Relationship between nutritional adequacy and anthropometric profile characteristics according to playing position in professional female soccer players: A cross-sectional study

Relationship between nutritional adequacy and anthropometric profile characteristics according to playing position in professional female soccer players: A cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Relationship between nutritional adequacy and anthropometric profile characteristics according to playing position in professional female soccer players: A cross-sectional study Ángel Luis Kong-Lozano, Nicolás Leveau-Carrera, Rodrigo Aquino, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8291239/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Apr, 2026 Read the published version in BMC Sports Science, Medicine and Rehabilitation → Version 1 posted 16 You are reading this latest preprint version Abstract Background Anthropometric characterization and somatotype profiling in professional soccer players are essential for understanding the morphofunctional demands associated with playing positions. Objective To determine and compare anthropometric profile and somatotype characteristics across playing positions in professional female soccer players, and to analyze their relationship with nutritional adequacy. Methods A cross-sectional study was conducted with 43 players from two First Division teams in Trujillo (Peru). Anthropometric measurements followed ISAK standards, and body composition was estimated using Kerr’s five-component fractionation method. Somatotype was assessed using the Heath–Carter method. Nutritional adequacy was calculated according to playing position and international recommendations. Statistical analysis included the Kruskal-Wallis test with Bonferroni-corrected post-hoc comparisons for anthropometric variables, Chi-square tests with Cramer’s V effect size for nutritional adequacy, and Kendall’s Tau correlations for body composition associations. Statistical significance was set at p < 0.05. Results Goalkeepers presented significantly higher adipose mass (18.6 ± 2.5 kg) and muscle mass (33.1 ± 2.2 kg) than outfield players ( p < 0.05; ε² ≥0.21). Nutritional adequacy differed significantly by position, with goalkeepers showing 100% protein insufficiency ( p 0.05). However, in the total sample, protein adequacy was inversely correlated with adipose mass (Tb = − 0.60, p < 0.001), and lipid adequacy was negatively associated with muscle mass (Tb = − 0.52, p < 0.001). Conclusions Although no position-specific associations were identified, significant relationships emerged in the overall sample, suggesting that nutritional adequacy influences body composition independently of tactical role. Body composition somatotype nutrients requirements energy expenditure professional soccer female Figures Figure 1 BACKGROUND Women's soccer has experienced exponential growth worldwide, a phenomenon reflected in the significant increase in federated participation and the rising scientific interest regarding the biological and technical development of the sport ( 1 , 2 ). As the female game becomes more professionalized, the physical demands on players have intensified, requiring specific morphological characteristics, such as height, body mass, and body composition, to optimize performance, prevent injuries, and execute tactical roles effectively ( 3 , 4 ). These anthropometric profiles are not uniform; literature indicates they vary significantly according to playing position due to the distinct physiological demands of each role on the field ( 5 , 6 ). Success in football is multifactorial and depends much more on technical and tactical aspects than on anthropometric, physical and physiological ones. For instance, goalkeepers typically exhibit greater height, body mass, and absolute muscle mass compared to outfield players, traits necessary to cover the goal area and generate lower-limb power ( 7 , 8 ). Conversely, midfielders tend to present a balanced mesomorphic-ectomorphic somatotype with lower body fat levels to support high-volume running and aerobic endurance, while forwards often combine moderate musculature with low adiposity to facilitate acceleration and agility ( 9 , 10 ). However, these standards are largely based on European or North American populations. Recent evidence suggests that somatotype and body composition in female athletes can vary significantly based on geographic origin, ethnicity, and competitive level, meaning international references may not apply to all populations ( 11 , 12 ). To achieve and maintain these optimal physical profiles, nutritional adequacy is a determining factor. Professional female soccer players face high energy expenditures, often requiring between 2,000 and 3,000 kcal/day depending on training load and match demands ( 13 , 14 ). Current consensus statements recommend carbohydrate intakes of 5–8 g/kg/day to replenish glycogen stores and protein intakes of 1.2–2.0 g/kg/day to support muscle repair and adaptation ( 15 , 16 ). Furthermore, dietary lipids are crucial for female athletes, with recommendations suggesting they should comprise 20–35% of total energy intake. Adequate fat consumption is vital for meeting energy needs, absorbing fat-soluble vitamins, and maintaining essential hormonal functions, specifically menstrual health ( 17 , 18 ). Despite these clear guidelines, research indicates that professional female athletes frequently fail to meet their energy and macronutrient requirements. Studies in various competitive leagues have reported that players often exhibit low energy availability and insufficient intake of carbohydrates and lipids ( 19 , 20 ). This nutritional inadequacy is a significant concern because it can impact body composition negatively, leading to the loss of lean mass or the retention of fat mass due to metabolic adaptation, thereby hindering performance and increasing health risks ( 21 ). In the Latin American context, and specifically in Peru, there is a paucity of data describing the morphological characteristics of professional female soccer players. The average anthropometric profile of the Peruvian female population differs from European standards, characterized by shorter stature and a tendency towards an endomorphic somatotype ( 22 , 23 ). Consequently, applying international reference standards to local players may be inappropriate for talent identification or nutritional interventions. To date, no study has examined how nutritional adequacy correlates with position-specific anthropometric profiles in Peruvian professional soccer. Therefore, the aim of this study was to determine and compare anthropometric profile and somatotype characteristics across playing positions in professional female soccer players, and to analyze their relationship with nutritional adequacy. METHODS Study Design and Participants A descriptive, cross-sectional study was conducted during the 2025 competitive season. The study population consisted of 43 professional female soccer players belonging to two First Division clubs in Trujillo, Peru. Census sampling was employed to include the entire eligible population. Inclusion criteria were: (i) having a valid professional contract for the 2025 season; (ii) age between 18 and 35 years; (iii) participation in at least 50% of official and friendly matches; and (iv) accumulating a minimum of 10 hours of training per week. Players were excluded if they had sustained injuries preventing regular training in the two weeks prior to data collection, presented diagnosed metabolic or endocrine disorders (e.g., diabetes, thyroid disorders, PCOS), or had a history of disciplinary sanctions related to doping. Anthropometric Measurements Anthropometric variables were measured following the International Society for the Advancement of Kinanthropometry (ISAK) guidelines (24). All measurements were performed by ISAK-certified evaluators in a private room, with participants wearing light clothing and in a fasting state of at least two hours. To ensure data quality, the Technical Error of Measurement (TEM) was maintained within acceptable limits (<5% for skinfolds and <1% for other measures) (25). Body mass was measured using a digital scale (Omron, precision ±0.1 kg), and standing height was measured using a stadiometer (Seca 213, precision ±0.1 cm) with the head in the Frankfurt plane. The anthropometric profile included 25 variables: skinfolds (triceps, subscapular, supraspinale, abdominal, medial calf, and front thigh) measured with a constant-pressure caliper (Slim Guide, precision ±2 mm); girths (head, arm relaxed/flexed, forearm, chest, waist, gluteal, mid-thigh, and calf) measured with a steel tape (Ava Nutri, precision ±0.1 cm); and bone breadths (humerus and femur biepicondylar) measured with a small sliding caliper (Vitruvian, precision ±0.1 cm). Somatotype was calculated using the Heath-Carter method to determine endomorphy, mesomorphy, and ectomorphy components (26). Body composition was estimated using the Kerr five-component fractionation method (adipose, muscle, bone, residual, and skin masses), which is considered robust for athletic populations (27). Nutritional Assessment Dietary Intake Habitual dietary intake was assessed using a 24-hour dietary recall administered on three non-consecutive days (two training days and one rest day) to account for daily variability. Participants were required to detail all foods, beverages, and supplements consumed during each 24-hour period. To enhance the accuracy of portion size estimation and minimize recall bias, visual aids and photographic atlases from the "Auxiliary Tables for the Formulation and Evaluation of Dietary Regimens" (TAFERA) of the Peruvian Ministry of Health were utilized (28). The collected nutritional data were processed to quantify Total Energy Intake (TEI) and macronutrient distribution (proteins, carbohydrates, and lipids). Energy Expenditure Resting Metabolic Rate (RMR) was estimated using the Cunningham equation, which accounts for Fat-Free Mass (FFM) and is widely validated for athletic populations (RMR=500+22×FFM) (29). Total Energy Expenditure (TEE) was calculated individually for each of the three assessment days (two training days and one rest day) using the factorial method. Daily physical activity energy expenditure was estimated using Metabolic Equivalents (METs) from the Compendium of Physical Activities (30), based on the specific duration and intensity of the activities performed on each specific day. Finally, a weighted mean TEE was computed to represent the habitual energy requirement, ensuring temporal consistency with the dietary intake assessment. Nutritional Adequacy Nutritional adequacy was calculated as the ratio between the weighted mean intake and the estimated requirement for energy, proteins, carbohydrates, and lipids, expressed as a percentage (Adequacy=[Intake/Requirement]×100). For energy, Total Energy Intake (TEI) was compared against Total Energy Expenditure (TEE). For macronutrients, intake was compared against the specific recommendations previously described. Based on established protocols, adequacy levels were classified as: Insufficient (110%) (31). Statistical Analysis Statistical analyses were performed using SPSS software, version 30.0 (IBM Corp., Armonk, NY, USA). Data normality was assessed using the Shapiro-Wilk test. Descriptive statistics are presented as mean ± standard deviation (SD) for quantitative variables and frequencies (percentages) for categorical data. Differences in anthropometric and body composition variables between playing positions were evaluated using the Kruskal-Wallis H test. Effect sizes were estimated using Epsilon-squared (ε²). Pairwise comparisons were conducted using the Mann-Whitney U test with Bonferroni correction. Associations between nutritional adequacy and playing positions were analyzed using the Chi-square test (χ²), with Cramer’s V (V) calculated for effect size estimation. Correlations between nutritional adequacy and body composition were assessed using Kendall’s Tau-b (Tb) coefficients. Statistical significance was set at p < 0.05. Effect sizes were interpreted as follows: small (ε² 0.01–0.08; V 0.10), medium (ε² 0.08–0.26; V 0.30), and large (ε² ≥ 0.26; V 0.50). Ethics Approval and Consent to Participate The study was conducted in accordance with the Declaration of Helsinki (32) and approved by the Institutional Ethics Committee of Universidad Científica del Sur (Approval No. 132-DACND-DAFCS-U.CIENTIFICA-2025). Authorization was obtained from the sports management of both clubs. All participants provided written informed consent prior to data collection, and data anonymity was strictly preserved. RESULTS Anthropometric Characteristics The descriptive analysis of anthropometric profiles (Table 1) revealed statistically significant differences associated with playing positions, primarily distinguishing goalkeepers from outfield players. The Kruskal-Wallis test indicated significant variations across positions for body mass (p = 0.011, ε² = 0.26), height (p = 0.015), and Body Mass Index (p = 0.031). Post-hoc comparisons confirmed that goalkeepers exhibited significantly higher values for these variables compared to forwards, midfielders, and defenders, who showed no significant differences among themselves. Regarding body composition estimated by the Kerr method, significant differences were observed in absolute muscle mass (p = 0.014, ε² = 0.25) and adipose mass (p = 0.017, ε² = 0.24). Goalkeepers displayed significantly higher absolute muscle (33.10 ± 2.20 kg) and adipose mass (18.60 ± 2.50 kg) compared to all outfield positions. While forwards presented the lowest mean values for body mass and adipose mass numerically, these differences were not statistically significant compared to midfielders or defenders. Furthermore, no significant differences were found for somatotype components (endomorphy, mesomorphy, ectomorphy) across positions (p > 0.05), indicating a relatively homogeneous somatotypic profile across the squad despite the differences in body size. Nutritional Adequacy The assessment of nutritional intake adequacy revealed widespread dietary imbalances across the sample, with distinct distribution patterns illustrated in Figure 1. Statistical analysis confirmed that these variations were significantly associated with playing position (Table 2). A highly significant association was found for protein intake ( p <0.001), with a very large effect size (Cramer’s V=0.58). Specifically, 100% of goalkeepers exhibited insufficient protein intake, whereas a prevalence of excessive intake was observed in midfielders (90.9%) and defenders (87.5%). Similarly, energy intake differed significantly by position ( p =0.002), with a large effect size (V=0.49); goalkeepers showed the highest rate of insufficiency (75.0%). Carbohydrate intake also showed a significant association ( p =0.010, V=0.39), characterized by a trend towards excess in outfield players (reaching 100% in midfielders). Conversely, no significant association was found for lipid adequacy ( p =0.827), with insufficiency being the dominant pattern across the entire sample (74.4%). Global Association Analysis Correlation analysis of the total population (n=43) revealed robust and highly significant associations between nutritional adequacy and body composition (Table 3). Protein adequacy demonstrated a strong inverse correlation with adipose mass (Tb=-0.60, p <0.001) and a strong negative correlation with muscle mass (Tb=-0.53, p =0.003). Similarly, lipid adequacy displayed a strong negative correlation with muscle mass (Tb=-0.52, p <0.001). Unlike proteins and lipids, energy intake did not show a significant correlation with adipose mass (p=0.205). but maintained a significant negative association with muscle mass (Tb=-0.52, p <0.001). Association Analysis Stratified by Playing Position The analysis of the relationship between nutritional adequacy and body composition markers stratified by playing position is detailed in Additional file 1. Consistent with the reduced sample sizes within subgroups (n=4 to n=16), statistical significance was not reached for any association across positions ( p >0.05). However, strong correlation coefficients were observed in specific groups, suggesting potential trends that were likely masked by limited statistical power. For instance, forwards exhibited a strong negative correlation between protein adequacy and muscle mass (Tc=−0.69, p =0.060) and adipose mass (Tc=−0.47, p =0.235). Similarly, defenders showed a moderate-to-strong negative correlation between protein adequacy and adipose mass (Tb=−0.49, p =0.051). In goalkeepers and midfielders, correlations for several variables could not be computed due to the homogeneity of data (constant values) within these small subgroups. Somatotype Analysis Finally, the relationship between nutritional adequacy and somatotype components (Endomorphy, Mesomorphy, and Ectomorphy) was evaluated. No statistically significant associations were found in either the analysis stratified by playing position or the global analysis of the total sample ( p >0.05 for all comparisons). Detailed statistical results are provided in Additional file 2. DISCUSSION Guided by the objective to determine and compare anthropometric profile and somatotype characteristics across playing positions in professional female soccer players, and to analyze their relationship with nutritional adequacy, this study reveals a complex interaction between morphological specialization and nutritional strategies. While anthropometric profiles exhibited clear differentiation based on playing position, consistent with the functional demands of the sport, nutritional intake patterns appeared largely homogeneous and unbalanced across the squad. A critical observation was the robust statistical association found in the global population between high nutritional adequacy and favorable body composition (lower adiposity and higher muscle mass), a relationship that was statistically attenuated when stratified by specific playing positions due to sample size limitations. Regarding morphological characteristics, the data confirm that goalkeepers possess a distinct phenotype characterized by significantly greater height, body mass, and absolute muscle and adipose mass compared to outfield players. This aligns with recent international evidence, such as the findings by Petri et al. (8) and Strauss et al. (7), which describe goalkeepers as the tallest and heaviest players, a morphological adaptation necessary to maximize aerial reach and blocking presence in the goal area. However, a quantitative comparison with elite international cohorts reveals notable differences. For instance, while the height of the goalkeepers in this study is comparable to that of Chilean national team players (1.72 m) reported by Hernández-Mosqueira et al. (33), the sample exhibited a significantly higher body mass, approximately 13 kg heavier than the 66.7 kg average reported in the Chilean study. This suggests that while the structural requirement of height is met, there may be an excess of body mass not solely attributable to muscle tissue. Conversely, the outfield players presented an average height of approximately 1.59 m and weight of 57 kg, which mirrors almost exactly the profile of other South American professional cohorts as described by Hernandez-Martinez et al. (33), yet remains notably lower than elite European players who average between 1.65 and 1.68 m (Ruiz et al., 9). This reinforces the existence of a specific "regional phenotype" characterized by a lower center of gravity, which may offer biomechanical advantages in rotational agility and balance to compensate for the lack of vertical stature in match play, as suggested by biomechanical principles in soccer physiology (6). Furthermore, the findings underscore the limitations of relying solely on Body Mass Index (BMI) for athletic screening. While traditional metrics might classify players with substantial muscular development as "overweight", a trend frequently observed in power dependent positions like forwards, the use of the Kerr five component method allowed for a precise discrimination between adipose and muscle tissue (27). This distinction is supported by the systematic review of Sebastia-Rico et al. (5), which emphasizes that BMI fails to capture functional body composition in elite populations where skeletal muscle mass contributes disproportionately to total weight (34). Consequently, specific players in the studied cohort presented elevated BMI values yet maintained low adipose mass, indicating a favorable lean to fat ratio that would be misclassified by simpler anthropometric indices. The nutritional analysis uncovered a widespread pattern of imbalance, characterized by excessive protein intake and insufficient lipid consumption. Paradoxically, the global correlation analysis revealed that players with higher protein adequacy exhibited significantly lower adipose mass (Tb=−0.60, p < 0.001). From a bioenergetic perspective, this inverse relationship may be explained by the high Thermic Effect of Food associated with protein metabolism (16). However, a compelling finding from this research challenges the misconception that higher protein intake automatically translates to greater muscle mass accretion. While outfield players exhibited excessive protein consumption (exceeding international recommendations in >87% of cases), they did not display superior absolute muscle mass compared to goalkeepers, who conversely had 100% insufficient protein intake yet the highest muscle mass values. This aligns with recent evidence by Baranauskas et al. (12), who observed that exceeding protein thresholds does not yield additional anabolic benefits if energy balance or training stimuli are not optimized. Furthermore, this "muscle protein paradox" suggests that protein intake beyond the saturation point may be oxidized for energy rather than used for hypertrophy, particularly when carbohydrate availability is high (17). Thus, the superior muscle mass in goalkeepers appears to be driven more by their specific resistance training demands and total body size rather than dietary protein quantity per se. Conversely, the generalized insufficiency in lipid intake (83.7% of the sample) was strongly associated with lower muscle mass (Tb= −0.52, p < 0.001). This finding highlights a critical physiological concern documented by Slater et al. (17), as dietary lipids are essential substrates for the synthesis of cholesterol and anabolic hormones. In female athletes, chronic low lipid availability can suppress the hypothalamic pituitary gonadal axis (18), creating a subclinical environment that may compromise muscle repair and hypertrophy. These data suggest that the restriction of dietary fats, likely adopted as an empirical weight control strategy, might be inducing a counterproductive state, potentially limiting the capacity of players to accrue or maintain muscle mass despite high protein intake. A key methodological insight addresses the divergence between the stratified and global analyses. When data were segmented by playing position, statistical significance diminished (p > 0.05) for most variables (see Additional file 1). This absence of positional correlation does not necessarily imply different physiological responses among positions, but rather reflects a Type II statistical error driven by reduced statistical power in small subgroups, particularly in goalkeepers. However, when analyzed as a total population, robust physiological associations became evident. This indicates that metabolic responses to nutrient intake appear consistent across the cohort, meaning that regardless of tactical role, the physiological demand requires sufficient energy and lipids to sustain muscle mass, and high protein availability to minimize adiposity (15). Therefore, the global analysis provides the most robust evidence that nutritional status is a primary determinant of body composition quality in this population. Finally, the relationship between nutritional adequacy and somatotype components was evaluated. No statistically significant associations were found in either the stratified or global analysis. This suggests that while nutritional adequacy was significantly associated with modifiable tissues such as muscle and adipose mass, it was not related to the somatotypic profile in this cross sectional assessment, likely because somatotype represents a more stable phenotypic trait compared to body composition components (10). The primary limitation of this study was the small sample size of the goalkeeper subgroup, which restricted statistical power in stratified analyses, alongside the cross sectional design that precludes causal inferences. Additionally, self reported dietary data may be subject to underreporting. However, key strengths include the census sampling of two complete professional First Division squads, ensuring high representativeness of the specific study population, and the use of the Kerr five component fractionation method which offers superior anatomical precision compared to traditional BMI or two component models. CONCLUSION No statistically significant associations were found between nutritional adequacy and body composition when stratified by playing position, suggesting that dietary habits are homogeneous across the squad. However, global analysis of overall sample revealed that caloric adequacy is positively associated with muscle mass (p<0.001), whereas macronutrient imbalances (proteins, lipids, and carbohydrates) showed inverse relationships with muscle, adipose, and bone mass. Furthermore, the somatotype (meso-endomorphic) appeared stable and unrelated to current nutritional intake. Abbreviations BMI Body Mass Index ES Effect Size FFM Fat-Free Mass ISAK International Society for the Advancement of Kinanthropometry METs Metabolic Equivalents mTORC1 Mammalian Target of Rapamycin Complex 1 PCOS Polycystic Ovary Syndrome RMR Resting Metabolic Rate SD Standard Deviation TAFERA Tablas Auxiliares para la Formulación y Evaluación de Regímenes Alimentarios (Auxiliary Tables for the Formulation and Evaluation of Food Regimens) TEE Total Energy Expenditure TEF Thermic Effect of Food TEI Total Energy Intake TEM Technical Error of Measurement. Declarations Ethics approval and consent to participate The study was conducted in accordance with the ethical principles of the Declaration of Helsinki. The protocol was approved by the Institutional Ethics Committee of Universidad Científica del Sur (Approval No. 132-DACND-DAFCS-U.CIENTIFICA-2025). Before participating, all players were provided with detailed information about the purpose, procedures, and potential risks of the research. Written informed consent was obtained from each participant. Personal information and research data were anonymized and protected in accordance with confidentiality principles. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The authors received no financial support for the research, authorship, and/or publication of this article. Authors contributions LAKL, NLC and BSMG conceived and designed the study. LAKL and NLC performed data collection. LAKL and BSMG performed the statistical analysis and data interpretation. LAKL, NLC, BSMG, NECP and RA drafted the manuscript and provided critical revision for important intellectual content. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank the players, coaching staff, and management of Club Carlos A. Mannucci and Club Universidad César Vallejo for their collaboration and willingness to participate in this study. References FIFA. Women’s Football Strategy [Internet]. 2018 [cited 2024 Apr 29]. Available from: https://publications.fifa.com/es/vision-report-2021/11-goals/push-womens-football/ Iván-Baragaño I, Maneiro R. Investigación en fútbol femenino: antecedentes, progreso y futuros horizontes. 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Anthropometry, Body Composition, and Physical Fitness in Semi-Professional Soccer Players: Differences between Sexes and Playing Position. Appl Sci. 2023;13(3):1249. Tables Table 1. Descriptive anthropometric characteristics of professional female soccer players by playing position. Variable Forwards Midfielders Defenders Goalkeepers comparisons Mean SD Mean SD Mean SD Mean SD p-value † ES Age (years) 25.80 6.43 24.21 5.59 26.19 4.93 30.02 2.91 .256 0.10 Body Mass (kg) 57.18 8.24 a 56.82 6.25 a 58.10 3.90 a 79.95 3.34 b .011* 0.26 Height (m) 1.59 0.07 ab 1.58 0.03 a 1.60 0.04 a 1.70 0.04 b .015* 0.25 BMI (kg/m²) 22.59 3.55 ab 22.83 2.36 a 22.72 1.76 a 27.63 1.96 b .031* 0.21 Adipose Mass (kg) 12.70 3.10 a 12.60 2.20 a 12.10 1.90 a 18.60 2.50 b .017* 0.24 Muscle Mass (kg) 22.80 2.80 a 22.50 2.10 a 24.00 1.80 a 33.10 2.20 b .014* 0.25 Bone Mass (kg) 6.80 0.90 6.30 0.70 6.40 0.60 9.30 0.80 .094 0.15 Residual Mass (kg) 10.10 1.20 a 9.90 0.80 ab 10.10 0.70 a 12.40 1.10 b .009* 0.27 Skin Mass (kg) 4.80 0.50 a 5.20 0.40 ab 5.50 0.30 b 6.40 0.40 ab .009* 0.27 Endomorphy 4.18 1.25 3.68 0.63 3.61 0.72 4.33 0.17 .152 0.13 Mesomorphy 4.47 0.94 3.77 0.62 4.12 0.52 4.13 0.53 .755 0.03 Ectomorphy 1.53 0.74 1.67 0.67 1.49 0.45 0.98 0.45 .474 0.06 Note: SD = Standard Deviation; BMI = Body Mass Index; ES = Effect Size (Epsilon-squared ϵ 2 ). Body composition was estimated using the Kerr five-component method; Somatotype was evaluated using the Heath-Carter method. † Statistical comparisons were performed using the Kruskal-Wallis H test. Different superscripts ( a,b ) within the same row indicate significant differences between groups based on pairwise Mann-Whitney U comparisons with Bonferroni correction (p<0.05). *Denotes statistical significance. Table 2. Frequency of nutritional adequacy levels stratified by playing position and statistical association analysis. Nutrient Adequacy Level Forwards (n=12) Midfielders (n=11) Defenders (n=16) Goalkeepers (n=4) Total (n=43) Statistics n % n % n % n % n % p-value Cramer´s V Energy Insufficient 0 0.0 0 0.0 0 0.0 3 75.0 3 7.0 .002* 0.49 Optimal 6 50.0 1 9.1 10 62.5 1 25.0 18 41.9 Excessive 6 50.0 10 90.9 6 37.5 0 0.0 22 51.2 Proteins Insufficient 0 0.0 0 0.0 0 0.0 4 100.0 4 9.3 <.001* 0.71 Optimal 4 33.3 1 9.1 2 12.5 0 0.0 7 16.3 Excessive 8 66.7 10 90.9 14 87.5 0 0.0 32 74.4 Carbohydrates Insufficient 0 0.0 0 0.0 0 0.0 2 50.0 2 4.7 .003* 0.48 Optimal 3 25.0 0 0.0 1 6.3 2 50.0 6 14.0 Excessive 9 75.0 11 100.0 15 93.8 0 0.0 35 81.4 Lipids Insufficient 8 66.7 7 63.6 13 81.3 4 100.0 32 74.4 .827 0.18 Optimal 3 25.0 3 27.3 3 18.8 0 0.0 9 20.9 Excessive 1 8.3 1 9.1 0 0.0 0 0.0 2 4.7 Note: Data presented as frequency (n) and percentage (%). Statistical analysis performed using Chi-square test of independence. Effect size calculated using Cramer’s V (V) interpreted as: 0.10 small, 0.30 medium, 0.50 large. * Denotes statistical significance (p<0.05). Table 3. Relationship between nutritional adequacy and body compositions in the total sample. Nutritional Adequacy Body Compositions p Kendall´s Tau b Calories Adipose Mass .205 -.20 Muscle Mass <.001* -.52 Proteins Adipose Mass <.001* -.60 Muscle Mass .003* -.53 Bone Mass .041* -.37 Carbohydrates Adipose Mass .010* -.35 Muscle Mass .040* -.34 Lipids Muscle Mass <.001* -.52 Note: Correlations determined by Kendall's Tau-b coefficient. Interpretation of Tb: 0.50 (large). * Denotes statistical significance (p<0.05). Additional Declarations No competing interests reported. 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Pasto","correspondingAuthor":false,"prefix":"","firstName":"Nelson","middleName":"Enrique Conde","lastName":"Parada","suffix":""},{"id":566801778,"identity":"f6c691ad-11d1-4f0a-9362-331e40fd2625","order_by":4,"name":"Bryan Steve Martínez Galán","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABM0lEQVRIiWNgGAWjYBACAwTJfIAhAUixMR9sAHEZG/BpOQAm2RJAWiTY2BKJ0QJm8oDZEiCteLWYix1+9vlDwR05+f41Hz88qLhTx8fG3PjhB4ON7IYDvAcfYNFiOTvNeMYBg2fGjDPebpZIOPMM6DDGZskehjTjDQf4kg2wOex2gjHQL4cTmyXObmNIbDsswSbf2MbAw3A4ccMBHjMJrFrSP4O01LdJnHnGkPjvMMiWNsY/DP9BWsx/YNWSA7YlgYe/h40hsQGihZmH4QDYFmzet5ydU8xwxuCw4QwJNmOJhGOHJduAfpGWMUg2nnmYLxmbw8yl0zczVPw5LC/ff/jhxx81h/nl29gffnxTYSfbd7z34AccAQ0BEgkoDgZioPPwA/4DGEKEtIyCUTAKRsEIAQAK82fJ4r+bMwAAAABJRU5ErkJggg==","orcid":"","institution":"Scientific University of the South","correspondingAuthor":true,"prefix":"","firstName":"Bryan","middleName":"Steve 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16:39:21","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":126152,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8291239/v1/0bac5efac491cb315a26cd4a.html"},{"id":99286402,"identity":"d3cb6dbd-0036-4e3f-bf48-611be5d9bcf4","added_by":"auto","created_at":"2025-12-31 09:25:24","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":166349,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNutritional adequacy percentages for energy and macronutrients stratified by playing position. \u003c/strong\u003eThe box plots illustrate the distribution of nutritional adequacy. The central horizontal line represents the median, while the 'X' indicates the mean. Whiskers extend to 1.5 times the interquartile range. Individual dots represent outliers. Note the distinct trend of insufficiency in goalkeepers versus excessive protein/carbohydrate intake in outfield players, with generalized lipid insufficiency across all positions.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8291239/v1/e973d43cf2205972c4021586.jpg"},{"id":108437975,"identity":"bc49e987-592c-4cb3-9802-b4b31c072dbb","added_by":"auto","created_at":"2026-05-04 16:05:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":604740,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8291239/v1/73294405-aa8e-4d56-b0b0-4bbac2943432.pdf"},{"id":99286413,"identity":"693f77be-10d1-43ba-b73d-4cf90b1f2c36","added_by":"auto","created_at":"2025-12-31 09:25:26","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":22859,"visible":true,"origin":"","legend":"","description":"","filename":"BMCSSMRAdditionalfile1and2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8291239/v1/83e7992b1c79d73f6f3e528b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship between nutritional adequacy and anthropometric profile characteristics according to playing position in professional female soccer players: A cross-sectional study","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eWomen's soccer has experienced exponential growth worldwide, a phenomenon reflected in the significant increase in federated participation and the rising scientific interest regarding the biological and technical development of the sport (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). As the female game becomes more professionalized, the physical demands on players have intensified, requiring specific morphological characteristics, such as height, body mass, and body composition, to optimize performance, prevent injuries, and execute tactical roles effectively (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). These anthropometric profiles are not uniform; literature indicates they vary significantly according to playing position due to the distinct physiological demands of each role on the field (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSuccess in football is multifactorial and depends much more on technical and tactical aspects than on anthropometric, physical and physiological ones. For instance, goalkeepers typically exhibit greater height, body mass, and absolute muscle mass compared to outfield players, traits necessary to cover the goal area and generate lower-limb power (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Conversely, midfielders tend to present a balanced mesomorphic-ectomorphic somatotype with lower body fat levels to support high-volume running and aerobic endurance, while forwards often combine moderate musculature with low adiposity to facilitate acceleration and agility (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, these standards are largely based on European or North American populations. Recent evidence suggests that somatotype and body composition in female athletes can vary significantly based on geographic origin, ethnicity, and competitive level, meaning international references may not apply to all populations (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo achieve and maintain these optimal physical profiles, nutritional adequacy is a determining factor. Professional female soccer players face high energy expenditures, often requiring between 2,000 and 3,000 kcal/day depending on training load and match demands (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Current consensus statements recommend carbohydrate intakes of 5\u0026ndash;8 g/kg/day to replenish glycogen stores and protein intakes of 1.2\u0026ndash;2.0 g/kg/day to support muscle repair and adaptation (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Furthermore, dietary lipids are crucial for female athletes, with recommendations suggesting they should comprise 20\u0026ndash;35% of total energy intake. Adequate fat consumption is vital for meeting energy needs, absorbing fat-soluble vitamins, and maintaining essential hormonal functions, specifically menstrual health (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Despite these clear guidelines, research indicates that professional female athletes frequently fail to meet their energy and macronutrient requirements. Studies in various competitive leagues have reported that players often exhibit low energy availability and insufficient intake of carbohydrates and lipids (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). This nutritional inadequacy is a significant concern because it can impact body composition negatively, leading to the loss of lean mass or the retention of fat mass due to metabolic adaptation, thereby hindering performance and increasing health risks (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the Latin American context, and specifically in Peru, there is a paucity of data describing the morphological characteristics of professional female soccer players. The average anthropometric profile of the Peruvian female population differs from European standards, characterized by shorter stature and a tendency towards an endomorphic somatotype (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Consequently, applying international reference standards to local players may be inappropriate for talent identification or nutritional interventions. To date, no study has examined how nutritional adequacy correlates with position-specific anthropometric profiles in Peruvian professional soccer. Therefore, the aim of this study was to determine and compare anthropometric profile and somatotype characteristics across playing positions in professional female soccer players, and to analyze their relationship with nutritional adequacy.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Participants\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA descriptive, cross-sectional study was conducted during the 2025 competitive season. The study population consisted of 43 professional female soccer players belonging to two First Division clubs in Trujillo, Peru. Census sampling was employed to include the entire eligible population.\u003c/p\u003e\n\u003cp\u003eInclusion criteria were: (i) having a valid professional contract for the 2025 season; (ii) age between 18 and 35 years; (iii) participation in at least 50% of official and friendly matches; and (iv) accumulating a minimum of 10 hours of training per week. Players were excluded if they had sustained injuries preventing regular training in the two weeks prior to data collection, presented diagnosed metabolic or endocrine disorders (e.g., diabetes, thyroid disorders, PCOS), or had a history of disciplinary sanctions related to doping.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnthropometric Measurements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnthropometric variables were measured following the International Society for the Advancement of Kinanthropometry (ISAK) guidelines (24). All measurements were performed by ISAK-certified evaluators in a private room, with participants wearing light clothing and in a fasting state of at least two hours. To ensure data quality, the Technical Error of Measurement (TEM) was maintained within acceptable limits (\u0026lt;5% for skinfolds and \u0026lt;1% for other measures) (25).\u003c/p\u003e\n\u003cp\u003eBody mass was measured using a digital scale (Omron, precision ±0.1 kg), and standing height was measured using a stadiometer (Seca 213, precision ±0.1 cm) with the head in the Frankfurt plane. The anthropometric profile included 25 variables: skinfolds (triceps, subscapular, supraspinale, abdominal, medial calf, and front thigh) measured with a constant-pressure caliper (Slim Guide, precision ±2 mm); girths (head, arm relaxed/flexed, forearm, chest, waist, gluteal, mid-thigh, and calf) measured with a steel tape (Ava Nutri, precision ±0.1 cm); and bone breadths (humerus and femur biepicondylar) measured with a small sliding caliper (Vitruvian, precision ±0.1 cm).\u003c/p\u003e\n\u003cp\u003eSomatotype was calculated using the Heath-Carter method to determine endomorphy, mesomorphy, and ectomorphy components (26). Body composition was estimated using the Kerr five-component fractionation method (adipose, muscle, bone, residual, and skin masses), which is considered robust for athletic populations (27).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNutritional Assessment Dietary Intake\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHabitual dietary intake was assessed using a 24-hour dietary recall administered on three non-consecutive days (two training days and one rest day) to account for daily variability. Participants were required to detail all foods, beverages, and supplements consumed during each 24-hour period. To enhance the accuracy of portion size estimation and minimize recall bias, visual aids and photographic atlases from the \"Auxiliary Tables for the Formulation and Evaluation of Dietary Regimens\" (TAFERA) of the Peruvian Ministry of Health were utilized (28). The collected nutritional data were processed to quantify Total Energy Intake (TEI) and macronutrient distribution (proteins, carbohydrates, and lipids).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnergy Expenditure\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResting Metabolic Rate (RMR) was estimated using the Cunningham equation, which accounts for Fat-Free Mass (FFM) and is widely validated for athletic populations (RMR=500+22×FFM) (29). Total Energy Expenditure (TEE) was calculated individually for each of the three assessment days (two training days and one rest day) using the factorial method. Daily physical activity energy expenditure was estimated using Metabolic Equivalents (METs) from the Compendium of Physical Activities (30), based on the specific duration and intensity of the activities performed on each specific day. Finally, a weighted mean TEE was computed to represent the habitual energy requirement, ensuring temporal consistency with the dietary intake assessment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNutritional Adequacy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNutritional adequacy was calculated as the ratio between the weighted mean intake and the estimated requirement for energy, proteins, carbohydrates, and lipids, expressed as a percentage (Adequacy=[Intake/Requirement]×100). For energy, Total Energy Intake (TEI) was compared against Total Energy Expenditure (TEE). For macronutrients, intake was compared against the specific recommendations previously described. Based on established protocols, adequacy levels were classified as: Insufficient (\u0026lt;90%), Optimal (90–110%), or Excessive (\u0026gt;110%) (31).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using SPSS software, version 30.0 (IBM Corp., Armonk, NY, USA). Data normality was assessed using the Shapiro-Wilk test. Descriptive statistics are presented as mean ± standard deviation (SD) for quantitative variables and frequencies (percentages) for categorical data.\u003c/p\u003e\n\u003cp\u003eDifferences in anthropometric and body composition variables between playing positions were evaluated using the Kruskal-Wallis H test. Effect sizes were estimated using Epsilon-squared (ε²). Pairwise comparisons were conducted using the Mann-Whitney U test with Bonferroni correction.\u003c/p\u003e\n\u003cp\u003eAssociations between nutritional adequacy and playing positions were analyzed using the Chi-square test (χ²), with Cramer’s V (V) calculated for effect size estimation. Correlations between nutritional adequacy and body composition were assessed using Kendall’s Tau-b (Tb) coefficients.\u003c/p\u003e\n\u003cp\u003eStatistical significance was set at p \u0026lt; 0.05. Effect sizes were interpreted as follows: small (ε² 0.01–0.08; V 0.10), medium (ε² 0.08–0.26; V 0.30), and large (ε² ≥ 0.26; V 0.50).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki (32) and approved by the Institutional Ethics Committee of Universidad Científica del Sur (Approval No. 132-DACND-DAFCS-U.CIENTIFICA-2025). Authorization was obtained from the sports management of both clubs. All participants provided written informed consent prior to data collection, and data anonymity was strictly preserved.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eAnthropometric Characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe descriptive analysis of anthropometric profiles (Table 1) revealed statistically significant differences associated with playing positions, primarily distinguishing goalkeepers from outfield players. The Kruskal-Wallis test indicated significant variations across positions for body mass (p = 0.011, ε² = 0.26), height (p = 0.015), and Body Mass Index (p = 0.031). Post-hoc comparisons confirmed that goalkeepers exhibited significantly higher values for these variables compared to forwards, midfielders, and defenders, who showed no significant differences among themselves.\u003c/p\u003e\n\u003cp\u003eRegarding body composition estimated by the Kerr method, significant differences were observed in absolute muscle mass (p = 0.014, ε² = 0.25) and adipose mass (p = 0.017, ε² = 0.24). Goalkeepers displayed significantly higher absolute muscle (33.10 ± 2.20 kg) and adipose mass (18.60 ± 2.50 kg) compared to all outfield positions. While forwards presented the lowest mean values for body mass and adipose mass numerically, these differences were not statistically significant compared to midfielders or defenders. Furthermore, no significant differences were found for somatotype components (endomorphy, mesomorphy, ectomorphy) across positions (p \u0026gt; 0.05), indicating a relatively homogeneous somatotypic profile across the squad despite the differences in body size.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNutritional Adequacy\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe assessment of nutritional intake adequacy revealed widespread dietary imbalances across the sample, with distinct distribution patterns illustrated in Figure 1. Statistical analysis confirmed that these variations were significantly associated with playing position (Table 2). A highly significant association was found for protein intake (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), with a very large effect size (Cramer’s V=0.58). Specifically, 100% of goalkeepers exhibited insufficient protein intake, whereas a prevalence of excessive intake was observed in midfielders (90.9%) and defenders (87.5%). Similarly, energy intake differed significantly by position (\u003cem\u003ep\u003c/em\u003e=0.002), with a large effect size (V=0.49); goalkeepers showed the highest rate of insufficiency (75.0%). Carbohydrate intake also showed a significant association (\u003cem\u003ep\u003c/em\u003e=0.010, V=0.39), characterized by a trend towards excess in outfield players (reaching 100% in midfielders). Conversely, no significant association was found for lipid adequacy (\u003cem\u003ep\u003c/em\u003e=0.827), with insufficiency being the dominant pattern across the entire sample (74.4%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGlobal Association Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation analysis of the total population (n=43) revealed robust and highly significant associations between nutritional adequacy and body composition (Table 3). Protein adequacy demonstrated a strong inverse correlation with adipose mass (Tb=-0.60, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) and a strong negative correlation with muscle mass (Tb=-0.53, \u003cem\u003ep\u003c/em\u003e=0.003). Similarly, lipid adequacy displayed a strong negative correlation with muscle mass (Tb=-0.52,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e\u0026lt;0.001). Unlike proteins and lipids, energy intake did not show a significant correlation with adipose mass (p=0.205). but maintained a significant negative association with muscle mass (Tb=-0.52, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation Analysis Stratified by Playing Position\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of the relationship between nutritional adequacy and body composition markers stratified by playing position is detailed in Additional file 1. Consistent with the reduced sample sizes within subgroups (n=4 to n=16), statistical significance was not reached for any association across positions (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05). However, strong correlation coefficients were observed in specific groups, suggesting potential trends that were likely masked by limited statistical power. For instance, forwards exhibited a strong negative correlation between protein adequacy and muscle mass (Tc=−0.69,\u003cem\u003ep\u003c/em\u003e=0.060) and adipose mass (Tc=−0.47,\u003cem\u003ep\u003c/em\u003e=0.235). Similarly, defenders showed a moderate-to-strong negative correlation between protein adequacy and adipose mass (Tb=−0.49,\u003cem\u003ep\u003c/em\u003e=0.051). In goalkeepers and midfielders, correlations for several variables could not be computed due to the homogeneity of data (constant values) within these small subgroups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSomatotype Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinally, the relationship between nutritional adequacy and somatotype components (Endomorphy, Mesomorphy, and Ectomorphy) was evaluated. No statistically significant associations were found in either the analysis stratified by playing position or the global analysis of the total sample (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05 for all comparisons). Detailed statistical results are provided in Additional file 2.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eGuided by the objective to determine and compare anthropometric profile and somatotype characteristics across playing positions in professional female soccer players, and to analyze their relationship with nutritional adequacy, this study reveals a complex interaction between morphological specialization and nutritional strategies. While anthropometric profiles exhibited clear differentiation based on playing position, consistent with the functional demands of the sport, nutritional intake patterns appeared largely homogeneous and unbalanced across the squad. A critical observation was the robust statistical association found in the global population between high nutritional adequacy and favorable body composition (lower adiposity and higher muscle mass), a relationship that was statistically attenuated when stratified by specific playing positions due to sample size limitations.\u003c/p\u003e\n\u003cp\u003eRegarding morphological characteristics, the data confirm that goalkeepers possess a distinct phenotype characterized by significantly greater height, body mass, and absolute muscle and adipose mass compared to outfield players. This aligns with recent international evidence, such as the findings by Petri et al. (8) and Strauss et al. (7), which describe goalkeepers as the tallest and heaviest players, a morphological adaptation necessary to maximize aerial reach and blocking presence in the goal area. However, a quantitative comparison with elite international cohorts reveals notable differences. For instance, while the height of the goalkeepers in this study is comparable to that of Chilean national team players (1.72 m) reported by Hernández-Mosqueira et al. (33), the sample exhibited a significantly higher body mass, approximately 13 kg heavier than the 66.7 kg average reported in the Chilean study. This suggests that while the structural requirement of height is met, there may be an excess of body mass not solely attributable to muscle tissue. Conversely, the outfield players presented an average height of approximately 1.59 m and weight of 57 kg, which mirrors almost exactly the profile of other South American professional cohorts as described by Hernandez-Martinez et al. (33), yet remains notably lower than elite European players who average between 1.65 and 1.68 m (Ruiz et al., 9). This reinforces the existence of a specific \"regional phenotype\" characterized by a lower center of gravity, which may offer biomechanical advantages in rotational agility and balance to compensate for the lack of vertical stature in match play, as suggested by biomechanical principles in soccer physiology (6).\u003c/p\u003e\n\u003cp\u003eFurthermore, the findings underscore the limitations of relying solely on Body Mass Index (BMI) for athletic screening. While traditional metrics might classify players with substantial muscular development as \"overweight\", a trend frequently observed in power dependent positions like forwards, the use of the Kerr five component method allowed for a precise discrimination between adipose and muscle tissue (27). This distinction is supported by the systematic review of Sebastia-Rico et al. (5), which emphasizes that BMI fails to capture functional body composition in elite populations where skeletal muscle mass contributes disproportionately to total weight (34). Consequently, specific players in the studied cohort presented elevated BMI values yet maintained low adipose mass, indicating a favorable lean to fat ratio that would be misclassified by simpler anthropometric indices.\u003c/p\u003e\n\u003cp\u003eThe nutritional analysis uncovered a widespread pattern of imbalance, characterized by excessive protein intake and insufficient lipid consumption. Paradoxically, the global correlation analysis revealed that players with higher protein adequacy exhibited significantly lower adipose mass (Tb=−0.60, p \u0026lt; 0.001). From a bioenergetic perspective, this inverse relationship may be explained by the high Thermic Effect of Food associated with protein metabolism (16). However, a compelling finding from this research challenges the misconception that higher protein intake automatically translates to greater muscle mass accretion. While outfield players exhibited excessive protein consumption (exceeding international recommendations in \u0026gt;87% of cases), they did not display superior absolute muscle mass compared to goalkeepers, who conversely had 100% insufficient protein intake yet the highest muscle mass values. This aligns with recent evidence by Baranauskas et al. (12), who observed that exceeding protein thresholds does not yield additional anabolic benefits if energy balance or training stimuli are not optimized. Furthermore, this \"muscle protein paradox\" suggests that protein intake beyond the saturation point may be oxidized for energy rather than used for hypertrophy, particularly when carbohydrate availability is high (17). Thus, the superior muscle mass in goalkeepers appears to be driven more by their specific resistance training demands and total body size rather than dietary protein quantity per se.\u003c/p\u003e\n\u003cp\u003eConversely, the generalized insufficiency in lipid intake (83.7% of the sample) was strongly associated with lower muscle mass (Tb= −0.52, p \u0026lt; 0.001). This finding highlights a critical physiological concern documented by Slater et al. (17), as dietary lipids are essential substrates for the synthesis of cholesterol and anabolic hormones. In female athletes, chronic low lipid availability can suppress the hypothalamic pituitary gonadal axis (18), creating a subclinical environment that may compromise muscle repair and hypertrophy. These data suggest that the restriction of dietary fats, likely adopted as an empirical weight control strategy, might be inducing a counterproductive state, potentially limiting the capacity of players to accrue or maintain muscle mass despite high protein intake.\u003c/p\u003e\n\u003cp\u003eA key methodological insight addresses the divergence between the stratified and global analyses. When data were segmented by playing position, statistical significance diminished (p \u0026gt; 0.05) for most variables (see Additional file 1). This absence of positional correlation does not necessarily imply different physiological responses among positions, but rather reflects a Type II statistical error driven by reduced statistical power in small subgroups, particularly in goalkeepers. However, when analyzed as a total population, robust physiological associations became evident. This indicates that metabolic responses to nutrient intake appear consistent across the cohort, meaning that regardless of tactical role, the physiological demand requires sufficient energy and lipids to sustain muscle mass, and high protein availability to minimize adiposity (15). Therefore, the global analysis provides the most robust evidence that nutritional status is a primary determinant of body composition quality in this population.\u003c/p\u003e\n\u003cp\u003eFinally, the relationship between nutritional adequacy and somatotype components was evaluated. No statistically significant associations were found in either the stratified or global analysis. This suggests that while nutritional adequacy was significantly associated with modifiable tissues such as muscle and adipose mass, it was not related to the somatotypic profile in this cross sectional assessment, likely because somatotype represents a more stable phenotypic trait compared to body composition components (10).\u003c/p\u003e\n\u003cp\u003eThe primary limitation of this study was the small sample size of the goalkeeper subgroup, which restricted statistical power in stratified analyses, alongside the cross sectional design that precludes causal inferences. Additionally, self reported dietary data may be subject to underreporting. However, key strengths include the census sampling of two complete professional First Division squads, ensuring high representativeness of the specific study population, and the use of the Kerr five component fractionation method which offers superior anatomical precision compared to traditional BMI or two component models.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eNo statistically significant associations were found between nutritional adequacy and body composition when stratified by playing position, suggesting that dietary habits are homogeneous across the squad. However, global analysis of overall sample revealed that caloric adequacy is positively associated with muscle mass (p\u0026lt;0.001), whereas macronutrient imbalances (proteins, lipids, and carbohydrates) showed inverse relationships with muscle, adipose, and bone mass. Furthermore, the somatotype (meso-endomorphic) appeared stable and unrelated to current nutritional intake.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEffect Size\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFFM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFat-Free Mass\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eISAK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Society for the Advancement of Kinanthropometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMETs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMetabolic Equivalents\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emTORC1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMammalian Target of Rapamycin Complex 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolycystic Ovary Syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eResting Metabolic Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTAFERA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTablas Auxiliares para la Formulaci\u0026oacute;n y Evaluaci\u0026oacute;n de Reg\u0026iacute;menes Alimentarios (Auxiliary Tables for the Formulation and Evaluation of Food Regimens)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTEE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal Energy Expenditure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTEF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThermic Effect of Food\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTEI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal Energy Intake\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTechnical Error of Measurement.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the ethical principles of the Declaration of Helsinki. The protocol was approved by the Institutional Ethics Committee of Universidad Científica del Sur (Approval No. 132-DACND-DAFCS-U.CIENTIFICA-2025). Before participating, all players were provided with detailed information about the purpose, procedures, and potential risks of the research. Written informed consent was obtained from each participant. Personal information and research data were anonymized and protected in accordance with confidentiality principles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no financial support for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLAKL, NLC and BSMG conceived and designed the study. LAKL and NLC performed data collection. LAKL and BSMG performed the statistical analysis and data interpretation. LAKL, NLC, BSMG, NECP and RA drafted the manuscript and provided critical revision for important intellectual content. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the players, coaching staff, and management of Club Carlos A. Mannucci and Club Universidad César Vallejo for their collaboration and willingness to participate in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFIFA. Women\u0026rsquo;s Football Strategy [Internet]. 2018 [cited 2024 Apr 29]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://publications.fifa.com/es/vision-report-2021/11-goals/push-womens-football/\u003c/span\u003e\u003cspan address=\"https://publications.fifa.com/es/vision-report-2021/11-goals/push-womens-football/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIv\u0026aacute;n-Baraga\u0026ntilde;o I, Maneiro R. Investigaci\u0026oacute;n en f\u0026uacute;tbol femenino: antecedentes, progreso y futuros horizontes. EFDeportes. 2023;28(300):127\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorton K, Olds T, Anthropometrica. A Textbook of Body Measurement for Sports and Health Courses. Sydney: UNSW; 1996.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReilly T, Bangsbo J, Franks A. Anthropometric and physiological predispositions for elite soccer. 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The Shape of Success: A Scoping Review of Somatotype in Modern Elite Athletes Across Various Sports. Sports. 2025;13(2):38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaranauskas M, Kupčiūnaitė I, Lieponienė J, Stukas R. Dominant Somatotype Development in Relation to Body Composition and Dietary Macronutrient Intake among High-Performance Athletes. Nutrients. 2024;16(10):1493.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams C, Serratosa L. Nutrition on match day. J Sports Sci. 2006;24(7):687\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaughan RJ, Burke LM, Dvorak J, Larson-Meyer DE, Peeling P, Phillips SM, Rawson ES, Walsh NP, Garthe I, Geyer H, Meeusen R, van Loon LJC, Shirreffs SM, Spriet LL, Stuart M, Vernec A, Currell K, Ali VM, Budgett RG, Ljungqvist A, Mountjoy M, Pitsiladis YP, Soligard T, Erdener U, Engebretsen L. IOC consensus statement: dietary supplements and the high-performance athlete. Br J Sports Med. 2018;52(7):439\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas DT, Erdman KA, Burke LM. Position of the Academy of Nutrition and Dietetics, Dietitians of Canada, and the American College of Sports Medicine: Nutrition and Athletic Performance. J Acad Nutr Diet. 2016;116(3):501\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeukendrup AE, Gleeson M. Sport Nutrition: An Introduction to Energy Production and Performance. 3rd ed. Champaign: Human Kinetics; 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlater G, Rice A, Burke LM. Macronutrient requirements for athletes: focus on fat intake and its role in muscle protein synthesis. Int J Sport Nutr Exerc Metab. 2020;30(2):95\u0026ndash;104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimopoulos AP. The importance of the omega-6/omega-3 fatty acid ratio in cardiovascular disease and other chronic diseases. Exp Biol Med (Maywood). 2008;233(6):674\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDobrowolski H, Włodarek D. Energy expenditure and nutritional needs satisfaction in female football players. Int J Environ Res Public Health. 2019;16(7):1134.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMicha R, Pe\u0026ntilde;alvo JL, Cudhea F, Imamura F, Rehm CD. Nutrient Adequacy in Endurance Athletes. Nutrients. 2023;15(1):123.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAragon AA, Schoenfeld BJ. Nutrient timing revisited: is there a post-exercise anabolic window? J Int Soc Sports Nutr. 2013;10(1):5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChirinos JL, Castillo H, Huam\u0026aacute;n J. Caracter\u0026iacute;sticas antropom\u0026eacute;tricas de la poblaci\u0026oacute;n femenina peruana. Rev Peru Med Exp Salud Publica. 2020;37(1):45\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez J, Quispe F. Perfil cineantropom\u0026eacute;trico de los futbolistas varones de 16 a 18 a\u0026ntilde;os del Club Centro Deportivo Municipal \u0026ndash; Per\u0026uacute; 2020 [Tesis de Licenciatura]. Lima: Universidad Mar\u0026iacute;a Auxiliadora; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStewart A, Marfell-Jones M, Olds T, de Ridder H. International standards for anthropometric assessment. Lower Hutt: International Society for the Advancement of Kinanthropometry; 2011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerini TA, de Oliveira GL, Ornellas JS, de Oliveira FP. Technical error of measurement in anthropometry. Rev Bras Med Esporte. 2005;11(1):81\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarter JEL, Heath BH. Somatotyping - Development and Applications. Cambridge: Cambridge University Press; 1990.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKerr DA. An anthropometric method for fractionation of skin, adipose, bone, muscle and residual tissue masses in males and females age 6 to 77 years [Master\u0026rsquo;s thesis]. Burnaby: Simon Fraser University; 1988.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinisterio de Salud. Tablas auxiliares para la formulaci\u0026oacute;n y evaluaci\u0026oacute;n de reg\u0026iacute;menes alimentarios \u0026ndash; TAFERA. Lima: Centro Nacional de Alimentaci\u0026oacute;n y Nutrici\u0026oacute;n; 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCunningham JJ. A reanalysis of the factors influencing basal metabolic rate in normal adults. Am J Clin Nutr. 1980;33(11):2372\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAinsworth BE, Haskell WL, Herrmann SD, Meckes N, Bassett DR Jr, Tudor-Locke C, Greer JL, Vezina J, Whitt-Glover MC, Leon AS. 2011 Compendium of Physical Activities: a second update of codes and MET values. Med Sci Sports Exerc. 2011;43(8):1575\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartinez-Ruiz NR, Moreno-Gonzalez J, Chavez-Sandoval BE, Ramirez-Silva I. Evaluaci\u0026oacute;n de la adecuaci\u0026oacute;n de la ingesta de energ\u0026iacute;a y nutrientes en adultos. Nutr Hosp. 2015;32(6):2708\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Medical Association. WMA Declaration of Helsinki \u0026ndash; Ethical Principles for Medical Research Involving Human Subjects. JAMA. 2013;310(20):2191\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHernandez-Martinez J, Perez-Carcamo J, Canales-Canales S, Co\u0026ntilde;api-Union B, Cid-Calfucura I, Herrera-Valenzuela T, Branco BHM. Vald\u0026eacute;s-Badilla P. Body Composition and Physical Performance by Playing Position in Amateur Female Soccer Players. Appl Sci. 2024;14(13):5665.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToro-Rom\u0026aacute;n V, Grijota FJ, Mu\u0026ntilde;oz D, Maynar-Mari\u0026ntilde;o M, Clemente-Gil S, Robles-Gil MC. Anthropometry, Body Composition, and Physical Fitness in Semi-Professional Soccer Players: Differences between Sexes and Playing Position. Appl Sci. 2023;13(3):1249.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Descriptive anthropometric characteristics of professional female soccer players by playing position.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"701\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eForwards\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMidfielders\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDefenders\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGoalkeepers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ecomparisons\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBody Mass (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.25\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.90\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.34\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.011*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHeight (m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.07\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.015*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.55\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.36\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.76\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.96\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.031*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAdipose Mass (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.10\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.20\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.90\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.50\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.017*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMuscle Mass (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.80\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.10\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.80\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.20\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.014*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBone Mass (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eResidual Mass (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.20\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.80\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.70\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.10\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.009*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSkin Mass (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.50\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.40\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.30\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.40\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.009*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEndomorphy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMesomorphy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEctomorphy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: SD = Standard Deviation; BMI = Body Mass Index; ES = Effect Size (Epsilon-squared ϵ\u003csup\u003e2\u003c/sup\u003e). Body composition was estimated using the Kerr five-component method; Somatotype was evaluated using the Heath-Carter method. \u003csup\u003e\u0026dagger;\u003c/sup\u003eStatistical comparisons were performed using the Kruskal-Wallis H test. Different superscripts (\u003csup\u003ea,b\u003c/sup\u003e) within the same row indicate significant differences between groups based on pairwise Mann-Whitney U comparisons with Bonferroni correction (p\u0026lt;0.05). *Denotes statistical significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Frequency of nutritional adequacy levels stratified by playing position and statistical association analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"1002\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNutrient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdequacy Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForwards (n=12)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMidfielders (n=11)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDefenders (n=16)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGoalkeepers (n=4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (n=43)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCramer\u0026acute;s V\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnergy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eInsufficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e75.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e62.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e41.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eExcessive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e90.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e51.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProteins\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eInsufficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eExcessive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e66.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e90.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e87.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e74.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCarbohydrates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eInsufficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.003*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e14.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eExcessive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e75.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e93.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e81.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLipids\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eInsufficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e66.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e63.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e81.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e74.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e27.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e20.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eExcessive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Data presented as frequency (n) and percentage (%). Statistical analysis performed using Chi-square test of independence. Effect size calculated using Cramer\u0026rsquo;s V (V) interpreted as: 0.10 small, 0.30 medium, 0.50 large. * Denotes statistical significance (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Relationship between nutritional adequacy and body compositions in the total sample.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNutritional Adequacy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBody Compositions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eKendall\u0026acute;s\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTau b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAdipose Mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMuscle Mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eProteins\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAdipose Mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMuscle Mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.003*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBone Mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.041*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCarbohydrates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAdipose Mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.010*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMuscle Mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.040*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLipids\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMuscle Mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Correlations determined by Kendall\u0026apos;s Tau-b coefficient. Interpretation of Tb: \u0026lt;0.10 (trivial), 0.10\u0026ndash;0.30 (small), 0.30\u0026ndash;0.50 (moderate), \u0026gt;0.50 (large). * Denotes statistical significance (p\u0026lt;0.05).\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Body composition, somatotype, nutrients requirements, energy expenditure, professional soccer, female","lastPublishedDoi":"10.21203/rs.3.rs-8291239/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8291239/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAnthropometric characterization and somatotype profiling in professional soccer players are essential for understanding the morphofunctional demands associated with playing positions.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo determine and compare anthropometric profile and somatotype characteristics across playing positions in professional female soccer players, and to analyze their relationship with nutritional adequacy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted with 43 players from two First Division teams in Trujillo (Peru). Anthropometric measurements followed ISAK standards, and body composition was estimated using Kerr\u0026rsquo;s five-component fractionation method. Somatotype was assessed using the Heath\u0026ndash;Carter method. Nutritional adequacy was calculated according to playing position and international recommendations. Statistical analysis included the Kruskal-Wallis test with Bonferroni-corrected post-hoc comparisons for anthropometric variables, Chi-square tests with Cramer\u0026rsquo;s V effect size for nutritional adequacy, and Kendall\u0026rsquo;s Tau correlations for body composition associations. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eGoalkeepers presented significantly higher adipose mass (18.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5 kg) and muscle mass (33.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2 kg) than outfield players (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ε\u0026sup2; \u0026ge;0.21). Nutritional adequacy differed significantly by position, with goalkeepers showing 100% protein insufficiency (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, V\u0026thinsp;=\u0026thinsp;0.58). No significant associations were found between nutritional adequacy and body composition when stratified by playing position (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, in the total sample, protein adequacy was inversely correlated with adipose mass (Tb\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.60, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and lipid adequacy was negatively associated with muscle mass (Tb\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.52, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAlthough no position-specific associations were identified, significant relationships emerged in the overall sample, suggesting that nutritional adequacy influences body composition independently of tactical role.\u003c/p\u003e","manuscriptTitle":"Relationship between nutritional adequacy and anthropometric profile characteristics according to playing position in professional female soccer players: A cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-31 09:25:20","doi":"10.21203/rs.3.rs-8291239/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-23T10:42:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-22T21:26:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-22T08:53:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23043986454708021783822554760818570137","date":"2026-01-12T21:25:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198398775153821929042704205358267640344","date":"2026-01-12T11:47:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"125789370416027750798898316460439459912","date":"2026-01-12T11:19:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68104314085308238660526986518835139404","date":"2026-01-12T10:03:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-10T13:36:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-02T12:22:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57023649310563789008516336021374658835","date":"2026-01-02T12:10:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4708383267790544722882262423372272211","date":"2025-12-31T19:16:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-25T12:35:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-10T07:48:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-08T00:46:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-08T00:46:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Sports Science, Medicine and Rehabilitation","date":"2025-12-06T01:07:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c30aacb8-109a-47d0-a413-bb6eed888303","owner":[],"postedDate":"December 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T16:05:08+00:00","versionOfRecord":{"articleIdentity":"rs-8291239","link":"https://doi.org/10.1186/s13102-026-01702-z","journal":{"identity":"bmc-sports-science-medicine-and-rehabilitation","isVorOnly":false,"title":"BMC Sports Science, Medicine and Rehabilitation"},"publishedOn":"2026-04-29 15:57:06","publishedOnDateReadable":"April 29th, 2026"},"versionCreatedAt":"2025-12-31 09:25:20","video":"","vorDoi":"10.1186/s13102-026-01702-z","vorDoiUrl":"https://doi.org/10.1186/s13102-026-01702-z","workflowStages":[]},"version":"v1","identity":"rs-8291239","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8291239","identity":"rs-8291239","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}