
==== Front
Eur J Sport Sci
Eur J Sport Sci
10.1002/(ISSN)1536-7290
EJSC
European Journal of Sport Science
1746-1391
1536-7290
John Wiley and Sons Inc. Hoboken

39106159
10.1002/ejsc.12167
EJSC12167
Original Paper
ORIGINAL PAPER
Physiology and Nutrition
Association between muscle‐localized bioelectrical impedance analysis parameters and performance in a multi‐set exercise on the isokinetic dynamometer in young women
Fukuoka Aryanne H. https://orcid.org/0000-0002-7561-9591
1 2 aryanneuenp@gmail.com

Oliveira Núbia M. 2
Matias Catarina N. 3
Guariglia Débora A. 1
Guerra‐Júnior Gil 2
Gonçalves Ezequiel M. 1 2
1 Health Sciences Center State University of Northern Parana (UENP) Jacarezinho Parana Brazil
2 Laboratory of Growth and Development (LabCreD) Center for Investigation in Pediatrics (CIPED) School of Medical Sciences (FCM) State University of Campinas (UNICAMP) Campinas Sao Paulo Brazil
3 CIDEFES: Centro de Investigação em Desporto, Educação Física, Exercício e Saúde Universidade Lusófona Lisbon Portugal
* Correspondence
Aryanne H. Fukuoka, Health Sciences Center, State University of Northern Parana (UENP), Alameda Padre Magno, 134, Jardim Europa, Jacarezinho 86400‐000, Parana, Brazil.
Email: aryanneuenp@gmail.com

06 8 2024
9 2024
24 9 10.1002/ejsc.v24.9 13191327
11 5 2024
12 12 2023
24 6 2024
© 2024 The Author(s). European Journal of Sport Science published by Wiley‐VCH GmbH on behalf of European College of Sport Science.
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

Abstract

This study aimed to verify the relationship between changes in thigh muscle‐localized bioelectrical impedance analysis (ML‐BIA) parameters and performance in a multiple‐set exercise. The sample consisted of 30 female university students (22.1 ± 3.2 years). The ML‐BIA parameters, including localized muscle resistance (ML‐R), reactance (ML‐Xc), and phase angle (ML‐AngF), were evaluated using a tetrapolar bioelectric impedance device operating at a frequency of 50 KHz. The multiple sets protocol was performed with an isokinetic dynamometer. For body composition, total and leg lean soft tissue (LST) were evaluated using dual X‐ray absortiometry. Student's t‐test for paired samples was used to compare the ML‐BIA parameters and thigh circumference pre and postexercise. Linear regression analysis was performed to verify the ∆ML‐PhA as a predictor of peak torque for the three sets alone while controlling for total and leg LST. There were differences in the ML‐R (∆ = 0.02 ± 1.45 Ω; p = 0.001; and E.S = 0.19), ML‐Xc (∆ = 2.90 ± 4.12 Ω; p = 0.043; and E.S = 0.36), and thigh circumference (∆ = 0.82 ± 0.60 cm; p < 0.001; and E.S = 0.16) pre‐ and post‐multiple sets. ΔML‐PhA was a predictor of performance in the first set (p = 0.002), regardless of total and leg LST. However, the ΔML‐PhA lost its explanatory power in the other sets (second and third), and the variables that best explained performance were total and leg LST. The ML‐BIA (ML‐R and ML‐Xc) parameters were sensitive and changed after the multiple sets protocol, and the ΔML‐PhA was a predictor of performance in the first set regardless of the total and leg LST.

Highlights

Muscle‐localized bioimpedance analysis (ML‐BIA) is a noninvasive and inexpensive method used to assess specific muscle groups. It is already known that raw parameters, such as Xc, R, and PhA, can reflect cell hydration, volume, and membrane integrity. We demonstrate that these parameters can also reflect acute changes resulting from exercise.

The muscle resistance and ML‐Xc values decreased after the exercise protocol, accompanied by increases in thigh circumference. These findings contribute to explaining the phenomenon of local muscle swelling.

The ∆AngF accounted for 39%, 28%, and 24% of the variation in the first, second, and third sets of the multiple series exercise. Notably, in the first set, it remained a significant predictor even when controlled for total and leg lean soft tissue.

muscle localized
muscle strength
university students
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior 10.13039/501100002322 88887.830994/2023‐00 1577/202388881.859218/2023‐01 source-schema-version-number2.0
cover-dateSeptember 2024
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.4.8 mode:remove_FC converted:03.09.2024
Aryanne H. Fukuoka and Núbia M. Oliveira contributed equally to this work.
==== Body
pmc1 INTRODUCTION

Muscle‐ localized bioelectrical impedance analysis (ML‐BIA) is a noninvasive, painless, and inexpensive method to assess specific muscle groups (Cebrián‐Ponce et al., 2021). This method can quantify the muscular electrical behavior (Rutkove, 2009), providing information about its composition and structure (Sanchez et al., 2017a). The ML‐BIA involves applying an electrical current to biological tissue, similar to the traditional BIA method. The tissue's response to the current flow provides biophysical parameters, such as resistance (ML‐R), which indicates the amount of water and electrolytes in the tissue, and reactance (ML‐Xc), which arises from the capacitive property of cell membranes. Additionally, PhA represents the geometrical quantification of the ratio between Xc and R. PhA is recognized as an indicator of the intra/extracellular water ratio and cell integrity and is associated with muscle strength and power (Custódio Martins et al., 2022; Oliveira et al., 2023; Sardinha et al., 2023; Ward et al., 2023). The ML‐BIA has been used to investigate muscle changes related to various disorders, such as amyotrophic lateral sclerosis (Tarulli et al., 2009), muscular atrophy and dystrophy (Rutkove et al., 2010, 2014; Statland et al., 2016), sarcopenia (Aaron et al., 2006; Rutkove et al., 2008; de‐Mateo‐Silleras et al., 2018), and muscular injuries in athletes (Francavilla et al., 2015; Nescolarde et al., 2013, 2015, 2017, 2020).

Regarding muscle adaptations induced by exercise and its impact on ML‐BIA parameters, there are still few studies. Researchers (Mascherini et al., 2015) reported a decrease in ML‐R and increases in ML‐PhA of the quadriceps muscles after 50 days of soccer training; nevertheless, there were no significant changes in ML‐Xc. However, most studies using ML‐BIA investigated acute adaptations (<24 h) to physical exercise (Freeborn et al., 2019, 2020; Fu et al., 2018; Huang et al., 2020; Li et al., 2016; Shiffman et al., 2003) showing changes mainly in the ML‐R value, indicative of resistance to the electrical current through intra and extracellular ionic fluids, demonstrating that this is the most sensitive parameter to acute changes to exercise. Nonetheless, all these studies analyzed only arm muscles; as this is a small muscle group, it does not allow extrapolation of this behavior to other muscle groups. Another factor that may play an essential role in the ML‐BIA analysis is sex, with women having higher values of ML‐R/H (adjusted by the segment length) and lower values of ML‐Xc/H and ML‐PhA/H (Mascherini et al., 2017); nevertheless, the aforementioned studies comprised only male samples.

Resistance training is one of the primary interventions for gaining muscle mass, promoting health, and performance benefits (Kraemer et al., 2002), such as reductions in body fat (Wewege et al., 2022), osteoporosis risk (Kitsuda et al., 2021; Zhao et al., 2015), improvements in muscle function (Seo et al., 2021), strength (Lopez et al., 2021), power, resistance (Kraemer et al., 2002), and hypertrophy (Schoenfeld et al., 2017). The progression of a weight training program for healthy adults is specific, individualized, and manipulated by variables, such as muscle action, exercises, order, load, volume, interval, speed, and frequency (American College of Sports Medicine, 2009), and must be different between beginners, intermediate, and advanced practitioners. Among training programs, multiple sets have been associated with significant gains in muscle size compared to a single‐set program (Krieger, 2010).

Multiple‐set exercises induce acute effects on the skeletal muscle, characterized by increased intramuscular pressure due to fluid accumulation and damage to muscle fibers. These effects lead to spontaneous shortening of muscle fibers, accompanied by increased muscle hardness, cross‐sectional area, and circumference (Akagi et al., 2015; Damas et al., 2016; Dankel et al., 2020; Yasuda et al., 2015). Despite previous studies (Freeborn et al., 2019, 2020; Fu et al., 2018; Huang et al., 2020; Li et al., 2016) have demonstrated that these acute effects may influence ML‐BIA parameters, limited research has explored the acute impact of multiple sets in lower limb exercises on ML‐BIA parameters, particularly in women.

Therefore, using an isokinetic dynamometer, this study aimed to verify the behavior of ML‐BIA parameters before and after an exercise protocol and the relationship between changes in thigh ML‐BIA parameters and performance in a multiple‐set exercise in young women. Our hypothesis is that the ML‐R parameter will exhibit the highest sensitivity to exercise‐induced alterations and will decrease possibly due to a higher local fluid volume immediately postexercise (Damas et al., 2016; Dankel et al., 2020; Freeborn et al., 2020). Conversely, regarding the ML‐Xc parameter, although muscle damage typically results in its reduction (Nescolarde et al., 2013, 2015) and previous studies have demonstrated decreases after exercise in arm muscles (Freeborn et al., 2019, 2020), it remains unclear whether an acute multi‐set lower limb exercise protocol will induce significant changes in muscular structures to prompt notable reductions in this parameter. Theoretically, PhA demonstrates an inverse correlation with the R parameter and a positive association with Xc (Baumgartner et al., 1988) with the latter exerting a greater influence on PhA alterations. A decrease in PhA, indicating reduced Xc, is linked to diminished body cell mass and compromised selective permeability function of cellular membranes (Sardinha et al., 2023). Consequently, we propose that significant variations in PhA values will primarily depend on the behavior of the Xc parameter.

2 MATERIALS AND METHODS

2.1 Participants

The sample consisted of 30 young women college students (22.1 ± 3.2 years; 63.37 ± 9.5 kg; and 162.4 ± 4.2 cm) body composition (total lean soft tissue [LST]): 38.7 ± 4.6 kg; total fat mass (32.8 ± 7.1%; leg LST: 7.0 ± 1.1 kg; and leg fat mass 4.3 ± 1.4 kg). Inclusion criteria were (I) being female; (II) age ≥18 years; and (III) being a college student. The exclusion criteria were (I) did not signed the informed consent term; (II) having metal objects on the body that cannot be removed; (III) using medications and/or supplements that could interfere with test results, and (IV) having physical limitations to perform the isokinetic test.

All participants who agreed to participate in the study signed the free and informed consent term. This research was approved by the ethics committee nº55537521.7.0000.8123 following the Declaration of Helsinki of research involving human beings.

2.2 Study design

This research is part of a cross‐sectional study in which the participants visited our facilities twice for the assessments. Data collection took place at the Multiuser Laboratory of Biodynamics of Human Movement—Health Sciences Center—State University of Northern Paraná (UENP), Jacarezinho, Parana, Brazil. Participants also attended a Medical Diagnostic Imaging Center to perform the body composition exam. In the first visit, we assessed anthropometric measurements, followed by total and ML‐BIA measurements. Subsequently, participants underwent the multiple sets protocol on the isokinetic dynamometer, after which BIA measurements were immediately taken again. In the second visit, participants attended the Medical Diagnostic Imaging Center for dual X‐ray absortiometry (DXA) assessment. The evaluations were previously scheduled with the participants, and the two visits were separated by 48 h of distance. The assessments took place in the evening, during college class hours, and the subjects were instructed about clothing and being adequately hydrated and fed or asked to be fasted according to the characteristics of each test.

2.3 Anthropometric measurements

To assess body mass, the participant should stand upright, looking forward, arms hanging loosely at the side of the body, and feet slightly apart. The evaluation was performed using a digital scale (Lider/P‐200C) with a precision of 100 g. Height was measured in the same position using a stadiometer (WELMY R‐110) with a precision of 0.1 cm. The thigh circumference was measured at the midpoint between the anterior inferior iliac crest and the superior pole of the patella. Circumference measurements were taken using a tape measure (MacroLife®). Three measurements were performed, maintaining the mean value between measurements.

2.4 Isokinetic dynamometer test

A polyarticular isokinetic dynamometer, Biodex®, System 3 model (Biodex), calibrated according to the manufacturer's specifications and recommendations, was used to determine the lower limb muscle strength. The test was performed on the right lower limb. Knee flexion and extension movements were performed, with three sets of 10 repetitions, with 1 min of rest between sets. The isokinetic parameters with their respective values were obtained by the equipment integrated program, and for this study, we used peak torque (muscle strength).

2.5 Body composition assessment

Body composition was assessed using the dual‐energy x‐ray absorptiometry (DXA) DPX‐NT model (GE Medical Lunar System). The device provided total and segmental body measurements. The participants were positioned in dorsal decubitus and free of any metallic object in the body. All measurements and instrument calibration were performed following the procedures recommended by the manufacturer. Total and leg Lean Soft Tissue (LST) and %fat mass (%FM) values were determined.

The tetrapolar BIA device, model Quantum II (RJL Systems, Detroit, USA), operating at a single frequency of 50 KHz, was utilized to measure ML‐BIA parameters. For the localized assessment of the quadriceps, the participant was in the supine position, with the electrodes attached 5 and 10 cm distally from the anterior inferior iliac spine and close to the superior pole of the patella (Figure 1). The accuracy of our BIA device was determined by the coefficient of variation (%CV) and the technical error of measurement (TEM) based on the test–retest method. To assess only the equipment variation, two measurements were obtained from eight individuals at the same time with the individual in the same position. The %CV was 0.66 and 0.77 for ML‐Xc and ML‐R, respectively, and the TEM was 0.71 Ω for ML‐R and 0.25 Ω for ML‐Xc. The variation of BIA parameters on subsequent days was also determined in 12 subjects. The %CV was 4.90 and 4.41 for ML‐Xc and ML‐R, respectively, and the TEM was 3.45 Ω for ML‐R and 0.71 Ω for ML‐Xc. The ML‐PhA was calculated using the equation: PhA=arctangentXcR×180π

FIGURE 1 ML‐BIA electrodes positioning on the anterior thigh muscles.

2.6 Statistical analysis

Data were analyzed using SPSS version 25.0. The Shapiro–Wilk normality test was performed. When the data did not show normal distribution, they were transformed by log or Bloom scores. Data are presented as mean ± standard deviation and minimum and maximum values. ANOVA for repeated measures was used to compare peak torque between multiple sets. Student's t‐test for paired samples and the Wilcoxon test were performed to verify changes in thigh ML‐BIA parameters and thigh circumference pre and post‐performance tests. For effect size (E.S) measurements, the Glass's delta and the r value were calculated using the formulas (M‐post‐Mpre)/SDpre and r = z/√n, respectively. The Glass's delta was interpreted (Sawilowsky, 2009) as 0.01 = very small, 0.2 = small, 0.5 = medium, 0.8 = large, 1.2 = very large, and 2.0 = huge, while the r value was interpreted (Cohen, 1992) as 0.00–0.19 = trivial, 0.20–0.49 = small, 0.50–0.79 = moderate, and ≥0.80 as large. Changes in BIA parameters before and after exercise (Mpost–Mpre) were calculated (∆ML‐R, ∆ML‐Xc, and ∆ML‐PhA). The Pearson correlation coefficient was used to verify the correlation between the ML‐BIA parameters, body composition, and peak torque of the three sets. Linear regression analysis was performed to verify the relationship between ∆ML‐PhA and peak torque of the three sets and adjusted using the total and leg LST. For all analyses, a p‐value <0.05 was considered significant.

3 RESULTS

There were no differences between the three sets of peak torque (F = 0.240; p = 0.709) as illustrated in Figure 2.

FIGURE 2 Violin plots of first, second, and third sets of peak torque.

Differences were found in the measurements of ML‐R (∆ = −2.90 ± 4.12 Ω; p = 0.001), ML‐Xc (∆ = −0.77 ± 2.48 Ω; p = 0.043), and thigh circumference (∆ = 0.82 ± 0.60 cm; p < 0.001) pre and post‐multiple sets on the isokinetic dynamometer as shown in Table 1.

TABLE 1 Comparison of ML‐BIA parameters and circumference before and after multiple sets.

	Before	After	Δ	p	E. S	
ML‐R (ohm)	56.6 ± 8.6	53.7 ± 7.4	−2.90 ± 4.12	0.001	0.19 a	
ML‐Xc (ohm)	12.0 (3.0)	12.0 (7.0)	−0.77 ± 2.48	0.043	0.36 b	
ML‐PhA (ohm)	12.6 (2.1)	12.9 (2.1)	−0.02 ± 1.45	0.453	0.14 b	
Thigh circumference (cm)	56.1 ± 4.9	56.9 ± 5.0	0.82 ± 0.60	<0.001	0.16 a	
Note: Values are expressed as mean ± standard deviation or median (interquartile range). Bold values are significant.

Abbreviations: E. S, effect size; ML‐PhA, muscle‐localized phase angle; ML‐R, muscle localized resistance; ML‐Xc, muscle‐localized reactance.

a Delta of Glass.

b r.

The correlation analysis showed that the ∆ML‐PhA (Ω) and ∆ML‐Xc (Ω) were inversely correlated with the first set (p < 0.001; p = 0.008), second set (p = 0.002; p = 0.034), and third set (p = 0.004; p = 0.048) of peak torque, respectively. Regarding body composition, the total LST showed a direct correlation in the three sets of peak torque (first set p = 0.002; second set p < 0.001; and third set p < 0.001). The leg LST did not correlate with the first set of peak torque, but it showed a direct correlation in the second (p = 0.005) and third sets (p < 0.001).

An association between the peak torque for each exercise set on the isokinetic dynamometer and the ∆ML‐PhA (post and pre‐multiple sets protocol) of the thigh was found, explaining 39%, 28%, and 24% of the peak torque variation of the first, second, and third sets (Figure 3 left upper, middle, and lower panels, respectively). At the same time, LST explained 28% (first set), 39% (second set), and 55% (third set) of the peak torque variation (Figure 3 right upper, middle, and lower panels, respectively).

FIGURE 3 Linear relation between performance in multiple sets, ∆ML‐PhA, and total LST.

When the thigh ∆ML‐PhA was adjusted for the total LST (model 1) and the leg LST (model 2) at peak torque of multiple sets, thigh ∆ML‐PhA remained significant only in the first set (model 1: β = 0.484; p = 0.005 and model 2: β = 0.605; p = 0.001). For the average peak torque, when the ∆ML‐PhA was adjusted for total and leg LSTs, it remained independently significant in all models (model 1: β = 0.374; p = 0.022 and model 2: β = 0.485; p = 0.007) (Table 2).

TABLE 2 Linear regression analysis showing the relation of peak torque and ΔML‐PhA in the multiple sets adjusted by total and right leg LST.

	B	SE	β	p	r 2	p	
Peak torque–first set	
Model 1	ΔML‐PhA	0.043	0.014	0.484	0.005	0.446	<0.001	
LST total (g)	5958	0.000	0.316	0.056			
Model 2	ΔML‐PhA	0.054	0.015	0.605	0.001	0.369	0.001	
LST right leg (g)	6219	0.000	0.076	0.649			
Peak torque–second set	
Model 1	ΔML‐PhA	0.029	0.015	0.310	0.061	0.444	<0.001	
LST total (g)	9609	0.000	0.489	0.005			
Model 2	ΔML‐PhA	0.038	0.016	0.409	0.022	0.335	0.002	
LST right leg (g)	2703	0.000	0.316	0.073			
Peak torque–third set	
Model 1	ΔML‐PhA	0.018	0.014	0.189	0.188	0.566	<0.001	
LST total (g)	1360	0.000	0.662	<0.001			
Model 2	ΔML‐PhA	0.028	0.015	0.292	0.075	0.423	<0.001	
LST right leg (g)	4474	0.000	0.499	0.004			
Average peak torque–multiple sets	
Model 1	ΔML‐PhA	0.033	0.014	0.374	0.022	0.482	<0.001	
LST total (g)	8657	0.000	0.459	0.006			
Model 2	ΔML‐PhA	0.043	0.015	0.485	0.007	0.366	0.001	
LST right leg (g)	2100	0.000	0.255	0.133			
Note: Bold values are significant.

Abbreviations: ∆ML‐PhA, changes observed for muscle localized PhA; LST, lean soft tissue.

4 DISCUSSION

The objective of our study was to investigate the relationship between ML‐BIA parameters and exercise performance in young women. We found that ML‐ R and Xc decreased after the multiple sets exercise (p < 0.05). Furthermore, ΔML‐PhA was a predictor of performance in the first set (p = 0.002), regardless of the total and leg LST. However, ΔML‐PhA lost its explanatory power through the other sets (second and third), and the variables that best explained the performance were the total and leg LST. The ∆ML‐PhA showed a positive association with mean peak torque, regardless of total and leg LST.

No significant changes were observed for ML‐PhA; however, differences were noted in ML‐R, ML‐Xc, and thigh circumference. The expected decrease in ML‐R and increase in thigh circumference suggest an increase of local fluid volume in the exercised region. Consequently, the tissue offers less resistance to electrical current flow, thereby decreasing ML‐R values. This is similar to a study that reported increases in biceps thickness assessed by ultrasound after several sets of low‐load resistance exercise (barbell curl) until exhaustion; this could result from an increase in blood flow and edema‐induced muscle swelling (Yasuda et al., 2015). Regarding the ML‐Xc parameter, despite previous findings indicating reductions after exercise protocols in arm muscles (Freeborn et al., 2019, 2020), we were uncertain whether our exercise protocol would effectively decrease Xc values measured in the quadriceps region. However, our results confirmed a significant reduction caused by the multiple‐set leg extension exercise protocol. The decrease in the Xc parameter may indicate exercise‐induced changes in tissue structures, possibly due to muscle fiber damage. However, further investigation is necessary to confirm this, including measurements of both tissue impedance and markers of muscle damage. Additionally, previous research has demonstrated that in professional football players, the degree of injury correlates with lower ML‐Xc values (Rutkove et al., 2008).

Although the multiple sets protocol (3 sets of 10 repetitions, with 60 s of interval) did not induce significant changes in peak torque, it was enough to cause a decrease in ML‐R and ML‐Xc values without causing changes in ML‐ PhA. Similarly, it was verified by Huang and colleagues, that in the biceps brachii, a decrease in the ML‐R derived from the BIA was verified at different intensities (20%, 40%, and 60% of the maximum voluntary contraction), also without alterations in the ML‐PhA (Huang et al., 2020). Conversely, researchers observed changes in ML‐R after the three sets of barbell curls with dumbbells, and in ML‐Xc and ML‐PhA only after the 4th set (Freeborn et al., 2019); in addition, during the 10 sets with repetitions up to failure, changes in ML‐R, ML‐Xc, and ML‐PhA occurred earlier with lower intensities (60% of 1RM) compared to higher ones (75% of 1RM), suggesting that changes in these parameters are related to greater volume (sets/repetitions). Another noteworthy aspect of our study is that the protocol was conducted on an isokinetic dynamometer, where the load imposed by the device adjusts according to the force that the individual can apply in each repetition. As a result, this may have contributed to less fatigue and muscular damage over the sets, potentially influencing the observed changes. Although significant, these changes were not of great magnitude, as demonstrated by the small effect sizes for ML‐R (E. S = 0.19) and Xc (E. S = 0.36), and were not sufficient to alter ML‐PhA.

Previous studies have shown a relationship between total PhA and muscle power (Martins et al., 2021; Nabuco et al., 2019) and performance levels (Micheli et al., 2014) in soccer athletes, upper limb strength and lower limb power in physically active adults (Fukuoka et al., 2022), and athletes from different sports (Koury et al., 2014); nevertheless to the best of our knowledge, this is the first study that sought to verify the relationship between ∆ML‐PhA and changes in isokinetic multiple sets performance. In the first set, the ∆ML‐PhA was a predictor of performance regardless of body composition; however, losing explanatory power from the second set. Interestingly, ∆ML‐PhA changes occurred heterogeneously, with values decreasing in about half of the sample and ML‐PhA values increasing in the other half. What may help to justify these observed differences was that the part of the sample that showed increases in ML‐PhA values after multiple set. also had the highest values of total and leg LST and peak torque compared to those that decreased the ML‐PhA values after multiple sets, and as demonstrated, LST was a predictor of performance in this sample. Similar to a previous study (Fukuoka et al., 2022), we found a direct relationship between LST and performance, that is, the greater the total and leg LSTs, the greater the performance values at peak torque.

ML‐BIA has been used to investigate muscle changes related to muscle injuries in athletes (Francavilla et al., 2015; Nescolarde et al., 2013, 2015, 2017, 2020), and in general, the impedance values of the injured muscle decrease, according to the degree of injury, indicate an increase in inflammation and fluid accumulation (Nescolarde et al., 2013, 2015) and its gradual recovery over time. The direct measure of muscle changes requires expensive and invasive methods such as magnetic resonance imaging and biopsies. In this sense, these are initial studies showing that ML‐BIA can be a practical, noninvasive, and relatively inexpensive follow‐up technique to assess the muscle quality and health (Sanchez et al., 2017b).

The portability of BIA devices allows the adoption of this method to monitor changes in tissues during activities outside of controlled clinical environments. Considering training control and prescription, the status of fatigue and muscle damage decreases strength and performance during exercise (Clarkson et al., 1992). It has been shown that BIA parameters can be indicative of muscle function (power and strength), and declines in Xc and PhA follow decreases in muscle strength (Matias et al., 2021; Norman et al., 2015; Oliveira et al., 2023). Given the relationship between ∆ML‐PhA and strength performance in multiple set exercises demonstrated in our study, coupled with the practical limitations of isokinetic dynamometers and the care and preparation required for 1RM tests, this method could serve as an accessible and safe alternative tool for monitoring overtraining and muscular fatigue.

Despite these relevant results, some limitations were acknowledged and should be considered. Our results are of practical laboratory interest that use the same model of equipment; therefore, this method may not apply to other BIA models, and it should be tested to be confirmed. The study design is cross‐sectional, not being able to determine the cause and effect relationship. Therefore, for now it should be used only in a transversal approach and not have a recommended standardization of electrode placement in a localized manner; nevertheless, Figure 1 discloses and addresses this point of interest. Besides all these, also some strengths must be pointed out. This study is unprecedented, verifying the ML‐BIA parameters change in response to a multiple‐set exercise in which the sample comprises women. Strength performance was determined by equipment considered to be the gold standard (isokinetic dynamometer). The body composition assessment was not carried out using predictive equations; we used DXA, a method with demonstrated high validity and reproducibility for body composition evaluation.

5 CONCLUSION

In this sample of young women, a multiple‐set lower limb exercise protocol induced changes in the ML‐R and Xc measured in the quadriceps. ∆ML‐PhA showed an association with the performance of the multiple‐set exercise in the first set and in the average of peak torque regardless of total and right leg LST. These findings may show that the ML‐BIA parameters are sensitive to exercise acute changes in the skeletal muscle and could be used as indicators of muscle strength in young women. Methods for quantifying muscular alterations outside of the controlled laboratory environment can assist in training prescription and monitoring, prevent overtraining, and may aid in identifying risks of musculoskeletal injuries.

AUTHOR CONTRIBUTIONS

Aryanne H. Fukuoka; Núbia M. Oliveira, Ezequiel M. Gonçalves: study concept, design and data collection, Aryanne H. Fukuoka, Ezequiel M. Gonçalves: data analysis, interpretation and statistical analyses, Aryanne H. Fukuoka: writing of the manuscript, Ezequiel M. Gonçalves, Catarina N. Matias, Débora A. Guariglia, Gil Guerra‐Júnior: reviewing of the manuscript and editing of the manuscript.

CONFLICT OF INTEREST STATEMENT

The authors declare that they have no conflicts of interest to disclose.

ACKNOWLEDGMENTS

The authors would like to thank CAPES for providing scholarship and the participants who gave their time and effort to participate in the study. Funded by 1577/2023 (Grant numbers: 88881.859218/2023‐01).
==== Refs
REFERENCES

Aaron, R. , G. J. Esper , C. A. Shiffman , K. Bradonjic , K. S. Lee , and S. B. Rutkove . 2006. “Effects of Age on Muscle as Measured by Electrical Impedance Myography.” Physiological Measurement 27 (10 ): 953–959. 10.1088/0967-3334/27/10/002.16951455
Akagi, R. , J. Tanaka , T. Shikiba , and H. Takahashi . 2015. “Muscle Hardness of the Triceps Brachii Before and After a Resistance Exercise Session: A Shear Wave Ultrasound Elastography Study.” Acta Radiologica 56 (12 ): 1487–1493. 10.1177/0284185114559765.25422513
American College of Sports Medicine . 2009. “Progression Models in Resistance Training for Healthy Adults.” Medicine & Science in Sports & Exercise 41 (3 ): 687–708. 10.1249/mss.0b013e3181915670.19204579
Baumgartner, R. N. , W. C. Chumlea , and A. F. Roche . 1988. “Bioelectric Phase Angle and Body Composition1 .” The American Journal of Clinical Nutrition 48 (May ): 16–23. 10.1093/ajcn/48.1.16.3389323
Cebrián‐Ponce, Á. , A. Irurtia , M. Carrasco‐Marginet , G. Saco‐Ledo , M. Girabent‐Farrés , and J. Castizo‐Olier . 2021. “Electrical Impedance Myography in Health and Physical Exercise: A Systematic Review and Future Perspectives.” Frontiers in Physiology 12 (September ). 10.3389/fphys.2021.740877.
Clarkson, P. M. , K. Nosaka , and B. Braun . 1992. “Muscle Function after Exercise‐Induced Muscle Damage and Rapid Adaptation.” Medicine & Science in Sports & Exercise 24 (5 ): 512–520. 10.1249/00005768-199205000-00004.1569847
Cohen, J. 1992. “A Power Primer.” Psychological Bulletin 112 (1 ): 155–159. 10.1037/0033-2909.112.1.155.19565683
Custódio Martins, P. , T. R. de Lima , A. M. Silva , and D. A. Santos Silva . 2022. “Association of Phase Angle with Muscle Strength and Aerobic Fitness in Different Populations: A Systematic Review.” Nutrition 93 : 111489. 10.1016/j.nut.2021.111489.34688022
Damas, F. , S. M. Phillips , M. E. Lixandrão , F. C. Vechin , C. A. Libardi , H. Roschel , V. Tricoli , and C. Ugrinowitsch . 2016. “Early Resistance Training—Induced Increases in Muscle Cross—Sectional Area Are Concomitant With Edema—Induced Muscle Swelling.” European Journal of Applied Physiology 116 (1 ): 49–56. 10.1007/s00421-015-3243-4.26280652
Dankel, S. J. , and B. M. Razzano . 2020. “The Impact of Acute and Chronic Resistance Exercise on Muscle Stiffness: A Systematic Review and Meta‐Analysis.” Journal of Ultrasound 23 (4 ): 473–480: 10.1007/s40477-020-00486-3.32533552
de‐Mateo‐Silleras, B. , M. A. Camina‐Martín , J. M. de‐Frutos‐Allas , S. de‐la‐Cruz‐Marcos , L. Carreño‐Enciso , and M. P. Redondo‐del‐Río . 2018. “Bioimpedance Analysis as an Indicator of Muscle Mass and Strength in a Group of Elderly Subjects.” Experimenal Gerontology 113 (September ): 113–119. 10.1016/j.exger.2018.09.025.
Francavilla, V. C. , T. Bongiovanni , F. Genovesi , P. Minafra , and G. Francavilla . 2015. “Localized Bioelectrical Impedance Analysis: How Useful Is it in the Follow‐Up of Muscle Injury? A Case Report.” Medicina dello Sport 68 (2 ).
Freeborn, T. J. , and B. Fu . 2019. “Time‐course Bicep Tissue Bio‐Impedance Changes Throughout a Fatiguing Exercise Protocol.” Medical Engineering & Physics 69 : 109–115. 10.1016/j.medengphy.2019.04.006.31056402
Freeborn, T. J. , G. Regard , and B. Fu . 2020. “Localized Bicep Tissue Bioimpedance Alterations Following Eccentric Exercise in Healthy Young Adults.” IEEE Access 8 : 23100–23109. 10.1109/access.2020.2970314.
Fu, B. , and T. J. Freeborn . 2018. “Biceps Tissue Bioimpedance Changes from Isotonic Exercise‐Induced Fatigue at Different Intensities.” Biomedical Physics & Engineering Express 4 (2 ): 025037. 10.1088/2057-1976/aaabed.
Fukuoka, A. H. , N. M. de Oliveira , C. N. Matias , F. J. Teixeira , C. P. Monteiro , M. J. Valamatos , J. F. Reis , and E. M. Gonçalves . 2022. “Association between Phase Angle from Bioelectric Impedance and Muscular Strength and Power in Physically Active Adults.” Biology 11 (9 ): 1–10. 10.3390/biology11091255.
Huang, L. K. , L. N. Huang , Y. M. Gao , Z. Lucev Vasic , M. Cifrek , and M. Du . 2020. “Electrical Impedance Myography Applied to Monitoring of Muscle Fatigue during Dynamic Contractions.” IEEE Access 8 : 13056–13065. 10.1109/access.2020.2965982.
Kitsuda, Y. , T. Wada , H. Noma , M. Osaki , and H. Hagino . 2021. “Impact of High—Load Resistance Training on Bone Mineral Density in Osteoporosis and Osteopenia: A Meta—Analysis.” Journal of bone and mineral metabolism 39 (0123456789 ): 787–803: 10.1007/s00774-021-01218-1.33851269
Koury, J. C. , N. M. F. Trugo , and A. G. Torres . 2014. “Phase Angle and Bioelectrical Impedance Vectors in Adolescent and Adult Male Athletes.” International Journal of Sports Physiology and Performance 9 (5 ): 798–804. 10.1123/ijspp.2013-0397.24414089
Kraemer, W. J. , N. A. Ratamess , and D. N. French . 2002. “Resistance Training for Health and Performance.” Current Sports Medicine Reports 1 (3 ): 165–171. 10.1249/00149619-200206000-00007.12831709
Krieger, J. W. 2010. “Single vs. Multiple Sets of Resistance.” The Journal of Strength & Conditioning Research 24 (4 ): 1150–1159. 10.1519/jsc.0b013e3181d4d436.20300012
Li, L. , H. Shin , X. Li , S. Li , and P. Zhou . 2016. “Localized Electrical Impedance Myography of the Biceps Brachii Muscle during Different Levels of Isometric Contraction and Fatigue.” Sensors 16 (4 ): 581. 10.3390/s16040581.27110795
Lopez, P. , R. Radaelli , D. R. Taaffe , R. U. Newton , D. A. Galvão , G. S. Trajano , J. L. Teodoro , W. J. Kraemer , K. Häkkinen , and R. S. Pinto . 2021. “Resistance Training Load Effects on Muscle Hypertrophy and Strength Gain: Systematic Review and Network Meta‐Analysis.” Medicine & Science in Sports & Exercise 53 (6 ): 1206–1216. 10.1249/mss.0000000000002585.33433148
Martins, P. C. , A. S. Teixeira , L. G. Antonacci Guglielmo , J. S. Francisco , D. A. S. Silva , F. Y. Nakamura , and L. R. A. de Lima . 2021. “Phase Angle Is Related to 10 M and 30 M Sprint Time and Repeated‐Sprint Ability in Young Male Soccer Players.” International Journal of Environmental Research and Public Health 18 (9 ): 1–13. 10.3390/ijerph18094405.
Mascherini, G. , J. Castizo‐Olier , A. Irurtia , C. Petri , and G. Galanti . 2017. “Differences between the Sexes in Athletes’ Body Composition and Lower Limb Bioimpedance Values.” Muscles, Ligaments and Tendons Journal 7 (4 ): 573–581. 10.32098/mltj.04.2017.12.29721459
Mascherini, G. , C. Petri , and G. Galanti . 2015. “Integrated Total Body Composition and Localized Fat‐free Mass Assessment.” Sport Sciences for Health 11 (2 ): 217–225. 10.1007/s11332-015-0228-y.
Matias, C. N. , F. Campa , C. L. Nunes , R. Francisco , F. Jesus , M. Cardoso , Maria J. Valamatos , et al. 2021. “Phase Angle Is a Marker of Muscle Quantity and Strength in Overweight/obese Former Athletes.” International Journal of Environmental Research and Public Health 18 (12 ): 1–10. 10.3390/ijerph18126649.
Micheli, M. L. , L. Pagani , M. Marella , M. Gulisano , A. Piccoli , F. Angelini , M. Burtscher , and H. Gatterer . 2014. “Bioimpedance and Impedance Vector Patterns as Predictors of League Level in Male Soccer Players.” International Journal of Sports Physiology and Performance 9 (3 ): 532–539. 10.1123/ijspp.2013-0119.23881291
Nabuco, H. C. G. , A. M. Silva , L. B. Sardinha , F. B. Rodrigues , C. M. Tomeleri , F. C. P. Ravagnani , E. S. Cyrino , and C. F. C. Ravagnani . 2019. “Phase Angle Is Moderately Associated with Short‐Term Maximal Intensity Efforts in Soccer Players.” Int J Sports Med 40 (11 ): 739–743. 10.1055/a-0969-2003.31437860
Nescolarde, L. , J. Terricabras , S. Mechó , G. Rodas , and J. Yanguas . 2020. “Differentiation between Tendinous, Myotendinous and Myofascial Injuries by L‐BIA in Professional Football Players.” Frontiers in Physiology 11 (September ): 1–12. 10.3389/fphys.2020.574124.32038307
Nescolarde, L. , J. Yanguas , H. Lukaski , X. Alomar , J. Rosell‐Ferrer , and G. Rodas . 2013. “Localized Bioimpedance to Assess Muscle Injury.” Physiological Measurement 34 (2 ): 237–245. 10.1088/0967-3334/34/2/237.23354019
Nescolarde, L. , J. Yanguas , H. Lukaski , X. Alomar , J. Rosell‐Ferrer , and G. Rodas . 2015. “Effects of Muscle Injury Severity on Localized Bioimpedance Measurements.” Physiological Measurement 36 (1 ): 27–42. 10.1088/0967-3334/36/1/27.25500910
Nescolarde, L. , J. Yanguas , J. Terricabras , H. Lukaski , X. Alomar , J. Rosell‐Ferrer , and G. Rodas . 2017. “Detection of Muscle Gap by L‐BIA in Muscle Injuries: Clinical Prognosis.” Physiological Measurement 38 (7 ): L1–L9. 10.1088/1361-6579/aa7243.28636566
Norman, K. , R. Wirth , M. Neubauer , R. Eckardt , and N. Stobäus . 2015. “The Bioimpedance Phase Angle Predicts Low Muscle Strength, Impaired Quality of Life, and Increased Mortality in Old Patients with Cancer.” Journal of the American Medical Directors Association 16 (2 ): 173.e17–173.e22: 10.1016/j.jamda.2014.10.024.
Oliveira, N. M. , A. H. Fukuoka , C. N. Matias , G. G. E. Guerra‐Júnior , and E. M. Gonçalves . 2023. “Is Muscle Localized Phase Angle an Indicator of Muscle Power and Strength in Young Women?” Physiological Measurement 44 : 12. 10.1088/1361-6579/ad10c5.
Rutkove, S. B. 2009. “Electrical Impedance Myography: Background, Current State, and Future Directions.” Muscle & Nerve 40 (6 ): 936–946. 10.1002/mus.21362.19768754
Rutkove, S. B. , P. M. Fogerson , L. P. Garmirian , and A. W. Tarulli . 2008. “Reference Values for 50‐kHz Electrical Impedance Myography.” Muscle & Nerve 38 (3 ): 1128–1132. 10.1002/mus.21075.18642375
Rutkove, S. B. , T. R. Geisbush , A. Mijailovic , I. Shklyar , A. Pasternak , N. Visyak , J. S. Wu , C. Zaidman , and B. T. Darras . 2014. “Cross‐Sectional Evaluation of Electrical Impedance Myography and Quantitative Ultrasound for the Assessment of Duchenne Muscular Dystrophy in a Clinical Trial Setting.” Pediatric Neurology 51 (1 ): 88–92: 10.1016/j.pediatrneurol.2014.02.015.24814059
Rutkove, S. B. , J. M. Shefner , M. Gregas , H. Butler , J. Caracciolo , C. Lin , P. M. Fogerson , P. Mongiovi , and B. T. Darras . 2010. “Characterizing Spinal Muscular Atrophy with Electrical Impedance Myography.” Muscle & Nerve 42 (6 ): 915–921. 10.1002/mus.21784.21104866
Sanchez, B. , and S. B. Rutkove . 2017a. “Electrical Impedance Myography and its Applications in Neuromuscular Disorders.” Neurotherapeutics 14 (1 ): 107–118: 10.1007/s13311-016-0491-x.27812921
Sanchez, B. , and S. B. Rutkove . 2017b. “Present Uses, Future Applications, and Technical Underpinnings of Electrical Impedance Myography.” Current Neurology and Neuroscience Reports 17 (11 ): 86. 10.1007/s11910-017-0793-3.28933017
Sardinha, L. B. , and G. B. Rosa . 2023. “Phase Angle, Muscle Tissue, and Resistance Training.” Reviews in Endocrine & Metabolic Disorders 24 (3 ): 393–414. 10.1007/s11154-023-09791-8.36759377
Sawilowsky, S. S. 2009. “Very Large and Huge Effect Sizes.” Journal of Modern Applied Statistical Methods 8 (2 ): 597–599. 10.22237/jmasm/1257035100.
Schoenfeld, B. J. , J. Grgic , D. Ogborn , and J. W. Krieger . 2017. “Strength and Hypertrophy Adaptations between Low‐vs. High‐Load Resistance Training: A Systematic Review and Meta‐Analysis.” The Journal of Strength & Conditioning Research 31 (12 ): 3508–3523. 10.1519/jsc.0000000000002200.28834797
Seo, M. W. , S. W. Jung , S. W. Kim , J. M. Lee , H. C. Jung , and J. K. Song . 2021. “Effects of 16 Weeks of Resistance Training on Muscle Quality and Muscle Growth Factors in Older Adult Women with Sarcopenia: A Randomized Controlled Trial.” International Journal of Environmental Research and Public Health 18 (13 ): 1–13. 10.3390/ijerph18136762.
Shiffman, C. A. , R. Aaron , and S. B. Rutkove . 2003. “Electrical Impedance of Muscle during Isometric Contraction.” Physiological Measurement 24 (1 ): 213–234. 10.1088/0967-3334/24/1/316.12636198
Statland, J. M. , C. Heatwole , K. Eichinger , N. Dilek , W. B. Martens , and R. Tawil . 2016. “Electrical Impedance Myography in Facioscapulohumeral Muscular Dystrophy.” Muscle & Nerve 54 (4 ): 696–701. 10.1002/mus.25065.26840230
Tarulli, A. W. , L. P. Garmirian , P. M. Fogerson , and S. B. Rutkove . 2009. “Localized Muscle Impedance Abnormalities in Amyotrophic Lateral Sclerosis.” Journal of Clinical Neuromuscular Disease 10 (3 ): 90–96. 10.1097/cnd.0b013e3181934423.19258856
Ward, L. C. , and S. Brantlov . 2023. “Bioimpedance Basics and Phase Angle Fundamentals.” Reviews in Endocrine and Metabolic Disorders 24 (3 ): 381–391: 10.1007/s11154-022-09780-3.36749540
Wewege, M. A. , I. Desai , C. Honey , B. Coorie , M. D. Jones , B. K. Clifford , H. B. Leake , and A. D. Hagstrom . 2022. “The Effect of Resistance Training in Healthy Adults on Body Fat Percentage, Fat Mass and Visceral Fat: A Systematic Review and Meta‐Analysis.” Sport Medicine 52 (2 ): 287–300: 10.1007/s40279-021-01562-2.
Yasuda, T. , K. Fukumura , H. Iida , and T. Nakajima . 2015. “Effect of Low‐Load Resistance Exercise with and Without Blood Flow Restriction to Volitional Fatigue on Muscle Swelling.” European Journal of Applied Physiology 115 (5 ): 919–926. 10.1007/s00421-014-3073-9.25491331
Zhao, R. , M. Zhao , and Z. Xu . 2015. “The Effects of Differing Resistance Training Modes on the Preservation of Bone Mineral Density in Postmenopausal Women: A Meta‐Analysis.” Osteoporosis International 26 (5 ): 1605–1618. 10.1007/s00198-015-3034-0.25603795
