2026-07-13T01:40:05Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/119042026-07-08T00:05:29Zcom_10259_11209com_10259_5086com_10259_2604col_10259_11210
Repositorio Institucional de la Universidad de Burgos
author
Martínez Ferrán, María
author
Rafei, Eleh
author
Romero-Morales, Carlos
author
Pérez-Ruiz, Margarita
author
Lam-Meléndez, Alberto
author
Munguía-Izquierdo, Diego
author
Pareja-Galeano, Helios
2026-07-07T11:21:40Z
2026-07-07T11:21:40Z
2022-01
https://hdl.handle.net/10259/11904
10.3390/app12020727
2076-3417
Body composition is a determinant of performance in soccer. To estimate the body fat percentage (%BF), dual energy X-ray absorptiometry (DXA) is effective though this method is expensive and not readily accessible. This study examines the validity of widely used field methods based on anthropometric data and bioelectrical impedance analysis (BIA). Participants were 21 male Spanish First Division soccer players aged between 22 and 35 years. In each participant, body fat mass was determined by BIA and using 18 anthropometric equations including skinfold (SKF) measurements. DXA was used as reference. Correlation with DXA measurements was excellent for all equations and separate SKF measurements yet only moderate for BIA. However, only the equation recently developed for use in soccer players based on iliac crest and triceps SKFs showed no significant or standardized differences with DXA-derived %BF and these measurements also had the lowest bias. Our findings suggest that when DXA is not available, the best field method for %BF assessment in footballers is the equation based on iliac crest and triceps SKF. As another good option, we propose the sum of triceps, subscapular, supraspinal, and abdominal SKFs, as this combination also showed good correlation with DXA.
eng
Atribución 4.0 Internacional
Football
Fat mass
Bioelectrical impedance
Dual X-ray absorptiometry
Skinfolds
Optimizing Field Body Fat Percentage Assessment in Professional Soccer Players
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/11904/1/Martinez-AS_2022.pdf
File
MD5
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284223
application/pdf
Martinez-AS_2022.pdf