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Measurement of lean body mass using bioelectrical impedance analysis: a consideration of the pros and cons.
Abstract
The assessment of body composition has important applications in the evaluation of nutritional status and estimating potential health risks. Bioelectrical impedance analysis (BIA) is a valid method for the assessment of body composition. BIA is an alternative to more invasive and expensive methods like dual-energy X-ray absorptiometry, computerized tomography, and magnetic resonance imaging. Bioelectrical impedance analysis is an easy-to-use and low-cost method for the estimation of fat-free mass (FFM) in physiological and pathological conditions. The reliability of BIA measurements is influenced by various factors related to the instrument itself, including electrodes, operator, subject, and environment. BIA assumptions beyond its use for body composition are the human body is empirically composed of cylinders, FFM contains virtually all the water and conducting electrolytes in the body, and its hydration is constant. FFM can be predicted by BIA through equations developed using reference methods. Several BIA prediction equations exist for the estimation of FFM, skeletal muscle mass (SMM), or appendicular SMM. The BIA prediction models differ according to the characteristics of the sample in which they have been derived and validated in addition to the parameters included in the multiple regression analysis. In choosing BIA equations, it is important to consider the characteristics of the sample in which it has been developed and validated, since, for example, age- and ethnicity-related differences could sensitively affect BIA estimates.
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“However, it is essential that predictive equations are selected for subjects with similar characteristics regarding gender, age group, pubertal stage, ethnicity, and nutritional status (20, 21).”
From Development and Cross-Validation of a Predictive Equation for Fat-Free Mass in Brazilian Adolescents by Bioelectrical Impedance (2022) · cites this paper as [21]
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Jing, W., Jing, W., Luo, S., Chen, Y., Guo, Q., & Yang, J. (2026) · Frontiers in Medical Technology · doi:10.3389/fmedt.2026.1789046
Udroiu, I.-V., Albai, A., Lazăr, S., Braha, A., Gaiţă, L., Timar, B., & Sima, A. C. (2026) · Diagnostics · doi:10.3390/diagnostics16172837
Bekir, W. M. A., Mourgan, F. H. A. E., & AlMatar, M. (2026) · Biomedical and Biotechnology Research Journal (BBRJ) · doi:10.4103/bbrj.bbrj_101_26
Asensio, L. E., López, P. P., Izaola-Jáuregui, O., Cebriá, Á., González, M. P. B., Hoyos, E. G., Gutiérrez, J. G., Primo, D., Saavedra-Vasquez, M. A., Godoy, E. J., et al. (2026) · Endocrinología Diabetes y Nutrición · doi:10.1016/j.endinu.2026.501928
Metadata
- Authors
- Sergi, G., De Rui, M., Stubbs, B., Veronese, N., & Manzato, E.
- Affiliations
- University of Padua (IT)
- National Health Service (GB)
- Venue
- Aging Clinical and Experimental Research
- Published
- 2016-08-27
- Volume
- 29
- Issue
- 4
- Pages
- 591-597
- ISSN
- 1594-0667 (ISSN-L)
- 1720-8319
- OpenAlex
- W2516060484
- Cited by
- 291
- References
- 41
- Citations by year
- 2026: 20 · 2025: 39 · 2024: 44 · 2023: 35 · 2022: 37 · 2021: 38 · 2020: 33 · 2019: 22 · 2018: 18 · 2017: 5
- FWCI
- 1.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Aging Clinical and Experimental Research (publishedVersion)
- PubMed (publishedVersion)
- Nova Science Publishers (Nova Science Publishers, Inc.) (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion)
- Topics
- Health Sciences › Medicine › Physiology (medical) › Body Composition Measurement Techniques
- Health Sciences › Medicine › Physiology (medical) › Nutrition and Health in Aging
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrical and Bioimpedance Tomography
- Keywords
- bioelectrical impedance analysis, lean body mass, fat-free mass, body composition, skeletal muscle mass, appendicular skeletal muscle mass, measurement reliability, hydration, nutritional assessment, aging
- MeSH terms
- Aging, Body Composition, Humans, Nutritional Status, Regression Analysis, Thinness, Reproducibility of Results, Electric Impedance, Muscle, Skeletal
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