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Lean soft tissue and appendicular lean soft tissue in adolescent soccer players: new predictive equations using raw
Marcus Vinicius de Oliveira Cattem1, Gabriela Morgado de Oliveira Coelho1, Gabriel Gazone Dos Santos2
1Nutrition Institute, State University of Rio de Janeiro, Rio de Janeiro, Brazil.
Background:
As determinants of soccer player performance, lean soft tissue (LST) and appendicular lean soft tissue (ALST) require accurate, segment-specific assessment.
Objective:
This study aimed to develop LST and ALST predictive equations from multi-frequency bioelectrical impedance analysis data at 50 and 500 kHz using raw data from an octapolar device and evaluate their predictive performance using dual-energy X-ray absorptiometry as a reference method via split-sample validation.
Methods:
Multiple linear regression models were developed using body mass, impedance index, and maturity offset as predictors. Model performance was evaluated using adjusted R², standard error of estimate (SEE), root mean square error, mean absolute error, Lin's concordance correlation coefficient (CCC), and Bland-Altman agreement analysis. Participants were randomly allocated into a development (n = 100) and an independent validation subset (n = 49).
Results:
Predictive performance was comparable between frequencies, with high explanatory power (R² for LST = 0.953-0.955; ALST = 0.931-0.932) and low prediction error (SEE ≤ 1.65 kg). Maturity offset contributed to LST models only at both frequencies (P < 0.001). In the validation subset, concordance was higher for LST (CCC = 0.973 at 50 kHz and 0.974 at 500 kHz) than for ALST (CCC = 0.955 at both frequencies), whose lower confidence bound fell within the moderate range. Absolute limits of agreement were narrower for ALST, but relative limits of agreement were larger for ALST (∼7.4-7.7%) than for LST (∼5.5-6.0%), and proportional bias was detected in ALST models.
Conclusions:
These equations show strong potential for group-level estimation in adolescent soccer players. Individual-level application should consider this direction-dependent error, particularly for ALST, and longitudinal monitoring requires further validation.