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Multi-Frequency Bioimpedance Analysis in Practice: A Review of Validated Prediction Equations for Key Body

D Kampo¹, E Závodná, V Vondra

¹Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic, Department of Physiology and Pathophysiology, Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic. Eva.Zavodna@osu.cz.

Physiological Research

|January 9, 2026

Summary

This review synthesizes prediction equations for body composition using bioelectrical impedance analysis (BIA). Updated, population-specific models are needed to improve accuracy for total body water and fat-free mass estimation.

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Area of Science:

Physiology
Biomedical Engineering

Background:

Traditional bioelectrical impedance analysis (BIA) models for body composition may not reflect current populations or technologies.
Established equations for total body water (TBW) and fat-free mass (FFM) require updates.
Advancements in multi-frequency BIA devices offer potential for improved accuracy.

Purpose of the Study:

To comprehensively synthesize validated prediction equations for body composition using BIA.
To identify gaps and limitations in existing BIA prediction models.
To highlight the need for updated, population-specific BIA equations.

Main Methods:

Systematic review of studies published between 2000 and April 2025.
Inclusion of 43 studies developing 98 unique prediction equations.
Equations derived using reference methods like deuterium dilution, DXA, or multi-component models.

Main Results:

Most equations focused on estimating fat-free mass (FFM) and total body water (TBW).
A significant lack of prediction models for extracellular water (ECW), intracellular water (ICW), and bone mineral content (BMC) was observed.
Geographic and demographic imbalances in study populations were identified.

Conclusions:

There is a critical need for updated, population-specific BIA prediction models.
External validation of BIA models across diverse populations and health conditions is essential.
Continued refinement of BIA prediction models is crucial for enhanced clinical applicability.