Development and validation of an ADL dependency model for Centenarians: a CHCCS-based cross-sectional cohort study

Development and validation of an ADL dependency model for Centenarians: a CHCCS-based cross-sectional cohort 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 Development and validation of an ADL dependency model for Centenarians: a CHCCS-based cross-sectional cohort study Songmei Han, Mingzhi Shen, Fan Bu, Jianbo Wu, Dongxu zhao, Fei Hua This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6738344/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Objective This study aimed to investigate the influencing factors of activities of daily life (ADL) among centenarians and to develop and validate a prediction model of ADL dependency for this population. Methods A total of 952 eligible centenarians from the China Hainan Centenarian Cohort Study (CHCCS) were included. An ADL score below 90 was defined as ADL dependence. Participants were randomly divided into development (70%) and validation (30%) groups. Univariate and multivariate logistic regression analysis (LRA) of the development group were used to identify independent risk factors related to ADL dependency. The selected variables were employed for modeling and nomogram construction. The model's performance was assessed using the receiver operating characteristic (ROC) curve, calibration plots, net reclassification index (NRI), and integrated discrimination improvement (IDI) scores. Decision curve analysis (DCA) was utilized to evaluate the clinical utility of the model. Results The development group comprised 668 participants, and the validation group included 284. After variable selection via univariate and multivariate logistic regression analyses, eight factors—residential type, chronic pain, incontinence, weight loss, napping, social participation, BMI, and albumin—were incorporated into the prediction model. The area under curve (AUC) the ROC curve was 0.796 (95% CI: 0.763–0.829) for the development group and 0.800 (95% CI: 0.750–0.851) for the validation group. Calibration plots, NRI, and IDI indicated a good fit of the model in both groups. The DCA demonstrated clinical effectiveness. Conclusions Factors such as living alone, experiencing chronic pain, incontinence, weight loss, absence of napping, lack of social participation, low BMI, and low albumin levels were identified as risk factors for ADL dependency among centenarians. The tailored prediction model encompassing these eight factors is suitable for early identification and prediction of ADL dependency in extremely elderly individuals. Activities of daily life ADL dependency Centenarian Prediction model CHCCS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction With the advancement of human civilization and medical sciences, life expectancy has steadily increased, leading to an ongoing global trend of population aging [ 1 ]. This trend is particularly pronounced in countries such as Japan, South Korea, and China [ 2 – 4 ]. Although an aging population reflects societal progress, it also brings challenges, including a rise in chronic disease prevalence, increased economic burdens, and labor shortages. Promoting healthy aging remains a paramount strategy [ 5 ]. Notably, centenarians, who live significantly longer—over 20 years more—than the average elderly person (76.1 years in China), serve as exemplars of successful aging and have thus garnered considerable attention in longevity research [ 6 – 10 ]. Activities of daily life (ADL) are crucial for maintaining independence in daily tasks and social interactions, reflecting an individual's self-care capacity. Impairment in these abilities is common among the elderly and increases with age [ 11 ], especially among centenarians. The prevalence of functional disability rises from 6.9% among those aged 65–79 to 23.6% in those 80–89 years old, and 42.7% in nonagenarians. Such impairments can lead to psychological distress, increased caregiving needs, decreased social participation, and a significant impact on both physical and mental health, potentially increasing mortality risk [ 12 ]; indeed, the mortality rate reaches 46.8% within two years of disability onset in those over 65 [ 13 ]. Given their advanced age, centenarians experience pronounced vulnerability due to diminished physical function and the exacerbated impact of mobility impairments. Addressing how to effectively preserve the basic self-care capabilities of centenarians is a fundamental and challenging task. The ADL scale, which assesses self-care tasks such as bathing, dressing, and eating [ 14 ], is commonly used but can be complex, especially for specific groups like centenarians. Currently, the assessment models derived from studies are often intricate, with limited research on simplified methods tailored for everyday assessment of centenarians [ 15 – 17 ]. Drawing on data from the China Hainan Centenarian Cohort Study (CHCCS) and existing ADL assessments, this study aims to: (1) identify factors influencing the ADL of centenarians to enhance their quality of life, and (2) develop and validate a streamlined, visual clinical prediction model based on ADL dependency. This research seeks to provide insights that could enhance the self-care capabilities of centenarians. 2 Methods 2.1 Study design and population This study was conducted following the guidelines of the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) [ 18 ]. Data for this retrospective cohort study were sourced from the CHCCS, a longitudinal cohort study conducted by Hainan Hospital of PLA General Hospital in collaboration with the Department of Civil Affairs of Hainan Province [ 19 ]. A total of 1,811 centenarians (age ≥ 100 years) were identified by the Department of Civil Affairs of Hainan Province by June 30, 2014. Each centenarian underwent physical examinations, laboratory tests, and structured interviews conducted by a trained interview team. At the conclusion of the data collection, 338 subjects were unreachable, 203 refused participation, and 268 had passed away before the investigation. After age verification and baseline data filtering, 952 centenarians were included in the final analysis for model development and internal validation. Participants were randomly assigned to the development group (668 individuals) and the validation group (284 individuals). The data screening process is illustrated in Fig. 1 . 2.2 Investigation Baseline data were obtained through one-on-one interviews conducted by trained interviewers. The collected data included demographic information (accurate birth date, gender, education, ethnicity, marital status, pre-retirement occupation, and residential type), personal habits (smoking and drinking habits, social participation), current health status (chronic pain, incontinence, falls in the past year, napping habits), and past medical history (cardiovascular diseases, hypertension, diabetes). Height and weight were measured by qualified medical staff, and BMI was calculated using the standard formula [ 20 ]. 2.3 Laboratory tests Fasting venous blood samples were collected and analyzed in the Central Laboratory of Hainan Hospital of PLA General Hospital. Cytological components, such as red blood cell (RBC) count and hemoglobin concentration, as well as biochemical markers including alanine transaminase (ALT), aspartate aminotransferase (AST), albumin, cholesterol, and triglycerides, were measured. 2.4 Physical assessment The ADL was assessed using the Barthel Index. This scale includes 10 items, each scored as follows: 0 (completely reliable), 5 (partially reliable), and 10 (independent), with a total score of 100. A lower score indicates greater dependency. An ADL score of less than 90 was considered indicative of physical dependency. 2.5 Statistical protocols Statistical analyses and graphical plots were performed using R 4.1.3 software. Missing data (< 5%) were handled using multiple imputation techniques. Continuous variables were expressed as median [interquartile range, (IQR)], and group differences were evaluated using the Mann-Whitney U-test. Categorical variables were reported as frequencies (percentages), and differences between groups were compared using the Chi-square test. A P < 0.05 was considered statistically significant. Univariate logistic regression analyses (LRA) was performed to explore the relationship between ADL and other variables using the development group dataset. Variables with P < 0.05 in univiarate analysis were included in the multivariate logistic regression analysis. The most relevant variables for the final model predicting physical impairment were selected using backward stepwise regression, based on the minimum Akaike information criterion (AIC). A nomogram was generated based on the final model. The discrimination of the prediction model was assessed using the receiver operating characteristic (ROC) curve in both the development and validation datasets. An area under the curve (AUC) > 0.7 was considered indicative of high discriminative ability. Model validation was conducted using the net reclassification index (NRI) and integrated discrimination improvement (IDI). Decision curve analysis (DCA) was used to evaluate the clinical benefit of the model in both datasets. 3 Results 3.1 Characteristic of centenaries Baseline characteristics are presented in Table 1 . A total of 952 centenarians were included in the final analysis, of which 535 (56.20%) were physically impaired and 417 (43.80%) were physically normal. Apart from chronic pain ( P = 0.033), diabetes ( P = 0.014) and ADL ( P = 0.037), the baseline characteristics were similar between the development and validation groups. Table 1 Baseline characteristic of centenarians in development and validation group Variables All Development group Validation group Z/χ 2 P value Number [n(%)] 952 668(70.19) 284(29.83) Age (years) 102(101, 104) 102(101, 104) 102(101, 104) -0.609 0.542 Gender [n(%)] 1.072 0.301 Male 169(17.75) 113(16.92) 56(19.72) Female 783(82.25) 555(83.08) 228(80.28) Ethnicity [n(%)] 0.517 0.472 Han 837(87.92) 584(87.43) 253(89.08) Minority 115(12.08) 84(12.57) 31(10.92) Marriage [n(%)] 0.268 0.605 Single 861(90.44) 602(90.12) 259(91.20) Coupled 91(9.56) 66(9.88) 25(8.80) Education [n(%)] 2.725 0.099 Educated 82(8.61) 51(7.63) 31(10.92) Uneducated 870(91,39) 617(92.37) 253(89.08) Work type [n(%)] 2.330 0.127 Heavy physical 430(45.17) 291(43.56) 139(48.94) Mild physical 522(54.83) 377(56.44) 145(51.06) Residential type [n(%)] 0.712 0.399 Alone 124(13.03) 83(12.43) 41(14.44) With family 828(86.97) 585(87.57) 243(85.56) Smoke [n(%)] 0.169 0.681 Yes 98(10.29) 67(10.03) 31(10.92) No 854(89.71) 601(89.97) 253(89.08) Drink [n(%)] 0.269 0.604 Yes 103(10.82) 70(10.48) 33(11.62) No 849(89.18) 598(89.52) 251(88.38) Chronic pain [n(%)] 4.520 0.033 Yes 388(40.76) 287(42.96) 101(35.56) No 564(59.24) 381(57.04) 183(64.44) Incontinence [n(%)] 0.551 0.458 Yes 132(13.87) 89(13.32) 43(15.14) No 820(86.13) 579(86.68) 241(84.86) Recent fall [n(%)] 1.542 0.214 Yes 247(25.95) 181(27.10) 66(23.24) No 705(74.05) 487(72.90) 218(76.76) Weight loss [n(%)] 0.411 0.522 Yes 82(8.61) 55(8.23) 27(9.51) No 870(91.39) 613(91.77) 257(90.49) Napping [n(%)] 1.068 0.301 Yes 799(83.93) 566(84.73) 233(82.04) No 153(16.07) 102(15.27) 51(17.96) Social participant [n(%)] 0.000 0.984 Yes 727(76.37) 510(76.35) 217(76.41) No 225(23.63) 158(23.65) 67(23.59) Cardiovascular diseases [n(%)] 3.817 0.051 Yes 36(3.78) 16(2.40) 20(7.04) No 916(96.22) 648(97.60) 268(92.96) Hypertension [n(%)] 0.098 0.754 Yes 225(23.63) 156(23.35) 69(24.30) No 727(76.37) 512(76.65) 215(75.70) Diabetes [n(%)] 5.977 0.014 Yes 91(9.56) 74(11.08) 17(5.99) No 861(90.44) 594(88.92) 267(94.01) BMI, kg/m 2 18.15(16.63, 19.56) 18.15(16.46, 19.57) 18.15(16.67, 19.43) -0.368 0.713 RBC, ×10 9 /L 4.01(3.62, 4.36) 4.01(3.62, 4.37) 4.01(3.64, 4.36) -0.711 0.477 Hemoglobin, g/L 114(103, 124) 114(103, 124) 114(102, 124) -0.543 0.587 ALT, U/L 9.3(7.3, 11.8) 9.4(7.3, 11.8) 9.2(7.4, 11.8) -0.154 0.878 AST, U/L 20.8(17.6, 24.3) 20.9(17.6, 24.5) 20.6(17.5, 24.0) -0.723 0.470 Albumin, g/L 38.5(36.1, 41.2) 38.4(36.1, 41.2) 38.6(36.1, 41.5) -0.727 0.467 Cholesterol, U/L 4.62(4.04, 5.25) 4.65(4.09, 5.25) 4.55(3.86, 5.25) -0.949 0.342 Triglyceride, U/L 1.05(0.80, 1.38) 1.04(0.82, 1.35) 1.08(0.77, 1.42) -0.119 0.906 ADL [n(%)] 4.346 0.037 < 90 535(56.20) 390(58.38) 145(51.06) ≥ 90 417(43.80) 278(41.62) 139(48.94) Abbreviations: ADL activity of daily life; ALT Alanine Transaminase, AST Aspartate Aminotransferase, BMI: Body Mass Index; RBC: Red Blood Cell Table 2 Univariate and multivariate LRA of the development dataset Variables Univariate analysis Multivariate analysis OR (95% CI) P value OR (95% CI) P value Age 1.021(0.964–1.082) 0.480 Gender Male 0.650(0.433–0.976) 0.038 1.347(0.844–2.151) 0.211 Female Ref. Ref. Ethnicity Han 0.753(0.468–1.211) 0.242 Minority Ref. Marriage Single 1.108(0.659–1.862) 0.700 Coupled Ref. Education Educated 0.723(0.408–1.281) 0.266 Uneducated Ref. Work type Heavy physical 1.195(0.876–1.632) 0.261 Mild physical Ref. Residential type Alone 0.446(0.280–0.713) 0.001 0.474(0.274–0.821) 0.008 With family Ref. Ref. Smoke Yes 0.810(0.488–1.345) 0.416 No Ref. Drink Yes 0.730(0.444–1.199) 0.214 No Ref. Chronic pain Yes 1.868(1.360–2.566) 0.000 1.651(1.145–2.381) 0.007 No Ref. Ref. Incontinence Yes 4.934(2.679–9.085) 0.000 3.687(1.869–7.275) 0.000 No Ref. Ref. Recent fall Yes 1.387(0.975–1.974) 0.069 No Ref. Weight loss Yes 5.434(2.420–12.200) 0.000 4.438(1.799–10.947) 0.001 No Ref. Ref. Napping Yes 1.800(1.178–2.751) 0.007 1.752(1.072–2.863) 0.025 No Ref. Ref. Social participant Yes 0.099(0.056–0.172) 0.000 0.124(0.068–0.225) 0.000 No Ref. Ref. Cardiovascular diseases Yes 0.464(0.187–1.151) 0.098 No Ref. Hypertension Yes 0.964(0.670–1.385) 0.842 No Ref. Diabetes Yes 1.655(1.019–2.688) 0.042 1.589(0.902–2.799) 0.109 No Ref. Ref. BMI 0.927(0.880–0.977) 0.004 0.918(0.864–0.975) 0.005 RBC 0.878(0.679–1.134) 0.318 Hemoglobin 0.991(0.981-1.000) 0.053 ALT 0.988(0.967–1.009) 0.267 AST 0.994(0.976–1.012) 0.514 Albumin 0.865(0.827–0.905) 0.000 0.896(0.848–0.947) 0.000 Cholesterol 0.814(0.692–0.957) 0.013 0.966(0.786–1.186) 0.739 Triglyceride 0.870(0.687-1.100) 0.245 Abbreviations: ADL activity of daily life; ALT Alanine Transaminase, AST Aspartate Aminotransferase, BMI Body Mass Index; CI confidence interval; OR odds ratio; RBC Red Blood Cell 3.2 LRA and modeling Univariate LRA identified 11 predictors ( P < 0.05) from 25 eligible candidates. These predictors were then subjected to multivariable LRA. After applying backward regression, the model yielded a minimal AIC (AIC = 733.097), incorporating residential type, chronic pain, incontinence, weight loss, napping, social participation, BMI, and albumin as predictors. 3.3 Development of the nomogram The eight predictors selected through backward multivariable LRA were used to formulate the predicting equation: Logit (1/1- P ) = 5.417–0.807 × residential type + 0.474 × chronic pain + 1.381 × incontinence + 1.506 × weight loss + 0.587 × napping + 2.075 × social participation – 0.082 × BMI – 0.133 × albumin. A nomogram was constructed to predict centenarian physical disability. As shown in Fig. 2 , points are assigned for each parameter based on the centenarian’s individual status, enabling a match of the total points with the likelihood of physical disability. 3.4 Discrimination of the nomogram As illustrated in Fig. 3 and Supplementary Table 2, the ROC curves for both the development and validation datasets exhibited good predictive capability, with AUCs of 0.796 (95% CI: 0.763–0.829) and 0.800 (95% CI: 0.750–0.851), respectively. These curves also demonstrated superior predictive accuracy compared to models using randomly selected variables (incontinence, social participation, recent fall, and BMI). 3.5 Validation of the nomogram Internal validation was employed. The Hosmer-Lemeshow test confirmed a good fit for our nomogram in both development ( P = 0.300) and validation ( P = 0.560) datasets. The calibration plots (Fig. 4 ) for both datasets showed a close correlation between predicted and actual probabilities ( P = 0.778 vs. P = 0.716). NRI [0.028 (95% CI: 0.020–0.230)] and IDI [0.019 (95% CI: 0.005–0.033)] further affirmed the model's accuracy (Supplementary Fig. 1). 3.6 Efficiency of the model The clinical efficacy of the model in both development and validation datasets was evidenced by Decision Curve Analyses (DCA), which showed a significant benefit over a wide range of thresholds compared to individual predictors (incontinence, social participation, recent fall, and BMI) (Fig. 5 ). 4 Discussions Due to the accumulation of various injuries and diseases, elderly individuals often exhibit a high incidence of disability. The decline of motor ability in the elderly is often accompanied by the loss of self-care ability [ 21 ]. The results of this study showed that the proportion of ADL impairment in centenarians was 56.2%, which was markedly higher than in individuals younger than 100 years [ 22 ]. All these suggest that more resources, including public policy, nursing, and economic support, should be allocated to enhance the quality of life for centenarians. In this study, logistic regression was used to identify eight variables that were strongly correlated with ADL impairment in the development dataset, and these were used to create a prediction equation for ADL impairment. Comprehensive validation demonstrated that the formula provided excellent discrimination, calibration, and clinical benefits in both development and validation datasets, suggesting that the model had high accuracy in predicting the incidence of ADL impairment in centenarians. Our nomogram indicates that risk factors for ADL impairment among centenarians include incontinence, chronic body pain, recent three-month weight loss, napping, lack of social communication, low BMI, and low serum albumin levels. Incontinence is considered to be an independent risk factor for ADL impairment. A community-based cross-sectional survey showed a significant association between activity limitation and urinary incontinence [ 23 , 24 ]. The adjusted odds ratio (OR) for disabilities associated with urinary incontinence was 1.96 (95% CI: 1.07, 3.58) compared to women without this condition [ 25 ]. In addition, incontinence and ADL impairment may be complications resulting from acute stroke, spinal cord injury, and other conditions, often accompanied by comorbidities, suggesting central nervous system involvement. Both incontinence and mobility impairment significantly affect physical ADL [ 26 ]. Disability may induce chronic pain. A study of 426 participants aged 71 to 80 years highlighted that chronic pain significantly impacted the prevalence of ADL and/or IADL disabilities in the elderly [ 27 ]. The OR for ADL disability among African Americans who reported body pain was 4.06 (95% CI: 2.63–6.26) for women and 6.44 (95% CI: 2.84–14.57), for men [ 28 ]. Even in the absence of prior disability, chronic pain in the musculoskeletal system may restrict physical functions in the elderly, leading to significant ADL limitations, and this limitation is closely related to the location, intensity, and duration of pain. For example, arthritis significantly increases the incidence of ADL disability [ 29 ]. Weight loss, low BMI, and low serum albumin are indicators of malnutrition. This study suggests a strong relationship between nutrition and ADL in centenarians, warranting increased attention. Research on the elderly over 75 years old showed that underweight individuals not only had a high incidence of ADL disability but also a strong association with disability [ 30 ]. In a study of nonagenarians and centenarians, the OR for ADL disability was 1.5 times higher in underweight older adults compared to those with a normal BMI [ 31 ]. Sarcopenia, resulting from malnutrition, was significantly associated with ADL disability (OR = 1.94, 95% CI: 1.37–2.75) [ 32 ]. Conversely, increased food intake can significantly reduce the relative risk (RR) of impaired physical function in the Japanese elderly [ 33 ]. In addition, when centenarians experience weight loss, low BMI, and low serum albumin, it indicates abnormal metabolic function. An extremely low or high BMI can have adverse metabolic effects, which are risk factors for disability in older adults [ 34 , 35 ]. Metabolic syndrome was also associated with ADL disability (OR = 1.81, 95%CI: 1.22–3.45) in a study of the elderly over 90 years [ 22 ]. Studies on the effect of sleep on the ADL of the elderly mainly focus on nighttime sleep, and napping is a neglected factor. Sleep complaints, including napping frequency, were associated with an increased risk of ADL disability (HR: 1.27,95% CI: 1.10–1.47), and mobility disability (HR: 1.27, 95% CI: 1.09–1.48) [ 36 ]. People with ADL impairment also had impaired sleep quality (OR = 1.44, 95%CI: 1.20–1.72, P < 0.0001) [ 37 ]. Centenarians whose sleep time ≥ 2 h in the daytime were more likely to develop ADL disability (OR: 2.75, 95%CI: 1.56–4.83) [ 38 ]. Therefore, this study supports previous research indicating that napping is crucial for maintaining health in centenarians. Residential type is one of the influencing factors of disability progression in the elderly [ 39 ]. The elderly living alone had the highest incidence of disability [ 40 ]. However, one interesting conclusion from this study was that living with family members was correlated with a higher probability of ADL disability, which contradicts previous findings. In China, influenced by traditional concepts, family members carry the main burden of elderly care. Many elderly people who are capable of taking care of themselves prefer to live apart from their children to reduce dependence on their children and society. Assistance from children is typically sought only when the elderly become incapacitated. Therefore, when the elderly live with their children, it often indicates the onset of disability [ 41 ]. The current study also found that a lack of social communication was strongly associated with a high incidence of ADL disability in centenarians. A large-scale survey involving 16536 elderly participants revealed that alienation from friends and relatives was associated with a higher incidence of disability [ 42 ]. A Study from the China Health and Retirement Longitudinal Study (CHARLS) found that for people older than 65, ADL disability was significantly associated with social isolation (OR = 1.18, 95%CI: 1.07–1.30), but not with loneliness [ 43 ]. Chronic conditions in the elderly often lead to disability, which in turn restricts social participation [ 44 ]. In addition, the deterioration of socioeconomic status significantly influences an individual's social participation and thus leads to ADL disability [ 45 ]. In general, for centenarians, guardians should closely monitor the nutritional status of these individuals, enhance nutritional supplementation, and prepare special digestible and absorbable foods for them. Moreover, family members should maintain communication with centenarians to prevent feelings of loneliness [ 46 ], thus reducing the incidence of ADL dependency. Finally, guardians, especially those in nursing homes, should provide tailored personal care and psychological support to enhance the quality of life for centenarians facing irreversible physical conditions such as incontinence and chronic pain [ 47 ]. Limitations Firstly, due to the nature of observational studies, while this study incorporated as many potential influencing factors of ADL disability in centenarians as possible, some factors may still have been overlooked. Secondly, 203 centenarians died before the investigation during the course of this study, and these individuals, who may have been in worse physical condition, were not included, potentially leading to overly optimistic results. In addition, being an isolated island, Hainan Province has distinct characteristics that might yield different results from mainland China and other parts of the world. Abbreviations ADL Activities of Daily Life AIC Akaike Information Criterion ALT Alanine Transaminase AST Aspartate Aminotransferase AUC Area Under the Curve BMI Body Mass Index CHARLS China Health and Retirement Longitudinal Study CHCCS China Hainan Centenarian Cohort Study CI Confidence Interval DCA Decision Curve Analysis IDI Integrated Discrimination Improvement IQR Interquartile Range LRA logistic Regression Analysis NRI Net Reclassification Index RBC Red Blood Cell ROC Receiver Operating Characteristic Curve TRIPOD Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis Declarations Ethics statement The CHCCS was approved by the Ethics Committee of Hainan Hospital of Chinese People’s Liberation Army General Hospital (Sanya, Hainan; Number: 301HNLL-2016-01). Written informed consents were signed before investigation for all participants. Acknowledgements We thank all the colleagues that devoted to the accomplishment of the CHCCS database. Author contributions DXZ and FH designed and concepted of this study. JBW and FB analyzed the raw data. SMH and MZS completed the original manuscript. DXZ and FH revised the manuscript. SMH and MZS contributed equally to the current study. Funing The current study was supported by grant from the Natural Science Foundation of Hainan Province (ZDYF2023SHFZ145). Conflict of interests All authors declare no conflict of interests. References Ng R, Chow TYJ. Aging Narratives Over 210 Years (1810–2019). journals Gerontol Ser B Psychol Sci social Sci. 2021;76(9):1799–807. Chen Z, Yu J, Song Y, Chui D. Aging Beijing: challenges and strategies of health care for the elderly. Ageing Res Rev. 2010;9(Suppl 1):S2–5. Liu Y, Kobayashi S, Karako K, Song P, Tang W. The latest policies, practices, and hotspots in research in conjunction with the aging of Japan's population. Biosci Trends. 2024;18(3):219–23. Hyun KR, Kang S, Lee S. Population Aging and Healthcare Expenditure in Korea. 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Urinary incontinence and disability in community-dwelling women: a cross-sectional study. Neurourol Urodyn. 2015;34(6):539–43. Liu C, Shu R, Liang H, Liang Y. Multimorbidity Patterns and the Disablement Process among Public Long-Term Care Insurance Claimants in the City of Yiwu (Zhejiang Province, China). Int J Environ Res Public Health 2022, 19(2). Ćwirlej-Sozańska AB, Sozański B, Wiśniowska-Szurlej A, Wilmowska-Pietruszyńska A. An assessment of factors related to disability in ADL and IADL in elderly inhabitants of rural areas of south-eastern Poland. Annals agricultural Environ medicine: AAEM. 2018;25(3):504–11. Walker JL, Thorpe RJ Jr., Harrison TC, Baker TA, Cary M, Szanton SL, Allaire JC, Whitfield KE. The Relationship between Pain, Disability, and Sex in African Americans. Pain Manage nursing: official J Am Soc Pain Manage Nurses. 2016;17(5):294–301. Rodriguez MA, Chou LN, Sodhi JK, Markides KS, Ottenbacher KJ, Snih SA. Arthritis, physical function, and disability among older Mexican Americans over 23 years of follow-up. Ethn Health. 2022;27(8):1915–31. Lee E, Jun SS. Trajectories of Disability and Related Factors in Korean Adults Aged ≥ 75 years. J Appl gerontology: official J South Gerontological Soc. 2023;42(9):1953–64. Yang M, Hao Q, Luo L, Ding X, Wu H, Zhang Y, Dong B. Body mass index and disability in Chinese nonagenarians and centenarians. J Am Med Dir Assoc. 2014;15(4):e303301–306. Li Q, Cen W, Yang T, Tao S. Association between depressive symptoms and sarcopenia among middle-aged and elderly individuals in China: the mediation effect of activities of daily living (ADL) disability. BMC Psychiatry. 2024;24(1):432. Yoshida D, Ohara T, Hata J, Shibata M, Hirakawa Y, Honda T, Uchida K, Takasugi S, Kitazono T, Kiyohara Y, et al. Dairy consumption and risk of functional disability in an elderly Japanese population: the Hisayama Study. Am J Clin Nutr. 2019;109(6):1664–71. Su P, Ding H, Zhang W, Duan G, Yang Y, Long J, Du L, Xie C, Jin C, Hu C, et al. Joint Association of Obesity and Hypertension with Disability in the Elderly– A Community-Based Study of Residents in Shanghai, China. J Nutr Health Aging. 2017;21(4):362–9. Zhang H, Wang ZH, Wang LM, Qi SG, Li ZX. [Study on activities of daily living disability in community-dwelling older adults in China]. Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi. 2019;40(3):266–71. Park M, Buchman AS, Lim AS, Leurgans SE, Bennett DA. Sleep complaints and incident disability in a community-based cohort study of older persons. Am J geriatric psychiatry: official J Am Association Geriatric Psychiatry. 2014;22(7):718–26. Steptoe A, Di Gessa G. Mental health and social interactions of older people with physical disabilities in England during the COVID-19 pandemic: a longitudinal cohort study. Lancet Public health. 2021;6(6):e365–73. Yang S, Wang S, Liu G, Li R, Li X, Chen S, Zhao Y, Liu M, Liu Y, He Y. The relationship between sleep status and activity of daily living: based on China Hainan centenarians cohort study. BMC Geriatr. 2023;23(1):796. Pan C, Cao N, Kelifa MO, Luo S. Age and cohort trends in disability among Chinese older adults. Front public health. 2023;11:998948. Henning-Smith C, Shippee T, Capistrant B. Later-Life Disability in Environmental Context: Why Living Arrangements Matter. Gerontologist. 2018;58(5):853–62. Chen W, Fang Y, Mao F, Hao S, Chen J, Yuan M, Han Y, Hong YA. Assessment of Disability among the Elderly in Xiamen of China: A Representative Sample Survey of 14,292 Older Adults. PLoS ONE. 2015;10(6):e0131014. Qiao R, Jia S, Zhao W, Xia X, Su Q, Hou L, Li D, Hu F, Dong B. Prevalence and correlates of disability among urban-rural older adults in Southwest China: a large, population-based study. BMC Geriatr. 2022;22(1):517. Guo L, An L, Luo F, Yu B. Social isolation, loneliness and functional disability in Chinese older women and men: a longitudinal study. Age Ageing. 2021;50(4):1222–8. Griffith LE, Raina P, Levasseur M, Sohel N, Payette H, Tuokko H, van den Heuvel E, Wister A, Gilsing A, Patterson C. Functional disability and social participation restriction associated with chronic conditions in middle-aged and older adults. J Epidemiol Commun Health. 2017;71(4):381–9. Liu H, Wang M. Socioeconomic status and ADL disability of the older adults: Cumulative health effects, social outcomes and impact mechanisms. PLoS ONE. 2022;17(2):e0262808. Liu Y, Li H, Wu B, Liu X, Chen H, Jin HY, Wu C. Association between primary caregiver type and mortality among Chinese older adults with disability: a prospective cohort study. BMC Geriatr. 2021;21(1):268. Webster-Dekker KE, Lu Y, Perkins SM, Ellis J, Gates M, Otis L, Winton R, Hacker E. Factors associated with change in activities of daily living performance in home health care patients with diabetes. Geriatric Nurs (New York NY). 2024;59:543–8. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfigure1.pdf Suplementarytables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 11 May, 2026 Reviews received at journal 06 Jul, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers invited by journal 23 Jun, 2025 Editor invited by journal 28 May, 2025 Editor assigned by journal 27 May, 2025 Submission checks completed at journal 27 May, 2025 First submitted to journal 24 May, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6738344","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475395743,"identity":"a6e2d079-7953-4c6a-a593-1cd5303ef811","order_by":0,"name":"Songmei Han","email":"","orcid":"","institution":"the Third Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Songmei","middleName":"","lastName":"Han","suffix":""},{"id":475395744,"identity":"228920be-4b63-4eee-a29c-35d5680e75f4","order_by":1,"name":"Mingzhi Shen","email":"","orcid":"","institution":"Hainan Hospital of Chinese People’s Liberation Army General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mingzhi","middleName":"","lastName":"Shen","suffix":""},{"id":475395745,"identity":"50b95326-02c2-46f7-b6b6-2e17ed77f5ee","order_by":2,"name":"Fan Bu","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Bu","suffix":""},{"id":475395746,"identity":"00728592-b2db-48fc-a0cc-f6123f1e09c9","order_by":3,"name":"Jianbo Wu","email":"","orcid":"","institution":"the Second Naval Hospital of Southern Theater command of People’s Liberation Army","correspondingAuthor":false,"prefix":"","firstName":"Jianbo","middleName":"","lastName":"Wu","suffix":""},{"id":475395747,"identity":"a8a803a2-5897-4252-9405-e1ef2d7ce088","order_by":4,"name":"Dongxu zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYFCCBIYDHypsGPiYSdDC+HDGmTQGNlK0MBvzth1mYCNag3x7jpk0D9t5eTZ2HjPpAgY7Od0GAloYe56lSc7huW3YxgzUMoMh2djsAAEtzBLJxyTeSNxmBGvhYTiQuI2QFjaJxDYJHoNz9sRr4ZFIPmzIk3AgkXgtEjzPEh/OOJCc3MbMVmzNY0CEX4AhZnDg4z87237+wxtv81TYyRHUggQ4DBgYDIhXDgLsD0hTPwpGwSgYBSMGAAA4JDgF1S3RigAAAABJRU5ErkJggg==","orcid":"","institution":"the Second Naval Hospital of Southern Theater command of People’s Liberation Army","correspondingAuthor":true,"prefix":"","firstName":"Dongxu","middleName":"","lastName":"zhao","suffix":""},{"id":475395748,"identity":"2fa02aaf-90ec-4d88-b882-7b15f053e521","order_by":5,"name":"Fei Hua","email":"","orcid":"","institution":"the Third Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Hua","suffix":""}],"badges":[],"createdAt":"2025-05-24 10:08:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6738344/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6738344/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85618832,"identity":"f7efed42-e3e3-4b67-a16b-ae719441a8f1","added_by":"auto","created_at":"2025-06-29 14:52:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":175966,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant selection\u003c/p\u003e","description":"","filename":"Figure121.png","url":"https://assets-eu.researchsquare.com/files/rs-6738344/v1/da473b2c06a56aa0d2681aae.png"},{"id":85617216,"identity":"eaf4bb04-4d3a-4b88-afb5-9e4a3f2bc125","added_by":"auto","created_at":"2025-06-29 14:44:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19763,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for centenarian physical disability prediction model\u003c/p\u003e","description":"","filename":"Figure225.png","url":"https://assets-eu.researchsquare.com/files/rs-6738344/v1/d038884509c70550bb35f596.png"},{"id":85617214,"identity":"170b70b9-3335-44d3-b104-fd8e7646a485","added_by":"auto","created_at":"2025-06-29 14:44:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":75849,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curves of the development (Panel A) and validation (Panel B) group\u003c/p\u003e","description":"","filename":"Figure3A1.png","url":"https://assets-eu.researchsquare.com/files/rs-6738344/v1/4da18c5b8040b17b50d60e79.png"},{"id":85617218,"identity":"62cb600f-cd99-426f-8594-ccae19784e63","added_by":"auto","created_at":"2025-06-29 14:44:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":68699,"visible":true,"origin":"","legend":"\u003cp\u003eThe calibration plots of the development (Panel A) and validation (Panel B) group\u003c/p\u003e","description":"","filename":"Figure4A1.png","url":"https://assets-eu.researchsquare.com/files/rs-6738344/v1/e00bfccda16232dcfb9f8d86.png"},{"id":85617224,"identity":"1ee2d640-a8eb-4ec2-906d-4fd80f85bb0b","added_by":"auto","created_at":"2025-06-29 14:44:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":68724,"visible":true,"origin":"","legend":"\u003cp\u003eDCA curves of the development (Panel A) and validation (Panel B) group\u003c/p\u003e","description":"","filename":"Figure5A1.png","url":"https://assets-eu.researchsquare.com/files/rs-6738344/v1/e9f66756b2a3ca3c50f9c05a.png"},{"id":85619433,"identity":"59e6fd03-b9c2-444a-bd04-4e19012e973e","added_by":"auto","created_at":"2025-06-29 15:00:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1528656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6738344/v1/1bfe6011-6f05-4d2a-8d48-9fc042204941.pdf"},{"id":85617212,"identity":"6a62ff73-1921-49f4-8313-2d438104d735","added_by":"auto","created_at":"2025-06-29 14:44:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":44309,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6738344/v1/60a88b9390d917a42f4ca7d3.pdf"},{"id":85618833,"identity":"907f8e78-1a03-44c4-96b0-b18ea89cd0d0","added_by":"auto","created_at":"2025-06-29 14:52:23","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":386385,"visible":true,"origin":"","legend":"","description":"","filename":"Suplementarytables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6738344/v1/7b74af6c8ea86e6a93560ccb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of an ADL dependency model for Centenarians: a CHCCS-based cross-sectional cohort study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWith the advancement of human civilization and medical sciences, life expectancy has steadily increased, leading to an ongoing global trend of population aging [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This trend is particularly pronounced in countries such as Japan, South Korea, and China [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although an aging population reflects societal progress, it also brings challenges, including a rise in chronic disease prevalence, increased economic burdens, and labor shortages. Promoting healthy aging remains a paramount strategy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Notably, centenarians, who live significantly longer\u0026mdash;over 20 years more\u0026mdash;than the average elderly person (76.1 years in China), serve as exemplars of successful aging and have thus garnered considerable attention in longevity research [\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eActivities of daily life (ADL) are crucial for maintaining independence in daily tasks and social interactions, reflecting an individual's self-care capacity. Impairment in these abilities is common among the elderly and increases with age [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], especially among centenarians. The prevalence of functional disability rises from 6.9% among those aged 65\u0026ndash;79 to 23.6% in those 80\u0026ndash;89 years old, and 42.7% in nonagenarians. Such impairments can lead to psychological distress, increased caregiving needs, decreased social participation, and a significant impact on both physical and mental health, potentially increasing mortality risk [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]; indeed, the mortality rate reaches 46.8% within two years of disability onset in those over 65 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Given their advanced age, centenarians experience pronounced vulnerability due to diminished physical function and the exacerbated impact of mobility impairments. Addressing how to effectively preserve the basic self-care capabilities of centenarians is a fundamental and challenging task.\u003c/p\u003e \u003cp\u003eThe ADL scale, which assesses self-care tasks such as bathing, dressing, and eating [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], is commonly used but can be complex, especially for specific groups like centenarians. Currently, the assessment models derived from studies are often intricate, with limited research on simplified methods tailored for everyday assessment of centenarians [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Drawing on data from the China Hainan Centenarian Cohort Study (CHCCS) and existing ADL assessments, this study aims to: (1) identify factors influencing the ADL of centenarians to enhance their quality of life, and (2) develop and validate a streamlined, visual clinical prediction model based on ADL dependency. This research seeks to provide insights that could enhance the self-care capabilities of centenarians.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and population\u003c/h2\u003e \u003cp\u003eThis study was conducted following the guidelines of the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Data for this retrospective cohort study were sourced from the CHCCS, a longitudinal cohort study conducted by Hainan Hospital of PLA General Hospital in collaboration with the Department of Civil Affairs of Hainan Province [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A total of 1,811 centenarians (age\u0026thinsp;\u0026ge;\u0026thinsp;100 years) were identified by the Department of Civil Affairs of Hainan Province by June 30, 2014. Each centenarian underwent physical examinations, laboratory tests, and structured interviews conducted by a trained interview team. At the conclusion of the data collection, 338 subjects were unreachable, 203 refused participation, and 268 had passed away before the investigation. After age verification and baseline data filtering, 952 centenarians were included in the final analysis for model development and internal validation. Participants were randomly assigned to the development group (668 individuals) and the validation group (284 individuals). The data screening process is illustrated 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 Investigation\u003c/h2\u003e \u003cp\u003e Baseline data were obtained through one-on-one interviews conducted by trained interviewers. The collected data included demographic information (accurate birth date, gender, education, ethnicity, marital status, pre-retirement occupation, and residential type), personal habits (smoking and drinking habits, social participation), current health status (chronic pain, incontinence, falls in the past year, napping habits), and past medical history (cardiovascular diseases, hypertension, diabetes). Height and weight were measured by qualified medical staff, and BMI was calculated using the standard formula [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Laboratory tests\u003c/h2\u003e \u003cp\u003eFasting venous blood samples were collected and analyzed in the Central Laboratory of Hainan Hospital of PLA General Hospital. Cytological components, such as red blood cell (RBC) count and hemoglobin concentration, as well as biochemical markers including alanine transaminase (ALT), aspartate aminotransferase (AST), albumin, cholesterol, and triglycerides, were measured.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Physical assessment\u003c/h2\u003e \u003cp\u003eThe ADL was assessed using the Barthel Index. This scale includes 10 items, each scored as follows: 0 (completely reliable), 5 (partially reliable), and 10 (independent), with a total score of 100. A lower score indicates greater dependency. An ADL score of less than 90 was considered indicative of physical dependency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical protocols\u003c/h2\u003e \u003cp\u003eStatistical analyses and graphical plots were performed using R 4.1.3 software. Missing data (\u0026lt;\u0026thinsp;5%) were handled using multiple imputation techniques. Continuous variables were expressed as median [interquartile range, (IQR)], and group differences were evaluated using the Mann-Whitney U-test. Categorical variables were reported as frequencies (percentages), and differences between groups were compared using the Chi-square test. A \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eUnivariate logistic regression analyses (LRA) was performed to explore the relationship between ADL and other variables using the development group dataset. Variables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univiarate analysis were included in the multivariate logistic regression analysis. The most relevant variables for the final model predicting physical impairment were selected using backward stepwise regression, based on the minimum Akaike information criterion (AIC). A nomogram was generated based on the final model.\u003c/p\u003e \u003cp\u003eThe discrimination of the prediction model was assessed using the receiver operating characteristic (ROC) curve in both the development and validation datasets. An area under the curve (AUC)\u0026thinsp;\u0026gt;\u0026thinsp;0.7 was considered indicative of high discriminative ability. Model validation was conducted using the net reclassification index (NRI) and integrated discrimination improvement (IDI). Decision curve analysis (DCA) was used to evaluate the clinical benefit of the model in both datasets.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Characteristic of centenaries\u003c/h2\u003e \u003cp\u003eBaseline characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 952 centenarians were included in the final analysis, of which 535 (56.20%) were physically impaired and 417 (43.80%) were physically normal. Apart from chronic pain (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033), diabetes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014) and ADL (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037), the baseline characteristics were similar between the development and validation groups.\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 characteristic of centenarians in development and validation group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"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 \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\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDevelopment group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValidation group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ/χ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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\u003eNumber [n(%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e668(70.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e284(29.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102(101, 104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102(101, 104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102(101, 104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender [n(%)]\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.301\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e169(17.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113(16.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56(19.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e783(82.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e555(83.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e228(80.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity [n(%)]\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e837(87.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e584(87.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e253(89.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115(12.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84(12.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(10.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarriage [n(%)]\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e861(90.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e602(90.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e259(91.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoupled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91(9.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66(9.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(8.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation [n(%)]\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82(8.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51(7.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(10.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUneducated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e870(91,39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e617(92.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e253(89.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork type [n(%)]\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy physical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e430(45.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e291(43.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139(48.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild physical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e522(54.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e377(56.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e145(51.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidential type [n(%)]\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124(13.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83(12.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41(14.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e828(86.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e585(87.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e243(85.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke [n(%)]\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98(10.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67(10.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(10.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e854(89.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e601(89.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e253(89.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink [n(%)]\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103(10.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70(10.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(11.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e849(89.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e598(89.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e251(88.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic pain [n(%)]\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e388(40.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e287(42.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101(35.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e564(59.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e381(57.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e183(64.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncontinence [n(%)]\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132(13.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89(13.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43(15.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e820(86.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e579(86.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e241(84.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecent fall [n(%)]\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247(25.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181(27.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66(23.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e705(74.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e487(72.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e218(76.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight loss [n(%)]\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82(8.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55(8.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(9.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e870(91.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e613(91.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e257(90.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNapping [n(%)]\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e799(83.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e566(84.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e233(82.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e153(16.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102(15.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51(17.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial participant [n(%)]\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e727(76.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e510(76.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e217(76.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225(23.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158(23.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67(23.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular diseases [n(%)]\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36(3.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(7.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e916(96.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e648(97.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e268(92.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension [n(%)]\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225(23.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156(23.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69(24.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e727(76.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e512(76.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e215(75.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes [n(%)]\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91(9.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74(11.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(5.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e861(90.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e594(88.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e267(94.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.15(16.63, 19.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.15(16.46, 19.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.15(16.67, 19.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.01(3.62, 4.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.01(3.62, 4.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.01(3.64, 4.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114(103, 124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114(103, 124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114(102, 124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.3(7.3, 11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.4(7.3, 11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.2(7.4, 11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.8(17.6, 24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.9(17.6, 24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.6(17.5, 24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.5(36.1, 41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.4(36.1, 41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.6(36.1, 41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.62(4.04, 5.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.65(4.09, 5.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.55(3.86, 5.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05(0.80, 1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04(0.82, 1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08(0.77, 1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADL [n(%)]\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e535(56.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e390(58.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e145(51.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e417(43.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e278(41.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139(48.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eAbbreviations: ADL activity of daily life; ALT Alanine Transaminase, AST Aspartate Aminotransferase, BMI: Body Mass Index; RBC: Red Blood Cell\u003c/em\u003e\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=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate LRA of the development dataset\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.021(0.964\u0026ndash;1.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c6\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.650(0.433\u0026ndash;0.976)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.347(0.844\u0026ndash;2.151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.211\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.753(0.468\u0026ndash;1.211)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarriage\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.108(0.659\u0026ndash;1.862)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoupled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.723(0.408\u0026ndash;1.281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUneducated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork type\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy physical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.195(0.876\u0026ndash;1.632)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild physical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidential type\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.446(0.280\u0026ndash;0.713)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.474(0.274\u0026ndash;0.821)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke\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 \u003ctd align=\"left\" colname=\"c6\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.810(0.488\u0026ndash;1.345)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink\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 \u003ctd align=\"left\" colname=\"c6\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.730(0.444\u0026ndash;1.199)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic pain\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 \u003ctd align=\"left\" colname=\"c6\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.868(1.360\u0026ndash;2.566)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.651(1.145\u0026ndash;2.381)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncontinence\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 \u003ctd align=\"left\" colname=\"c6\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.934(2.679\u0026ndash;9.085)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.687(1.869\u0026ndash;7.275)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecent fall\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 \u003ctd align=\"left\" colname=\"c6\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.387(0.975\u0026ndash;1.974)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight loss\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 \u003ctd align=\"left\" colname=\"c6\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.434(2.420\u0026ndash;12.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.438(1.799\u0026ndash;10.947)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNapping\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 \u003ctd align=\"left\" colname=\"c6\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.800(1.178\u0026ndash;2.751)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.752(1.072\u0026ndash;2.863)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.025\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial participant\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 \u003ctd align=\"left\" colname=\"c6\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.099(0.056\u0026ndash;0.172)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.124(0.068\u0026ndash;0.225)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular 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 \u003ctd align=\"left\" colname=\"c6\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.464(0.187\u0026ndash;1.151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\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 \u003ctd align=\"left\" colname=\"c6\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.964(0.670\u0026ndash;1.385)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\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 \u003ctd align=\"left\" colname=\"c6\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.655(1.019\u0026ndash;2.688)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.589(0.902\u0026ndash;2.799)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.109\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.927(0.880\u0026ndash;0.977)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.918(0.864\u0026ndash;0.975)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.878(0.679\u0026ndash;1.134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.991(0.981-1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.988(0.967\u0026ndash;1.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.994(0.976\u0026ndash;1.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.865(0.827\u0026ndash;0.905)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.896(0.848\u0026ndash;0.947)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.814(0.692\u0026ndash;0.957)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.966(0.786\u0026ndash;1.186)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.870(0.687-1.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eAbbreviations: ADL activity of daily life; ALT Alanine Transaminase, AST Aspartate Aminotransferase, BMI Body Mass Index; CI confidence interval; OR odds ratio; RBC Red Blood Cell\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 LRA and modeling\u003c/h2\u003e \u003cp\u003eUnivariate LRA identified 11 predictors (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from 25 eligible candidates. These predictors were then subjected to multivariable LRA. After applying backward regression, the model yielded a minimal AIC (AIC\u0026thinsp;=\u0026thinsp;733.097), incorporating residential type, chronic pain, incontinence, weight loss, napping, social participation, BMI, and albumin as predictors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Development of the nomogram\u003c/h2\u003e \u003cp\u003eThe eight predictors selected through backward multivariable LRA were used to formulate the predicting equation: Logit (1/1-\u003cem\u003eP\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;5.417\u0026ndash;0.807 \u0026times; residential type\u0026thinsp;+\u0026thinsp;0.474 \u0026times; chronic pain\u0026thinsp;+\u0026thinsp;1.381 \u0026times; incontinence\u0026thinsp;+\u0026thinsp;1.506 \u0026times; weight loss\u0026thinsp;+\u0026thinsp;0.587 \u0026times; napping\u0026thinsp;+\u0026thinsp;2.075 \u0026times; social participation \u0026ndash; 0.082 \u0026times; BMI \u0026ndash; 0.133 \u0026times; albumin. A nomogram was constructed to predict centenarian physical disability. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, points are assigned for each parameter based on the centenarian\u0026rsquo;s individual status, enabling a match of the total points with the likelihood of physical disability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Discrimination of the nomogram\u003c/h2\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplementary Table\u0026nbsp;2, the ROC curves for both the development and validation datasets exhibited good predictive capability, with AUCs of 0.796 (95% CI: 0.763\u0026ndash;0.829) and 0.800 (95% CI: 0.750\u0026ndash;0.851), respectively. These curves also demonstrated superior predictive accuracy compared to models using randomly selected variables (incontinence, social participation, recent fall, and BMI).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Validation of the nomogram\u003c/h2\u003e \u003cp\u003eInternal validation was employed. The Hosmer-Lemeshow test confirmed a good fit for our nomogram in both development (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.300) and validation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.560) datasets. The calibration plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) for both datasets showed a close correlation between predicted and actual probabilities (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.778 vs. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.716). NRI [0.028 (95% CI: 0.020\u0026ndash;0.230)] and IDI [0.019 (95% CI: 0.005\u0026ndash;0.033)] further affirmed the model's accuracy (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Efficiency of the model\u003c/h2\u003e \u003cp\u003eThe clinical efficacy of the model in both development and validation datasets was evidenced by Decision Curve Analyses (DCA), which showed a significant benefit over a wide range of thresholds compared to individual predictors (incontinence, social participation, recent fall, and BMI) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussions","content":"\u003cp\u003eDue to the accumulation of various injuries and diseases, elderly individuals often exhibit a high incidence of disability. The decline of motor ability in the elderly is often accompanied by the loss of self-care ability [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The results of this study showed that the proportion of ADL impairment in centenarians was 56.2%, which was markedly higher than in individuals younger than 100 years [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. All these suggest that more resources, including public policy, nursing, and economic support, should be allocated to enhance the quality of life for centenarians.\u003c/p\u003e \u003cp\u003eIn this study, logistic regression was used to identify eight variables that were strongly correlated with ADL impairment in the development dataset, and these were used to create a prediction equation for ADL impairment. Comprehensive validation demonstrated that the formula provided excellent discrimination, calibration, and clinical benefits in both development and validation datasets, suggesting that the model had high accuracy in predicting the incidence of ADL impairment in centenarians.\u003c/p\u003e \u003cp\u003eOur nomogram indicates that risk factors for ADL impairment among centenarians include incontinence, chronic body pain, recent three-month weight loss, napping, lack of social communication, low BMI, and low serum albumin levels.\u003c/p\u003e \u003cp\u003eIncontinence is considered to be an independent risk factor for ADL impairment. A community-based cross-sectional survey showed a significant association between activity limitation and urinary incontinence [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The adjusted odds ratio (OR) for disabilities associated with urinary incontinence was 1.96 (95% CI: 1.07, 3.58) compared to women without this condition [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In addition, incontinence and ADL impairment may be complications resulting from acute stroke, spinal cord injury, and other conditions, often accompanied by comorbidities, suggesting central nervous system involvement. Both incontinence and mobility impairment significantly affect physical ADL [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDisability may induce chronic pain. A study of 426 participants aged 71 to 80 years highlighted that chronic pain significantly impacted the prevalence of ADL and/or IADL disabilities in the elderly [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The OR for ADL disability among African Americans who reported body pain was 4.06 (95% CI: 2.63\u0026ndash;6.26) for women and 6.44 (95% CI: 2.84\u0026ndash;14.57), for men [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Even in the absence of prior disability, chronic pain in the musculoskeletal system may restrict physical functions in the elderly, leading to significant ADL limitations, and this limitation is closely related to the location, intensity, and duration of pain. For example, arthritis significantly increases the incidence of ADL disability [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWeight loss, low BMI, and low serum albumin are indicators of malnutrition. This study suggests a strong relationship between nutrition and ADL in centenarians, warranting increased attention. Research on the elderly over 75 years old showed that underweight individuals not only had a high incidence of ADL disability but also a strong association with disability [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In a study of nonagenarians and centenarians, the OR for ADL disability was 1.5 times higher in underweight older adults compared to those with a normal BMI [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Sarcopenia, resulting from malnutrition, was significantly associated with ADL disability (OR\u0026thinsp;=\u0026thinsp;1.94, 95% CI: 1.37\u0026ndash;2.75) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Conversely, increased food intake can significantly reduce the relative risk (RR) of impaired physical function in the Japanese elderly [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, when centenarians experience weight loss, low BMI, and low serum albumin, it indicates abnormal metabolic function. An extremely low or high BMI can have adverse metabolic effects, which are risk factors for disability in older adults [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Metabolic syndrome was also associated with ADL disability (OR\u0026thinsp;=\u0026thinsp;1.81, 95%CI: 1.22\u0026ndash;3.45) in a study of the elderly over 90 years [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies on the effect of sleep on the ADL of the elderly mainly focus on nighttime sleep, and napping is a neglected factor. Sleep complaints, including napping frequency, were associated with an increased risk of ADL disability (HR: 1.27,95% CI: 1.10\u0026ndash;1.47), and mobility disability (HR: 1.27, 95% CI: 1.09\u0026ndash;1.48) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. People with ADL impairment also had impaired sleep quality (OR\u0026thinsp;=\u0026thinsp;1.44, 95%CI: 1.20\u0026ndash;1.72, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Centenarians whose sleep time\u0026thinsp;\u0026ge;\u0026thinsp;2 h in the daytime were more likely to develop ADL disability (OR: 2.75, 95%CI: 1.56\u0026ndash;4.83) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Therefore, this study supports previous research indicating that napping is crucial for maintaining health in centenarians.\u003c/p\u003e \u003cp\u003eResidential type is one of the influencing factors of disability progression in the elderly [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The elderly living alone had the highest incidence of disability [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. However, one interesting conclusion from this study was that living with family members was correlated with a higher probability of ADL disability, which contradicts previous findings. In China, influenced by traditional concepts, family members carry the main burden of elderly care. Many elderly people who are capable of taking care of themselves prefer to live apart from their children to reduce dependence on their children and society. Assistance from children is typically sought only when the elderly become incapacitated. Therefore, when the elderly live with their children, it often indicates the onset of disability [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe current study also found that a lack of social communication was strongly associated with a high incidence of ADL disability in centenarians. A large-scale survey involving 16536 elderly participants revealed that alienation from friends and relatives was associated with a higher incidence of disability [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. A Study from the China Health and Retirement Longitudinal Study (CHARLS) found that for people older than 65, ADL disability was significantly associated with social isolation (OR\u0026thinsp;=\u0026thinsp;1.18, 95%CI: 1.07\u0026ndash;1.30), but not with loneliness [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Chronic conditions in the elderly often lead to disability, which in turn restricts social participation [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In addition, the deterioration of socioeconomic status significantly influences an individual's social participation and thus leads to ADL disability [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn general, for centenarians, guardians should closely monitor the nutritional status of these individuals, enhance nutritional supplementation, and prepare special digestible and absorbable foods for them. Moreover, family members should maintain communication with centenarians to prevent feelings of loneliness [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], thus reducing the incidence of ADL dependency. Finally, guardians, especially those in nursing homes, should provide tailored personal care and psychological support to enhance the quality of life for centenarians facing irreversible physical conditions such as incontinence and chronic pain [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFirstly, due to the nature of observational studies, while this study incorporated as many potential influencing factors of ADL disability in centenarians as possible, some factors may still have been overlooked. Secondly, 203 centenarians died before the investigation during the course of this study, and these individuals, who may have been in worse physical condition, were not included, potentially leading to overly optimistic results. In addition, being an isolated island, Hainan Province has distinct characteristics that might yield different results from mainland China and other parts of the world.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eActivities of Daily Life\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAkaike Information Criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlanine Transaminase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAspartate Aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea Under the Curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHARLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChina Health and Retirement Longitudinal Study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHCCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChina Hainan Centenarian Cohort Study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDecision Curve Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIDI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntegrated Discrimination Improvement\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile Range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLRA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elogistic Regression Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNet Reclassification Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRed Blood Cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver Operating Characteristic Curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTRIPOD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTransparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CHCCS was approved by the Ethics Committee of Hainan Hospital of Chinese People\u0026rsquo;s\u003c/p\u003e\n\u003cp\u003eLiberation Army General Hospital (Sanya, Hainan; Number: 301HNLL-2016-01). Written informed consents were signed before investigation for all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the colleagues that devoted to the accomplishment of the CHCCS database.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDXZ and FH designed and concepted of this study. JBW and FB analyzed the raw data. SMH and MZS completed the original manuscript. DXZ and FH revised the manuscript. SMH and MZS contributed equally to the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuning\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study was supported by grant from the Natural Science Foundation of Hainan Province (ZDYF2023SHFZ145).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no conflict of interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNg R, Chow TYJ. Aging Narratives Over 210 Years (1810\u0026ndash;2019). journals Gerontol Ser B Psychol Sci social Sci. 2021;76(9):1799\u0026ndash;807.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Z, Yu J, Song Y, Chui D. Aging Beijing: challenges and strategies of health care for the elderly. 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Int J Epidemiol. 2018;47(3):694\u0026ndash;h695.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiolero A. Body mass index as socioeconomic indicator. BMJ (Clinical Res ed). 2021;373:n1158.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng PP, Guo ZL, Du XJ, Yang HM, Wang ZJ. Prevalence of Disability among the Chinese Older Population: A Systematic Review and Meta-Analysis. Int J Environ Res Public Health 2022, 19(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang M, Xu H, Yang L, Jiang J, Dong B. Metabolic syndrome and disability in Chinese nonagenarians and centenarians. Aging Clin Exp Res. 2018;30(8):943\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeter RM, Palanisamy K, Kumar D, Joseph A. Prevalence of activity limitation and its associated predictor among the elderly in Tamil Nadu, India: A community-based cross-sectional study. 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Am J geriatric psychiatry: official J Am Association Geriatric Psychiatry. 2014;22(7):718\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteptoe A, Di Gessa G. Mental health and social interactions of older people with physical disabilities in England during the COVID-19 pandemic: a longitudinal cohort study. Lancet Public health. 2021;6(6):e365\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang S, Wang S, Liu G, Li R, Li X, Chen S, Zhao Y, Liu M, Liu Y, He Y. The relationship between sleep status and activity of daily living: based on China Hainan centenarians cohort study. BMC Geriatr. 2023;23(1):796.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan C, Cao N, Kelifa MO, Luo S. Age and cohort trends in disability among Chinese older adults. Front public health. 2023;11:998948.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenning-Smith C, Shippee T, Capistrant B. Later-Life Disability in Environmental Context: Why Living Arrangements Matter. Gerontologist. 2018;58(5):853\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen W, Fang Y, Mao F, Hao S, Chen J, Yuan M, Han Y, Hong YA. Assessment of Disability among the Elderly in Xiamen of China: A Representative Sample Survey of 14,292 Older Adults. PLoS ONE. 2015;10(6):e0131014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQiao R, Jia S, Zhao W, Xia X, Su Q, Hou L, Li D, Hu F, Dong B. Prevalence and correlates of disability among urban-rural older adults in Southwest China: a large, population-based study. BMC Geriatr. 2022;22(1):517.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo L, An L, Luo F, Yu B. Social isolation, loneliness and functional disability in Chinese older women and men: a longitudinal study. Age Ageing. 2021;50(4):1222\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGriffith LE, Raina P, Levasseur M, Sohel N, Payette H, Tuokko H, van den Heuvel E, Wister A, Gilsing A, Patterson C. Functional disability and social participation restriction associated with chronic conditions in middle-aged and older adults. J Epidemiol Commun Health. 2017;71(4):381\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu H, Wang M. Socioeconomic status and ADL disability of the older adults: Cumulative health effects, social outcomes and impact mechanisms. PLoS ONE. 2022;17(2):e0262808.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Li H, Wu B, Liu X, Chen H, Jin HY, Wu C. Association between primary caregiver type and mortality among Chinese older adults with disability: a prospective cohort study. BMC Geriatr. 2021;21(1):268.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWebster-Dekker KE, Lu Y, Perkins SM, Ellis J, Gates M, Otis L, Winton R, Hacker E. Factors associated with change in activities of daily living performance in home health care patients with diabetes. Geriatric Nurs (New York NY). 2024;59:543\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Activities of daily life, ADL dependency, Centenarian, Prediction model, CHCCS","lastPublishedDoi":"10.21203/rs.3.rs-6738344/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6738344/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to investigate the influencing factors of activities of daily life (ADL) among centenarians and to develop and validate a prediction model of ADL dependency for this population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 952 eligible centenarians from the China Hainan Centenarian Cohort Study (CHCCS) were included. An ADL score below 90 was defined as ADL dependence. Participants were randomly divided into development (70%) and validation (30%) groups. Univariate and multivariate logistic regression analysis (LRA) of the development group were used to identify independent risk factors related to ADL dependency. The selected variables were employed for modeling and nomogram construction. The model's performance was assessed using the receiver operating characteristic (ROC) curve, calibration plots, net reclassification index (NRI), and integrated discrimination improvement (IDI) scores. Decision curve analysis (DCA) was utilized to evaluate the clinical utility of the model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e The development group comprised 668 participants, and the validation group included 284. After variable selection via univariate and multivariate logistic regression analyses, eight factors\u0026mdash;residential type, chronic pain, incontinence, weight loss, napping, social participation, BMI, and albumin\u0026mdash;were incorporated into the prediction model. The area under curve (AUC) the ROC curve was 0.796 (95% CI: 0.763\u0026ndash;0.829) for the development group and 0.800 (95% CI: 0.750\u0026ndash;0.851) for the validation group. Calibration plots, NRI, and IDI indicated a good fit of the model in both groups. The DCA demonstrated clinical effectiveness.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFactors such as living alone, experiencing chronic pain, incontinence, weight loss, absence of napping, lack of social participation, low BMI, and low albumin levels were identified as risk factors for ADL dependency among centenarians. The tailored prediction model encompassing these eight factors is suitable for early identification and prediction of ADL dependency in extremely elderly individuals.\u003c/p\u003e","manuscriptTitle":"Development and validation of an ADL dependency model for Centenarians: a CHCCS-based cross-sectional cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-29 14:44:18","doi":"10.21203/rs.3.rs-6738344/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"104189074509253613770875125430691616656","date":"2026-05-12T01:12:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-06T04:10:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"58618512380015721333581624548125445047","date":"2025-06-25T20:40:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-23T18:11:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-28T10:44:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-27T08:49:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-27T08:45:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2025-05-24T09:52:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1d54a784-11f9-4ca6-909c-83ca37669abb","owner":[],"postedDate":"June 29th, 2025","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"104189074509253613770875125430691616656","date":"2026-05-12T01:12:28+00:00","index":82,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-29T14:44:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-29 14:44:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6738344","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6738344","identity":"rs-6738344","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}