Field-Based Assessment of Body Composition: Independent and Combined Contributions of Anthropometry, Bioimpedance, and Ultrasound

Background and aims: Accurate assessment of body composition in field settings remains challenging, as commonly used techniques such as anthropometry, bioelectrical impedance analysis (BIA), and ultrasound (US) provide only partial and method-specific information. This study investigated whether integrating these approaches improves the prediction of fat mass (FM) and appendicular lean soft mass (ALSM). Methods Ninety-six adults (42 women, 54 men; 18–84 years) were assessed using surface anthropometry, foot-to-hand BIA, and B-mode US. Dual-energy X-ray absorptiometry served as the reference method. Bivariate correlations and multiple linear regression models adjusted for sex and age were used to evaluate the independent predictive value of each technique, whereas hierarchical models tested the incremental explained variance (ΔR²) obtained by combining methods. To facilitate model integration, only selected anthropometric variables were retained in the hierarchical analyses. Results For FM, anthropometry showed the highest predictive power (R²=0.87), followed by US (R²=0.80), whereas BIA explained less variance (R²=0.32). In hierarchical models, US markedly improved FM prediction when added to anthropometric girths (ΔR²=0.48, p < 0.001), whereas anthropometry provided a smaller but significant improvement when added after US (ΔR²=0.06, p < 0.001). For ALSM, anthropometry was the strongest individual predictor (R²=0.91), followed by BIA (R²=0.82) and US (R²=0.81). Anthropometry provided the largest incremental contribution when added to either BIA or US (ΔR²=0.11–0.14, p < 0.001), whereas US did not further improve ALSM prediction once anthropometry and BIA were included. Conclusions Combining two techniques improved predictive accuracy, supporting a multimodal approach to body composition evaluation. In addition, a new standardized US protocol was presented.