The purpose of this investigation was to expand upon the limited existing research examining the test–retest reliability, cross-sectional validity and longitudinal validity of a sample of bioelectrical impedance analysis (BIA) devices as compared with a laboratory four-compartment (4C) model. Seventy-three healthy participants aged 19–50 years were assessed by each of fifteen BIA devices, with resulting body fat percentage estimates compared with a 4C model utilising air displacement plethysmography, dual-energy X-ray absorptiometry and bioimpedance spectroscopy. A subset of thirty-seven participants returned for a second visit 12–16 weeks later and were included in an analysis of longitudinal validity. The sample of devices included fourteen consumer-grade and one research-grade model in a variety of configurations: hand-to-hand, foot-to-foot and bilateral hand-to-foot (octapolar). BIA devices demonstrated high reliability, with precision error ranging from 0·0 to 0·49 %. Cross-sectional validity varied, with constant error relative to the 4C model ranging from −3·5 (sd 4·1) % to 11·7 (sd 4·7) %, standard error of the estimate values of 3·1–7·5 % and Lin’s concordance correlation coefficients (CCC) of 0·48–0·94. For longitudinal validity, constant error ranged from −0·4 (sd 2·1) % to 1·3 (sd 2·7) %, with standard error of the estimate values of 1·7–2·6 % and Lin’s CCC of 0·37–0·78. While performance varied widely across the sample investigated, select models of BIA devices (particularly octapolar and select foot-to-foot devices) may hold potential utility for the tracking of body composition over time, particularly in contexts in which the purchase or use of a research-grade device is infeasible.

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.

Assessing children’s growth adequately is important due to the necessary prevention of adequate body composition, especially at pre-pubertal age. Simpler measurements such as anthropometry or bioimpedance, using equations validated in Caucasian children, have been demonstrated to overestimate or underestimate fat mass percentage (FM%) or fat-free mass (FFM) in Chilean children. In a sample of 424 children (198 boys and 226 girls) of 7–9 years old, the three component (3C) model was assessed, where total body water was determined by 2H dilution and body volume by air displacement plethysmography, in order to design and validate anthropometry and bioimpedance equations. The FM (%) equation specific for Chilean children was validated as (1·743 × BMI z-score) + (0·727 × triceps skinfold) + (0·385 × biceps skinfold) + 15·985, against the 3C model (R2 0·79). The new FFM equation (kg) generated was (log FFM = (0·018 × age) + (0·047 × sex) + (0·006 × weight) + (0·027 × resistance) + 2·071), with an R2 0·93 (female = 1 and male = 2). The Bland–Altman analysis shows a mean difference of 0·27 (sd 3·5) for the FM% in the whole group as well as 0·004 (sd 0·9) kg is the mean difference for the bioelectrical impedance analysis (BIA) FFM (kg) equation. The new equations for FM (%) and FFM (kg) in Chilean children will provide a simple and valid tool for the assessment of body composition in cohort studies or to assess the impact of nutritional programmes or public policies.

This study reports the validity of body fat percentage (BF%) estimates from several commonly employed techniques as compared with a five-component (5C) model criterion. Healthy adults (n 170) were assessed by dual-energy X-ray absorptiometry (DXA), air displacement plethysmography (ADP), multiple bioimpedance techniques and optical scanning. Output was also used to produce a criterion 5C model, multiple variants of three- and four-component models (3C; 4C) and anthropometry-based BF% estimates. Linear regression, Bland–Altman analysis and equivalence testing were performed alongside evaluation of the constant error (CE), total error (TE), se of the estimate (SEE) and coefficient of determination (R2). The major findings were (1) differences between 5C, 4C and 3C models utilising the same body volume (BV) and total body water (TBW) estimates are negligible (CE ≤ 0·2 %; SEE < 0·5 %; TE ≤ 0·5 %; R2 1·00; 95 % limits of agreement (LOA) ≤ 0·9 %); (2) moderate errors from alternate TBW or BV estimates in multi-component models were observed (CE ≤ 1·3 %; SEE ≤ 2·1 %; TE ≤ 2·2 %; R2 ≥ 0·95; 95 % LOA ≤ 4·2 %); (3) small differences between alternate DXA (i.e. tissue v. region) and ADP (i.e. Siri v. Brozek equations) estimates were observed, and both techniques generally performed well (CE < 3·0 %; SEE ≤ 2·3 %; TE ≤ 3·6 %; R2 ≥ 0·88; 95 % LOA ≤ 4·8 %); (4) bioimpedance technologies performed well but exhibited larger individual-level errors (CE < 1·0 %; SEE ≤ 3·1 %; TE ≤ 3·3 %; R2 ≥ 0·94; 95 % LOA ≤ 6·2 %) and (5) anthropometric equations generally performed poorly (CE 0·6– 5·7 %; SEE ≤ 5·1 %; TE ≤ 7·4 %; R2 ≥ 0·67; 95 % LOA ≤ 10·6 %). Collectively, the data presented in this manuscript can aid researchers and clinicians in selecting an appropriate body composition assessment method and understanding the associated errors when compared with a reference multi-component model.

Estrogen-based hormone replacement therapy (HRT) may be associated with deceleration of cellular aging. We investigated whether long-term HRT has effects on leukocyte (LTL) or mean and minimum skeletal muscle telomere length (SMTL) in a design that controls for genotype and childhood environment. Associations between telomeres, body composition, and physical performance were also examined. Eleven monozygotic twin pairs (age 57.6 ± 1.8 years) discordant for HRT were studied. Mean duration of HRT use was 7.3 ± 3.7 years in the user sister, while their co-twins had never used HRT. LTL was measured by qPCR and SMTLs by southern blot. Body and muscle composition were estimated by bioimpedance and computed tomography, respectively. Physical performance was measured by jumping height and grip strength. HRT users and non-users did not differ in LTL or mean or minimum SMTL. Within-pair correlations were high in LTL (r = 0.69, p = .020) and in mean (r = 0.74, p = .014) and minimum SMTL (r = 0.88, p = .001). Body composition and performance were better in users than non-users. In analyses of individuals, LTL was associated with BMI (r2 = 0.30, p = .030), percentage total body (r2 = 0.43, p = .014), and thigh (r2 = 0.55, p = .004) fat, while minimum SMTL was associated with fat-free mass (r2 = 0.27, p = .020) and thigh muscle area (r2 = 0.42, p = .016). We found no associations between HRT use and telomere length. Longer LTLs were associated with lower total and regional fat, while longer minimum SMTLs were associated with higher fat-free mass and greater thigh muscle area. This suggests that telomeres measured from different tissues may have different associations with measures of body composition.

A method, electrical impedance spectroscopy (EIS), is introduced to study seed viability non-destructively. Snap bean (Phaseolus vulgaris L.) seeds were studied by EIS to determine the most sensitive EIS parameter(s) and the optimal range of moisture content (MC) for separation of viable and non-viable seeds. Hydrated seeds exhibited two impedance arcs in the complex plane at the frequency range from 60 Hz to 8 MHz, and impedance spectra of viable and non-viable seeds differed. The hydrated seeds were best-modelled by an equivalent electrical circuit with two distributed circuit elements in series with a resistor (Voigt model). Moisture content and seed viability had strong effects on the EIS parameters. The most sensitive EIS parameters for detecting the differences between viable and non-viable seeds were the capacitance log(C2), the resistance R2, the resistance ratio R2/R1 and the apex ratio, which all represent specific features of the impedance spectrum. The highest differentiation in the EIS parameters between the viable and non-viable seeds occurred in partially imbibed seeds between MC of 40 and 45% (fresh weight basis).

Using percentage body fat (BF%) from a three-compartment (3C) model (body density from underwater weighing (UWW) and bone-mineral mass from dual-energy X-ray absorptiometry (DXA)) as a criterion, we studied the accuracy of UWW, DXA, two skinfold equations, and two bioimpedance (BIA) equations. Thirty-four women (aged 16–20 years) with BF% 13·5–31·1 volunteered. UWW underestimated BF% by −0·5 BF% (95 % CI: −1·0; −0·02), whereas DXA overestimated it by 7·3 BF% (95% CI5.8;8·8). Skinfolds underestimated and BIA overpredicted BF%. The differences between 3C and UWW, skinfolds (Durnin & Womersley, 1974) and BIA (Deurenberg et al. 1990) were dependent (range of r values: −0.63 to −0.79; P < 0·0001) on BF%, causing an overestimation of lean subjects' (UWW, BIA) or an underestimation of normal-weight subjects' (UWW, skinfolds) BF%. The 3C model and UWW gave comparable body-composition results for healthy young women with BF% of approximately 20–25. Based on a significant mean difference from the 3C model, and a large standard error of the estimate, we do not regard DXA as superior to skinfolds or BIA to assess BF%.