Prediction of body water compartments by raw bioelectrical impedance parameters in athletes: Comparison between series and parallel measurements

Francisco, Rúben; Jesus, Filipe; Nunes, Catarina L; Carvalho, André Lopes; Alvim, Marta; Campa, Francesco; Sardinha, Luís B.; Mendonça, Gonçalo V.; Lukaski, Henry C.; Silva, Analiza M.

doi:10.1111/sms.14443

Cited by 4 publications

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bioelectrical reactance is related to the capacitance properties of the cell membrane and variations that can occur depending on its integrity, function, and composition [ 17 ]. Notably, these properties can be attributed to theoretical models, according to which biological tissues are traditionally conceived as electrical circuits arranged in series with each other [ 18 ]. Starting from the relationships between resistance and reactance with TBW [ 19 ], numerous BIA-based predictive equations have been developed over the years [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

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

Campa,

Coratella,

Cerullo

et al. 2024

J Transl Med

Self Cite

View full text Add to dashboard Cite

The appropriate use of predictive equations in estimating body composition through bioelectrical impedance analysis (BIA) depends on the device used and the subject’s age, geographical ancestry, healthy status, physical activity level and sex. However, the presence of many isolated predictive equations in the literature makes the correct choice challenging, since the user may not distinguish its appropriateness. Therefore, the present systematic review aimed to classify each predictive equation in accordance with the independent parameters used. Sixty-four studies published between 1988 and 2023 were identified through a systematic search of international electronic databases. We included studies providing predictive equations derived from criterion methods, such as multi-compartment models for fat, fat-free and lean soft mass, dilution techniques for total-body water and extracellular water, total-body potassium for body cell mass, and magnetic resonance imaging or computerized tomography for skeletal muscle mass. The studies were excluded if non-criterion methods were employed or if the developed predictive equations involved mixed populations without specific codes or variables in the regression model. A total of 106 predictive equations were retrieved; 86 predictive equations were based on foot-to-hand and 20 on segmental technology, with no equations used the hand-to-hand and leg-to-leg. Classifying the subject’s characteristics, 19 were for underaged, 26 for adults, 19 for athletes, 26 for elderly and 16 for individuals with diseases, encompassing both sexes. Practitioners now have an updated list of predictive equations for assessing body composition using BIA. Researchers are encouraged to generate novel predictive equations for scenarios not covered by the current literature.Registration code in PROSPERO: CRD42023467894.

show abstract