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Article

Bioelectrical Impedance Vector Analysis Discriminates Aerobic Power in Futsal Players: The Role of Body Composition

by
Catarina N. Matias
1,2,†,
Francesco Campa
3,*,†,
Giuseppe Cerullo
4,
Giuseppe D’Antona
5,
Rita Giro
1,
João Faleiro
1,6,
Joana F. Reis
7,8,
Cristina P. Monteiro
7,8,
Maria J. Valamatos
7,9 and
Filipe J. Teixeira
1,7,10
1
Bettery Life Lab, Innovation Direction, Bettery S.A., 2740-262 Lisboa, Portugal
2
CIDEFES—Universidade Lusófona, 1749-024 Lisboa, Portugal
3
Department for Life Quality Studies, University of Bologna, 47921 Rimini, Italy
4
Department of Movement Sciences and Wellbeing, University of Naples Parthenope, 80133 Naples, Italy
5
Centro di Ricerca Interdipartimentale nelle Attività Motorie e Sportive (CRIAMS)—Sport Medicine Centre, University of Pavia, 27058 Voghera, Italy
6
AC Oulu Football Club, 90100 Oulu, Finland
7
Interdisciplinary Center for the Study of Human Performance (CIPER), Faculdade de Motricidade Humana, Universidade de Lisboa, 1495-761 Cruz-Quebrada, Portugal
8
Laboratory of Physiology and Biochemistry of Exercise, Faculdade de Motricidade Humana, Universidade de Lisboa, 1495-761 Cruz-Quebrada, Portugal
9
Neuromuscular Research Lab, Faculdade Motricidade Humana, Universidade Lisboa, 1495-761 Cruz-Quebrada, Portugal
10
Atlântica, Instituto Universitário, Fábrica da Pólvora de Barcarena, 2730-036 Barcarena, Portugal
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Biology 2022, 11(4), 505; https://doi.org/10.3390/biology11040505
Submission received: 4 March 2022 / Revised: 18 March 2022 / Accepted: 23 March 2022 / Published: 25 March 2022
(This article belongs to the Special Issue Evaluation of Bioelectrical Proprieties and Body Composition in Athletes and Exercisers)
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Abstract

:

Simple Summary

Bioelectrical and body composition proprieties are linked to health status and physical performance. The bioelectrical impedance vector analysis (BIVA) is a valid approach for qualitatively and timely assessing body components in sports practice. However, it is still unclear, if bioimpedance vector patterns can provide relevant information regarding the aerobic power. For the first time, this study shows that BIVA is useful in discriminating professional futsal players according to their VO2max. In addition, fat mass and bioelectrical phase angle were identified as valid predictors for aerobic power.

Abstract

Aims: The present study aimed to assess the ability of bioelectrical impedance vector analysis (BIVA) in discriminating fitness levels in futsal players, exploring the association of body composition and bioelectrical parameters with aerobic power. Methods: Forty-eight professional futsal players (age 23.8 ± 5.3 years) were involved in a cross-sectional study during their pre-season phase. Fat mass (FM) and muscle mass were determined by dual-energy X-ray absorptiometry. VO2max was obtained by indirect calorimetry through a graded exercise test performed on a treadmill. Bioelectrical resistance (R), reactance (Xc), and phase angle (PhA) were directly measured using a foot-to-hand bioimpedance technology at a 50 kHz frequency. Bioelectric R and Xc were standardized for the participants’ height and used to plot the bioimpedance vector in the R-Xc graph according to the BIVA approach. Results: The participants divided into groups of VO2max limited by tertiles showed significant differences in mean vector position in the R-Xc graph (p < 0.001), where a higher VO2max resulted in a longer vector and upper positioning. FM, muscle mass, and PhA differed (p < 0.01) among the athletes grouped by tertiles of VO2max, where athletes with a greater aerobic power showed a lower percentage of FM and a higher percentage of muscle mass and PhA. FM and PhA were associated with VO2max (FM: r = −0.658, p < 0.001; PhA: r = 0.493, p < 0.001). These relationships remained significant after adjusting for age and body mass (FM: ß = −0.335, p = 0.046; PhA: ß = 0.351, p = 0.003). Conclusions: Bioelectrical impedance vectors positioned on the lower pole of the R-Xc graph identified futsal players with a lower VO2max, while longer vectors corresponded to a greater aerobic power. Additionally, PhA, that describes the vector direction, was positively associated with VO2max, while a higher FM negatively affected VO2max in the futsal players. BIVA and PhA evaluation may represent a valid support for screening the aerobic fitness level in professional futsal players, when more sophisticated assessment methods are not available.

1. Introduction

Futsal is a 5-a-side indoor adaptation of soccer that is officially recognized by the soccer’s international governing body (FIFA). Despite increasing in popularity around the world, with over 12 million athletes participating in over 100 nations, the body of literature related to futsal is still scarce. The futsal game is played between two teams, each with five players: one goalkeeper and four outfielders, usually known as defenders, right or left-wingers, and pivots. Competitive matches include periods of intermittent and high-intensity activity that require significant physical, technical, and tactical effort [1,2]. The highest metabolic contribution is provided by the aerobic pathway as recently reported in professional futsal players during a simulated futsal game [3]. The maximal oxygen uptake (VO2max) is among the most important determinants for endurance performance, as well as mortality in the general population [4,5]. In this regard, VO2max is considered the gold standard for assessing aerobic fitness. However, it is possible to observe a lack of scientific data related to aerobic capacity of futsal players. The VO2max mirrors the maximum amount of oxygen an individual can use to support the oxidative production of energy [6]. The most common and reliable method to assess VO2max is the cardiopulmonary exercise test, recognized a valuable measure not only to assess the functional status and oxygen availability during exercise training but also to discriminate cardiorespiratory, pulmonary and musculoskeletal function at the same average oxygen consumption [7]. For the first time, Alvarez et al. [8] showed that high values of VO2max were essential for futsal athletes at the professional level, suggesting the relevance of aerobic power in futsal [8]. Subsequently, other authors confirmed that VO2max may be assumed as a discriminative physiological parameter in futsal played at recreational or professional levels [9,10,11]. Nevertheless, differences in this regard may be a result of genetic factors [4], players role [8], training load [11] or body composition [12]. In general, increased body fat is associated with decreased value of VO2max, while FFM is positively correlated with VO2max [13,14]. Futsal players usually display a low percentage of fat (~15%) with a higher lean mass in high-level players [12]. Taken together, these data might suggest a close relationship between body composition and aerobic power in this sportive population.
Body composition parameters can be accurately determined using laboratory techniques and procedures [15]. To date, the state-of-art method for determining body fat is identified in a specific formula proposed by Wang et al. [15], which requires the assessment of bone mineral content by dual-energy X-ray absorptiometry (DXA), total body water (TBW) by deuterium dilution, and body volume by air displacement plethysmography. In addition to bone mineral content, for which it is considered the gold standard, DXA allows for accurate estimations of lean soft and muscle tissues [16]. However, these procedures are difficult to use in the practical context due to their high cost and the need for specialized personnel [17]. The bioelectrical impedance analysis (BIA) has been suggested as an accurate method for assessing body composition in athletes, provided that specific procedures are used [17]. In particular, single frequency BIA provides body composition estimations with a good agreement respect to the aforementioned reference methods [17,18]. However, the most innovative use of BIA consists in the evaluation of the raw bioelectrical parameters through the vector analysis (BIVA) [18,19]. Indeed, the bioimpedance can be considered as the bivariate result of the bioelectrical resistance (R) and reactance (Xc) and graphically represented as a vector into a graph [18,20]. Particularly, the direction of the vector is determinate by the phase angle (PhA), which can be directly calculated as the arctangent of Xc and R [18]. In BIVA, the vector length is representative of the TBW, where a higher fluid content corresponds to long vectors. Additionally, PhA is positively associated with the intracellular-to-extracellular water (ICW/ECW) ratio [21] and can be evaluated when more sophisticate methods for determining body fluids are not available. The evaluation of BIVA patterns represents a qualitative examination of body composition and avoids the use of prediction equations. Recent literature reviews have shown how BIVA is able to discriminate different body morphologies and identify adaptations related to sports performance in athletes [18,19,22].
To the best of our knowledge, the ability of BIVA and the role of the bioelectrical properties in discriminating aerobic power in futsal players is still unexplored. The use of BIVA could serve as a practical tool suited for nutritionists and futsal coaches and team staff in obtaining information on VO2max. Therefore, the present study aimed to investigate the ability of BIVA in defining aerobic power in futsal players, clarifying the role of body composition in the VO2max prediction. Our hypothesis was that BIVA would be informative for aerobic performance and that body composition would be highly correlated with VO2max.

2. Materials and Methods

2.1. Study Design and Participants

In line with previous studies to evaluate the ability of BIVA in discriminating physical characteristics, by grouping the participants into tertiles of VO2max, a cross sectional study design is presented. DXA was used as a support to obtain additionally information related to body composition. The sample size was assessed a priori using GPower (version 3.1, Dusseldorf, Germany). It was calculated considering a large effect size (appropriate for calculating effect size within a multiple regression model with continuous independent and dependent variables), with a 5% type I error, 85% power, and an α level = 0.05, which resulted in the appropriate sample size for conducting this study. The final sample was comprised of both elite and sub elite players (age 23.8 ± 5.3 years). The elite players (n = 16) participated in the Major Portuguese National Futsal League “LIGA PLACARD” and had a sport-specific training frequency of 5 sessions per week and a game frequency of 1 per week. The sub elite players (n = 32) participated in either the second (n = 16) or third (n = 16) Portuguese National Futsal League and they endured 3 training sessions of ~1.5 h per week plus a weekend game. This investigation was approved by the Faculty of Human Kinetics Institutional Review Board (approval number 37/2021) and conformed to all standards of human research set out in the declaration of Helsinki. After a detailed explanation of the procedures, the participants signed an informed consent.

2.2. Procedures

All evaluations of the participants were performed early in the morning (7.00 a.m.) after a 12-h fast and without consumption of alcohol, caffeine/stimulant beverages and at least 12 h from the last exercise session. Body composition assessments were performed at fast, while the cardiopulmonary exercise test (CPET) to access VO2max was made in a fed state, with a meal replacement bar (nutritional composition: 231 kcal, 14 g of fat, 12 g of carbohydrate and 13 g of protein) being provided prior to testing.
All measurements were performed in the pre-season period following the timeline depicted in Figure 1.
The participants had their weight and height measured wearing minimal clothes and without shoes to the nearest 0.1 kg and 0.1 cm, respectively with a scale and a stadiometer (Seca, Hamburg, Germany). Body composition was determined through two methodologies:
(a)
Whole-body DXA scan (Horizon Wi, Waltham, MA, USA) according to the procedures recommended by the manufacturer. The DXA measurements included whole-body estimations of absolute and percentage of fat mass (FM, kg and %) and lean soft tissue, from which muscle mass was calculated using the Kim’s formula [16].
(b)
Whole-body BIA using a single frequency of 50 kHz device (BIA 101 BIVA® PRO, Akern Systems, Firenze, Italy). After cleaning the skin with isotropy alcohol, four low intrinsic impedance adhesive electrodes (Biatrodes Akern Srl, Firenze, Italy) were placed on the hands back and other four electrodes on the neck of the corresponding feet, according to the guidelines for athletes [18]. From the raw data R and Xc, PhA was calculated as the arctangent of Xc/R × 180/π. BIVA was applied standardizing R and Xc for the subjects’ stature in meters.
After body composition assessments, a maximal incremental test with expired gas analysis (Quark, Cosmed, Italy) was performed on a motor-driven treadmill. After 3 min of warm-up at 5 km∙h−1, participants began the test at 6 km∙h−1 and 2% grade. Each minute the speed increased 1 km∙h−1 until volitional exhaustion, so that fatigue would be induced within 8–12 min [23]. The highest 30 s average of VO2 values reached during the exercise phase of the incremental test were considered to be the VO2max. At the end of the test, all subjects met at least two of the following criteria: respiratory quotient greater than 1.10; heart rate equal to or greater than 95% of predicted maximal HR; and increments in VO2 below 2 mL∙kg−1∙min−1 despite an increase in speed [24].

2.3. Statistical Analysis

Data were analyzed with IBM SPSS Statistics, version 24.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics (mean ± standard deviation) were calculated for all measurements. To verify the normality of the data, the Shapiro-Wilk test was applied. The participants were divided into groups limited by tertiles of VO2max [first tertile (T1), second tertile (T2) and third tertile (T3)] and a one-way ANOVA was performed to evaluate differences in body composition. When a significant F ratio was obtained, the Bonferroni post hoc test was used to assess the differences between the three groups, setting the significance at p < 0.016. The unpaired-sample Hotelling’s T2 test was used to compare the mean impedance vectors among the participants grouped into tertiles. Mahalanobis distance (D2), which represents a multivariate measure of effect and a multivariate measure of distance, was calculated to determine the magnitude of changes in the mean group vectors. D2 was interpreted according to the following Stevens’s [25] guidelines: 0.25–0.49: small; 0.5–0.99; ≥1: large. Bivariate correlations were performed in preliminary analysis and multiple regression analysis was used to determine if body components were significant predictors of VO2max after correcting for age and body mass.

3. Results

No difference for age among the three groups was found (F = 0.41, p = 0.661). Table 1 shows the descriptive characteristics for the athletes divided according to tertiles of VO2max. The athletes with lower VO2max (T1) showed higher body mass and absolute (kg) and relative (%) FM than the other groups. Additionally, athletes included into the T1 reported lower percentage of muscle mass, Xc/H, and PhA than the T3 group.
Figure 2 shows the relative amount of FM and muscle mass according with the three—compartments tissue model.
Figure 3 shows the BIVA patterns for the three groups. The athletes with higher VO2max showed a mean vector displaced in the higher part of the graph, out of the 50th percentile of the tolerance ellipses of the general population [23]. The T2 and T3 groups fell into the 50th percentile of the R-Xc graph. Significant bivariate differences in R/H and Xc/H among groups were found, as shown in Figure 1. Figure 1 the mean vectors with their 95% confidence ellipses are compared with the target zone proposed for the athletic population. The T1 and T3 groups showed different vector directions, as highlighted by the difference in PhA among these two groups.
VO2max was negatively correlated with FM (r = −0.658, p < 0.001), while positively associated with PhA (r = 0.493, p < 0.001), as showed in Figure 4. No association between muscle mass and VO2max was found (r = −0.082, p = 0.581) (Figure 4).
The results of the multivariate regression analysis showed that FM% and PhA remained significant predictor even when adjusted for age and body mass, as reported in Table 2.

4. Discussion

The present study aimed to assess the ability of BIVA in discriminating the variability in VO2max and the role of body composition in determining VO2max in elite and sub-elite futsal players. When the futsal players were grouped in tertiles for VO2max, they showed specific positions on the R-Xc graph, suggesting different body composition features. Particularly, the participants with a greater aerobic power presented a higher percentage of muscle mass and a lower percentage of FM. Bioelectrical PhA and FM were identified as valid predictors of VO2max, regardless of body mass and age. To date, this is the first study to investigate these relations in futsal players, suggesting that BIVA can be used as a tool to evaluate aerobic power.
BIVA showed that the participants with a lower VO2max (T1 group) presented a shorter mean vector than the futsal players with a higher VO2max (T3 group). The vector length is inversely related with TBW that is determined by the amount of intra and extra cellular fluids [21]. The shorter vector of the T1 group may be due to a higher TBW with a lower ICW/ECW ratio. Indeed, PhA, calculated as the arctangent between Xc and R, is directly related to the ICW/ECW ratio [20]. In the participants with similar R/H, the low PhA was also due to a lower Xc/H, which is known to be negatively associated with ECW [28] and representative of the integrity of cell membranes and tissue interfaces [29]. In healthy athletes not affected by acute or chronic fatigue, a high PhA could be the consequence of a high percentage of muscle mass and a low percentage of FM [30], as it turned out in the present study. In fact, a high level of FM may lead to a fluid retention into the extracellular spaces and in some cases was stated that generate inflammation [31,32]. Conversely, a high level of muscle mass encompasses a fluid balance in favor of the intracellular compartment [30]. A shorter and rightward vector may then represent a situation of higher FM% and lower PhA, both values that negatively affect aerobic power.
The results of the present study identified FM and PhA as valid predictors of VO2max, even when adjusted for body mass and age. Previous studies are in line with these results reporting the negative effect of FM on aerobic power [14,33,34]. However, while it was suggested that FFM is positively associated with and enhanced aerobic power [14], in the present study the muscle mass, one of the major components of the FFM, did not result among the VO2max predictors. It could be speculated that over a certain threshold, increases in muscle mass do not lead to VO2max gains, as already suggested by a previous study on combat athletes [35]. Several studies investigated the role of body composition in determining aerobic fitness [13,14,34]. However, all of them considered only FM and FFM. The quantification of FFM preclude the evaluations of several body components and their distribution among the body. For example, different distributions of ICW and ECW can result in a similar TBW. In this regard, the body fluid distribution reflected by PhA was identified as a VO2max determinant in the present study. Therefore, evaluating FFM without considering its sub-components may result in a poor assessment of body composition. The negative influence of FM on flexibility and mobility has already been reported in previous findings [33,36]. Additionally, PhA associations with anaerobic performance, such as sprints, and aerobic outcomes like running time in endurance competitions have been described in the literature [37,38]. Therefore, practitioners should consider reducing FM and increasing PhA in order to achieve a greater aerobic power. Both these adaptations can be achieved through nutritional and training interventions [22,31]. In contrast, muscle mass does not seem to influence fitness levels in futsal players. However, body composition may play only a moderate part in the prediction of aerobic power. Therefore, physiological characteristics such as ventilatory functions as well as biomechanical and technical features involved in the running economy should also be considered across the competitive season in futsal players [11].
Previous research studies showed that a leftward vector displacement was associated with an improvement in aerobic and anaerobic performance [39,40]. In particular, vector shifting from the right to the left side and from the bottom to the upper part of the R-Xc graph occurred after a training macrocycle in swimmers [40]. This also results in a FM reduction and in an aerobic performance improvement [28]. Therefore, monitoring vector displacements may provide important insights related to changes in body composition and fitness levels. In this regard, reference zones among the R-Xc graph were proposed for the athletic population, considering the first phase of the season when a high fitness level should be achieved after the preparatory phase [26]. However, these target zones have been identified in athletes involved in several different sports and may not be used to just characterize futsal players. Also, reference percentiles for athletes have been proposed [41], identifying an average value of 7.7 degree around the 50th percentile. In this study, to achieve these BIA-related standards, the athletes included in the T1 group should reduce FM and ECW in order to move the vector to the left side of the R-Xc graph. On the contrary, T3 athletes should increase PhA to induce a shortening and a leftward displacement of the vector. The futsal players with a higher VO2max of our study presented lower values than previous data reported in literature on elite futsal players [8]. This may be due to the different competitive phases evaluated in these studies. In fact, the participants of the present study were assessed during the preparatory phase where physical adaptations are not achieved yet. Further studies should assess if vector displacements to defined target zone and PhA increases would results in a VO2max improvement.
The strength of the present study resides in the reference procedures used for determining VO2max, body composition, and bioelectrical parameters. In fact, breath-by-breath devices, DXA, and BIA are among the most accurate methods to quantify VO2max and fat, estimate muscle mass, and detect bioelectrical phase angle, respectively. Although more body composition parameters such as TBW, ECW, and ICW would have been estimated with BIA, we preferred to avoid the use of BIA-based predictive equations since no specific formulas for futsal players are available in literature to date [18], therefore this non-specificity would have resulted in estimation errors. In this regard, the lack of dilution techniques as reference procedures for determining body fluids should be recognized as a limitation. Additionally, DXA cannot be assumed as the state-of-the-art method for evaluating FM, but it is employed in conjunction with air plethysmography and dilution techniques in the four-compartment molecular model, which is currently considered the gold standard procedure [18,42]. In the same way, magnetic resonance would be used for quantifying muscle mass instead of DXA. Furthermore, the results of this study cannot be generalized, but could be applicable to assessments performed with foot-to-hand technology at a single frequency of 50 kHz, since differences in the analysis were highlighted between bioelectrical parameters measured using different type of devices [43,44].

5. Conclusions

Body fat and bioelectrical phase angle can be considered as valid predictors of VO2max in professional futsal players. In contrast, muscle mass is not associated with aerobic power. Bioelectrical impedance vectors positioned on the lower pole of the R-Xc graph were found in futsal players with a lower VO2max. Using BIVA, the length of the vector reflects the body fluid content, while phase angle is informative of their distributions among the intra and extra cellular spaces. Inferior aerobic power was found in athletes with higher TBW, lower phase angle and higher body fat, regardless of their muscle mass. BIVA seems to be a feasible field method for assessing body composition while avoiding the use of prediction equations.

Author Contributions

Conceptualization, C.N.M. and F.J.T.; Data curation, C.N.M. and F.J.T.; Formal analysis, F.C.; Investigation, R.G., J.F., J.F.R., C.P.M., M.J.V. and F.J.T.; Methodology, F.C. and J.F.R.; Supervision, C.N.M. and F.J.T.; Writing—original draft, F.C. and G.C.; Writing—review & editing, C.N.M., G.D., R.G., J.F., J.F.R., C.P.M., M.J.V. and F.J.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This investigation was approved by the Faculty of Human Kinetics Institutional Review Board (approval number 37/2021) and conformed to all standards of human research set out in the declaration of Helsinki.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data can be obtained from the corresponding author on [email protected].

Acknowledgments

The authors express their gratitude to all the participants involved in this study.

Conflicts of Interest

F.J.T., C.N.M., J.F. and R.G. are currently employees of a biotechnology company (Bettery S.A.) that produces dietary supplements. Other authors declare no conflict of interest.

References

  1. Borges, L.; Dermargos, A.; Gorjão, R.; Cury-Boaventura, M.F.; Hirabara, S.M.; Abad, C.C.; Pithon-Curi, T.C.; Curi, R.; Barros, M.P.; Hatanaka, E. Updating futsal physiology, immune system, and performance. Res. Sports Med. 2021, 1–18. [Google Scholar] [CrossRef] [PubMed]
  2. Barbero-Alvarez, J.C.; Soto, V.M.; Barbero-Alvarez, V.; Granda-Vera, J. Match analysis and heart rate of futsal players during competition. J. Sports Sci. 2008, 26, 63–73. [Google Scholar] [CrossRef] [PubMed]
  3. Da Silva, H.S.; Nakamura, F.Y.; Papoti, M.; da Silva, A.S.; Dos-Santos, J.W. Relationship Between Heart Rate, Oxygen Consumption, and Energy Expenditure in Futsal. Front. Psychol. 2021, 12, 2896. [Google Scholar] [CrossRef] [PubMed]
  4. Lundby, C.; Montero, D.; Joyner, M. Biology of VO2max: Looking under the physiology lamp. Acta Physiol. 2017, 220, 218–228. [Google Scholar] [CrossRef] [PubMed]
  5. Pedersen, B.K.; Saltin, B. Exercise as medicine—Evidence for prescribing exercise as therapy in 26 different chronic diseases. Scand. J. Med. Sci. Sports 2015, 25, 1–72. [Google Scholar] [CrossRef] [Green Version]
  6. Bundy, M.; Leaver, A. Training and conditioning. In A Guide to Sports and Injury Management; Elsevier: Amsterdam, The Netherlands, 2010; pp. 1–9. [Google Scholar]
  7. Greco, D.; Calanni, L.; Cerullo, G.; Negro, M.; D’Antona, G. Comparison of Cardiorespiratory and Metabolic Responses between Kettlebell Half Marathon and Treadmill Running at the Same Average Oxygen Consumption: A Case Study. J. Sci. Sport Exerc. 2021, 3, 379–384. [Google Scholar] [CrossRef]
  8. Álvarez, J.C.B.; D’ottavio, S.; Vera, J.G.; Castagna, C. Aerobic Fitness in Futsal Players of Different Competitive Level. J. Strength Cond. Res. 2009, 23, 2163–2166. [Google Scholar] [CrossRef]
  9. Baroni, B.M.; Leal Junior, E.C.P. Aerobic capacity of male professional futsal players. J. Sports Med. Phys. Fit. 2010, 50, 395–399. [Google Scholar]
  10. Beato, M.; Coratella, G.; Schena, F. Brief review of the state of art in futsal. J. Sports Med. Phys. Fit. 2016, 56, 428–432. [Google Scholar]
  11. Naser, N.; Ali, A.; Macadam, P. Physical and physiological demands of futsal. J. Exerc. Sci. Fit. 2017, 15, 76–80. [Google Scholar] [CrossRef]
  12. Spyrou, K.; Freitas, T.T.; Marín-Cascales, E.; Alcaraz, P.E. Physical and Physiological Match-Play Demands and Player Characteristics in Futsal: A Systematic Review. Front. Psychol. 2020, 11, 2870. [Google Scholar] [CrossRef] [PubMed]
  13. Köhler, A.; King, R.; Bahls, M.; Groß, S.; Steveling, A.; Gärtner, S.; Schipf, S.; Gläser, S.; Völzke, H.; Felix, S.B.; et al. Cardiopulmonary fitness is strongly associated with body cell mass and fat-free mass. Scand. J. Med. Sci. Sports 2018, 28, 1628–1635. [Google Scholar] [CrossRef] [PubMed]
  14. Mondal, H. Effect of BMI, Body Fat Percentage and Fat Free Mass on Maximal Oxygen Consumption in Healthy Young Adults. J. Clin. Diagn. Res. 2017, 11, CC17. [Google Scholar] [CrossRef] [PubMed]
  15. Wang, Z.; Pi-Sunyer, F.X.; Kotler, D.P.; Wielopolski, L.; Withers, R.T.; Pierson, R.N.J.; Heymsfield, S.B. Multicomponent methods: Evaluation of new and traditional soft tissue mineral models by in vivo neutron activation analysis. Am. J. Clin. Nutr. 2002, 76, 968–974. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Kim, J.; Wang, Z.; Heymsfield, S.B.; Baumgartner, R.N.; Gallagher, D. Total-body skeletal muscle mass: Estimation by a new dual-energy X-ray absorptiometry method. Am. J. Clin. Nutr. 2002, 76, 378–383. [Google Scholar] [CrossRef] [Green Version]
  17. Campa, F.; Gobbo, L.A.; Stagi, S.; Cyrino, L.T.; Toselli, S.; Marini, E.; Coratella, G. Bioelectrical impedance analysis versus reference methods in the assessment of body composition in athletes. Eur. J. Appl. Physiol. 2022, 122, 561–589. [Google Scholar] [CrossRef]
  18. Campa, F.; Toselli, S.; Mazzilli, M.; Gobbo, L.A.; Coratella, G. Assessment of Body Composition in Athletes: A Narrative Review of Available Methods with Special Reference to Quantitative and Qualitative Bioimpedance Analysis. Nutrients 2021, 13, 1620. [Google Scholar] [CrossRef]
  19. Lukaski, H.; Raymond-Pope, C.J. New Frontiers of Body Composition in Sport. Int. J. Sports Med. 2021, 42, 588–601. [Google Scholar] [CrossRef]
  20. Bongiovanni, T.; Mascherini, G.; Genovesi, F.; Pasta, G.; Iaia, F.M.; Trecroci, A.; Ventimiglia, M.; Alberti, G.; Campa, F. Bioimpedance Vector References Need to Be Period-Specific for Assessing Body Composition and Cellular Health in Elite Soccer Players: A Brief Report. J. Funct. Morphol. Kinesiol. 2020, 5, 73. [Google Scholar] [CrossRef]
  21. Marini, E.; Campa, F.; Buffa, R.; Stagi, S.; Matias, C.N.; Toselli, S.; Sardinha, L.B.; Silva, A.M. Phase angle and bioelectrical impedance vector analysis in the evaluation of body composition in athletes. Clin. Nutr. 2020, 39, 447–454. [Google Scholar] [CrossRef]
  22. Di Vincenzo, O.; Marra, M.; Scalfi, L. Bioelectrical impedance phase angle in sport: A systematic review. J. Int. Soc. Sports Nutr. 2019, 16, 49. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Buchfuhrer, M.J.; Hansen, J.E.; Robinson, T.E.; Sue, D.Y.; Wasserman, K.; Whipp, B.J. Optimizing the exercise protocol for cardiopulmonary assessment. J. Appl. Physiol. 1983, 55, 1558–1564. [Google Scholar] [CrossRef] [PubMed]
  24. Howley, E.T.; Bassett, D.R.J.; Welch, H.G. Criteria for maximal oxygen uptake: Review and commentary. Med. Sci. Sports Exerc. 1995, 27, 1292–1301. [Google Scholar] [CrossRef] [PubMed]
  25. Stevens, J. Applied Multivariate Statistics for the Social Sciences; Lawrence Erlbaum Associates Inc.: Mahwah, NJ, USA, 2002. [Google Scholar]
  26. Campa, F.; Matias, C.; Gatterer, H.; Toselli, S.; Koury, J.C.; Andreoli, A.; Melchiorri, G.; Sardinha, L.B.; Silva, A.M. Classic Bioelectrical Impedance Vector Reference Values for Assessing Body Composition in Male and Female Athletes. Int. J. Environ. Res. Public Health 2019, 16, 5066. [Google Scholar] [CrossRef] [Green Version]
  27. Piccoli, A.; Nigrelli, S.; Caberlotto, A.; Bottazzo, S.; Rossi, B.; Pillon, L.; Maggiore, Q. Bivariate normal values of the bioelectrical impedance vector in adult and elderly populations. Am. J. Clin. Nutr. 1995, 61, 269–270. [Google Scholar] [CrossRef]
  28. Francisco, R.; Matias, C.N.; Santos, D.A.; Campa, F.; Minderico, C.S.; Rocha, P.; Heymsfield, S.B.; Lukaski, H.; Sardinha, L.B.; Silva, A.M. The Predictive Role of Raw Bioelectrical Impedance Parameters in Water Compartments and Fluid Distribution Assessed by Dilution Techniques in Athletes. Int. J. Environ. Res. Public Health 2020, 17, 759. [Google Scholar] [CrossRef] [Green Version]
  29. Matias, C.N.; Monteiro, C.P.; Santos, D.A.; Martins, F.; Silva, A.M.; Laires, M.J.; Sardinha, L.B. Magnesium and phase angle: A prognostic tool for monitoring cellular integrity in judo athletes. Magnes. Res. 2015, 28, 92–98. [Google Scholar] [CrossRef]
  30. Campa, F.; Silva, A.M.; Matias, C.N.; Monteiro, C.P.; Paoli, A.; Nunes, J.P.; Talluri, J.; Lukaski, H.; Toselli, S. Body Water Content and Morphological Characteristics Modify Bioimpedance Vector Patterns in Volleyball, Soccer, and Rugby Players. Int. J. Environ. Res. Public Health 2020, 17, 6604. [Google Scholar] [CrossRef]
  31. Barrea, L.; Muscogiuri, G.; Pugliese, G.; Laudisio, D.; de Alteriis, G.; Graziadio, C.; Colao, A.; Savastano, S. Phase Angle as an Easy Diagnostic Tool of Meta-Inflammation for the Nutritionist. Nutrients 2021, 13, 1446. [Google Scholar] [CrossRef]
  32. Barrea, L.; Muscogiuri, G.; Laudisio, D.; Di Somma, C.; Salzano, C.; Pugliese, G.; de Alteriis, G.; Colao, A.; Savastano, S. Phase Angle: A Possible Biomarker to Quantify Inflammation in Subjects with Obesity and 25(OH)D Deficiency. Nutrients 2019, 11, 1747. [Google Scholar] [CrossRef] [Green Version]
  33. Campa, F.; Semprini, G.; Judice, P.B.; Messina, G.; Toselli, S. Anthropometry, Physical and Movement Features, and Repeated-sprint Ability in Soccer Players. Int. J. Sports Med. 2019, 40, 100–109. [Google Scholar] [CrossRef] [PubMed]
  34. Durkalec-Michalski, K.; Nowaczyk, P.M.; Podgórski, T.; Kusy, K.; Osiński, W.; Jeszka, J. Relationship between body composition and the level of aerobic and anaerobic capacity in highly trained male rowers. J. Sports Med. Phys. Fit. 2019, 59, 1526–1535. [Google Scholar] [CrossRef] [PubMed]
  35. Beekley, M.D.; Abe, T.; Kondo, M.; Midorikawa, T.; Yamauchi, T. Comparison of normalized maximum aerobic capacity and body composition of sumo wrestlers to athletes in combat and other sports. J. Sports Sci. Med. 2006, 5, 13–20. [Google Scholar]
  36. Toselli, S.; Campa, F. Anthropometry and Functional Movement Patterns in Elite Male Volleyball Players of Different Competitive Levels e. J. Strength Cond. Res. 2018, 32, 2601–2611. [Google Scholar] [CrossRef] [PubMed]
  37. Martins, P.C.; Teixeira, A.S.; Guglielmo, L.G.A.; Francisco, J.S.; Silva, D.A.S.; Nakamura, F.Y.; de Lima, L.R.A. Phase Angle Is Related to 10 m and 30 m Sprint Time and Repeated-Sprint Ability in Young Male Soccer Players. Int. J. Environ. Res. Public Health 2021, 18, 4405. [Google Scholar] [CrossRef] [PubMed]
  38. Genton, L.; Mareschal, J.; Norman, K.; Karsegard, V.L.; Delsoglio, M.; Pichard, C.; Graf, C.; Herrmann, F.R. Association of phase angle and running performance. Clin. Nutr. ESPEN 2020, 37, 65–68. [Google Scholar] [CrossRef] [PubMed]
  39. Campa, F.; Matias, C.N.M.; Teixeira, F.J.; Reis, J.F.; Valamatos, M.J.; Toselli, S.; Monteiro, C.P. Leucine metabolites do not induce changes in phase angle, bioimpedance vector analysis patterns, and strength in resistance trained men. Appl. Physiol. Nutr. Metab. 2020, 46, 669–675. [Google Scholar] [CrossRef]
  40. Reis, J.F.; Matias, C.N.; Campa, F.; Morgado, J.P.; Franco, P.; Quaresma, P.; Almeida, N.; Curto, D.; Toselli, S.; Monteiro, C.P. Bioimpedance Vector Patterns Changes in Response to Swimming Training: An Ecological Approach. Int. J. Environ. Res. Public Health 2020, 17, 4851. [Google Scholar] [CrossRef]
  41. Campa, F.; Thomas, D.M.; Watts, K.; Clark, N.; Baller, D.; Morin, T.; Toselli, S.; Koury, J.C.; Melchiorri, G.; Andreoli, A.; et al. Reference Percentiles for Bioelectrical Phase Angle in Athletes. Biology 2022, 11, 264. [Google Scholar] [CrossRef]
  42. Silva, A.M. Structural and functional body components in athletic health and performance phenotypes. Eur. J. Clin. Nutr. 2019, 73, 215–224. [Google Scholar] [CrossRef]
  43. Dellinger, J.R.; Johnson, B.A.; Benavides, M.L.; Moore, M.L.; Stratton, M.T.; Harty, P.S.; Siedler, M.R.; Tinsley, G.M. Agreement of bioelectrical resistance, reactance, and phase angle values from supine and standing bioimpedance analyzers. Physiol. Meas. 2021, 42, 35003. [Google Scholar] [CrossRef] [PubMed]
  44. Stratton, M.T.; Smith, R.W.; Harty, P.S.; Rodriguez, C.; Johnson, B.A.; Dellinger, J.R.; Williams, A.D.; White, S.J.; Benavides, M.L.; Tinsley, G.M. Longitudinal agreement of four bioimpedance analyzers for detecting changes in raw bioimpedance during purposeful weight gain with resistance training. Eur. J. Clin. Nutr. 2021, 75, 1060–1068. [Google Scholar] [CrossRef] [PubMed]
Biology 11 00505 g001 550
Figure 1. Timeline of stations performed by the futsal players involved in the study.
Figure 1. Timeline of stations performed by the futsal players involved in the study.
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Figure 2. Body composition assessed with DXA according to the three-compartments tissue model in the futsal players grouped by tertiles of VO2max.
Figure 2. Body composition assessed with DXA according to the three-compartments tissue model in the futsal players grouped by tertiles of VO2max.
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Figure 3. On the left, mean impedance vectors with their 95% confidence ellipses for VO2max tertiles and Hotelling’s T2 test results; vector direction defined by phase angle (PhA) is also shown. On the right side, the target zone for athletes [26] is reported and the tolerance ellipses for the general population [27] depicted in the background.
Figure 3. On the left, mean impedance vectors with their 95% confidence ellipses for VO2max tertiles and Hotelling’s T2 test results; vector direction defined by phase angle (PhA) is also shown. On the right side, the target zone for athletes [26] is reported and the tolerance ellipses for the general population [27] depicted in the background.
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Figure 4. Correlation between fat mass (panel (a)) and muscle mass (panel (b)) phase angle (panel (c)) with VO2max in the futsal players.
Figure 4. Correlation between fat mass (panel (a)) and muscle mass (panel (b)) phase angle (panel (c)) with VO2max in the futsal players.
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Table
Table 1. Body composition characteristics (mean ± standard deviation) for the athletes grouped by tertiles of VO2max and results of the analysis of variance (ANOVA) are shown.
Table 1. Body composition characteristics (mean ± standard deviation) for the athletes grouped by tertiles of VO2max and results of the analysis of variance (ANOVA) are shown.
VariableT1, n = 16
(V02max = 42.9 ± 3.7 mL/kg/min)		T2, n = 16
(VO2max = 50.1 ± 1.7 mL/kg/min)		T3, n = 16
(VO2max = 58.2 ± 3.9 mL/kg/min)		ANOVA
Body mass (kg)81.0 ± 0.6 2,371.5 ± 7.5 1,366.2 ± 5.4 1,2F = 15.5, p < 0.001
BMI (kg/m2)24.3 ± 3.1 323.3 ± 1.522.0 ± 1.6 1F = 4.64, p = 0.015
DXA
Muscle mass (kg)33.3 ± 3.631.2 ± 4.231.8 ± 4.2F = 1.1, p = 0.349
Muscle mass (%)41.6 ± 6.8 343.8 ± 3.948.3 ± 7.5 1F = 4.7, p = 0.014
Fat mass (kg)17.9 ± 6.0 2,312.8 ± 3.0 1,39.8 ± 2.1 1,2F = 16.1, p < 0.001
Fat mass (%)22.1 ± 5.5 2,318.2 ± 3.3 1,315.2 ± 2.5 1,2F = 11.8, p < 0.001
BIA
R/H (ohm/m)279.3 ± 27.3282.9 ± 18.5293.4 ± 22.3F = 1.6, p = 0.212
Xc/H (ohm/m)33.7 ± 3.0 335.5 ± 3.038.4 ± 3.3 1F = 9.3, p < 0.001
Phase angle (degree)6.9 ± 0.4 37.2 ± 0.47.5 ± 0.5 1F = 7.7, p = 0.001
Note: BMI = body mass index, DXA = dual-energy X-ray absorptiometry, BIA = bioelectrical impedance analysis, R/H = resistance standardized for height, Xc/H = reactance standardized for height. 1 = different (p < 0.016) from T1; 2 = different from T2; 3 = different from T3.
Table
Table 2. Adjusted models using fat and muscle mass, and phase angle as independent variables for determining VO2max.
Table 2. Adjusted models using fat and muscle mass, and phase angle as independent variables for determining VO2max.
ModelR2Std. Errorβ95% CIp-Value
Fat mass
Model a0.4530.23−0.335−0.94, −0.020.046
Muscle mass
Model a0.4100.150.120−0.21, 0.640.371
Phase angle
Model a0.5111.640.3511.95, 8.540.003
Abbreviations: R2, coefficient of determination; β, Unstandardized coefficients beta; CI, confidence interval; Std. Error, standard error; VO2max, maximum rate of oxygen consumption. a, adjusted for age and body mass.
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Matias, C.N.; Campa, F.; Cerullo, G.; D’Antona, G.; Giro, R.; Faleiro, J.; Reis, J.F.; Monteiro, C.P.; Valamatos, M.J.; Teixeira, F.J. Bioelectrical Impedance Vector Analysis Discriminates Aerobic Power in Futsal Players: The Role of Body Composition. Biology 2022, 11, 505. https://doi.org/10.3390/biology11040505

AMA Style

Matias CN, Campa F, Cerullo G, D’Antona G, Giro R, Faleiro J, Reis JF, Monteiro CP, Valamatos MJ, Teixeira FJ. Bioelectrical Impedance Vector Analysis Discriminates Aerobic Power in Futsal Players: The Role of Body Composition. Biology. 2022; 11(4):505. https://doi.org/10.3390/biology11040505

Chicago/Turabian Style

Matias, Catarina N., Francesco Campa, Giuseppe Cerullo, Giuseppe D’Antona, Rita Giro, João Faleiro, Joana F. Reis, Cristina P. Monteiro, Maria J. Valamatos, and Filipe J. Teixeira. 2022. "Bioelectrical Impedance Vector Analysis Discriminates Aerobic Power in Futsal Players: The Role of Body Composition" Biology 11, no. 4: 505. https://doi.org/10.3390/biology11040505

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