Influence of Acute Water Ingestion and Prolonged Standing on Raw Bioimpedance and Subsequent Body Fluid and Composition Estimates.

This study evaluated the influence of acute water ingestion and maintaining an upright posture on raw bioimpedance and subsequent estimates of body fluids and composition. Twenty healthy adults participated in a randomized crossover study. In both conditions, an overnight food and fluid fast was followed by an initial multi-frequency bioimpedance assessment (InBody 770). Participants then ingested 11 mL/kg of water (water condition) or did not (control condition) during a 5-minute period. Thereafter, bioimpedance assessments were performed every 10 minutes for one hour with participants remaining upright throughout. Linear mixed effects models were used to examine the influence of condition and time on raw bioimpedance, body fluids, and body composition. Water consumption increased impedance of the arms but not trunk or legs. However, drift in leg impedance was observed, with decreasing values over time in both conditions. No effects of condition on body fluids were detected, but total body water and intracellular water decreased by ~0.5 kg over time in both conditions. Correspondingly, lean body mass did not differ between conditions but decreased over the measurement duration. The increase in body mass in the water condition was detected exclusively as fat mass, with final fat mass values ~1.3 kg higher than baseline and also higher than the control condition. Acute water ingestion and prolonged standing exert practically meaningful effects on relevant bioimpedance variables quantified by a modern, vertical multi-frequency analyzer. These findings have implications for pre-assessment standardization, methodological reporting, and interpretation of assessments.

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.

A Field-based Three-Compartment Model Derived from Ultrasonography and Bioimpedance for Estimating Body Composition Changes.

PURPOSE: The purpose of this study was to assess the agreement between a field-based three-compartment (3CFIELD) model and a laboratory-based three-compartment (3CLAB) model for tracking body composition changes over time. METHODS: Resistance-trained males completed a supervised nutrition and resistance training intervention. Before and after the intervention, assessments were performed via air displacement plethysmography (ADP), bioimpedance spectroscopy (BIS), portable ultrasonography (US), and bioelectrical impedance analysis (BIA). ADP body density and BIS body water were used within the reference 3CLAB model, whereas US-derived body density and BIA body water were used within the 3CFIELD model. Two-compartment model body composition estimates provided by US and BIA were also examined. Changes in fat-free mass and fat mass were analyzed using repeated-measures ANOVA, equivalence testing, Bland-Altman analysis, linear regression, and related validity analyses. RESULTS: Significant increases in fat-free mass (3CLAB, 4.0 ± 4.5 kg; 3CFIELD, 3.9 ± 4.2 kg; US, 3.2 ± 4.3 kg; BIA, 3.9 ± 4.2 kg) and fat mass (3CLAB, 1.3 ± 2.2 kg; 3CFIELD, 1.4 ± 2.2 kg; US, 2.1 ± 2.6 kg; BIA, 1.4 ± 2.9 kg) were detected by all methods. However, only the 3CFIELD model demonstrated equivalence with the 3CLAB model. In addition, the 3CFIELD model exhibited superior performance to US and BIA individually, as indicated by the total error (3CFIELD, 1.0 kg; US, 1.8 kg; BIA, 1.6 kg), 95% limits of agreement (3CFIELD, ±2.1 kg; US, ±3.3 kg; BIA, ±3.1 kg), correlation coefficients (3CFIELD, 0.79-0.82; US, 0.49-0.55; BIA, 0.61-0.72), and additional metrics. CONCLUSIONS: The present study demonstrated the potential usefulness of a 3CFIELD model incorporating US and BIA data for tracking body composition changes over time, as well as its superiority to US or BIA individually. As such, this accessible multicompartment model may be suitable for implementation in field or limited-resource settings.

Correction to: Effects of Bang® Keto Coffee Energy Drink on Metabolism and Exercise Performance in Resistance-Trained Adults: A Randomized, Double-blind, Placebo-controlled, Crossover Study.

An amendment to this paper has been published and can be accessed via the original article.

Effects of Bang® Keto Coffee Energy Drink on Metabolism and Exercise Performance in Resistance-Trained Adults: A Randomized, Double-blind, Placebo-controlled, Crossover Study.

BACKGROUND: Energy drinks are often consumed by the general population, as well as by active individuals seeking to enhance exercise performance and augment training adaptations. However, limited information is available regarding the efficacy of these products. Thus, the purpose of this study was to determine the effects of a commercially available caffeine- and protein-containing energy drink on metabolism and muscular performance. METHODS: Sixteen resistance-trained males (n = 8; mean ± SD; age: 22.4 ± 4.9 years; body mass: 78.8 ± 14.0 kg; body fat: 15.3 ± 6.4%) and females (n = 8; age: 24.5 ± 4.8 years; body mass: 67.5 ± 11.9 kg; body fat: 26.6 ± 7.1%) participated in this randomized, double-blind, placebo-controlled, crossover study. Following a familiarization visit, participants completed two identical visits to the laboratory separated by 5-10 days, each of which consisted of indirect calorimetry energy expenditure (EE) assessments before and after consumption of the beverage (Bang® Keto Coffee; 130 kcal, 300 mg caffeine, 20 g protein) or placebo (30 kcal, 11 mg caffeine, 1 g protein) as well as after exercise testing. In addition, participants' subjective feelings of energy, fatigue, and focus as well as muscular performance (leg press one-repetition maximum and repetitions to fatigue, maximal isometric and isokinetic squat testing) were assessed. Multiple repeated measures ANOVAs with Tukey post-hoc tests were used to analyze data. Estimates of effect size were quantified via partial eta squared (ηP2) and Hedge's g. RESULTS: A significant interaction effect was identified for EE (p < 0.001, ηP2 = 0.52) but not respiratory exchange ratio (p = 0.17, ηP2 = 0.11). Following consumption of the beverage, EE was 0.18 [corrected] kcal·min- 1 greater than placebo at the post-beverage time point (p < 0.001) and 0.08 [corrected] kcal·min- 1 greater than placebo at the post-exercise time point (p = 0.011). However, no between-condition differences were detected for any subjective or muscular performance outcomes. CONCLUSIONS: The results of this study suggest that consumption of the energy drink had minimal effects on lower-body muscular performance and subjective factors in the context of a laboratory setting. However, the beverage was found to significantly increase energy expenditure compared to placebo immediately following ingestion as well as during the recovery period after an exercise bout, suggesting that active individuals may improve acute metabolic outcomes via consumption of a caffeine- and protein-containing energy drink. TRIAL REGISTRATION: This trial was prospectively registered at ClinicalTrials.gov (Identifier: NCT04180787 ; Registered 29 November 2019).