Timed Up and Go Test and Prediction of Injurious Falls among Older Adults by Sex: A Population-based Cohort Study

Timed Up and Go Test and Prediction of Injurious Falls among Older Adults by Sex: A Population-based 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 Timed Up and Go Test and Prediction of Injurious Falls among Older Adults by Sex: A Population-based Cohort Study Jiyun Kim, Sookja Choi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4521597/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Dec, 2024 Read the published version in BMC Geriatrics → Version 1 posted 10 You are reading this latest preprint version Abstract Background This study aimed to determine whether sex - specific timed up and go (TUG) test results can predict injurious fall occurrence in older adults and identify risk factors for injurious falls based on TUG results. Methods Data were obtained from an older adult cohort database provided by the National Health Insurance Service, which included 34,030 individuals aged 66 years or older who underwent life-transition health examinations in 2007 and 2008 and were followed up until 2019. To identify the risk factors for injurious falls, this study performed a Cox proportional hazard regression analysis by sex, with individual characteristics, including TUG test results, as independent variables. Results The TUG test predicted the occurrence of injurious falls in older adult men but not in older adult women. Among men with abnormal TUG results, those with high systolic blood pressure had a greater risk of injurious falls. In women, urinary dysfunction, hearing impairment, low BMI, high systolic blood pressure and fasting blood glucose level, depressive symptoms, and bone mineral density were identified as risk factors for injurious falls—regardless of the TUG test results. The factors predictive of injurious falls according to the TUG test differed by sex. Conclusion The TUG test is useful for predicting injurious falls in older adult men. This study identified other risk factors for injurious falls among older adult women that can be used in prevention strategies. fall older adult timed up and go test risk factor sex difference Figures Figure 1 1. Introduction Falls are the leading cause of injury-related morbidity and mortality among older adults. The fall experience rate among older adults is 7.2%, and the average number of falls is 1.6 in South Korea [1]. A total of 72.5% of South Korean older adults who fell required hospitalization for treatment. Injurious falls can cause hip fractures [2,3] and traumatic brain injury [4] and increase the possibility of death in people over the age of 80 [5]. The timed up and go (TUG) test is used to assess physical function and predict falls by testing the movement and balance of older adults [6]. The advantages of the test include the fact that it does not require special equipment, facilities, or space; the existence of extensive evidence on the relationship between TUG test results and falls; and its reliability [7,8]. Regarding the relationship between the TUG test and falls, while a large number of studies have investigated common falls [8], fewer have focused on injurious falls [9]. Since 2007, the TUG test has been included as a standard test for South Korean older adults aged 66 years and above as a life cycle test. All South Korean citizens are covered by the national health insurance scheme, and the database of health examinations and treatments is secured and managed by the National Health Insurance Corporation [10]. Given these findings, confirming the existence of a relationship between the TUG test and the rate of injurious falls in South Korean older adults could serve as a basis for providing various interventions for individuals with poor TUG results to prevent injurious falls [9]. Research has shown that there are sex differences in the risk factors for falls [11] and the frequency of injurious falls [12] among older adults. One reason for this is sex differences in age-related changes in physical function [13]. Although aging leads to a decline in physical function in both men and women, a previous study revealed an association of lower extremity function with muscle quality and physical activity in men and an association of lower extremity function with muscle quality and body fat in women [14]. This study longitudinally examined whether the TUG test can predict injurious falls by sex and which injurious fall-related factors are associated with physical function (as assessed by the TUG test) by sex. 2. Materials and methods 2.1 Study design and data sources 2.1.1 Study design This retrospective cohort study used data from the National Health Information Database (NHID) of South Korea. The study participants were 66 years old and underwent the National Screening Program for Transitional Ages (NSPTA) from 2007 to 2008. Observations began on the date they received the examination in the program and ended either on the date of the outcome event (i.e., injurious fall or all-cause death) for participants who experienced the event, or on 31 December 2019 for participants who did not experience the event. We used survival analysis to compare the events between participants with normal and abnormal TUG test results. 2.1.2 Data sources The NHID is a public database on healthcare utilization, health screening, sociodemographic variables, and mortality for the entire population of South Korea. It was developed and distributed by the Korean National Health Insurance Service (KNHIS) under the Ministry of Health and Welfare in South Korea. This study used the older adult cohort database of the NHID (2002–2019), which was extracted using simple random sampling, to establish a representative dataset of 558,147 older adults. The participants accounted for approximately 10% of the total population of people aged 60 or older in the NHID in 2002 and were followed for 18 years until 2019. Participants were disqualified in the event of emigration or death. This study also included the NSPTA, which targets people aged 40 or 66 years. These data were utilized for categorizing the age and sex of each participant and strengthening post examination counseling. Only patients aged 66 years who underwent the TUG test in 2007 or 2008 were included [15]. The database included data on participant demographics (i.e., age, sex, residential area, and income status); survey-based data on medical history and health behavior; screening results regarding height, weight, and physical function tests (i.e., TUG and unipedal stance tests); and laboratory tests. The exclusion criteria included those who were diagnosed with injurious falls as a primary or secondary diagnosis (W00, W01, W05–W10, and W17–W19 from the 10th revision of the International Classification of Diseases [ICD]) in 2007 or 2008 or who had missing values. After excluding participants for whom information for the TUG test questionnaire was missing, a total sample of 34,030 participants and their follow-up data through 2019 were collected (Fig. 1 ). [Insert Fig. 1 about here] 2.2 Ethical approval To obtain the database, the researcher prepared a research plan and received approval for conducting the study from the ethics review board of the institution to which the research was affiliated (IRB No. 1044396-202203-HR-073-01). The research team submitted a request for data use to the KNHIS, along with the results of the research ethics review process. After review, the KNHIS gave the research team permission to remotely analyze the data. The data were encrypted to protect personal identities, and the requirement for informed consent was waived by the relevant body. The researcher collected the data for analysis by connecting the data from various databases, all of which were anonymized. 2.3 Variables 2.3.1 Injurious falls Injurious falls were defined as falls that caused injuries requiring inpatient or outpatient treatment, as defined by the International Classification of Disease [ICD] and previous studies [11,16]. In this study, hospital medical record claims data up to 2019 were reviewed, and injurious falls were defined as occurring when diagnosed using the codes W00, W01, W05–W10, and W17–W19 from the 10th revision of the ICD. 2.3.2 Lower extremity function Lower extremity function was assessed using the TUG test, a reliable and valid test for quantifying functional mobility in older adults and assessing fall risk in community settings. This study was conducted according to the recommendations provided in the NSPTA manual. The TUG test involved assessing how many seconds it took for the person being tested to sit on a chair, stand, and walk three meters at a comfortable speed, walk back to the chair, and sit down again. Participants were categorized into two groups according to test results: normal (≤ 10 sec) and abnormal (> 10 sec). 2.3.3 Covariates We followed the risk factors for falls proposed by Deandrea S. et al. (2010) [17]. Depressive symptoms were measured using three items from the Geriatric Depression Scale [15], namely, loss of activities/interests, feelings of worthlessness, and feelings of hopelessness. A positive answer to any item was defined as a depressive mood. For the ability to perform activities of daily living (ADLs), participants were asked if it was possible for them to perform the following six activities in their daily lives: eating, dressing, going to the bathroom, bathing, preparing meals, and going out. ADLs were divided into two groups; if any of the six items were ‘no’, they were coded in the ADL ‘poor’ group, and the rest were coded in the ‘good’ group. Visual impairment was assessed using visual acuity testing. Blindness in any eye was categorized as ‘abnormal’, and no blindness in any eye was categorized as ‘normal’. Hearing impairment was measured using pure-tome audiometry or a whispered voice test; a result of dichotomously recorded hearing loss on any side or both sides was classified as ‘abnormal’, whereas other results were classified as ‘normal’. Urinary dysfunction and previous fall experience were measured by asking ‘Do you have dysuria?’ and ‘Have you fallen within the past six months?’, respectively. For each of these two items, each group was further categorized into two groups (yes or no). Body mass index (BMI) was calculated in kilograms per meter squared, and participants were categorized as underweight (less than 18.0), normal (18.0–22.9), or overweight (23 or greater). Participants were classified into two groups (normal or abnormal) for systolic blood pressure and diastolic blood pressure according to the reference values of 140 mmHg and 90 mmHg for systolic and diastolic blood pressure, respectively. We used clinical laboratory results for hemoglobin, fasting blood glucose, and total cholesterol levels. Participants were divided into two groups for hemoglobin levels: anemic (<10 g/dL for women and <12 g/dL for men, based on the Korean National Health Examination criteria) and nonanemic. In women, osteoporosis was measured by bone mineral density (BMD) and defined according to the World Health Organization T score criteria using three groups (normal, ≥ -1.0; osteopenia, -2.4 to -1.1; osteoporosis, ≤ -2.5). 2.4 Statistical analysis Frequencies and percentages were calculated for all variables and compared by sex using the chi-square test. The Cox proportional hazards model was used to estimate hazard ratios (HRs), and a 95% confidence interval (CI) was used to predict fall occurrence according to TUG test results. The control variables were sex, fall experience, urinary dysfunction, visual impairment, hearing impairment, BMI, blood pressure, blood glucose, total cholesterol, hemoglobin, depressive symptoms, ADL, and BMD (only including women). Using subgroup analysis and the same model, we identified the risk factors affecting injurious fall occurrence according to sex and TUG score. All the statistical tests were two-tailed and performed using SAS software (version 9.4; SAS Institute Inc., Cary, NC, USA), with a 95% significance level. 3. Results The general characteristics of the 34,030 participants at baseline (2007 and 2008) are depicted in Table 1. There were 878 cases (2.58%) of injurious falls among the 34,030 participants, with an incidence of 2.2 cases per 1,000 person–years, and the incidence was greater among women (3.38%) than men (1.65%). Regarding sex, 17,759 (46.3%) were men, and 18,271 (53.7%) were women. The percentages of individuals in the abnormal TUG test group were 15.3% among men and 21.1% among women, and the percentages of individuals with previous falls were 8.8% and 12.7%, respectively. On average, men experienced more urinary dysfunction (19.5%), visual impairment (1.7%), hearing impairment (8.7%), higher glucose levels (12.9%), and abnormal ADLs (8.4%) than women did. Women had a greater incidence of injurious falls (3.4%), depressive symptoms (37.7%), falls (12.7%), and overweight (69.9%) than men. [Insert Table 1 about here] Tables 2 and 3 present the results of the multivariate Cox regression model for injurious falls according to sex. Regarding sex, men in the abnormal TUG test group had a significantly greater risk of injurious falls than did those in the normal TUG test group (HR: 1.52, 95% CI: 1.13–2.04). The TUG test score was not a significant predictor of injurious falls in women. In men, previous fall experience was also associated with a significantly greater risk of injurious falls, even after adjusting for several potentially confounding variables (HR: 2.98, 95% CI: 2.17–4.10). According to the subgroup analysis for men (normal vs. abnormal TUG groups), multivariate Cox proportional hazards regression analysis revealed that the HRs for injurious falls increased 4.17-fold (95% CI: 2.98–5.86) in the normal TUG group, and having abnormal systolic blood pressure (HR: 2.65, 95% CI: 1.50–4.68) and total cholesterol level (HR: 4.22, 95% CI: 2.40–7.41) in the abnormal TUG group also increased the HRs for injurious falls. In the abnormal TUG test group, there was a 4.42-fold increase in the HR for injurious falls in patients with poor ADLs (vs. good ADLs; 95% CI: 2.51–7.79). [Insert Table 2 about here] Table 3 presents the findings of the association of TUG test results with injurious falls. In women, TUG test scores were not a predictor of injurious falls; however, urinary dysfunction (HR: 1.61, 95% CI: 1.30–2.00), BMI (HR: 1.99, 95% CI: 1.18–3.34), blood pressure (HR: 1.79, 95% CI: 1.30–2.16), blood sugar level (HR: 1.68, 95% CI: 1.30–2.16), BMD (osteopenia HR: 1.57, 95% CI: 1.12–2.20; osteoporosis HR: 1.87, 95% CI: 1.35–2.60), and hearing impairment (HR: 0.56, 95% CI: 0.36–0.87) were statistically significant predictors. Furthermore, underweight older adult women had a 1.99 times greater risk of injurious falls (95% CI, 1.18–3.34) than did those in the normal weight group. Regarding BMD, women with osteopenia had a 1.57-fold (95% CI: 1.12–2.20) greater risk of injurious falls, and women with osteoporosis had a 1.87-fold greater risk (95% CI: 1.35–2.60). [Insert Table 3 about here] 4. Discussion The TUG test is one of the most frequently used procedures to evaluate the possibility of falls in older adults [18] and can be used to predict the occurrence of falls in community-dwelling older adult women [19], and its usefulness relies on high specificity rather than sensitivity [8,20,21]. The TUG test results were shown to predict the occurrence of injurious falls in men but not in women. This result resonates with a previous study in which fall incidence and risk factors differed by sex [11]. In this study, previous fall experience was an important predictor of falls in men. This finding is consistent with prior evidence showing that fall recurrence [22-24] and falling experience are risk factors for injurious falls [11,25]. Among older adult men, the risk of injurious falls was greater in the abnormal TUG test group in patients with abnormal blood pressure, elevated cholesterol levels, and impaired independence from daily living (ADL) ability. In a cohort study conducted in Sweden, an association between high systolic pressure and the occurrence of injurious falls was confirmed in a group with low functional status [26]. Similar to the current study, the results of Ek et al.’s study showed that injurious fall risk differed depending on ADLs in men but not in women [11]. Therefore, more educational interventions are needed to prevent falls among older adult men with abnormal TUG results. Furthermore, important fall prevention intervention focus points for this group may include cardiovascular health management, including cholesterol, and detection of ADL changes. In older adult women, the risk factors for injurious falls were urinary dysfunction, underweight status, and low bone density, regardless of the TUG test results. Urinary dysfunction, such as incontinence, is a well-known risk factor for injurious falls in hospitals [27]. Underweight older adult women in our sample showed a greater risk of injurious falls than did normal weight older adult women. In previous investigations, low body weight was also shown to be a risk factor for increased fractures or falls [28-30]; however, the mechanisms by which low body weight increases the likelihood of injurious falls remain unclear. Some hypotheses for this finding are presented herein. A lower BMI has been associated with decreased BMD, soft tissue loss, and muscular weakness, thereby increasing the risk of falls or fractures [31]. Additionally, while a lower BMI is associated with a lower BMD, a BMI increase has been shown to be associated with an increase in adipose tissue, providing further support against fractures [32]. Being underweight often relates to undernutrition or malnutrition, and if persistent, both can worsen BMD [33], thereby increasing injurious fall risk. Prolonged exposure to trauma in the absence of a fat cushion has also been shown to potentially influence the risk of falls or fractures [34,35]. In addition, a low BMI was shown to be strongly associated with sarcopenia development. Sarcopenia diminishes physical strength and muscular performance, leading to injuries that increase the probability of a fall or fracture [36,37]; however, muscle mass may not provide adequate bone protection, and reduced muscle strength may increase the risk of fall-related injuries [38,39]. Studies have also demonstrated that hearing loss is associated with a high probability of falling [40]. However, in this study, hearing loss in women with normal TUG results was associated with a reduced risk of injurious falls. It is possible that the risk of injurious falls in older adults diagnosed with hearing loss is lower because those with hearing loss may engage in less outdoor activity than those without hearing loss [41]. However, follow-up studies are required to confirm the relationship between hearing loss and injurious falls. In summary, the TUG test is a relatively simple test that has been shown, based on our findings, to be potentially useful in health interventions aimed at dealing with injurious fall risk factors and tailored to individuals by sex. In male older adults, fall prevention interventions could be designed to divide participants into normal and abnormal groups according to their TUG test results. Moreover, since the TUG test did not predict the occurrence of falls in older adult women, future research is needed to further assess whether the TUG test can be useful in predicting falls in this population. Regarding study limitations, this study could not control for the participants’ demographic characteristics. As we tracked and analyzed individuals reaching the age of 66 years according to sex, demographic variables such as sex and age were considered, but others such as education level and marital status were not. These variables were analyzed in other studies but could not be handled. Moreover, this study could not analyze data on BMD T scores for older adult men, as their claims data did not contain this information. This study’s results may have also underestimated injurious fall incidence because the claims data were analyzed while tracking older adults in the community who underwent lifecycle examinations. This implies that cases of minor injurious falls that did not require hospitalization or treatment were not considered in the analysis. In the case of an injury due to a fall, treatment costs are high, and people could become impaired in the performance of their ADLs; therefore, identifying the related risk factors for cases of minor injurious falls and using them in preventive techniques may be important steps of future research. Regarding strengths, this study conducted a cohort analysis of older adults aged 66 years in 2007 and 2008 and analyzed their follow-up data for more than 10 years. It also examined injurious falls using objective claims data from the KNHIS. 5. Conclusion The TUG test is useful for predicting injurious falls in older adult men but not in older adult women. In older adult women, various factors can predict injurious falls regardless of the TUG test. The factors predictive of injurious falls according to the TUG test differed by sex. Abbreviations ADLs, activities of daily living. BMD, bone mineral density. BMI, body mass index. TUG, timed up and go test. CI, Confidence interval HR, Hazard ratio Declarations Ethics approval and consent to participate: This study was reviewed and approved by an affiliated institution (IRB No. 1044396-202203-HR-073-01), and permission to analyze the data remotely was provided by the National Health Insurance Service (NHIS). Access to the National Health Insurance Sharing Service database (NHISS) for research purposes was provided only through legal procedures after deliberation (NHIS-2022-2-362). In addition, it was impossible to confirm the patient’s personal information because the information had been encrypted. Informed consent was not needed. Consent for publication : Not applicable Availability of data and materials: Data from this study must be used in accordance with National Health Insurance Service policies. The information on how to request the database is available at https://nhiss.nhis.or.kr/bd/ab/bdaba021eng.do. The details and cost of the database are described in https://nhiss.nhis.or.kr/bd/ab/bdaba022eng.do. To request the database, visit https://nhiss.nhis.or.kr/bd/ay/bdaya001iv.do. (only available in Korean). Competing Interests : The authors declare no competing interests. Funding: This research was funded by the National Research Foundation of Korea (NRF) grant number NRF-2020R1F1A1076167. Authors’ Contributions: JK led the study design, interpreted the data, and wrote the paper. SC performed the statistical analysis and assisted with the data interpretation and wrote the paper. SC served as the corresponding author and directed the study. All the authors have read and approved the final version of the manuscript. Acknowledgments: None References Lee Y, Kim S, Hwang N, et al. 2020 Survey of the Elderly(online). Available at: https://www.mohw.go.kr/board.es?mid=a10107010100&bid=0040&act=view&list_no=374195&tag=&cg_code=&list_depth=1 . Accessed August 23, 2023. Berry SD, Miller RR. 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Associations between self‐reported hearing loss and outdoor activity limitations, psychological distress and self‐reported memory loss among older people: Analysis of the 2016 Comprehensive Survey of Living Conditions in Japan. Geriatr Gerontol Int. 2019;19(8):747-754. doi:10.1111/ggi.13708 Tables Table 1. Descriptive characteristics of the study participants at baseline (2007–2008) Variables Sample Men Women Chi-square test p value n % n % n % Total 34,030 100 15,759 46.3 18,271 53.7 Injurious falls* No 33,152 97.4 15,499 98.4 17,653 96.6 101.0 <.001 Yes 878 2.6 260 1.6 618 3.4 TUG group Normal 27,757 81.6 13,343 84.7 14,414 78.9 187.9 <.001 Abnormal 6,273 18.4 2,416 15.3 3,857 21.1 Previous fall experience Yes 3,716 10.9 1,395 8.8 2,321 12.7 129.0 <.001 No 30,314 89.1 14,364 91.2 15,950 87.3 Urinary dysfunction Yes 6,076 17.8 3,077 19.5 2,999 16.4 55.8 <.001 No 27,954 82.2 12,682 80.5 15,272 83.6 Visual impairment Normal 33,536 98.5 15,493 98.3 18,043 98.7 11.4 0.00 Abnormal 494 1.5 266 1.7 228 1.3 Hearing impairment Normal 31,416 92.3 14,380 91.2 17,036 93.2 47.3 <.001 Abnormal 2,614 7.7 1,379 8.8 1,235 6.8 BMI, kg/m 2 <18.0 (underweight) 759 2.2 443 2.8 316 1.7 242.6 <.001 18.1~22.9 (normal weight) 10,710 31.5 5,527 35.1 5,183 28.4 ≥23.0 (overweight) 22,561 66.3 9,789 62.1 12,772 69.9 Systolic blood pressure (mmHg) <140 24,725 72.7 11,486 72. 9 13,239 72.5 0.8 0.37 ≥140 9,305 27.3 4,273 27.1 5,032 27.5 Diastolic blood pressure (mmHg) <90 28,058 82.5 12,907 81.9 15,151 82.9 6.1 0.01 ≥90 5,972 17.5 2,852 18.1 3,120 17.1 Fasting blood glucose level (mg/dL) <126 30,264 88.9 13,714 87.0 16,550 90.6 108.8 <.001 ≥126 3,766 11.1 2,045 13.0 1,721 9.4 Total cholesterol level (mg/dL) <240 29,286 86.1 14,287 90.7 14,999 82.1 517.7 <.001 ≥240 4,744 13.9 1,472 9.3 3,272 17.9 Hemoglobin level Nonanemic 33,387 98.1 15,262 96.8 18,125 99.2 253.0 <.001 Anemic 643 1.9 497 3.2 146 0.8 Depressive symptom No 22,576 66.3 11,190 71.0 11,386 62.3 286.1 <.001 Yes 11,454 33.7 4,569 29.0 6,885 37.7 ADL Good 31,673 93.1 14,440 91.6 17,233 94.3 94.9 <.001 Poor 2,357 6.9 1,319 8.4 1,038 5.7 BMD T score ≥-1.0 (normal) 2,264 14.3 -2.4–-1.0 (osteopenia) 6,194 39.1 ≤-2.5 (osteoporosis) 7,366 46.6 *During the 12- or 13-year follow-up period (until 2019). Abbreviations: TUG, timed up and go test; ADL, activities of daily living; BMI, body mass index; BMD, bone mineral density. Table 2. Cox proportional regression analysis of men Total Men Normal TUG group Abnormal TUG group HR (95% CI) HR (95% CI) HR (95% CI) TUG group (ref: normal) Abnormal 1.52 (1.13–2.04) Previous fall experience (ref: no) Yes 2.98 (2.17–4.10) 4.17 (2.98–5.84) 0.37 (0.09–1.58) Urinary dysfunction (ref: no) Yes 0.77 (0.55–1.06) 0.81 (0.57–1.16) 0.56 (0.25–1.27) Visual impairment (ref: normal) Abnormal 1.01 (0.42–2.45) Hearing impairment (ref: normal) Abnormal 1.03 (0.68–1.57) 1.26 (0.81–1.96) 0.34 (0.08–1.39) BMI (ref: normal) Underweight 0.95 (0.44–2.06) 1.01 (0.44–2.33) 0.75 (0.10–5.82) Overweight 1.08 (0.83–1.40) 1.08 (0.80–1.45) 0.92 (0.52–1.65) Systolic blood pressure (ref: <140 mmHg) ≥140 0.99 (0.71–1.39) 0.58 (0.38–0.90) 2.65 (1.50–4.68) Diastolic blood pressure (ref: <90 mmHg) ≥90 0.69 (0.45–1.05) 1.14 (0.70–1.83) 0.23 (0.10–0.57) Fasting blood glucose level (ref: <126 mg/dL) ≥126 1.14 (0.81–1.62) 1.18 (0.80–1.75) 1.11 (0.52–2.34) Total cholesterol level (ref: <240 mg/dL) ≥240 1.31 (0.89–1.92) 0.55 (0.29–1.05) 4.22 (2.40–7.41) Hemoglobin level (ref: nonanemic) Anemic 1.34 (0.73–2.47) 1.37 (0.70–2.70) 1.40 (0.33–5.96) Depressive symptom (ref: no) Yes 1.00 (0.76–1.31) 1.17 (0.87–1.58) 0.68 (0.36–1.31) ADL (ref: good) Poor 1.41 (0.97–2.06) 0.69 (0.38–1.23) 4.42 (2.51–7.79) Total* 15,757 13,343 2,414 Event 258 200 58 Censored 15,499 13,143 2,356 No. of observations read 15,759 13,343 2,416 *No. of observations used for Cox proportional models Abbreviations: HR, hazard ratio; 95% CI, 95% confidence interval; TUG, timed up and go test; ADL, activities of daily living; BMI, body mass index Table 3. Cox proportional regression analysis of women Total Women Normal TUG group Abnormal TUG group HR (95% CI) HR (95% CI) HR (95% CI) TUG group (ref: normal) Abnormal 1.00 (0.81–1.25) Previous fall experience (ref: no) Yes 0.92 (0.70–1.20) 0.81 (0.59–1.12) 1.19 (0.71–1.97) Urinary dysfunction (ref: no) Yes 1.61 (1.30–2.00) 1.59 (1.24–2.03) 1.60 (1.02–2.52) Visual impairment (ref: normal) Abnormal 0.85 (0.35–2.05) 1.12 (0.46–2.71) 0.00 (0.00-.) Hearing impairment (ref: normal) Abnormal 0.56 (0.36–0.87) 0.38 (0.2–0.70) 1.16 (0.60–2.23) BMI (ref: normal) Underweight 1.99 (1.18–3.34) 2.44 (1.44–4.12) 0.00 (0.00-.) Overweight 0.95 (0.77–1.16) 0.80 (0.64–0.99) 1.89 (1.15–3.13) Systolic blood pressure (ref: <140 mmHg) ≥140 1.79 (1.30–2.16) 2.12 (1.67–2.68) 0.95 (0.57–1.60) Diastolic blood pressure (ref: <90 mmHg) ≥90 0.56 (0.73–1.17) 0.57 (0.41–0.78) 0.56 (0.28–1.15) Fasting blood glucose level (ref: <126 mg/dL) ≥126 1.68 (1.30–2.16) 1.73 (1.30–2.29) 1.55 (0.89–2.70) Total cholesterol level (ref: <240 mg/dL) ≥240 0.92 (0.73–1.17) 1.01 (0.78–1.32) 0.62 (0.35–1.10) Hemoglobin level (ref: nonanemic) Anemic 1.04 (0.39–2.79) 1.31 (0.49–3.50) 0.00 (0.00-.) Depressive symptom (ref: no) Yes 1.21 (1.01–1.46) 1.13 (0.92–1.39) 1.55 (1.04–2.32) ADL (ref: good) Poor 1.04 (0.72–1.52) 1.12 (0.74–1.71) 0.81 (0.35–1.85) BMD T score (ref: normal) -2.4–-1.0 (osteopenia) 1.57 (1.12–2.20) 1.55 (1.08–2.22) 1.62 (0.61–4.30) ≤-2.5 (osteoporosis) 1.87 (1.35–2.60) 1.49 (1.04–2.13) 4.42 (1.78–10.93) Total* 15,671 12,377 3,294 Event 474 371 103 Censored 15,197 12,006 3,191 No. of observations read 18,271 14,414 3,857 *No. of observations used in the Cox proportional hazards models. Abbreviations: HR, hazard ratio; 95% CI, 95% confidence interval; TUG, timed up and go test; ADL, activities of daily living; BMI, body mass index; BMD, bone mineral density. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Dec, 2024 Read the published version in BMC Geriatrics → Version 1 posted Editorial decision: Revision requested 30 Aug, 2024 Reviews received at journal 22 Aug, 2024 Reviews received at journal 11 Aug, 2024 Reviewers agreed at journal 20 Jul, 2024 Reviewers agreed at journal 15 Jul, 2024 Reviewers invited by journal 26 Jun, 2024 Editor invited by journal 24 Jun, 2024 Editor assigned by journal 20 Jun, 2024 Submission checks completed at journal 20 Jun, 2024 First submitted to journal 03 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4521597","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":321187504,"identity":"466ef233-a554-4bb9-b910-f28b82b2c5f1","order_by":0,"name":"Jiyun Kim","email":"","orcid":"","institution":"Gachon University","correspondingAuthor":false,"prefix":"","firstName":"Jiyun","middleName":"","lastName":"Kim","suffix":""},{"id":321187506,"identity":"769f9c96-bd11-433a-a337-c31950439270","order_by":1,"name":"Sookja Choi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIie3QMQrCMBTG8U+EuAS7vlLUK0SE6iA9S0PByUFwdaiLLqJrwEsognOgUJcewNGpcycnUYsgOJmOgvlvD/KD9wLYbD+ZZoAYwnnPVJGM4MbVCUqCBEJXJZ15kl+KSV0eTklOmAVwt/o7ETrtd5Vg8piNfEIawWuGBoKMeVxweTyjJEyjzU2LxS9C8qAaV8K9AoF+ESF3xH2qLTQ8Eylv8V0lwp7KxtOBXEfcXZkWU+VHFbdHa7M87c/FNWhTZlqM9McQAsZLACc2v7HZbLY/7wlAGzylo/3y7wAAAABJRU5ErkJggg==","orcid":"","institution":"Chung-Ang University","correspondingAuthor":true,"prefix":"","firstName":"Sookja","middleName":"","lastName":"Choi","suffix":""}],"badges":[],"createdAt":"2024-06-03 11:48:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4521597/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4521597/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12877-024-05588-9","type":"published","date":"2024-12-23T15:57:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60598841,"identity":"17d862dc-91d5-4073-be91-3b30a89d0948","added_by":"auto","created_at":"2024-07-18 15:55:51","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":310505,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of sample selection procedure\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4521597/v1/ee37eae215083159de9b76fc.jpeg"},{"id":72640722,"identity":"19f81e80-36c5-44b2-a444-9aecfdc197cd","added_by":"auto","created_at":"2024-12-30 16:09:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1208292,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4521597/v1/6826b9e8-1129-4119-879e-cdd0161f39e7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Timed Up and Go Test and Prediction of Injurious Falls among Older Adults by Sex: A Population-based Cohort Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFalls are the leading cause of injury-related morbidity and mortality among older adults. The fall experience rate among older adults is 7.2%, and the average number of falls is 1.6 in South Korea [1]. A total of 72.5% of South Korean older adults who fell required hospitalization for treatment. Injurious falls can cause hip fractures [2,3] and traumatic brain injury [4] and increase the possibility of death in people over the age of 80 [5].\u003c/p\u003e \u003cp\u003eThe timed up and go (TUG) test is used to assess physical function and predict falls by testing the movement and balance of older adults [6]. The advantages of the test include the fact that it does not require special equipment, facilities, or space; the existence of extensive evidence on the relationship between TUG test results and falls; and its reliability [7,8]. Regarding the relationship between the TUG test and falls, while a large number of studies have investigated common falls [8], fewer have focused on injurious falls [9].\u003c/p\u003e \u003cp\u003eSince 2007, the TUG test has been included as a standard test for South Korean older adults aged 66 years and above as a life cycle test. All South Korean citizens are covered by the national health insurance scheme, and the database of health examinations and treatments is secured and managed by the National Health Insurance Corporation [10]. Given these findings, confirming the existence of a relationship between the TUG test and the rate of injurious falls in South Korean older adults could serve as a basis for providing various interventions for individuals with poor TUG results to prevent injurious falls [9].\u003c/p\u003e \u003cp\u003eResearch has shown that there are sex differences in the risk factors for falls [11] and the frequency of injurious falls [12] among older adults. One reason for this is sex differences in age-related changes in physical function [13]. Although aging leads to a decline in physical function in both men and women, a previous study revealed an association of lower extremity function with muscle quality and physical activity in men and an association of lower extremity function with muscle quality and body fat in women [14]. This study longitudinally examined whether the TUG test can predict injurious falls by sex and which injurious fall-related factors are associated with physical function (as assessed by the TUG test) by sex.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and data sources\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Study design\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study used data from the National Health Information Database (NHID) of South Korea. The study participants were 66 years old and underwent the National Screening Program for Transitional Ages (NSPTA) from 2007 to 2008. Observations began on the date they received the examination in the program and ended either on the date of the outcome event (i.e., injurious fall or all-cause death) for participants who experienced the event, or on 31 December 2019 for participants who did not experience the event. We used survival analysis to compare the events between participants with normal and abnormal TUG test results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Data sources\u003c/h2\u003e \u003cp\u003eThe NHID is a public database on healthcare utilization, health screening, sociodemographic variables, and mortality for the entire population of South Korea. It was developed and distributed by the Korean National Health Insurance Service (KNHIS) under the Ministry of Health and Welfare in South Korea.\u003c/p\u003e \u003cp\u003eThis study used the older adult cohort database of the NHID (2002\u0026ndash;2019), which was extracted using simple random sampling, to establish a representative dataset of 558,147 older adults. The participants accounted for approximately 10% of the total population of people aged 60 or older in the NHID in 2002 and were followed for 18 years until 2019. Participants were disqualified in the event of emigration or death. This study also included the NSPTA, which targets people aged 40 or 66 years. These data were utilized for categorizing the age and sex of each participant and strengthening post examination counseling. Only patients aged 66 years who underwent the TUG test in 2007 or 2008 were included [15]. The database included data on participant demographics (i.e., age, sex, residential area, and income status); survey-based data on medical history and health behavior; screening results regarding height, weight, and physical function tests (i.e., TUG and unipedal stance tests); and laboratory tests.\u003c/p\u003e \u003cp\u003eThe exclusion criteria included those who were diagnosed with injurious falls as a primary or secondary diagnosis (W00, W01, W05\u0026ndash;W10, and W17\u0026ndash;W19 from the 10th revision of the International Classification of Diseases [ICD]) in 2007 or 2008 or who had missing values. After excluding participants for whom information for the TUG test questionnaire was missing, a total sample of 34,030 participants and their follow-up data through 2019 were collected (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e[Insert\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003eabout here]\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.2 Ethical approval\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo obtain the database, the researcher prepared a research plan and received approval for conducting the study from the ethics review board of the institution to which the research was affiliated (IRB No. 1044396-202203-HR-073-01). The research team submitted a request for data use to the KNHIS, along with the results of the research ethics review process. After review, the KNHIS gave the research team permission to remotely analyze the data. The data were encrypted to protect personal identities, and the requirement for informed consent was waived by the relevant body. The researcher collected the data for analysis by connecting the data from various databases, all of which were anonymized.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.3 Variables\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3.1 Injurious falls\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInjurious falls were defined as falls that caused injuries requiring inpatient or outpatient treatment, as defined by the International Classification of Disease [ICD] and previous studies [11,16]. In this study,\u0026nbsp;hospital medical record claims data up to 2019 were reviewed, and injurious falls were defined as occurring when diagnosed using the codes W00, W01, W05\u0026ndash;W10, and W17\u0026ndash;W19 from the 10th revision of the ICD.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3.2 Lower extremity function\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLower extremity function was assessed using the TUG test, a reliable and valid test for quantifying functional mobility in older adults and assessing fall risk in community settings. This study was conducted according to the recommendations provided in the NSPTA manual. The TUG test involved assessing how many seconds it took for the person being tested to sit on a chair, stand, and walk three meters at a comfortable speed, walk back to the chair, and sit down again. Participants were categorized into two groups according to test results: normal (\u0026le; 10 sec) and abnormal (\u0026gt; 10 sec).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3.3 Covariates\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe followed the risk factors for falls proposed by Deandrea S. et al. (2010) [17].\u003csup\u003e\u0026nbsp;\u003c/sup\u003eDepressive symptoms were measured using three items from the Geriatric Depression Scale [15],\u0026nbsp;namely, loss of activities/interests, feelings of worthlessness, and feelings of hopelessness. A positive answer to any item was defined as a depressive mood. For the ability to perform activities of daily living (ADLs), participants were asked if it was possible for them to perform the following six activities in their daily lives:\u003csup\u003e\u0026nbsp;\u003c/sup\u003eeating, dressing, going to the bathroom, bathing, preparing meals, and going out. ADLs were divided into two groups; if any of the six items were \u0026lsquo;no\u0026rsquo;, they were coded in the ADL \u0026lsquo;poor\u0026rsquo; group, and the rest were coded in the \u0026lsquo;good\u0026rsquo; group. Visual impairment was assessed using visual acuity testing. Blindness in any eye was categorized as \u0026lsquo;abnormal\u0026rsquo;, and no blindness in any eye was categorized as \u0026lsquo;normal\u0026rsquo;. Hearing impairment was measured using pure-tome audiometry or a whispered voice test; a result of dichotomously recorded hearing loss on any side or both sides was classified as \u0026lsquo;abnormal\u0026rsquo;, whereas other results were classified as \u0026lsquo;normal\u0026rsquo;. Urinary dysfunction and previous fall experience were measured by asking \u0026lsquo;Do you have dysuria?\u0026rsquo; and \u0026lsquo;Have you fallen within the past six months?\u0026rsquo;, respectively. For each of these two items, each group was further categorized into two groups (yes or no). Body mass index (BMI) was calculated in kilograms per meter squared, and participants were categorized as underweight (less than 18.0), normal (18.0\u0026ndash;22.9), or overweight (23 or greater). Participants were classified into two groups (normal or abnormal) for systolic blood pressure and diastolic blood pressure according to the reference values of 140 mmHg and 90 mmHg for systolic and diastolic blood pressure, respectively.\u003c/p\u003e\n\u003cp\u003eWe used clinical laboratory results for hemoglobin, fasting blood glucose, and total cholesterol levels. Participants were divided into two groups for hemoglobin levels: anemic (\u0026lt;10 g/dL for women and \u0026lt;12 g/dL for men, based on the Korean National Health Examination criteria) and nonanemic.\u003c/p\u003e\n\u003cp\u003eIn women, osteoporosis was measured by bone mineral density (BMD) and defined according to the World Health Organization T score criteria using three groups (normal, \u0026ge; -1.0; osteopenia, -2.4 to -1.1; osteoporosis, \u0026le; -2.5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.4 Statistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrequencies and percentages were calculated for all variables and compared by sex using the chi-square test. The Cox proportional hazards model was used to estimate hazard ratios (HRs), and a 95% confidence interval (CI) was used to predict fall occurrence according to TUG test results. The control variables were sex, fall experience, urinary dysfunction, visual impairment, hearing impairment, BMI, blood pressure, blood glucose, total cholesterol, hemoglobin, depressive symptoms, ADL, and BMD (only including women). Using subgroup analysis and the same model, we identified the risk factors affecting injurious fall occurrence according to sex and TUG score. All the statistical tests were two-tailed and performed using SAS software (version 9.4; SAS Institute Inc., Cary, NC, USA), with a 95% significance level.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe general characteristics of the 34,030 participants at baseline (2007 and 2008) are depicted in Table 1. There were 878 cases (2.58%) of injurious falls among the 34,030 participants, with an incidence of 2.2 cases per 1,000 person\u0026ndash;years, and the incidence was greater among women (3.38%) than men (1.65%). Regarding sex, 17,759 (46.3%) were men, and 18,271 (53.7%) were women. The percentages of individuals in the abnormal TUG test group were 15.3% among men and 21.1% among women, and the percentages of individuals with previous falls were 8.8% and 12.7%, respectively.\u003c/p\u003e\n\u003cp\u003eOn average, men experienced more urinary dysfunction (19.5%), visual impairment (1.7%), hearing impairment (8.7%), higher glucose levels (12.9%), and abnormal ADLs (8.4%) than women did. Women had a greater incidence of injurious falls (3.4%), depressive symptoms (37.7%), falls (12.7%), and overweight (69.9%) than men.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e[Insert Table 1 about here]\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTables 2 and 3 present the results of the multivariate Cox regression model for injurious falls according to sex. Regarding sex, men in the abnormal TUG test group had a significantly greater risk of injurious falls than did those in the normal TUG test group (HR: 1.52, 95% CI: 1.13\u0026ndash;2.04). The TUG test score was not a significant predictor of injurious falls in women. In men, previous fall experience was also associated with a significantly greater risk of injurious falls, even after adjusting for several potentially confounding variables (HR: 2.98, 95% CI: 2.17\u0026ndash;4.10). According to the subgroup analysis for men (normal vs. abnormal TUG groups), multivariate Cox proportional hazards regression analysis revealed that the HRs for injurious falls increased 4.17-fold (95% CI: 2.98\u0026ndash;5.86) in the normal TUG group, and having abnormal systolic blood pressure (HR: 2.65, 95% CI: 1.50\u0026ndash;4.68) and total cholesterol level (HR: 4.22, 95% CI: 2.40\u0026ndash;7.41) in the abnormal TUG group also increased the HRs for injurious falls. In the abnormal TUG test group, there was a 4.42-fold increase in the HR for injurious falls in patients with poor ADLs (vs. good ADLs; 95% CI: 2.51\u0026ndash;7.79).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e[Insert Table 2 about here]\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 presents the findings of the association of TUG test results with injurious falls. In women, TUG test scores were not a predictor of injurious falls; however, urinary dysfunction (HR: 1.61, 95% CI: 1.30\u0026ndash;2.00), BMI (HR: 1.99, 95% CI: 1.18\u0026ndash;3.34), blood pressure (HR: 1.79, 95% CI: 1.30\u0026ndash;2.16), blood sugar level (HR: 1.68, 95% CI: 1.30\u0026ndash;2.16), BMD (osteopenia HR: 1.57, 95% CI: 1.12\u0026ndash;2.20; osteoporosis HR: 1.87, 95% CI: 1.35\u0026ndash;2.60), and hearing impairment (HR: 0.56, 95% CI: 0.36\u0026ndash;0.87) were statistically significant predictors. Furthermore, underweight older adult women had a 1.99 times greater risk of injurious falls (95% CI, 1.18\u0026ndash;3.34) than did those in the normal weight group. Regarding BMD, women with osteopenia had a 1.57-fold (95% CI: 1.12\u0026ndash;2.20) greater risk of injurious falls, and women with osteoporosis had a 1.87-fold greater risk (95% CI: 1.35\u0026ndash;2.60).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e[Insert Table 3 about here]\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe TUG test is one of the most frequently used procedures to evaluate the possibility of falls in older adults [18] and can be used to predict the occurrence of falls in community-dwelling older adult women [19],\u0026nbsp;and its usefulness relies on high specificity rather than sensitivity [8,20,21].\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;TUG test results were shown to predict the occurrence of injurious falls in men but not in women. This result resonates with a previous study in which fall incidence and risk factors differed by sex [11].\u0026nbsp;In this study, previous fall experience was an important predictor of falls in men. This finding is consistent with prior evidence showing that fall recurrence [22-24] and falling experience are risk factors for injurious falls [11,25].\u003c/p\u003e\n\u003cp\u003eAmong older adult men, the risk of injurious falls was greater in the abnormal TUG test group in patients with abnormal blood pressure, elevated cholesterol levels, and impaired independence from daily living (ADL) ability. In a cohort study conducted in Sweden, an association between high systolic pressure and the occurrence of injurious falls was confirmed in a group with low functional status [26].\u0026nbsp;Similar to the current study, the results of Ek et al.\u0026rsquo;s study showed that injurious fall risk differed depending on ADLs in men but not in women [11].\u0026nbsp;Therefore, more educational interventions are needed to prevent falls among older adult men with abnormal TUG results. Furthermore, important fall prevention intervention focus points for this group may include cardiovascular health management, including cholesterol, and detection of ADL changes.\u003c/p\u003e\n\u003cp\u003eIn older adult women, the risk factors for injurious falls were urinary dysfunction, underweight status, and low bone density, regardless of the TUG test results. Urinary dysfunction, such as incontinence, is a well-known risk factor for injurious falls in hospitals [27].\u0026nbsp;Underweight older adult women in our sample showed a greater risk of injurious falls than did normal weight older adult women. In previous investigations, low body weight was also shown to be a risk factor for increased fractures or falls [28-30]; however, the mechanisms by which low body weight increases the likelihood of injurious falls remain unclear. Some hypotheses for this finding are presented herein. A lower BMI has been associated with decreased BMD, soft tissue loss, and muscular weakness, thereby increasing the risk of falls or fractures [31].\u0026nbsp;Additionally, while a lower BMI is associated with a lower BMD, a BMI increase has been shown to be associated with an increase in adipose tissue, providing further support against fractures [32].\u0026nbsp;Being underweight often relates to undernutrition or malnutrition, and if persistent, both can worsen BMD [33],\u0026nbsp;thereby increasing injurious fall risk. Prolonged exposure to trauma in the absence of a fat cushion has also been shown to potentially influence the risk of falls or fractures [34,35].\u003csup\u003e\u0026nbsp;\u003c/sup\u003eIn addition, a low BMI was shown to be strongly associated with sarcopenia development. Sarcopenia diminishes physical strength and muscular performance, leading to injuries that increase the probability of a fall or fracture [36,37];\u0026nbsp;however, muscle mass may not provide adequate bone protection, and reduced muscle strength may increase the risk of fall-related injuries [38,39].\u003c/p\u003e\n\u003cp\u003eStudies have also demonstrated that hearing loss is associated with a high probability of falling [40].\u003csup\u003e\u0026nbsp;\u003c/sup\u003eHowever, in this study, hearing loss in women with normal TUG results was associated with a reduced risk of injurious falls. It is possible that the risk of injurious falls in older adults diagnosed with hearing loss is lower because\u0026nbsp;those with hearing loss\u0026nbsp;may engage in less\u0026nbsp;outdoor\u0026nbsp;activity than those without hearing loss [41].\u0026nbsp;However, follow-up studies are required to confirm the relationship between hearing loss and injurious falls.\u003c/p\u003e\n\u003cp\u003eIn summary, the TUG test is a relatively simple test that has been shown, based on our findings, to be potentially useful in health interventions aimed at dealing with injurious fall risk factors and tailored to individuals by sex. In male older adults, fall prevention interventions could be designed to divide participants into normal and abnormal groups according to their TUG test results. Moreover, since the TUG test did not predict the occurrence of falls in older adult women, future research is needed to further assess whether the TUG test can be useful in predicting falls in this population.\u003c/p\u003e\n\u003cp\u003eRegarding study limitations, this study could not control for the participants\u0026rsquo; demographic characteristics. As we tracked and analyzed individuals reaching the age of 66 years according to sex, demographic variables such as sex and age were considered, but others such as education level and marital status were not. These variables were analyzed in other studies but could not be handled. Moreover, this study could not analyze data on BMD T scores for older adult men, as their claims data did not contain this information. This study\u0026rsquo;s results may have also underestimated injurious fall incidence because the claims data were analyzed while tracking older adults in the community who underwent lifecycle examinations. This implies that cases of minor injurious falls that did not require hospitalization or treatment were not considered in the analysis. In the case of an injury due to a fall, treatment costs are high, and people could become impaired in the performance of their ADLs; therefore, identifying the related risk factors for cases of minor injurious falls and using them in preventive techniques may be important steps of future research. Regarding strengths, this study conducted a cohort analysis of older adults aged 66 years in 2007 and 2008 and analyzed their follow-up data for more than 10 years. It also examined injurious falls using objective claims data from the KNHIS.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe TUG test is useful for predicting injurious falls in older adult men but not in older adult women. In older adult women, various factors can predict injurious falls regardless of the TUG test. The factors predictive of injurious falls according to the TUG test differed by sex.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eADLs,\u0026nbsp;activities of daily living.\u003c/p\u003e\n\u003cp\u003eBMD, bone mineral density.\u003c/p\u003e\n\u003cp\u003eBMI, body mass index.\u003c/p\u003e\n\u003cp\u003eTUG, timed up and go test.\u003c/p\u003e\n\u003cp\u003eCI, Confidence interval\u003c/p\u003e\n\u003cp\u003eHR, Hazard ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThis study was reviewed and approved by an affiliated institution (IRB No. 1044396-202203-HR-073-01),\u0026nbsp;and permission to analyze the data remotely was\u0026nbsp;provided\u0026nbsp;by the National Health Insurance Service (NHIS).\u0026nbsp;Access to the National Health Insurance Sharing Service database (NHISS) for research purposes was provided only through legal procedures after deliberation (NHIS-2022-2-362). In addition, it was impossible to confirm the patient\u0026rsquo;s personal information because the information had been encrypted. Informed consent was not needed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e Data from this study must be used in accordance with National Health Insurance Service policies. The information on how to request the database is available at https://nhiss.nhis.or.kr/bd/ab/bdaba021eng.do. The details and cost of the database are described in https://nhiss.nhis.or.kr/bd/ab/bdaba022eng.do. To request the database, visit https://nhiss.nhis.or.kr/bd/ay/bdaya001iv.do. (only available in Korean).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e: The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research was funded by the National Research Foundation of Korea (NRF) grant number NRF-2020R1F1A1076167.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u0026nbsp;\u003c/strong\u003eJK led the study design, interpreted the data, and wrote the paper. SC performed the statistical analysis and assisted with\u0026nbsp;the\u0026nbsp;data interpretation and wrote the paper. SC served as the corresponding author and directed the study. All the authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLee Y, Kim S, Hwang N, et al. 2020 Survey of the Elderly(online). Available at: \u003cem\u003ehttps://www.mohw.go.kr/board.es?mid=a10107010100\u0026amp;bid=0040\u0026amp;act=view\u0026amp;list_no=374195\u0026amp;tag=\u0026amp;cg_code=\u0026amp;list_depth=1\u003c/em\u003e. Accessed August 23, 2023.\u003c/li\u003e\n\u003cli\u003eBerry SD, Miller RR. Falls: Epidemiology, pathophysiology, and relationship to fracture. Curr Osteoporos Rep. 2008;6(4):149-154. doi:10.1007/s11914-008-0026-4\u003c/li\u003e\n\u003cli\u003eGong XF, Li XP, Zhang LX, et al. Current status and distribution of hip fractures among older adults in China. 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Bone Metabolism in Obesity and Weight Loss. Annu Rev Nutr. 2012;32(1):287-309. doi:10.1146/annurev.nutr.012809.104655\u003c/li\u003e\n\u003cli\u003eFawzy T, Muttappallymyalil J, Sreedharan J, et al. Association between Body Mass Index and Bone Mineral Density in Patients Referred for Dual-Energy X-ray Absorptiometry Scan in Ajman, UAE. J Osteoporos. 2011;2011:1-4. doi:10.4061/2011/876309\u003c/li\u003e\n\u003cli\u003eBonjour JP, Schurch MA, Rizzoli R. Nutritional aspects of hip fractures. Bone. 1996;18(3):S139-S144. doi:10.1016/8756-3282(95)00494-7\u003c/li\u003e\n\u003cli\u003eLee HD, Han S, Jang HD, et al. Cumulative Burden of Being Underweight Increases the Risk of Hip Fracture: A Nationwide Population-Based Cohort Study. Healthcare. 2022;10(12):2568. doi:10.3390/healthcare10122568\u003c/li\u003e\n\u003cli\u003eLandi F, Liperoti R, Russo A, et al. Sarcopenia as a risk factor for falls in elderly individuals: Results from the ilSIRENTE study. Clinical Nutrition. 2012;31(5):652-658. doi:10.1016/j.clnu.2012.02.007\u003c/li\u003e\n\u003cli\u003eTokeshi S, Eguchi Y, Suzuki M, et al. Relationship between Skeletal Muscle Mass, Bone Mineral Density, and Trabecular Bone Score in Osteoporotic Vertebral Compression Fractures. Asian Spine J. 2021;15(3):365-372. doi:10.31616/asj.2020.0045\u003c/li\u003e\n\u003cli\u003eScott D, Daly RM, Sanders KM, Ebeling PR. Fall and Fracture Risk in Sarcopenia and Dynapenia With and Without Obesity: the Role of Lifestyle Interventions. Curr Osteoporos Rep. 2015;13(4):235-244. doi:10.1007/s11914-015-0274-z\u003c/li\u003e\n\u003cli\u003eP\u0026eacute;rez-L\u0026oacute;pez FR, Ara I. Fragility fracture risk and skeletal muscle function. Climacteric. 2016;19(1):37-41. doi:10.3109/13697137.2015.1115261\u003c/li\u003e\n\u003cli\u003eJiam NTL, Li C, Agrawal Y. Hearing loss and falls: A systematic review and meta-analysis. Laryngoscope. 2016;126(11):2587-2596. doi:10.1002/LARY.25927\u003c/li\u003e\n\u003cli\u003eIwagami M, Kobayashi Y, Tsukazaki E, et al. Associations between self‐reported hearing loss and outdoor activity limitations, psychological distress and self‐reported memory loss among older people: Analysis of the 2016 Comprehensive Survey of Living Conditions in Japan. Geriatr Gerontol Int. 2019;19(8):747-754. doi:10.1111/ggi.13708\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eDescriptive characteristics of the study participants at baseline (2007\u0026ndash;2008)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"683\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.373352855051245%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.373352855051245%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.373352855051245%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eWomen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eChi-square test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.095238095238095%\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.238095238095237%\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.238095238095237%\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.238095238095237%\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e34,030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e15,759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e46.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e18,271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e53.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u003cstrong\u003eInjurious falls*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e33,152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e97.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e15,499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e98.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e17,653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e96.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\"\u003e\n \u003cp\u003e101.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTUG group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e27,757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e81.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e13,343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e84.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e14,414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e78.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\"\u003e\n \u003cp\u003e187.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e6,273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e18.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e2,416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e15.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e3,857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e21.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevious fall experience\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e3,716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e1,395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e2,321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\"\u003e\n \u003cp\u003e129.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e30,314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e89.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e14,364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e91.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e15,950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e87.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrinary dysfunction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e6,076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e17.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e3,077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e2,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\"\u003e\n \u003cp\u003e55.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e27,954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e82.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e12,682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e80.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e15,272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e83.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVisual impairment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e33,536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e98.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e15,493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e98.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e18,043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e98.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHearing impairment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e31,416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e92.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e14,380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e91.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e17,036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e93.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\"\u003e\n \u003cp\u003e47.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e2,614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e1,379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e1,235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u0026lt;18.0 (underweight)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\"\u003e\n \u003cp\u003e242.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e18.1~22.9 (normal weight)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e10,710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e31.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e5,527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e35.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e5,183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e28.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u0026ge;23.0 (overweight)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e22,561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e66.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e9,789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e62.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e12,772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e69.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSystolic blood pressure (mmHg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u0026lt;140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e24,725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e72.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e11,486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e72. 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e13,239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e72.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u0026ge;140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e9,305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e27.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e4,273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e27.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e5,032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e27.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiastolic blood pressure (mmHg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u0026lt;90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e28,058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e82.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e12,907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e81.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e15,151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e82.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u0026ge;90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e5,972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e17.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e2,852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e3,120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFasting blood glucose level (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u0026lt;126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e30,264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e88.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e13,714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e87.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e16,550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e90.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\"\u003e\n \u003cp\u003e108.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u0026ge;126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e3,766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e2,045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e1,721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal cholesterol level (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u0026lt;240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e29,286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e86.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e14,287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e90.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e14,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e82.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\"\u003e\n \u003cp\u003e517.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u0026ge;240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e4,744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e1,472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e3,272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemoglobin level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u0026nbsp; Nonanemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e33,387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e98.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e15,262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e96.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e18,125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e99.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\"\u003e\n \u003cp\u003e253.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003eAnemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepressive symptom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e22,576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e66.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e11,190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e71.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e11,386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e62.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\"\u003e\n \u003cp\u003e286.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e11,454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e33.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e4,569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e29.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e6,885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e37.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u003cstrong\u003eADL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e31,673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e93.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e14,440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e91.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e17,233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e94.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\"\u003e\n \u003cp\u003e94.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e2,357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e1,319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e1,038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMD T score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u0026ge;-1.0 (normal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e2,264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e-2.4\u0026ndash;-1.0 (osteopenia)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e6,194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e39.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.08931185944363%\"\u003e\n \u003cp\u003e\u0026le;-2.5 (osteoporosis) \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\" valign=\"top\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.345534407027818%\"\u003e\n \u003cp\u003e7,366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.027818448023426%\"\u003e\n \u003cp\u003e46.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420204978038067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.370424597364568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*During the 12- or 13-year follow-up period (until 2019).\u003c/p\u003e\n\u003cp\u003eAbbreviations: TUG, timed up and go test; ADL, activities of daily living; BMI, body mass index; BMD, bone mineral density.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Cox proportional regression analysis of men\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"105%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" valign=\"top\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" rowspan=\"2\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.43217665615142%\" colspan=\"2\" style=\"width: 39.6504%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.45631067961165%\" valign=\"top\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.854368932038835%\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal TUG group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.689320388349515%\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbnormal TUG group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" valign=\"top\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eTUG group (ref: normal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" valign=\"top\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" valign=\"top\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" valign=\"top\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e1.52 (1.13\u0026ndash;2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" valign=\"top\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" valign=\"top\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003ePrevious fall experience (ref: no)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" valign=\"top\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" valign=\"top\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" valign=\"top\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e2.98 (2.17\u0026ndash;4.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e4.17 (2.98\u0026ndash;5.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e0.37 (0.09\u0026ndash;1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eUrinary dysfunction (ref: no)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" valign=\"top\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" valign=\"top\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" valign=\"top\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e0.77 (0.55\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e0.81 (0.57\u0026ndash;1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e0.56 (0.25\u0026ndash;1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eVisual impairment (ref: normal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" valign=\"top\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" valign=\"top\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" valign=\"top\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e1.01 (0.42\u0026ndash;2.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" valign=\"top\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" valign=\"top\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eHearing impairment (ref: normal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" valign=\"top\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" valign=\"top\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" valign=\"top\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e1.03 (0.68\u0026ndash;1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e1.26 (0.81\u0026ndash;1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e0.34 (0.08\u0026ndash;1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eBMI (ref: normal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" valign=\"top\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" valign=\"top\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" valign=\"top\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e0.95 (0.44\u0026ndash;2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e1.01 (0.44\u0026ndash;2.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e0.75 (0.10\u0026ndash;5.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e1.08 (0.83\u0026ndash;1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e1.08 (0.80\u0026ndash;1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e0.92 (0.52\u0026ndash;1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eSystolic blood pressure (ref: \u0026lt;140 mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" valign=\"top\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" valign=\"top\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" valign=\"top\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003e\u0026ge;140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e0.99 (0.71\u0026ndash;1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e0.58 (0.38\u0026ndash;0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e2.65 (1.50\u0026ndash;4.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eDiastolic blood pressure (ref: \u0026lt;90 mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" valign=\"top\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" valign=\"top\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" valign=\"top\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003e\u0026ge;90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e0.69 (0.45\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e1.14 (0.70\u0026ndash;1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e0.23 (0.10\u0026ndash;0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eFasting blood glucose level (ref: \u0026lt;126 mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" valign=\"top\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" valign=\"top\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" valign=\"top\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003e\u0026ge;126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e1.14 (0.81\u0026ndash;1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e1.18 (0.80\u0026ndash;1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e1.11 (0.52\u0026ndash;2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eTotal cholesterol level (ref: \u0026lt;240 mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" valign=\"top\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" valign=\"top\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" valign=\"top\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003e\u0026ge;240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e1.31 (0.89\u0026ndash;1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e0.55 (0.29\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e4.22 (2.40\u0026ndash;7.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eHemoglobin level (ref: nonanemic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" valign=\"top\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" valign=\"top\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" valign=\"top\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eAnemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e1.34 (0.73\u0026ndash;2.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e1.37 (0.70\u0026ndash;2.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e1.40 (0.33\u0026ndash;5.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eDepressive symptom (ref: no)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" valign=\"top\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" valign=\"top\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" valign=\"top\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e1.00 (0.76\u0026ndash;1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e1.17 (0.87\u0026ndash;1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e0.68 (0.36\u0026ndash;1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eADL (ref: good)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" valign=\"top\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" valign=\"top\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" valign=\"top\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e1.41 (0.97\u0026ndash;2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e0.69 (0.38\u0026ndash;1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e4.42 (2.51\u0026ndash;7.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eTotal*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e15,757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e13,343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e2,414\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eEvent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eCensored\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e15,499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e13,143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e2,356\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.798107255520506%\" style=\"width: 42.6816%;\"\u003e\n \u003cp\u003eNo. of observations read\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.769716088328074%\" style=\"width: 17.9746%;\"\u003e\n \u003cp\u003e15,759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.189274447949526%\" style=\"width: 19.5938%;\"\u003e\n \u003cp\u003e13,343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.242902208201894%\" style=\"width: 19.9062%;\"\u003e\n \u003cp\u003e2,416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*No. of observations used for Cox proportional models\u003c/p\u003e\n\u003cp\u003eAbbreviations: HR, hazard ratio; 95% CI, 95% confidence interval; TUG, timed up and go test; ADL, activities of daily living; BMI, body mass index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Cox proportional regression analysis of women\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"102%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.08943089430894%\" valign=\"top\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" rowspan=\"2\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.91056910569106%\" colspan=\"2\" style=\"width: 38.082%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWomen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.97148676171079%\" valign=\"top\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.014256619144604%\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal TUG group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.014256619144604%\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbnormal TUG group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" valign=\"top\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eTUG group (ref: normal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"top\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e1.00 (0.81\u0026ndash;1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003ePrevious fall experience (ref: no)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"top\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e0.92 (0.70\u0026ndash;1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e0.81 (0.59\u0026ndash;1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e1.19 (0.71\u0026ndash;1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eUrinary dysfunction (ref: no)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"top\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e1.61 (1.30\u0026ndash;2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e1.59 (1.24\u0026ndash;2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e1.60 (1.02\u0026ndash;2.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eVisual impairment (ref: normal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"top\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e0.85 (0.35\u0026ndash;2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e1.12 (0.46\u0026ndash;2.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e0.00 (0.00-.)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eHearing impairment (ref: normal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"top\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e0.56 (0.36\u0026ndash;0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e0.38 (0.2\u0026ndash;0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e1.16 (0.60\u0026ndash;2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eBMI (ref: normal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"top\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e1.99 (1.18\u0026ndash;3.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e2.44 (1.44\u0026ndash;4.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e0.00 (0.00-.)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e0.95 (0.77\u0026ndash;1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e0.80 (0.64\u0026ndash;0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e1.89 (1.15\u0026ndash;3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eSystolic blood pressure (ref: \u0026lt;140 mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"top\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003e\u0026ge;140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e1.79 (1.30\u0026ndash;2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e2.12 (1.67\u0026ndash;2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e0.95 (0.57\u0026ndash;1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eDiastolic blood pressure (ref: \u0026lt;90 mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"top\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003e\u0026ge;90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e0.56 (0.73\u0026ndash;1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e0.57 (0.41\u0026ndash;0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e0.56 (0.28\u0026ndash;1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eFasting blood glucose level (ref: \u0026lt;126 mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"top\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003e\u0026ge;126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e1.68 (1.30\u0026ndash;2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e1.73 (1.30\u0026ndash;2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e1.55 (0.89\u0026ndash;2.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eTotal cholesterol level (ref: \u0026lt;240 mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"top\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003e\u0026ge;240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e0.92 (0.73\u0026ndash;1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e1.01 (0.78\u0026ndash;1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e0.62 (0.35\u0026ndash;1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eHemoglobin level (ref: nonanemic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"top\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eAnemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e1.04 (0.39\u0026ndash;2.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e1.31 (0.49\u0026ndash;3.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e0.00 (0.00-.)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eDepressive symptom (ref: no)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"top\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e1.21 (1.01\u0026ndash;1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e1.13 (0.92\u0026ndash;1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e1.55 (1.04\u0026ndash;2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eADL (ref: good)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"top\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e1.04 (0.72\u0026ndash;1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e1.12 (0.74\u0026ndash;1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e0.81 (0.35\u0026ndash;1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eBMD T score (ref: normal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" valign=\"top\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003e -2.4\u0026ndash;-1.0 (osteopenia)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e1.57 (1.12\u0026ndash;2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e1.55 (1.08\u0026ndash;2.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e1.62 (0.61\u0026ndash;4.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003e\u0026le;-2.5 (osteoporosis) \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e1.87 (1.35\u0026ndash;2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e1.49 (1.04\u0026ndash;2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e4.42 (1.78\u0026ndash;10.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eTotal*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e15,671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e12,377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e3,294\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eEvent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eCensored\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e15,197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e12,006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e3,191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.159609120521175%\" style=\"width: 46.0293%;\"\u003e\n \u003cp\u003eNo. of observations read\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03257328990228%\" style=\"width: 16.418%;\"\u003e\n \u003cp\u003e18,271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 17.8457%;\"\u003e\n \u003cp\u003e14,414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" style=\"width: 20.0293%;\"\u003e\n \u003cp\u003e3,857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*No. of observations used in the Cox proportional hazards models.\u003c/p\u003e\n\u003cp\u003eAbbreviations:\u0026nbsp;HR, hazard ratio; 95% CI, 95% confidence interval; TUG, timed up and go test; ADL, activities of daily living; BMI, body mass index; BMD, bone mineral density.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"fall, older adult, timed up and go test, risk factor, sex difference","lastPublishedDoi":"10.21203/rs.3.rs-4521597/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4521597/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study aimed to determine whether sex\u003cb\u003e-\u003c/b\u003especific timed up and go (TUG) test results can predict injurious fall occurrence in older adults and identify risk factors for injurious falls based on TUG results.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were obtained from an older adult cohort database provided by the National Health Insurance Service, which included 34,030 individuals aged 66 years or older who underwent life-transition health examinations in 2007 and 2008 and were followed up until 2019. To identify the risk factors for injurious falls, this study performed a Cox proportional hazard regression analysis by sex, with individual characteristics, including TUG test results, as independent variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe TUG test predicted the occurrence of injurious falls in older adult men but not in older adult women. Among men with abnormal TUG results, those with high systolic blood pressure had a greater risk of injurious falls. In women, urinary dysfunction, hearing impairment, low BMI, high systolic blood pressure and fasting blood glucose level, depressive symptoms, and bone mineral density were identified as risk factors for injurious falls\u0026mdash;regardless of the TUG test results. The factors predictive of injurious falls according to the TUG test differed by sex.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe TUG test is useful for predicting injurious falls in older adult men. This study identified other risk factors for injurious falls among older adult women that can be used in prevention strategies.\u003c/p\u003e","manuscriptTitle":"Timed Up and Go Test and Prediction of Injurious Falls among Older Adults by Sex: A Population-based Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 15:55:46","doi":"10.21203/rs.3.rs-4521597/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-30T05:35:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-22T09:42:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-11T21:25:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121717613510422931413091412330462498937","date":"2024-07-20T11:53:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68285850486765424815582462357873388706","date":"2024-07-16T01:37:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-26T11:23:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-24T17:29:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-20T10:24:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-20T10:22:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2024-06-03T11:46:38+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":"df12b759-fa8a-4d48-aada-85f3dacec1e2","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-30T16:03:53+00:00","versionOfRecord":{"articleIdentity":"rs-4521597","link":"https://doi.org/10.1186/s12877-024-05588-9","journal":{"identity":"bmc-geriatrics","isVorOnly":false,"title":"BMC Geriatrics"},"publishedOn":"2024-12-23 15:57:10","publishedOnDateReadable":"December 23rd, 2024"},"versionCreatedAt":"2024-07-18 15:55:46","video":"","vorDoi":"10.1186/s12877-024-05588-9","vorDoiUrl":"https://doi.org/10.1186/s12877-024-05588-9","workflowStages":[]},"version":"v1","identity":"rs-4521597","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4521597","identity":"rs-4521597","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}