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Updated: May 13, 2026

Evaluation of Fluid Overload by Bioelectrical Impedance Vectorial Analysis
Published on: August 17, 2022
The science of bioelectrical impedance-derived phase angle: insights from body composition in youth
Gil B Rosa1, Henry C Lukaski2, Luís B Sardinha3
1Exercise and Health Laboratory, Faculdade de Motricidade Humana, CIPER, Universidade de Lisboa, Cruz-Quebrada, Portugal. gilrosa@campus.ul.pt.
Abstract:
Despite bioelectrical impedance analysis (BIA)-derived phase angle (PhA) being recognized as a global marker of health, reflecting both cellular integrity and fluid distribution, its biological determinants still need to be described in youth. This narrative review provides a comprehensive framework examining to what extent dielectric properties shaping PhA are influenced by qualitative and quantitative determinants at multiple levels of body composition in healthy and clinical pediatric populations. At the atomic-molecular level, water content, glycogen, lipids, and ionic concentrations are expected to influence PhA by affecting electrical conductivity and/or capacitance. While the increase in the absolute values of intracellular (ICW) and extracellular water (ECW) enhances electric conductivity, an increase in the relative portion of ECW is expected to reflect hydration imbalances with an impact on electrical pathways. At the cellular level, body cell mass is a key determinant of PhA, mainly due to the presence of skeletal muscle cells favoring conductive and capacitive properties. At the tissue level, skeletal muscle architecture and orientation strongly influence conductivity, while increases in skeletal muscle mass positively impact PhA by enhancing electric conductivity and capacitance. Beyond the theoretical insights presented in this review, careful interpretation of dielectric data remains crucial due to the lack of methodological standardization. Future research should prioritize validated reference methods, investigate longitudinal changes, integrate localized BIA, and explore additional BIA models to refine the interpretation of PhA.
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