Few investigations have evaluated the validity of current body composition technology among racially and ethnically diverse populations. This study assessed the validity of common body composition methods in a multi-ethnic sample stratified by race and ethnicity. One hundred and ten individuals (55 % female, age: 26·5 (sd 6·9) years) identifying as Asian, African American/Black, Caucasian/White, Hispanic, Multi-racial and Native American were enrolled. Seven body composition models (dual-energy X-ray absorptiometry (DXA), air displacement plethysmography (ADP), two bioelectrical impedance devices (BIS, IB) and three multi-compartment models) were evaluated against a four-compartment criterion model by assessing total error (TE) and standard error of the estimate. For the total sample, measures of % fat and fat-free mass (FFM) from multi-compartment models were all excellent to ideal (% fat: TE = 0·94–2·37 %; FFM: TE = 0·72–1·78 kg) compared with the criterion. % fat measures were very good to excellent for DXA, ADP and IB (TE = 2·52–2·89 %) and fairly good for BIS (TE = 4·12 %). For FFM, single device estimates were good (BIS; TE = 3·12 kg) to ideal (DXA, ADP, IB; TE = 1·21–2·15 kg). Results did not vary meaningfully between each race and ethnicity, except BIS was not valid for African American/Black, Caucasian/White and Multi-racial participants for % fat (TE = 4·3–4·9 %). The multi-compartment models evaluated can be utilised in a multi-ethnic sample and in each individual race and ethnicity to obtain highly valid results for % fat and FFM. Estimates from DXA, ADP and IB were also valid. The BIS may demonstrate greater TE for all racial and ethnic cohorts and results should be interpreted cautiously.

Understanding the effects of acute feeding on body composition and metabolic measures is essential to the translational component and practical application of measurement and clinical use. To investigate the influence of acute feeding on the validity of dual-energy X-ray absorptiometry (DXA), a four-compartment model (4C) and indirect calorimetry metabolic outcomes, thirty-nine healthy young adults (n 19 females; age: 21·8 (sd 3·1) years, weight; 71·5 (sd 10·0) kg) participated in a randomised cross-over study. Subjects were provided one of four randomised meals on separate occasions (high carbohydrate, high protein, ad libitum or fasted baseline) prior to body composition and metabolic assessments. Regardless of macronutrient content, acute feeding increased DXA percent body fat (%fat) for the total sample and females (average constant error (CE):–0·30 %; total error (TE): 2·34 %), although not significant (P = 0·062); the error in males was minimal (CE: 0·11 %; TE: 0·86 %). DXA fat mass (CE: 0·26 kg; TE: 0·75 kg) and lean mass (LM) (CE: 0·83 kg; TE: 1·23 kg) were not altered beyond measurement error for the total sample. 4C %fat was significantly impacted from all acute feedings (avg CE: 0·46 %; TE: 3·7 %). 4C fat mass (CE: 0·71 kg; TE: 3·38 kg) and fat-free mass (CE: 0·55 kg; TE: 3·05 kg) exceeded measurement error for the total sample. RMR was increased for each feeding condition (TE: 1666·9 kJ/d; 398 kcal/d). Standard pre-testing fasting guidelines may be important when evaluating DXA and 4C %fat, whereas additional DXA variables (fat mass and LM) may not be significantly impacted by an acute meal. Measuring body composition via DXA under less stringent pre-testing guidelines may be valid and increase feasibility of testing in clinical settings.

The present study reports the validity of multiple assessment methods for tracking changes in body composition over time and quantifies the influence of unstandardised pre-assessment procedures. Resistance-trained males underwent 6 weeks of structured resistance training alongside a hyperenergetic diet, with four total body composition evaluations. Pre-intervention, body composition was estimated in standardised (i.e. overnight fasted and rested) and unstandardised (i.e. no control over pre-assessment activities) conditions within a single day. The same assessments were repeated post-intervention, and body composition changes were estimated from all possible combinations of pre-intervention and post-intervention data. Assessment methods included dual-energy X-ray absorptiometry (DXA), air displacement plethysmography, three-dimensional optical imaging, single- and multi-frequency bioelectrical impedance analysis, bioimpedance spectroscopy and multi-component models. Data were analysed using equivalence testing, Bland–Altman analysis, Friedman tests and validity metrics. Most methods demonstrated meaningful errors when unstandardised conditions were present pre- and/or post-intervention, resulting in blunted or exaggerated changes relative to true body composition changes. However, some methods – particularly DXA and select digital anthropometry techniques – were more robust to a lack of standardisation. In standardised conditions, methods exhibiting the highest overall agreement with the four-component model were other multi-component models, select bioimpedance technologies, DXA and select digital anthropometry techniques. Although specific methods varied, the present study broadly demonstrates the importance of controlling and documenting standardisation procedures prior to body composition assessments across distinct assessment technologies, particularly for longitudinal investigations. Additionally, there are meaningful differences in the ability of common methods to track longitudinal body composition changes.

This cross-sectional study compared body fat percentage (BF%) obtained from a four-compartment (4C) model with BF% from hydrometry (using 2H2O), dual-energy X-ray absorptiometry (DXA) and densitometry among the three main ethnic groups (Chinese, Malays and Indians) in Singapore, and determined the suitability of two-compartment (2C) models as surrogate methods for assessing BF% among different ethnic groups. A total of 291 subjects (108 Chinese, seventy-six Malays, 107 Indians) were selected to ensure an adequate representation of age range (18–75 years) and BMI range (16–40 kg/m2) of the general adult population, with almost equal numbers from each gender group. Body weight was measured, together with body height, total body water by 2H2O dilution, densitometry with Bodpod® and bone mineral content with Hologic® QDR-4500. BF% measurements with a 4C model for the subgroups were: Chinese females 33.5 (SD 7.5), CHINESE MALES 24.4 (sd 6.1), Malay females 37.8 (sd 6.3), Malay males 26.0 (sd 7.6), Indian females 38.2 (sd 7.0), Indian males 28.1 (sd 5.5). Differences between BF% measured by the 4C and 2C models (hydrometry, DXA and densitometry) were found, with underestimation of BF% in all the ethnic-gender groups by DXA of 2.1–4.2 BF% and by densitometry of 0.5–3.2 BF%). On a group level, the differences in BF% between the 4C model and 2H2O were the lowest (0.0–1.4 BF% in the different groups), while differences between the 4C model and DXA were the highest. Differences between the 4C model and 2H2O and between the 4C model and DXA were positively correlated with the 4C model, water fraction (fwater) of fat-free mass (FFM) and the mineral fraction (fmineral) of FFM, and negatively correlated with density of the FFM (DFFM), while the difference between 4C model and densitometry correlated with these variables negatively and positively respectively (i.e. the correlations were opposite). The largest contributors to the observed differences were fwater and DFFM. When validated against the reference 4C model, 2C models were found to be unsuitable for accurate measurements of BF% at the individual level, owing to the high errors and violation of assumptions of constant hydration of FFM and DFFM among the ethnic groups. On a group level, the best 2C model for measuring BF% among Singaporeans was found to be 2H2O.

The Tanita body-fat analyser is a novel device to estimate body fat, based on the principles of bioelectrical impedance. It differs from other impedance systems which use surface electrodes in that the subjects stand bare-footed on a metal sole-plate which incorporates the electrodes, hence impedance is measured through the legs and lower trunk. In 104 men and 101 women (16–78 years and BMI 16–41 kg/m2) the mean bias in body-fat mass measured using the Tanita body-fat analyser was 0·8 (2SD 7·9) KG RELATIVE TO A FOUR-COMPARTMENT MODEL. THIS IS COMPARABLE TO THE OTHER PREDICTION TECHNIQUES TESTED (CONVENTIONAL TETRAPOLAR IMPEDANCE -1·3 (2sd 6·9) kg, skinfold thicknesses 0·3 (2sd 7·4) kg, and BMI-based formulas -0·2 (2sd 9·0) kg and -0·6 (2sd 8·5) kg), but the agreement was poorer than for ‘reference’ methods to measure body fat (density 0·2 (2sd 3·7) kg, total body water -0·9 (2sd 3·4) kg and dual-energy X-ray absorptiometry 0·1 (2sd 5·0) kg). The present paper also describes the derivation of a new prediction equation for the calculation of body composition from the Tanita body-fat analyser. The equation incorporates sex, age, and a log-transformation of height, weight and the measured impedance to predict body fat measured by a four-compartment model. This approach is recommended in the derivation of other prediction equations in body composition analysis. Using this novel prediction equation the residual standard deviations were 4·8 % for men and 3·3 % for women. A similar analysis using data collected with a conventional tetrapolar system yielded residual standard deviations of 4·3 % for men and 3·1 % for women. This demonstrates that the practical simplicity of the novel Tanita method is not associated with a clinically significant decrement in performance relative to a traditional impedance device.