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

doi:10.21203/rs.3.rs-4988908/v1

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

https://doi.org/10.21203/rs.3.rs-4988908/v1

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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/m²; 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

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.

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.

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/mm² [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 (m²). 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 (f²) for multiple regression analysis using the following equation: R² ∕ (1 - R²) [36]. The statistical power of the analysis based on f², 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).

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 (R² = 0.338, f² = 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 (R² = 0.272, f² = 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/m²; 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/m²), 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/m²), 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/m²	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.

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/m²; 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.

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.

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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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

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

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Abstract

Objective

Methods

Results

Conclusions

Figures

Key summary points

Introduction

Materials and Methods

Study design and subjects

Outcome measurement

Assessment of gait independent

APMT measurement

SMI measurement

Other assessment

Statistical analysis

Results

Discussion

Conclusions

Declarations

Conflict of Interest Statement

Acknowledgments

References

Status:

Version 1