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Review

Body Composition of Male Professional Soccer Players Using Different Measurement Methods: A Systematic Review and Meta-Analysis

by
Jaime Sebastiá-Rico
1,2,3,
Jose M. Soriano
3,4,*,
Noelia González-Gálvez
5,* and
José Miguel Martínez-Sanz
2,6
1
Area of Nutrition, University Clinic of Nutrition, Physical Activity and Physiotherapy (CUNAFF), Lluís Alcanyís Foundation-University of Valencia, 46020 Valencia, Spain
2
Food and Nutrition Research Group (ALINUT), University of Alicante, 03690 Alicante, Spain
3
Food & Health Lab, Institute of Materials Science, University of Valencia, 46980 Paterna, Spain
4
Joint Research Unit of Endocrinology, Nutrition and Clinical Dietetics, University of Valencia-Health Research Institute La Fe, 46026 Valencia, Spain
5
Sports Injury Prevention Research Group, Faculty of Sport, Catholic University of Murcia (UCAM), 30107 Murcia, Spain
6
Nursing Department, Faculty of Health Sciences, University of Alicante, 03690 Alicante, Spain
*
Authors to whom correspondence should be addressed.
Nutrients 2023, 15(5), 1160; https://doi.org/10.3390/nu15051160
Submission received: 6 February 2023 / Revised: 22 February 2023 / Accepted: 23 February 2023 / Published: 25 February 2023
(This article belongs to the Special Issue The Impact of Nutritional Strategies on Muscle Health)
Browse Figures
Versions Notes

Abstract

:
The performance of male soccer players (MSP) depends on multiple factors such as body composition. The physical demands of modern soccer have changed, so the ideal body composition (BC) requirements must be adapted to the present. The aim of this systematic review and meta-analysis was to describe the anthropometric, BC, and somatotype characteristics of professional MSP and to compare the values reported according to the methods and equations used. We systematically searched Embase, PubMed, SPORTDiscus, and Web of Science following the PRISMA statement. Random-effects meta-analysis, a pooled summary of means, and 95% CI (method or equation) were calculated. Random models were used with the restricted maximum likelihood (REML) method. Seventy-four articles were included in the systematic review and seventy-three in the meta-analysis. After comparing the groups according to the assessment method (kinanthropometry, bioimpedance, and densitometry), significant differences were found in height, fat mass in kilograms, fat mass percentage, and fat-free mass in kilograms (p = 0.001; p < 0.0001). Taking into account the equation used to calculate the fat mass percentage and ∑skinfolds, significant differences were observed in the data reported according to groups (p < 0.001). Despite the limitations, this study provides useful information that could help medical technical staff to properly assess the BC of professional MSP, providing a range of guidance values for the different BC.

Graphical Abstract

1. Introduction

Kinanthropometry is the area of science responsible for measuring the composition of the human body. Changes in lifestyle, nutrition, physical activity, and ethnic composition of populations are some of the factors that can cause alterations in body dimensions [1]. In sports (including soccer), anthropometry, bioelectrical impedance analysis (BIA) and dual X-ray absorptiometry (DXA) are the main methods used to assess body composition [2,3].
Anthropometry refers to the different measurements taken of the size and proportions of the human body by which, through equations, an estimation of the percentage of fat mass (FM), and by derivation, the fat-free mass (FFM), can be obtained [4]. This evaluation method has been described by different institutions, with the International Society for the Advancement of Kinanthropometry (ISAK) being the gold standard institution, according to ISO 7250-1:2017 and the ISAK standard [1]. This method is useful for nutritional and training control and monitoring [2,3].
BIA is a non-invasive and easy-to-apply method based on the principle that states that the conductivity of body water varies between the different compartments and can therefore be used for the calculation of total body water, FM, and FFM [5,6]. The method consists of measuring the resistance of the body to the flow of a current. In the adipose compartments, a greater resistance will be observed as these compartments are poorer conductors of electricity due to their low water volume, whereas muscle tissues, as they have a high-water content, together with a high concentration of electrolytes, will act as good electrical conductors [4].
DXA is an indirect method used to measure muscle mass (MM), FM, and bone mineral density (BMD) through photon attenuation (X-rays). Soft tissues, due to their high water and organic compound content, reduce the photon flux to a lesser extent than bone tissue, and the pixels of the bone compartment are more clearly distinguishable [6]. It is therefore considered the gold standard method for the assessment of bone mineral status [7,8].
Another of the most relevant tools used in the study of body composition is somatotype, defined as the quantification of the shape and composition of the human body through the numerical quantification of three components, by using different anthropometric formulas and measurements [9]. Derived from the somatotype, the somatochart is the expression of the three components (endomorphy, mesomorphy, and ectomorphy) as a graphic representation. In the field of sports, it is useful to be able to compare the somatotype of the athlete being evaluated against the reference somatotype of the sport he/she practices, based on a wide collection of data [10].
Therefore, the assessment of body composition plays a crucial role in athletes, as it directly affects both performance and sporting results in competitions [11]. In soccer, body composition is crucial for achieving an optimal physical level, which can translate into a good level of play, as performance in soccer depends on multiple technical, biomechanical, tactical, mental, and physiological factors, as well as nutritional and training control [12].
The aim of the present systematic review with meta-analysis was to describe the anthropometric characteristics, body composition, and somatotype of professional male soccer players, and to compare the values reported according to the methods and equations used. Consequently, the initial hypotheses were:
Hypothesis 1 (H1). 
Depending on the measurement instrument used, different values of body composition will be observed for the same compartment, especially in the weight and percentage of the FM.
Hypothesis 2 (H2). 
There will be differences between anthropometric equations for the same body compartment.

2. Materials and Methods

2.1. Type of Study

This systematic review and meta-analysis is based on existing evidence on anthropometric characteristics, body composition, and somatotype, of professional male soccer players. It was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [13].

2.2. Information Sources and Search Strategy

The databases searched to obtain the most current data were PubMed-MEDLINE, Embase, SPORTDiscus, and Web of Science. To find the largest number of available articles related to the research aim, the words used in the search strategy were defined considering: (1) soccer (football); (2) anthropometry, body composition and somatotype; (3) athlete (professional or elite); (4) the descriptors of the Medical Subjects Headings (MeSH); (5) other terms described in MeSH as “entry terms”, which include the terminology prior to the setting-up of the MeSH register; and (6) the terms (tiab) or (Title/Abstract) attached to the “entry terms” or MeSH, which allow the localization of these terms in the title and abstract of the articles. The search strategy used for PubMed was: (“Soccer” (Mesh) OR “soccer player” (Title/Abstract) OR “fútbol” (Title/Abstract) OR “soccer” (Title/Abstract) OR “football” (Title/Abstract)) AND (“Anthropometry” (MeSH) OR “Anthropometry” (Title/Abstract) OR “Body composition” (MeSH) OR “Body composition” (Title/Abstract) OR “Skinfolds” (Title/Abstract) OR “Skinfold Thickness” (MeSH) OR “Somatotypes” (Mesh) OR “Somatotypes” (Title/Abstract) OR “Body Build” (Title/Abstract) OR “Body Type” (Title/Abstract) OR “Endomorph” (Title/Abstract) OR “Mesomorph” (Title/Abstract) OR “Ectomorph” (Title/Abstract) OR “Absorptiometry, Photon” (Mesh) OR “Absorptiometry, Photon” (Title/Abstract) OR “Electric Impedance” (Mesh) OR “Electric Impedance” (Title/Abstract) OR “Bioimpedance” (Title/Abstract) OR “DXA” (Title/Abstract) OR “Dual Energy X-ray Absorptiometry” (Title/Abstract)) NOT (“youth” (Title/Abstract) OR “young” (Title/Abstract) OR “semi-professional” (Title/Abstract) OR “amateur” (Title/Abstract) OR “collegiate” (Title/Abstract) OR “pre-adolescent” (Title/Abstract) OR “recreational” (Title/Abstract) OR “adolescent” (Title/Abstract) OR “junior” (Title/Abstract) OR “referee” (Title/Abstract) OR “referees” (Title/Abstract) OR “gaelic” (Title/Abstract) OR “rugby” (Title/Abstract) OR “american football” (Title/Abstract) OR “female” (Title/Abstract) OR “women” (Title/Abstract)) AND (“professional” (Title/Abstract) OR “elite” (Title/Abstract)).
The search strategy was adapted for each of the databases consulted through the Polyglot Search of the Systematic Review Accelerator tool (accessed on 1 December 2021. https://sr-accelerator.com/#/polyglot).
The timeframe for the search included studies from the year 2000 until November 2021. Due to the fact that the physical demands of soccer players have evolved over the decades, they are currently more demanding and require different body composition characteristics [14].

2.3. Eligibility Criteria

The Participants, Intervention, Comparison, and Outcomes (PICO) criteria for the inclusion and exclusion criteria are shown on Table 1. No limits were placed in relation to the publication status of the study (pre-print, post-print, first online, or final).
The exclusion criteria included: (a) studies published in a language other than Spanish and/or English, and (b) narrative, systematic reviews, and/or meta-analyses.

2.4. Article Management Process

All the documents found were incorporated into the Zotero citation manager in a separate folder, depending on the database where they were found. A common folder was created to detect and delete duplicated articles using the software’s degree of data overlap. The final database was exported in RIS format to be imported into the article screening system for further processing by the researchers.

2.5. Study Selection

All retrieved articles were screened in duplicate. The first screening, based on the title and abstract, was independently conducted in all the studies by two authors (JS-R and JMM-S). During the processes of identifying and screening, a third researcher was consulted (JMS) to determine if the documents that led to discrepancies between authors had to be included or excluded. The articles eligible for a full-text review were then screened by the same authors (JS-R and JMM-S), independently and in duplicate. The rejected articles were then duly identified using the eligibility criterion previously established. Additional reviewers (JMS, NG-G) provided advice when feedback about doubtful documents was required.

2.6. Data Extraction

The studies’ information was extracted following a blinded and duplicated protocol by two authors (JS-R and JMM-S) using a previously piloted data extraction survey created for this review. The data extraction protocol for this study consisted of the following variables:
  • Study: Authors and year of publication
  • Country and competition category: Geographical area and competitive category from which the data comes. The latter was included to differentiate between professional league categories within the same country.
  • Sample: Number of subjects.
  • Time of Season: Included to differentiate values collected between different cycles of a natural season (if specified).
  • Method of analysis: It was included to differentiate the values collected between the three methods of evaluation of body composition.
  • Measuring instruments: description of the material used in the evaluation.
  • Main results: Kinanthropometric characteristics and values of FM, MM, bone mass (BM), and body water

2.7. Study Quality and Data Collection

Two researchers (JS-R and JMM-S) examined the quality of the studies using the Agency for Health Research and Quality (AHRQ) Methodology Checklist [15]. A third reviewer (JMS) was consulted to resolve discrepancies. A score above 8 was considered a high-quality study. Egger’s bias statistic [16] was used to assess the risk of bias, and funnel plots were created. When a meta-analysis was based on a small number of studies, the ability of Egger’s test to detect bias is limited [17]. Therefore, this test was only performed when there were at least 10 studies included in the meta-analysis [16]

2.8. Statistical Analysis

The meta-analysis was performed with the R software version 3.6.0. Copyright (C) 2019 (R Foundation for Statistical Computing). The meta-analysis was performed for continuous data using sample (n), mean (M), and standard deviation (SD) of each output from each study. Some studies had more than one group and were treated as other subgroups in the analysis. In the random-effects meta-analysis, a pooled summary mean, and 95%CI were calculated. Studies were weighted according to sample within and between studies. A pooled summary mean and 95%CI were calculated for subgroups (method or equation) in order to compare the differences between groups. Random models using the restricted maximum likelihood method (REML) were utilized. The heterogeneity was measured using the I2 statistic, considering a high heterogeneity if I2 ≥ 75%. The level of significance adopted was 5% (p < 0.05).

3. Results

A total of 74 studies were included in the systematic review, of which 32 were based on the use of anthropometry [18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49], 21 in BIA [50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70], 13 in DXA [71,72,73,74,75,76,77,78,79,80,81,82,83], 3 combined anthropometry and BIA [84,85,86], 3 combined anthropometry and DXA [87,88,89], and 2 combined BIA and DXA [90,91], while 73 articles were included in the meta-analysis (Figure 1).
Table 2, Table 3 and Table 4 show the qualitative characteristics of the included articles differentiated by measurement methods (anthropometry, BIA, and DXA, respectively). Table 5 shows the evaluation of methodological quality as assessed with the Methodology Checklist from the Agency for Health Research and Quality (AHRQ) [15].
The sample comprising the different articles included in the review amounted to a total of 5197 soccer players.
In relation to the anthropometric measuring instrument, the main one used was the Harpenden plicometer (n = 25) [18,19,20,21,23,24,25,26,28,31,33,35,36,37,39,40,41,44,46,47,48,86,87,88,89], followed by the Holtain (n = 8) [22,29,34,38,42,43,84,85] and the Slimguide (n = 5) [27,30,32,45,49] ones. For BIA, the most commonly used models were the Tanita BC 418 MA (n = 5) [58,62,64,67,86], Akern BIA 101 (n = 4) [57,65,84,85], and Tanita TBF 543 (n = 2) [52,60]. Lastly, the most common DXA machine models were GE Lunar Prodigy (n = 4) [73,87,89,90], Hologic QDR Series Delphi A (n = 2) [82,91], Hologic QDR Series Discovery A (n = 2) [71,76] and Hologic Discovery W (n = 2) [78,79].
Of the 38 articles included in which anthropometry was used as a method of assessment, 15 used the ISAK protocol [22,28,30,31,32,34,35,41,43,45,49,85,86,88,89], 2 utilized the protocol from the Anthropometric Standardization Reference Manual (ASRM) [29,38], 1 used the protocol from the International Biological Program (IBP) [47], 1 used the protocol from the American College of Sports Medicine (ACSM) [46], and the remaining 19 articles did not specify the protocol applied [18,19,20,21,23,24,25,26,27,33,36,37,39,40,42,44,48,84,87].
As for the sum of skinfolds, the most commonly used formulas were the sum of six skinfolds (triceps, subscapular, supraspinale, abdominal, mid-thigh, and calf) (n = 4) [22,32,34,45], followed by the sum of eight folds (biceps, triceps, subscapular, iliac crest, supraspinale, abdominal, mid-thigh, and calf) (n = 3) [31,40,41].
Regarding the percentage of FM, the most commonly used formula was that of Durnin and Womersley, 1974 (n = 10) [19,21,23,24,25,26,31,48,87,89], followed by Jackson and Pollock’s seven folds, 1978 (n = 6) [36,39,46,47,87,89], and Faulkner, 1968 (n = 4) [27,34,37,89]. In the case of the MM percentage, the most commonly used formula was Matiegka, 1921 (n = 3) [28,34,44], followed by Kerr, 1988 (n = 1). Finally, for the percentage of bone mass (BM), the two equally used formulas were those of Matiegka, 1921 and Kerr, 1988 (n = 1) [44,89]. Tables S1-S3 show the body composition characteristics of included studies for each of the methods analyzed.
Table 6 and Supplementary Figures S1–S9 show the meta-analysis grouped according to the method of variable assessment (1 = anthropometry, 2 = BIA, 3 = DXA) mean, standard deviation, sample size, weight, and subgroup meta-analysis (pooled summary mean, 95%CI, Tau2, Chi2, df, p, I2) and for the total (pooled summary mean, 95%CI, Tau2, Chi2, df, p, I2 and test for subgroup differences: Chi2, df, p). The sample had a mean age of 24.50 years old, and the following mean values were found: 179.76 cm in height, 76.29 kg in weight, 12.48 kg of FM, 11.85% of FM, 66.10 kg of FFM, 81.36% of FFM, 39.28 kg of MM and 52.03% of MM. The comparison of the groups according to the assessment method (anthropometry, BIA, and DXA) did not show significant differences in age (p = 0.45), (Supplementary Figure S1), weight (p = 0.11) (Supplementary Figure S3), percentage of FFM (p = 0.59) (Supplementary Figure S7), kilograms of MM (p = 0.37) (Supplementary Figure S8) or percentage of MM (p = 0.38) (Supplementary Figure S9). However, there were significant differences in height, with method 2 (BIA) showing the greatest height (p = 0.001) (Supplementary Figure S2); in kilograms of FM, with method 1 showing the highest value (p < 0.0001) (Supplementary Figure S4), in the percentage of FM, with method 3 showing the highest value (p < 0.0001) (Supplementary Figure S5), in kilograms of FFM, with method 1 showing the highest value (p = 0.0004) (Supplementary Figure S6).
Taking into account the equation used for the calculation of the percentage of FM, a mean of 10.19% was obtained, with significant differences in the data reported according to groups (p < 0.001), with equations 3 (Durnin and Womersley, 1974) and 6 (Faulkner, 1968) indicating the highest values, and equations 1 (Carter, 1982) and 26 (Yuhasz, 1974) the lowest values (Table 7 and Figure 2).
According to the equation used to calculate the sum of skinfolds, Equation (5) (triceps, subscapular, supraspinale, abdominal, anterior thigh, calf) showed a mean of 52.18 mm, while Equation (6) (triceps, biceps, subscapular, iliac crest, supraspinale, abdominal, anterior thigh, calf) showed a value of 59.93 mm (Table 7 and Figure 3).
Likewise, the data (pooled summary mean, 95%CI, Tau2, Chi2, df, p, I2) for endomorphy, ectomorphy, and mesomorphy are shown in Figure 4. The studies report a mean of 2.32 points for endomorphy, 2.32 points for ectomorphy, and 5.15 points for mesomorphy. Figure 5 shows the data for bone mineral content (BMC), BMD, and water body. With respect to BMC, mean values of 3.17 kg were reported, with respect to BMD values, 1.33 g/cm3, and with respect to water values, 47.52 l mean values were reported.

4. Discussion

This is the first systematic review with meta-analysis to assess the body composition of professional male soccer players, as well as the influences of the measurement method used. The main findings of this work were: (1) there are differences in the FM assessed between the different methods, with higher percentages using DXA followed by BIA and anthropometry, with a mean value of 11.85%; (2) there are significant differences between the different anthropometric formulas to assess the percentage of FM, with higher values in Durnin and Womersley, 1974 and Faulkner, 1968, and lower values in Yuhasz, 1974 and Carter, 1982; (3) no significant differences were observed in the measurement of MM through anthropometry and BIA, with a mean of 52.03% and 39.28 kg; (4) the weight of the FFM was higher with anthropometry, followed by BIA and DXA, with a mean value of 66.10 kg, although no significant differences were observed in the percentage of FFM through BIA and DXA, with a mean of 82.36%; (5) the mean somatotype was balanced mesomorphic.

4.1. Measurement Instruments

The anthropometric method is one of the most widely used methods in soccer to assess body composition, as it is inexpensive, easy to transport, non-invasive, simple to apply, and validated by the scientific community, although it requires the anthropometrist to be trained and qualified [6,8]. There are many types of plicometers available on the market, some of which are cheap and accessible, but not valid because they do not meet the technical specifications in relation to the pressure exerted on the subcutaneous tissue and/or an erroneous calibration [4,92]. In our work, the Harpenden model was the most commonly used, followed by the Holtain and Slimguide ones. This coincides with the literature, as Harpenden is the most traditionally used model in scientific research, being considered the gold standard method [93,94]. Recently, the agreement of these three analog models, together with a fourth digital model, Lipowise, was evaluated to establish the differences between the sum of skinfolds, and the estimation of FM and adipose tissue using different formulas. The authors concluded that the measurement data were similar, although the Holtain and Slimguide models were more similar to each other and tended to overestimate the result as compared to the Harpenden model [95]. It is therefore recommended that measurements be made with the same model of plicometer to monitor an individual or to compare the measured results with other studies [8,95,96].
In addition, there are many formulas for estimating the different compartments of body composition, which have an impact on the results obtained, and which are heterogeneous and not comparable with each other [97,98]. Recently, Martínez-Ferran et al. tested 21 professional soccer players from the Spanish league to discover which FM formula and sum of skinfolds correlated best with DXA as the gold standard method when assessing FM. The formula proposed by Suarez-Arrones et al., 2018, and the sum of four skinfolds (triceps, subscapular, supraspinale, and abdominal) were found to have the highest agreement [99]. In addition, this formula required only the triceps and iliac crest fold, which could reduce the time spent on the anthropometric assessment of soccer teams that are assessed on the same day and at different times in the season [99]. This correlation has been previously investigated in soccer players [89,91,100], but their results pointed to a higher correlation to formulas such as Faulkner, 1968 [101], Eston, 2005 [102], Withers et al., 1987 [103] or Durnin and Womersley, 1974 [104]. In our work, neither of the two proposals by Martínez-Ferran et al. were included, with the formula from Durnin and Womersley, 1974 [104], and the sum of six skinfolds (triceps, subscapular, supraspinale, abdominal, mid-thigh, and calf) being the most widely used in the studies included. For this reason, caution should be exercised in the selection of the formulas to be used when assessing soccer players, and the validity of the proposals by Martínez-Ferran et al. should be further verified [97].
With respect to BIA, it is frequently used in soccer due to the ease of transport (for some models) and speed of use [6]. However, it is probably not the most appropriate method, as the presence of certain diseases, treatments, or clinical situations, as well as the rules of use, can alter the results of this instrument [5,6]. In addition, several published studies point to the existence of a low correlation of FM results between BIA and DXA in professional soccer players, in contrast to some anthropometric formulas [91,99], in agreement with the results of this work. However, the combination of anthropometry and BIA could allow for a more complete assessment of body composition, as BIA adequately assesses water status and cell mass, thus complementing the anthropometric method [84]. Determining water status prior to training and/or competitions could improve hydration patterns and prevent adverse effects such as dehydration and hyperhydration, which have an impact on sports performance or risk of injury [105,106]. Despite their usefulness, the percentage and weight of body water were only collected in a few articles included in the present review, with a value of 47.52 L on average [57,64,66,69,84] (Supplementary Figure S10), as more importance has been traditionally given to the total weight and FM than to the rest of the components [8]. In relation to DXA, although it is considered the gold standard method, it has limitations that can affect the calculations of the measurements, such as the dimensions of the subject to be analyzed (both in height and width), the high economic cost, the experience needed for processing and interpreting the results, or software updates with new algorithms to calculate body composition [6,8,75,107]. In the UEFA expert group statement, the average values of FM of elite male soccer players measured by DXA varied between 8–13%, although lower and higher values have also been reported [2]. Several of the included studies agreed with this range [71,72,73,75,76,78,90], with most of them exceeding it [74,77,79,80,81,82,83,87,88,89,91], but in no case was it below this value. In fact, the mean of the meta-analysis performed provided a value of 13.46%. This does not necessarily translate into poorer performance, as optimal physique varies according to playing position, physiology, and style of play depending on the team and/or coach [2]. Finally, despite being considered the reference method for the evaluation of bone mineral status [7,8], few studies assessed the BMC [75,77,78,79,89], with a value of 3.17 kg (Supplementary Figure S11), or BMD [71,74,75,77,78,79,89] with a value of 1.33 g/cm3 (Supplementary Figure S12).

4.2. Body Composition Values

Presently, there are multiple body composition references for all professional sports modalities, many of them being considered a goal to achieve for athletes [108,109]. Body composition is crucial for achieving an optimal physical level, which can translate into a good level of play, as performance in soccer depends on multiple technical, biomechanical, tactical, mental, and physiological factors [2,12]. In fact, during soccer practice, there are a multitude of movements that are affected by weight, such as accelerations, changes in direction, or vertical jumps, so muscle training, impact loading, and body fat reduction are important in physical preparation to improve performance in soccer [71,110]. For this reason, the physiological assessment of a soccer player has become more important in recent years, to the extent that it is not only based on technical quality, but also on the physical abilities of the player [111]. Moreover, the physical demands of elite soccer players have been increasing in recent decades, not only in the amount of training and/or competitions, but also in the intensity of effort during matches, and a shorter recovery period between competitions or training sessions [14,112,113]. It is coherent to think that body composition has also evolved over time. However, there are only a few reference values of the different body components in professional soccer players that can be used by the medical technical staff [2,3].
This is not the case in other team sports such as basketball [114] or handball [115], where reference values for some body components, such as FM, are available, although these studies did not specify either the formulas for estimating FM, MM, or BM, or the model of the instrument used. These limitations may influence reliability, reproducibility, and application in clinical practice.
In relation to FM, Radzimiński et al. evaluated the relationship between speed, aerobic capacity, body composition (through BIA), and distance covered during official matches, of 23 professional players participating in the international competition Europa League, concluding that players with a lower percentage of FM and higher aerobic capacity covered the longest distances and at a higher speed during competitions [70,116,117]. It has also been observed that a higher percentage of FM is negatively associated with the 20 m sprint speed [118]. Regarding MM, Ayotte et al. assessed whether body mass gain from strength training would impair the aerobic capacity of 11 elite soccer players, with the results showing that it did not negatively impact aerobic capacity, but significantly increased it [2,119,120]. In our work, for the first time to our knowledge, it was possible to make a proposal of ranges of guidance values for the different body compartments (Figure 6).
Recently, Moya-Amaya et al. verified the somatotype trend in the last few decades, of professional male soccer players, observing a decrease in the endomorphic component, evolving from balanced mesomorphy to ecto-mesomorphy [121]. This may be important, because the somatotype that is most prone to injury is the balanced ectomorph (85%), as opposed to the ecto-mesomorph (50%), meso-ectomorph (45%), or balanced mesomorph (44%) [98]. In our study, similar results were observed [22,28,29,30,32,38,44,45,49,85,86], although the total mean endomorphy, ectomorphy, and mesomorphy values were 2.32, 2.32, and 5.15, respectively, resulting in a balanced mesomorph somatotype. These findings relate to the usefulness of the rest of the body compartments, beyond FM, for the performance and health of the soccer player.

4.3. Limitations

This study has limitations. Firstly, the existing heterogeneity in the equations for estimating body composition using the anthropometric method, times in the season in which the assessment was carried out, playing positions, and measuring instruments for evaluating the different body components, made it difficult to compare the results. In addition, not all studies that used the anthropometric method provided details on which measurement protocol they applied, so the methodology and anthropometrists could be biased. Moreover, while certain measurement instruments were excluded in the anthropometry section, as they were not valid according to the ISAK protocol, no limiting criteria were applied for BIA or DXA instruments. This is an important aspect, as there is a risk of unifying tetrapolar and octapolar BIA values, as well as single and multi-frequency methods. Another limitation is that although the data collection was limited to male soccer players, not all countries show the same professional level of soccer, with some countries having higher Fédération Internationale de Football Association (FIFA) rankings than others [122]. While it is true that they are all professional players, they do not have the same economic level, sporting facilities, or physical demands, which can affect many factors that could influence their body composition and sporting performance.
In spite of this, and the limitations of our study, it is the only study currently available in the scientific literature that provides a complete description of the main instruments for assessing body composition of male professional soccer players. Based on the current situation of the sport, our research aims to be the first study to propose a range of guidance values of anthropometric measurements, FM, MM, and BM in general male professional soccer players.

4.4. Future Research and Practical Application

Although the importance of assessing MM, BM, and body water, and their relationship to sports performance has been highlighted in recent years, studies continue to focus on total weight and FM. For future studies it is recommended to (1) clearly describe the procedures and protocols applied when performing the measurements, (2) specify the reliability, calibration of the measuring instruments, and the technical error of measurement, (3) clearly monitor and report the hydration and nutrition status prior to the measurement, (4) specify the competitive level of the athlete, (5) specify the level of the athlete’s performance, (6) specify the competitive level of the sample by reporting the country and/or region and the name of the league in which the players were competing at the time of the study, (7) report the playing position of the players and the exact time in the season in which the measurements were taken, (8) show all body composition characteristics of the different methods used, as well as the anthropometric and somatotype values, and (9) investigate body composition values through different assessment methods and different playing positions in women’s professional soccer.
Based on the observed results and the experience of the authors, it is proposed (1) to have as many equations and assessment methods as possible; (2) to replace parameters such as “ideal weight” with the sum of skinfolds; (3) to rely on guidance values indicated in the scientific literature, as well as on the evolution of the team and/or players during the season; and finally (4) to work with the medical technical staff and the players and exchange information and impressions.

5. Conclusions

This systematic review with meta-analysis provides useful information that could help medical technical staff to adequately assess the body composition of male professional soccer players. In conclusion: (1) the somatotype tendency is balanced mesomorphic; (2) the mean Σ6 and Σ8 skinfolds are 52.18 and 59.93 mm, respectively; (3) there are significant differences in the measurement of the height, percentage, and kilograms of FM, and kilograms of FFM, with oscillating values depending on the method and/or formula applied; and (4) there are no significant differences between measurement methods for the calculation of the weight, percentage of FFM, and percentage and kilograms of MM.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu15051160/s1, Table S1: Body composition characteristics of included studies with anthropometry; Table S2: Body composition characteristics of included studies with BIA; Table S3. Body composition characteristics of included studies with DXA; Figure S1. Forest plot of age according to assessment method; Figure S2. Forest plot of height according to assessment method; Figure S3. Forest plot of weight according to assessment method; Figure S4. Forest plot of fat mass kilograms according to assessment method; Figure S5. Forest plot of fat mass percentage according to assessment method; Figure S6. Forest plot of fat-free mass kilograms according to assessment method; Figure S7. Forest plot of fat-free mass percentage according to assessment method; Figure S8. Forest plot of muscle mass kilograms according to assessment method; Figure S9. Forest plot of muscle mass percentage according to assessment method; Figure S10. Forest plot of total body water; Figure S11. Forest plot of bone mineral content; Figure S12. Forest plot of bone mineral density

Author Contributions

J.M.M.-S.; data curation and analysis, J.S.-R. and N.G.-G.; project administration J.M.S. and J.M.M.-S.; writing—original draft preparation, J.S.-R., N.G.-G.; writing—review and editing, J.S.-R., J.M.S., N.G.-G., and J.M.M.-S.; visualization and supervision J.M.S. and J.M.M.-S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available in the tables of this article. The data presented in this study are available on request from the corresponding author.

Acknowledgments

We are grateful to Mario Fon (native English speaker) for the review of the English grammar and style of the current report. This study is part of the doctoral thesis of Jaime Sebastiá Rico.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

ACSM: American College of Sports Medicine; AHRQ: Agency for Health Research and Quality; ASRM: Anthropometric Standardization Reference Manual; BIA: bioelectrical impedance analysis; BM: bone mass; BMC: bone mineral content; BMD: bone mineral density; DXA: dual X-ray absorptiometry; FIFA: Fédération Internationale de Football Association; FFM: fat-free mass; FM: fat mass; IBP: International Biological Program; ISAK: International Society for the Advancement of Kinanthropometry; MM: muscle mass; PICO: Population/Intervention/Comparison/Outcomes; PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses; REML: restricted maximum likelihood.

References

  1. What Is ISAK? Available online: https://www.isak.global/WhatIsIsak/#GoToKina (accessed on 22 May 2022).
  2. Collins, J.; Maughan, R.J.; Gleeson, M.; Bilsborough, J.; Jeukendrup, A.; Morton, J.P.; Phillips, S.M.; Armstrong, L.; Burke, L.M.; Close, G.L.; et al. UEFA Expert Group Statement on Nutrition in Elite Football. Current Evidence to Inform Practical Recommendations and Guide Future Research. Br. J. Sports Med. 2021, 55, 416. [Google Scholar] [CrossRef] [PubMed]
  3. Abreu, R.; Figueiredo, P.; Beckert, P.; Marques, J.P.; Amorim, S.; Caetano, C.; Carvalho, P.; Sá, C.; Cotovio, R.; Cruz, J.; et al. Portuguese Football Federation Consensus Statement 2020: Nutrition and Performance in Football. BMJ Open Sport Exerc. Med. 2021, 7, e001082. [Google Scholar] [CrossRef] [PubMed]
  4. Alvero Cruz, J.R.; Cabañas, M.D.; Herrero de Lucas, A.; Martinez Riaza, L.; Moreno Pascual, C.; Porta Manzañido, J.; Sillero Quintana, M.; Sirvent Belando, J.E. Protocolo de Valoración de La Composición Corporal Para El Reconocimiento Médico-Deportivo. Documento de Consenso Del Grupo Español de Cineantropometría (GREC) de La Federación Española de Medicina Del Deporte (FEMEDE). Versión 2010. Arch. Med. Deporte 2010, 27, 330. [Google Scholar]
  5. Alvero-Cruz, J.R.; Correas Gómez, L.; Ronconi, M.; Fernández Vázquez, R.; Porta i Manzañido, J. La bioimpedancia eléctrica como método de estimación de la composición corporal, normas prácticas de utilización. Rev. Andal. Med. Deporte 2011, 4, 167–174. [Google Scholar]
  6. Moreira, O.C.; Aubin, D.A.A.; de Oliveira, C.E.P.; Luján, R.C. Métodos de evaluación de la composición corporal: Una revisión actualizada de descripción, aplicación, ventajas y desventajas. Arch. Med. Deporte Rev. Fed. Esp. Med. Deporte Confed. Iberoam. Med. Deporte 2015, 32, 387–394. [Google Scholar]
  7. Lewiecki, E.M. Update on Bone Density Testing. Curr. Osteoporos. Rep. 2005, 3, 136–142. [Google Scholar] [CrossRef]
  8. Kasper, A.M.; Langan-Evans, C.; Hudson, J.F.; Brownlee, T.E.; Harper, L.D.; Naughton, R.J.; Morton, J.P.; Close, G.L. Come Back Skinfolds, All Is Forgiven: A Narrative Review of the Efficacy of Common Body Composition Methods in Applied Sports Practice. Nutrients 2021, 13, 1075. [Google Scholar] [CrossRef]
  9. Carter, J. The Heath-Carter Anthropometric Somatotype—Instruction Manual; Department of Exercise and Nutritional Sciences San Diego State University: San Diego, CA, USA, 2002; pp. 1–26. [Google Scholar]
  10. Sanz, J.M.M.; Otegui, A.U.; Guerrero, J.; Barrios, V. El somatotipo-morfología en los deportistas. ¿Cómo se calcula? ¿Cuáles son las referencias internacionales para comparar con nuestros deportistas? Lect. Educ. Física Deport. 2011, 159, 4. [Google Scholar]
  11. Ackland, T.R.; Lohman, T.G.; Sundgot-Borgen, J.; Maughan, R.J.; Meyer, N.L.; Stewart, A.D.; Müller, W. Current Status of Body Composition Assessment in Sport: Review and Position Statement on Behalf of the Ad Hoc Research Working Group on Body Composition Health and Performance, under the Auspices of the I.O.C. Medical Commission. Sports Med. Auckl. NZ 2012, 42, 227–249. [Google Scholar] [CrossRef] [PubMed]
  12. Cavia, M.; Moreno, A.; Fernández-Trabanco, B.; Carrillo, C.; Alonso-Torre, S. Anthropometric Characteristics and Somatotype of Professional Soccer Players by Position. J. Sports Med. Ther. 2019, 4, 073–080. [Google Scholar] [CrossRef] [Green Version]
  13. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef] [PubMed]
  14. Wallace, J.L.; Norton, K.I. Evolution of World Cup Soccer Final Games 1966-2010: Game Structure, Speed and Play Patterns. J. Sci. Med. Sport 2014, 17, 223–228. [Google Scholar] [CrossRef] [PubMed]
  15. Zeng, X.; Zhang, Y.; Kwong, J.S.W.; Zhang, C.; Li, S.; Sun, F.; Niu, Y.; Du, L. The Methodological Quality Assessment Tools for Preclinical and Clinical Studies, Systematic Review and Meta-Analysis, and Clinical Practice Guideline: A Systematic Review. J. Evid.-Based Med. 2015, 8, 2–10. [Google Scholar] [CrossRef] [PubMed]
  16. Egger, M.; Davey Smith, G.; Schneider, M.; Minder, C. Bias in Meta-Analysis Detected by a Simple, Graphical Test. BMJ 1997, 315, 629–634. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Sterne, J.A.C.; Sutton, A.J.; Ioannidis, J.P.A.; Terrin, N.; Jones, D.R.; Lau, J.; Carpenter, J.; Rücker, G.; Harbord, R.M.; Schmid, C.H.; et al. Recommendations for Examining and Interpreting Funnel Plot Asymmetry in Meta-Analyses of Randomised Controlled Trials. BMJ 2011, 343, d4002. [Google Scholar] [CrossRef] [Green Version]
  18. Al-Hazzaa, H.M.; Almuzaini, K.S.; Al-Refaee, S.A.; Sulaiman, M.A.; Dafterdar, M.Y.; Al-Ghamedi, A.; Al-Khuraiji, K.N. Aerobic and Anaerobic Power Characteristics of Saudi Elite Soccer Players. J. Sports Med. Phys. Fit. 2001, 41, 54–61. [Google Scholar]
  19. Bekris, E.; Mylonis, E.; Gioldasis, A.; Gissis, I.; Natalia, K. Aerobic and Anaerobic Capacity of Professional Soccer Players in Annual Macrocycle. J. Phys. Educ. Sport 2016, 16, 527–533. [Google Scholar] [CrossRef]
  20. Boone, J.; Vaeyens, R.; Steyaert, A.; Bossche, L.V.; Bourgois, J. Physical Fitness of Elite Belgian Soccer Players by Player Position. J. Strength Cond. Res. 2012, 26, 2051–2057. [Google Scholar] [CrossRef] [Green Version]
  21. Carling, C.; Orhant, E. Variation in Body Composition in Professional Soccer Players: Interseasonal and Intraseasonal Changes and the Effects of Exposure Time and Player Position. J. Strength Cond. Res. 2010, 24, 1332–1339. [Google Scholar] [CrossRef] [Green Version]
  22. Casajús, J.A. Seasonal Variation in Fitness Variables in Professional Soccer Players. J. Sports Med. Phys. Fit. 2001, 41, 463–469. [Google Scholar]
  23. Chaouachi, A.; Manzi, V.; Chaalali, A.; Wong, D.P.; Chamari, K.; Castagna, C. Determinants Analysis of Change-of-Direction Ability in Elite Soccer Players. J. Strength Cond. Res. 2012, 26, 2667–2676. [Google Scholar] [CrossRef] [PubMed]
  24. Dey, S.K.; Kar, N.; Debray, P. Anthropometric, motor ability and physiological profiles of indian national club footballers: A comparative study. S. Afr. J. Res. Sport Phys. Educ. Recreat. 2010, 32, 43–56. [Google Scholar] [CrossRef] [Green Version]
  25. Fessi, M.S.; Zarrouk, N.; Filetti, C.; Rebai, H.; Elloumi, M.; Moalla, W. Physical and Anthropometric Changes during Pre- and in-Season in Professional Soccer Players. J. Sports Med. Phys. Fit. 2016, 56, 1163–1170. [Google Scholar]
  26. Filaire, E.; Lac, G.; Pequignot, J.-M. Biological, Hormonal, and Psychological Parameters in Professional Soccer Players throughout a Competitive Season. Percept. Mot. Skills 2003, 97, 1061–1072. [Google Scholar] [CrossRef] [PubMed]
  27. Grazioli, R.; Loturco, I.; Baroni, B.M.; Oliveira, G.S.; Saciura, V.; Vanoni, E.; Dias, R.; Veeck, F.; Pinto, R.S.; Cadore, E.L. Coronavirus Disease-19 Quarantine Is More Detrimental Than Traditional Off-Season on Physical Conditioning of Professional Soccer Players. J. Strength Cond. Res. 2020, 34, 3316–3320. [Google Scholar] [CrossRef]
  28. Gutiérrez, C.R.; Monroy, S.E. Anthropometric and physiological characteristics in elite soccer players. Arch. Med. Deporte 2005, 22, 33–37. [Google Scholar]
  29. Hazir, T. Physical Characteristics and Somatotype of Soccer Players According to Playing Level and Position. J. Hum. Kinet. 2010, 26, 83–95. [Google Scholar] [CrossRef]
  30. Henriquez-Olguin, C.; Baez, E.; Ramirez-Campillo, R.; Canas, R. Somatotype Profile of Professional Male Soccer Chilean Players. Int. J. Morphol. 2013, 31, 225–230. [Google Scholar] [CrossRef] [Green Version]
  31. Iga, J.; Scott, M.; George, K.; Drust, B. Seasonal Changes in Multiple Indices of Body Composition in Professional Football Players. Int. J. Sports Med. 2014, 35, 994–998. [Google Scholar] [CrossRef]
  32. Jorquera Aguilera, C.; Rodriguez Rodriguez, F.; Torrealba Vieira, M.I.; Campos Serrano, J.; Gracia Leiva, N.; Holway, F. Anthropometric Characteristics of Chilean Professional Football Players. Int. J. Morphol. 2013, 31, 609–614. [Google Scholar] [CrossRef] [Green Version]
  33. Kalapotharakos, V.I.; Ziogas, G.; Tokmakidis, S.P. Seasonal Aerobic Performance Variations in Elite Soccer Players. J. Strength Cond. Res. 2011, 25, 1502–1507. [Google Scholar] [CrossRef] [PubMed]
  34. Lago-Penas, C.; Rey, E.; Lago-Ballesteros, J.; Dominguez, E.; Casais, L. Seasonal Variations in Body Composition and Fitness Parameters According to Individual Percentage of Training Completion in Professional Soccer Players. Int. Sport Med. J. 2013, 14, 205–215. [Google Scholar]
  35. Manuel Vega, J.; Gonzalez-Artetxe, A.; Ander Aguinaco, J.; Los Arcos, A. Assessing the Anthropometric Profile of Spanish Elite Reserve Soccer Players by Playing Position over a Decade. Int. J. Environ. Res. Public Health 2020, 17, 5446. [Google Scholar] [CrossRef] [PubMed]
  36. Michailidis, Y. Stress Hormonal Analysis in Elite Soccer Players during a Season. J. Sport Health Sci. 2014, 3, 279–283. [Google Scholar] [CrossRef] [Green Version]
  37. Najafi, A.; Shakerian, S.; Habibi, A.; Shabani, M.; Fatemi, R. The comparison of some anthropometric, body composition indexes and vo2max of ahwaz elite soccer players of different playing positions. Pedagog. Psychol. Med.-Biol. Probl. Phys. Train. Sports 2015, 19, 64–68. [Google Scholar] [CrossRef] [Green Version]
  38. Orhan, O.; Sagir, M.; Zorba, E. Comparison of Somatotype Values of Football Players in Two Professional League Football Teams According to the Positions. Coll. Antropol. 2013, 37, 401–405. [Google Scholar]
  39. Ostojic, S.M. Seasonal Alterations in Body Composition and Sprint Performance of Elitesoccer Players. J. Exerc. Physiol. Online 2003, 6, 24–27. [Google Scholar]
  40. Owen, A.L.; Wong, D.P.; Paul, D.; Dellal, A. Effects of a Periodized Small-Sided Game Training Intervention on Physical Performance in Elite Professional Soccer. J. Strength Cond. Res. 2012, 26, 2748–2754. [Google Scholar] [CrossRef]
  41. Owen, A.L.; Lago-Peñas, C.; Dunlop, G.; Mehdi, R.; Chtara, M.; Dellal, A. Seasonal Body Composition Variation Amongst Elite European Professional Soccer Players: An Approach of Talent Identification. J. Hum. Kinet. 2018, 62, 177–184. [Google Scholar] [CrossRef] [Green Version]
  42. Petri, C.; Mascherini, G.; Pengue, L.; Galanti, G. Dietary Habits in Elite Soccer Players. Sport Sci. Health 2016, 12, 113–119. [Google Scholar] [CrossRef]
  43. Pietraszewska, J.; Struzik, A.; Burdukiewicz, A.; Stachon, A.; Pietraszewski, B. Relationships between Body Build and Knee Joint Flexor and Extensor Torque of Polish First-Division Soccer Players. Appl. Sci. 2020, 10, 783. [Google Scholar] [CrossRef] [Green Version]
  44. Pireva, A. Anthropometric and Body Composition Differences Among Elite Kosovo Basketball, Handball and Soccer Players. Int. J. Morphol. 2019, 37, 1067–1072. [Google Scholar] [CrossRef] [Green Version]
  45. Rodríguez Rodríguez, F.; López-Fuenzalida, A.; Holway, F.; Jorquera-Aguilera, C. [Anthropometric differences per playing position in Chilean professional footballers]. Nutr. Hosp. 2019, 36, 846–853. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Sotiropoulos, A.; Travlos, A.K.; Gissis, I.; Souglis, A.G.; Grezios, A. The Effect of a 4-Week Training Regimen on Body Fat and Aerobic Capacity of Professional Soccer Players during the Transition Period. J. Strength Cond. Res. 2009, 23, 1697–1703. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Sporis, G.; Jukic, I.; Ostojic, S.M.; Milanovic, D. Fitness Profiling in Soccer: Physical and Physiologic Characteristics of Elite Players. J. Strength Cond. Res. 2009, 23, 1947–1953. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Voutselas, V.; Papanikolaou, Z.; Soulas, D.; Famisis, K. Years of Training and Hamstring-Quadriceps Ratio of Soccer Players. Psychol. Rep. 2007, 101, 899–906. [Google Scholar] [CrossRef] [PubMed]
  49. Zuniga Galaviz, U.; Osorio Gutierrez, A.; de Toledo Dominguez, I.J.; Herrera Perea, R. Somatotype of Mexican Soccer Players from Different Competition Level. Retos-Nuevas Tend. Educ. Fis. Deporte Recreacion 2018, 34, 100–102. [Google Scholar]
  50. Andreoli, A.; Melchiorri, G.; Brozzi, M.; Di Marco, A.; Volpe, S.L.; Garofano, P.; Di Daniele, N.; De Lorenzo, A. Effect of Different Sports on Body Cell Mass in Highly Trained Athletes. Acta Diabetol. 2003, 40 (Suppl. 1), S122–S125. [Google Scholar] [CrossRef] [PubMed]
  51. Matković, B.R.; Misigoj-Duraković, M.; Matković, B.; Janković, S.; Ruzić, L.; Leko, G.; Kondric, M. Morphological Differences of Elite Croatian Soccer Players According to the Team Position. Coll. Antropol. 2003, 27 (Suppl. 1), 167–174. [Google Scholar]
  52. Dupont, G.; Akakpo, K.; Berthoin, S. The Effect of In-Season, High-Intensity Interval Training in Soccer Players. J. Strength Cond. Res. 2004, 18, 584–589. [Google Scholar] [CrossRef]
  53. Al-Jaser, T.A.; Hasan, A.A.A. Fluid Loss and Body Composition of Elite Kuwaiti Soccer Players during a Soccer Match. J. Sports Med. Phys. Fit. 2006, 46, 281–285. [Google Scholar]
  54. Clark, N.A.; Edwards, A.M.; Morton, R.H.; Butterly, R.J. Season-to-Season Variations of Physiological Fitness Within a Squad of Professional Male Soccer Players. J. Sports Sci. Med. 2008, 7, 157–165. [Google Scholar] [PubMed]
  55. Hoppe, M.W.; Baumgart, C.; Sperlich, B.; Ibrahim, H.; Jansen, C.; Willis, S.J.; Freiwald, J. Comparison between Three Different Endurance Tests in Professional Soccer Players. J. Strength Cond. Res. 2013, 27, 31–37. [Google Scholar] [CrossRef] [PubMed]
  56. Suda, Y.; Umeda, T.; Watanebe, K.; Kuroiwa, J.; Sasaki, E.; Tsukamoto, T.; Takahashi, I.; Matsuzaka, M.; Iwane, K.; Nakaji, S. Changes in Neutrophil Functions during a 10-Month Soccer Season and Their Effects on the Physical Condition of Professional Japanese Soccer Players. Lumin. J. Biol. Chem. Lumin. 2013, 28, 121–128. [Google Scholar] [CrossRef]
  57. Micheli, M.L.; Pagani, L.; Marella, M.; Gulisano, M.; Piccoli, A.; Angelini, F.; Burtscher, M.; Gatterer, H. Bioimpedance and Impedance Vector Patterns as Predictors of League Level in Male Soccer Players. Int. J. Sports Physiol. Perform. 2014, 9, 532–539. [Google Scholar] [CrossRef]
  58. Semjon, M.; Botek, M.; Svozil, Z.; McKune, A.J. Positional Differences in the Cardiorespiratory, Autonomic, and Somatic Profiles of Professional Soccer Players. Acta Gymnica 2016, 46, 90–96. [Google Scholar] [CrossRef] [Green Version]
  59. Aras, D.; Karakoc, B.; Koz, M.; Bizati, O. The Effects of Active Recovery and Carbohydrate Intake on HRV during 48 Hours in Athletes after a Vigorous-Intensity Physical Activity. Sci. Sports 2017, 32, 295–302. [Google Scholar] [CrossRef]
  60. Requena, B.; García, I.; Suárez-Arrones, L.; Sáez de Villarreal, E.; Naranjo Orellana, J.; Santalla, A. Off-Season Effects on Functional Performance, Body Composition, and Blood Parameters in Top-Level Professional Soccer Players. J. Strength Cond. Res. 2017, 31, 939–946. [Google Scholar] [CrossRef]
  61. Kafedžić, E.; Čović, N.; Jelešković, E.; Alić, H.; Ibrahimović, M.; Talović, M. Preseason aerobic physiological characteristics in bosnia and herzegovina professional football players. Homo Sport 2018, 4, 5–9. [Google Scholar]
  62. Marcos, M.A.; Koulla, P.M.; Anthos, Z.I. Preseason Maximal Aerobic Power in Professional Soccer Players Among Different Divisions. J. Strength Cond. Res. 2018, 32, 356–363. [Google Scholar] [CrossRef]
  63. Clemente, F.M.; Nikolaidis, P.T.; Rosemann, T.; Knechtle, B. Dose-Response Relationship Between External Load Variables, Body Composition, and Fitness Variables in Professional Soccer Players. Front. Physiol. 2019, 10, 443. [Google Scholar] [CrossRef] [PubMed]
  64. Gardasevic, J.; Bjelica, D.; Vasiljevic, I. Morphological Characteristics and Body Composition of Elite Soccer Players in Montenegro. Int. J. Morphol. 2019, 37, 284–288. [Google Scholar] [CrossRef] [Green Version]
  65. Pietraszewska, J.; Burdukiewicz, A.; Zagrodna, A.; Stachon, A.; Andrzejewska, J. Anthropometric Profile and Serum 25-Hydroxyvitamin d-3 Levels in Elite Soccer Players. South Afr. J. Res. Sport Phys. Educ. Recreat. 2019, 41, 93–102. [Google Scholar]
  66. Dagcilar, K.; Ozturk, M. An Evaluation of Nutritional Knowledge Levels, Nutritional Intake, and Anthropometric Features of Northern Cyprus Professional Football Players. Med. Sport 2020, 73, 81–89. [Google Scholar] [CrossRef]
  67. Gardasevic, J.; Bjelica, D. Body Composition Differences between Football Players of the Three Top Football Clubs. Int. J. Morphol. 2020, 38, 153–158. [Google Scholar] [CrossRef] [Green Version]
  68. Granero-Gil, P.; Gómez-Carmona, C.D.; Bastida-Castillo, A.; Rojas-Valverde, D.; de la Cruz, E.; Pino-Ortega, J. Influence of Playing Position and Laterality in Centripetal Force and Changes of Direction in Elite Soccer Players. PLoS ONE 2020, 15, e0232123. [Google Scholar] [CrossRef] [Green Version]
  69. Ksiazek, A.; Zagrodna, A.; Slowinska-Lisowska, M. Assessment of the Dietary Intake of High-Rank Professional Male Football Players during a Preseason Training Week. Int. J. Environ. Res. Public Health 2020, 17, 8567. [Google Scholar] [CrossRef]
  70. Radzimiński, Ł.; Szwarc, A.; Padrón-Cabo, A.; Jastrzębski, Z. Correlations between Body Composition, Aerobic Capacity, Speed and Distance Covered among Professional Soccer Players during Official Matches. J. Sports Med. Phys. Fit. 2020, 60, 257–262. [Google Scholar] [CrossRef]
  71. Sutton, L.; Scott, M.; Wallace, J.; Reilly, T. Body Composition of English Premier League Soccer Players: Influence of Playing Position, International Status, and Ethnicity. J. Sports Sci. 2009, 27, 1019–1026. [Google Scholar] [CrossRef]
  72. Wittich, A.; Oliveri, M.B.; Rotemberg, E.; Mautalen, C. Body Composition of Professional Football (Soccer) Players Determined by Dual X-Ray Absorptiometry. J. Clin. Densitom. Off. J. Int. Soc. Clin. Densitom. 2001, 4, 51–55. [Google Scholar] [CrossRef]
  73. Reinke, S.; Karhausen, T.; Doehner, W.; Taylor, W.; Hottenrott, K.; Duda, G.N.; Reinke, P.; Volk, H.-D.; Anker, S.D. The Influence of Recovery and Training Phases on Body Composition, Peripheral Vascular Function and Immune System of Professional Soccer Players. PLoS ONE 2009, 4, e4910. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Gerosa-Neto, J.; Rossi, F.E.; Silva, C.B.; Campos, E.Z.; Fernandes, R.A.; Freitas Júnior, I.F. Body Composition Analysis of Athletes from the Elite of Brazilian Soccer Players. Motricidade 2014, 10, 105–110. [Google Scholar] [CrossRef]
  75. Milanese, C.; Cavedon, V.; Corradini, G.; De Vita, F.; Zancanaro, C. Seasonal DXA-Measured Body Composition Changes in Professional Male Soccer Players. J. Sports Sci. 2015, 33, 1219–1228. [Google Scholar] [CrossRef] [PubMed]
  76. Milsom, J.; Naughton, R.; O’Boyle, A.; Iqbal, Z.; Morgans, R.; Drust, B.; Morton, J.P. Body Composition Assessment of English Premier League Soccer Players: A Comparative DXA Analysis of First Team, U21 and U18 Squads. J. Sports Sci. 2015, 33, 1799–1806. [Google Scholar] [CrossRef]
  77. Sanchez-Urena, B.; Araya-Ramirez, F.; Blanco-Romero, L.; Crespo-Coco, C. Comparison of Two Methods to Measure Body Composition in Costa Rican Professional Soccer Players. Mhsalud-Rev. En Cienc. Mov. Hum. Salud 2016, 12, 1–13. [Google Scholar] [CrossRef] [Green Version]
  78. Devlin, B.L.; Leveritt, M.D.; Kingsley, M.; Belski, R. Dietary Intake, Body Composition, and Nutrition Knowledge of Australian Football and Soccer Players: Implications for Sports Nutrition Professionals in Practice. Int. J. Sport Nutr. Exerc. Metab. 2017, 27, 130–138. [Google Scholar] [CrossRef]
  79. Devlin, B.L.; Kingsley, M.; Leveritt, M.D.; Belski, R. Seasonal Changes in Soccer Players’ Body Composition and Dietary Intake Practices. J. Strength Cond. Res. 2017, 31, 3319–3326. [Google Scholar] [CrossRef]
  80. Khalladi, K.; Farooq, A.; Souissi, S.; Herrera, C.P.; Chamari, K.; Taylor, L.; El Massioui, F. Inter-Relationship between Sleep Quality, Insomnia and Sleep Disorders in Professional Soccer Players. BMJ Open Sport Exerc. Med. 2019, 5, e000498. [Google Scholar] [CrossRef] [Green Version]
  81. Randell, R.K.; Carter, J.M.; Jeukendrup, A.E.; Lizarraga, M.A.; Yanguas, J.I.; Rollo, I. Fat Oxidation Rates in Professional Soccer Players. Med. Sci. Sports Exerc. 2019, 51, 1677–1683. [Google Scholar] [CrossRef]
  82. Suarez-Arrones, L.; Lara-Lopez, P.; Maldonado, R.; Torreno, N.; De Hoyo, M.; Nakamura, F.Y.; Di Salvo, V.; Mendez-Villanueva, A. The Effects of Detraining and Retraining Periods on Fat-Mass and Fat-Free Mass in Elite Male Soccer Players. PeerJ 2019, 7, e7466. [Google Scholar] [CrossRef] [Green Version]
  83. McEwan, G.P.; Drobnic, F.; Lizarraga, A.; Gómez Díaz, A.; Pons, E.; Dello Iacon, A.; Unnithan, V. Changes in Markers of Body Composition of Professional Male Soccer Players during Pre-Season. Sports Med. Health Sci. 2020, 2, 166–171. [Google Scholar] [CrossRef] [PubMed]
  84. Mascherini, G.; Petri, C.; Galanti, G. Integrated Total Body Composition and Localized Fat-Free Mass Assessment. Sport Sci. Health 2015, 11, 217–225. [Google Scholar] [CrossRef]
  85. Campa, F.; Bongiovanni, T.; Matias, C.N.; Genovesi, F.; Trecroci, A.; Rossi, A.; Iaia, F.M.; Alberti, G.; Pasta, G.; Toselli, S. A New Strategy to Integrate Heath-Carter Somatotype Assessment with Bioelectrical Impedance Analysis in Elite Soccer Player. Sports 2020, 8, 142. [Google Scholar] [CrossRef] [PubMed]
  86. Castro Jimenez, L.E.; Arguello Gutierrez, Y.P.; Sanchez Rojas, I.A.; Jazmin Galvez, A.; Melo Buitrago, P.J. Relationship between Dermatoglyphic Markers and Morphofunctional Profile in Professional Soccer Players from Bogota, Colombia. Retos-Nuevas Tend. Educ. Fis. Deporte Recreacion 2021, 182–190. [Google Scholar]
  87. Novack, L.F.; Ferreira, G.A.; Coelho, R.L.; Osiecki, R. Novel Equations to Predict Body Fat Percentage of Brazilian Professional Soccer Players: A Case Study. Mot. Rev. Educ. Física 2014, 20, 402–407. [Google Scholar] [CrossRef]
  88. López-Taylor, J.R.; González-Mendoza, R.G.; Gaytán-González, A.; Jiménez-Alvarado, J.A.; Villegas-Balcázar, M.; Jáuregui-Ulloa, E.E.; Torres-Naranjo, F. Accuracy of Anthropometric Equations for Estimating Body Fat in Professional Male Soccer Players Compared with DXA. J. Sports Med. Hindawi Publ. Corp. 2018, 2018, 6843792. [Google Scholar] [CrossRef] [Green Version]
  89. Kammerer López, M.; Ceballos Feria, N.D.C.; Mayor Rengifo, M.C.; Hoyos García, H.H.; Gómez Velásquez, S. [Evaluation of the accuracy of different body composition prediction formulas, compared to Dual Energy X-ray Absorptiometry, in soccer players of Colombian professional teams]. Nutr. Hosp. 2021, 38, 290–297. [Google Scholar] [CrossRef]
  90. Svantesson, U.; Zander, M.; Klingberg, S.; Slinde, F. Body Composition in Male Elite Athletes, Comparison of Bioelectrical Impedance Spectroscopy with Dual Energy X-Ray Absorptiometry. J. Negat. Results Biomed. 2008, 7, 1. [Google Scholar] [CrossRef] [Green Version]
  91. Suarez-Arrones, L.; Petri, C.; Maldonado, R.A.; Torreno, N.; Munguia-Izquierdo, D.; Di Salvo, V.; Mendez-Villanueva, A. Body Fat Assessment in Elite Soccer Players: Cross-Validation of Different Field Methods. Sci. Med. Footb. 2018, 2, 203–208. [Google Scholar] [CrossRef]
  92. Olds, T.; Norton, K.I.; Australian Sports Commission (Eds.) Anthropometrica: A Textbook of Body Measurement for Sports and Health Courses; UNSW Press: Sydney, Australia, 1996; ISBN 978-0-86840-223-9. [Google Scholar]
  93. Lohman, T.G.; Pollock, M.L. Skinfold Measurement: Which Caliper? How Much Training? J. Phys. Educ. Recreat. 1981, 52, 27–29. [Google Scholar] [CrossRef]
  94. Orphanidou, C.; McCargar, L.; Birmingham, C.L.; Mathieson, J.; Goldner, E. Accuracy of Subcutaneous Fat Measurement: Comparison of Skinfold Calipers, Ultrasound, and Computed Tomography. J. Am. Diet. Assoc. 1994, 94, 855–858. [Google Scholar] [CrossRef] [PubMed]
  95. Esparza-Ros, F.; Moreira, A.C.; Vaquero-Cristóbal, R.; Barrigas, C.; Albaladejo-Saura, M.; Vieira, F. Differences between Four Skinfold Calipers in the Assessment of Adipose Tissue in Young Adult Healthy Population. Nutrients 2022, 14, 2085. [Google Scholar] [CrossRef] [PubMed]
  96. Ros, F.E.; Vaquero-Cristóbal, R.; Marfell-Jones, M. Protocolo Internacional Para la Valoración Antropométrica; UCAM Universidad Católica de Murcia, Ed.; Sociedad Internacional para el Avance de la Cineantropometría (ISAK): Murcia, Spain, 2019; ISBN 978-84-92986-17-0. [Google Scholar]
  97. Vaquero-Cristóbal, R.; Albaladejo-Saura, M.; Luna-Badachi, A.E.; Esparza-Ros, F. Differences in Fat Mass Estimation Formulas in Physically Active Adult Population and Relationship with Sums of Skinfolds. Int. J. Environ. Res. Public Health 2020, 17, E7777. [Google Scholar] [CrossRef] [PubMed]
  98. Cabañas, M.D.; Esparza, F. Compendio de Cineantropometría; CTO Editorial: Madrid, Spain, 2009. [Google Scholar]
  99. Martinez-Ferran, M.; Rafei, E.; Romero-Morales, C.; Pérez-Ruiz, M.; Lam-Meléndez, A.; Munguia-Izquierdo, D.; Pareja-Galeano, H. Optimizing Field Body Fat Percentage Assessment in Professional Soccer Players. Appl. Sci. 2022, 12, 727. [Google Scholar] [CrossRef]
  100. Reilly, T.; George, K.; Marfell-Jones, M.; Scott, M.; Sutton, L.; Wallace, J.A. How Well Do Skinfold Equations Predict Percent Body Fat in Elite Soccer Players? Int. J. Sports Med. 2009, 30, 607–613. [Google Scholar] [CrossRef] [PubMed]
  101. Faulkner, J.A. Physiology of Swimming. Res. Q. Am. Assoc. Health Phys. Educ. Recreat. 1966, 37, 41–54. [Google Scholar] [CrossRef]
  102. Eston, R.G.; Rowlands, A.V.; Charlesworth, S.; Davies, A.; Hoppitt, T. Prediction of DXA-Determined Whole Body Fat from Skinfolds: Importance of Including Skinfolds from the Thigh and Calf in Young, Healthy Men and Women. Eur. J. Clin. Nutr. 2005, 59, 695–702. [Google Scholar] [CrossRef] [Green Version]
  103. Withers, R.T.; Craig, N.P.; Bourdon, P.C.; Norton, K.I. Relative Body Fat and Anthropometric Prediction of Body Density of Male Athletes. Eur. J. Appl. Physiol. 1987, 56, 191–200. [Google Scholar] [CrossRef]
  104. Durnin, J.V.; Womersley, J. Body Fat Assessed from Total Body Density and Its Estimation from Skinfold Thickness: Measurements on 481 Men and Women Aged from 16 to 72 Years. Br. J. Nutr. 1974, 32, 77–97. [Google Scholar] [CrossRef] [Green Version]
  105. Martínez-Sanz, J.M.; Fernández Nuñez, A.; Sospedra, I.; Martínez-Rodríguez, A.; Domínguez, R.; González-Jurado, J.A.; Sánchez-Oliver, A.J. Nutrition-Related Adverse Outcomes in Endurance Sports Competitions: A Review of Incidence and Practical Recommendations. Int. J. Environ. Res. Public Health 2020, 17, E4082. [Google Scholar] [CrossRef]
  106. Kerksick, C.M.; Wilborn, C.D.; Roberts, M.D.; Smith-Ryan, A.; Kleiner, S.M.; Jäger, R.; Collins, R.; Cooke, M.; Davis, J.N.; Galvan, E.; et al. ISSN Exercise & Sports Nutrition Review Update: Research & Recommendations. J. Int. Soc. Sports Nutr. 2018, 15, 38. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  107. Nuñez, F.J.; Munguia-Izquierdo, D.; Petri, C.; Suarez-Arrones, L. Field Methods to Estimate Fat-Free Mass in International Soccer Players. Int. J. Sports Med. 2019, 40, 619–624. [Google Scholar] [CrossRef] [PubMed]
  108. Sala, V.P.; Riera, J.; Ballarini, P.-A.G.; Martínez, F.D.; Banquells, M.; Ruiz, O. Características antropométricas, composición corporal y somatotipo por deportes.: Datos de referencia del CAR de San Cugat, 1989-2013. Apunts Med. Esport 2015, 50, 65–72. [Google Scholar]
  109. Moreno, A.C. Variables Antropométricas de la Población Deportista Española: 60; ISBN 978-84-7949-220-5.
  110. Stølen, T.; Chamari, K.; Castagna, C.; Wisløff, U. Physiology of Soccer: An Update. Sports Med. Auckl. NZ 2005, 35, 501–536. [Google Scholar] [CrossRef]
  111. Herrero de Lucas, A.; Cabañas Armesilla, M.D.; Maestre López, I. Morfotipo del futbolista profesional de la Comunidad Autónoma de Madrid. Composición corporal. 2004, 12, 72–77. [Google Scholar] [CrossRef]
  112. Nassis, G.P.; Massey, A.; Jacobsen, P.; Brito, J.; Randers, M.B.; Castagna, C.; Mohr, M.; Krustrup, P. Elite Football of 2030 Will Not Be the Same as That of 2020: Preparing Players, Coaches, and Support Staff for the Evolution. Scand. J. Med. Sci. Sports 2020, 30, 962–964. [Google Scholar] [CrossRef]
  113. Silva, J.R. The Soccer Season: Performance Variations and Evolutionary Trends. PeerJ 2022, 10, e14082. [Google Scholar] [CrossRef]
  114. Sansone, P.; Makivic, B.; Csapo, R.; Hume, P.; Martínez-Rodríguez, A.; Bauer, P. Body Fat of Basketball Players: A Systematic Review and Meta-Analysis. Sports Med.—Open 2022, 8, 26. [Google Scholar] [CrossRef]
  115. Rodríguez, A.M.; Olcina, M.M.; García, M.H.; Arias, J.Á.R.; Sánchez, J.S.; Sáez, J.A.S. Body Composition Characteristics of Handball Players: Systematic Review. Arch. Med. Deporte Rev. Fed. Esp. Med. Deporte Confed. Iberoam. Med. Deporte 2020, 37, 52–61. [Google Scholar]
  116. Figueiredo, D.H.; Dourado, A.C.; Stanganelli, L.C.R.; Gonçalves, H.R. Evaluation of Body Composition and Its Relationship with Physical Fitness in Professional Soccer Players at the Beginning of Pre-Season. Retos Nuevas Tend. En Educ. Física Deporte Recreación 2021, 40, 117–125. [Google Scholar]
  117. Campa, F.; Semprini, G.; Júdice, 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]
  118. Nikolaidis, P.T.; Ruano, M.A.G.; de Oliveira, N.C.; Portes, L.A.; Freiwald, J.; Leprêtre, P.M.; Knechtle, B. Who Runs the Fastest? Anthropometric and Physiological Correlates of 20 m Sprint Performance in Male Soccer Players. Res. Sports Med. Print 2016, 24, 341–351. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  119. Ayotte, B.; Carey, V.; Charron, J.; Ibo, J.; Ferland, P.-M.; Comtois, A.S. Increase In Body Mass Do Not Negatively Affect Aerobic Capacity In Elite Male Soccer Players: 67. Med. Sci. Sports Exerc. 2021, 53, 21. [Google Scholar] [CrossRef]
  120. Peñailillo, L.; Espíldora, F.; Jannas-Vela, S.; Mujika, I.; Zbinden-Foncea, H. Muscle Strength and Speed Performance in Youth Soccer Players. J. Hum. Kinet. 2016, 50, 203–210. [Google Scholar] [CrossRef]
  121. Moya-Amaya, H.; Molina-López, A.; Berral-Aguilar, A.J.; Rojano-Ortega, D.; Berral-de-la-Rosa, F.J.; Moya-Amaya, H.; Molina-López, A.; Berral-Aguilar, A.J.; Rojano-Ortega, D.; Berral-de-la-Rosa, F.J. Migración Del Somatotipo En Jugadores de Fútbol Profesional En Las Últimas Décadas. Int. J. Morphol. 2022, 40, 327–333. [Google Scholar] [CrossRef]
  122. Clasificación Masculina. Available online: https://www.fifa.com/es/fifa-world-ranking/origin1904-p.cxm.fifa.com/fifa-world-ranking/men (accessed on 10 October 2022).
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Figure 1. Flow diagram showing the process used to select the studies.
Figure 1. Flow diagram showing the process used to select the studies.
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Figure 2. Forest plot fat mass percentage according to equation.
Figure 2. Forest plot fat mass percentage according to equation.
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Figure 3. Forest plot sum of folds according to equation.
Figure 3. Forest plot sum of folds according to equation.
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Figure 4. Forest plot (a) endomorphy, (b) ectomorphy, and (c) mesomorphy.
Figure 4. Forest plot (a) endomorphy, (b) ectomorphy, and (c) mesomorphy.
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Figure 5. Forest plot (a) BMC, (b) BMD, and (c) body water.
Figure 5. Forest plot (a) BMC, (b) BMD, and (c) body water.
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Figure 6. Guidance values for body composition differentiated by measurement method.
Figure 6. Guidance values for body composition differentiated by measurement method.
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Table
Table 1. The inclusion criteria applied in the study followed the Population, Intervention, Comparison, and Outcomes (PICO) strategy.
Table 1. The inclusion criteria applied in the study followed the Population, Intervention, Comparison, and Outcomes (PICO) strategy.
PopulationInterventionComparisonOutcomes
Male soccer players who train with the aim of competing or improving their physical performance (excludes physical activity for health or aesthetics).
Professional category.
Absence of pathologies (healthy subjects).		Anthropometry.
Bioimpedance (BIA).
Dual X-ray Absorptiometry (DXA).		Measurement methods.
Season.
Equations.		Anthropometric characteristics: skinfolds, girth, breadth, heights, lengths, body composition, and somatotype.
Percentages and values of fat mass, muscle mass, bone mass and body water.
Table
Table 2. Descriptive characteristics of the included studies with anthropometry.
Table 2. Descriptive characteristics of the included studies with anthropometry.
Authors and YearSample Size (n)Age (Years)Country Competition CategoryTime of the SEAMeasuring InstrumentsProtocol
Al-Hazzaa et al., 2001 [18]T = 2325.2 ± 3.3SA. National teamPreparation for the FR World CupBAS Seca, PLI Harpenden-
Casajús, 2001 [22]T = 1525.8 ± 3.1ES. 1st DivisiónDuring competitive SEAEST Rabonne Chesterman, BAS Rabonne Chesterman, Small sliding caliper Rabonne Chesterman, Measuring tape GPM Siber-Hegner Maschinen, PLI HoltainISAK
Filaire et al., 2003 [26]T = 2025.1 ± 0.4FR. Ligue 1Start of pre-SEA, start and end of SEA, start of 2nd pre-SEAPLI Harpenden-
Ostojic. 2003 [39]T = 3023.5 ± 3.1EN. National LeagueStart pre-SEA, start, middle and end SEA, and start 2nd pre-SEABAS Avery 3306 ABV, PLI Harpenden-
Gutierrez and Monroy, 2005 [28]T = 2029.0 ± 3.0MX. National teamPreparation for the World Cup South Korea and JapanBAS Bame, Swiss anthropometer type Martin, PLI HarpendenISAK
Voutselas et al., 2007 [48]T = 7220.1 ± 5.2GR. Super League 1 and 2-PLI Harpenden-
Sotiropoulos et al., 2009 [46]T = 58; CG = 20; EG = 38CG = 24.4 ± 2.9; EG = 23.2 ± 2.5GR. Super League GRBeginning and end of the transition periodPLI HarpendenACSM
Sporis et al., 2009 [47]T = 27028.3 ± 5.9HR. Prva HNL2 consecutive pre-SEAsBAS Seca, PLI John Bull CaliperIBP
Carling and Orhant, 2010 [21]T = 2624.4 ± 4.1FR. Ligue 13 consecutive full SEAs (5 moments)BAS Holtain, PLI Harpenden-
Dey et al., 2010 [24]T = 15023.3 ± 3.5IN. IN Super League-PLI Harpenden-
Hazir, 2010 [29]T = 305; SL = 161; FL = 144;SL = 25.7 ± 3.7
FL = 24.1 ± 4.2		TR. Süper Lig (161) and TFF 1. Lig (144)5 beginnings of the transition period for the SEA halfBAS Tanita TBF 401A, EST Holtain, Bicondylar caliper Holtain, PLI HoltainASRM
Kalapotharakos et al., 2011 [33]T = 1225 ± 5GR. Super League GRPre-SEA start, start and mid-SEAPLI Harpenden-
Boone et al., 2012 [20]T = 28925.4 ± 4.9BE. Jupiler Pro League2–4 weeks prior to start of SEABAS Seca, PLI Harpenden-
Chaouachi et al., 2012 [23]T = 2319 ± 1TN. Ligue 1Last SEA stageBAS Seca, EST Holtain, PLI Harpenden-
Owen et al., 2012 [40]T = 1524.5 ± 3.4SCO. Scottish PremiershipDuring a 4-week break SEAPLI Harpenden-
Henríquez-Olguín et al., 2013 [30]T = 10023.0 ± 4.4CL. Not specified2 SEA startsBAS Tanita TBF 401A, Kit Health & Performance®ISAK
Jorquera et al., 2013 [32]T = 406; DEF = 124; CEN = 134; DEL = 93; POR = 48DEF = 25.3 ± 4; CEN = 25.2 ± 4.7; DEL = 23.5 ± 4.1; POR = 25.1 ± 5.5CL. 1st división (326) and 1st B (80)-BAS Tanita, Kit Gaucho Pro “Mercosur”ISAK
Lago-Peñas et al., 2013 [34]T = 4225.0 ± 5.2ES. 1st DivisiónDuring 1st half SEAPLI Holtain 610ISAK
Orhan et al., 2013 [38]1st team = 24
2nd team = 24		23.29 ± 2.12
25.12 ± 3.60		TR. Süper Lig-PLI HoltainASRM
Iga et al., 2014 [34]T = 3520 ± 4EN. Not specifiedStart and end of pre-SEA, 1st and 2nd half and end of SEAPLI HarpendenISAK
Michailidis, 2014 [36]T = 15-GR. Super League GreeceStart and end pre-SEA, middle and end SEABAS Tanita BC 418, EST Seca 208, PLI Harpenden-
Novack et al., 2014 [87]T = 3121.48 ± 3.38BR. Not specified-DXA GE Lunar Prodigy software 8.50.093, PLI Harpenden-
Mascherini et al., 2015 [84]T = 5922.47 ± 5.58IT. Serie APre-SEA start and 50 days laterBAS Akern BIA 101 Sport Edition, Measuring tape Holtain, PLI Holtain-
Najafi et al., 2015 [37]T = 6024.31 ± 4.20IR. Iran Pro League and Azadegan League-BAS Seca, PLI Harpenden-
Bekris et al., 2016 [19]T = 2424.3 ± 4.3GR. Super League GreeceStart preSEA, start, end 1st half and endSEABAS Seca 710, EST Seca, PLI Harpenden-
Fessi et al., 2016 [25]T = 1723.7 ± 3.2QA. Qatar Stars LeagueStart and end pre-SEA, and mid-SEABAS ADE Electronic Column Scales, EST Holtain, PLI Harpenden-
Petri et al., 2016 [42]T = 2827.88 ± 4.55IT. Serie APre-SEA start, SEA start and endBIO BIA 101 Sport, PLI Holtain-
Lopez-Taylor et al., 2018 [88]T = 13123.2 (20.5–26.8)MX. Liga Premier-DXA Hologic QDR4500 Explorer software 12.1, BAS Tanita TBF 410, EST Seca 213, Bicondylar calliper Campbell 10, PLI HarpendenISAK
Owen et al., 2018 [41]T = 2224.0 ± 3.7EU. Not specifiedStart and end pre-SEA, mid-SEA, end mid-SEA transition period and end SEABAS CIRCA, EST CIRCA, Bicondylar calliper Gulick, PLI HarpendenISAK
Zuñiga et al., 2018 [49]T = 78; 1st DIV = 18; 1st “a” DIV = 19; 2nd DIV = 24; 3rd DIV = 171st DIV = 25.8 ± 5.2; 1st “a” DIV = 23.4 ± 1.6;
2nd DIV = 18.9 ± 1.6; 3rd DIV = 16.0 ± 0.9		MX. Liga MX (18), Liga de Expansión MX (19), Liga Premier (24) and Liga TDP (17)Pre-SEABAS Tanita Inner Scan BC 532, EST Holtain, Measuring tape Lufkin, Bicondylar caliper Campbell 10, PLI SlimguideISAK
Pireva, 2019 [44]T = 118-XK. Superliga-BAS Tanita HD-351, EST Seca, PLI John Bull Caliper-
Rodríguez-Rodríguez et al., 2019 [45]T = 339; DEF = 119; CEN = 133; DEL = 94; POR = 44DEF = 25.3 ± 4.8; CEN = 25.2 ± 4.8; DEL = 23.5 ± 4.1; POR = 25.1 ± 5.5CL. 1st división-Kit Gaucho Pro “Mercosur”ISAK
Campa et al., 2020 [85]T = 176Development G = 27.4 ± 4.3; Cross-Validation G = 28.0 ± 5.0IT. Serie A-BAS Seca 877, EST Seca 217, Measuring tape Lufkin, Bicondylar caliper GMP, PLI HoltainISAK
Grazioli et al., 2020 [27]T = 2326.3 ± 5.6BR. Brasileirão Serie APre-SEA start and 63 days after quarantineBAS Urano PP180A, PLI Slimguide-
Vega et al., 2020 [35]T = 41-ES. 1st División and 2nd División10 full SEAsBAS Seca 719, EST Seca 213, PLI HarpendenISAK
Pietraszewska et al., 2020 [43]T = 3719–30PL. EkstraklasaDuring competitive SEAAnthropological instruments Siber Hegner Machinery Ltd., PLI HoltainISAK
Castro Jiménez et al., 2021 [86]T = 2421.0 ± 1.9CO. 1st B-BIO InBody 770, EST Seca, Bicondylar caliper Holtain, PLI Harpenden HSK-BIISAK
Kammerer López et al., 2021 [89]T = 7923.0 ± 4.4CO. 1st A and 1st BDuring competitive SEADXA GE Lunar Prodigy, BAS Seca 874, EST Seca 213, Measuring tape Lufkin, Bicondylar caliper Slimguide, PLI HarpendenISAK
1st = first; 2nd = second; 3rd = third; ACSM = American College of Sports Medicine; ASRM = Anthropometric Standardization Reference Manual; BAS = scale; BE = Belgium; BIO = bioimpedance; BR = Brazil; CEN = midfielders; CG = control group; CL = Chile; CO = Colombia; DEF = defenders; DEL = Forwards; DXA = Dual X-ray absorptiometry; EG = Experimental group; EN = England; ES = Spain; EST = Stadiometer; EU = European players; FR = France; G = Group; GR = Greece; HR = Croatia; IBP = International Biological Program; IN = India; IR = Iran; IT = Italy; ISAK = International Society for the Advancement of Kinanthropometry; MX = Mexico; PL = Poland; PLI = plicometer; POR = goalkeepers; QA = Qatar; SA = Saudi Arabia; SCO = Scotland; T = Total; SEA = Season; TN = Tunisia; TR = Turkey; XK = Kosovo.
Table
Table 3. Descriptive characteristics of the included studies with BIA.
Table 3. Descriptive characteristics of the included studies with BIA.
Authors and YearSample Size (n)Age (Years)Country. Competition CategoryTime of the SEAMeasuring Instruments
Andreoli et al., 2003 [50]T = 48; Serie A = 16; Serie B = 14; Serie C = 18Serie A = 25.9 ± 4.2; Serie B = 25.1 ± 2.6; Serie C = 25.1 ± 5.7IT. Serie A (16), Serie B (14) and Serie C (18)-BAS Invernizzi, EST Invernizzi, Xitron 4000B
Matković et al., 2003 [51]T = 5723.2 ± 3.4HR. Prva HNLDuring competitive SEABody analyzer Danninger
Dupont et al., 2004 [52]T = 2220.2 ± 0.7FR. Ligue 1During competitive SEA 1st and 2nd periodsTanita TBF 543
Al-Jaser and Hasan, 2006 [53]T = 924 ± 4.7KW. Not specified5 pre-SEA matchesBiodynamics 310e
Clark et al., 2008 [54]T = 4226.0 ± 4.3EN. Football League Championship3 complete SEAs (pre-SEA start, mid and end SEA)EST Seca 240, Tanita TBF 551
Svantesson et al., 2008 [90]T = 1724.1 ± 3.8SE. AllsvenskanSpringDXA GE Lunar Prodigy, EST Hultafors, Xitron Hydra 4200
Hoppe et al., 2013 [55]T = 1123.8 ± 3.0DE. Dritte Liga1st week pre-SEABodystat QuadScan 4000
Suda et al., 2013 [56]T = 2124.7 ± 5.2JP. J2 LeaguePre-SEA final, mid-term and SEA finalTanita MC 190
Micheli et al., 2014 [57]T = 21926.1 ± 4.4IT. Serie A and Serie B1st half SEAAkern BIA 101
Mascherini et al., 2015 [84]T = 5922.47 ± 5.58IT. Serie APre-SEA start and 50 days laterMeasuring tape Holtain, PLI Holtain, Akern BIA 101 Sport Edition
Semjon et al., 2016 [58]T = 120; central DEF = 18; full DEF = 15; central CEN = 24; wingers = 18; DEL = 34; POR = 11n.r.; 27.3 ± 6.2; 26.7 ± 4.8; 25.8 ± 5.3; 25.3 ± 4.2; 24.0 ± 3.6; 26.6 ± 6.5RC. Českou fotbalovou ligu6 consecutive pre-SEAsBAS Leifheit Soehnle 7307, Tanita BC 418 MA
Aras et al., 2017 [59]T = 1218.33 ± 0.98TR. Not specified-BAS Jawon Medical
Requena et al., 2017 [60]T = 1926.2 ± 2.8ES. 1ª DivisiónMid SEA, SEA final and start of pre-SEATanita TBF 543
Kafedžić et al., 2018 [61]T = 3923.5 ± 4.6BA. Premier League2 pre-SEA startsHolton Anthropometer, Tanita BC 420SMA
Marcos et al., 2018 [62]T = 23325.37 ± 5.06CY. 1st DivisiónStart pre-SEAEST Leicester, Tanita BC 418 MA
Suarez-Arrones et al., 2018 [91]T = 1827.6 ± 3.0IT. Serie ASEA finalDXA Hologic QDR Series Delphi A software 13.3:3, BAS OHAUS, EST Seca 213, Tanita MC-180 MAIII
Clemente et al., 2019 [63]T = 2324.7 ± 2.8PT. 2nd LigaPre-SEA start and SEA startEST Seca 242, Seca mBCA 515
Gardasevic et al., 2019 [64]T = 7022.84 ± 4.47ME. Prva Crnogorska LigaSEA finalTanita BC 418 MA
Pietraszewska et al., 2019 [65]T = 2925.6 ± 5.8PL. EkstraklasaDuring competitive SEAAkern BIA 101 Sport Edition
Campa et al., 2020 [85]T = 176Development G = 27.4 ± 4.3; Cross-Validation G = 28.0 ± 5.0IT. Serie A-BAS Seca 877, EST Seca 217, Measuring tape Lufkin, Bicondylar calliper GMP, PLI Holtain, Akern BIA 101
Dağcilar and Öztürk, 2020 [66]T = 19124.7 ± 5.5CY. 1st DivisiónDuring competitive SEATanita SC 330
Gardasevic and Bjelica, 2020 [67]T = 5322.75 ± 4.16XK. SuperligaSEA finalTanita BC 418 MA
Granero-Gil et al., 2020 [68]T = 3026.57 ± 5.56RU. Russian Premier LeagueDuring competitive SEAEST Seca, Tanita SC-240
Książek et al., 2020 [69]T = 2627.0 ± 3.7PL. EkstraklasaPre-SEAAkern
Radzimiński et al., 2020 [70]T = 2327.9 ± 4.58Europa League participants13 weeks during competitive SEATanita MC-780
Castro Jiménez et al., 2021 [86]T = 2421.0 ± 1.9CO. 1st B-EST Seca, Bicondylar calliper Holtain, PLI Harpenden HSK-BI, Tanita BC 418 MA
1st = first; 2nd = second; BA = Bosnia and Herzegovina; BAS = scale; CEN = midfielders; CO = Colombia; CY = Cyprus; DE = Germany; DEF = defenders; DEL = forwards; DXA = dual X-ray absorptiometry; ES = Spain; EST = stadiometer; FR = France; G = group; HR = Croatia; IT = Italy; JP = Japan; KW = Kuwait; ME = Montenegro; PL = Poland; PLI = plicometer; POR = goalkeepers; PT = Portugal; RC = Czech Republic; RU = Russia; SE = Sweden; T = Total; SEA = season; TR = Turkey; XK = Kosovo; n.r. = not reported.
Table
Table 4. Descriptive characteristics of the included studies with DXA.
Table 4. Descriptive characteristics of the included studies with DXA.
Authors and YearSample Size (n)Age (Years)Country. Competition CategoryTime of the SEAMeasuring Instruments
Wittich et al., 2001 [72]T = 4223.2 ± 3.5AR. 1st División3 pre-SEAsGE Lunar DPX-L software 1.33
Svantesson et al., 2008 [90]T = 1724.1 ± 3.8SE. AllsvenskanSpringBAS The Advanced Weighing System 31, EST Hultafors, GE Lunar Prodigy
Reinke et al., 2009 [73]T = 1025.3 ± 5.1DE. BundesligaSEA final, final of the transitional summer period and final of the pre-SEAGE Lunar Prodigy software Lunar enCORE 2002
Sutton et al., 2009 [71]T = 6426.2 ± 4.0EN. Premier League-BAS y EST Seca 702, Hologic QDR Series Discovery A software 12:4:3
Gerosa-Neto et al., 2014 [74]T = 8223.6 ± 4.2BR. Brasileirão Serie APre-SEABAS Filizola, EST Sanny, GE Lunar DPX-MD software 4.7
Novack et al., 2014 [87]T = 3121.48 ± 3.38BR. Not specified-PLI Harpenden, GE Lunar Prodigy software 8.50.093
Milanese et al., 2015 [75]T = 2927.5 ± 4.38IT. Serie A3 full SEAsBAS Tanita BWB-800MA, EST Harpenden, QDR Explorer W software 12.6.1
Milsom et al., 2015 [76]T = 2724.1 ± 3.9EN. Premier League3 full SEAs (different periods)BAS Seca, Hologic QDR Series Discovery A
Sánchez-Ureña et al., 2016 [77]T = 10624.53 ± 4.77CR. Fútbol de 1st División-BAS Tanita HD-313, EST Tanita, GE enCORE 2011 software 13.6
Devlin et al., 2017 [78]T = 1827 ± 5AU. A-LeaguePre-SEA finalBAS Wedderburn WM203, EST Seca SE206, Hologic Discovery W
Devlin et al., 2017b [79]T = 1825 ± 5AU. A-LeaguePre-SEA start, SEA start, SEA middle and SEA finalBAS Wedderburn WM203, EST Seca SE206, Hologic Discovery W
Lopez-Taylor et al., 2018 [88]T = 13123.2 (20.5–26.8)MX. Liga Premier-Bicondylar caliper Campbell 10, PLI Harpenden, BAS Tanita TBF 410, EST Seca 213, Hologic QDR4500 Explorer software 12.1
Suarez-Arrones et al., 2018 [91]T = 1827.6 ± 3.0IT. Serie ASEA finalBAS OHAUS, EST Seca 213, BIO Tanita MC-180 MAIII, Hologic QDR Series Delphi A software 13.3:3
Khalladi et al., 2019 [80]T = 11123.7 ± 4.8QA. Qatar Stars LeagueDuring competitive SEAEST Seca 242, GE Medical SysSEA Lunar software enCORE 12.10
Randell et al., 2019 [81]T = 1625 ± 4
26 ± 4		ES. 1st División2 consecutive pre-SEAsGE Lunar iDXA
Suarez-Arrones et al., 2019 [82]T = 1027.3 ± 2.8IT. Serie ASEA final, start and pre-SEA finalBAS OHAUS, EST Seca 213, Hologic QDR Series Delphi A software 13.3:3
McEwan et al., 2020 [83]T = 2025.1 ± 4.1ES. 1st DivisiónStart and end of two pre-SEAsGE Lunar
Kammerer López et al., 2021 [89]T = 7923.0 ± 4.4CO. 1st A y 1st BDuring competitive SEAMeasuring tape Lufkin, Bicondylar caliper Slimguide, PLI Harpenden, BAS Seca 874, EST Seca 213, GE Lunar Prodigy
1st = first; 2nd = second; AR = Argentina; AU = Australia; BAS = Scale; BIO = bioimpedance; BR = Brazil; CO = Colombia; CR = Costa Rica; DE = Germany; EN = England; ES = Spain; EST = stadiometer; IT = Italy; MX = Mexico; PLI = plicometer; QA = Qatar; SE = Sweden; T = Total; SEA = season.
Table
Table 5. Analysis of methodological quality according to the Agency for Health Research and Quality (AHRQ) Methodology Checklist.
Table 5. Analysis of methodological quality according to the Agency for Health Research and Quality (AHRQ) Methodology Checklist.
Authors and Year1234567891011Total *
Al-Hazzaa et al., 2001 [18]10110-000104
Al-Jaser and Hasan, 2006 [53]10110-110117
Andreoli et al., 2003 [50]10010-010104
Aras et al., 2017 [59]10010-000103
Bekris et al., 2016 [19]101101010117
Boone et al., 2012 [20]10110-010105
Campa et al., 2020 [85]10010-010104
Carling and Orhant, 2010 [21]101101110118
Casajús, 2001 [22]101101010117
Castro Jiménez et al., 2021 [86]11010-010105
Chaouachi et al., 2012 [23]10110-010105
Clark et al., 2008 [54]11110-010117
Clemente et al., 2019 [63]11110-010117
Dağcilar and Öztürk, 2020 [66]10110-100105
Devlin et al., 2017 [78]101101010106
Devlin et al., 2017b [79]101101010117
Dey et al., 2010 [24]11010-000104
Dupont et al., 2004 [52]10110-010116
Fessi et al., 2016 [25]10110-110117
Filaire et al., 2003 [26]11110-000116
Gardasevic and Bjelica, 2020 [67]10110-000104
Gardasevic et al., 2019 [64]10110-000104
Gerosa-Neto et al., 2014 [74]10110-000104
Granero-Gil et al., 2020 [68]11110-000105
Grazioli et al., 2020 [27]10110-010116
Gutierrez and Monroy, 2005 [28]101101010106
Hazir, 2010 [29]10110-0101-5
Henríquez-Olguín et al., 2013 [30]10110-000115
Hoppe et al., 2013 [55]10110-010105
Iga et al., 2014 [34]10110-010116
Jorquera et al., 2013 [32]10010-010104
Kafedžić et al., 2018 [61]10110-010116
Kalapotharakos et al., 2011 [33]10110-000115
Kammerer López et al., 2021 [89]111101010107
Khalladi et al., 2019 [80]11110-100106
Książek et al., 2020 [69]11110-010106
Lago-Peñas et al., 2013 [34]11110-010106
Lopez-Taylor et al., 2018 [88]100101010116
Marcos et al., 2018 [62]11110-010106
Mascherini et al., 2015 [84]11110-110118
Matković et al., 2003 [51]10110-000104
McEwan et al., 2020 [83]10110-010116
Michailidis, 2014 [36]10110-110117
Micheli et al., 2014 [57]10110-010105
Milanese et al., 2015 [75]101101110118
Milsom et al., 2015 [76]101101010117
Najafi et al., 2015 [37]10010-000103
Novack et al., 2014 [87]110101010106
Orhan et al., 2013 [38]10010-010104
Ostojic. 2003 [39]10110-000115
Owen et al., 2012 [40]10110-010105
Owen et al., 2018 [41]101101010117
Petri et al., 2016 [42]10110-010116
Pietraszewska et al., 2019 [65]10110-010105
Pietraszewska et al., 2020 [43]11010-010105
Pireva, 2019 [44]11010-000104
Radzimiński et al., 2020 [70]11110-010117
Randell et al., 2019 [81]10110-010116
Reinke et al., 2009 [73]10110-000115
Requena et al., 2017 [60]10110-110117
Rodríguez-Rodríguez et al., 2019 [45]110101010106
Sánchez-Ureña et al., 2016 [77]10010-010104
Semjon et al., 2016 [58]11110-100118
Sotiropoulos et al., 2009 [46]11110-010106
Sporis et al., 2009 [47]11110-010117
Suarez-Arrones et al., 2018 [91]101101010106
Suarez-Arrones et al., 2019 [82]111101010118
Suda et al., 2013 [56]10110-010116
Sutton et al., 2009 [71]10010-010104
Svantesson et al., 2008 [90]101101010106
Vega et al., 2020 [35]11110-010117
Voutselas et al., 2007 [48]10010-010104
Wittich et al., 2001 [72]10110-101117
Zuñiga et al., 2018 [49]10110-010105
* 0 = worst; 11 = best value; - = unclear.
Table
Table 6. Meta-analysis by groups according to the evaluation method used.
Table 6. Meta-analysis by groups according to the evaluation method used.
AuthorsGMCI 95%Weight (%)MCI95%p
Age
Anthropometry[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,37,38,39,40,41,42,43,45,46,47,48,49,84,85,86,87,88,89]4824.2023.45; 24.9549.324.5024.04; 24.970.45
BIA[50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,84,85,86,90,91]3524.7924.02; 25.5634.1
DXA[71,72,73,74,75,76,77,78,79,80,81,82,83,87,88,89,90,91]1724.7823.90; 25.6716.7
Height
Anthropometry[18,19,20,21,22,23,24,25,26,28,29,30,31,32,33,34,35,37,38,39,40,41,42,43,44,45,46,47,48,49,84,85,86,87,88,89]120179.01178.32; 179.7058.4179.76179.22; 180.30<0.01
BIA[50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,84,85,86,90,91]52181.17180.17; 182.1624.1
DXA[71,72,73,74,75,76,77,78,79,80,81,82,83,87,88,89,90,91]38180.37178.91; 181.8317.5
Weight
Anthropometry[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,84,85,86,87,88,89]7075.6074.57; 76.6256.876.2775.51; 77.030.11
BIA[50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,84,85,86,90,91]3877.0875.88; 78.2730.5
DXA[71,72,73,74,75,76,77,78,79,80,81,82,83,87,88,89,90,91]1677.4174.41; 80.1912.7
Fat mass kilograms
Anthropometry[34,41,44,45,89]1414.7212.82; 16.6140.712.4811.41; 13.55<0.01
BIA[50,56,57,65,69,85]912.0310.41; 13.6625.6
DXA[72,73,74,75,76,78,79,81,82,83,87,89]1210.079.35; 10.7933.7
Fat mass percentage
Anthropometry[18,19,20,21,22,23,24,25,26,27,28,31,33,34,35,36,37,39,44,46,47,48,87,88,89]4410.609.73; 11.4744.811.8511.28; 12.43<0.01
BIA[50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,85,86,90,91]4012.6011.76; 13.4438.4
DXA[71,72,73,74,75,76,77,78,79,80,81,82,83,87,88,89,90,91]1713.4612.20; 14.7316.8
Fat-Free mass kilograms
Anthropometry[21,22,36,39,41,42,44]969.4467.56; 71.3222.966.1064.65; 67.55<0.01
BIA[50,51,56,57,65,66,69,84,85,90]1366.3164.67; 67.9637.2
DXA[72,73,74,75,76,77,80,81,82,83,87,89,90]1463.9161.09; 66.7439.9
Fat-Free mass percentage
BIA[63,65,69]382.7775.55; 89.9848.982.3680.30; 84.420.59
DXA[71,72,89]381.8479.84; 83.8151.1
Muscle mass kilograms
Anthropometry[34,44,45,89]738.4536.17; 40.7348.739.2837.34; 41.230.37
BIA[64,65,67,69,86]840.0536.46; 43.6451.3
Muscle mass percentage
Anthropometry[28,34,44,89]450.2145.45; 54.9757.352.0346.90; 57.170.38
BIA[61,65,69]354.4934.67; 74.3142.7
G = groups; M = mean.
Table
Table 7. Meta-analysis by groups according to the equation used.
Table 7. Meta-analysis by groups according to the equation used.
AuthorsGMCI 95%Weight (%)MCI95%p
Fat mass
Equation (1)[22,89]38.197.24; 9.158.510.199.41; 10.97<0.01
Equation (3)[19,21,23,24,25,26,31,48,87,89]1312.1811.06; 13.3035.9
Equation (6)[27,34,37,89]411.1610.42; 11.911.3
Equation (9)[36,39,46,47,87,89]69.547.45; 11.6216.8
Equation (11)[33,87]29.23−6.33; 24.785.3
Equation (20)[31,88,89]210.029.77; 10.275.7
Equation (26)[87,88]67.146.87; 7.4116.6
Sum of skinfold
Equation (5)[22,32,34,45]1152.1849.49; 54.8778.453.9551.19; 56.70<0.01
Equation (6)[31,40,41]459. 9355.34; 64.5221.6
Fat mass: Equation (1) = Carter, 1982; Equation (3) = Durnin and Womersley, 1974; Equation (6) = Faulkner, 1968; Equation (9) = Jackson and Pollock 7 folds, 1978; Equation (11) = Jackson and Pollock 3 folds, 1978; Equation (20) = Reilly, 2009; Equation (26) = Yuhasz, 1974. Sum of skinfolds: Equation (5) = Σ6 skinfolds (triceps, subscapular, supraspinale, abdominal, mid-thigh, and calf); Equation (6) = Σ8 folds (biceps, triceps, subscapular, iliac crest, supraspinale, abdominal, mid-thigh, and calf). G = groups; M = mean.
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Sebastiá-Rico, J.; Soriano, J.M.; González-Gálvez, N.; Martínez-Sanz, J.M. Body Composition of Male Professional Soccer Players Using Different Measurement Methods: A Systematic Review and Meta-Analysis. Nutrients 2023, 15, 1160. https://doi.org/10.3390/nu15051160

AMA Style

Sebastiá-Rico J, Soriano JM, González-Gálvez N, Martínez-Sanz JM. Body Composition of Male Professional Soccer Players Using Different Measurement Methods: A Systematic Review and Meta-Analysis. Nutrients. 2023; 15(5):1160. https://doi.org/10.3390/nu15051160

Chicago/Turabian Style

Sebastiá-Rico, Jaime, Jose M. Soriano, Noelia González-Gálvez, and José Miguel Martínez-Sanz. 2023. "Body Composition of Male Professional Soccer Players Using Different Measurement Methods: A Systematic Review and Meta-Analysis" Nutrients 15, no. 5: 1160. https://doi.org/10.3390/nu15051160

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