Health science reports · 2026
Body Composition Differences Across Ranks and Sexes of Competitive Swimmers: A Cross-Sectional Study.
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Mauro Mario, Moro Federica, Coloretti Vittorio, Bosetto Pietro, Cortesi Matteo, Toselli Stefania
Аннотация
BACKGROUND AND AIMS: Body composition (BC) plays a key role in assessing athletes' health. However, many factors, including sex and performance rank, may influence BC and should be considered when assessing it. Moreover, BC is estimated indirectly, and body volume (BV) represents a key component of densitometric assessment. This investigation aimed to compare swimmers' BC across sexes and performance ranks, compare methods for BF estimation, and develop a model for predicting BV.
METHODS: This was a cross-sectional study. Participants underwent air-displacement plethysmography (ADP), anthropometric measurements, and bioimpedance analysis (BIA) according to standardized procedures. Sixty-four competitive (13.6 ± 1.9 training hours per week) swimmers (17.8 ± 4.4 years, 56.3% male; 13.6 ± 1.9 training hours per week) were enrolled from the national competitive list. They were stratified by sex and classified into three groups according to performance rank (Best World Aquatic score, BWA). A two-way ANCOVA (sex and rank), adjusted for age, was used to compare BC variables. A bootstrap model-averaging approach was used to select a linear regression model for predicting BV.
RESULTS: Males showed higher BV, total body water (TBW), and fat-free mass (FFM) than females. Most rank-related differences were attenuated after adjustment for age; however, BWA showed a strong positive correlation with limb girths and FFM (Pearson rho > 0.5). BF estimated using specific cut-offs exhibited the lowest error propagation (reduction of 6.42%). The best model was selected with a 18.81% frequency (adjusted R 2 = 0.981; RMSE = 1.541), and the variables that showed an adequate bootstrap inclusion frequency (> 60%) were stature (β = 0.342), stretched arm (β = 0.905), hip (β = 0.117), thigh (β = 0.659) and calf (β = 0.508) girths, with a mean relative conditional bias of 1.79%.
CONCLUSION: The proposed BF estimation method showed lower error propagation in this sample, while the anthropometric model provided an accurate internal prediction of BV. These findings support the use of predicted BV for subsequent BF estimation using specific cut-offs, although external validation of the BV prediction model is required.
METHODS: This was a cross-sectional study. Participants underwent air-displacement plethysmography (ADP), anthropometric measurements, and bioimpedance analysis (BIA) according to standardized procedures. Sixty-four competitive (13.6 ± 1.9 training hours per week) swimmers (17.8 ± 4.4 years, 56.3% male; 13.6 ± 1.9 training hours per week) were enrolled from the national competitive list. They were stratified by sex and classified into three groups according to performance rank (Best World Aquatic score, BWA). A two-way ANCOVA (sex and rank), adjusted for age, was used to compare BC variables. A bootstrap model-averaging approach was used to select a linear regression model for predicting BV.
RESULTS: Males showed higher BV, total body water (TBW), and fat-free mass (FFM) than females. Most rank-related differences were attenuated after adjustment for age; however, BWA showed a strong positive correlation with limb girths and FFM (Pearson rho > 0.5). BF estimated using specific cut-offs exhibited the lowest error propagation (reduction of 6.42%). The best model was selected with a 18.81% frequency (adjusted R 2 = 0.981; RMSE = 1.541), and the variables that showed an adequate bootstrap inclusion frequency (> 60%) were stature (β = 0.342), stretched arm (β = 0.905), hip (β = 0.117), thigh (β = 0.659) and calf (β = 0.508) girths, with a mean relative conditional bias of 1.79%.
CONCLUSION: The proposed BF estimation method showed lower error propagation in this sample, while the anthropometric model provided an accurate internal prediction of BV. These findings support the use of predicted BV for subsequent BF estimation using specific cut-offs, although external validation of the BV prediction model is required.