Association between intrinsic capacity, nighttime sleep duration and falls in middle‐aged and older adults: a longitudinal study

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To that end, the present study aims to explore the association between IC and falls, as well as the mediating role of NSD. Methods Data from four waves of the China Health and Retirement Longitudinal Study (CHARLS) were utilized. The primary measure, IC, was constructed from five dimensions. The relationships between IC, NSD, and falls were assessed using Cox proportional hazards regression models, with a P value of less than 0.05 considered statistically significant. Results This study included 4,789 participants. Over a 7-year period, 1,118 new falls were recorded. In the Cox regression model, higher levels of IC were significantly associated with a reduced risk of falling, both during nighttime sleep and overall. Specifically, for every one-unit increase in IC, the hazard ratio (HR) for falling was 0.863 (95% CI: 0.819,0.909). For every additional hour of nighttime sleep, the HR for falling was 0.936 (95% CI: 0.905,0.967). Mediation analysis revealed that sleep played a mediating role in the association between IC and falling. Conclusions Chinese patients with low IC and a short nighttime sleep duration had a significantly increased risk of falls, highlighting the importance of early assessment and prevention of fall risk. Intrinsic capacity nighttime sleep fall risk middle-aged and elderly individuals CHARLS Figures Figure 1 Figure 2 Figure 3 1. Introduction Falling refers to the sudden, involuntary, and unintentional change in an individual's body position, resulting in the body dropping to the ground or a lower surface[ 1 , 2 ]. According to the Global Burden of Disease, Injury, and Risk Factor Study, 12% of unintentional injury deaths are caused by falls, a figure that has risen to 65%. Falls not only lead to fatalities among middle-aged and elderly individuals but also cause serious injuries such as hip fractures and traumatic brain injuries[ 3 – 5 ]. In China, it is estimated that approximately 50 million elderly people experience at least one fall each year, with 36 to 44% of them requiring emergency medical treatment[ 6 ]. Despite extensive research on fall risk conducted over the past few decades, there has been an increase in fall-related injuries across adults of all ages, highlighting the need for new fall prevention strategies[ 7 ]. Intrinsic Capacity (IC) is a capability-based approach proposed by the World Health Organization (WHO) to promote healthy aging in the broadest sense. Of note, IC gradually declines throughout adulthood as individuals age. This concept differs from other aging frameworks in several important ways. First, IC provides a continuous and comprehensive measure of overall health that can be assessed throughout most of life rather than focusing solely on impairments in later life or specific aspects of physical function[ 8 ]. IC encompasses five dimensions: physical, mental, cognitive, and sensory abilities[ 9 ]. Early assessment and timely intervention can significantly delay functional decline, improve the quality of life for older adults, and promote healthy aging. A previous study revealed that IC is related to the risk of falls[ 10 ]. Nevertheless, this research was conducted in Belgium with a limited sample size. Meanwhile, a cross-sectional study has shown that nighttime sleep duration is associated with IC and may play a crucial role in its maintenance[ 11 ]. Currently, no studies have examined the relationship between nighttime sleep duration (NSD), IC, and falls. In the present study, the China Health and Retirement Longitudinal Study (CHARLS) was utilized to investigate the association between IC and falls in middle-aged and elderly Chinese individuals, as well as to investigate the mediating role of NSD. 2. Materials and methods 2.1 Population and Data Sources The data for this study were sourced from CHARLS, a cohort study focusing on Chinese residents aged 45 years and older. Notably, CHARLS gathered comprehensive information on participants' demographics, health status, socioeconomic background, family structure, and social activities. Using a multistage probability sampling technique, participants were recruited from 28 Chinese provinces through a four-stage stratified cluster sampling method, achieving a response rate of over 80%[ 12 ]. This study was approved by the Biomedical Ethics Review Board of Peking University (IRB00001052-11015), and all participants provided written informed consent. Detailed information regarding the CHARLS data is available at the following website: http://charls.pku.edu.cn/en . We conducted four rounds of the CHARLS core survey [2011 (baseline), 2013, 2015, and 2018], which included 17,705 participants. A total of 12,916 participants were excluded for the following reasons: falls at baseline (n = 1,884), missing baseline fall data (n = 5,520), missing demographic data (n = 173), missing health-related factors (n = 2,137), missing sleep data (n = 1,616), and missing lifestyle follow-up data (n = 1,586). As a result, 4,789 participants were included in the final analysis. The details of the selection process are shown in Fig. 1 . 2.2 Measurement of NSD The self-reported data on nightly sleep duration were obtained from the 2011 CHARLS follow-up questionnaire. Participants were asked, "Over the past month, what was the average number of hours of actual sleep you obtained per night? (Note: This may be less than the total hours you spent in bed)" (Question ID: DA049). The reported sleep duration ranged from 0 to 24 hours per day. 2.3 Operational definition of IC IC refers to the physical and mental resources available to an individual at any given time. As defined by WHO, IC consists of five core domains: locomotion, sensory function (hearing and vision), vitality, cognition, and psychological well-being. This construct offers a comprehensive assessment of the functional status of older adults. Each domain is classified as either intact functioning (score of 1) or impaired functioning (score of 0) based on validated thresholds. Since sensory function includes two components, hearing and vision, the total IC score ranges from 0 to 6, with higher scores indicating greater capacity[ 13 ]. This is consistent with a recent study validated using the CHARLS dataset[ 14 ]. Measurement of IC (1) Locomotion: Completing five sit-to-stand tests independently in 14 seconds or less was scored as 1 point while completing the test in more than 14 seconds was scored as 0 points[ 14 ]; (2) Vitality: Body mass index (BMI) was calculated, with the threshold based on the Malnutrition Universal Screening Tool. A BMI less than 18.5 kg/m² was scored as 0 points, while a BMI of 18.5 kg/m² or higher was scored as 1 point[ 15 ]; (3) Hearing: Participants were asked, "How is your hearing?" If they responded "poor," they were scored as 0 points. If they answered "fair," "good," "very good," or "excellent," they were scored as 1 point; (4) Vision: Participants were asked, "How good is your eyesight for seeing things at a distance?" and "How good is your eyesight for seeing things up close?" If they answered "fair," "good," "very good," or "excellent" to both questions, they were scored 1 point. If they answered "poor" to either question, they were scored 0 points. (5) Cognition: The Telephone Interview of Cognitive Status (TICS), which assesses memory ability and mental status, was used to determine the cut-off value based on the average minus one standard deviation[ 16 ]. Memory: This aspect was evaluated through immediate and delayed recall of 10 unrelated words. Participants were asked to recall the words after approximately 2 and 4 minutes. A total of 20 points were awarded if the participant recalled all 20 words correctly. Mental status: Participants could earn five points for correct orientation (day, month, year, day of the week, and season), five points for accurately performing a calculation (subtracting 7 from 100 five times in a row), and one point for visual construction (reproducing a picture of two five-pointed stars shown by the interviewer). The total score for mental status was 11 points. (6) Psychology: The Epidemiological Studies Depression Scale[ 17 ] was used to assess depression. A score of less than 12 is considered normal and is assigned 1 point, while a score of 12 or greater is considered impaired and is assigned 0 points. 2.4 Fall The primary outcome was fall incidents, which were self-reported by participants. These incidents were recorded in the 2011 to 2018 survey with the question: "Have you experienced any falls since your last visit?" Participants responded with either "yes" or "no." 2.5 Accompanying factors The covariates included socio-demographic information, lifestyle behaviors, and health-related factors. The sociodemographic variables included age, gender, marital status (married and living with a spouse, married but living without a spouse, and single, divorced, or widowed), residence (rural and urban), and education level (less than lower secondary education and secondary or above). Lifestyle behaviors and health-related factors included BMI, smoking (yes or no), drinking (non-drinker, drink less than once a month, and drink more than once a month), and annual household expenditure. 2.6 Statistical Methods Baseline characteristics were presented using the mean and standard deviation for continuous variables and percentages for categorical variables. Categorical variables were analyzed using chi-square tests, while continuous variables were analyzed using t-tests. The number of follow-ups was recorded from the baseline date to either the diagnosis date or December 31, 2018, whichever occurred first. The Cox proportional hazards model estimated the association between IC and falls. We established Model 1 (the coarse model) for the initial analysis. Meanwhile, Model 2 was adjusted for confounding factors such as age, sex, residence, marital status, and education level. Model 3 was further adjusted for alcohol consumption, smoking, and BMI. Model 4 added household expenditure to Model 3, and Model 5 was further adjusted for sleep duration. We also conducted a mediation analysis to quantify the proportion of sleep's impact on intrinsic ability and falls. First, a linear regression model was fitted to assess the effect of intrinsic ability on NSD. After adjusting for various covariates, we applied the Cox proportional hazards model to examine the relationship between the mediator (NSD), intrinsic ability, and falls, calculating both direct and indirect effects. The mediation ratio of NSD was then determined. To ensure robust statistical inference, resampling methods (Monte Carlo simulation) were used to estimate the Confidence interval(CI) and P values for these effects. In the subgroup analysis, the samples were stratified by age (< 65 years, ≧65 years), sex, place of residence, education level, marital status, smoking, alcohol consumption, and consumption level (categorized into low, low-middle, upper-middle, and high based on quartiles). A P value of < 0.05 was considered statistically significant for each analysis. R statistical software (Version 4.4.1) was used for the analysis. 3. Results 3.1 Descriptive Statistics The characteristics of the 4,789 participants in this study, stratified by fall status, are summarized in Table 1 . Of the total, 2,451 (51.20%) were female. During the follow-up period, 1,118 (23.35%) experienced a new onset of falls. The analysis indicated that older age, female sex, single/divorced/widowed marital status, lower education level, non-smoking status, lower household expenditure, and shorter sleep duration were associated with an increased risk of falling. Table 1 Baseline characteristics of the longitudinal study population by fall Characteristic Overall(n = 4789) Non-Fall(n = 3671) Fall (n = 1118) P value Age (Mean (SD)) 56.4 (8.5) 56.0 (8.4) 57.6 (8.6) < 0.001 Gender (%) female 2451 (51.2) 1813 (49.4) 638 (57.1) 0.04 male 2338 (48.8) 1858 (50.6) 480 (42.9) Residence (%) Rural 3928 (82.0) 2998 (81.7) 930 (83.2) 0.266 Urban 861 (18.0) 673 (18.3) 188 (16.8) Marital_status (%) Married and living with a spouse 4215 (88.0) 3273 (89.2) 942 (84.3) < 0.001 Married but living without a spouse 185 ( 3.9) 135 ( 3.7) 50 ( 4.5) Single, divorced, and windowed 389 ( 8.1) 263 ( 7.2) 126 (11.3) Education_level (%) Less than lower secondary education 4169 (87.1) 3165 (86.2) 1004 (89.8) 0.002 secondary or above 620 (12.9) 506 (13.8) 114 (10.2) Smoking (%) Non-smoker 2894 (60.4) 2172 (59.2) 722 (64.6) 0.001 Smoker 1895 (39.6) 1499 (40.8) 396 (35.4) Drinking (%) Drink but less than once a month 389 ( 8.1) 308 ( 8.4) 81 ( 7.2) 0.102 Drink more than once a month 1100 (23.0) 862 (23.5) 238 (21.3) Non-drinker 3300 (68.9) 2501 (68.1) 799 (71.5) BMI (Mean (SD)) 23.9 (10.1) 23.9 (8.6) 23.9 (14.1) 0.96 Annual_Household_Expenditure (mean (SD)) 7072.4 (9119.9) 7229.2 (9505.6) 6557.6 (7700.6) 0.031 Nighttime sleep duration(Mean (SD)) 6.5 (1.7) 6.6 (1.7) 6.3 (1.8) < 0.001 3.2 Association between IC and fall As shown in Fig. 2 , univariate analysis revealed that each 1-point increase in IC was associated with a significant 17.9% reduction in the risk of falling (HR = 0.821, 95% CI: 0.782,0.861). After adjusting for confounders including age, sex, place of residence, education level, marital status, smoking, alcohol consumption, BMI, household expenditure, and sleep duration, the hazard ratios (HRs) for falls were 0.862 (95% CI = 0.819–0.908), 0.862 (95% CI = 0.818–0.907), 0.863 (95% CI = 0.819–0.909) and 0.874 (95% CI = 0.830–0.922), across the sequential models (all P < 0.001). When NSD was included alone in the Cox regression model (Model 5, Table 2 ), a significant inverse relationship was observed between NSD and fall risk, with each additional hour of nighttime sleep associated with a 6.4% reduction in fall risk and a corresponding HR of 0.936 (95% CI = 0.905–0.967). When both IC and NSD were included in the same model (Model 6, Table 2 ), the effect estimates for IC and NSD remained largely unchanged, indicating independent contributions of both variables to fall risk. Table 2 Hazard ratios and 95% CI of fall status for IC and sleep. Models IC-MetS Sleep-MetS HR (95% CI) P value HR (95% CI) P value Model 1 IC 0.821(0.782,0.861) < 0.001 - - Model 2 IC 0.862(0.819,0.908) < 0.001 - - Model 3 IC 0.862(0.818,0.907) < 0.001 - - Model 4 IC 0.863(0.819,0.909) < 0.001 - - Model 5 Sleep - - 0.936(0.905,0.967) < 0.001 Model 6 IC + Sleep 0.874(0.830,0.922) < 0.001 0.949(0.918,0.981) < 0.01 Model 1:Unadjusted Model 2: Adjustments in Model 1 age, gender, Residence, education_level, Marital_status Model 3: Adjustments in Model 2 plus drinking, smoking, BMI Model 4: Adjustments in Model 3 plus Annual_Household_Expenditure Model 5:Univariate model, after adjusting for all covariates, only sleep was applied in the model; Model 6: Additive models, IC and sleep were applied in the model, with IC plus sleep; 3.3 Intermediary effects As shown in Fig. 3 , after adjusting for covariates, NSD was significantly associated with IC (β = 0.229, P < 0.001). Resampling analysis revealed that the total effect of IC on falls was 1.637 (95% CI = 1.320–1.900), with an indirect effect mediated through sleep of 0.131 (95% CI = 0.045–0.220). These findings indicate a significant mediating role of sleep in the relationship between intrinsic capacity and fall risk, explaining 7.87% variance of the total effect. 3.4 Subgroup analyses and interactions To further explore the association between IC and the incidence of falls, we performed subgroup and interaction analyses stratified by age, sex, place of residence, education level, marital status, smoking, alcohol consumption, and household expenditure, as shown in Fig. 3 . These analyses revealed a significant interaction between marital status and fall risk ( P for interaction = 0.003), with a stronger protective effect of IC observed among participants who were married and living with a spouse, yielding an effect size of 0.83 (95% CI = 0.78–0.88). This may be attributed to the greater availability of social support among individuals who are married and living with a spouse. 4. Discussion In this nationally representative study of middle-aged and elderly Chinese adults, we found that a higher IC was associated with a reduced incidence of falls. Moreover, longer NSD appeared to enhance the protective effect of IC against falls. To our knowledge, this is the first study to employ COX regression models and mediation analyses to examine the relationship between IC and falls while also exploring the mediating role of NSD. A previous Chinese study investigating the association between IC and inpatient falls found that higher IC composite scores were linked to a reduced risk of falls and frailty among elderly inpatients[ 18 ]. While consistent with our findings, that study was limited by its small sample size and cross-sectional design. Similarly, other studies have explored the independent effects of specific IC components, such as cognitive function, mood[ 19 – 21 ], and fall risk[ 22 ]. These studies suggest that impairments in particular cognitive domains, including processing speed and executive function, may predict injurious falls. Nonetheless, these studies have traditionally relied on disease-centered health concepts or single-domain health indicators and have primarily focused on older adults. In contrast, recent research has underscored the substantial public health burden of falls among individuals aged 40 to 64 years old. This concern arises not only from the significant consequences for labor force participation and public healthcare expenditure but also from the potential for timely interventions to prevent adverse outcomes[ 23 ]. Therefore, greater attention should be directed toward both middle-aged and elderly adults. The association between IC, NSD, and fall risk observed in this study may be explained by several mechanisms. First, poor nutritional status, especially insufficient protein intake and vitamin D deficiency, can lead to decreased muscle mass and function, contributing to the development of sarcopenia. These conditions may result in slower gait speed, reduced balance, and limited mobility, thereby increasing the likelihood of recurrent falls[ 24 ]. Second, cognitive impairment or motor dysfunction may disrupt the processing of sensory information essential for maintaining balance during ambulation, as well as impair the ability of older adults to accurately perceive and assess fall risk[ 20 , 22 ]. Third, depressive symptoms such as negative self-appraisal, cognitive changes, and reduced physical activity may heighten the fear of falling, thereby increasing the risk of tripping or losing balance[ 25 ]. Fourth, sensory impairments, including deficits in vision[ 26 ] and hearing[ 27 ], have been identified as predictors of increased fall risk. Furthermore, the interplay between multiple compromised IC components and coexisting chronic conditions may exacerbate functional decline, ultimately contributing to the likelihood of falls. The mediating role of NSD may be attributed to several factors. One potential reason is that sleep deprivation disrupts cerebrospinal fluid circulation, impairing the clearance of neurotoxic substances such as β-amyloid. This can lead to neuronal damage and diminished synaptic plasticity, directly affecting the cognitive domain of intrinsic ability[ 28 ]. In addition, sleep deprivation may impair the prefrontal regulation of emotional control centers, thereby exacerbating anxiety and depressive tendencies[ 29 ]. Besides, sleep may modulate sensory functions through molecular signaling pathways that support neuroplasticity, enhancing the brain’s ability to adapt and respond to sensory inputs[ 30 ]. The strengths of our study include the large sample size, national representativeness, and the use of a robust statistical model to assess the relationship between IC, NSD, and falls. However, several limitations should be acknowledged and taken into consideration. Firstly, self-reported data on IC, NSD, and falls may be subject to recall bias. Secondly, despite adjusting for numerous potential confounders, residual confounding cannot be entirely excluded. Lastly, while longitudinal studies tend to show stronger associations between IC, NSD, and falls compared to cross-sectional studies, our study cannot establish causality or clarify the underlying biological mechanisms. Thus, further experimental research is warranted to substantiate our findings. 5. Conclusions In conclusion, the incidence of falls decreased with increasing IC and longer NSD. This highlights the importance of focusing on populations with low IC and short NSD, as targeted preventive measures and further research are needed to address fall risk in these subgroups. Declarations Ethics approval and consent to participate This study was performed in line with the principles of the Declaration of Helsinki and all participants signed informed consents before participation. Approval was granted by the Ethical Review Committee at Peking University (CHARLS: IRB00001052-11015, CFPS: IRB00001052-14010). Informed consent was obtained from all individual participants enrolled in the study. Clinical trial number Not applicable The authors Consent to Publication NA Data availability statemen t The CHARLS dataset is publicly available online, accessible at http://charls.pku.edu.cn/en. Conflicts of Interest The authors declare no conflict of interest in this study. Funding This research received no external funding. Acknowledgement There is no acknowledgement. Consent to Participate declaration The authors of this article consent. Authors’ contributions ChunQiao Wu: Data curation, Formal analysis, and Writing - original draft; ZhiYing Fei: Writing- Reviewing and Editing; Jiaxin Zhang: Writing- Reviewing and Editing； Shulin Lu: Writing- Reviewing and Editing; Hongying Pan: Conceptualization, Writing - original draft, Writing - review & editing, and Validation. References De Cillisy, F., et al., Fall-detection solution for mobile platforms using accelerometer and gyroscope data. Annu Int Conf IEEE Eng Med Biol Soc, 2015. 2015: p. 3727-30. Li Z, Ye ZH, Sheng JH. Risk factor analysis and preventive strategies for falls and bed falls. Chin Hosp Manage J. 2008;24(9):646-647. 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Womens Midlife Health, 2020. 6: p. 4. Trevisan, C., et al., Nutritional Status, Body Mass Index, and the Risk of Falls in Community-Dwelling Older Adults: A Systematic Review and Meta-Analysis. J Am Med Dir Assoc, 2019. 20(5): p. 569-582.e7. Kvelde, T., et al., Physiological and cognitive mediators for the association between self-reported depressed mood and impaired choice stepping reaction time in older people. J Gerontol A Biol Sci Med Sci, 2010. 65(5): p. 538-44. Kulmala, J., et al., Poor vision accompanied with other sensory impairments as a predictor of falls in older women. Age Ageing, 2009. 38(2): p. 162-7. Heitz, E.R., et al., Self-Reported Hearing Loss and Nonfatal Fall-Related Injury in a Nationally Representative Sample. J Am Geriatr Soc, 2019. 67(7): p. 1410-1416. Bello-Corral, L., et al., Implications of gut and oral microbiota in neuroinflammatory responses in Alzheimer's disease. Life Sci, 2023. 333: p. 122132. Kahn, M., G. Sheppes, and A. Sadeh, Sleep and emotions: bidirectional links and underlying mechanisms. Int J Psychophysiol, 2013. 89(2): p. 218-28. Blumberg, M.S., J.C. Dooley, and A. Tiriac, Sleep, plasticity, and sensory neurodevelopment. Neuron, 2022. 110(20): p. 3230-3242. 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-6629092","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466586636,"identity":"2473d84e-c5fd-41e8-b6b8-e0a8787ff06b","order_by":0,"name":"Chunqiao Wu","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chunqiao","middleName":"","lastName":"Wu","suffix":""},{"id":466586637,"identity":"fd86b1d5-289c-48a4-a033-0e0583e9b219","order_by":1,"name":"Zhiying Fei","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhiying","middleName":"","lastName":"Fei","suffix":""},{"id":466586638,"identity":"f3919a36-f186-47c8-84c2-c03f104bd7fd","order_by":2,"name":"Jiaxin Zhang","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiaxin","middleName":"","lastName":"Zhang","suffix":""},{"id":466586639,"identity":"1d3ad1a5-e62a-43bc-8ad3-27a24b400ceb","order_by":3,"name":"Shulin Lu","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shulin","middleName":"","lastName":"Lu","suffix":""},{"id":466586640,"identity":"40d05873-716c-4a05-b086-5d19859a7097","order_by":4,"name":"Hongying Pan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIie3RsQrCMBCA4SuRuES6dmpf4YqjPkxc7CR06lwp6OIDBBx8BX2D1AOnoquDg5NzRgcFo4OjjZtg/u3gPnIQAJ/vB0sY01riMAbQduQOJJ3zkTb5uO9O4CD6tTLbUfmaXEhQCSSBOltNGwRTEITL8jPpMp5bcppMywYDtSeITrrtFba25DKpoEHWmxFgJFsuszuWUMaf5O5GOlgrJCmeJHAhacWlNjhOFezyerHPRHRsIUlIZORtmCSKNudrMYhD1XbYu0i/PlO47tvC8otln8/n+6setOBISELXCSQAAAAASUVORK5CYII=","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hongying","middleName":"","lastName":"Pan","suffix":""}],"badges":[],"createdAt":"2025-05-09 13:23:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6629092/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6629092/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84276218,"identity":"35b5a030-1b2b-428a-9775-63d403806eb3","added_by":"auto","created_at":"2025-06-10 05:39:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":256932,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow chart of participants selection process.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6629092/v1/6e1a6ba0b5979b5250088215.png"},{"id":84276217,"identity":"76151634-9853-4c19-b1c9-b3da789d2f4c","added_by":"auto","created_at":"2025-06-10 05:39:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":387540,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup and interaction analyses\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6629092/v1/4d2c09998691dee98fc36bac.png"},{"id":84276216,"identity":"3f988ea9-c056-43a5-9294-f67e0a33c10e","added_by":"auto","created_at":"2025-06-10 05:39:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":74028,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePath diagram of the association between Intrinsic capacity and fall, sleep as a mediator.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNotes: ***P-value \u0026lt; 0.001. 95 % CI in the parentheses are shown. Models\u003c/p\u003e\n\u003cp\u003econtrol for gender, age, education, marital status, region, BMI, smoking,\u003c/p\u003e\n\u003cp\u003edrinking, and number of chronic conditions.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6629092/v1/eb2ad7b8b314736db5347d76.png"},{"id":101471469,"identity":"c557dd25-35f0-45ef-b393-dc256ba2f0aa","added_by":"auto","created_at":"2026-01-30 05:41:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1727563,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6629092/v1/42cbd89a-aa4d-4609-be4f-3944d9672372.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between intrinsic capacity, nighttime sleep duration and falls in middle‐aged and older adults: a longitudinal study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFalling refers to the sudden, involuntary, and unintentional change in an individual's body position, resulting in the body dropping to the ground or a lower surface[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to the Global Burden of Disease, Injury, and Risk Factor Study, 12% of unintentional injury deaths are caused by falls, a figure that has risen to 65%. Falls not only lead to fatalities among middle-aged and elderly individuals but also cause serious injuries such as hip fractures and traumatic brain injuries[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In China, it is estimated that approximately 50\u0026nbsp;million elderly people experience at least one fall each year, with 36 to 44% of them requiring emergency medical treatment[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Despite extensive research on fall risk conducted over the past few decades, there has been an increase in fall-related injuries across adults of all ages, highlighting the need for new fall prevention strategies[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIntrinsic Capacity (IC) is a capability-based approach proposed by the World Health Organization (WHO) to promote healthy aging in the broadest sense. Of note, IC gradually declines throughout adulthood as individuals age. This concept differs from other aging frameworks in several important ways. First, IC provides a continuous and comprehensive measure of overall health that can be assessed throughout most of life rather than focusing solely on impairments in later life or specific aspects of physical function[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. IC encompasses five dimensions: physical, mental, cognitive, and sensory abilities[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Early assessment and timely intervention can significantly delay functional decline, improve the quality of life for older adults, and promote healthy aging.\u003c/p\u003e \u003cp\u003eA previous study revealed that IC is related to the risk of falls[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Nevertheless, this research was conducted in Belgium with a limited sample size. Meanwhile, a cross-sectional study has shown that nighttime sleep duration is associated with IC and may play a crucial role in its maintenance[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Currently, no studies have examined the relationship between nighttime sleep duration (NSD), IC, and falls.\u003c/p\u003e \u003cp\u003eIn the present study, the China Health and Retirement Longitudinal Study (CHARLS) was utilized to investigate the association between IC and falls in middle-aged and elderly Chinese individuals, as well as to investigate the mediating role of NSD.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Population and Data Sources\u003c/h2\u003e \u003cp\u003eThe data for this study were sourced from CHARLS, a cohort study focusing on Chinese residents aged 45 years and older. Notably, CHARLS gathered comprehensive information on participants' demographics, health status, socioeconomic background, family structure, and social activities. Using a multistage probability sampling technique, participants were recruited from 28 Chinese provinces through a four-stage stratified cluster sampling method, achieving a response rate of over 80%[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This study was approved by the Biomedical Ethics Review Board of Peking University (IRB00001052-11015), and all participants provided written informed consent. Detailed information regarding the CHARLS data is available at the following website: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://charls.pku.edu.cn/en\u003c/span\u003e\u003cspan address=\"http://charls.pku.edu.cn/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eWe conducted four rounds of the CHARLS core survey [2011 (baseline), 2013, 2015, and 2018], which included 17,705 participants. A total of 12,916 participants were excluded for the following reasons: falls at baseline (n\u0026thinsp;=\u0026thinsp;1,884), missing baseline fall data (n\u0026thinsp;=\u0026thinsp;5,520), missing demographic data (n\u0026thinsp;=\u0026thinsp;173), missing health-related factors (n\u0026thinsp;=\u0026thinsp;2,137), missing sleep data (n\u0026thinsp;=\u0026thinsp;1,616), and missing lifestyle follow-up data (n\u0026thinsp;=\u0026thinsp;1,586). As a result, 4,789 participants were included in the final analysis. The details of the selection process are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measurement of NSD\u003c/h2\u003e \u003cp\u003eThe self-reported data on nightly sleep duration were obtained from the 2011 CHARLS follow-up questionnaire. Participants were asked, \"Over the past month, what was the average number of hours of actual sleep you obtained per night? (Note: This may be less than the total hours you spent in bed)\" (Question ID: DA049). The reported sleep duration ranged from 0 to 24 hours per day.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Operational definition of IC\u003c/h2\u003e \u003cp\u003eIC refers to the physical and mental resources available to an individual at any given time. As defined by WHO, IC consists of five core domains: locomotion, sensory function (hearing and vision), vitality, cognition, and psychological well-being. This construct offers a comprehensive assessment of the functional status of older adults. Each domain is classified as either intact functioning (score of 1) or impaired functioning (score of 0) based on validated thresholds. Since sensory function includes two components, hearing and vision, the total IC score ranges from 0 to 6, with higher scores indicating greater capacity[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This is consistent with a recent study validated using the CHARLS dataset[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMeasurement of IC\u003c/p\u003e \u003cp\u003e(1) Locomotion: Completing five sit-to-stand tests independently in 14 seconds or less was scored as 1 point while completing the test in more than 14 seconds was scored as 0 points[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e];\u003c/p\u003e \u003cp\u003e(2) Vitality: Body mass index (BMI) was calculated, with the threshold based on the Malnutrition Universal Screening Tool. A BMI less than 18.5 kg/m\u0026sup2; was scored as 0 points, while a BMI of 18.5 kg/m\u0026sup2; or higher was scored as 1 point[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e];\u003c/p\u003e \u003cp\u003e (3) Hearing: Participants were asked, \"How is your hearing?\" If they responded \"poor,\" they were scored as 0 points. If they answered \"fair,\" \"good,\" \"very good,\" or \"excellent,\" they were scored as 1 point;\u003c/p\u003e \u003cp\u003e(4) Vision: Participants were asked, \"How good is your eyesight for seeing things at a distance?\" and \"How good is your eyesight for seeing things up close?\" If they answered \"fair,\" \"good,\" \"very good,\" or \"excellent\" to both questions, they were scored 1 point. If they answered \"poor\" to either question, they were scored 0 points.\u003c/p\u003e \u003cp\u003e(5) Cognition: The Telephone Interview of Cognitive Status (TICS), which assesses memory ability and mental status, was used to determine the cut-off value based on the average minus one standard deviation[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Memory: This aspect was evaluated through immediate and delayed recall of 10 unrelated words. Participants were asked to recall the words after approximately 2 and 4 minutes. A total of 20 points were awarded if the participant recalled all 20 words correctly. Mental status: Participants could earn five points for correct orientation (day, month, year, day of the week, and season), five points for accurately performing a calculation (subtracting 7 from 100 five times in a row), and one point for visual construction (reproducing a picture of two five-pointed stars shown by the interviewer). The total score for mental status was 11 points.\u003c/p\u003e \u003cp\u003e(6) Psychology: The Epidemiological Studies Depression Scale[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] was used to assess depression. A score of less than 12 is considered normal and is assigned 1 point, while a score of 12 or greater is considered impaired and is assigned 0 points.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Fall\u003c/h2\u003e \u003cp\u003eThe primary outcome was fall incidents, which were self-reported by participants. These incidents were recorded in the 2011 to 2018 survey with the question: \"Have you experienced any falls since your last visit?\" Participants responded with either \"yes\" or \"no.\"\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Accompanying factors\u003c/h2\u003e \u003cp\u003eThe covariates included socio-demographic information, lifestyle behaviors, and health-related factors.\u003c/p\u003e \u003cp\u003eThe sociodemographic variables included age, gender, marital status (married and living with a spouse, married but living without a spouse, and single, divorced, or widowed), residence (rural and urban), and education level (less than lower secondary education and secondary or above).\u003c/p\u003e \u003cp\u003eLifestyle behaviors and health-related factors included BMI, smoking (yes or no), drinking (non-drinker, drink less than once a month, and drink more than once a month), and annual household expenditure.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.6 Statistical Methods\u003c/h3\u003e\n\u003cp\u003eBaseline characteristics were presented using the mean and standard deviation for continuous variables and percentages for categorical variables. Categorical variables were analyzed using chi-square tests, while continuous variables were analyzed using t-tests. The number of follow-ups was recorded from the baseline date to either the diagnosis date or December 31, 2018, whichever occurred first. The Cox proportional hazards model estimated the association between IC and falls. We established Model 1 (the coarse model) for the initial analysis. Meanwhile, Model 2 was adjusted for confounding factors such as age, sex, residence, marital status, and education level. Model 3 was further adjusted for alcohol consumption, smoking, and BMI. Model 4 added household expenditure to Model 3, and Model 5 was further adjusted for sleep duration.\u003c/p\u003e \u003cp\u003eWe also conducted a mediation analysis to quantify the proportion of sleep's impact on intrinsic ability and falls. First, a linear regression model was fitted to assess the effect of intrinsic ability on NSD. After adjusting for various covariates, we applied the Cox proportional hazards model to examine the relationship between the mediator (NSD), intrinsic ability, and falls, calculating both direct and indirect effects. The mediation ratio of NSD was then determined. To ensure robust statistical inference, resampling methods (Monte Carlo simulation) were used to estimate the Confidence interval(CI) and \u003cem\u003eP\u003c/em\u003e values for these effects.\u003c/p\u003e \u003cp\u003eIn the subgroup analysis, the samples were stratified by age (\u0026lt;\u0026thinsp;65 years, ≧65 years), sex, place of residence, education level, marital status, smoking, alcohol consumption, and consumption level (categorized into low, low-middle, upper-middle, and high based on quartiles). A \u003cem\u003eP\u003c/em\u003e value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant for each analysis. R statistical software (Version 4.4.1) was used for the analysis.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Descriptive Statistics\u003c/h2\u003e \u003cp\u003eThe characteristics of the 4,789 participants in this study, stratified by fall status, are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Of the total, 2,451 (51.20%) were female. During the follow-up period, 1,118 (23.35%) experienced a new onset of falls. The analysis indicated that older age, female sex, single/divorced/widowed marital status, lower education level, non-smoking status, lower household expenditure, and shorter sleep duration were associated with an increased risk of falling.\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\u003eBaseline characteristics of the longitudinal study population by fall\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\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall(n\u0026thinsp;=\u0026thinsp;4789)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-Fall(n\u0026thinsp;=\u0026thinsp;3671)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFall\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1118)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (Mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56.4 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.0 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.6 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender (%)\u003c/b\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\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\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2451 (51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1813 (49.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e638 (57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2338 (48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1858 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e480 (42.9)\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\u003e\u003cb\u003eResidence (%)\u003c/b\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\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\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3928 (82.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2998 (81.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e930 (83.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e861 (18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e673 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e188 (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\u003e\u003cb\u003eMarital_status (%)\u003c/b\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\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\u003eMarried and living with a spouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4215 (88.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3273 (89.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e942 (84.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried but living without a spouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e185 ( 3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e135 ( 3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50 ( 4.5)\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\u003eSingle, divorced, and windowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e389 ( 8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e263 ( 7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e126 (11.3)\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\u003e\u003cb\u003eEducation_level (%)\u003c/b\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\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\u003eLess than lower secondary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4169 (87.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3165 (86.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1004 (89.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esecondary or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e620 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e506 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e114 (10.2)\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\u003e\u003cb\u003eSmoking (%)\u003c/b\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\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\u003eNon-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2894 (60.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2172 (59.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e722 (64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1895 (39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1499 (40.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e396 (35.4)\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\u003cb\u003eDrinking (%)\u003c/b\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\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\u003eDrink but less than once a month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e389 ( 8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e308 ( 8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81 ( 7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink more than once a month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1100 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e862 (23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e238 (21.3)\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\u003eNon-drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3300 (68.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2501 (68.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e799 (71.5)\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\u003cb\u003eBMI (Mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.9 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.9 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.9 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnnual_Household_Expenditure (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7072.4 (9119.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7229.2 (9505.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6557.6 (7700.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNighttime sleep duration(Mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.5 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.6 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.3 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Association between IC and fall\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, univariate analysis revealed that each 1-point increase in IC was associated with a significant 17.9% reduction in the risk of falling (HR\u0026thinsp;=\u0026thinsp;0.821, 95% CI: 0.782,0.861). After adjusting for confounders including age, sex, place of residence, education level, marital status, smoking, alcohol consumption, BMI, household expenditure, and sleep duration, the hazard ratios (HRs) for falls were 0.862 (95% CI\u0026thinsp;=\u0026thinsp;0.819\u0026ndash;0.908), 0.862 (95% CI\u0026thinsp;=\u0026thinsp;0.818\u0026ndash;0.907), 0.863 (95% CI\u0026thinsp;=\u0026thinsp;0.819\u0026ndash;0.909) and 0.874 (95% CI\u0026thinsp;=\u0026thinsp;0.830\u0026ndash;0.922), across the sequential models (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When NSD was included alone in the Cox regression model (Model 5, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), a significant inverse relationship was observed between NSD and fall risk, with each additional hour of nighttime sleep associated with a 6.4% reduction in fall risk and a corresponding HR of 0.936 (95% CI\u0026thinsp;=\u0026thinsp;0.905\u0026ndash;0.967). When both IC and NSD were included in the same model (Model 6, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the effect estimates for IC and NSD remained largely unchanged, indicating independent contributions of both variables to fall risk.\u003c/p\u003e \u003cp\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\u003e\u003cb\u003eHazard ratios and 95% CI of fall status for IC and sleep.\u003c/b\u003e\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eIC-MetS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eSleep-MetS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 1\u003c/b\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\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\u003eIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.821(0.782,0.861)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2\u003c/b\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\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\u003eIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.862(0.819,0.908)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 3\u003c/b\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\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\u003eIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.862(0.818,0.907)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 4\u003c/b\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\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\u003eIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.863(0.819,0.909)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 5\u003c/b\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\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\u003eSleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.936(0.905,0.967)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 6\u003c/b\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\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\u003eIC\u0026thinsp;+\u0026thinsp;Sleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.874(0.830,0.922)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.949(0.918,0.981)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 1:Unadjusted\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 2: Adjustments in Model 1 age, gender, Residence, education_level, Marital_status\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 3: Adjustments in Model 2 plus drinking, smoking, BMI\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 4: Adjustments in Model 3 plus Annual_Household_Expenditure\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 5:Univariate model, after adjusting for all covariates, only sleep was applied in the model;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 6: Additive models, IC and sleep were applied in the model, with IC plus sleep;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Intermediary effects\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, after adjusting for covariates, NSD was significantly associated with IC (β\u0026thinsp;=\u0026thinsp;0.229, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Resampling analysis revealed that the total effect of IC on falls was 1.637 (95% CI\u0026thinsp;=\u0026thinsp;1.320\u0026ndash;1.900), with an indirect effect mediated through sleep of 0.131 (95% CI\u0026thinsp;=\u0026thinsp;0.045\u0026ndash;0.220). These findings indicate a significant mediating role of sleep in the relationship between intrinsic capacity and fall risk, explaining 7.87% variance of the total effect.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Subgroup analyses and interactions\u003c/h2\u003e \u003cp\u003eTo further explore the association between IC and the incidence of falls, we performed subgroup and interaction analyses stratified by age, sex, place of residence, education level, marital status, smoking, alcohol consumption, and household expenditure, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. These analyses revealed a significant interaction between marital status and fall risk (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.003), with a stronger protective effect of IC observed among participants who were married and living with a spouse, yielding an effect size of 0.83 (95% CI\u0026thinsp;=\u0026thinsp;0.78\u0026ndash;0.88). This may be attributed to the greater availability of social support among individuals who are married and living with a spouse.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this nationally representative study of middle-aged and elderly Chinese adults, we found that a higher IC was associated with a reduced incidence of falls. Moreover, longer NSD appeared to enhance the protective effect of IC against falls. To our knowledge, this is the first study to employ COX regression models and mediation analyses to examine the relationship between IC and falls while also exploring the mediating role of NSD.\u003c/p\u003e \u003cp\u003eA previous Chinese study investigating the association between IC and inpatient falls found that higher IC composite scores were linked to a reduced risk of falls and frailty among elderly inpatients[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. While consistent with our findings, that study was limited by its small sample size and cross-sectional design. Similarly, other studies have explored the independent effects of specific IC components, such as cognitive function, mood[\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and fall risk[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These studies suggest that impairments in particular cognitive domains, including processing speed and executive function, may predict injurious falls. Nonetheless, these studies have traditionally relied on disease-centered health concepts or single-domain health indicators and have primarily focused on older adults. In contrast, recent research has underscored the substantial public health burden of falls among individuals aged 40 to 64 years old. This concern arises not only from the significant consequences for labor force participation and public healthcare expenditure but also from the potential for timely interventions to prevent adverse outcomes[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Therefore, greater attention should be directed toward both middle-aged and elderly adults.\u003c/p\u003e \u003cp\u003eThe association between IC, NSD, and fall risk observed in this study may be explained by several mechanisms. First, poor nutritional status, especially insufficient protein intake and vitamin D deficiency, can lead to decreased muscle mass and function, contributing to the development of sarcopenia. These conditions may result in slower gait speed, reduced balance, and limited mobility, thereby increasing the likelihood of recurrent falls[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Second, cognitive impairment or motor dysfunction may disrupt the processing of sensory information essential for maintaining balance during ambulation, as well as impair the ability of older adults to accurately perceive and assess fall risk[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Third, depressive symptoms such as negative self-appraisal, cognitive changes, and reduced physical activity may heighten the fear of falling, thereby increasing the risk of tripping or losing balance[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Fourth, sensory impairments, including deficits in vision[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and hearing[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], have been identified as predictors of increased fall risk. Furthermore, the interplay between multiple compromised IC components and coexisting chronic conditions may exacerbate functional decline, ultimately contributing to the likelihood of falls.\u003c/p\u003e \u003cp\u003eThe mediating role of NSD may be attributed to several factors. One potential reason is that sleep deprivation disrupts cerebrospinal fluid circulation, impairing the clearance of neurotoxic substances such as β-amyloid. This can lead to neuronal damage and diminished synaptic plasticity, directly affecting the cognitive domain of intrinsic ability[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In addition, sleep deprivation may impair the prefrontal regulation of emotional control centers, thereby exacerbating anxiety and depressive tendencies[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Besides, sleep may modulate sensory functions through molecular signaling pathways that support neuroplasticity, enhancing the brain\u0026rsquo;s ability to adapt and respond to sensory inputs[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe strengths of our study include the large sample size, national representativeness, and the use of a robust statistical model to assess the relationship between IC, NSD, and falls. However, several limitations should be acknowledged and taken into consideration. Firstly, self-reported data on IC, NSD, and falls may be subject to recall bias. Secondly, despite adjusting for numerous potential confounders, residual confounding cannot be entirely excluded. Lastly, while longitudinal studies tend to show stronger associations between IC, NSD, and falls compared to cross-sectional studies, our study cannot establish causality or clarify the underlying biological mechanisms. Thus, further experimental research is warranted to substantiate our findings.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn conclusion, the incidence of falls decreased with increasing IC and longer NSD. This highlights the importance of focusing on populations with low IC and short NSD, as targeted preventive measures and further research are needed to address fall risk in these subgroups.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki and all participants signed informed consents before participation. Approval was granted by the Ethical Review Committee at Peking University (CHARLS: IRB00001052-11015, CFPS: IRB00001052-14010). Informed consent was obtained from all individual participants enrolled in the study.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe authors Consent to Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statemen\u003c/strong\u003et\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe CHARLS dataset is publicly available online, accessible at \u0026nbsp;http://charls.pku.edu.cn/en.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no acknowledgement.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent to Participate declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors of this article consent.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChunQiao Wu:\u003c/strong\u003e Data curation, Formal analysis, and Writing - original draft; \u003cstrong\u003eZhiYing Fei:\u0026nbsp;\u003c/strong\u003eWriting- Reviewing and Editing;\u003cstrong\u003e\u0026nbsp;Jiaxin Zhang:\u0026nbsp;\u003c/strong\u003eWriting- Reviewing and Editing；\u003cstrong\u003eShulin Lu:\u0026nbsp;\u003c/strong\u003eWriting- Reviewing and Editing; \u003cstrong\u003eHongying Pan:\u0026nbsp;\u003c/strong\u003eConceptualization, Writing - original draft, Writing - review \u0026amp; editing, and Validation.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDe Cillisy, F., et al., Fall-detection solution for mobile platforms using accelerometer and gyroscope data. 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Sadeh, Sleep and emotions: bidirectional links and underlying mechanisms. Int J Psychophysiol, 2013. 89(2): p. 218-28.\u003c/li\u003e\n\u003cli\u003eBlumberg, M.S., J.C. Dooley, and A. Tiriac, Sleep, plasticity, and sensory neurodevelopment. Neuron, 2022. 110(20): p. 3230-3242.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Intrinsic capacity, nighttime sleep, fall risk, middle-aged and elderly individuals, CHARLS","lastPublishedDoi":"10.21203/rs.3.rs-6629092/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6629092/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe association between intrinsic capacity (IC), nighttime sleep duration (NSD), and falls is poorly understood. To that end, the present study aims to explore the association between IC and falls, as well as the mediating role of NSD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData from four waves of the China Health and Retirement Longitudinal Study (CHARLS) were utilized. The primary measure, IC, was constructed from five dimensions. The relationships between IC, NSD, and falls were assessed using Cox proportional hazards regression models, with a \u003cem\u003eP\u003c/em\u003e value of less than 0.05 considered statistically significant.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThis study included 4,789 participants. Over a 7-year period, 1,118 new falls were recorded. In the Cox regression model, higher levels of IC were significantly associated with a reduced risk of falling, both during nighttime sleep and overall. Specifically, for every one-unit increase in IC, the hazard ratio (HR) for falling was 0.863 (95% CI: 0.819,0.909). For every additional hour of nighttime sleep, the HR for falling was 0.936 (95% CI: 0.905,0.967). Mediation analysis revealed that sleep played a mediating role in the association between IC and falling.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eChinese patients with low IC and a short nighttime sleep duration had a significantly increased risk of falls, highlighting the importance of early assessment and prevention of fall risk.\u003c/p\u003e","manuscriptTitle":"Association between intrinsic capacity, nighttime sleep duration and falls in middle‐aged and older adults: a longitudinal study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 05:39:01","doi":"10.21203/rs.3.rs-6629092/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":"7c38d449-8f55-421f-80df-18a0e7502e25","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-30T05:40:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-10 05:39:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6629092","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6629092","identity":"rs-6629092","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}