Body Composition of Male Professional Soccer Players Using Different Measurement Methods: A Systematic Review and Meta-Analysis.
Jaime Sebastiá-RicoJosé Miguel Soriano Del CastilloNoelia González-GálvezJosé Miguel Martínez SanzPublished in: Nutrients (2023)
The performance of male soccer players (MSP) depends on multiple factors such as body composition. The physical demands of modern soccer have changed, so the ideal body composition (BC) requirements must be adapted to the present. The aim of this systematic review and meta-analysis was to describe the anthropometric, BC, and somatotype characteristics of professional MSP and to compare the values reported according to the methods and equations used. We systematically searched Embase, PubMed, SPORTDiscus, and Web of Science following the PRISMA statement. Random-effects meta-analysis, a pooled summary of means, and 95% CI (method or equation) were calculated. Random models were used with the restricted maximum likelihood (REML) method. Seventy-four articles were included in the systematic review and seventy-three in the meta-analysis. After comparing the groups according to the assessment method (kinanthropometry, bioimpedance, and densitometry), significant differences were found in height, fat mass in kilograms, fat mass percentage, and fat-free mass in kilograms ( p = 0.001; p < 0.0001). Taking into account the equation used to calculate the fat mass percentage and ∑skinfolds, significant differences were observed in the data reported according to groups ( p < 0.001). Despite the limitations, this study provides useful information that could help medical technical staff to properly assess the BC of professional MSP, providing a range of guidance values for the different BC.
Keyphrases
- body composition
- systematic review
- meta analyses
- resistance training
- adipose tissue
- bone mineral density
- fatty acid
- plasmodium falciparum
- healthcare
- randomized controlled trial
- body mass index
- physical activity
- public health
- mental health
- electronic health record
- clinical trial
- case control
- machine learning
- postmenopausal women
- study protocol
- big data
- high intensity
- phase iii