The Distribution and Associated Factors of SARC-F and SARC-CalF in Community-Dwelling Older Adults of Kinmen

The Distribution and Associated Factors of SARC-F and SARC-CalF in Community-Dwelling Older Adults of Kinmen | 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 The Distribution and Associated Factors of SARC-F and SARC-CalF in Community-Dwelling Older Adults of Kinmen Ching-Sung Ho, Shen-Ming Lee, Meng-Chi Chen, Chia-Ming Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4169906/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose This study aims to assess the distribution of SARC-F and SARC-CalF scores and identify associated risk factors among the elderly in Kinmen. Methods A community-based cross-sectional study was conducted in Kinmen in 2022 at the community care stations. The sample consisted of 305 individuals aged over 65. The outcome variable was the SARC-Ca1F score, which was categorized as normal (< 11) or abnormal ( ≧ 11). Chi-square, and binary logistic regression analysis were conducted for inferential statistical analysis. Results A total of 3.9% exhibited abnormal SARC-F values ( ≧ 4), and 14.4% had abnormal SARC-Ca1F values ( ≧ 11). Younger age, higher educational level, and BMI ≧ 27 were independently associated with a lower abnormal rate of SARC-Ca1F scores in older adults in Kinmen. When considering the interaction effect between MNA scores, marital status, age and educational level, it was observed that single elders showed a higher abnormal rate of SARC-Ca1F values (OR = 2.299, p = 0.023). Those using Kinmenese and Chinese or solely Kinmenese had a higher abnormal rate of SARC-Ca1F values compared to others, (OR = 5.902 and OR = 9.341, respectively). Individuals with a BMI ≧ 27 exhibited a significantly lower abnormal rate of SARC-Ca1F values compared to those with a BMI falling between 22-23.99, (OR = 0.174). Conclusions Among the elderly population in Kinmen, individuals with younger age, higher education levels, and BMI ≧ 27 exhibit a lower prevalence of sarcopenia. Conversely, individuals who are single or use Kinmenese as their native language show a higher likelihood of developing sarcopenia, highlighting unique demographic influences. SARC-F SARC-CalF sarcopenia Kinmen elders Key summary points Aim: The objective of this study is to evaluate the distribution of SARC-F and SARC-CalF scores and identify the associated risk factors among the elderly population in Kinmen. Findings: Individuals with younger age, higher education levels, and BMI ≧27 exhibit a lower prevalence of sarcopenia. Conversely, those who are single or use Kinmenese as their native language show a higher likelihood of developing sarcopenia. Message: This is the inaugural analysis of sarcopenia prevalence among the elderly in Kinmen, utilizing the SARC-F and SARC-CalF tools. Introduction Population aging is a global phenomenon, with nearly every country experiencing growth in both the size and proportion of older individuals in the population. The absolute number of older individuals is projected to more than double by 2050. The overall proportion of individuals aged 65 years or over is expected to increase from 10 percent in 2021 to 17 percent by 2050 [ 1 ]. In line with this trend, statistics from the Executive Yuan indicate that the population of individuals aged 65 and above in Taiwan is anticipated to surpass 20% by the year 2025 [ 2 ]. Sarcopenia, characterized by the decline of skeletal muscle tissue with age, emerges as a significant contributor to functional decline and loss of independence in older adults [ 3 ]. Elderly individuals experiencing severe sarcopenia coupled with impaired physical performance, face an elevated risk of short-term mortality [ 4 ]. Current estimates suggest that sarcopenia affects approximately 10% of older adults worldwide [ 5 ]. The reported prevalence rates of sarcopenia in community-dwelling older adults from previous studies range between 5% and 13% [ 6 ]. The SARC-F and SARC-CalF questionnaires were developed to assess sarcopenia in the elderly. SARC-F is a self-administered questionnaire that evaluates the level of difficulty experienced in five components: strength, assistance in walking, rising from a chair, climbing stairs, and falls. Each item is scored on a 3-level score ranging from 0 to 2 points, indicating none (0), some (1), or a lot (2) of difficulty, with falls being assessed as none (0), 1–3 times (1), or ≧ 4 times (2). The total score ranges from 0 to 10, with the criterion for sarcopenia set at 4 points [ 7 ]. Previous reports highlight a strong association between a diagnosis of sarcopenia using SARC-F and reduced physical performance, lower quality of life, and an increased risk of death and hospitalization [ 8 – 9 ]. SARC-CalF incorporates the five items from SARC-F and introduces an additional item—the calf circumference (CC) item, measured on the right calf in a standing position at its greatest circumference. CC values of ≤ 33 cm for women and ≤ 34 cm for men appear to be the optimal cut-off values for identifying sarcopenia in community-dwelling older adults [ 10 ]. The CC item is scored at 0 points if its value is above the cut-off point and 10 points if below or equal to the cut-off. A total score of ≥ 11 points indicates a risk of sarcopenia [ 11 ]. SARC-CalF has demonstrated a significant improvement in sensitivity and overall diagnostic accuracy over SARC-F in screening for sarcopenia among community-dwelling older adults [ 12 ]. The population exhibiting sarcopenia demonstrated a significant correlation with malnutrition and BMI. Malnutrition is linked to diverse geriatric syndromes, including sarcopenia, dependency, falls, and fractures. It repercussions extend to increased risk of pressure ulcers, cognitive decline, infections, prolonged hospital stays, higher costs, and elevated mortality rates [ 13 ]. BMI was primarily associated with sarcopenia in both men and women across various definitions, with a higher BMI demonstrating an inverse relationship with the likelihood of sarcopenia [ 14 ]. The Mini Nutritional Assessment (MNA) stands out as a specific tool for evaluating malnutrition in geriatric settings, adept at identifying elderly individuals with sarcopenia. Studies indicate a gradual decline in MNA scores with the severity of sarcopenia [ 15 ]. Moreover, it demonstrates a robust predictive effect on a low muscle-mass index in hospitalized patients [ 16 ]. Kinmen, a small island covering 150 square kilometers, is situated in proximity to the major Chinese city of Xiamen. Administered as part of Taiwan, it boasts a permanent resident population of nearly 60,000 people. The residents of Kinmen are diverse, with some being native-born individuals who use Kinmenese as their primary language, while others may communicate in Chinese or Taiwanese due to their educational background, job requirements, or having immigrated from Taiwan. There is only one hospital in Kinmen, it is a district hospital with 300 beds, subordinated to Ministry of Health and Welfare. Compared to the main island of Taiwan, Kinmen is a region with relatively limited healthcare resources. The primary objectives of this study were to assess the distribution of SARC-F and SARC-CalF scores and investigate associated risk factors among the elderly population in Kinmen. These risk factors include sociodemographic variables and Mini Nutritional Assessment (MNA) values, contributing to a comprehensive understanding of the health dynamics within this unique island community. Material and methods Study design A community-based cross-sectional study was conducted in Kinmen from September 2022 to November 2022 at the Community Care Stations. The study sample consisted of individuals aged over 65 and older who participated in community meals. The inclusion criteria for the samples were: 1) aged 65 or above, 2) capable of independent walking and clear communication, 3) no mental disorders. Trained interviewers collected the questionnaires through face-to-face interviews. Clinical data were gathered from medical records at the end of one year. Out of 311 individuals in the sample, 305 valid questionnaires were collected after excluding 6 invalid ones. This yielded an impressive questionnaire response rate of 98.07%. To ensure confidentiality, data analysis was conducted anonymously, and the study protocol was approved by the Medical Ethics Committee of National Cheng Kung University (IRB no. 111-406-2). Measurements The study’s outcome variable was the SARC-Ca1F score of the elderly in Kinmen, categorized as normal (< 11) and abnormal ( ≧ 11). The independent variables included gender, age, educational level, marital and living status, current language usage, and health-related factors such as BMI and MNA scores. BMI was calculated as the weight in kilograms divided by the square of the height in meters, following the definitions of lean (BMI < 22 kg/m 2 ), normal weight (BMI: 22-23.9 kg/m 2 ), overweight (BMI: 24-26.9 kg/m 2 ), and obese (BMI ≧ 27 kg/m 2 ), as proposed by the Ministry of Health and Welfare in Taiwan [ 17 ]. The MNA score comprises 18 items grouped into four rubrics: anthropometric assessment, general assessment, short dietary assessment, and subjective assessment. Threshold values of ≥ 24 for well-nourished, 17-23.5 suggest being at risk of malnutrition, and < 17 signify malnourished [ 18 – 19 ]. Note, there were only 12 individuals with a SARC-F value ≧ 4, so the inferential statistical analysis mainly compared the distribution of SARC-Ca1F values. Statistical analysis Frequent analyses, mean value, and standard deviation were utilized to assess the distribution of samples’ SARC-F and SARC-Ca1F values, sociodemographic and health-related factors. In addition, inferential statistical analysis, including chi-square tests and binary logistic regression analysis, was conducted to determine the relationship between samples’ SARC-Ca1F values and the variables of interest. For data analysis, we employed the SPSS software package (version 27.0). A significance level of 0.05 was set, although higher levels of significance were also considered. Results Characteristics of the study sample A total of 305 individuals, comprising 202 women and 103 men, volunteered for the study. The mean age was 74.53±7.02 years. Of the participants, 114 (37.4%) were illiterate, with the majority being married (70.2%). Additionally, 206 (67.5%) were living with their offspring, while 16.1% were living alone. Regarding language usage, 107 (35.1%) of them used both Kinmenese and Chinese as their main language, and 107 only used Kinmenese. At the time of the study, the mean value of BMI was 24.62±3.65. Notably, 75 individuals (24.6%) had a BMI less than 22, and 69 (22.6%) had a BMI equal to or higher than 27. 90.5% of the sample had MNA scores greater than 23.5. Abnormal SARC-F values (≧4) were observed in 12 individuals (3.9%), while 14.4% exhibited abnormal SARC-Ca1F values (≧11). The basic sociodemographic characteristics and health-related results of the sample are detailed in Table 1. Sample Characteristics, Health-related Status, and SARC-Ca1F The highest abnormal rate of SARC-Ca1F values, at 30.3%, was observed in individuals over 80, and this difference was statistically significant across different age groups (p<0.001). For the samples whose SARC-Ca1F values were abnormal, 24.6% of them were illiterate, compared to 9.2% for samples with 1 to 6 years, and 7.5% for those with 7 or more years of schooling, demonstrating a statistically significant difference (p<0.001). Individuals with no marital partner had a significantly higher abnormal rate of SARC-Ca1F values than those with a spouse (22.0% vs. 11.2%, p=0.013). Individuals who used Kinmenese as their main language exhibited the highest abnormal rate of SARC-Ca1F values (24.3%), compared to those who used Kinmenese and Chinese (14.0%) and others (3.3%), (p<0.001). The group of samples with BMI ≧27 had the lowest abnormal rate of SARC-Ca1F values (4.3%) compared to the others, showing a statistically significant difference (p=0.036). The people with a MNA value ≦23.5 had a higher abnormal rate of SARC-Ca1F values (34.5%) compared to those whose MNA value was ≧24 (12.3%), (p=0.004). Binary Logistic Regressions The abnormal rate of SARC-Ca1F values among Kinmen elderly was predicted by age, educational level, current using language, BMI and MNA value, as indicated in the results of the binary logistic regression analyses presented in Table 3. Illiterate individuals had a higher abnormal rate of SARC-Ca1F values than those with an educational level of ≧7 years (OR=3.121, 95% C.I.= 1.025 to 9.507). Those using both Kinmenese and Chinese, as well as those using only Kinmenese as their main language, had a higher abnormal rate of SARC-Ca1F values than others (OR=8.593 and 6.607, respectively). In comparison to the sample with a BMI between 22-23.99, individuals with a BMI≧27 had a significantly lower abnormal rate of SARC-Ca1F values (OR=0.182, 95% C.I.= 0.046 to 0.726). While individuals with MNA scores≦23.5 showed a higher abnormal rate of SARC-Ca1F values, the result did not reach statistical significance (p=0.271). The relationship of MNA status and sociodemographic factors The distribution of MNA scores and marriage status showed a statistically significant difference among various age groups and educational levels. A higher percentage of MNA scores ≦23.5 was found in people aged ≧75 (14.6% vs. 6.0%, 0.375 (95% C.I.=0.171~0.826), p=0.011) and those with illiteracy (14.9% vs. 6.3%, 2.614 (95% C.I.=1.199~5.699), p=0.012). Furthermore, individuals with higher age or illiteracy had a higher rate of being single (43.1% vs. 20.9%, 0.349 (95% C.I.= 0.201~0.578), p<0.001, and 40.4% vs. 23.6%, 2.195 (95% C.I.= 1.329~3.625), p=0.002, respectively). These results indicate the presence of an interaction effect between MNA scores, marital status, and age and educational level factors. (Table 4). Table 5 illustrates the various factors associated with the SARC-Ca1F value. Single individuals showed a higher abnormal rate of SARC-Ca1F values, (OR=2.299, p=0.023). Moreover, individuals who used Kinmenese and Chinese, or exclusively Kinmenese, had a higher abnormal rate of SARC-Ca1F values compared to others, (OR=5.902 and OR=9.341, respectively). Additionally, individuals with a BMI ≧27 demonstrated a significantly lower abnormal rate of SARC-Ca1F values compared to individuals with a BMI falling between 22-23.99, with an odds ratio of 0.174 (95% C.I.= 0.045 to 0.668). The sample whose MNA scores were equal to or less than 23.5 showed a higher abnormal rate of SARC-Ca1F values, with a p value of 0.051. Discussion To the best of our knowledge, this study is the first analysis of sarcopenia prevalence among the elderly in Kinmen, utilizing the SARC-F and SARC-CalF tools. Our findings reveal that 3.9% of the elderly population scored ≧ 4 on SARC-F, while 14.4% scored ≧ 11 on SARC-Ca1F in Kinmen. These results suggest that the prevalence of sarcopenia among the elderly in Kinmen is lower than in other regions [ 20 ]. While SARC-F demonstrated its value in predicting clinically significant outcomes, including functional impairment, hospitalization, poor quality of life, and mortality [ 12 ]. SARC-CalF, with calf circumference cut-off values predicting low muscle mass at ≤ 34 cm in men and ≤ 33 cm in women, emerges as a more effective screening tool for sarcopenia in community-dwelling older adults compared to SARC-F and other criteria [ 10 ]. Future research is crucial to validate this estimate, not only considering the rapid health and sociodemographic changes underway in Kinmen associated with population aging but also due to the inadequacy of local medical resources. Sarcopenia and diminished physical performance are closely linked to the overall health of the elderly [ 21 ]. We have observed an age-related increase in the prevalence of elders with SARC-Ca1F scores of ≧ 11. Specifically, individuals aged 75 or older face a more than four times greater risk of having SARC-Ca1F scores ≧ 11 compared to those under 70. It is crucial to devise effective interventions for preventing sarcopenia in individuals aged 75 and above. While several studies have suggested that men are more susceptible to sarcopenia and tend to experience more substantial loss of muscle mass compared to women [ 22 – 23 ], our findings in Kinmen show a higher prevalence of SARC-Ca1F ≧ 11 in females, although this difference did not reach statistical significance. Evaluating the gender effect on the distribution of sarcopenia among elders in Kinmen, and proposing effective strategies for prevention of sarcopenia progress is essential. The presented study identifies educational attainment as a determinant of potential sarcopenia when adjusted for other known risk factors. Older adults with fewer years of education faced an increased risk of potential sarcopenia compared to those with higher education [ 24 ]. It is evident that individuals with higher educational levels possess a better understanding of health status and are more inclined to engage in health promotion activities. Designing effective health education programs for less educated elderly individuals could prove beneficial in preventing sarcopenia. To enhance the precision of this study and mitigate the impact of confounding variables, we performed an analysis of covariance. This approach helps eliminate the effects of extraneous sources of variance that might otherwise inflate the experimental error [ 25 ]. The research found a statistically significant difference in the distribution of marriage status and MNA scores among different age groups and educational levels (Table 4 ). The findings indicate an interaction between marital status and MNA scores concerning age groups and educational levels. It has been established that family function is significantly associated with sarcopenia [ 26 ]. In our study, after adjusting for age, education level, and health conditions, elders without a spouse demonstrated a higher likelihood of experiencing sarcopenia. Additionally, we observed that elders living with offspring have the lowest prevalence of sarcopenia compared to those living with a spouse or living alone; however, the difference did not reach statistical significance. When older adults experience a well-functioning family life or receive family support, their health functions contribute to good daily functioning and self-care ability [ 27 ], especially considering that, for the majority of Chinese elderly individuals, families constitute the primary source of social support a widely acknowledged determinant of health [ 28 ]. Subsequent studies should assess whether positive family functioning proves effective in influencing psychological outcomes, dietary behaviours, nutritional status, and reducing the risk of sarcopenia in the elderly. Our research findings highlight a significant association between language preference and sarcopenia risk among elderly individuals in Kinmen. Specifically, those who primarily use Kinmenese as their language exhibit a considerably higher rate of abnormal SARC-Ca1F scores (> 11) compared to those who do not primarily use Kinmenese. This suggests that native Kinmenese elders face a higher risk of sarcopenia than their counterparts in the migrant population. Several studies have demonstrated variations in lean body mass composition and sarcopenia prevalence across different ethnic and racial groups [ 29 ]. Additionally, differences were observed in the prevalence of obesity, lack of leisure-time physical activity, and diagnosed diabetes among elderly individuals of different races [ 30 ]. Further research is needed to delve into the relationship between health behaviours and sarcopenia among diverse ethnic groups in Kinmen. Being overweight, even obese, is associated with better survival rates when compared to a low BMI, which may be related to better metabolic reserves [ 13 ]. We found that the elders in Kinmen with a BMI ≧ 27 had a significantly lower risk of SARC-Ca1F ≧ 11 compared with those with a BMI between 22-23.99. Previous research ssuggests that obese individuals with a higher amount of fat mass may also have a greater lean mass, masking the inadequate muscle mass for their size and preserving lean muscle mass more effectively than those with a lower BMI [ 13 , 31 ]. It is observed that BMI may serve as a suboptimal indicator of adiposity among older adults, as body composition undergoes changes during aging, marked by an increase in adiposity levels and a decrease in muscle mass [ 32 ]. Further investigation is needed to understand the mechanisms underlying the association between elevated body weight and the risk of sarcopenia. Conclusion The elderly population in Kinmen presents a distinct health profile, there are some significant factors related to sarcopenia in this area. The statistically significant association between this diminished prevalence and key factors, including younger age, higher educational levels, and a BMI exceeding 27, underscores the importance of considering demographic and lifestyle factors when evaluating sarcopenia prevalence in different regions. Recognizing the influence of age, education, and BMI on sarcopenia rates provides valuable insights for targeted interventions and healthcare strategies tailored to the unique characteristics of the elderly population in Kinmen. Limitations The present study employs purposive sampling, and the research sample is drawn from community care centers in Kinmen with higher participation willingness. As a result, the generalizability of the research findings is limited and may not represent all elderly individuals aged 65 and above in Kinmen, as well as the elderly in other regions. Secondly, the study only measured the research sample in the year 2022, making it challenging to infer the long-term outcomes of the research sample. Thirdly, the analysis focused on the distribution of SARC-Ca1F scores in the elderly, and further exploration is required to investigate its correlation with sarcopenia. Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Ethics approval The study protocol was approved by the Medical Ethics Committee of National Cheng Kung University (IRB no. 111-406-2). Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author Contributions All authors contributed to the study conception and design. Methodology visualization, writing original draft preparation were performed by Ching-Sung Ho. Methodology visualization, writing reviewing and editing were performed by Shen-Ming Lee. 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Divers Equality Health Care 15(4). https://doi.org/10.21767/2049-5471.1000173 Denny CH, Holtzman D, Goins RT, Croft JB (2005) Disparities in chronic disease risk factors and health status between American Indian/Alaska Native and White elders: findings from a telephone survey, 2001 and 2002. Am J Public Health 95(5):825–827. https://doi.org/10.2105/AJPH.2004.043489 Sousa-Santos AR, Afonso C, Borges N, Santos A, Padrão P, Moreira P, Amaral TF (2019) Factors associated with sarcopenia and undernutrition in older adults. Nutr Dietetics 76(5):604–612. https://doi.org/10.1111/1747-0080.12542 Santos ALM, d., Amaral TM d. S. P. F. d., Borges NP (2015) G. F. B. Undernutrition and associated factors in a Portuguese older adult community. Revista de Nutrição, 28 , 231–240 https://doi.org/10.1590/1415-52732015000300001 Tables Table 1. Distribution of sociodemographic factors and biological indicators in community-dwelling older adults of Kinmen N % Male 103 33.8 Female 202 66.2 Age 74.53±7.02 65-69 96 31.5 70-74 86 28.2 75-79 57 18.7 ≧80 66 21.6 Educational level illiteracy 114 37.4 <=6 years 98 32.1 ≧7 years 93 30.5 Marriage status married 214 70.2 unmarried 2 0.7 divorced/separated 9 3.0 widow 80 26.2 with spouse 214 70.2 single 91 28.9 Living status living with offspring 206 67.5 living with spouse 50 16.4 living alone 49 16.1 Current using language Kinmenese and Chinese 107 35.1 Kinmenese only 107 35.1 the Other 91 29.8 BMI 24.62±3.65 <22 75 24.6 22-23.99 64 21.0 24-26.99 97 31.8 ≧27 69 22.6 MNA 26.67±2.41 ≦23.5 29 9.5 ≧24 276 90.5 SARC-F <4 293 96.1 ≧4 12 3.9 SARC-Ca1F <11 261 85.6 ≧11 44 14.4 Total 305 100 Table 2. Distribution of SARC-Ca1F in different factors. SARC-Ca1F <11 N (%) ≧11 N (%) p value Gender 0.228 Male 92(89.3) 11(10.7) Female 169(83.7) 33(16.3) Age <0.001 65-69 92(95.8) 4(4.2) 70-74 77(89.5) 9(10.5) 75-79 46(80.7) 11(19.3) ≧80 46(69.7) 20(30.3) Education level <0.001 illiteracy 86(75.4) 28(24.6) <=6 years 89(90.8) 9(9.2) ≧7 years 86(92.5) 7(7.5) Marriage status 0.013 with spouse 190(88.8) 24(11.2) single 71(78.0) 20(22.0) Living status 0.245 living with offspring 181(87.9) 25(12.1) living with spouse 41(82.0) 9(18.0) living alone 39(79.6) 10(20.4) Current using language <0.001 Kinmenese and Chinese 92(86.0) 15(14.0) Kinmenese only 81(75.7) 26(24.3) the Other 88(96.7) 3(3.3) BMI 0.036 <22 61(81.3) 14(18.7) 22-23.99 51(79.7) 13(20.3) 24-26.99 83(85.6) 14(14.4) ≧27 66(95.7) 3(4.3) MNA 0.004 ≦23.5 19(65.5) 10(34.5) ≧24 242(87.7) 34(12.3) Total 261(85.6) 44(14.4) Table 3. Logistic regression analysis of SARC-Ca1F in different factors. ORs 95% C.I. P value Age 65-69 Reference 70-74 2.634 0.736~9.432 0.137 75-79 4.064 1.144~14.436 0.030 ≧80 4.826 1.360~17.127 0.015 Educational level illiteracy 3.121 1.025~9.507 0.045 <=6 years 1.424 0.453~4.476 0.545 ≧7 years Reference Marriage status with spouse Reference single 1.585 0.737~3.408 0.239 Current using language Kinmenese and Chinese 8.593 2.167~34.068 0.002 Kinmenese 6.607 1.803~24.219 0.004 the Other Reference BMI <22 0.795 0.286~2.206 0.660 22-23.99 Reference 24-26.99 0.655 0.254~1.689 0.381 ≧27 0.182 0.046~0.726 0.016 MNA ≦23.5 1.907 0.604~6.028 0.271 ≧24 Reference R 2 =0.300 Table 4. The relationship of MNA, Marriage status and sociodemographic factors MNA ≦23.5 N (%) >23.5 N (%) OR (95% C.I.) p value Age 0.375 (0.171~0.826) 0.011 65-74 11(6.0) 171(94.0) ≧75 18(14.6) 105(85.4) Education level 2.614 (1.199~5.699) 0.012 illiteracy 17(14.9) 97(85.1) literacy 12(6.3) 179(93.7) Marriage status single N (%) with spouse N (%) p value Age 0.349 (0.201~0.578) <0.001 65-74 38(20.9) 144(79.1) ≧75 53(43.1) 70(56.9) Education level 2.195 (1.329~3.625) 0.002 illiteracy 46(40.4) 68(59.6) literacy 45(23.6) 146(76.4) Table 5. Logistic regression analysis of SARC-Ca1F in different factors. ORs 95% C.I. P value Marriage status with spouse Reference single 2.299 1.122~4.712 0.023 Current using language Kinmenese and Chinese 5.902 1.611~21.621 0.007 Kinmenese 9.341 2.668~32.710 <0.001 the Other Reference BMI <22 0.464 0.266~1.829 0.698 22-23.99 Reference 24-26.99 0.698 0.306~1.814 0.518 ≧27 0.174 0.045~0.668 0.011 MNA ≦23.5 2.878 0.998~8.305 0.051 ≧24 Reference R 2 =0.221 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4169906","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":286943451,"identity":"1dbdf4dc-b3eb-42da-93e1-13eb6466c64f","order_by":0,"name":"Ching-Sung Ho","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsUlEQVRIiWNgGAWjYFACHgaGDxCWAfFaGGfAtSQQqYWZhyQt/P1nDz623WGd2MDevE2C8cdhwlokbuQlG+eeSU9s4DlWJsGQQIQWAwkeM+nctsOJDRI5ZkAtt4nQwn/GTNoSpEX+DbFaGHLMpBnBtvAQqUXiRo6xYW9bunEbT1qxRULaf8Ja+PvPGD742WYt289+eOONDzZphLVAATMDG4hKIFoDSMsoGAWjYBSMApwAAE8TMqEq0rsSAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-3864-945X","institution":"National Quemoy University","correspondingAuthor":true,"prefix":"","firstName":"Ching-Sung","middleName":"","lastName":"Ho","suffix":""},{"id":286943452,"identity":"c4f45e9a-c668-4abf-b696-13f2f8b10cff","order_by":1,"name":"Shen-Ming Lee","email":"","orcid":"","institution":"Feng Chia University","correspondingAuthor":false,"prefix":"","firstName":"Shen-Ming","middleName":"","lastName":"Lee","suffix":""},{"id":286943453,"identity":"fd1d1860-2f2b-43ee-aa20-5c9ce897bf39","order_by":2,"name":"Meng-Chi Chen","email":"","orcid":"","institution":"National Quemoy University","correspondingAuthor":false,"prefix":"","firstName":"Meng-Chi","middleName":"","lastName":"Chen","suffix":""},{"id":286943454,"identity":"921ff5f1-6c11-4f36-b951-0cea65d9d8f1","order_by":3,"name":"Chia-Ming Lin","email":"","orcid":"","institution":"Feng Chia University","correspondingAuthor":false,"prefix":"","firstName":"Chia-Ming","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2024-03-26 12:43:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4169906/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4169906/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55594485,"identity":"04c832aa-c89f-4f7b-a7a6-d2cd9ef1c2fd","added_by":"auto","created_at":"2024-04-30 10:13:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":277213,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4169906/v1/e80747a7-f634-4f08-b1e1-8e72a01fd814.pdf"}],"financialInterests":"","formattedTitle":"The Distribution and Associated Factors of SARC-F and SARC-CalF in Community-Dwelling Older Adults of Kinmen","fulltext":[{"header":"Key summary points","content":"\u003cp\u003eAim:\u003c/p\u003e\n\u003cp\u003eThe objective of this study is to evaluate the distribution of SARC-F and SARC-CalF scores and identify the associated risk factors among the elderly population in Kinmen.\u003c/p\u003e\n\u003cp\u003eFindings:\u003c/p\u003e\n\u003cp\u003eIndividuals with younger age, higher education levels, and BMI\u0026nbsp;≧27 exhibit a lower prevalence of sarcopenia. Conversely, those who are single or use Kinmenese as their native language show a higher likelihood of developing sarcopenia.\u003c/p\u003e\n\u003cp\u003eMessage:\u003c/p\u003e\n\u003cp\u003eThis is the inaugural analysis of sarcopenia prevalence among the elderly in Kinmen, utilizing the SARC-F and SARC-CalF tools.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003ePopulation aging is a global phenomenon, with nearly every country experiencing growth in both the size and proportion of older individuals in the population. The absolute number of older individuals is projected to more than double by 2050. The overall proportion of individuals aged 65 years or over is expected to increase from 10 percent in 2021 to 17 percent by 2050 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In line with this trend, statistics from the Executive Yuan indicate that the population of individuals aged 65 and above in Taiwan is anticipated to surpass 20% by the year 2025 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSarcopenia, characterized by the decline of skeletal muscle tissue with age, emerges as a significant contributor to functional decline and loss of independence in older adults [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Elderly individuals experiencing severe sarcopenia coupled with impaired physical performance, face an elevated risk of short-term mortality [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Current estimates suggest that sarcopenia affects approximately 10% of older adults worldwide [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The reported prevalence rates of sarcopenia in community-dwelling older adults from previous studies range between 5% and 13% [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe SARC-F and SARC-CalF questionnaires were developed to assess sarcopenia in the elderly. SARC-F is a self-administered questionnaire that evaluates the level of difficulty experienced in five components: strength, assistance in walking, rising from a chair, climbing stairs, and falls. Each item is scored on a 3-level score ranging from 0 to 2 points, indicating none (0), some (1), or a lot (2) of difficulty, with falls being assessed as none (0), 1\u0026ndash;3 times (1), or ≧\u0026thinsp;4 times (2). The total score ranges from 0 to 10, with the criterion for sarcopenia set at 4 points [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Previous reports highlight a strong association between a diagnosis of sarcopenia using SARC-F and reduced physical performance, lower quality of life, and an increased risk of death and hospitalization [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. SARC-CalF incorporates the five items from SARC-F and introduces an additional item\u0026mdash;the calf circumference (CC) item, measured on the right calf in a standing position at its greatest circumference. CC values of \u0026le;\u0026thinsp;33 cm for women and \u0026le;\u0026thinsp;34 cm for men appear to be the optimal cut-off values for identifying sarcopenia in community-dwelling older adults [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The CC item is scored at 0 points if its value is above the cut-off point and 10 points if below or equal to the cut-off. A total score of \u0026ge;\u0026thinsp;11 points indicates a risk of sarcopenia [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. SARC-CalF has demonstrated a significant improvement in sensitivity and overall diagnostic accuracy over SARC-F in screening for sarcopenia among community-dwelling older adults [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe population exhibiting sarcopenia demonstrated a significant correlation with malnutrition and BMI. Malnutrition is linked to diverse geriatric syndromes, including sarcopenia, dependency, falls, and fractures. It repercussions extend to increased risk of pressure ulcers, cognitive decline, infections, prolonged hospital stays, higher costs, and elevated mortality rates [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. BMI was primarily associated with sarcopenia in both men and women across various definitions, with a higher BMI demonstrating an inverse relationship with the likelihood of sarcopenia [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Mini Nutritional Assessment (MNA) stands out as a specific tool for evaluating malnutrition in geriatric settings, adept at identifying elderly individuals with sarcopenia. Studies indicate a gradual decline in MNA scores with the severity of sarcopenia [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, it demonstrates a robust predictive effect on a low muscle-mass index in hospitalized patients [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKinmen, a small island covering 150 square kilometers, is situated in proximity to the major Chinese city of Xiamen. Administered as part of Taiwan, it boasts a permanent resident population of nearly 60,000 people. The residents of Kinmen are diverse, with some being native-born individuals who use Kinmenese as their primary language, while others may communicate in Chinese or Taiwanese due to their educational background, job requirements, or having immigrated from Taiwan. There is only one hospital in Kinmen, it is a district hospital with 300 beds, subordinated to Ministry of Health and Welfare. Compared to the main island of Taiwan, Kinmen is a region with relatively limited healthcare resources. The primary objectives of this study were to assess the distribution of SARC-F and SARC-CalF scores and investigate associated risk factors among the elderly population in Kinmen. These risk factors include sociodemographic variables and Mini Nutritional Assessment (MNA) values, contributing to a comprehensive understanding of the health dynamics within this unique island community.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eA community-based cross-sectional study was conducted in Kinmen from September 2022 to November 2022 at the Community Care Stations. The study sample consisted of individuals aged over 65 and older who participated in community meals. The inclusion criteria for the samples were: 1) aged 65 or above, 2) capable of independent walking and clear communication, 3) no mental disorders. Trained interviewers collected the questionnaires through face-to-face interviews. Clinical data were gathered from medical records at the end of one year. Out of 311 individuals in the sample, 305 valid questionnaires were collected after excluding 6 invalid ones. This yielded an impressive questionnaire response rate of 98.07%. To ensure confidentiality, data analysis was conducted anonymously, and the study protocol was approved by the Medical Ethics Committee of National Cheng Kung University (IRB no. 111-406-2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasurements\u003c/h2\u003e \u003cp\u003eThe study\u0026rsquo;s outcome variable was the SARC-Ca1F score of the elderly in Kinmen, categorized as normal (\u0026lt;\u0026thinsp;11) and abnormal (\u0026thinsp;≧\u0026thinsp;11). The independent variables included gender, age, educational level, marital and living status, current language usage, and health-related factors such as BMI and MNA scores. BMI was calculated as the weight in kilograms divided by the square of the height in meters, following the definitions of lean (BMI\u0026thinsp;\u0026lt;\u0026thinsp;22 kg/m\u003csup\u003e2\u003c/sup\u003e), normal weight (BMI: 22-23.9 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (BMI: 24-26.9 kg/m\u003csup\u003e2\u003c/sup\u003e), and obese (BMI\u0026thinsp;≧\u0026thinsp;27 kg/m\u003csup\u003e2\u003c/sup\u003e), as proposed by the Ministry of Health and Welfare in Taiwan [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The MNA score comprises 18 items grouped into four rubrics: anthropometric assessment, general assessment, short dietary assessment, and subjective assessment. Threshold values of \u0026ge;\u0026thinsp;24 for well-nourished, 17-23.5 suggest being at risk of malnutrition, and \u0026lt;\u0026thinsp;17 signify malnourished [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNote, there were only 12 individuals with a SARC-F value\u0026thinsp;≧\u0026thinsp;4, so the inferential statistical analysis mainly compared the distribution of SARC-Ca1F values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFrequent analyses, mean value, and standard deviation were utilized to assess the distribution of samples\u0026rsquo; SARC-F and SARC-Ca1F values, sociodemographic and health-related factors. In addition, inferential statistical analysis, including chi-square tests and binary logistic regression analysis, was conducted to determine the relationship between samples\u0026rsquo; SARC-Ca1F values and the variables of interest.\u003c/p\u003e \u003cp\u003eFor data analysis, we employed the SPSS software package (version 27.0). A significance level of 0.05 was set, although higher levels of significance were also considered.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCharacteristics of the study sample\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of\u0026nbsp;305\u0026nbsp;individuals, comprising\u0026nbsp;202 women and 103 men,\u0026nbsp;volunteered for the study. The mean age was 74.53±7.02\u0026nbsp;years. Of the participants, 114\u0026nbsp;(37.4%) were illiterate,\u0026nbsp;with the majority being\u0026nbsp;married (70.2%). Additionally,\u0026nbsp;206\u0026nbsp;(67.5%) were living with their offspring, while\u0026nbsp;16.1% were living alone. Regarding language usage, 107 (35.1%) of them used\u0026nbsp;both\u0026nbsp;Kinmenese and Chinese\u0026nbsp;as their\u0026nbsp;main language,\u0026nbsp;and 107\u0026nbsp;only\u0026nbsp;used\u0026nbsp;Kinmenese. At the time of the study,\u0026nbsp;the mean value of BMI was 24.62±3.65.\u0026nbsp;Notably,\u0026nbsp;75\u0026nbsp;individuals\u0026nbsp;(24.6%)\u0026nbsp;had a\u0026nbsp;BMI less than 22,\u0026nbsp;and 69 (22.6%)\u0026nbsp;had a BMI\u0026nbsp;equal\u0026nbsp;to\u0026nbsp;or higher than 27. 90.5% of the sample\u0026nbsp;had\u0026nbsp;MNA scores\u0026nbsp;greater\u0026nbsp;than 23.5. Abnormal SARC-F values (≧4) were observed in 12 individuals (3.9%), while 14.4% exhibited abnormal SARC-Ca1F values (≧11). The basic sociodemographic characteristics and health-related results of the sample are detailed in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSample Characteristics, Health-related Status, and SARC-Ca1F\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe highest abnormal rate of\u0026nbsp;SARC-Ca1F values, at 30.3%,\u0026nbsp;was\u0026nbsp;observed\u0026nbsp;in\u0026nbsp;individuals\u0026nbsp;over 80,\u0026nbsp;and\u0026nbsp;this\u0026nbsp;difference\u0026nbsp;was\u0026nbsp;statistically significant\u0026nbsp;across\u0026nbsp;different age groups\u0026nbsp;(p\u0026lt;0.001). For the samples whose\u0026nbsp;SARC-Ca1F\u0026nbsp;values\u0026nbsp;were abnormal, 24.6% of them were illiterate, compared to 9.2% for samples with 1 to 6 years, and 7.5% for those with 7 or more years of schooling, demonstrating a statistically significant difference (p\u0026lt;0.001).\u0026nbsp;Individuals with no marital partner\u0026nbsp;had a significantly higher abnormal rate of\u0026nbsp;SARC-Ca1F\u0026nbsp;values\u0026nbsp;than those with\u0026nbsp;a\u0026nbsp;spouse (22.0% vs. 11.2%, p=0.013).\u0026nbsp;Individuals\u0026nbsp;who used Kinmenese\u0026nbsp;as their\u0026nbsp;main language\u0026nbsp;exhibited\u0026nbsp;the highest abnormal rate of\u0026nbsp;SARC-Ca1F\u0026nbsp;values (24.3%),\u0026nbsp;compared to those\u0026nbsp;who used Kinmenese and Chinese (14.0%) and others (3.3%), (p\u0026lt;0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe group of samples\u0026nbsp;with\u0026nbsp;BMI\u0026nbsp;≧27 had the lowest\u0026nbsp;abnormal rate of\u0026nbsp;SARC-Ca1F\u0026nbsp;values\u0026nbsp;(4.3%)\u0026nbsp;compared to\u0026nbsp;the others,\u0026nbsp;showing\u0026nbsp;a statistically significant difference (p=0.036).\u0026nbsp;The people with\u0026nbsp;a\u0026nbsp;MNA value\u0026nbsp;≦23.5\u0026nbsp;had a higher abnormal rate of\u0026nbsp;SARC-Ca1F\u0026nbsp;values\u0026nbsp;(34.5%)\u0026nbsp;compared to those whose\u0026nbsp;MNA value\u0026nbsp;was\u0026nbsp;≧24 (12.3%), (p=0.004).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBinary Logistic Regressions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;abnormal rate of\u0026nbsp;SARC-Ca1F\u0026nbsp;values\u0026nbsp;among Kinmen elderly was predicted by age, educational level,\u0026nbsp;current using language, BMI\u0026nbsp;and MNA value,\u0026nbsp;as indicated in\u0026nbsp;the results of\u0026nbsp;the\u0026nbsp;binary logistic regression analyses presented in Table 3.\u0026nbsp;Illiterate individuals\u0026nbsp;had\u0026nbsp;a\u0026nbsp;higher abnormal rate of\u0026nbsp;SARC-Ca1F\u0026nbsp;values\u0026nbsp;than those\u0026nbsp;with\u0026nbsp;an\u0026nbsp;educational level\u0026nbsp;of\u0026nbsp;≧7\u0026nbsp;years\u0026nbsp;(OR=3.121, 95% C.I.= 1.025\u0026nbsp;to\u0026nbsp;9.507). Those using\u0026nbsp;both\u0026nbsp;Kinmenese and Chinese,\u0026nbsp;as well as those using only\u0026nbsp;Kinmenese\u0026nbsp;as their\u0026nbsp;main language,\u0026nbsp;had\u0026nbsp;a higher\u0026nbsp;abnormal rate of\u0026nbsp;SARC-Ca1F\u0026nbsp;values\u0026nbsp;than\u0026nbsp;others (OR=8.593\u0026nbsp;and 6.607, respectively). In comparison to the sample with a BMI between\u0026nbsp;22-23.99,\u0026nbsp;individuals with a\u0026nbsp;BMI≧27 had a significantly lower abnormal rate of\u0026nbsp;SARC-Ca1F\u0026nbsp;values\u0026nbsp;(OR=0.182, 95% C.I.= 0.046\u0026nbsp;to 0.726).\u0026nbsp;While individuals\u0026nbsp;with\u0026nbsp;MNA scores≦23.5\u0026nbsp;showed a\u0026nbsp;higher\u0026nbsp;abnormal rate of\u0026nbsp;SARC-Ca1F\u0026nbsp;values, the result did not reach statistical significance\u0026nbsp;(p=0.271).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThe relationship of MNA status and sociodemographic factors\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe distribution of MNA scores and marriage status showed a statistically significant difference among\u0026nbsp;various\u0026nbsp;age groups and educational levels. A higher percentage of MNA scores\u0026nbsp;≦23.5 was found in people aged\u0026nbsp;≧75 (14.6% vs. 6.0%, 0.375 (95% C.I.=0.171~0.826), p=0.011) and those with illiteracy (14.9% vs. 6.3%, 2.614 (95% C.I.=1.199~5.699), p=0.012). Furthermore, individuals with higher age or illiteracy had a higher rate of being single (43.1% vs. 20.9%, 0.349 (95% C.I.= 0.201~0.578), p\u0026lt;0.001, and 40.4% vs. 23.6%, 2.195 (95% C.I.= 1.329~3.625), p=0.002, respectively). These results indicate the presence of an interaction effect between MNA scores, marital status, and age and educational level\u0026nbsp;factors. (Table 4).\u003c/p\u003e\n\u003cp\u003eTable 5 illustrates the various factors associated with the SARC-Ca1F value. Single individuals showed a higher abnormal rate of SARC-Ca1F values, (OR=2.299, p=0.023). Moreover, individuals who used Kinmenese and Chinese, or exclusively Kinmenese, had a higher abnormal rate of SARC-Ca1F values compared to others, (OR=5.902 and OR=9.341, respectively). Additionally, individuals with a BMI ≧27 demonstrated a significantly lower abnormal rate of SARC-Ca1F values compared to individuals with a BMI falling between 22-23.99, with an odds ratio of 0.174 (95% C.I.= 0.045 to 0.668). The sample whose MNA scores were equal to or less than 23.5 showed a higher abnormal rate of SARC-Ca1F values, with a p value of 0.051.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this study is the first analysis of sarcopenia prevalence among the elderly in Kinmen, utilizing the SARC-F and SARC-CalF tools. Our findings reveal that 3.9% of the elderly population scored\u0026thinsp;≧\u0026thinsp;4 on SARC-F, while 14.4% scored\u0026thinsp;≧\u0026thinsp;11 on SARC-Ca1F in Kinmen. These results suggest that the prevalence of sarcopenia among the elderly in Kinmen is lower than in other regions [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. While SARC-F demonstrated its value in predicting clinically significant outcomes, including functional impairment, hospitalization, poor quality of life, and mortality [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. SARC-CalF, with calf circumference cut-off values predicting low muscle mass at \u0026le;\u0026thinsp;34 cm in men and \u0026le;\u0026thinsp;33 cm in women, emerges as a more effective screening tool for sarcopenia in community-dwelling older adults compared to SARC-F and other criteria [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Future research is crucial to validate this estimate, not only considering the rapid health and sociodemographic changes underway in Kinmen associated with population aging but also due to the inadequacy of local medical resources.\u003c/p\u003e \u003cp\u003eSarcopenia and diminished physical performance are closely linked to the overall health of the elderly [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. We have observed an age-related increase in the prevalence of elders with SARC-Ca1F scores of ≧\u0026thinsp;11. Specifically, individuals aged 75 or older face a more than four times greater risk of having SARC-Ca1F scores\u0026thinsp;≧\u0026thinsp;11 compared to those under 70. It is crucial to devise effective interventions for preventing sarcopenia in individuals aged 75 and above.\u003c/p\u003e \u003cp\u003eWhile several studies have suggested that men are more susceptible to sarcopenia and tend to experience more substantial loss of muscle mass compared to women [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], our findings in Kinmen show a higher prevalence of SARC-Ca1F\u0026thinsp;≧\u0026thinsp;11 in females, although this difference did not reach statistical significance. Evaluating the gender effect on the distribution of sarcopenia among elders in Kinmen, and proposing effective strategies for prevention of sarcopenia progress is essential.\u003c/p\u003e \u003cp\u003eThe presented study identifies educational attainment as a determinant of potential sarcopenia when adjusted for other known risk factors. Older adults with fewer years of education faced an increased risk of potential sarcopenia compared to those with higher education [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. It is evident that individuals with higher educational levels possess a better understanding of health status and are more inclined to engage in health promotion activities. Designing effective health education programs for less educated elderly individuals could prove beneficial in preventing sarcopenia.\u003c/p\u003e \u003cp\u003eTo enhance the precision of this study and mitigate the impact of confounding variables, we performed an analysis of covariance. This approach helps eliminate the effects of extraneous sources of variance that might otherwise inflate the experimental error [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The research found a statistically significant difference in the distribution of marriage status and MNA scores among different age groups and educational levels (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The findings indicate an interaction between marital status and MNA scores concerning age groups and educational levels.\u003c/p\u003e \u003cp\u003eIt has been established that family function is significantly associated with sarcopenia [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In our study, after adjusting for age, education level, and health conditions, elders without a spouse demonstrated a higher likelihood of experiencing sarcopenia. Additionally, we observed that elders living with offspring have the lowest prevalence of sarcopenia compared to those living with a spouse or living alone; however, the difference did not reach statistical significance. When older adults experience a well-functioning family life or receive family support, their health functions contribute to good daily functioning and self-care ability [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], especially considering that, for the majority of Chinese elderly individuals, families constitute the primary source of social support a widely acknowledged determinant of health [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Subsequent studies should assess whether positive family functioning proves effective in influencing psychological outcomes, dietary behaviours, nutritional status, and reducing the risk of sarcopenia in the elderly.\u003c/p\u003e \u003cp\u003eOur research findings highlight a significant association between language preference and sarcopenia risk among elderly individuals in Kinmen. Specifically, those who primarily use Kinmenese as their language exhibit a considerably higher rate of abnormal SARC-Ca1F scores (\u0026gt;\u0026thinsp;11) compared to those who do not primarily use Kinmenese. This suggests that native Kinmenese elders face a higher risk of sarcopenia than their counterparts in the migrant population. Several studies have demonstrated variations in lean body mass composition and sarcopenia prevalence across different ethnic and racial groups [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, differences were observed in the prevalence of obesity, lack of leisure-time physical activity, and diagnosed diabetes among elderly individuals of different races [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Further research is needed to delve into the relationship between health behaviours and sarcopenia among diverse ethnic groups in Kinmen.\u003c/p\u003e \u003cp\u003eBeing overweight, even obese, is associated with better survival rates when compared to a low BMI, which may be related to better metabolic reserves [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. We found that the elders in Kinmen with a BMI\u0026thinsp;≧\u0026thinsp;27 had a significantly lower risk of SARC-Ca1F\u0026thinsp;≧\u0026thinsp;11 compared with those with a BMI between 22-23.99. Previous research ssuggests that obese individuals with a higher amount of fat mass may also have a greater lean mass, masking the inadequate muscle mass for their size and preserving lean muscle mass more effectively than those with a lower BMI [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. It is observed that BMI may serve as a suboptimal indicator of adiposity among older adults, as body composition undergoes changes during aging, marked by an increase in adiposity levels and a decrease in muscle mass [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Further investigation is needed to understand the mechanisms underlying the association between elevated body weight and the risk of sarcopenia.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe elderly population in Kinmen presents a distinct health profile, there are some significant factors related to sarcopenia in this area. The statistically significant association between this diminished prevalence and key factors, including younger age, higher educational levels, and a BMI exceeding 27, underscores the importance of considering demographic and lifestyle factors when evaluating sarcopenia prevalence in different regions. Recognizing the influence of age, education, and BMI on sarcopenia rates provides valuable insights for targeted interventions and healthcare strategies tailored to the unique characteristics of the elderly population in Kinmen.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003e The present study employs purposive sampling, and the research sample is drawn from community care centers in Kinmen with higher participation willingness. As a result, the generalizability of the research findings is limited and may not represent all elderly individuals aged 65 and above in Kinmen, as well as the elderly in other regions. Secondly, the study only measured the research sample in the year 2022, making it challenging to infer the long-term outcomes of the research sample. Thirdly, the analysis focused on the distribution of SARC-Ca1F scores in the elderly, and further exploration is required to investigate its correlation with sarcopenia.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics approval\u003c/h2\u003e \u003cp\u003e The study protocol was approved by the Medical Ethics Committee of National Cheng Kung University (IRB no. 111-406-2).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eAll authors contributed to the study conception and design. Methodology visualization, writing original draft preparation were performed by Ching-Sung Ho. Methodology visualization, writing reviewing and editing were performed by Shen-Ming Lee. Investigation, data curation, software, validation were performed by Meng-Chi Chen. Software, validation were performed by Chia-Ming Lin, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWilmoth JR et al (2023) World social report 2023: Leaving no one behind in an ageing world. UN\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDepartment of Information Services, Executive Yuan. White paper to outline comprehensive policies for aged society. 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Am J Public Health 95(5):825\u0026ndash;827. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2105/AJPH.2004.043489\u003c/span\u003e\u003cspan address=\"10.2105/AJPH.2004.043489\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSousa-Santos AR, Afonso C, Borges N, Santos A, Padr\u0026atilde;o P, Moreira P, Amaral TF (2019) Factors associated with sarcopenia and undernutrition in older adults. Nutr Dietetics 76(5):604\u0026ndash;612. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1747-0080.12542\u003c/span\u003e\u003cspan address=\"10.1111/1747-0080.12542\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantos ALM, d., Amaral TM d. S. P. F. d., Borges NP (2015) G. F. B. Undernutrition and associated factors in a Portuguese older adult community. \u003cem\u003eRevista de Nutri\u0026ccedil;\u0026atilde;o, 28\u003c/em\u003e, 231\u0026ndash;240\u003c/span\u003e \u003cspan\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/1415-52732015000300001\u003c/span\u003e\u003cspan address=\"10.1590/1415-52732015000300001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Distribution of sociodemographic factors and biological\u0026nbsp;indicators in\u0026nbsp;community-dwelling older adults of Kinmen\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"416\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\" valign=\"top\"\u003e\u003cbr\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e33.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e66.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eAge \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;74.53\u0026plusmn;7.02 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003e65-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e31.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003e70-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e28.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003e75-79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e18.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003e≧80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\" valign=\"top\"\u003e\n \u003cp\u003eEducational level \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003eilliteracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e37.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003e\u0026lt;=6 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e32.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003e≧7\u0026nbsp;years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e30.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\" valign=\"top\"\u003e\n \u003cp\u003eMarriage status \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003emarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e70.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003eunmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003edivorced/separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003ewidow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e26.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003ewith spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e70.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003esingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\" valign=\"top\"\u003e\n \u003cp\u003eLiving status \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003eliving with offspring\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003eliving with spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003eliving alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e16.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\" valign=\"top\"\u003e\n \u003cp\u003eCurrent using language \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003eKinmenese and Chinese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e35.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003eKinmenese only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e35.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003ethe Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e29.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eBMI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;24.62\u0026plusmn;3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003e\u0026lt;22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e24.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003e22-23.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e21.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003e24-26.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e31.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003e≧27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMNA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;26.67\u0026plusmn;2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003e≦23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003e≧24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e90.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eSARC-F\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003e\u0026lt;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e96.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003e≧4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eSARC-Ca1F\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003e\u0026lt;11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e85.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\"\u003e\n \u003cp\u003e≧11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.43269230769231%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.567307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Distribution of SARC-Ca1F in different factors.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.724550898203596%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.712574850299404%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSARC-Ca1F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.562874251497007%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.64940239043825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;11\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e≧11\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.52589641434263%\" valign=\"top\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.64940239043825%\" valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.52589641434263%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.210268948655255%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e92(89.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e11(10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.210268948655255%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e169(83.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e33(16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.64940239043825%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.52589641434263%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.64940239043825%\"\u003e\n \u003cp\u003e65-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e92(95.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e4(4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.52589641434263%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.64940239043825%\"\u003e\n \u003cp\u003e70-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e77(89.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e9(10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.52589641434263%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.64940239043825%\"\u003e\n \u003cp\u003e75-79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e46(80.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e11(19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.52589641434263%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.64940239043825%\"\u003e\n \u003cp\u003e≧80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e46(69.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e20(30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.52589641434263%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.64940239043825%\" valign=\"top\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.52589641434263%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.210268948655255%\"\u003e\n \u003cp\u003eilliteracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e86(75.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e28(24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.210268948655255%\"\u003e\n \u003cp\u003e\u0026lt;=6 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e89(90.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e9(9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.210268948655255%\"\u003e\n \u003cp\u003e≧7\u0026nbsp;years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e86(92.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e7(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.64940239043825%\" valign=\"top\"\u003e\n \u003cp\u003eMarriage status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.52589641434263%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.210268948655255%\"\u003e\n \u003cp\u003ewith spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e190(88.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e24(11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.64940239043825%\"\u003e\n \u003cp\u003esingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e71(78.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e20(22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.52589641434263%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.64940239043825%\" valign=\"top\"\u003e\n \u003cp\u003eLiving status \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.52589641434263%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.210268948655255%\"\u003e\n \u003cp\u003eliving with offspring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e181(87.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e25(12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.210268948655255%\"\u003e\n \u003cp\u003eliving with spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e41(82.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e9(18.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.210268948655255%\"\u003e\n \u003cp\u003eliving alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e39(79.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e10(20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.64940239043825%\" valign=\"top\"\u003e\n \u003cp\u003eCurrent using language \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.52589641434263%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.210268948655255%\"\u003e\n \u003cp\u003eKinmenese and Chinese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e92(86.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e15(14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.210268948655255%\"\u003e\n \u003cp\u003eKinmenese only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e81(75.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e26(24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.210268948655255%\"\u003e\n \u003cp\u003ethe Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e88(96.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.894865525672373%\" valign=\"top\"\u003e\n \u003cp\u003e3(3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.64940239043825%\" valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.52589641434263%\" valign=\"top\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.64940239043825%\"\u003e\n \u003cp\u003e\u0026lt;22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e61(81.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e14(18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.52589641434263%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.64940239043825%\"\u003e\n \u003cp\u003e22-23.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e51(79.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e13(20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.52589641434263%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.64940239043825%\"\u003e\n \u003cp\u003e24-26.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e83(85.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e14(14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.52589641434263%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.64940239043825%\"\u003e\n \u003cp\u003e≧27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e66(95.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e3(4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.52589641434263%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.64940239043825%\" valign=\"top\"\u003e\n \u003cp\u003eMNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.52589641434263%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.64940239043825%\"\u003e\n \u003cp\u003e≦23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e19(65.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e10(34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.52589641434263%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.64940239043825%\"\u003e\n \u003cp\u003e≧24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e242(87.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e34(12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.52589641434263%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.64940239043825%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e261(85.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91235059760956%\" valign=\"top\"\u003e\n \u003cp\u003e44(14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.52589641434263%\" 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\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Logistic regression analysis of SARC-Ca1F\u0026nbsp;in different factors.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"546\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"bottom\"\u003e\n \u003cp\u003eORs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"bottom\"\u003e\n \u003cp\u003e95% C.I.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003e65-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003e70-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003e2.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e0.736~9.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003e75-79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003e4.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e1.144~14.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003e≧80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003e4.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e1.360~17.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eEducational level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003eilliteracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003e3.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e1.025~9.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003e\u0026lt;=6 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003e1.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e0.453~4.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003e≧7\u0026nbsp;years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eMarriage status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003ewith spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003esingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003e1.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e0.737~3.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eCurrent using language\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003eKinmenese and Chinese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003e8.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e2.167~34.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003eKinmenese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003e6.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e1.803~24.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003ethe Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003e\u0026lt;22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e0.286~2.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003e22-23.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003e24-26.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003e0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e0.254~1.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003e≧27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e0.046~0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eMNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003e≦23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003e1.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e0.604~6.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003e≧24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e=0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 4. The relationship of MNA,\u0026nbsp;Marriage status and sociodemographic factors\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.6028880866426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.628158844765345%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.425992779783392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.342960288808664%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.65641952983725%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003e≦23.5\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;23.5\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.455696202531644%\" valign=\"top\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003cp\u003e(95% C.I.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.65641952983725%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.455696202531644%\" valign=\"top\"\u003e\n \u003cp\u003e0.375 (0.171~0.826)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.65641952983725%\"\u003e\n \u003cp\u003e65-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003e11(6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e171(94.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.455696202531644%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.65641952983725%\"\u003e\n \u003cp\u003e≧75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003e18(14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e105(85.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.455696202531644%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.65641952983725%\" valign=\"top\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.455696202531644%\" valign=\"top\"\u003e\n \u003cp\u003e2.614 (1.199~5.699)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.042735042735046%\"\u003e\n \u003cp\u003eilliteracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.435897435897434%\" valign=\"top\"\u003e\n \u003cp\u003e17(14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e97(85.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.444444444444443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.042735042735046%\"\u003e\n \u003cp\u003eliteracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.435897435897434%\" valign=\"top\"\u003e\n \u003cp\u003e12(6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e179(93.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.444444444444443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.6028880866426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.628158844765345%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMarriage status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.425992779783392%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.342960288808664%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.65641952983725%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003esingle\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003ewith spouse\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.455696202531644%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.65641952983725%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.455696202531644%\" valign=\"top\"\u003e\n \u003cp\u003e0.349 (0.201~0.578)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.65641952983725%\"\u003e\n \u003cp\u003e65-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003e38(20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e144(79.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.455696202531644%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.65641952983725%\"\u003e\n \u003cp\u003e≧75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003e53(43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e70(56.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.455696202531644%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.65641952983725%\" valign=\"top\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.455696202531644%\" valign=\"top\"\u003e\n \u003cp\u003e2.195 (1.329~3.625)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.370705244122966%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.042735042735046%\"\u003e\n \u003cp\u003eilliteracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.435897435897434%\" valign=\"top\"\u003e\n \u003cp\u003e46(40.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e68(59.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.444444444444443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.042735042735046%\"\u003e\n \u003cp\u003eliteracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.435897435897434%\" valign=\"top\"\u003e\n \u003cp\u003e45(23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e146(76.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.444444444444443%\" 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\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5. Logistic regression analysis of SARC-Ca1F\u0026nbsp;in different factors.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"546\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"bottom\"\u003e\n \u003cp\u003eORs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"bottom\"\u003e\n \u003cp\u003e95% C.I.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eMarriage status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003ewith spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003esingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003e2.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e1.122~4.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eCurrent using language\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003eKinmenese and Chinese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003e5.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e1.611~21.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003eKinmenese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003e9.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e2.668~32.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003ethe Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003e\u0026lt;22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e0.266~1.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003e22-23.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003e24-26.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e0.306~1.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003e≧27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e0.045~0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eMNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003e≦23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003e2.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e0.998~8.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003e≧24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36928702010969%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.012797074954296%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.250457038391225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.36745886654479%\" valign=\"top\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e=0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"SARC-F, SARC-CalF, sarcopenia, Kinmen, elders","lastPublishedDoi":"10.21203/rs.3.rs-4169906/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4169906/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study aims to assess the distribution of SARC-F and SARC-CalF scores and identify associated risk factors among the elderly in Kinmen.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA community-based cross-sectional study was conducted in Kinmen in 2022 at the community care stations. The sample consisted of 305 individuals aged over 65. The outcome variable was the SARC-Ca1F score, which was categorized as normal (\u0026lt;\u0026thinsp;11) or abnormal (\u0026thinsp;≧\u0026thinsp;11). Chi-square, and binary logistic regression analysis were conducted for inferential statistical analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 3.9% exhibited abnormal SARC-F values (\u0026thinsp;≧\u0026thinsp;4), and 14.4% had abnormal SARC-Ca1F values (\u0026thinsp;≧\u0026thinsp;11). Younger age, higher educational level, and BMI\u0026thinsp;≧\u0026thinsp;27 were independently associated with a lower abnormal rate of SARC-Ca1F scores in older adults in Kinmen. When considering the interaction effect between MNA scores, marital status, age and educational level, it was observed that single elders showed a higher abnormal rate of SARC-Ca1F values (OR\u0026thinsp;=\u0026thinsp;2.299, p\u0026thinsp;=\u0026thinsp;0.023). Those using Kinmenese and Chinese or solely Kinmenese had a higher abnormal rate of SARC-Ca1F values compared to others, (OR\u0026thinsp;=\u0026thinsp;5.902 and OR\u0026thinsp;=\u0026thinsp;9.341, respectively). Individuals with a BMI\u0026thinsp;≧\u0026thinsp;27 exhibited a significantly lower abnormal rate of SARC-Ca1F values compared to those with a BMI falling between 22-23.99, (OR\u0026thinsp;=\u0026thinsp;0.174).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAmong the elderly population in Kinmen, individuals with younger age, higher education levels, and BMI\u0026thinsp;≧\u0026thinsp;27 exhibit a lower prevalence of sarcopenia. Conversely, individuals who are single or use Kinmenese as their native language show a higher likelihood of developing sarcopenia, highlighting unique demographic influences.\u003c/p\u003e","manuscriptTitle":"The Distribution and Associated Factors of SARC-F and SARC-CalF in Community-Dwelling Older Adults of Kinmen","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-08 18:31:16","doi":"10.21203/rs.3.rs-4169906/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"917c83ae-ca62-451e-97a7-6f77f47e0287","owner":[],"postedDate":"April 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-30T10:05:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-08 18:31:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4169906","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4169906","identity":"rs-4169906","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}