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Body Composition of Elite Female Players in Five Different Sports Games

Abstract

The goal of this study was to identify and compare body composition (BC) variables in elite female athletes (age ± years): volleyball (27.4 ± 4.1), softball (23.6 ± 4.9), basketball (25.9 ± 4.2), soccer (23.2 ± 4.2) and handball (24.0 ± 3.5) players. Fat-free mass (FFM), fat mass, percentage of fat mass (FMP), body cell mass (BCM), extracellular mass (ECM), their ratio, the percentage of BCM in FFM, the phase angle (α), and total body water, with a distinction between extracellular (ECW) and intracellular water, were measured using bioimpedance analysis. MANOVA showed significant differences in BC variables for athletes in different sports (F60.256 = 2.93, p < 0.01, η2 = 0.407). The results did not indicate any significant differences in FMP or α among the tested groups (p > 0.05). Significant changes in other BC variables were found in analyses when sport was used as an independent variable. Soccer players exhibited the most distinct BC, differing from players of other sports in 8 out of 10 variables. In contrast, the athletes with the most similar BC were volleyball and basketball players, who did not differ in any of the compared variables. Discriminant analysis revealed two significant functions (p < 0.01). The first discriminant function primarily represented differences based on the FFM proportion (volleyball, basketball vs. softball, soccer). The second discriminant function represented differences based on the ECW proportion (softball vs. soccer). Although all of the members of the studied groups competed at elite professional levels, significant differences in the selected BC variables were found. The results of the present study may serve as normative values for comparison or target values for training purposes.

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Planning Training Loads for The 400 M Hurdles in Three-Month Mesocycles Using Artificial Neural Networks

– an initial study. In: Proceedings 28th European Conference on Modelling and Simulation ECMS , 382-387; 2014 Lapkova D Pluhacek M Kominkova Oplatkova Z Adamek M. Using artificial neural network for the kick techniques classification – an initial study Proceedings 28th European Conference on Modelling and Simulation ECMS 382 387 2014 Maszczyk A, Roczniok R, Waśkiewicz Z, Czuba M, Mikołajec K, Zając A, Stanula A. Application of regression and neural models to predict competitive swimming performance. Percept Motor Skill , 2012; 114(2): 610–626 10.2466/05.10.PMS

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Compliance Issues in Higher Education

References Capitalizing on Complexity (2010). IBM . Retrieved from http://public.dhe.ibm.com/common/ssi/ecm/gb/en/gbe03297usen/GBE03297USEN.PDF [30.06.2015]. Collet, H. (2008). IT Controls Automation and Database Management: Defending Against the Insider Threat. In: Governance, Risk, and Compliance Handbook: Technology, Finance, Environmental, and International Guidance and Best Practices (ed A. Tarantino), Hoboken: John Wiley & Sons. Ch. 23. COSO (2015). Internal Control – Integrated Framework, Executive Summary . Retrieved from http

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