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High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a

Francesco Campa1, Giuseppe Coratella2, Giuseppe Cerullo3

  • 1Department of Biomedical Sciences, University of Padua, Padua, Italy. francesco.campa@unipd.it.

Journal of Translational Medicine
|May 29, 2024
PubMed
Summary

This review classifies bioelectrical impedance analysis (BIA) predictive equations for body composition, aiding practitioners in selecting appropriate methods. It provides an updated list of 106 equations based on diverse subject characteristics and technology.

Keywords:
BIAFat massFat-free massFitnessResistance trainingTotal body water

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Area of Science:

  • Body composition analysis
  • Bioelectrical impedance analysis (BIA)
  • Predictive equation classification

Background:

  • Accurate body composition estimation using BIA relies on appropriate predictive equations, influenced by subject demographics and health status.
  • Numerous isolated BIA predictive equations exist, complicating the selection of the most suitable one for specific populations.
  • A systematic review is needed to classify existing BIA predictive equations based on their independent parameters.

Approach:

  • A systematic literature search identified 64 studies published between 1988 and 2023.
  • Included studies derived predictive equations from criterion methods (e.g., multi-compartment models, MRI/CT).
  • Excluded studies using non-criterion methods or mixed populations without specific regression variables.

Key Points:

  • A total of 106 predictive equations were retrieved and classified.
  • 86 equations utilized foot-to-hand technology, and 20 used segmental technology.
  • Equations were categorized for specific populations: underaged (19), adults (26), athletes (19), elderly (26), and individuals with diseases (16).

Conclusions:

  • This review offers an updated, classified list of BIA predictive equations for body composition assessment.
  • The classification aids practitioners in choosing the most appropriate equation based on subject characteristics.
  • Further research is encouraged to develop new predictive equations for underrepresented scenarios.