Comparison of Indirect Calorimetry and Common Prediction Equations for Evaluating Changes in Resting Metabolic Rate Induced by Resistance Training and a Hypercaloric Diet.

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.

Predicting Adaptations to Resistance Training Plus Overfeeding Using Bayesian Regression: A Preliminary Investigation.

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.

Longitudinal agreement of four bioimpedance analyzers for detecting changes in raw bioimpedance during purposeful weight gain with resistance training.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Agreement of bioelectrical resistance, reactance, and phase angle values from supine and standing bioimpedance analyzers.

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.

Explaining Discrepancies Between Total and Segmental DXA and BIA Body Composition Estimates Using Bayesian Regression.

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 (ß) 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 (|ß|: 0.20-0.82). Additionally, extracellular fluid percentage was a predictor for nearly all outcomes (|ß|: 0.19-0.40). Height influenced the agreement between whole-body estimates (|ß|: 0.74-0.77), while the mass, length, and composition of body segments were predictors for segmental LST estimates (|ß|: 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.

3-Dimensional optical scanning for body composition assessment: A 4-component model comparison of four commercially available scanners.

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®, Naked Labs®, Size Stream®, and Styku®) 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® 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.

Segmental body composition evaluation by bioelectrical impedance analysis and dual-energy X-ray absorptiometry: Quantifying agreement between methods.

BACKGROUND & AIMS: Segmental estimates add specificity to body composition evaluation and could potentially have greater health and function implications than whole-body estimates alone. The aim of this study was to quantify the level of agreement between total and segmental fat mass (FM) and lean soft tissue (LST) estimates from dual-energy x-ray absorptiometry (DXA) and single-frequency bioelectrical impedance analysis (SFBIA). METHODS: Assessments via DXA (GE® Lunar Prodigy) and SFBIA (RJL Systems® Quantum V) were performed in 179 adults (103 F, 76 M; 30% racial/ethnic minorities). Total and segmental FM and LST estimates were compared in the entire sample, females, and males using null hypothesis significance testing (NHST; via paired-samples t-tests), equivalence testing with 5% equivalence regions, Bland-Altman analysis with linear regression, and additional error metrics. RESULTS: In females and the entire sample, all LST variables except LSTARMS exhibited equivalence between methods, despite statistically significant differences via NHST for most variables. In males, only estimates of LSTTOTAL and appendicular lean soft tissue (ALST) were equivalent between methods. LST variables exhibited minimal proportional bias. All FM variables failed to exhibit equivalence, and most FM variables were underestimated by SFBIA. The magnitude of relative errors for FM generally appeared larger in males than females. Proportional bias was observed for FMLEGS and FMARMS, as well as FMTOTAL in females only. ALST estimates were equivalent between DXA and SFBIA in all analyses, did not differ between methods based on NHST, exhibited relatively low errors, and displayed no proportional bias. CONCLUSIONS: In the context of the present study, DXA and SFBIA LST estimates appear to exhibit better overall agreement than FM estimates. Additionally, overall agreement between SFBIA and DXA may be superior in females as compared to males. The relatively strong agreement between ALST estimates indicates potential utility of SFBIA for clinical applications, such as evaluation of sarcopenia. Further investigation into the explanatory physiological (e.g. hydration) and anthropometric (e.g. segment lengths, circumferences, and volumes) variables predicting individual discrepancies between DXA and BIA is warranted.

Digital anthropometry via three-dimensional optical scanning: evaluation of four commercially available systems.

BACKGROUND: Digital anthropometry is increasingly accessible due to commercial availability of three-dimensional optical scanners (3DO). METHODS: One hundred and seventy-nine participants were assessed by four 3DO systems (FIT3D®, Size Stream®, Styku®, and Naked Labs®) in duplicate, air displacement plethysmography (ADP), and dual-energy x-ray absorptiometry (DXA). Test-retest precision was evaluated, and validity of total and regional volumes was established. RESULTS: All scanners produced precise estimates, with root mean square coefficient of variation (RMS-%CV) of 1.1-1.3% when averaged across circumferences and 1.9-2.3% when averaged across volumes. Precision for circumferences generally decreased in the order of: hip, waist and thigh, chest, neck, and arms. Precision for volumes generally decreased in the order of: total body volume (BV), torso, legs, and arms. Total BV was significantly underestimated by Styku® (constant error [CE]: -10.1 L; root mean square error [RMSE]: 10.5 L) and overestimated by Size Stream® (CE: 8.0 L; RMSE: 8.3 L). Total BV did not differ between ADP and FIT3D® (CE: -3.9 L; RMSE: 4.2 L) or DXA BV equations (CE: 0-1.4 L; RMSE: 0.7-1.5 L). Torso volume was overestimated and leg and arm volumes were underestimated by all 3DO. No total or regional 3DO volume estimates exhibited equivalence with reference methods using 5% equivalence regions, and proportional bias of varying magnitudes was observed. CONCLUSIONS: All 3DO produced precise anthropometric estimates, although variability in specific precision estimates was observed. 3DO BV estimates did not exhibit equivalence with reference methods. Conversely, DXA-derived total BV exhibited superior validity and equivalence with ADP.