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Assessment of adult body composition using bioelectrical impedance: comparison of researcher calculated to machine outputted values

Lookup NU author(s): Professor Mark Pearce

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).


Abstract

Objectives: To explore the usefulness of Bioelectrical Impedance Analysis (BIA) for general use by identifying best-evidenced formulae to calculate lean and fat mass, comparing these to historical gold standard data and comparing these results with machine-generated output. In addition, we explored how to best to adjust lean and fat estimates for height and how these overlapped with body mass index (BMI).Design: Cross-sectional observational study within population representative cohort study.Setting: Urban community, North East EnglandParticipants: Sample of 506 mothers of children aged 7-8 years, mean age 36.3 years.Methods: Participants were measured at a home visit using a portable height measure and leg-to-leg BIA machine (Tanita TBF-300MA).Measures: Height, weight, bioelectrical impedance (BIA).Outcome measures: Lean and fat mass calculated using best-evidenced published formulae as well as machine-calculated lean and fat mass data.Results: Estimates of lean mass were similar to historical results using gold standard methods. When compared with the machine-generated values, there were wide limits of agreement for fat mass and a large relative bias for lean that varied with size. Lean and fat residuals adjusted for height differed little from indices of lean (or fat)/height2. Of 112 women with BMI >30 kg/m(2), 100 (91%) also had high fat, but of the 16 with low BMI (<19 kg/m(2)) only 5 (31%) also had low fat.Conclusions: Lean and fat mass calculated from BIA using published formulae produces plausible values and demonstrate good concordance between high BMI and high fat, but these differ substantially from the machine-generated values. Bioelectrical impedance can supply a robust and useful field measure of body composition, so long as the machine-generated output is not used.


Publication metadata

Author(s): Franco-Villoria M, Wright CM, McColl JH, Sherriff A, Pearce MS, Gateshead Millennium Core Study Team

Publication type: Article

Publication status: Published

Journal: BMJ Open

Year: 2016

Volume: 6

Issue: 1

Online publication date: 07/01/2016

Acceptance date: 14/12/2015

Date deposited: 15/09/2016

ISSN (electronic): 2044-6055

Publisher: BMJ Group

URL: http://dx.doi.org/10.1136/bmjopen-2015-008922

DOI: 10.1136/bmjopen-2015-008922


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