Accuracy of determining gait independence using adductor pollicis muscle thickness and skeletal muscle mass index in community-dwelling older adults undergoing outpatient rehabilitation

Accuracy of determining gait independence using adductor pollicis muscle thickness and skeletal muscle mass index in community-dwelling older adults undergoing outpatient rehabilitation | 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 Accuracy of determining gait independence using adductor pollicis muscle thickness and skeletal muscle mass index in community-dwelling older adults undergoing outpatient rehabilitation Taisei Ishimoto, Takehiro Fujimoto, Ken Hisamatsu, Nozomi Matsudaira, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4988908/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective The accuracy of determining gait independence using adductor pollicis muscle thickness (APMT) and skeletal muscle mass index (SMI) in community-dwelling older adults undergoing outpatient rehabilitation remains unclear. The purpose of this study was to compare the accuracy of determining gait independence using APMT and SMI in community-dwelling older adults undergoing outpatient rehabilitation. Methods This study included 98 older adults (mean age: 85.2 years). Subjects were received outpatient rehabilitation one to three times a week. The main outcomes were gait independence (functional independence measure gait score: 6 or 7), skeletal muscle mass index (SMI), and APMT. A receiver operating characteristic (ROC) curve of APMT and SMI for gait independence were created, and a cut-off value were calculated using the Youden index. Additionally, the area under the curve (AUC) s of the APMT model and the SMI model were compared using the Delong test. Results Among the 98 subjects, 53 (54.1%) were in the gait independent group. The cut-off value of APMT calculated from the ROC curve was 13mm; the sensitivity and specificity were 67.9% and 86.7%, respectively; and the AUC was 0.800. The cut-off value of SMI calculated from the ROC curve was 4.6kg/m 2 ; the sensitivity and specificity were 90.6% and 26.7%, respectively; and the AUC was 0.582. The AUC for the APMT model was significantly higher than the SMI model ( P < 0.001). Conclusions The results of this study show that the cut-off value of APMT for determining the gait independence was 13 mm. In addition, APMT had a higher accuracy of determining gait independence than SMI. This indicates that measuring APMT is more useful for predicting gait independence than SMI in community-dwelling older adults undergoing outpatient rehabilitation. gait independence adductor pollicis muscle thickness skeletal muscle mass index community-dwelling older adults outpatient rehabilitation Figures Figure 1 Figure 2 Figure 3 Key summary points Aim To compare the accuracy of determining gait independence using adductor pollicis muscle thickness (APMT) and skeletal muscle mass index (SMI) in community-dwelling older adults undergoing outpatient rehabilitation. Findings The results of this study show that the cut-off value of APMT for determining the gait independence was 13 mm. In addition, APMT had a higher accuracy of determining gait independence than SMI. Message APMT is more useful for predicting gait independence in community-dwelling older adults undergoing outpatient rehabilitation. On the other hand, SMI may not be useful for predicting gait independence in community-dwelling older adults undergoing outpatient rehabilitation. Introduction Loss of skeletal muscle mass with aging has been investigated in many studies, which have reported an association with increased mortality and readmission rates [1.2]. Additionally, previous studies indicated that older adults who were not able to walk independently have less quadriceps muscle mass than similarly aged people who were able to walk independently [ 3 ]. Therefore, measuring muscle mass is important to improve gait independence in older adults. The standard for measuring muscle mass is the skeletal muscle mass index (SMI) [ 4 ], which is associated with clinical outcomes [ 5 – 8 ]. On the other hand, there are some reports that show no relationship between SMI and physical function or mortality [ 9 – 13 ]. In addition, bioimpedance analysis (BIA) equipment to measure SMI is expensive and may not be available at all facilities. In the absence of BIA equipment, calf circumference is recommended for screening for sarcopenia [ 4 ]. However, in older adults, there are reports that low skeletal muscle mass cannot be determined from the calf circumference alone because the calf circumference is easily overestimated due to the amount of subcutaneous fat and edema [14.15]. In recent years, adductor pollicis muscle thickness (APMT) has attracted attention as an anthropometric measurement that reflects muscle mass [ 16 – 18 ]. APMT is a measurement that is less likely to overestimate muscle mass because there is little subcutaneous fat at the measurement site [ 16 – 18 ]. APMT is associated with grip strength, nutritional status, low SMI, sarcopenia, and mortality [ 19 – 25 ]. Based on the results of previous studies on SMI and APMT [ 9 – 13 , 16 – 18 ], we speculate that APMT will have a higher accuracy in determining gait independence than SMI. However, there are no studies comparing the accuracy of predicting gait independence using APMT and SMI. If it turns out that APMT is a better predictive indicator than SMI for predicting gait independence in older adults, it will be clinically meaningful because APMT is inexpensive and easy to measure. The purpose of this study was to compare the accuracy of determining gait independence using the APMT and SMI in community-dwelling older adults undergoing outpatient rehabilitation. Materials and Methods Study design and subjects This study was a cross-sectional study design. The individuals involved in this study were older adults undergoing outpatient rehabilitation at Akahige Clinic. Subjects have chronic condition and are undergoing rehabilitation to improve their physical function and activities of daily living. Subjects who were under 65 years of age and those with flawed data were excluded. Out of the total 131 subjects, 98 were chosen for the study. The remaining 33 subjects were excluded because they did not meet the age requirement (2 subjects), or had data defects (31 subjects). Subjects were provided outpatient rehabilitation one to three times per week. All subjects or their guardians provided informed consent before the study, and the study was approved by the ethics committee of our institution. Outcome measurement Primary outcomes were gait independence, APMT, and SMI. In addition, age, height, body weight, body mass index (BMI), number of medications, comorbidities, and swallowing function were evaluated. Assessment of gait independent Gait independence was assessed using the functional independence measure (FIM) gait score [ 25 ]. FIM gait score ranged from 1 to 7 as follows: Total assistance as score 1, maximal assistance as score 2, moderate assistance as score 3, minimal assistance as score 4, supervision or monitoring as score 5, modified independence as score 6, complete independence as score 7. Subjects with FIM gait score of 6 or 7 were gait independent group [ 27 ] and those with FIM gait score of 1 to 5 were non-gait independent group. APMT measurement The APTM was assessed to measure muscle mass. Subjects sat with their hand on the table (with the elbow to be flexed approximately ,90-degree angle) [ 19 ]. The Lange caliper (Lange, Santa Cruz, CA, USA) was used to pinch the adductor pollicis muscle in the vertex of an imaginary triangle formed by extension of thumb and index finger muscle with a continuous pressure of 10 g/mm 2 [ 28 ]. The maximum value of three measurements was used as the measured value [ 16 , 29 ]. Additionally, APMT was measured on both sides and the maximum value was used for analysis. SMI measurement Skeletal muscle mass was evaluated using the BIA method (InBody 470, InBody Japan, Japan), and the SMI was calculated by dividing the limb skeletal muscle mass by the square of height [ 5 ]. Other assessment BMI was calculated by dividing the weight (kg) by the height squared (m 2 ). Body weight was measured using a digital scale in the standing position and recorded in 0.1 kg increments, while height was measured in the standing position using a height scale and recorded in 0.5 cm increments. Comorbidities were assessed using the updated Charlson Comorbidity Index (UCCI) [ 30 ]. Comorbidities assessed by UCCI have been reported to be associated with mortality in various diseases [ 31 , 32 ]. The UCCI valuates 12 conditions related to chronic diseases, including chronic lung disease, rheumatic diseases, diabetes with chronic complications, renal disease, congestive heart failure, dementia, mild liver disease, hemiplegia or paraplegia, malignant tumors including leukemia and lymphoma as score 1, moderate to severe liver disease, AIDS/HIV as score 4, and metastatic solid tumors as score 6, which are then combined to form a score [ 30 ]. The swallowing function was evaluated using the Food Intake Level Scale (FILS), a 10-point scale that assesses the patient's oral intake status [ 33 ]. Swallowing function was selected as a measurement because it is related to skeletal muscle mass [ 34 , 35 ]. Statistical analysis All statistical analyses were performed using Easy R. Variables were assessed for normality using the Shapiro–Wilk test. An unpaired t-test, or Mann–Whitney U test was performed to compare the outcomes between the gait independent group and non-gait independent group. Multiple regression analysis using the forced entry method was performed with FIM gait score as the dependent variable. APMT, SMI, BMI, FILS, age, sex, UCCI, and number of medications were included as independent variables. In this study, APMT and SMI were entered into separate multiple regression models. To assess multicollinearity, we used the variance inflation factor. A variance inflation factor value of more than 10 was considered as the presence of multicollinearity. Logistic regression analysis using the forced entry method was performed with gait independence as the objective variable. APMT, SMI, BMI, FILS, age, sex, UCCI, and number of medications were included as independent variables. In this study, APMT and SMI were entered into separate logistic regression models. Additionally, the sample size for the gait independent group was 45 subjects, so APMT, BMI, FILS, and propensity score were used as independent variables in the logistic regression analysis [ 24 ]. The propensity score was calculated using age, UCCI, and number of medications. In this study, gait independent group was coded as 0, and the gait dependent group was coded as 1. A receiver operating characteristic (ROC) curve of APMT and SMI for gait independence were created, and cut-off values were calculated using the Youden index. Additionally, the AUCs of the APMT model and the SMI model were compared using the Delong test. Statistical significance was set at P < 0.05. In addition, we calculated the effect size ( f 2 ) for multiple regression analysis using the following equation: R 2 ∕ (1 - R 2 ) [ 36 ]. The statistical power of the analysis based on f 2 , an alpha error of 0.05, total sample size, and the number of predictor variables was calculated using G*Power version 3.1.9.2 (Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany). Results Among the 98 subjects, 53 (54.1%) were in the gait independent group. The mean ages of gait independent group and non-gait independent group were 83.8 and 86.9 years, respectively. Table 1 shows the measurement data of the subjects and the results of the comparison between the gait independent and non-gait independent groups. A result of comparison between both groups showed significant differences in age ( P = 0.01), body weight ( P < 0.001), BMI ( P < 0.001), UCCI ( P = 0.02), FILS ( P < 0.001), FIM gait score ( P < 0.001), APMT ( P < 0.001). Table 2 shows the results of multiple regression analysis of the APMT entry model with FIM gait score as the dependent variable. APMT ( β = 0.362, P < 0.01) and number of medication ( β = -0.207, P < 0.01) were significantly associated with FIM gait score ( R 2 = 0.338, f 2 = 0.510, statistical power = 1.000). Table 3 shows the results of multiple regression analysis of the SMI entry model with FIM gait score as the dependent variable. Age ( β = -0.228, P = 0.01), BMI ( β = 0.257, P < 0.01), number of medication ( β = -0.240, P < 0.01), FILS ( β = 0.188, P = 0.04) were significantly associated with FIM gait score ( R 2 = 0.272, f 2 = 0.374, statistical power = 0.999). SMI showed no significant association with FIM gait score ( β = -0.079, P = 0.41). Table 4 shows the results of logistic regression analysis of the APMT entry model with gait independence as the dependent variable. APMT (odds ratio = 0.712, P = 0.02) was significantly associated with gait independence. Table 5 shows the results of logistic regression analysis of the SMI entry model with gait independence as the dependent variable. BMI (odds ratio = 0.744, P < 0.01) was significantly associated with gait independence. SMI showed no significant association with gait independence (odds ratio = 1.500, P = 0.18). No multicollinearity between variables was observed in all multivariate analyses. The cut-off value of APMT calculated from the ROC curve was 13mm; the sensitivity and specificity were 67.9% and 86.7%, respectively; and the AUC was 0.800 (Table 6 , Fig. 1). The cut-off value of SMI calculated from the ROC curve was 4.6kg/m 2 ; the sensitivity and specificity were 90.6% and 26.7%, respectively; and the AUC was 0.582 (Table 6 , Fig. 2). The results of comparing the AUCs for the APMT and SMI models are shown in Fig. 3. The AUCs for the APMT and SMI models were 0.800 (95% CI: 0.713–0.887) and 0.582 (95% CI: 0.468–0.697), respectively (Fig. 3). The AUC for the APMT model was significantly higher than the SMI model ( P < 0.001) (Fig. 3). Table 1 Participants characteristics and comparison results between the gait independent and non-gait independent groups Total (n = 98) gait independent group (n = 53) Non-gait independent group (n = 45) P -value 95%CI Age (years), mean (SD) 85.2 (6.3) 83.8 (6.3) 86.9 (5.9) 0.01 a -5.5 to -0.6 Height (m), mean (SD) 1.49 (1.44–1.55) 1.48 (1.44–1.56) 1.49 (1.44–1.55) 0.78 b - Body weight (kg), median (IQR) 51.2 (10.1) 54.4 (10.3) 47.4 (8.6) < 0.001 a 3.2–10.8 BMI (kg/m 2 ), median (IQR) 22.8 (3.9) 24 (3.7) 21.3 (2.8) < 0.001 a 1.4–4.1 Number of medications, median (IQR) 5 (4–8) 5 (4–7) 7 (4–10) 0.21 b - UCCI, median (IQR) 2 (0–2) 0 (0–2) 2 (0–2) 0.02 b - FILS, median (IQR) 10 (10–10) 10 (10–10) 10 (10–10) < 0.001 b - FIM gait score, mean (SD) 6 (5–6) 6 (6–7) 5 (5–5) < 0.001 b - APMT (mm), median (IQR) 13 (12–15) 14 (13–16) 12 (11–13) < 0.001 b - SMI (kg/m 2 ), median (IQR) 5.6 (5–6.2) 5.7 (5.0–6.4) 5.5 (4.6–6.1) 0.16 b - APMT, adductor pollicis muscle thickness; BMI, body mass index; CI, confidence interval; FILS, Food Intake Level Scale; IQR, interquartile range; SD, standard deviation; SMI, skeletal muscle mass index; UCCI, updated Charlson Comorbidity Index. a Unpaired t-test, b Mann-Whitney test. Table 2 Results of multiple regression analysis with FIM gait score as the dependent variable (APMT entry model). B SE β VIF P -value Age -0.021 0.012 -0.152 1.189 0.09 Sex 0.233 0.214 0.105 1.361 0.28 BMI 0.020 0.025 0.085 1.501 0.40 Number of medications -0.048 0.019 -0.207 1.045 0.01 UCCI -0.084 0.056 -0.133 1.166 0.14 FILS 0.205 0.152 0.120 1.170 0.18 APMT 0.118 0.038 0.362 1.993 < 0.01 APMT, adductor pollicis muscle thickness; B, partial regression coefficient; BMI, body mass index; FILS, Food Intake Level Scale; SE, standard error; UCCI, update Charlson Comorbidity Index; VIF, variance inflation factor. Table 3 Results of multiple regression analysis with FIM gait score as the dependent variable (SMI entry model). B SE β VIF P -value Age -0.032 0.012 -0.228 1.114 0.01 Sex -0.078 0.217 -0.035 1.269 0.72 BMI 0.063 0.023 0.257 1.153 < 0.01 Number of medications -0.056 0.020 -0.240 1.028 < 0.01 UCCI -0.112 0.059 -0.176 1.145 0.06 FILS 0.321 0.157 0.188 1.129 0.04 SMI -0.025 0.030 -0.079 1.217 0.41 B, partial regression coefficient; BMI, body mass index; FILS, Food Intake Level Scale; SE, standard error; SMI, skeletal muscle mass index; UCCI, update Charlson Comorbidity Index; VIF, variance inflation factor. Table 4 Results of logistic regression analysis with gait independence as the objective variable (APMT entry model). B SE OR 95%CI VIF P -value APMT -0.340 0.151 0.712 0.529–0.957 1.214 0.02 BMI -0.125 0.090 0.883 0.740–1.050 1.178 0.16 FILS -0.985 0.798 0.373 0.078–1.790 1.025 0.22 Odds ratio was adjusted by propensity score calculated using age, sex, updated Charlson comorbidity index, and number of medications. APMT, adductor pollicis muscle thickness; B, partial regression coefficient; BMI, body mass index; CI, confidence interval; FILS, food intake level scale; OR, odds ratio; SE, standard error; VIF, variance inflation factor. Table 5 Results of logistic regression analysis with gait independence as the objective variable (SMI entry model). B SE OR 95%CI VIF P -value SMI 0.406 0.303 1.500 0.828–2.720 1.411 0.18 BMI -0.295 0.100 0.744 0.612–0.905 1.389 < 0.01 FILS -1.347 0.756 0.260 0.059–1.140 1.034 0.07 Odds ratio was adjusted by propensity score calculated using age, sex, updated Charlson comorbidity index, and number of medications. B, partial regression coefficient; BMI, body mass index; CI, confidence interval; FILS, food intake level scale; OR, odds ratio; SE, standard error; SMI, skeletal muscle mass index; VIF, variance inflation factor. Table 6 Gait independence cut-off values and determination accuracy in APMT and SMI. Cut-off Sensitivity, % Specificity, % PPV, % NPV, % +LR -LR AUC (95%CI) APMT 13mm 67.9 86.7 81.2 76.1 5.11 0.37 0.800 (0.713–0.887) SMI 4.6kg/m 2 90.6 26.7 51.2 77.0 1.24 0.35 0.582 (0.468–0.697) APMT, adductor pollicis muscle thickness; AUC, area under the curve; CI, confidence interval; LR, likelihood ratio; NPV, negative predict value; PPV, positive predict value; SMI, skeletal muscle mass index. Discussion This is the first study to compare the accuracy of APMT and SMI in determining gait independence in community-dwelling older adults undergoing outpatient rehabilitation. The results showed that APMT had higher accuracy in determining gait independence than SMI. The cut-off value for APMT to determine the gait independence was 13 mm; the sensitivity and specificity were 67.9% and 86.7%, respectively; and the AUC was 0.800. A previous study has reported that the cut-off value for determining low SMI using APMT is 13 mm [ 24 ]. Considering the results of a previous study [ 24 ] and our study, the cut-off value in this study is appropriate. APMT is a measurement that is less likely to overestimate muscle mass because there is low subcutaneous fat at the measurement site [ 16 – 18 ]. The fact that APMT contains low subcutaneous fat and is less likely to overestimate muscle mass may be a factor in the high accuracy of APMT in determining gait independence. On the other hand, the cut-off value for SMI to determine the gait independence was 4.6kg/m 2 ; the sensitivity and specificity were 90.6% and 26.7%, respectively; and the AUC was 0.582. A previous study has reported that SMI in older hospitalized patients does not predict gait independence at discharge [ 37 ]. Muscle mass measurement using the SMI overestimates muscle mass in older adults with edema [ 38 ]. Furthermore, among community-dwelling older adults with an average age of 77.6 years, 26% had an extracellular water (ECW)-to-total body water (TBW) ratio of 0.4 or higher [ 39 ]. ECW/TBW ratio is used as an edema index, and ECW/TBW ratio of 0.4 or higher indicating edema [ 40 ]. Considering that ECW/TBW ratio increases with age [ 41 ] and that the subjects in this study (average age 85.2 years) were older than those in a previous study [ 39 ], it is possible that more subjects had edema compared with a previous study [ 39 ]. Based on findings of edema and ECW/TBW ratio in older adults [ 38 – 41 ], we speculate that the low accuracy of the SMI in determining gait independence in this study is due to SMI overestimating muscle mass. The SMI overestimates muscle mass in the same way that calf circumference easily overestimates due to subcutaneous fat and edema [14.15]. Therefore, like calf circumference, SMI may not be suitable for measuring muscle mass in some subjects. However, in this study, ECW/TBW ratio was not measured. Therefore, in the future, we will conduct a more detailed investigation by measuring ECW/TBW ratio. This study has two strengths. First, this is the first study to compare the accuracy of determining gait independence using the APMT and SMI in community-dwelling older adults undergoing outpatient rehabilitation. As a result, APMT had significantly higher accuracy in determining gait independence than SMI. APMT is a useful measurement that reflects gait independence in community-dwelling older adults undergoing outpatient rehabilitation. Second, no significant relationship was found between SMI and gait independence. SMI may not be a useful measurement that reflects gait independence in community-dwelling older adults undergoing outpatient rehabilitation. The finding that APMT is more strongly related to gait independence than SMI is clinically significant as it can be measured without expensive equipment such as BIA. Furthermore, APMT measurement is simple and easy to incorporate into clinical practice. This study has three limitations. First, the subjects in this study received outpatient rehabilitation one to three times a week to ensure a regular exercise routine. Therefore, the results of this study may differ if older adults not receiving outpatient rehabilitation were included. Second, we mentioned the possibility of edema in the subjects as a factor that did not show a relationship between SMI and gait independence. However, this study did not measure ECW/TBW ratio, which is an indicator of edema. In future study, we should conduct a more detailed investigation by measuring ECW/TBW ratio. Specifically, the question is whether the relationship between APMT and gait independence is affected by the ECW/TBW ratio. If the relationship between APMT and gait independence is not affected by the ECW/TBW ratio, then APMT would be an easier muscle mass measurement method to use in clinical practice than SMI. Third, in this study, there were few male subjects, so we were unable to calculate cut-off values ​​for APMT and SMI separately for male and female. Therefore, it may not be appropriate to use the cut-off values ​​in this study in clinical settings. However, it is important to clarify that APMT is a measurement that reflects gait independence better than SMI in community-dwelling older adults undergoing outpatient rehabilitation. In the future, it will be necessary to increase the sample size and calculate cut-off values ​​separately for male and female. Conclusions The results of this study show that APMT had a higher accuracy of determining gait independence than SMI in community-dwelling older adults undergoing outpatient rehabilitation. This indicates that measuring APMT is more useful for predicting gait independence than SMI in community-dwelling older adults undergoing outpatient rehabilitation. Declarations Conflict of Interest Statement The authors declare that there are no conflicts of interest. Funding details This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Acknowledgments We thank the subjects and staff who helped with this study. This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors. Statement of Authorship The conception and design of the study, acquisition of data, or analysis and interpretation of data; TI, TF, KH, NM, HH, RH, YT, and NA. Drafting the article: TI, TF, KH, NM, HH, RH, YT, and NA. Final approval of the version to be submitted; TI, TF, KH, NM, HH, RH, YT, and NA. 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Ther Adv Neurol Disord 15:17562864221140180 Dos Reis AS, Santos HO, Limirio LS, de Oliveira EP (2018) Adductor pollicis muscle thickness has a low association with muscle mass and lean mass in kidney transplantation patients. Clin Nutr ESPEN 28:110–113 Gonzalez MC, Duarte RR, Budziareck MB (2010) Adductor pollicis muscle: reference values of its thickness in a healthy population. Clin Nutr 29(2):268–271 Quan H, Li B, Couris CM, Fushimi K, Graham P, Hider P et al (2011) Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol 173(6):676–682 Grosso G, di Francesco F, Vizzini G, Mistretta A, Pagano D, Echeverri GJ et al (2012) The Charlson comorbidity index as a predictor of outcomes in liver transplantation: single-center experience. Transpl Proc 44(5):1298–1302 Di Donato V, D'Oria O, Giannini A, Bogani G, Fischetti M, Santangelo G et al (2022) Age-Adjusted Charlson Comorbidity Index Predicts Survival in Endometrial Cancer Patients. Gynecol Obstet Invest 87(3–4):191–199 Nishida T, Yamabe K, Honda S (2020) Dysphagia is associated with oral, physical, cognitive and psychological frailty in Japanese community-dwelling elderly persons. Gerodontology 37(2):185–190 Maeda K, Takaki M, Akagi J (2017) Decreased Skeletal Muscle Mass and Risk Factors of Sarcopenic Dysphagia: A Prospective Observational Cohort Study. J Gerontol Biol Sci Med Sci 72(9):1290–1294 Wakabayashi H, Matsushima M, Uwano R, Watanabe N, Oritsu H, Shimizu Y (2015) Skeletal muscle mass is associated with severe dysphagia in cancer patients. J Cachexia Sarcopenia Muscle 6(4):351–357 Selya AS, Rose JS, Dierker LC, Hedeker D, Mermelstein RJ (2012) A practical guide to calculating Cohen’s f 2 , a measure of local effect size, from PROC MIXED. Front Psychol 3:111 Kurita M, Fujita T, Kasahara R, Ohira Y, Otsuki K, Yamamoto Y (2021) Cutoff Value for a Nutritional Indicator Related to Gait Independence in Elderly Fracture Patients: A Preliminary Study. Phys Ther Res 25(1):26–30 Chamney PW, Wabel P, Moissl UM, Müller MJ, Bosy-Westphal A, Korth O et al (2007) A whole-body model to distinguish excess fluid from the hydration of major body tissues. Am J Clin Nutr 85(1):80–89 Hioka A, Akazawa N, Okawa N, Nagahiro S (2022) Extracellular water-to-total body water ratio is an essential confounding factor in bioelectrical impedance analysis for sarcopenia diagnosis in women. Eur Geriatr Med 13(4):789–794 Nishimura N, Hori S, Tomizawa M, Yoneda T, Nakai Y, Miyake M et al (2022) Relevance of the perioperative edema index measured by bioelectrical impedance analysis for prediction of cardiovascular disease in living-donor kidney transplantation. Int J Urol 29(4):309–316 Hioka A, Akazawa N, Okawa N, Nagahiro S (2024) Influence of aging on extracellular water-to-total body water ratio in community-dwelling females. Clin Nutr ESPEN 60:73–78 Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revisions 07 Nov, 2024 Reviewers agreed at journal 08 Sep, 2024 Reviewers invited by journal 03 Sep, 2024 Editor assigned by journal 30 Aug, 2024 First submitted to journal 28 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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-4988908","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":348906248,"identity":"acf86952-99d3-4867-b067-eb2b01c14cea","order_by":0,"name":"Taisei Ishimoto","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYJAC5h8MDHL8DAxsMIEEwlqAaowlG0jVkrjhAEILfqDb3p34uPCHnbHxjeRnDz5UMMgDXfjsAT4tZmfObjaekZAsZ3YjzdxwxhkGw5kNDOkGeLXcyN0mwZPAbGx2I8FMmreNIcHgAEOaBBFa6hM3z0j/RrwWaZ6Ew4kbJHKItQXoF8MZaceNJc68KZOccUbCcGYzIb8c79344INNtRx/e/o2iQ8VNvL87D1pD/BpQQCBBBAJdBIzTxpxOhj4D8BY7MeI1DIKRsEoGAUjBAAA2PhIed/jTjgAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-8385-6055","institution":"Akahige clinic","correspondingAuthor":true,"prefix":"","firstName":"Taisei","middleName":"","lastName":"Ishimoto","suffix":""},{"id":348906249,"identity":"1cc21f5a-8702-41e3-b127-2036f37b107d","order_by":1,"name":"Takehiro Fujimoto","email":"","orcid":"","institution":"Akahige clinic","correspondingAuthor":false,"prefix":"","firstName":"Takehiro","middleName":"","lastName":"Fujimoto","suffix":""},{"id":348906250,"identity":"3ff1dcee-650d-4a27-a7d2-373ddb96d305","order_by":2,"name":"Ken Hisamatsu","email":"","orcid":"","institution":"Akahige clinic","correspondingAuthor":false,"prefix":"","firstName":"Ken","middleName":"","lastName":"Hisamatsu","suffix":""},{"id":348906251,"identity":"d415b4ba-8cae-4051-ae6c-5436309573d8","order_by":3,"name":"Nozomi Matsudaira","email":"","orcid":"","institution":"Akahige clinic","correspondingAuthor":false,"prefix":"","firstName":"Nozomi","middleName":"","lastName":"Matsudaira","suffix":""},{"id":348906252,"identity":"43f99487-a199-4773-937c-e53d2da57635","order_by":4,"name":"Hikaru Hayashi","email":"","orcid":"","institution":"Akahige clinic","correspondingAuthor":false,"prefix":"","firstName":"Hikaru","middleName":"","lastName":"Hayashi","suffix":""},{"id":348906253,"identity":"5a423f57-c9b5-447f-af75-729e9272a02c","order_by":5,"name":"Risako Hashimoto","email":"","orcid":"","institution":"Akahige clinic","correspondingAuthor":false,"prefix":"","firstName":"Risako","middleName":"","lastName":"Hashimoto","suffix":""},{"id":348906254,"identity":"abc20d81-6ddb-4cef-83a8-fb750b4a2bc5","order_by":6,"name":"Yoshio Toyota","email":"","orcid":"","institution":"Akahige clinic","correspondingAuthor":false,"prefix":"","firstName":"Yoshio","middleName":"","lastName":"Toyota","suffix":""},{"id":348906255,"identity":"b393cd55-b368-4149-9e3e-537c2e1b75f3","order_by":7,"name":"Naoki Akazawa","email":"","orcid":"","institution":"Nagoya University Graduate School of Medicine Faculty of Medicine: Nagoya Daigaku Daigakuin Igakukei Kenkyuka Igakubu","correspondingAuthor":false,"prefix":"","firstName":"Naoki","middleName":"","lastName":"Akazawa","suffix":""}],"badges":[],"createdAt":"2024-08-28 07:12:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4988908/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4988908/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65914627,"identity":"aeef07d5-b4e3-4a1d-9b4f-741b1b6dfe03","added_by":"auto","created_at":"2024-10-04 10:25:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":614899,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curve: Diagnostic performance of gait independence using adductor pollicis muscle thickness.\u003c/p\u003e","description":"","filename":"figure1APMTgait.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4988908/v1/4aec6510721aa33ea2dd3c58.jpg"},{"id":65914630,"identity":"feddca64-0fc1-4109-994a-5da964b10cf6","added_by":"auto","created_at":"2024-10-04 10:25:28","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":616384,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curve: Diagnostic performance of gait independence using skeletal muscle mass index.\u003c/p\u003e","description":"","filename":"figure2APMTgait.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4988908/v1/bdbdfb8591b024dde32f01e4.jpg"},{"id":65914817,"identity":"5a861e4c-8c07-4393-8d77-a2090cf00832","added_by":"auto","created_at":"2024-10-04 10:33:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":619496,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of receiver operating characteristic curves using Delong test\u003c/p\u003e","description":"","filename":"figure3APMTgait.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4988908/v1/f55e48ae010a77c7f4225876.jpg"},{"id":65915812,"identity":"3cfbf7ab-b91f-4405-82d7-04a71857b25c","added_by":"auto","created_at":"2024-10-04 10:41:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":697051,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4988908/v1/27f6e09e-93e6-4417-9dd1-244aeee68e5c.pdf"}],"financialInterests":"","formattedTitle":"Accuracy of determining gait independence using adductor pollicis muscle thickness and skeletal muscle mass index in community-dwelling older adults undergoing outpatient rehabilitation","fulltext":[{"header":"Key summary points","content":"\u003cp\u003e\u003cstrong\u003eAim\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo compare the accuracy of determining gait independence using adductor pollicis muscle thickness (APMT) and skeletal muscle mass index (SMI) in community-dwelling older adults undergoing outpatient rehabilitation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of this study show that the cut-off value of APMT for determining the gait independence was 13 mm. In addition, APMT had a higher accuracy of determining gait independence than SMI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMessage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAPMT is more useful for predicting gait independence in community-dwelling older adults undergoing outpatient rehabilitation. On the other hand, SMI may not be useful for predicting gait independence in community-dwelling older adults undergoing outpatient rehabilitation.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eLoss of skeletal muscle mass with aging has been investigated in many studies, which have reported an association with increased mortality and readmission rates [1.2]. Additionally, previous studies indicated that older adults who were not able to walk independently have less quadriceps muscle mass than similarly aged people who were able to walk independently [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, measuring muscle mass is important to improve gait independence in older adults.\u003c/p\u003e \u003cp\u003eThe standard for measuring muscle mass is the skeletal muscle mass index (SMI) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], which is associated with clinical outcomes [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. On the other hand, there are some reports that show no relationship between SMI and physical function or mortality [\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition, bioimpedance analysis (BIA) equipment to measure SMI is expensive and may not be available at all facilities. In the absence of BIA equipment, calf circumference is recommended for screening for sarcopenia [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, in older adults, there are reports that low skeletal muscle mass cannot be determined from the calf circumference alone because the calf circumference is easily overestimated due to the amount of subcutaneous fat and edema [14.15]. In recent years, adductor pollicis muscle thickness (APMT) has attracted attention as an anthropometric measurement that reflects muscle mass [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. APMT is a measurement that is less likely to overestimate muscle mass because there is little subcutaneous fat at the measurement site [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. APMT is associated with grip strength, nutritional status, low SMI, sarcopenia, and mortality [\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on the results of previous studies on SMI and APMT [\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], we speculate that APMT will have a higher accuracy in determining gait independence than SMI. However, there are no studies comparing the accuracy of predicting gait independence using APMT and SMI. If it turns out that APMT is a better predictive indicator than SMI for predicting gait independence in older adults, it will be clinically meaningful because APMT is inexpensive and easy to measure. The purpose of this study was to compare the accuracy of determining gait independence using the APMT and SMI in community-dwelling older adults undergoing outpatient rehabilitation.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and subjects\u003c/h2\u003e \u003cp\u003eThis study was a cross-sectional study design. The individuals involved in this study were older adults undergoing outpatient rehabilitation at Akahige Clinic. Subjects have chronic condition and are undergoing rehabilitation to improve their physical function and activities of daily living. Subjects who were under 65 years of age and those with flawed data were excluded. Out of the total 131 subjects, 98 were chosen for the study. The remaining 33 subjects were excluded because they did not meet the age requirement (2 subjects), or had data defects (31 subjects). Subjects were provided outpatient rehabilitation one to three times per week. All subjects or their guardians provided informed consent before the study, and the study was approved by the ethics committee of our institution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eOutcome measurement\u003c/h2\u003e \u003cp\u003ePrimary outcomes were gait independence, APMT, and SMI. In addition, age, height, body weight, body mass index (BMI), number of medications, comorbidities, and swallowing function were evaluated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of gait independent\u003c/h2\u003e \u003cp\u003eGait independence was assessed using the functional independence measure (FIM) gait score [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. FIM gait score ranged from 1 to 7 as follows: Total assistance as score 1, maximal assistance as score 2, moderate assistance as score 3, minimal assistance as score 4, supervision or monitoring as score 5, modified independence as score 6, complete independence as score 7. Subjects with FIM gait score of 6 or 7 were gait independent group [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and those with FIM gait score of 1 to 5 were non-gait independent group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAPMT measurement\u003c/h2\u003e \u003cp\u003eThe APTM was assessed to measure muscle mass. Subjects sat with their hand on the table (with the elbow to be flexed approximately ,90-degree angle) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The Lange caliper (Lange, Santa Cruz, CA, USA) was used to pinch the adductor pollicis muscle in the vertex of an imaginary triangle formed by extension of thumb and index finger muscle with a continuous pressure of 10 g/mm\u003csup\u003e2\u003c/sup\u003e [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The maximum value of three measurements was used as the measured value [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, APMT was measured on both sides and the maximum value was used for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSMI measurement\u003c/h2\u003e \u003cp\u003eSkeletal muscle mass was evaluated using the BIA method (InBody 470, InBody Japan, Japan), and the SMI was calculated by dividing the limb skeletal muscle mass by the square of height [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eOther assessment\u003c/h2\u003e \u003cp\u003eBMI was calculated by dividing the weight (kg) by the height squared (m\u003csup\u003e2\u003c/sup\u003e). Body weight was measured using a digital scale in the standing position and recorded in 0.1 kg increments, while height was measured in the standing position using a height scale and recorded in 0.5 cm increments. Comorbidities were assessed using the updated Charlson Comorbidity Index (UCCI) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Comorbidities assessed by UCCI have been reported to be associated with mortality in various diseases [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The UCCI valuates 12 conditions related to chronic diseases, including chronic lung disease, rheumatic diseases, diabetes with chronic complications, renal disease, congestive heart failure, dementia, mild liver disease, hemiplegia or paraplegia, malignant tumors including leukemia and lymphoma as score 1, moderate to severe liver disease, AIDS/HIV as score 4, and metastatic solid tumors as score 6, which are then combined to form a score [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The swallowing function was evaluated using the Food Intake Level Scale (FILS), a 10-point scale that assesses the patient's oral intake status [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Swallowing function was selected as a measurement because it is related to skeletal muscle mass [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using Easy R. Variables were assessed for normality using the Shapiro\u0026ndash;Wilk test. An unpaired t-test, or Mann\u0026ndash;Whitney U test was performed to compare the outcomes between the gait independent group and non-gait independent group. Multiple regression analysis using the forced entry method was performed with FIM gait score as the dependent variable. APMT, SMI, BMI, FILS, age, sex, UCCI, and number of medications were included as independent variables. In this study, APMT and SMI were entered into separate multiple regression models. To assess multicollinearity, we used the variance inflation factor. A variance inflation factor value of more than 10 was considered as the presence of multicollinearity. Logistic regression analysis using the forced entry method was performed with gait independence as the objective variable. APMT, SMI, BMI, FILS, age, sex, UCCI, and number of medications were included as independent variables. In this study, APMT and SMI were entered into separate logistic regression models. Additionally, the sample size for the gait independent group was 45 subjects, so APMT, BMI, FILS, and propensity score were used as independent variables in the logistic regression analysis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The propensity score was calculated using age, UCCI, and number of medications. In this study, gait independent group was coded as 0, and the gait dependent group was coded as 1. A receiver operating characteristic (ROC) curve of APMT and SMI for gait independence were created, and cut-off values were calculated using the Youden index. Additionally, the AUCs of the APMT model and the SMI model were compared using the Delong test. Statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. In addition, we calculated the effect size (\u003cem\u003ef\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) for multiple regression analysis using the following equation: \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e ∕ (1 - \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The statistical power of the analysis based on \u003cem\u003ef\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e, an alpha error of 0.05, total sample size, and the number of predictor variables was calculated using G*Power version 3.1.9.2 (Heinrich-Heine-Universit\u0026auml;t D\u0026uuml;sseldorf, D\u0026uuml;sseldorf, Germany).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAmong the 98 subjects, 53 (54.1%) were in the gait independent group. The mean ages of gait independent group and non-gait independent group were 83.8 and 86.9 years, respectively. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the measurement data of the subjects and the results of the comparison between the gait independent and non-gait independent groups. A result of comparison between both groups showed significant differences in age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), body weight (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), BMI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), UCCI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), FILS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), FIM gait score (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), APMT (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results of multiple regression analysis of the APMT entry model with FIM gait score as the dependent variable. APMT (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.362, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and number of medication (\u003cem\u003eβ\u003c/em\u003e = -0.207, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were significantly associated with FIM gait score (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.338, \u003cem\u003ef\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.510, statistical power\u0026thinsp;=\u0026thinsp;1.000). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the results of multiple regression analysis of the SMI entry model with FIM gait score as the dependent variable. Age (\u003cem\u003eβ\u003c/em\u003e = -0.228, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), BMI (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.257, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), number of medication (\u003cem\u003eβ\u003c/em\u003e = -0.240, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), FILS (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.188, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04) were significantly associated with FIM gait score (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.272, \u003cem\u003ef\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.374, statistical power\u0026thinsp;=\u0026thinsp;0.999). SMI showed no significant association with FIM gait score (\u003cem\u003eβ\u003c/em\u003e = -0.079, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.41). Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the results of logistic regression analysis of the APMT entry model with gait independence as the dependent variable. APMT (odds ratio\u0026thinsp;=\u0026thinsp;0.712, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) was significantly associated with gait independence. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the results of logistic regression analysis of the SMI entry model with gait independence as the dependent variable. BMI (odds ratio\u0026thinsp;=\u0026thinsp;0.744, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) was significantly associated with gait independence. SMI showed no significant association with gait independence (odds ratio\u0026thinsp;=\u0026thinsp;1.500, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.18). No multicollinearity between variables was observed in all multivariate analyses. The cut-off value of APMT calculated from the ROC curve was 13mm; the sensitivity and specificity were 67.9% and 86.7%, respectively; and the AUC was 0.800 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Fig.\u0026nbsp;1). The cut-off value of SMI calculated from the ROC curve was 4.6kg/m\u003csup\u003e2\u003c/sup\u003e; the sensitivity and specificity were 90.6% and 26.7%, respectively; and the AUC was 0.582 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Fig.\u0026nbsp;2). The results of comparing the AUCs for the APMT and SMI models are shown in Fig.\u0026nbsp;3. The AUCs for the APMT and SMI models were 0.800 (95% CI: 0.713\u0026ndash;0.887) and 0.582 (95% CI: 0.468\u0026ndash;0.697), respectively (Fig.\u0026nbsp;3). The AUC for the APMT model was significantly higher than the SMI model (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipants characteristics and comparison results between the gait independent and non-gait independent groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;98)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003egait independent group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-gait independent group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;45)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.2 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.8 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.9 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.5 to -0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (m), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.49 (1.44\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48 (1.44\u0026ndash;1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.49 (1.44\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody weight (kg), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.2 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.4 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.4 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.2\u0026ndash;10.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.8 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.3 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.4\u0026ndash;4.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of medications, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (4\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (4\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (4\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCCI, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFILS, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (10\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (10\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (10\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIM gait score, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (5\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (6\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (5\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPMT (mm), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (12\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (13\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (11\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMI (kg/m\u003csup\u003e2\u003c/sup\u003e), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.6 (5\u0026ndash;6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.7 (5.0\u0026ndash;6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.5 (4.6\u0026ndash;6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAPMT, adductor pollicis muscle thickness; BMI, body mass index; CI, confidence interval; FILS, Food Intake Level Scale; IQR, interquartile range; SD, standard deviation; SMI, skeletal muscle mass index; UCCI, updated Charlson Comorbidity Index.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e Unpaired t-test, \u003csup\u003eb\u003c/sup\u003e Mann-Whitney test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of multiple regression analysis with FIM gait score as the dependent variable (APMT entry model).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of medications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFILS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAPMT, adductor pollicis muscle thickness; B, partial regression coefficient; BMI, body mass index; FILS, Food Intake Level Scale; SE, standard error; UCCI, update Charlson Comorbidity Index; VIF, variance inflation factor.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of multiple regression analysis with FIM gait score as the dependent variable (SMI entry model).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of medications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFILS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eB, partial regression coefficient; BMI, body mass index; FILS, Food Intake Level Scale; SE, standard error; SMI, skeletal muscle mass index; UCCI, update Charlson Comorbidity Index; VIF, variance inflation factor.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of logistic regression analysis with gait independence as the objective variable (APMT entry model).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.529\u0026ndash;0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.740\u0026ndash;1.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFILS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.078\u0026ndash;1.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eOdds ratio was adjusted by propensity score calculated using age, sex, updated Charlson comorbidity index, and number of medications.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAPMT, adductor pollicis muscle thickness; B, partial regression coefficient; BMI, body mass index; CI, confidence interval; FILS, food intake level scale; OR, odds ratio; SE, standard error; VIF, variance inflation factor.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of logistic regression analysis with gait independence as the objective variable (SMI entry model).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.828\u0026ndash;2.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.612\u0026ndash;0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFILS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.059\u0026ndash;1.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eOdds ratio was adjusted by propensity score calculated using age, sex, updated Charlson comorbidity index, and number of medications.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eB, partial regression coefficient; BMI, body mass index; CI, confidence interval; FILS, food intake level scale; OR, odds ratio; SE, standard error; SMI, skeletal muscle mass index; VIF, variance inflation factor.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGait independence cut-off values and determination accuracy in APMT and SMI.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCut-off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity, %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity, %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePPV, %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNPV, %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+LR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-LR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAUC (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e76.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.800 (0.713\u0026ndash;0.887)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.6kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e77.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.582 (0.468\u0026ndash;0.697)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eAPMT, adductor pollicis muscle thickness; AUC, area under the curve; CI, confidence interval; LR, likelihood ratio; NPV, negative predict value; PPV, positive predict value; SMI, skeletal muscle mass index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first study to compare the accuracy of APMT and SMI in determining gait independence in community-dwelling older adults undergoing outpatient rehabilitation. The results showed that APMT had higher accuracy in determining gait independence than SMI.\u003c/p\u003e \u003cp\u003eThe cut-off value for APMT to determine the gait independence was 13 mm; the sensitivity and specificity were 67.9% and 86.7%, respectively; and the AUC was 0.800. A previous study has reported that the cut-off value for determining low SMI using APMT is 13 mm [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Considering the results of a previous study [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and our study, the cut-off value in this study is appropriate. APMT is a measurement that is less likely to overestimate muscle mass because there is low subcutaneous fat at the measurement site [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The fact that APMT contains low subcutaneous fat and is less likely to overestimate muscle mass may be a factor in the high accuracy of APMT in determining gait independence.\u003c/p\u003e \u003cp\u003eOn the other hand, the cut-off value for SMI to determine the gait independence was 4.6kg/m\u003csup\u003e2\u003c/sup\u003e; the sensitivity and specificity were 90.6% and 26.7%, respectively; and the AUC was 0.582. A previous study has reported that SMI in older hospitalized patients does not predict gait independence at discharge [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Muscle mass measurement using the SMI overestimates muscle mass in older adults with edema [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Furthermore, among community-dwelling older adults with an average age of 77.6 years, 26% had an extracellular water (ECW)-to-total body water (TBW) ratio of 0.4 or higher [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. ECW/TBW ratio is used as an edema index, and ECW/TBW ratio of 0.4 or higher indicating edema [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Considering that ECW/TBW ratio increases with age [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and that the subjects in this study (average age 85.2 years) were older than those in a previous study [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], it is possible that more subjects had edema compared with a previous study [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Based on findings of edema and ECW/TBW ratio in older adults [\u003cspan additionalcitationids=\"CR39 CR40\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], we speculate that the low accuracy of the SMI in determining gait independence in this study is due to SMI overestimating muscle mass. The SMI overestimates muscle mass in the same way that calf circumference easily overestimates due to subcutaneous fat and edema [14.15]. Therefore, like calf circumference, SMI may not be suitable for measuring muscle mass in some subjects. However, in this study, ECW/TBW ratio was not measured. Therefore, in the future, we will conduct a more detailed investigation by measuring ECW/TBW ratio.\u003c/p\u003e \u003cp\u003eThis study has two strengths. First, this is the first study to compare the accuracy of determining gait independence using the APMT and SMI in community-dwelling older adults undergoing outpatient rehabilitation. As a result, APMT had significantly higher accuracy in determining gait independence than SMI. APMT is a useful measurement that reflects gait independence in community-dwelling older adults undergoing outpatient rehabilitation. Second, no significant relationship was found between SMI and gait independence. SMI may not be a useful measurement that reflects gait independence in community-dwelling older adults undergoing outpatient rehabilitation. The finding that APMT is more strongly related to gait independence than SMI is clinically significant as it can be measured without expensive equipment such as BIA. Furthermore, APMT measurement is simple and easy to incorporate into clinical practice.\u003c/p\u003e \u003cp\u003eThis study has three limitations. First, the subjects in this study received outpatient rehabilitation one to three times a week to ensure a regular exercise routine. Therefore, the results of this study may differ if older adults not receiving outpatient rehabilitation were included. Second, we mentioned the possibility of edema in the subjects as a factor that did not show a relationship between SMI and gait independence. However, this study did not measure ECW/TBW ratio, which is an indicator of edema. In future study, we should conduct a more detailed investigation by measuring ECW/TBW ratio. Specifically, the question is whether the relationship between APMT and gait independence is affected by the ECW/TBW ratio. If the relationship between APMT and gait independence is not affected by the ECW/TBW ratio, then APMT would be an easier muscle mass measurement method to use in clinical practice than SMI. Third, in this study, there were few male subjects, so we were unable to calculate cut-off values ​​for APMT and SMI separately for male and female. Therefore, it may not be appropriate to use the cut-off values ​​in this study in clinical settings. However, it is important to clarify that APMT is a measurement that reflects gait independence better than SMI in community-dwelling older adults undergoing outpatient rehabilitation. In the future, it will be necessary to increase the sample size and calculate cut-off values ​​separately for male and female.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe results of this study show that APMT had a higher accuracy of determining gait independence than SMI in community-dwelling older adults undergoing outpatient rehabilitation. This indicates that measuring APMT is more useful for predicting gait independence than SMI in community-dwelling older adults undergoing outpatient rehabilitation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of Interest Statement\u003c/h2\u003e \u003cp\u003eThe authors declare that there are no conflicts of interest.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFunding details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe thank the subjects and staff who helped with this study. This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eStatement of Authorship\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eThe conception and design of the study, acquisition of data, or analysis and interpretation of data; TI, TF, KH, NM, HH, RH, YT, and NA.\u003c/li\u003e\n \u003cli\u003eDrafting the article: TI, TF, KH, NM, HH, RH, YT, and NA.\u003c/li\u003e\n \u003cli\u003eFinal approval of the version to be submitted; TI, TF, KH, NM, HH, RH, YT, and NA.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLiu P, Hao Q, Hai S, Wang H, Cao L, Dong B (2017) Sarcopenia as a predictor of all-cause mortality among community-dwelling older people: A systematic review and meta-analysis. 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Am J Clin Nutr 85(1):80\u0026ndash;89\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHioka A, Akazawa N, Okawa N, Nagahiro S (2022) Extracellular water-to-total body water ratio is an essential confounding factor in bioelectrical impedance analysis for sarcopenia diagnosis in women. Eur Geriatr Med 13(4):789\u0026ndash;794\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishimura N, Hori S, Tomizawa M, Yoneda T, Nakai Y, Miyake M et al (2022) Relevance of the perioperative edema index measured by bioelectrical impedance analysis for prediction of cardiovascular disease in living-donor kidney transplantation. Int J Urol 29(4):309\u0026ndash;316\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHioka A, Akazawa N, Okawa N, Nagahiro S (2024) Influence of aging on extracellular water-to-total body water ratio in community-dwelling females. Clin Nutr ESPEN 60:73\u0026ndash;78\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-geriatric-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"EGEM","sideBox":"Learn more about [European Geriatric Medicine](https://www.springer.com/journal/41999)","snPcode":"41999","submissionUrl":"https://www.editorialmanager.com/egem/default2.aspx","title":"European Geriatric Medicine","twitterHandle":"","acdcEnabled":false,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"gait independence, adductor pollicis muscle thickness, skeletal muscle mass index, community-dwelling older adults, outpatient rehabilitation","lastPublishedDoi":"10.21203/rs.3.rs-4988908/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4988908/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThe accuracy of determining gait independence using adductor pollicis muscle thickness (APMT) and skeletal muscle mass index (SMI) in community-dwelling older adults undergoing outpatient rehabilitation remains unclear. The purpose of this study was to compare the accuracy of determining gait independence using APMT and SMI in community-dwelling older adults undergoing outpatient rehabilitation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study included 98 older adults (mean age: 85.2 years). Subjects were received outpatient rehabilitation one to three times a week. The main outcomes were gait independence (functional independence measure gait score: 6 or 7), skeletal muscle mass index (SMI), and APMT. A receiver operating characteristic (ROC) curve of APMT and SMI for gait independence were created, and a cut-off value were calculated using the Youden index. Additionally, the area under the curve (AUC) s of the APMT model and the SMI model were compared using the Delong test.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the 98 subjects, 53 (54.1%) were in the gait independent group. The cut-off value of APMT calculated from the ROC curve was 13mm; the sensitivity and specificity were 67.9% and 86.7%, respectively; and the AUC was 0.800. The cut-off value of SMI calculated from the ROC curve was 4.6kg/m\u003csup\u003e2\u003c/sup\u003e; the sensitivity and specificity were 90.6% and 26.7%, respectively; and the AUC was 0.582. The AUC for the APMT model was significantly higher than the SMI model (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe results of this study show that the cut-off value of APMT for determining the gait independence was 13 mm. In addition, APMT had a higher accuracy of determining gait independence than SMI. This indicates that measuring APMT is more useful for predicting gait independence than SMI in community-dwelling older adults undergoing outpatient rehabilitation.\u003c/p\u003e","manuscriptTitle":"Accuracy of determining gait independence using adductor pollicis muscle thickness and skeletal muscle mass index in community-dwelling older adults undergoing outpatient rehabilitation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-04 10:25:23","doi":"10.21203/rs.3.rs-4988908/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2024-11-07T07:01:20+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-09-08T07:12:53+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-03T11:14:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-30T05:32:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Geriatric Medicine","date":"2024-08-28T20:11:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-geriatric-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"EGEM","sideBox":"Learn more about [European Geriatric Medicine](https://www.springer.com/journal/41999)","snPcode":"41999","submissionUrl":"https://www.editorialmanager.com/egem/default2.aspx","title":"European Geriatric Medicine","twitterHandle":"","acdcEnabled":false,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"45cd104b-5553-4e94-81f9-6f8fbfa159ac","owner":[],"postedDate":"October 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-12-20T10:00:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-04 10:25:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4988908","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4988908","identity":"rs-4988908","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}