Measurement of lean body mass using bioelectrical impedance analysis: a consideration of the pros and cons
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 l...
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| Published in: | Aging clinical and experimental research Vol. 29; no. 4; pp. 591 - 597 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Cham
Springer International Publishing
01.08.2017
Springer Nature B.V |
| Subjects: | |
| ISSN: | 1720-8319, 1594-0667, 1720-8319 |
| Online Access: | Get full text |
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| Summary: | 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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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23 |
| ISSN: | 1720-8319 1594-0667 1720-8319 |
| DOI: | 10.1007/s40520-016-0622-6 |