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首页> 外文期刊>European journal of clinical nutrition >Effect of total body water estimates via bioimpedance on bod pod-based three-compartment body fat models
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Effect of total body water estimates via bioimpedance on bod pod-based three-compartment body fat models

机译：Effect of total body water estimates via bioimpedance on bod pod-based three-compartment body fat models

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Background/objectives Previous research has compared 2- and 3-compartment (2C and 3C, respectively) models against criterion 4-compartment (4C) models while utilizing the same body density (D-b) method for all measures. This design induces an inherent bias and obscures the added benefit of a 3C model over the simpler 2-compartment (2C) models. Purpose The purpose of this study was to determine the effect of total body water estimates via single-frequency (SF-BIA) and multi-frequency (MF-BIA) bioimpedance analysis on body fat estimates derived from air displacement plethysmography (ADP)-derived 3C models. Subjects/methods A sample of 95 females and 82 males (n = 177) participated in this study. Underwater weighing, dual energy X-ray absorptiometry, and bioimpedance spectroscopy were used to calculate percent fat (Fat) via a criterion 4C model (4C(CRITERION)). Fat was predicted via 3C(MFBIA) (ADP and MF-BIA), 3C(SFBIA) (ADP and SF-BIA), and a stand-alone 2-compartment (2C) model, based upon ADP, when using Siri and Brozek body density conversion formulas (2C(SIRI) and 2C(BROZEK). respectively). Results The standard error of estimate (SEE) was lowest for 3C(SFBIA) when evaluated in females and males (2.72 and 2.31, respectively) and highest for 2C(SIRI) (3.98 and 3.84, respectively). Similarly, the total error (TE) for females and males was lowest for 3C(SFBIA) (3.21 and 2.67, respectively) and highest for 2C(SIRI) (4.58 and 4.48, respectively) and 2C(BROZEK) (4.65 and 4.33, respectively). Conclusions Results suggest that SF-BIA and MF-BIA can improve the estimation of Fat, beyond simpler 2C models, when integrated with ADP in a more advanced 3C model. Furthermore, the present study revealed that 3C(SFBIA) was the best overall prediction model based upon TE values. The current study results support the integration of ADP and bioimpedance technology as part of a 3C model for the improvement of Fat estimates over simpler 2C models.

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