, bedtime, wake time, and sleep midpoint are frequently used as indicators of circadian rhythm. This study revealed that the mean sleep midpoint of participants was 01:56, and sleep midpoint was significantly correlated with the MoCA-B score. In binary logistic regression analysis, after adjustment for confounding variables, participants with a poor sleep midpoint had 1.9 times (OR = 1.955, 95% CI: 1.142–3.349) the risk of developing MCI compared with those with a healthy sleep midpoint domain.To date, research on the association between sleep time and cognitive function among older adults remains limited.A study by Wu et al.[45] of 4,601 community-dwelling older adults in China showed that an excessively early median sleep time (earlier than 01:30) was associated with lower MMSE scores (β: -0.36 to -0.34; 95% CI: -0.60 to -0.10), consistent with the findings of the present study.In addition, similar findings have been reported in several large-sample cross-sectional surveys[46, 47]. Sleep duration is one of the most extensively investigated sleep characteristics, and no consensus has been reached regarding the association between sleep duration and cognitive function.Several previous studies[48–50]have suggested that extreme sleep duration ( too short or too long) can increase the risk of cognitive decline or dementia.Keage et al. [51]reported that short nocturnal sleep duration (≤ 6.5 h) was a risk factor for cognitive decline, whereas long nocturnal sleep duration (≥ 9 h) had no significant effect on cognitive function. However, that study did not examine the nonlinear association between sleep duration and cognitive function.A meta-analysis [52] demonstrated that sleep duration exhibited a U-shaped relationship with cognitive function.Insufficient sleep ( 10 h/d for nocturnal sleep, > 12.5 h/d for total sleep) was associated with an increased risk of all-cause cognitive impairment or Alzheimer’s disease (AD).Of note, although all the aforementioned studies reported an association between abnormal sleep duration and cognitive function, they used inconsistent cut-off values for defining abnormal sleep duration. This may account for the finding that several studies, including the present one [34, 35, 37], failed to identify a significant association between sleep duration and cognitive function.This study adopted 7 and 9 hours as the cut-off values for sleep duration, whereas participants’ sleep duration was concentrated within 6–8 hours. The insufficient number of extreme values resulted in failure to detect the effect of extreme sleep duration, suggesting that subsequent studies should verify whether the selected cut-off values are supported by local population evidence. Previous studies[34, 53]have confirmed that poor self-reported sleep quality is associated with cognitive decline, and suggest that it could serve as an early marker of cognitive decline in older adults. However, some other studies have failed to identify an association between sleep quality and cognitive function.In the MrOS Sleep Cohort, a study of 3132 community-dwelling older adults found no association between PSQI scores, sleep quality as categorized by the PSQI, and global cognitive function, attention or alertness[54], which is consistent with the findings of the present study.Potential explanations for this inconsistency may be related to the approach used to assess sleep quality in the present study. We only adopted Item 6 of the Pittsburgh Sleep Quality Index (PSQI) as the indicator for evaluating sleep quality. As a single subjective assessment item, it has inherent shortcomings including restricted coverage of assessment dimensions and high susceptibility to individual subjective cognitive bias, which may consequently compromise the accuracy and reliability of the study findings.In addition, heterogeneity in the study population may also account for the inconsistent results.A study on the risk of subjective cognitive decline[55] found that the correlation between poor sleep quality and subjective cognitive decline only existed in community-dwelling older adults, with no such association observed in older adults living in nursing homes. Alertness refers to the ability to maintain wakefulness during the daytime. This sleep indicator is commonly measured using the Epworth Sleepiness Scale (ESS), which is applied to identify the presence of excessive daytime sleepiness (EDS) in older adults.Two cross-sectional studies [34, 56] demonstrated that EDS was strongly associated with poorer cognitive function after adjusting for potential confounding factors. A longitudinal study by Gabelle et al. [57]also confirmed that participants with EDS had an increased risk of a decline in Mini-Mental State Examination (MMSE) scores (OR = 2.46; 95% CI: 1.28–4.71).However, the present study found no association between alertness and cognitive function, which is consistent with the findings of previous studies[37, 58]. The results of this study revealed that only 15.6% of the participants were classified as having poor alertness, and such a low proportion may have attenuated the statistical power of the study results to a certain extent. Several limitations should be noted in this study.First, the cross-sectional design precluded the establishment of a causal relationship between sleep health and cognitive function. Second, sleep health was assessed using subjective questionnaires in this study, which may be susceptible to recall bias. The lack of objective measurements may have led to the underdetection of potential associations.Third, a convenience sampling strategy was adopted, and all participants were recruited from three nursing homes in Wuhan, resulting in a small sample size and insufficient representativeness and generalizability of the study sample.In the future, multi-center and long-term cohort studies combining subjective and objective measurement methods (e.g., sleep diaries and wearable devices) are required to further explore the mechanistic relationship between sleep health and cognitive function in older adults.Nevertheless, these findings carry important implications for the development of sleep management strategies targeted at maintaining and promoting cognitive function among older adults. Conclusion Based on the SATED sleep health model, this study found that impaired sleep health and abnormalities in individual sleep domains, particularly sleep efficiency and sleep midpoint, were significantly associated with an elevated risk of MCI among older adults. These associations remained after adjustment for confounding factors.The findings of this study provide a novel evidentiary perspective for research on the association between sleep and cognitive function, and highlight the multidimensional nature of sleep health.Implementing sleep health screening and management to improve sleep health in older adults may serve as an important strategy to delay or prevent cognitive decline. Abbreviations PSQI Pittsburgh Sleep Quality Index ESS Epworth Sleepiness Scale MCI Mild cognitive impairment Sleep HCS Sleep Health Composite Score SATED Satisfaction,Aterness, Timing, Efficiency, Duration Declarations Author contributions YL involved in conception of study, acquisition of data, data entry, interpretation of results and drafting manuscript. XXD involved in acquisition of data and finalization of manuscript. HH involved in conception of study, acquisition of data, interpretation of results and finalization of manuscript. XTW involved in acquisition of data and data entry. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Data Availability Data is provided within the manuscript or supplementary information files. Ethics approval and consent to participate This study adhered to the principles of the Declaration of Helsinki and received approval from the Ethics Committee of the Jianghan University (Approved No. of ethic committee: JHDXKJLL2025-261). Consent for publication Not applicable. Competing interests The authors declare no competing interests. Clinical trial number Not applicable. References Chinese Medical Doctor Association. Neurologist Branch; Writing Group of Chinese Guidelines on Cognitive Training. Chinese Guidelines on Cognitive Training (2022 Version). Chin Med J. 2022;102(37):2918–25. Petersen RC, Lopez O, Armstrong MJ, et al. Practice guideline update summary: Mild cognitive impairment: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology. Neurology. 2018;90:126–35. Jia L, Du Y, Chu L, et al. 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Screening for serious mental illness in the general population with the K6 screening scale: results from the WHO World Mental Health (WMH) survey initiative. Int J Methods Psychiatr Res. 2010;19(Suppl 1):4–22. Calfee KR, Lee S, Andel R. Multidimensional sleep health and cognitive function across adulthood. Sleep Health: J Natl Sleep Foundation. 2025;11:206–13. Boeve A, Halpin A, Michaud S, et al. Specific Sleep Health Domains as Predictors of Executive Function in Older Adults. J Neuropsychiatry Clin Neurosci. 2022;34:422–7. McSorley VE, Bin YS, Lauderdale DS. Associations of Sleep Characteristics With Cognitive Function and Decline Among Older Adults. Am J Epidemiol. 2019;188:1066–75. Liu R. Study on the Correlation Between Sleep Characteristics and Cognitive Function in the Elderly and Its Mechanisms. Shandong University; 2023. Blackwell T, Yaffe K, Laffan A, et al. Associations of objectively and subjectively measured sleep quality with subsequent cognitive decline in older community-dwelling men: the MrOS sleep study. Sleep. 2014;37:655–63. Lim ASP, Kowgier M, Yu L, et al. Sleep Fragmentation and the Risk of Incident Alzheimer’s Disease and Cognitive Decline in Older Persons. Sleep. 2013;36:1027–32. André C, Tomadesso C, de Flores R, et al. Brain and cognitive correlates of sleep fragmentation in elderly subjects with and without cognitive deficits. Alzheimers Dement (Amst). 2019;11:142–50. Hita-Yañez E, Atienza M, Gil-Neciga E, et al. Disturbed sleep patterns in elders with mild cognitive impairment: the role of memory decline and ApoE ε4 genotype. Curr Alzheimer Res. 2012;9:290–7. Branger P, Arenaza-Urquijo EM, Tomadesso C, et al. Relationships between sleep quality and brain volume, metabolism, and amyloid deposition in late adulthood. Neurobiol Aging. 2016;41:107–14. Vasciaveo V, Iadarola A, Casile A, et al. Sleep fragmentation affects glymphatic system through the different expression of AQP4 in wild type and 5xFAD mouse models. Acta Neuropathol Commun. 2023;11:16. Liu R, Ren Y, Hou T, et al. Associations of sleep timing and time in bed with dementia and cognitive decline among Chinese older adults: A cohort study. J Am Geriatr Soc. 2022;70:3138–51. Blackwell T, Yaffe K, Ancoli-Israel S, et al. Association of sleep characteristics and cognition in older community-dwelling men: the MrOS sleep study. Sleep. 2011;34:1347–56. Wu X, Liao J, Chen X, et al. The independent and combined associations of nocturnal sleep duration, sleep midpoint, and sleep onset latency with global cognitive function in older Chinese adults. Geroscience. 2025;47:3433–45. Fang S-C, Huang C-J, Wu Y-L, et al. Effects of napping on cognitive function modulation in elderly adults with a morning chronotype: A nationwide survey. J Sleep Res. 2019;28:e12724. Wang J, Li YR, Jiang CQ, et al. Chronotype and cognitive function: Observational study and bidirectional Mendelian randomization. EClinicalMedicine. 2022;53:101713. Ma Y, Liang L, Zheng F, et al. Association Between Sleep Duration and Cognitive Decline. JAMA Netw Open. 2020;3:e2013573. Liu S, Hu Z, Guo Y, et al. Association of sleep quality and nap duration with cognitive frailty among older adults living in nursing homes. Front Public Health. 2022;10:963105. Manousakis JE, Nicholas C, Scovelle AJ, et al. Associations between sleep and verbal memory in subjective cognitive decline: A role for semantic clustering. Neurobiol Learn Mem. 2019;166:107086. Keage HAD, Banks S, Yang KL, et al. What sleep characteristics predict cognitive decline in the elderly? Sleep Med. 2012;13:886–92. Xu W, Tan C-C, Zou J-J, et al. Sleep problems and risk of all-cause cognitive decline or dementia: an updated systematic review and meta-analysis. J Neurol Neurosurg Psychiatry. 2020;91:236–44. Waller KL, Mortensen EL, Avlund K, et al. Subjective sleep quality and daytime sleepiness in late midlife and their association with age-related changes in cognition. Sleep Med. 2016;17:165–73. Blackwell T, Yaffe K, Ancoli-Israel S, et al. Association of sleep characteristics and cognition in older community-dwelling men: the MrOS sleep study. Sleep. 2011;34:1347–56. Hu Q, Song Y, Wang S, et al. Association of subjective cognitive complaints with poor sleep quality: A cross-sectional study among Chinese elderly. Int J Geriatr Psychiatry. 2023;38:e5956. Merlino G, Piani A, Gigli GL, et al. Daytime sleepiness is associated with dementia and cognitive decline in older Italian adults: a population-based study. Sleep Med. 2010;11:372–7. Gabelle A, Gutierrez L-A, Jaussent I, et al. Excessive Sleepiness and Longer Nighttime in Bed Increase the Risk of Cognitive Decline in Frail Elderly Subjects: The MAPT-Sleep Study. Front Aging Neurosci. 2017;9:312. Elwood PC, Bayer AJ, Fish M, et al. Sleep disturbance and daytime sleepiness predict vascular dementia. J Epidemiol Community Health. 2011;65:820–4. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8801555","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":606177485,"identity":"7f1c74c1-8ea3-491c-997a-94aa93ec8f81","order_by":0,"name":"Yang Li","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Li","suffix":""},{"id":606177487,"identity":"00b3077c-02bf-4bd5-9957-93917677097d","order_by":1,"name":"Xinxiu Dong","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xinxiu","middleName":"","lastName":"Dong","suffix":""},{"id":606177490,"identity":"e8ef1464-a1ea-4278-88ea-a33601e71b8a","order_by":2,"name":"Hui Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYDACCQgpx8be2PjwAylajPl5DjcbS5CghSFx5oz0NgEeYnTIz24+9vBrm0XihpsP24D67eR0GwhoYZxzLN1Ytk3CeMPtxLYHBQzJxmYHCGhhlsgxk5Zsk5AFamk3kGA4kLiNkBY2qBbGDTcPtknwEKOFB6hF8mObhOLMGYxEapGQSEuTZjgHCuREYCAbEOEX+RnJxyR/lNUBo/L4w4cfKuzkCGoBAWZeNhjTgAjlIMD44w+RKkfBKBgFo2BkAgCORD8VPJXtQgAAAABJRU5ErkJggg==","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Hui","middleName":"","lastName":"Hu","suffix":""},{"id":606177491,"identity":"a2eb5eb5-2390-4a18-b65d-e5bcc0f95fcc","order_by":3,"name":"Xiaotong Wang","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaotong","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-02-06 01:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8801555/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8801555/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107358922,"identity":"5672465e-c834-41af-af1a-86da5cc4e3ea","added_by":"auto","created_at":"2026-04-20 17:39:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":660811,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8801555/v1/c229b8d2-d118-4d46-a371-d03a597494a0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship between Multidimensional Sleep Health and Cognitive Function in Older Adults in Nursing Homes: A Cross-Sectional Study","fulltext":[{"header":"Background","content":"\u003cp\u003eCognitive impairment refers to a group of syndromes characterized by core symptoms of acquired cognitive damage, which can lead to reduced ability in daily life and work, and may or may not be accompanied by psychobehavioral abnormalities. According to the severity of the condition, it can be classified into subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].Among these, Alzheimer's disease (AD) is the most common type of dementia.MCI is a transitional stage between the cognitive changes associated with normal aging and clinical dementia, and it constitutes a pathological condition. Individuals with MCI exhibit certain impairments in cognitive domains including memory, language, attention, visuospatial function, executive function, and orientation, while maintaining intact daily living abilities[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].Epidemiological data show that the prevalence rate of dementia among people aged 60 years and above in China is 6.0%, and that of MCI is 15.5%[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It is estimated that 15%~28% of elderly individuals with MCI progress to dementia within one year, and more than 70% do so within 5\u0026ndash;10 years[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, another study[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] has indicated that approximately 44% of elderly individuals with MCI can revert to normal cognitive function after one year of cognitive management. Thus,MCI stages represent the preclinical phases of dementia, characterized by bidirectional transformability and plasticity, and serve as the \"golden window period\" for the early intervention, prevention and treatment of dementia.\u003c/p\u003e \u003cp\u003eAs a basic physiological need of humans, sleep is closely related to physical and mental health[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].Adequate sleep not only maintains physical vitality but also plays a crucial role in the regulation of cognitive function, memory consolidation, and emotional regulation[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].Quality sleep facilitates the clearance of intracerebral metabolites, supports brain function repair, and helps maintain homeostasis[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In recent years, sleep has been identified as a modifiable risk factor for MCI[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, abnormal changes in sleep patterns are expected to serve as potentially valuable signals for the early identification of MCI[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].Data show that globally, 30%-40% of elderly individuals suffer from sleep disorders, and 88% of those aged 65 years and above experience sleep problems[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A meta-analysis[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] has revealed that the prevalence of sleep disorders among elderly individuals in China is 46%. Elderly people with sleep disorders have a 1.39 times higher risk of developing MCI than those with normal sleep[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and this risk will further increase the likelihood of progressing to dementia[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Compared with healthy elderly individuals, patients with MCI and AD have poorer sleep quality, often accompanied by various sleep disorder manifestations, such as shortened total sleep time, increased number of nocturnal awakenings, decreased sleep efficiency, exacerbated sleep fragmentation, and sleep-related daytime dysfunction[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].Especially in nursinghomes, residents showed significantly poorer sleep compared to community-dwelling older adults[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], caused by factors such as environmental noise, nocturnal care practices and social disengagement.In summary, both cognitive decline and sleep problems are common geriatric health issues during aging. On the one hand, the incidence of sleep problems is relatively high among elderly patients with MCI; on the other hand, sleep problems can increase the risk of cognitive decline and accelerate the progression of MCI to dementia. Relevant clinical guidelines[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]have explicitly identified sleep as a modifiable risk factor for cognitive impairment and incorporated sleep management into the primary prevention system for cognitive impairment. Early screening, systematic assessment, and scientific management of sleep health in the elderly living in nursing homes can serve as a reasonable, effective, and easily scalable intervention strategy to delay the progression of cognitive decline.\u003c/p\u003e \u003cp\u003eHowever, previous studies have some limitations: most studies have used a single scale or have focused on the association between individual sleep characteristics (such as sleep quality and sleep duration) and cognitive outcomes[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], overlooking the multidimensional nature of sleep.In 2014, Buysse[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] first proposed the concept of \"sleep health\", defining it as a multiple dimensions of sleep that adapts to individual, social, and environmental needs and promotes physical and mental health. Meanwhile, he also proposed the SATED Scale (Satisfaction/quality, Alertness, Timing, Efficiency, and Duration Scale) to assess five sleep dimensions: satisfaction, alertness, sleep timing, sleep efficiency, and sleep duration[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].To date, existing studies have explored the correlation between sleep health and metabolic diseases as well as mental health outcomes[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], but few have focused on the association between sleep health and cognitive function. Cognitive decline and sleep problems associated with aging not only seriously affect the quality of life of patients and their caregivers but also impose a heavy economic burden, psychological stress, and physical health impairments, making them a pressing major public health issue in contemporary society. Therefore, from a multidimensional perspective of sleep health, this study systematically investigates the correlation between sleep health and cognitive function in older adults, aiming to provide new insights and evidence for the early prevention and control of cognitive impairment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePopulation\u003c/h2\u003e \u003cp\u003eThis study recruited participants from 3 nursing home in Wuhan using a convenience sampling method. The sample size was calculated using the formula of [Z\u0026sup2;P(1-P)]/d\u0026sup2;, with a significance level set at 0.05 and CI of 95% [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].The prevalence of MCI among older adults in Chinese nursing home was set at 0.54[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] with a relative precision of 0.05. After accounting for a 10% invalid sample size, the final calculated sample size was 425 participants. Inclusion criteria: (1) Age\u0026thinsp;\u0026gt;\u0026thinsp;60 years; (2) Length of residence in the nursing home\u0026thinsp;\u0026ge;\u0026thinsp;6 months; (3) Possessing normal communication and comprehension abilities, and being able to complete the questionnaire.Exclusion criteria:(1) presence dementia such as Alzheimer\u0026rsquo;s disease, vascular dementia, and other neurological diseases that can cause brain dysfunction, which diagnosed by a medical institution; (2) Those with severe visual and hearing impairment; (3) Those with comorbid psychiatric or neurological disorders that result in the inability to communicate normally. Prior to the interviews, all participants provided informed consent. The study was approved by the Ethics Committee of Jianghan University (Approved No. of ethic committee: JHDXKJLL2025-261).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSleep health assessment\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSleep Health Composite Score(sleep HCS) was calculated using the SATED model sleep dimensions[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e],which includes sleep satisfaction/quality,aterness, sleep timing, sleep efficiency, sleep duration.Each domain was measured using a corresponding subjective sleep rating scale and was dichotomized (1\u0026thinsp;=\u0026thinsp;good, 0\u0026thinsp;=\u0026thinsp;poor) with established cutoff points (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) which were validated in previous Literature[\u003cspan additionalcitationids=\"CR24 CR25 CR26\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A sum of scores across the five domains was used as the composite sleep health score (Range\u0026thinsp;=\u0026thinsp;0\u0026ndash;5),A higher score indicates a higher level of sleep health[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eCognitive function assessment\u003c/h3\u003e\n\u003cp\u003eMontreal Cognitive Assessment (MoCA) Test is a widely used screening assessment tool for cognitive function of older adults. Studies have shown that the MoCA test has high sensitivity (80\u0026ndash;100%) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]and specificity (50\u0026ndash;76%) in identifying MCI, and it is more accurate than the Mini-Mental State Examination Scale in distinguishing between normal and MCI (Grade A recommendation)[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].The Montreal Cognitive Assessment Basic Version (MoCA-B) was used to evaluate the overall cognitive function of the elderly based on the expert consensus on neuropsychological assessment[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The Chinese version of MoCA-B is a revised edition tailored for older adults with low educational attainment[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and shows no ceiling effect among older adults with high educational attainment. MoCA-B consists of 8 cognitive domains: executive function, language, orientation, calculation, conceptual thinking, memory, visual perception, and attention(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.mocatest.org\" target=\"_blank\"\u003ewww.mocatest.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.mocatest.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, visit Basic section).MoCA-B has a score of 30, and a higher score indicates better cognitive function. The cut-off values for diagnosing MCI were stratified by educational level as follows: participants with \u0026le;\u0026thinsp;6 years of education and a score\u0026thinsp;\u0026le;\u0026thinsp;19; those with 7\u0026ndash;12 years of education and a score\u0026thinsp;\u0026le;\u0026thinsp;22; and those with \u0026gt;\u0026thinsp;12 years of education and a score\u0026thinsp;\u0026le;\u0026thinsp;24 were classified as having cognitive impairment.The Cronbach's α of the MoCA-B scale was 0.807, and the optimal sensitivity and specificity could be obtained in all education groups by using the above cut-off score[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Participants with MoCA-B score\u0026thinsp;\u0026le;\u0026thinsp;the critical value received phase II cognitive testing, including Activities of Daily Living Scale (ADL) and Clinical Dementia Rating (CDR). MCI was diagnosed according to Petersen criteria[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and relevant guidelines and expert consensus[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] as follows: (1) complaints of memory loss; (2)MoCA-B score less than the critical value; (3) normal or very slight impairment in activities of daily living, such as ADL score\u0026thinsp;\u0026lt;\u0026thinsp;16; (4) Clinical Dementia Rating (CDR)\u0026thinsp;=\u0026thinsp;0.5.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePotential covariates include socio-demographics (e.g.,gender,age, education, marital status, residential status,monthly income), life style factors (e.g., smorking,coffee, drink,tea,physical exercise,social activities), and sleep-related variables(e.g., number of chronic diseasess, mental health).Mental health status was assessed using the Kessler Psychological Distress Scale (K6), which comprises six psychological symptoms[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]:\u0026ldquo;nervous\u0026rdquo;, \u0026ldquo;depressed\u0026rdquo;, \u0026ldquo;restless\u0026rdquo;, \u0026ldquo;hopeless\u0026rdquo;, \u0026ldquo;having difficulty doing everything\u0026rdquo;, and \u0026ldquo;feeling worthless\u0026rdquo;. Each symptom was rated on a 5-point Likert scale. The total score of the scale ranged from 0 to 24, with a cut-off value of 12. A score higher than 12 indicated a high risk of mental disorder.All other variables measured using a survey designed by the research team.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eMissing data were complemented using multiple interpolation.Data were presented as mean (standard deviation,SD) or frequency (n) and proportion (%). Before statistical analysis,data were tested for independence, normality, and homogeneity of variance. Independent-samples t-test was used for continuous variables, and the χ\u0026sup2; test was adopted for categorical variables to compare the measurement data between the two groups.Correlation analysis was performed to examine the univariate associations between the sleep HCS and its domains, as well as cognitive function-related indicators. For continuous variables conforming to a normal distribution, Pearson correlation analysis was adopted; for dichotomous variables, point-biserial correlation analysis was employed.In the multivariate analysis,\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\u003eSleep health dimensions and cut points used to construct sleep health composite score\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssessment/Item\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCut point\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep satisfaction /quality[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep satisfaction refers to the evaluation of an individual's overall satisfaction with their own sleep status.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-rated sleep quality score (from PSQI Item 6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:Item 6 of the PSQI had a score of 0 or 1.\u003c/p\u003e \u003cp\u003e0:Item 6 of the PSQI had a score of 2 or 3.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAterness[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlertness refers to the ability to maintain wakefulness during the daytime.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUsing the Epworth Sleepiness Scale (ESS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:ESS\u0026lt;10\u003c/p\u003e \u003cp\u003e0:ESS\u0026gt;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep timing [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep timing refers to the consistent bedtime and wake time within a 24-hour period, and serves as a marker of circadian phase.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIt was expressed as the sleep midpoint, calculated by the formula:\u003c/p\u003e \u003cp\u003eBedtime + (Wake-up Time\u0026thinsp;\u0026minus;\u0026thinsp;Bedtime) / 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1: 2:00\u0026ndash;4:00 am\u003c/p\u003e \u003cp\u003e0: \u0026lt;2:00 am/\u0026gt;4:00 am\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep efficiency [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep efficiency is defined as the ease of falling asleep and returning to sleep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThree items from the PSQI (Items 1, 3, and 4) were used to calculate sleep efficiency.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:\u0026gt;85%\u003c/p\u003e \u003cp\u003e0:\u0026lt;85%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep duration[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep duration generally refers to the total accumulated sleep within a 24-hour period, including nocturnal sleep and daytime napping.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePSQI(Items 4):How many hours do you actually sleep each night?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:7\u0026minus;9h\u003c/p\u003e \u003cp\u003e0:\u0026lt;7h/\u0026gt;9h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ePSQI:Pittsburgh sleep quality index;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ebinary logistic regression analyses were used to determine the association between sleep health and MCI, and two models were developed.The unadjusted model no covariate was adjusted. The adjusted model further incorporated covariates with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariate analysis, including sex, age, marital status, educational level, monthly income, physical exercise, smoking, drinking and mental health.A two-tailed p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.All data analyses were performed using IBM SPSS software (version 27.0).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the participants\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThis study conducted face-to-face questionnaire surveys among 430 participants from 3 nursing homes in Wuhan from March to August 2025.After excluding invalid questionnaires, a total of 416 participants were finally included, with a questionnaire recovery rate of 96.74%.Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the characteristics of the participants (N\u0026thinsp;=\u0026thinsp;416). Their mean age was 70.55 years (SD\u0026thinsp;=\u0026thinsp;7.58). The mean body mass index(BMI) was 23.36 kg/m\u0026sup2; (SD\u0026thinsp;=\u0026thinsp;2.97). According to the diagnostic criteria of MCI based on the MoCA-B, 197 participants (47.35%) were classified as MCI. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e also shows the between-group comparisons of demographic variables between the MCI and non-MCI (NC)groups. Overall, age, gender, marital status, educational level, monthly income, frequency of physical activity, drinking and mental health were significantly associated with the risk of MCI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant characteristics and group comparison for MCI (N\u0026thinsp;=\u0026thinsp;416)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;416)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;219)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;197)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.5(7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.7(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.5(8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.4(2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.4(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.3(3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e171(41.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105(47.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66(33.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e245(58.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114(52.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e131(66.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e237(57.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139(63.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98(49.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle/divorce/separation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e179(43.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80(36.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99(50.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidential status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle room\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23(5.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15(6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8(4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDouble room\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e343(82.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e179(81.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e164(83.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriple room\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50(12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25(11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25(12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation leve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliteracy/Primary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36(8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11(5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25(12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105(25.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52(23.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53(26.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e169(40.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85(38.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84(42.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106(25.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71(32.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35(17.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly income (RMB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e146(35.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56(25.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90(45.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u0026ndash;4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e154(37.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80(36.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74(37.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116(27.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83(37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33(16.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120(28.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58(26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62(31.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-3times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e242(58.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125(57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e117(59.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54(13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36(16.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18(9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73(17.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35(16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38(19.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-3times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e282(67.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e144(65.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e138(70.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61(14.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40(18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21(10.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmorking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e146(35.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80(36.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66(33.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e270(64.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139(63.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e131(66.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e213(51.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92(42.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111(56.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e203(48.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127(58.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86(43.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of chronic diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89(21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49(22.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40(20.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e264(63.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139(63.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e125(63.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63(15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31(14.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32(16.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKessler Psychological Distress Scale Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.7(3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.8(3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.8(3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: BMI=Body Mass Index; data were presented as mean (SD) or n (%)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSleep health of the participants\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the sleep health characteristics of the participants. Based on the composite sleep health score, 1%, 16.1%, 25.2%, 28.4%, 20.9%, and 8.4% of participants achieved scores of 0, 1, 2, 3, 4, and 5, respectively. A total of 70.7% of participants had sleep problems in at least two domains. The mean sleep duration was 398.42 minutes (SD\u0026thinsp;=\u0026thinsp;79.17), the mean ESS score was 5.35 (SD\u0026thinsp;=\u0026thinsp;4.24), the mean sleep efficiency was 76.44%, and the mean sleep midpoint was 01:56.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the distribution of participants across each domain of sleep health, as well as the correlation analysis between these domains and the PSQI score.With regard to individual sleep domains, 204 participants (49.0%) had an earlier or later sleep midpoint; 146 (35.1%) had poor sleep quality;265 (63.7%) had low sleep efficiency;65 (15.6%) had excessive daytime sleepiness; and 214 (51.4%) had short sleep duration (\u0026lt;\u0026thinsp;7 hours), and 32 (7.7%) had long sleep duration (\u0026gt;\u0026thinsp;9 hours).The sleep HCS, sleep quality, sleep efficiency, and sleep duration was correlated with the PSQI score. Among these, the composite sleep health score showed the strongest correlation with the PSQI score (\u003cem\u003er\u003c/em\u003e = -0.644), whereas sleep midpoint and alterness was not correlated with the PSQI score.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSleep health and cognitive function of the participants (N\u0026thinsp;=\u0026thinsp;416)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN(%)/Mean(SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep health composite score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.77(1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67(16.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105(25.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118(28.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87(20.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35(8.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual sleep dimension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37(0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep duration (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e398.42(79.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e240\u0026ndash;600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAterness(ESS score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.35(4.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep efficiency (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.44(13.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u0026ndash;98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep midpoint(am)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1:56(0:46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0:00\u0026ndash;5:01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations between Sleep Health and global Pittsburgh Sleep Quality Index (PSQI) score\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN(%)/Mean(SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePSQI[Mean(SD)]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCorrelation analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep midpoint\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003csub\u003epb\u003c/sub\u003e = -0.059; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e212(51.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.61(3.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e204(49.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.03(3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep quality\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003csub\u003epb\u003c/sub\u003e = -0.678; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e270(64.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.01(2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e146(35.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.17(3.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep efficiency\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003csub\u003epb\u003c/sub\u003e = -0.482; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e151(36.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.50(2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e265(63.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.14(3.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlertness\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003csub\u003epb\u003c/sub\u003e=-\u0026minus;0.028;\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e351(84.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.77(3.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65(15.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.06(3.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep duration\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003csub\u003es\u003c/sub\u003e=-\u0026minus;0.475;\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170(40.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.15(2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e214(51.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.50(3.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32(7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.43(2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep HCS\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.77(1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.82(3.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er= -0.644; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: a:Point-biserial correlation analysis was used. b:Person correlation analysis was used.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ec:Spearman's rank-order correlation analysis was used.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociation Between Sleep Health and Cognitive Function\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the correlations between the sleep health and the MoCA-B score .The sleep HCS was positively correlated with the MoCA-B score (r\u0026thinsp;=\u0026thinsp;0.141), and was also correlated with executive function, calculation, memory, and attention.The MoCA-B score was correlated with sleep efficiency, sleep midpoint, and sleep quality, whereas its correlation with sleep duration and alterness was not statistically significant.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo further investigate the effects of sleep health and its individual domains on cognitive function, we entered sleep variables that showed significant associations in the univariate analysis into binary logistic regression models (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).In the unadjusted model, sleep efficiency (OR\u0026thinsp;=\u0026thinsp;1.766, 95% CI: 1.079\u0026ndash;2.890) and the sleep HCS (OR\u0026thinsp;=\u0026thinsp;0.499, 95% CI: 0.389\u0026ndash;0.640) were associated with the risk of MCI.After adjustment for covariates, sleep midpoint (OR\u0026thinsp;=\u0026thinsp;1.955, 95% CI: 1.142\u0026ndash;3.349), sleep efficiency (OR\u0026thinsp;=\u0026thinsp;1.969, 95% CI: 1.102\u0026ndash;3.521), and the sleep HCS (OR\u0026thinsp;=\u0026thinsp;0.493, 95% CI: 0.366\u0026ndash;0.665) emerged as significant predictors of MCI.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe correlations between sleep health and cognitive function(\u003cem\u003er\u003c/em\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoCA-B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eexecutive function\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrientation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecalculation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAbstracting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMemory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVisuospatial function\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLanguage skills\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAttention\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep duration\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.097*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.104*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep efficiency\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.110*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.117*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.143**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.127**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.098*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep midpoint\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.119*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.177**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.098*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlertness\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep quality\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.134**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.097*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.102*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.127**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.155**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep HCS\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.141**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.134**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.155**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.122*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.112*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNotes:*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01;a:Point-biserial correlation analysis was used. b:Person correlation analysis was used.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression models using each dimension of sleep health predicting MCI(N\u0026thinsp;=\u0026thinsp;416)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted model OR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted model OR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep midpoint\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.483(0.933\u0026thinsp;~\u0026thinsp;2.357)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.955(1.142\u0026thinsp;~\u0026thinsp;3.349)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep quality\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.186(0.733\u0026thinsp;~\u0026thinsp;1.919)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.409(0.767\u0026thinsp;~\u0026thinsp;2.591)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep efficiency\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.766(1.079\u0026thinsp;~\u0026thinsp;2.890)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.969(1.102\u0026thinsp;~\u0026thinsp;3.521)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep HCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.499(0.389\u0026thinsp;~\u0026thinsp;0.640)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.493(0.366\u0026thinsp;~\u0026thinsp;0.665)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: \u003csup\u003ea\u003c/sup\u003eReference group=Good sleep midpoint; \u003csup\u003eb\u003c/sup\u003eReference group=Good sleep quality; \u003csup\u003ec\u003c/sup\u003eReference group=Good sleep efficiency; OR=Odds ratio; 95%CI\u0026thinsp;=\u0026thinsp;95% Confidence Interval; adjusted for age, gender,marital, education, income, physical exercise, drinking ,mental health in model.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis cross-sectional study recruited older adults in nursing homes as participants, and explored the association between sleep health and cognitive function. The results revealed that 70.7% of participants experienced sleep health problems in at least two domains, with abnormal sleep efficiency being the most prevalent (63.7%). The mean sleep efficiency among participants was 76.44%, which was considerably lower than the clinical cut-off value of 85%.Our study shows that a higher sleep HCS was associated with a lower risk of MCI among older adults in nursing homes (OR\u0026thinsp;=\u0026thinsp;0.499, 95% CI: 0.389\u0026ndash;0.640). After adjusting for confounding covariates, the sleep HCS remained an independent protective factor for cognitive function. Specifically, the risk of incident MCI was reduced by approximately 50% with each increment in a healthy sleep domain (OR\u0026thinsp;=\u0026thinsp;0.493, 95% CI: 0.366\u0026ndash;0.665).Notably, In contrast to previous studies, sleep duration and sleep quality\u0026mdash;widely investigated indicators in sleep research\u0026mdash;showed no significant association with the risk of incident MCI, whereas sleep efficiency and sleep midpoint were associated with MCI risk. These findings are consistent with those study by Kristin[33].\u003c/p\u003e\n\u003cp\u003eUsing the SATED model, this study examined the relationship between multiple sleep health domains and cognitive function, providing a more comprehensive depiction of sleep health than single-dimensional indicators.To verify the validity of the Sleep HCS, this study examined the correlation between the Sleep HCS and PSQI scores, revealing a strong negative correlation(\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.644, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Scores from four domains, namely sleep quality, sleep efficiency, alertness and sleep duration, were all significantly correlated with the PSQI score, indicating a high degree of consistency between the Sleep HCS and the PSQI.This composite score can not only comprehensively and objectively reflect sleep health status, but also identify the specific weak domains of sleep health at the individual level, thereby providing an important evidence base for the development of subsequent targeted sleep interventions and health management for the elderly population.\u003c/p\u003e\n\u003cp\u003eAs a core component of overall sleep health, sleep efficiency may serve as a key driver of the cross-sectional association between sleep health and cognitive function. This study demonstrated that low sleep efficiency (\u0026lt;\u0026thinsp;85%) was a risk factor for incident MCI. After adjustment for potential confounding variables, participants with low sleep efficiency had 1.9 times the risk of developing MCI compared with those with high sleep efficiency(OR\u0026thinsp;=\u0026thinsp;1.969, 95% CI: 1.102\u0026ndash;3.521). Long sleep latency (\u0026ge;\u0026thinsp;30 min), Wake After Sleep Onset(WASO), and sleep fragmentation (SF) are all core indicators that reflect sleep efficiency.Numerous studies have confirmed that long sleep latency[33\u0026ndash;35], long WASO[36, 37], and sleep fragmentation[38\u0026ndash;40] are all significantly positively correlated with cognitive decline. In addition to its adverse impact on cognitive function, low sleep efficiency (\u0026lt;\u0026thinsp;85%) is also significantly associated with impairments in memory and verbal function.This may be related to the increased A\u0026beta; deposition in the frontal lobe and/or precuneus caused by both low sleep efficiency[41] and sleep fragmentation[42]. Low sleep efficiency is associated with white matter volume atrophy. Furthermore, sleep fragmentation can suppress the function of neuronal pathways, particularly the gamma-aminobutyric acid (GABA) and cyclic adenosine monophosphate (cAMP) pathways, thereby impairing synaptic plasticity [43]. Early impairment in synaptic plasticity may be a primary contributor to the development of memory deficits.One laboratory-based study also found that the influence of sleep continuity on cognitive function is independent of sleep duration. That is, even with sufficient sleep duration, disrupted sleep continuity can still exert adverse effects on cognitive function.Poor sleep efficiency is an extremely prevalent sleep problem among older adults residing in nursing homes. A study of older women[44] demonstrated that compared with older adults with higher sleep efficiency, those with poor sleep efficiency had a significantly elevated risk of nursing home admission (OR\u0026thinsp;=\u0026thinsp;3.25, 95% CI 1.35\u0026ndash;7.82), with their odds of being admitted to a nursing home being more than three times higher than the former. These findings indicate that poor sleep efficiency is not only an important factor driving older adults to enter nursing homes, but also one of the most prominent sleep problems among residents of such facilities.\u003c/p\u003e\n\u003cp\u003eIn the SATED model, sleep timing is assessed using the sleep midpoint, which is calculated as:sleep midpoint\u0026thinsp;=\u0026thinsp;bedtime + (wake time\u0026thinsp;\u0026minus;\u0026thinsp;bedtime) / 2.Accordingly, bedtime, wake time, and sleep midpoint are frequently used as indicators of circadian rhythm. This study revealed that the mean sleep midpoint of participants was 01:56, and sleep midpoint was significantly correlated with the MoCA-B score. In binary logistic regression analysis, after adjustment for confounding variables, participants with a poor sleep midpoint had 1.9 times (OR\u0026thinsp;=\u0026thinsp;1.955, 95% CI: 1.142\u0026ndash;3.349) the risk of developing MCI compared with those with a healthy sleep midpoint domain.To date, research on the association between sleep time and cognitive function among older adults remains limited.A study by Wu et al.[45] of 4,601 community-dwelling older adults in China showed that an excessively early median sleep time (earlier than 01:30) was associated with lower MMSE scores (\u0026beta;: -0.36 to -0.34; 95% CI: -0.60 to -0.10), consistent with the findings of the present study.In addition, similar findings have been reported in several large-sample cross-sectional surveys[46, 47].\u003c/p\u003e\n\u003cp\u003eSleep duration is one of the most extensively investigated sleep characteristics, and no consensus has been reached regarding the association between sleep duration and cognitive function.Several previous studies[48\u0026ndash;50]have suggested that extreme sleep duration ( too short or too long) can increase the risk of cognitive decline or dementia.Keage et al. [51]reported that short nocturnal sleep duration (\u0026le;\u0026thinsp;6.5 h) was a risk factor for cognitive decline, whereas long nocturnal sleep duration (\u0026ge;\u0026thinsp;9 h) had no significant effect on cognitive function. However, that study did not examine the nonlinear association between sleep duration and cognitive function.A meta-analysis [52] demonstrated that sleep duration exhibited a U-shaped relationship with cognitive function.Insufficient sleep (\u0026lt;\u0026thinsp;4 h/d) or excessive sleep (\u0026gt;\u0026thinsp;10 h/d for nocturnal sleep, \u0026gt; 12.5 h/d for total sleep) was associated with an increased risk of all-cause cognitive impairment or Alzheimer\u0026rsquo;s disease (AD).Of note, although all the aforementioned studies reported an association between abnormal sleep duration and cognitive function, they used inconsistent cut-off values for defining abnormal sleep duration. This may account for the finding that several studies, including the present one [34, 35, 37], failed to identify a significant association between sleep duration and cognitive function.This study adopted 7 and 9 hours as the cut-off values for sleep duration, whereas participants\u0026rsquo; sleep duration was concentrated within 6\u0026ndash;8 hours. The insufficient number of extreme values resulted in failure to detect the effect of extreme sleep duration, suggesting that subsequent studies should verify whether the selected cut-off values are supported by local population evidence.\u003c/p\u003e\n\u003cp\u003ePrevious studies[34, 53]have confirmed that poor self-reported sleep quality is associated with cognitive decline, and suggest that it could serve as an early marker of cognitive decline in older adults. However, some other studies have failed to identify an association between sleep quality and cognitive function.In the MrOS Sleep Cohort, a study of 3132 community-dwelling older adults found no association between PSQI scores, sleep quality as categorized by the PSQI, and global cognitive function, attention or alertness[54], which is consistent with the findings of the present study.Potential explanations for this inconsistency may be related to the approach used to assess sleep quality in the present study. We only adopted Item 6 of the Pittsburgh Sleep Quality Index (PSQI) as the indicator for evaluating sleep quality. As a single subjective assessment item, it has inherent shortcomings including restricted coverage of assessment dimensions and high susceptibility to individual subjective cognitive bias, which may consequently compromise the accuracy and reliability of the study findings.In addition, heterogeneity in the study population may also account for the inconsistent results.A study on the risk of subjective cognitive decline[55] found that the correlation between poor sleep quality and subjective cognitive decline only existed in community-dwelling older adults, with no such association observed in older adults living in nursing homes.\u003c/p\u003e\n\u003cp\u003eAlertness refers to the ability to maintain wakefulness during the daytime. This sleep indicator is commonly measured using the Epworth Sleepiness Scale (ESS), which is applied to identify the presence of excessive daytime sleepiness (EDS) in older adults.Two cross-sectional studies [34, 56] demonstrated that EDS was strongly associated with poorer cognitive function after adjusting for potential confounding factors. A longitudinal study by Gabelle et al. [57]also confirmed that participants with EDS had an increased risk of a decline in Mini-Mental State Examination (MMSE) scores (OR\u0026thinsp;=\u0026thinsp;2.46; 95% CI: 1.28\u0026ndash;4.71).However, the present study found no association between alertness and cognitive function, which is consistent with the findings of previous studies[37, 58]. The results of this study revealed that only 15.6% of the participants were classified as having poor alertness, and such a low proportion may have attenuated the statistical power of the study results to a certain extent.\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be noted in this study.First, the cross-sectional design precluded the establishment of a causal relationship between sleep health and cognitive function. Second, sleep health was assessed using subjective questionnaires in this study, which may be susceptible to recall bias. The lack of objective measurements may have led to the underdetection of potential associations.Third, a convenience sampling strategy was adopted, and all participants were recruited from three nursing homes in Wuhan, resulting in a small sample size and insufficient representativeness and generalizability of the study sample.In the future, multi-center and long-term cohort studies combining subjective and objective measurement methods (e.g., sleep diaries and wearable devices) are required to further explore the mechanistic relationship between sleep health and cognitive function in older adults.Nevertheless, these findings carry important implications for the development of sleep management strategies targeted at maintaining and promoting cognitive function among older adults.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBased on the SATED sleep health model, this study found that impaired sleep health and abnormalities in individual sleep domains, particularly sleep efficiency and sleep midpoint, were significantly associated with an elevated risk of MCI among older adults. These associations remained after adjustment for confounding factors.The findings of this study provide a novel evidentiary perspective for research on the association between sleep and cognitive function, and highlight the multidimensional nature of sleep health.Implementing sleep health screening and management to improve sleep health in older adults may serve as an important strategy to delay or prevent cognitive decline.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePSQI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Pittsburgh Sleep Quality Index\u003c/p\u003e\n\u003cp\u003eESS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Epworth Sleepiness Scale\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMCI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Mild cognitive impairment\u003c/p\u003e\n\u003cp\u003eSleep HCS \u0026nbsp; \u0026nbsp; Sleep Health Composite Score\u003c/p\u003e\n\u003cp\u003eSATED \u0026nbsp; \u0026nbsp; \u0026nbsp; Satisfaction,Aterness, Timing, Efficiency, Duration\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYL involved in conception of study, acquisition of data, data entry, interpretation of results and drafting manuscript. XXD involved in acquisition of data and finalization of manuscript. HH involved in conception of study, acquisition of data, interpretation of results and finalization of manuscript. XTW involved in acquisition of data and data entry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adhered to the principles of the Declaration of Helsinki and received approval from the Ethics Committee of the Jianghan University (Approved No. of ethic committee: JHDXKJLL2025-261).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChinese Medical Doctor Association. 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J Epidemiol Community Health. 2011;65:820\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cognitive function, Sleep health, Older adults","lastPublishedDoi":"10.21203/rs.3.rs-8801555/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8801555/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAs an increasingly prevalent geriatric health issue, mild cognitive impairment (MCI) is a critical transitional stage preceding dementia, with a high risk of progression to dementia but also substantial plasticity for intervention. Sleep problems are prevalent among elderly people in nursing homes and can increase the risk of cognitive impairment in the elderly. Although existing studies have explored the association between sleep problems and cognitive function.However, most previous studies have focused on single sleep characteristics rather than comprehensive sleep health.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted among 416 older adults aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years from 3 nursing homes in Wuhan. The Sleep Health Composite Score (Sleep HCS) based on the SATED model (Satisfaction/quality, Alertness, Timing, Efficiency, Duration) was used to assess multidimensional sleep health, while cognitive function was evaluated by using the Montreal Cognitive Assessment Basic Version (MoCA-B). Correlation analysis and binary logistic regression were performed to explore the associations between sleep health and MCI.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong participants, 47.35% were diagnosed with MCI, and 70.7% had sleep problems in at least two domains. Sleep HCS was positively correlated with MoCA-B scores (r\u0026thinsp;=\u0026thinsp;0.141, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and served as an independent protective factor for MCI (adjusted OR\u0026thinsp;=\u0026thinsp;0.493, 95% CI: 0.366\u0026ndash;0.665, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Specifically, poor sleep efficiency (adjusted OR\u0026thinsp;=\u0026thinsp;1.969, 95% CI: 1.102\u0026ndash;3.521, P\u0026thinsp;=\u0026thinsp;0.022) and abnormal sleep midpoint (adjusted OR\u0026thinsp;=\u0026thinsp;1.955, 95% CI: 1.142\u0026ndash;3.349, P\u0026thinsp;=\u0026thinsp;0.015) were significantly associated with an elevated risk of MCI.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eMultidimensional sleep health, particularly sleep efficiency and sleep midpoint, is closely associated with the risk of MCI in nursing home-dwelling older adults. The findings highlight the importance of comprehensive sleep health assessment and targeted sleep interventions to delay cognitive decline in this population.\u003c/p\u003e","manuscriptTitle":"Relationship between Multidimensional Sleep Health and Cognitive Function in Older Adults in Nursing Homes: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-16 20:17:13","doi":"10.21203/rs.3.rs-8801555/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4114d58a-7e73-48c1-ad04-ffb3a16b44c7","owner":[],"postedDate":"March 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T17:39:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-16 20:17:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8801555","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8801555","identity":"rs-8801555","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}