Skip to main content

Effectiveness of impedance parameters for muscle quality evaluation in healthy men


We investigated the relationship between impedance parameters and skeletal muscle function in the lower extremities, as well as the effectiveness of impedance parameters in evaluating muscle quality. Lower extremity impedance of 19 healthy men (aged 23–31 years) measured using the direct segmental multi-frequency bioelectrical impedance analysis were arc-optimized using the Cole–Cole model, following which phase angle (PA), \({R}_{i}/{R}_{e}\), and β were estimated. Skeletal muscle function was assessed by muscle thickness, muscle intensity, and isometric knee extension force (IKEF). IKEF was positively correlated with PA (r = 0.58, p < 0.01) and β (r = 0.34, p < 0.05) was negatively correlated with \({R}_{i}/{R}_{e}\) (r = − 0.43, p < 0.01). Stepwise multiple regression analysis results revealed that PA, β, and \({R}_{i}/{R}_{e}\) were correlated with IKEF independently of muscle thickness. This study suggests that arc-optimized impedance parameters are effective for evaluating muscle quality and prediction of muscle strength.


Skeletal muscle function has been shown to be influenced by both quantitative factors (e.g., number of muscle fibers and cross-sectional area) and qualitative factors [1]. Qualitative factors include an increase in noncontractile tissue (e.g., fatty infiltration in skeletal muscle and myofascial degeneration) [2]. These are known to be caused by inactivity even in young people [3]. In recent years, skeletal muscle dysfunction has attracted attention not only for the decline of physical function, but also for the risk of developing lifestyle diseases and mortality after their occurence, and for the quality of life [4,5,6]. Methods for evaluating muscle quality include physiological tests and diagnostic imaging tests such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasonography (US). Of these tests, US is noninvasive and does not limit the measurement location or posture. This test can be conducted in various settings such as medical institutions and sports facilities [7, 8]. Muscle intensity (MI), which quantifies the extent of black and white areas from cross-sectional images of the skeletal muscle taken with US, reflects noncontractile tissue (e.g., increase in intramyocellular lipids and connective tissue). MI is therefore expected to be an effective index for evaluating muscle quality [9]. However, there have been several problems regarding the reproducibility and sensitivity of evaluation with MI, including (1) fluctuating numerical values depending on the measurement method and instrument settings, making comparison with other research data difficult, and (2) the rate of change in noncontractile tissue and luminance is not linear [10].

Indirect body composition evaluation, which estimates skeletal muscle mass and body fat mass using differences in tissue electrical conductivity and transmittance in bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry, has recently become widespread. BIA is a non-invasive measurement technique based on the electrophysiological properties of biological tissues. Muscle mass evaluation using BIA has been shown to be as highly accurate as that by conventional measurement methods [11]. Impedance parameters of the biological tissue can also be qualitative factors in skeletal muscle evaluation because they reflect the mass and uniformity of cells and the condition of cell membranes [12]. Particularly, phase angle (PA), which is the phase difference between current and voltage, is related not only to survival rate, nutritional status [13], and the occurrence of sarcopenia and frailty [14], but also to muscle strength [15], exercise tolerance [16], and physical activity level [17] in the field of geriatrics as well as to physical fitness and sports science. Thus, it has attracted attention as a means of qualitative assessment of muscle cell function, which was previously difficult to evaluate noninvasively.

Thus, although impedance parameters are attractive for muscle quality evaluation, when used as objective indicators, they require correction to accommodate for changes in frequency characteristics associated with skeletal muscle physiology and decline in anatomical function due to aging or disease. A decline in skeletal muscle function causes changes in the cell membrane resistance and an increase in the noncontractile tissue, which changes the central relaxation frequency \({(f}_{c})\) at which reactance reaches its maximum value [18]. Thus, optimizing the arc using the Cole–Cole model is recommended to compensate for these changes and measure the maximum PA of the target muscle [19]. Employing the Cole–Cole model, the PA (PAcole) is calculated using \({R}_{i}/{R}_{e}\) (known as the intracellular fluid resistance-to-extracellular fluid resistance ratio) and the beta parameter (β) as shown in Eqs. 1 and 2, respectively. PAcole is an indicator of the structural perfection of skeletal muscle cells. \({R}_{i}/{R}_{e}\) refers to the balance between the intracellular fluid resistance and extracellular fluid resistance, and β refers to cell homogeneity [20]. Therefore, PAcole is said to be an indicator of the structural completeness of skeletal muscle cells [21]. Previous studies have shown that intracellular and extracellular water contents estimated using Ri/Re of the lower extremity are useful for evaluating the skeletal muscle mass [22]. β is one of the performance indices of the capacitor in the equivalent circuit model, and is an index of the uniformity of the measured structure. It is quantified on a scale from 0 to 1. 0, indicating non-uniform and perfectly uniform tissue, respectively [23]. It has been shown that changes in myofiber type and fatty infiltration could be assessed using this parameter [24].

However, to the best of our knowledge, there has been no study on the relationship between muscle strength and muscle mass and arc-optimized impedance parameters (PAcole, \({R}_{i}/{R}_{e}\), β), focusing on the lower extremity skeletal muscle function. We presumed that PAcole and \({R}_{i}/{R}_{e}\), β could be effective for the qualitative assessment of skeletal muscle composition related to muscle strength. This study aimed to verify the effectiveness of impedance parameters by simultaneously evaluating muscle thickness (MT) and MI with arc-optimized impedance parameters and US using Cole–Cole analysis for muscle quality evaluation to predict muscle strength.



Nineteen healthy adult men (aged 23–31 years), with a total of 38 left and right lower extremities, were included. The study protocol was approved by the ethics review board of the Kawasaki Medical School (approval No.: 2846). Written informed consent was obtained from all participants. The inclusion criteria were as follows: (a) no history of lower extremity trauma or surgery; (b) no history of neuromuscular disorders; (c) not using an artificial pacemaker; (d) the ability to provide informed consent with no serious cognitive impairment, and (e) no regular exercise routine. Height and weight were measured, with the subjects standing barefoot and wearing light training clothes. Measurements were taken to the nearest 0.1 cm and 0.1 kg, and the body mass index (BMI) was calculated. Table 1 shows the physical characteristics of the participants.

Table 1 Physical characteristics and muscle srength, quantity and quality of the participants

Experimental procedure

The impedance parameters were measured using direct segmental multi-frequency bioelectrical impedance analysis (DSM-BIA), MT, and MI were measured using US, and muscle strength was measured using isometric maximum muscle strength. The test procedures were uniform among all subjects. All assessments were performed on the same day. The subjects rested for 15 min in the supine position immediately before US and BIA measurements to stabilize body water [25].

Bioelectrical impedance

Lower extremity impedance was measured with DSM-BIA using the InBody S10 (InBody Japan, Tokyo, Japan), which has a tetrapolar eight-point tactile electrode system and three different frequencies (5, 50, 250 kHz). Eight electrodes were attached to the thumb and middle finger of the hand for the upper limb, and to the back of the endocarpus and exocarpus for the lower limb. DSM-BIA measurements have been shown to be as accurate as those of the DEXA [11]. Measurements were taken in the supine position after 15 min of rest. The subjects were instructed to refrain from alcohol intake and excessive exercise on the day before the test. Measurements were performed in a controlled clinic room with a room temperature of 24–26 °C. Contact between trunk and extremities was prevented by placing the upper and lower extremities in the 30° abduction position [25].

Cole–Cole model

The Cole–Cole model was used to estimate the impedance parameters optimized for the arc [19]. The arc of the Cole–Cole model is shown in Eq. 1 and Fig. 1, and the biological equivalent circuit model is shown in Eq. 2 and Fig. 2:

$$Z\left( f \right) = R + jX = Z_{\infty } + \frac{{Z_{0} - Z_{\infty } }}{{1 + \left( {j\frac{f}{{f_{c} }}} \right)^{\beta } }},$$
$$\frac{1}{Z\left( f \right)} = \frac{1}{{R_{e} }} + \frac{1}{{R_{i} + Z_{m} }}\left( f \right),$$
Fig. 1
figure 1

The arc of the Cole–Cole model

Fig. 2
figure 2

Equivalent circuit model of biological tissue

where \(f\) is the frequency, \(Z(f)\) is the complex-valued impedance as a function of \(f\), \(R\) is the resistance, \(X\) is the reactance, and j is an imaginary unit, \({Z}_{0}\) is \(R\) when \(f\) = 0, \({Z}_{0}\) is \(R\) when \(\mathrm{f}\) = \(\infty\),  \({R}_{e}\) is the extracellular fluid resistance, \({R}_{i}\) is the intracellular fluid resistance, and \({Z}_{m}(f)\) is the cellular membrane impedance as a function of \(f\). \({Z}_{\infty }\),\({Z}_{0}, {f}_{c}, \beta\) were estimated using Eq. 1. \(Z(f)\) and \({Z}_{m}(f)\) varies with \(f\):

$$R_{e} = Z_{0} ,$$
$$R_{i} = \frac{{Z_{0} Z_{\infty } }}{{Z_{0} + Z_{\infty } }},$$
$$Z_{m} \left( f \right) = \frac{{\left( {Z_{0} } \right)^{2} }}{{Z_{0} - Z_{\infty } }}\left( {j\frac{f}{{f_{c} }}} \right)^{ - \beta } .$$

\({R}_{\mathrm{c}}\) and \(-{X}_{c}\) were determined from the obtained \({Z}_{0}\) and \({Z}_{\infty }\) and is given by

$$R_{{\text{c}}} = \frac{{Z_{0} + Z_{\infty } }}{2},$$
$$- X_{c} = \frac{{\left( {Z_{0} - Z_{\infty } } \right)\sin \frac{\beta \pi }{2}}}{{2\left( {1 + \cos \frac{\beta \pi }{2}} \right)}},$$

where \({R}_{c}\) is \(R\) when the \(f\) is \({f}_{c}\) and \({X}_{c}\) is \(X\) when the \(f\) is \({f}_{c}.\)

\({\mathrm{PA}}_{\mathrm{cole}}\) was measured from the obtained \({R}_{c}\) and \(-{X}_{c}\) and is given by

$${\text{PA}}_{{{\text{cole}}}} = \arctan \left( {\frac{{ - X_{c} }}{{R_{c} }}} \right) = \frac{{R_{e} \sin \frac{\beta \pi }{2}}}{{\left( {R_{e} + 2R_{i} } \right)\left( {1 + \cos \frac{\beta \pi }{2}} \right)}} = \frac{{\sin \frac{\beta \pi }{2}}}{{\left( {1 + 2\frac{{R_{i} }}{{R_{e} }}} \right)\left( {1 + \cos \frac{\beta \pi }{2}} \right)}}.$$

The 50-kHz PA (PA50) used in previous research was added as a variable to evaluate the effects of the Cole–Cole analysis.

Search of \({{\varvec{Z}}}_{0}, {{\varvec{Z}}}_{\infty }{\varvec{\beta}},\) \({{\varvec{f}}}_{{\varvec{c}}}\)

The estimation of the four parameters was explored using an optimization method as shown in Eq. 9. Impedance and reactance data measured by DSM-BIA were used for the impedance data:

$$E = \mathop \sum \limits_{k = 1}^{3} \left. {\left\{ {\left( {R_{M} \left( {f_{k} } \right) - R_{E} \left( {f_{k} } \right)} \right)^{2} + \left( {X_{M} \left( {f_{k} } \right) - X_{E} \left( {f_{k} } \right)} \right)^{2} } \right.} \right\},$$

where \({f}_{1}\) = 5 kHz, \({f}_{2}\) = 50 kHz, \({f}_{3}\) = 250 kHz, \({R}_{M}\left({f}_{k}\right) and\) \({X}_{M}\left({f}_{k}\right)\) are the measured value of resistance and reactance at frequency \({f}_{k,}\) \({R}_{E}\left({f}_{k}\right) \mathrm\,and\,{X}_{E}\left({f}_{k}\right)\) are the resistance and reactance in Eq. 1 at the frequency \({f}_{k},\) respectively.

We searched for \({Z}_{0}, {Z}_{\infty }\beta\),\({f}_{c}\) so that the evaluated value E was the minimum.


Quadriceps femoris MT (QFMT) and MI (QFMI) were measured from cross-sectional images of the skeletal muscle using diagnostic ultrasound imaging equipment (SonoSite M-turbo, FUJIFILM). Measurements were taken in the brightness mode using a 6–15 MHz linear probe (56 mm). Hard-type echo gel (Conductor TM Transmission Gel, Chattanooga) was used to prevent the probe from touching the skin. Each measurement was taken twice. The target muscle group was the QF (rectus femoris, vastus medialis, vastus intermedius, and vastus lateralis). The measurement site for rectus femoris, vastus intermedius, and vastus lateralis was the midway point between the anterior superior iliac spine and the upper margin of patella, while that for vastus medialis was the point located 70% distal to the anterior superior iliac spine and the upper margin of the patella. Image analysis was performed using Image J-WinJP (LISIT, Tokyo, Japan) and using a partially modified method referencing the method proposed by Berger et al. [26]. Muscle luminance was measured using convex hull. After the upper and lower parts were surrounded to ensure the fascia was excluded, the values were quantified in the range of 0–255 with an 8-bit gray scale using histogram analysis, and the mean value of the area was calculated. QFMT (mm) and QFMI were calculated from the total value of the four muscles using the mean of two measurements taken for each muscle (Fig. 3). The measurements were taken by one examiner. The reliability of this measurement method has been demonstrated in previous studies [27].

Fig. 3
figure 3

Measurement of muscle thickness (MT) and muscle intensity (MI)

Muscle strength

Muscle strength was measured from isometric knee extension force (IKEF). The measurement was taken while the subject was seated in a chair without a backrest with the hip and knee joints at 90°. The equipment used for measurement was a handheld dynamometer (μ-TAS F-1, Anima, Japan). The sensor position was the distal part of the lower leg, and the arm length (m) was measured from the knee joint space to the center of the sensor. A 3-s maximum contraction was performed twice, with a 1-min break, and the maximum value (N) was used. The measured value was the value obtained by multiplying the measured value by the arm length (Nm).

Statistical analysis

All data are expressed as mean ± standard deviation. All statistical analyses were performed in EZR (Ver 1.4, Saitama Medical Center, Jichi Medical University, Saitama, Japan) [28]. Significance level was set as less than 5% (p < 0.05). Pearson's correlation coefficient was used for calculating the correlation of IKEF with QFMT, QFMI, PA50, PAcole, β, \({R}_{i}/{R}_{e}\), \({f}_{c}\), and BMI. Stepwise multiple regression analysis was conducted with IKEF as the dependent variable to examine the effects of PAcole and β, \({R}_{i}/{R}_{e}\) on IKEF. The linear models were as follows: Model 1, with QFMT, QFMI, and BMI as independent variables; Model 2, with PAcole added; and Model 3, with β and \({R}_{i}/{R}_{e}\) added. Variance inflation factor (VIF) was calculated to confirm the existence of multicollinearity.


The subjects’ physical characteristics and data of IKEF, QFMT, QFMI, and lower extremity impedance parameters are given in Tables 1 and 2, respectively. Data of the correlation between the subjects’ IKEF and QFMT, QFMI, and lower extremity impedance parameters are shown in Table 3. IKEF had a significantly positive correlation with MT (r = 0.58, p < 0.01), PAcole (r = 0.53, p < 0.01), and β (r = 0.55, p = 0.04) and a significantly negative correlation with \({R}_{i}/{R}_{e}\) (r = − 0.42, p < 0.01). The results of stepwise multiple regression analysis with IKEF as the objective variable are given in Table 4. QFMT was shown to be significantly related in Model 1. PAcole, and β and \({R}_{i}/{R}_{e}\) were selected as significant variables in Models 2 and 3, respectively. The VIF was within the range of 1.05–1.36 for all variables, indicating no multicollinearity.

Table 2 Impedance parameter of the lower extremity
Table 3 Correlation coefficients between muscle strength, muscle strength, muscle thickness, muscle intensity, impedance parameters and physical characteristics of the lower extremities (n=38)
Table 4 Predictors of muscle strength in lower extremity (n=38)


To the best of our knowledge, this is the first study to investigate the relationship between the two components (\({R}_{i}/{R}_{e}\), β) of the phase angle in the central frequency from the Cole–Cole model of site-specific impedances of the lower extremity, using optimization calculations and conventional assessment of skeletal muscle function. Previous studies have reported the relationship between whole-body PA50 and upper and lower extremity muscle strength and exercise tolerance [15, 16]. In this study, we demonstrated the correlation between lower extremity impedance parameters (PAcole, \({R}_{i}/{R}_{e}\), β, and \({f}_{c}\)) and QFMT, QFMI, IKEF, and BMI. Further, when PAcole (Model 2) and \({R}_{i}/{R}_{e}\) and β (Model 3) were added as independent variables in the stepwise multiple regression analysis where IKEF was set as a dependent variable, it resulted in increased R2 and decreased QFMT standardized partial regression coefficient (SC). A noteworthy finding is that in Model 3, both \({R}_{i}/{R}_{e}\) and β were shown to be factors that affect IKEF independent of QFMT. These results support our hypothesis that high muscle thickness and high PAcole are independently associated with IKEF in healthy men and that impedance parameters (PAcole, \({R}_{i}/{R}_{e}\), β) as a muscle quality evaluation parameter enhances the suitability of muscle strength evaluation. This study suggests that simultaneous evaluation of QFMT and impedance parameters (PAcole, \({R}_{i}/{R}_{e}\), β) could enable accurate estimation of muscle strength.

First, this study demonstrated the correlation between lower extremity impedance parameters (PAcole, \({R}_{i}/{R}_{e}\), β, and \({f}_{c}\)) and QFMT, QFMI, IKEF, and BMI (Table 3). Evaluation of skeletal muscle composition is important for predicting skeletal muscle function and physical function and for evaluating the effects of aging and disease [29]. PA50 has been shown to be related to muscle strength, exercise tolerance, fall history, and physical activity in the field of geriatrics, physical fitness, and sports science [15,16,17]. Furthermore, studies investigating changes before and after resistance training and age-related changes show that fluctuations in resistance and reactance associated with changes in skeletal muscle function affect PA50 [30]. Thus, although there are an increasing number of studies showing the relationship of skeletal muscle and physical function with impedance parameters, those studies use an impedance parameter with a frequency of only 50 kHz. Conventionally, the \({f}_{c}\) in biological tissues is approximated as 50 kHz, which is the reason for using the 50 kHz impedance value usually [31]. However, the change in muscle fiber size, increase in connective tissue, and fatty infiltration in skeletal muscle change the frequency characteristics of the skeletal muscle; therefore, the impedance parameter in \({f}_{c}\) using the Cole–Cole model is optimal for evaluating changes in skeletal muscle function [32]. In this study, the \({f}_{c}\) (38.1 ± 8.1 kHz) was lower than 50 kHz in all subjects. The results of this study showed a moderately positive correlation between IKEF and PAcole (r = 0.58 p < 0.01), although PA50 did not show a significant correlation (r = 0.11, p = 0.53). Furthermore, there was a positive correlation between IKEF and β (r = 0.34, p = 0.04), and a moderately negative correlation with \({R}_{i}/{R}_{e}\) (r = − 0.43, p < 0.01). A notable finding is that \({f}_{c}\) did not have a significant correlation with muscle strength (r = − 0.03, p = 0.8). Previous studies indicated significant correlations between, \({R}_{i}/{R}_{e}\) and/or \({f}_{c}\), which constitute PAcole. However, there was no correlation with muscle strength and muscle mass [22, 33,34,35,36]. Most of these studies focused on age-related changes in the aging process in older adults and aged mice, focusing on changes in the intracellular water to extracellular water ratio and the decrease in phase angle associated with age-related muscle mass loss [22, 33,34,35,36]. Conversely, as shown in Eq. 8 and Fig. 1, in addition to the intracellular water to extracellular water ratio (\({R}_{i}/{R}_{e}\) in the present study), the β has an effect on the phase angle. Despite the fact that β reflects cell homogeneity and may be affected by qualitative changes in skeletal muscle [20], no studies have focused on β. In the present study, β demonstrated a moderately positive correlation with IKEF, indicating that in addition to \({R}_{i}/{R}_{e}\), β is a factor that reflects the influence of skeletal muscle function.

Second, when PAcole (Model 2) and \({R}_{i}/{R}_{e}\) and β (Model 3) were added as independent variables in stepwise multiple regression analysis with IKEF as a dependent variable, it resulted in increased R2 and decreased QFMT SC in both models (Table 4). It has previously been reported that the prediction accuracy of muscle strength dramatically improves with a combination of muscle mass and muscle quality, rather than using muscle mass alone [37]. Additionally, the importance of evaluating muscle quality is increasing because the decline in muscle quality precedes the decline in muscle strength [38]. With the change from Models 1 to 2, the QFMT SC decreased from 0.57 to 0.46, while R2 increased from 0.29 to 0.53. The PAcole SC was 0.49, which was approximately the same value as that for QFMT, indicating that it had an effect independent of other variables. As expected, the analysis for predicting muscle strength produced results similar to those of the study conducted by Bourgeois et al. [37], where muscle mass and PA were set as variables. In Model 3, the components of PAcole were divided into \({R}_{i}/{R}_{e}\) and β, and the effects of those variables were analyzed. New findings in this study indicate that both \({R}_{i}/{R}_{e}\) (SC: − 0.43, p < 0.01) and β (SC: 0.55, p < 0.01) are factors that affect IKEF independently of QFMT. The low contribution of QFMI is due to the muscle quality evaluation with MI that tends not to show up as changes in the degree of whiteness unless there is more than a certain level of fascia degeneration or adipose tissue, which suggests that these changes may be underestimated in certain subjects [10]. In addition, as there is a difference in the percentage of fat infiltration between the distal part and the proximal part in the lower extremity [10], measurement accuracy can be improved by evaluating not just a single slice using US but evaluating a wide range. These findings suggest that using the impedance parameter in a wide range of tissues with DSM-BIA may enable more accurate detection of muscle quality condition than echo intensity (EI). The numerical values of \({R}_{i}/{R}_{e}\) and β in the evaluation of muscle quality may fluctuate due to various muscle quality disorders such as an increase in the noncontractile tissue, muscle fiber atrophy, and a decline in fascia function. Thus, further study is needed, analyzing each element separately. Our results show that \({R}_{i}/{R}_{e}\) and β can be used to evaluate muscle quality, which is difficult to express with EI, and that both are factors that independently affect muscle strength.

Evaluation of body composition using DSM-BIA is currently used in various fields such as medical care and athletes [39, 40]. Unlike CT and MRI, it does not require a special environment, and it is highly reproducible, making it widely used for training and evaluation of disease-related skeletal muscle mass loss. Evaluating muscle quality using a combination of PAcole, \({R}_{i}/{R}_{e}\), and β, rather than measuring skeletal muscle mass alone, may mean that skeletal muscle function can be predicted with higher accuracy than before. Prediction of skeletal muscle function using a combination of muscle mass and impedance parameters could be applied to various fields such as medical care, sports, and community-dwelling elderly once data have been accumulated considering race, gender, and age.

This study has several limitations. First, the subjects in this study were limited to healthy adult men. Given that gender, age, and nutritional status have an influence on the impedance parameters, the findings may not be applicable to other populations including women, the elderly, and sick patients. Second, it is necessary to consider that lower extremity impedance parameters in DSM-BIA may be affected by tissue impedance other than skeletal muscle. Furthermore, it has been shown that the 95% limits of agreement are larger than that of dual-energy X-ray absorptiometry and dilution-measured total body water methods [41]. In cases where it is possible to establish a measurement method that minimizes the effect of the skin and subcutaneous fat using electrical impedance myography applying BIA, it would be possible to improve the evaluation accuracy methods using skeletal muscle alone. Finally, this study did not perform physiological and anatomical evaluations showing evidence that PAcole, \({R}_{i}/{R}_{e}\), and β reflect the quality of the skeletal muscle.

Based on the results of this study, further research analyzing \({R}_{i}/{R}_{e}\) and β is required to increase the effectiveness of impedance parameters for evaluating muscle quality.


This study shows that PAcole, \({R}_{i}/{R}_{e}\), and β calculated using the Cole–Cole model are factors that independently affect muscle strength, even when muscle mass is added to the variables. Thus far, this is the first study showing an association between the lower extremity impedance parameters in DSM-BIA and skeletal muscle function. These factors can be measured noninvasively in a short period of time, making them effective as muscle quality indicators in a wide range of subjects. \({R}_{i}/{R}_{e}\) and β may present with more characteristic changes than the results of this study in the elderly and in patients with diseases, although these points need further investigation.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.


β :

Beta parameter (cell homogeneity)


Bioelectrical impedance analysis


Body mass index


Computed tomography


Direct segmental multi-frequency bioelectrical impedance analysis

f c :

Central relaxation frequency


Muscle intensity


Magnetic resonance imaging


Muscle thickness


Phase angle

PAcole :

Phase angle of Cole–Cole model

PA50 :

Phase angle of 50 kHz


Muscle thickness of quadriceps femoris


Muscle intensity of quadriceps femoris

R i /R e :

Ratio of intracellular fluid resistance to extracellular fluid resistance




Variance inflation factor


  1. Deschenes MR (2004) Effects of aging on muscle fibre type and size. Sports Med 34(12):809–824.

    Article  Google Scholar 

  2. Correa-de-Araujo R, Harris-Love MO, Miljkovic I, Fragala MS, Anthony BW, Manini TM (2017) The need for standardized assessment of muscle quality in skeletal muscle function deficit and other aging-related muscle dysfunctions: a symposium report. Front Physiol.

    Article  Google Scholar 

  3. Manini TM, Clark BC, Nalls MA, Goodpaster BH, Ploutz-Snyder LL, Harris TB (2007) Reduced physical activity increases intermuscular adipose tissue in healthy young adults. Am J Clin Nutr 85:377–384.

    CAS  Article  Google Scholar 

  4. Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T, Cooper C, Landi F, Rolland Y, Sayer AA, Schneider SM, Sieber CC, Topinkova E, Vandewoude M, Visser M, Zamboni M, Writing Group for the European Working Group on Sarcopenia in Older People 2 (EWGSOP2), and the Extended Group for EWGSOP2 (2019) Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing 48(1):16–31.

    Article  Google Scholar 

  5. Brown JC, Harhay MO, Harhay MN (2016) Sarcopenia and mortality among a population-based sample of community-dwelling older adults. J Cachexia Sarcopenia Muscle 7:290–298.

    Article  Google Scholar 

  6. Sayer AA, Syddall HE, Martin HJ, Dennison EM, Roberts HC, Cooper C (2006) Is grip strength associated with health related quality of life? Findings from the Hertfordshire Cohort Study. Age Ageing 35:409–415.

    Article  Google Scholar 

  7. Ticinesi A, Meschi T, Narici MV, Lauretani F, Maggio M (2017) Muscle ultrasound and sarcopenia in older individuals: a clinical perspective. J Am Med Dir Assoc 18(4):290–300.

    Article  Google Scholar 

  8. Loizides A, Gruber H, Peer S, Plaikner M (2017) Muscular injuries of athletes: Importance of ultrasound. Radiologe 57(12):1019–1028.

    CAS  Article  Google Scholar 

  9. Mayans D, Cartwright MS, Walker FO (2012) Neuromuscular ultrasonography: quantifying muscle and nerve measurements. Phys Med Rehabil Clin N Am 23:133–xii.

    Article  Google Scholar 

  10. Young H-J, Jenkins NT, Zhao Q, McCully KK (2015) Measurement of Intramuscular Fat by Muscle Echo Intensity. Muscle Nerve 52:963–971.

    Article  Google Scholar 

  11. Ling CHY, de Craen AJM, Slagboom PE, Gunn DA, Stokkel MPM, Westendrop RGJ, Maier AB (2011) Accuracy of direct segmental multi-frequency bioimpedance analysis in the assessment of total body and segmental body composition in middle-aged adult population. Clin Nutr 30:610–615.

    Article  Google Scholar 

  12. Foster KR, Lukaski HC (1996) Whole-body impedance—what does it measure? Am J Clin Nutr 64(3 Suppl):388S-396S.

    CAS  Article  Google Scholar 

  13. Mullie L, Obrand A, Bendayan M, Trnkus A, Ouimet MC, Moss E, Chen-Tournoux A, Rudski LG, Afilalo J (2018) Phase Angle as a Biomarker for Frailty and Postoperative Mortality: The BICS Study. J Am Heart Assoc 7(17):e008721.

    Article  Google Scholar 

  14. Kilic MK, Kizilarslanoglu MC, Arik G, Bolayir B, Kara O, Dogan Varan H, Sumer F, Kuyumcu ME, Halil M, Ulger Z (2017) Association of bioelectrical impedance analysis-derived phase angle and sarcopenia in older adults. Nutr Clin Pract 32(1):103–109.

    Article  Google Scholar 

  15. Hetherington-Rauth M, Baptista F, Sardinha LB (2019) BIA-assessed cellular hydration and muscle performance in youth, adults, and older adults. Clin Nutr.

    Article  Google Scholar 

  16. Stahn A, Strobel G, Terblanche E (2008) VO2max prediction from multi-frequency bioelectrical impedance analysis. Physiol Meas 29(2):193–203.

    Article  Google Scholar 

  17. Uemura K, Yamada M, Saho K (2019) Association of bio-impedance phase angle and physical activity level in older adult. Jpn Phys Ther Assoc 46(3):143–151.

    Article  Google Scholar 

  18. Arnold WD, Taylor RS, Li J, Nagy JA, Sanchez B, Rutkove SB (2017) Electrical impedance myography detects age-related muscle change in mice. PLoS ONE 12(10):e0185614.

    CAS  Article  Google Scholar 

  19. Cole KS, Cole RH (1941) Dispersion and absorption in dielectrics I. Alternating current characteristics. J Chem Phys 9:341–351

    CAS  Article  Google Scholar 

  20. Sanchez B, Rutkove SB (2017) Electrical impedance myography and its applications in neuromuscular disorders. Neurotherapeutics 14(1):107–118.

    Article  Google Scholar 

  21. Barbosa-Silva MC, Barros AJ, Wang J, Heymsfield SB, Pierson RN (2005) Bioelectrical impedance analysis: population reference values for phase angle by age and sex. Am J Clin Nutr 82(1):49–52.

    CAS  Article  Google Scholar 

  22. Yamada Y, Buehring B, Krueger D, Anderson RM, Schoeller DA, Binklet N (2016) Electrical properties assessed by bioelectrical impedance spectroscopy as biomarkers of age-related loss of skeletal muscle quantity and quality. J Gerontol A Biol Sci Med Sci 72:1180–1186.

    Article  Google Scholar 

  23. Fricke H, Morse S (1925) The electrical resistance and capacity of blood for frequencies between 800 and 4½ million cycles. J Gen Physiol 9(2):153–167.

    CAS  Article  Google Scholar 

  24. Kapur K, Nagy JA, Taylor RS, Sanchez B, Rutkove SB (2018) Estimating myofiber size with electrical impedance myography: a study in amyotrophic lateral sclerosis MICE: EIM estimates myofiber size. Muscle Nerve 58:713–717.

    CAS  Article  Google Scholar 

  25. Kushner RF, Gudivaka R, Schoeller DA (1996) Clinical characteristics influencing bioelectrical impedance analysis measurements. Am J Clin Nutr 64:423S-427S

    CAS  Article  Google Scholar 

  26. Berger J, Bunout D, Barrera G, de la Maza MP, Henriquez S, Leiva L, Hirsch S (2015) Rectus femoris (RF) ultrasound for the assessment of muscle mass in older people. Arch Gerontol Geriatr 61:33–38.

    Article  Google Scholar 

  27. Sato H, Kuniyasu K, Kobara K, Okada Y, Kawashima T, Shinonaga A, Yamamoto S, Yasunaga M, Hanayama K (2018) Verification of the accuracy of measuring the muscle cross- sectional area and muscle intensity of the rectus femoris using ultrasonography. Jpn J Compr Rehabil Sci 9:66–72.

    Article  Google Scholar 

  28. Kanda Y (2013) Investigation of the freely available easy-to-use software ‘EZR’’ for medical statistics’. Bone Marrow Transplant 48:452–458.

    CAS  Article  Google Scholar 

  29. Prado CM, Purcell SA, Alish C, Pereira SL, Deutz NE, Heyland DK, Goodpaster BH, Tappenden KA, Heymsfield SB (2018) Implications of low muscle mass across the continuum of care: a narrative review. Ann Med 50(8):675–693.

    Article  Google Scholar 

  30. Dos Santos L, Cyrino ES, Antunes M, Santos DA, Sardinha LB (2016) Changes in phase angle and body composition induced by resistance training in older women. Eur J Clin Nutr 70(12):1408–1413.

    Article  Google Scholar 

  31. Khalil SF, Mohktar MS, Ibrahim F (2014) The theory and fundamentals of bioimpedance analysis in clinical status monitoring and diagnosis of diseases. Sensors (Basel) 14(6):10895–928.

    Article  Google Scholar 

  32. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Gómez JM, Heitmann BL, Kent-Smith L, Melchior JC, Pirlich M, Scharfetter H, Schols AM, Pichard C, Composition of the ESPEN Working Group (2004) Bioelectrical impedance analysis–part I: review of principles and methods. Clin Nutr 23(5):1226–43.

    Article  Google Scholar 

  33. Taniguchi M, Yamada Y, Fukumoto Y, Sawano S, Minami S, Ikezoe T, Watanabe Y, Kimura M, Ichihashi N (2017) Increase in echo intensity and extracellular-to-intracellular water ratio is independently associated with muscle weakness in elderly women. Eur J Appl Physiol 117:2001–2007.

    Article  Google Scholar 

  34. Kapur K, Taylor RS, Qi K, Nagy JA, Li J, Sanchez B, Rutkove SB (2018) Predicting myofiber size with electrical impedance myography: a study in immature mice: EIM can predict myofiber size. Muscle Nerve 58:106–113.

    CAS  Article  Google Scholar 

  35. Yamada Y, Schoeller DA, Nakamura E, Morimoto T, Kimura M, Oda S (2010) Extracellular water may mask actual muscle atrophy during aging. J Gerontol A Biol Sci Med Sci 65A:510–516.

    Article  Google Scholar 

  36. Yoshida T, Yamada Y, Tanaka F, Yamagishi T, Shibata S, Kawakami Y (2018) Intracellular-to-total water ratio explains the variability of muscle strength dependence on the size of the lower leg in the elderly. Exp Gerontol 113:120–127.

    Article  Google Scholar 

  37. Bourgeois B, Fan B, Johannsen N, Gonzalez MC, Ng BK, Sommer MJ, Shepherd JA, Heymsfield SB (2019) Improved strength prediction combining clinically available measures of skeletal muscle mass and quality. Journal of Cachexia, Sarcopenia and Muscle 10:84–94.

    Article  Google Scholar 

  38. Addison O, Marcus RL, Lastayo PC, Ryan AS (2014) Intermuscular fat: a review of the consequences and causes. Int J Endocrinol 2014:309570.

    CAS  Article  Google Scholar 

  39. Martins PC, Moraes MS, Silva DAS (2020) Cell integrity indicators assessed by bioelectrical impedance: a systematic review of studies involving athletes. J Bodyw Mov Ther 24(1):154–164.

    Article  Google Scholar 

  40. Seino S, Shinkai S, Iijima K, Obuchi S, Fujiwara Y, Yoshida H, Kawai H, Nishi M, Murayama H, Taniguchi Y, Amano H, Takahashi R (2015) Reference values and age differences in body composition of community-dwelling older japanese men and women: a pooled analysis of Four Cohort Studies. PLoS ONE 10(7):e0131975.

    CAS  Article  Google Scholar 

  41. Ward LC (2019) Bioelectrical impedance analysis for body composition assessment: reflections on accuracy, clinical utility, and standardization. Eur J Clin Nutr 73(2):194–199.

    Article  Google Scholar 

Download references


The authors also thank all individuals who participated in the study. This study was not funded by any institutions, agencies, or companies.


Not applicable.

Author information

Authors and Affiliations



Conceptualization: NT, KK, KK, HK. Data curation and formal analysis: KK, KT. Investigation and methodology: NT, KT, KT. Wrote the paper: NT, KT. Study concept and design: NT, KK, KK, HK. Performed the experiments and acquisition of data: KK, KT. Analysis and interpretation of the data: NT, KT, KT. Drafted the paper: NT, KT. Critical revision: NT, KT, KK, KK, HK. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Hiroki Sato.

Ethics declarations

Ethics approval and consent to participate

The study protocol was approved by the ethics review board of Kawasaki Medical School (Approval No.: 2846). Written informed consent was obtained from all participants.

Consent for publication

Written informed consent for publication was obtained from all participants.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sato, H., Nakamura, T., Kusuhara, T. et al. Effectiveness of impedance parameters for muscle quality evaluation in healthy men. J Physiol Sci 70, 53 (2020).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • Phase angle
  • Bioelectrical impedance analysis
  • Cole–Cole model
  • Muscle quality