Due to inherent errors involved in the transformation of raw bioelectrical variables to body fluids or composition estimates, the sole use of resistance (R), reactance (Xc), and phase angle (φ) has been advocated when quantifying longitudinal changes. The aim of this investigation was to assess the ability of four bioimpedance analyzers to detect raw bioimpedance changes induced by purposeful weight gain with resistance training. Twenty-one resistance trained males completed a 6-week lifestyle intervention with the aim of purposeful weight gain. Bioimpedance analysis was performed before and after the intervention using four different analyzers (MFBIAInBody: InBody 770; MFBIASECA: Seca mBCA 515/514; BIS: ImpediMed SFB7; SFBIA: RJL Quantum V) for the quantification of R, Xc, and φ at the 50-kHz frequency. Repeated measures ANOVA and follow up tests were performed. Analysis revealed main effects of time and method for R, Xc, and φ (p ≤ 0.02), without significant time x method interactions (p ≥ 0.07). Follow up for time main effects indicated that, on average, R decreased by 4.5–5.8%, Xc decreased by 2.3–4.0%, and φ increased by 1.8–2.6% across time for all analyzers combined. However, varying levels of disagreement in absolute values were observed for each bioelectrical variable. The differences in absolute bioelectrical values suggests that analyzers should not be used interchangeably, which holds particular importance when reference values are utilized. Despite absolute differences, analyzers with varying characteristics demonstrated similar abilities to detect changes in R, Xc, and φ over time.

Relatively few investigations have reported purposeful overfeeding in resistance-trained adults. This preliminary study examined potential predictors of resistance training (RT) adaptations during a period of purposeful overfeeding and RT. Resistance-trained males (n = 28; n = 21 completers) were assigned to 6 weeks of supervised RT and daily consumption of a high-calorie protein/carbohydrate supplement with a target body mass (BM) gain of ≥0.45 kg·wk−1. At baseline and post-intervention, body composition was evaluated via 4-component (4C) model and ultrasonography. Additional assessments of resting metabolism and muscular performance were performed. Accelerometry and automated dietary interviews estimated physical activity levels and nutrient intake before and during the intervention. Bayesian regression methods were employed to examine potential predictors of changes in body composition, muscular performance, and metabolism. A simplified regression model with only rate of BM gain as a predictor was also developed. Increases in 4C whole-body fat-free mass (FFM; (mean ± SD) 4.8 ± 2.6%), muscle thickness (4.5 ± 5.9% for elbow flexors; 7.4 ± 8.4% for knee extensors), and muscular performance were observed in nearly all individuals. However, changes in outcome variables could generally not be predicted with precision. Bayes R2 values for the models ranged from 0.18 to 0.40, and other metrics also indicated relatively poor predictive performance. On average, a BM gain of ~0.55%/week corresponded with a body composition score ((∆FFM/∆BM)*100) of 100, indicative of all BM gained as FFM. However, meaningful variability around this estimate was observed. This study offers insight regarding the complex interactions between the RT stimulus, overfeeding, and putative predictors of RT adaptations.

ABSTRACT:Rodriguez, C, Harty, PS, Stratton, MT, Siedler, MR, Smith, RW, Johnson, BA, Dellinger, JR, Williams, AD, White, SJ, Benavides, ML, and Tinsley, GM. Comparison of indirect calorimetry and common prediction equations for evaluating changes in resting metabolic rate induced by resistance training and a hypercaloric diet. J Strength Cond Res 36(11): 3093-3104, 2022-The ability to accurately identify resting metabolic rate (RMR) changes over time allows practitioners to prescribe appropriate adjustments to nutritional intake. However, there is a lack of data concerning the longitudinal utility of commonly used RMR prediction equations. The purpose of this study was to evaluate the validity of several commonly used prediction equations to track RMR changes during a hypercaloric nutritional intervention and supervised resistance exercise training program. Twenty resistance-trained men completed the study. The protocol lasted 6 weeks, and subjects underwent RMR assessments by indirect calorimetry (IC) preintervention and postintervention to obtain reference values. Existing RMR prediction equations based on body mass (BM) or dual-energy X-ray absorptiometry fat-free mass (FFM) were also evaluated. Equivalence testing was used to evaluate whether each prediction equation demonstrated equivalence with IC. Null hypothesis significance testing was also performed, and Bland-Altman analysis was used alongside linear regression to assess the degree of proportional bias. Body mass and FFM increased by 3.6 ± 1.7 kg and 2.4 ± 1.6 kg, respectively. Indirect calorimetry RMR increased by 165 ± 97 kcal·d -1 , and RMR:FFM increased by 5.6 ± 5.2%. All prediction equations underestimated mean RMR changes relative to IC, with magnitudes ranging from 75 to 155 kcal·d -1 , while also displaying unacceptable levels of negative proportional bias. In addition, no equation demonstrated equivalence with IC. Common RMR prediction equations based on BM or FFM did not fully detect the increase in RMR observed with resistance training plus a hypercaloric diet. Overall, the evaluated prediction equations are unsuitable for estimating RMR changes in the context of this study.

Objective. Bioimpedance devices are commonly used to assess health parameters and track changes in body composition. However, the cross-sectional agreement between different devices has not been conclusively established. Thus, the objective of this investigation was to examine the agreement between raw bioelectrical variables (resistance, reactance, and phase angle at the 50 kHz frequency) obtained from three bioimpedance analyzers.Approach. Healthy male (n = 76, mean ± SD; 33.8 ± 14.5 years; 83.9 ± 15.1 kg; 179.4 ± 6.9 cm) and female (n = 103, mean ± SD; 33.4 ± 15.9 years; 65.6 ± 12.1 kg; 164.9 ± 6.4 cm) participants completed assessments using three bioimpedance devices: supine bioimpedance spectroscopy (BIS), supine single-frequency bioelectrical impedance analysis (SFBIA), and standing multi-frequency bioelectrical impedance analysis (MFBIA). Differences in raw bioelectrical variables between the devices were quantified via one-way analysis of variance for the total sample and for each sex. Equivalence testing was used to determine equivalence between methods.Main results. Significant differences in all bioelectrical variables were observed between the three devices when examining the total sample and males only. The devices appeared to exhibit slightly better agreement when analyzing female participants only. Equivalence testing using the total sample as well as males and females separately revealed that resistance and phase angle were equivalent between the supine devices (BIS, SFBIA), but not with the standing analyzer (MFBIA).Significance. The present study demonstrated disagreement between different bioimpedance analyzers for quantifying raw bioelectrical variables, with the poorest agreement between devices that employed different body positions during testing. These results suggest that researchers and clinicians should employ device-specific reference values to classify participants based on raw bioelectrical variables, such as phase angle. If reference values are needed but are unavailable for a particular bioimpedance analyzer, the set of reference values produced using the most similar analyzer and reference population should be selected.

Assessment methods such as dual-energy x-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA) are commonly used to determine molecular-level body composition estimates in both healthy and clinical populations. Though discrepancies in body composition estimates between BIA and DXA have been previously reported, virtually no investigations have explored potential predictor variables that may explain the disagreement between these methods. PURPOSE: To explore the physiological and anthropometric predictors of discrepancies between DXA and BIA total body composition estimates. METHODS: During a single visit, 103 female participants (Mean ± SD; 33.4 ± 15.9 years; 65.6 ± 12.1 kg; 165.5 ± 6.3 cm; 32.2 ± 7.4 DXA body fat percentage [BF%]) and 76 male participants (33.8 ± 14.5 years; 83.9 ± 15.1 kg; 178.8 ± 6.6 cm; 22.9 ± 8.0 DXA BF%) underwent body composition assessment via DXA and 8-point single-frequency BIA. Potential predictors of the discrepancies between DXA and BIA body composition estimates were obtained during the same visit using air displacement plethysmography, bioimpedance spectroscopy, and 3-dimensional optical scanning. Select DXA variables were also utilized as predictors. Ordinary least squares regression was conducted to predict the differences in total fat mass (FM) and total lean soft tissue (LST) between DXA and BIA. Standardized model coefficients (β), p-values for coefficients, and R2 values were generated. RESULTS: For FM estimates, significant predictors (p ≤ 0.01) of the differences between DXA and BIA were hydration of LST (TBW:LST; β = -0.82), height (β = -0.78), percentage of TBW as extracellular fluid (%ECF; β = -0.36), and the male sex (β = 0.26). For LST, significant predictors of the errors between DXA and BIA were TBW:LST (β = 0.85), height (β = 0.77), %ECF (β = 0.40), the male sex (β = -0.22), and racial identification as Black (β = -0.09). The R2 values for regression models predicting differences between DXA and BIA were 0.80 to 0.86 for FM and 0.73 to 0.87 for LST. CONCLUSION: Hydration variables and height are primary predictors of discrepancies between DXA and BIA total body composition estimates.

Estimating alterations in lean mass in response to various training interventions is a primary concern for many investigations. However, previous reports have suggested that lean mass estimates from weight loss interventions may be significantly altered by attempting to correct for changes in the fat-free component of adipose tissue (FFAT). This component, consisting primarily of water and protein, has been estimated as ~15% of adipose tissue (AT) mass. While a preliminary examination of this correction method has been conducted in the instance of weight loss, it has yet to be investigated after a period of purposeful weight gain and resistance training. PURPOSE: To examine the impact of corrections for FFAT on estimates of lean mass accretion during a period of weight gain and resistance training. METHODS: Twenty-one resistance trained males underwent 6 weeks of supervised training and followed a hypercaloric diet in order to elicit weight gain. Body composition was assessed preand post-intervention via dual energy xray absorptiometry (DXA). AT was estimated using DXA-derived fat mass (FM) in the equation: AT = FM/0.85. FFAT was then estimated via the equation: FFAT = 0.15 × AT. Lastly, FFAT was subtracted from DXA-derived lean mass (LMDXA) to yield the new corrected lean mass value (cLM). Changes in LMDXA and cLM in response to the training intervention were calculated, and dependent samples T-tests were employed to determine if significant differences were present between changes in LMDXA and cLM. RESULTS: Significant differences (p ≤ 0.001) were noted for estimates of LM gain, with a larger increase observed for LMDXA as compared to cLM (LMDXA :2.42 ± 1.58kg; cLM: 2.14 ± 1.65kg). CONCLUSION: Correcting DXA-derived LM for the fat-free component of adipose tissue reduces the magnitude of LM accretion after a period of weight gain. However, while LM estimates did significantly differ, the small degree to which they differed indicates questionable practical relevance of such corrections in future investigations.

Interest in evaluating the composition of specific anatomical regions has become commonplace in a variety of settings. Appendicular lean soft tissue (ALST) estimates are considered in the diagnosis of sarcopenia. While dual-energy x-ray absorptiometry (DXA) is viewed as a reference method for regional assessments, its availability is limited. Thus, explaining discrepancies in regional body composition estimates between DXA and the more accessible bioelectrical impedance analysis (BIA) is of utmost importance. PURPOSE: To assess the anthropometric and physiological predictors of variations between BIA and DXA segmental lean soft tissue (LST) estimates. METHODS: During a single visit, 179 participants (103 females, 76 males; Mean ± SD: 33.6 ± 15.3 years; 73.4 ± 16.2 kg; 171.2 ± 9.2 cm; 28.2 ± 8.9% DXA body fat %) underwent body composition assessments via DXA and 8-point single-frequency BIA. Potential predictors of discrepancies between DXA and BIA LST estimates were obtained from these methods and additional laboratory techniques. Specifically, air displacement plethysmography, 3-dimensional optical scanning, and bioimpedance spectroscopy were used to estimate body volume, anthropometrics, and hydration variables, respectively. Significant predictors (p ≤ 0.05) of the mean difference between DXA and BIA estimates of trunk LST (TLST) and ALST were established using ordinary least squares regression. Standardized model coefficients, p-values for coefficients, and R2 values were generated. RESULTS: For both TLST and ALST, extracellular fluid percentage, LST hydration, height, total LST mass, the male sex, and racial identification as Black significantly predicted discrepancies between DXA and BIA. Additional predictors for TLST discrepancies were DXA total fat mass (FM) to LST ratio and DXA TLST, while additional predictors of ALST discrepancies included DXA ALST, DXA FM to LST ratio of the legs, DXA appendicular FM, and DXA-derived volume of the arms and legs. Regression models including these significant predictor variables produced R2 values of 0.92 and 0.95 for TLST and ALST, respectively. CONCLUSIONS: Hydration variables, the quantity of LST in the region of interest, and height were the most influential predictor variables for discrepancies between DXA and BIA segmental LST estimates.

Background & aims: Body composition assessment via 3-dimensional optical (3DO) scanning has emerged as a rapid and simple evaluation method. The aim of this study was to establish the precision of body composition estimates from four commercially available 3DO scanners and evaluate their validity as compared to a reference 4-component (4C) model. Methods: The body composition of 171 participants was assessed using four commercially-available 3DO scanners (FIT3D (R), Naked Labs (R), Size Stream (R), and Styku (R)) and a 4C model utilizing data from dual-energy x-ray absorptiometry, air displacement plethysmography, and bioimpedance spectroscopy. Body composition estimates were compared via equivalence testing, Deming regression, Bland-Altman analysis, concordance correlation coefficients (CCC), root mean square error (RMSE), and related metrics. Precision metrics, including the root mean square coefficient of variation (RMS-%CV), precision error, and intraclass correlation coefficient, were generated for duplicate scans in 139 participants. Results: All scanners produced reasonably reliable estimates, with RMS-%CV of 2.3-4.3% for body fat percentage (BF%), 2.5-4.3% for fat mass (FM), and 0.7-1.4% for fat-free mass (FFM). ICC values ranged from 0.975 to 0.996 for BF% and 0.990 to 0.999 for FM and FFM. All scanners except Styku (R) demonstrated equivalence with 4C, using 5% equivalence regions, and constant errors of <1% for BF% and <= 0.5 kg for FM and FFM. However, the slopes of regression lines differed from the line of identity for most scanners and variables. CCC values ranged from 0.74 to 0.90 for BF%, 0.85 to 0.95 for FM, and 0.93 to 0.97 for FFM. RMSE values ranged from 3.7 to 6.1% for BF% and 2.8-4.6 kg for FM and FFM. Bland-Altman analysis indicated proportional bias of varying magnitudes was present for all scanners. Conclusions: Commercially available 3DO scanners produce relatively reliable body composition estimates. Three out of four scanners demonstrated equivalence with a 4C model for assessments of BF%, FM, and FFM, although other metrics of validity varied among scanners, and proportional bias was present for all scanners. (C) 2020 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

A common goal among athletes is to gain additional body mass (BM), particularly fat-free mass (FFM), in order to improve muscle strength, power, and overall performance. Athletes typically undergo BM accretion over a specific period of time in conjunction with high-volume resistance training (RT) in order to preferentially gain FFM and promote concomitant muscular performance improvements. PURPOSE: The purpose of this study was to examine the relationship between the rate of BM gain and the proportion of BM gained as FFM versus fat mass (FM) during a 6-week period of overfeeding and resistance training. METHODS: 21 resistance-trained males (mean ± SD: age = 22.6 ± 2.5 years; height = 177.8 ± 6.8 cm; BM = 73.3 ± 12.3 kg, body fat % = 14.8 ± 5.1%, bench press [BP] 1-repetition maximum [1RM] = 1.3 ± 0.3, leg press [LP] 1RM = 3.3 ± 0.9) were recruited and assigned to 6 weeks of RT for 3-days/week and instructed to consume a high-calorie protein/carbohydrate supplement daily. Prior to the intervention, participants performed 1RM tests for the BP and LP exercises to assess training status, with the minimum requirement for study participation being BP 1RM ≥ 1.0xBM and LP 1RM ≥ 2.0xBM. At baseline and post-intervention, body composition assessments were performed using dual-energy x-ray absorptiometry (DXA), air displacement plethysmography (ADP), and bioimpedance spectroscopy (BIS) in order to produce a criterion 4-compartment model. Simple linear regression was performed to determine if the relative rate of mass gain predicted the composition of mass gain (calculated as the change in fat-free mass divided by the change in body mass). Assumptions of normality, outliers, homogeneity, and independence were examined and addressed as needed. RESULTS: The change in BM, FM, and FFM were (mean ± SD) 5.6 ± 2.3%, 1.3 ± 14.8%, and 6.0 ± 2.1%, respectively. In the regression model, the relative rate of mass gain significantly predicted the composition of mass gain (β: -0.81 [-1.11, -0.50], mean [95% confidence interval]). Based on these data, for every 1% increase in the rate of relative mass gain, the percent of mass gained as FFM decreased by approximately 10% (with a 95% confidence interval of -6 to -13%). A rate of mass gain of 0.93%/week corresponded to 100% of mass being gained as FFM, with slower rates allowing for simultaneous FFM gain and FM loss. CONCLUSION: For individuals who are moderately welltrained with respect to resistance training, a rate of BM gain of ~1%/week may allow for nearly all mass to be gained as FFM while slower rates may allow for simultaneous increases in FFM and decreases in FM.

Two critical components of muscular performance are muscular strength (e.g., the maximal load that can be lifted for a given exercise) and muscular endurance (e.g., the maximal number of repetitions that can be performed at a given load). When seeking improvements in muscular performance, it is common to employ nutritional strategies that create an energy surplus and a resultant gain in body mass. Varying rates of body mass gain are often prescribed to optimize training adaptations, including improvements in muscular performance; however, the relationship between rate of body mass gain and muscular performance improvements, if any, is not entirely clear. PURPOSE: The purpose of this analysis was to elucidate if there is a relationship between the rate of body mass gain and changes in muscular performance resulting from a resistance training program. METHODS: Nineteen resistance-trained males (age: 21.7 ± 2.6; body mass [BM]: 74.1 ± 11.5 kg; body fat percentage: 13.7 ± 5.2%; bench press maximal strength: 1.3 ± 0.2 x BM; leg press maximal strength: 3.4 ± 0.9 x BM) completed a supervised resistance training program plus overfeeding. Muscular performance testing took place at baseline and after the 6week intervention. For the bench press and leg press exercises, strength was assessed via 1-repetition maximum (1RM), and endurance was assessed via repetitions to failure using 70% of the baseline 1RM. Simple linear regression analysis was used to determine if the relative rate of BM gain was related to relative improvements in maximal muscular strength and endurance. Standardized regression coefficients (β) and associated 95% confidence intervals (CI) were generated. RESULTS: The rate of BM gain was related to improvements in bench press 1RM (p=0.05; β=0.46 [0.02, 0.89], mean [95% CI]) and endurance (p=0.007, β =0.61 [0.23, 1.00]), but not leg press 1RM (p=0.16, β =0.33 [-0.11, 0.78]) or endurance (p=0.76, β = 0.08 [-0.42, 0.58]). A 1.0% increase in the relative rate of BM gain corresponded to relative increases of 1.2% (CI of 0.1 to 2.4%) in bench press 1RM and 6.7% (CI of 2.5 to 10.9%) in bench press repetitions to failure. CONCLUSION: The relative rate of body mass gain was positively related to performance improvements in the bench press exercise, but not the leg press exercise. One speculative explanation for this relationship is that the increase in upper body muscularity that results from body mass gain during resistance training could have decreased the range of motion on the bench press exercise, thereby facilitating easier execution of the movement for both strength and endurance tests.

Introduction/Background: Few investigations have sought to explain discrepancies between dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA) body composition estimates. The purpose of this analysis was to explore physiological and anthropometric predictors of discrepancies between DXA and BIA total and segmental body composition estimates. Methodology: Assessments via DXA (GE Lunar Prodigy) and single-frequency BIA (RJL Systems Quantum V) were performed in 179 adults (103 F, 76 M, age: 33.6 ± 15.3 yr; BMI: 24.9 ± 4.3 kg/m2). Potential predictor variables for differences between DXA and BIA total and segmental fat mass (FM) and lean soft tissue (LST) estimates were obtained from demographics and laboratory techniques, including DXA, BIA, bioimpedance spectroscopy, air displacement plethysmography, and 3-dimensional optical scanning. To determine meaningful predictors, Bayesian robust regression models were fit using a t-distribution and regularized hierarchical shrinkage “horseshoe” prior. Standardized model coefficients (β) were generated, and leave-one-out cross validation was used to assess model predictive performance. Results: LST hydration (i.e., total body water:LST) was a predictor of discrepancies in all FM and LST variables (|β|: 0.20–0.82). Additionally, extracellular fluid percentage was a predictor for nearly all outcomes (|β|: 0.19–0.40). Height influenced the agreement between whole-body estimates (|β|: 0.74–0.77), while the mass, length, and composition of body segments were predictors for segmental LST estimates (|β|: 0.23–3.04). Predictors of segmental FM errors were less consistent. Select sex-, race-, or age-based differences between methods were observed. The accuracy of whole-body models was superior to segmental models (leave-one-out cross-validation-adjusted R2 of 0.83–0.85 for FMTOTAL and LSTTOTAL vs. 0.20–0.76 for segmental estimates). For segmental models, predictive performance decreased in the order of: appendicular lean soft tissue, LSTLEGS, LSTTRUNK and FMLEGS, FMARMS, FMTRUNK, and LSTARMS. Conclusions: These findings indicate the importance of LST hydration, extracellular fluid content, and height for explaining discrepancies between DXA and BIA body composition estimates. These general findings and quantitative interpretation based on the presented data allow for a better understanding of sources of error between 2 popular segmental body composition techniques and facilitate interpretation of estimates from these technologies.