Rhodiola rosea (RR) is a plant whose bioactive components may function as adaptogens, thereby increasing resistance to stress and improving overall resilience. Some of these effects may influence exercise performance and adaptations. Based on studies of rodents, potential mechanisms for the ergogenic effects of RR include modulation of energy substrate stores and use, reductions in fatigue and muscle damage and altered antioxidant activity. At least sixteen investigations in humans have explored the potential ergogenicity of RR. These studies indicate acute RR supplementation (∼200 mg RR containing ∼1 % salidroside and ∼3 % rosavin, provided 60 min before exercise) may prolong time-to-exhaustion and improve time trial performance in recreationally active males and females, with limited documented benefits of chronic supplementation. Recent trials providing higher doses (∼1500 to 2400 mg RR/d for 4–30 d) have demonstrated ergogenic effects during sprints on bicycle ergometers and resistance training in trained and untrained adults. The effects of RR on muscle damage, inflammation, energy system modulation, antioxidant activity and perceived exertion are presently equivocal. Collectively, it appears that adequately dosed RR enhances dimensions of exercise performance and related outcomes for select tasks. However, the current literature does not unanimously show that RR is ergogenic. Variability in supplementation dose and duration, concentration of bioactive compounds, participant characteristics, exercise tests and statistical considerations may help explain these disparate findings. Future research should build on the longstanding use of RR and contemporary clinical trials to establish the conditions in which supplementation facilitates exercise performance and adaptations.

Automated visual anthropometrics produced by mobile applications are accessible and cost effective with the potential to assess clinically relevant anthropometrics without a trained technician present. Thus, the aim of this study was to evaluate the precision and agreement of smartphone-based automated anthropometrics against reference tape measurements. Waist and hip circumference (WC; HC), waist:hip ratio (WHR) and waist:height ratio (W:HT) were collected from 115 participants (69 F) using a tape measure and two smartphone applications (MeThreeSixty®, myBVI®) across multiple smartphone types. Precision metrics were used to assess test-retest precision of the automated measures. Agreement between the circumferences produced by each mobile application and the reference were assessed using equivalence testing and other validity metrics. All mobile applications across smartphone types produced reliable estimates for each variable with intraclass correlation coefficients ≥ 0·93 (all P < 0·001) and root mean square coefficient of variation between 0·5 and 2·5 %. Precision error for WC and HC was between 0·5 and 1·9 cm. WC, HC, and W:HT estimates produced by each mobile application demonstrated equivalence with the reference tape measurements using 5 % equivalence regions. Mean differences via paired t-tests were significant for all variables across each mobile application (all P < 0·050) showing slight underestimation for WC and slight overestimation for HC which resulted in a lack of equivalence for WHR compared with the reference tape measure. Overall, the results of our study support the use of WC and HC estimates produced from automated mobile applications, but also demonstrates the importance of accurate automation for WC and HC estimates given their influence on other anthropometric assessments and clinical health markers.

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.

Assessments of visceral adipose tissue (VAT) are critical in preventing metabolic disorders; however, there are limited measurement methods that are accurate and accessible for VAT. The purpose of this cross-sectional study was to evaluate the association between VAT estimates from consumer-grade devices and traditional anthropometrics and VAT and subcutaneous adipose tissue (SAT) from dual-energy X-ray absorptiometry (DXA). Data were collected from 182 participants (female = 114; White = 127; Black/African-American (BAA) = 48) which included anthropometrics and indices of VAT produced by near-infrared reactance spectroscopy (NIRS), visual body composition (VBC) and multifrequency BIA (MFBIA). VAT and SAT were collected using DXA. Bivariate and partial correlations were calculated between DXAVAT and DXASAT and other VAT estimates. All VAT indices had positive moderate–strong correlations with VAT (all P < 0·001) and SAT (all P < 0·001). Only waist:hip (r = 0·69), VATVBC (r = 0·84), and VATMFBIA (r = 0·86) had stronger associations with VAT than SAT (P < 0·001). Partial associations between VATVBC and VATMFBIA were only stronger for VAT than SAT in White participants (r = 0·67, P < 0·001) but not female, male, or BAA participants individually. Partial correlations for waist:hip were stronger for VAT than SAT, but only for male (r = 0·40, P < 0·010) or White participants (r = 0·48, P < 0·001). NIRS was amongst the weakest predictors of VAT which was highest in male participants (r = 0·39, P < 0·010) but non-existent in BAA participants (r = –0·02, P > 0·050) after adjusting for SAT. Both anthropometric and consumer-grade VAT indices are consistently better predictors of SAT than VAT. These data highlight the need for a standardised, but convenient, VAT estimation protocol that can account for the relationship between SAT and VAT that differs by sex/race.

This exploratory study investigated the effects of early v. delayed time-restricted eating (TRE) plus caloric restriction (CR) on body weight, body composition and cardiometabolic parameters in adults with overweight and obesity. Adults (20–40 years) were randomised to one of three groups for 8 weeks: early time-restricted eating (eTRE; 08.00–16.00) plus CR, delayed time-restricted eating (dTRE; 12.00–20.00) plus CR or only CR (CR; 08.00–20.00). All groups were prescribed a 25 % energy deficit relative to daily energy requirements. Thirteen participants completed the study in the eTRE and CR groups and eleven in the dTRE group (n 37). After the interventions, there was no significant difference between the three groups for any of the outcomes. Compared with baseline, significant decreases were observed in the body weight (eTRE group: −4·2 kg; 95 % CI, −5·6, −2·7; dTRE group: −4·8 kg; 95 % CI, −5·9, −3·7; CR: −4·0 kg; 95 % CI, −5·9, −2·1), fat mass (eTRE group: −2·9 kg; 95 % CI, −3·9, −1·9; dTRE group: −3·6 kg; 95 % CI, −4·6, −2·5; CR: −3·1 kg; 95 % CI, −4·3, −1·8) and fasting glucose levels (eTRE group: −4 mg/dl; 95 % CI, −8, −1; dTRE group: −2 mg/dl; 95 % CI, −8, 3; CR: −3 mg/dl; 95 % CI, −8, 2). In a free-living setting, TRE with a energetic deficit, regardless of the time of day, promotes similar benefits in weight loss, body composition and cardiometabolic parameters. However, given the exploratory nature of our study, further investigation is needed to confirm these findings.

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.

Previous studies evaluating the effects of betaine supplementation on body composition offer contradictory findings. This systematic review and meta-analysis assessed the effects of betaine supplementation on body composition indices (body mass (BM), BMI, body fat percentage (BFP), fat mass (FM), fat-free mass (FFM)), and dietary intakes. Studies examining the effects of betaine supplementation on body composition and dietary intakes published up to August 2021 were identified through PubMed, the Cochrane Library, Web of Science, Embase, SCOPUS and Ovid databases. Betaine supplementation failed to significantly affect BM ((weighted mean difference (WMD): −0·40 kg, 95 % CI −1·46, 0·64), P = 0·447), BMI ((WMD: −0·05 kg/m2, 95 % CI −0·36, 0·25), P = 0·719), BFP ((WMD: 0·26 %, 95 % CI −0·82, 1·36), P = 0·663), FM ((WMD: −0·57 kg, 95 % CI −2·14, 0·99), P = 0·473) and FFM ((WMD: 0·61 kg, 95 % CI −1·27, 2·49), P = 0·527). Subgroup analyses based on participant’s age (< 40 and > 40 years), sex, BMI, trial duration (< 8 and ≥ 8 weeks), betaine supplementation dosage (< 4 and ≥ 4 g) and health status (healthy or unhealthy) demonstrated similar results. Other than a potential negligible increase in protein intake (WMD: 3·56 g, 95 % CI 0·24, 6·88, P = 0·035), no changes in dietary intakes were observed following betaine supplementation compared with control. The present systematic review and meta-analysis does not show any beneficial effects of betaine supplementation on body composition indices (BM, BMI, FM and FFM).

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 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.

Eggs are considered a high-quality protein source for their complete amino acid profile and digestibility. Therefore, this study aimed to compare the effects of whole egg (WE) v. egg white (EW) ingestion during 12 weeks of resistance training (RT) on the skeletal muscle regulatory markers and body composition in resistance-trained men. Thirty resistance-trained men (mean age 24·6 (sd 2·7) years) were randomly assigned into the WE + RT (WER, n 15) or EW + RT (EWR, n 15) group. The WER group ingested three WE, while the EWR group ingested an isonitrogenous quantity of six EW per d immediately after the RT session. Serum concentrations of regulatory markers and body composition were measured at baseline and after 12 weeks. Significant main effects of time were observed for body weight (WER 1·7, EWR 1·8 kg), skeletal muscle mass (WER 2·9, EWR 2·7 kg), fibroblast growth factor-2 (WER 116·1, EWR 83·2 pg/ml) and follistatin (WER 0·05, EWR 0·04 ng/ml), which significantly increased (P < 0·05), and for fat mass (WER –1·9, EWR –1·1 kg), transforming growth factor-β1 (WER –0·5, EWR −0·1 ng/ml), activin A (WER –6·2, EWR –4·5 pg/ml) and myostatin (WER –0·1, EWR –0·06 ng/ml), which significantly decreased (P < 0·05) in both WER and EWR groups. The consumption of eggs absent of yolk during chronic RT resulted in similar body composition and functional outcomes as WE of equal protein value. EW or WE may be used interchangeably for the dietary support of RT-induced muscular hypertrophy when protein intake is maintained.

This systematic review and meta-analysis compared the effects of different rates of weight loss (WL), but equivalent total WL, on body composition and RMR. Studies examining gradual v. rapid WL on body composition and RMR in participants with overweight/obesity published up to October 2019 were identified through PubMed, the Cochrane Library, Web of Science, Embase, Scopus and Ovid databases. Meta-analysis was carried out using a fixed or random effects model as appropriate. Although the magnitude of WL was similar (mean difference 0·03 kg, 95 % CI –0·65, 0·71), gradual WL promoted greater reductions in fat mass (FM) (–1 kg, 95 % CI –1·70, –0·29) and body fat percentage (BFP) (–0·83 %, 95 % CI –1·49, –0·17). Gradual WL significantly preserved RMR compared with rapid WL (407·48 kJ, 95 % CI 76·76, 118·01). However, there was no significant difference in waist and hip circumferences, waist:hip ratio and fat-free mass (FFM) between gradual and rapid WL. The present systematic review and meta-analysis indicates beneficial effects of gradual WL, as compared with rapid WL, on FM, BFP and RMR in individuals with overweight/obesity. However, FFM changes and anthropometric indices did not significantly differ following different rates of WL.

This study was a randomised, double-blind, placebo-controlled cross-over trial examining the effects of β-hydroxy β-methylbutyrate free acid (HMB-FA) supplementation on muscle protein breakdown, cortisol, testosterone and resting energy expenditure (REE) during acute fasting. Conditions consisted of supplementation with 3 g/d HMB-FA or placebo during a 3-d meat-free diet followed by a 24-h fast. Urine was collected before and during the 24-h fast for analysis of 3-methylhistidine:creatinine ratio (3MH:CR). Salivary cortisol, testosterone, their ratio (T:C), and the cortisol awakening response were assessed. ANOVA was used to analyse all dependent variables, and linear mixed models were used to confirm the absence of carryover effects. Eleven participants (six females, five males) completed the study. Urinary HMB concentrations confirmed compliance with supplementation. 3MH:CR was unaffected by fasting and supplementation, but the cortisol awakening response differed between conditions. In both conditions, cortisol increased from awakening to 30 min post-awakening (P=0·01). Cortisol was reduced from 30 to 45 min post-awakening with HMB-FA (−32 %, d=−1·0, P=0·04), but not placebo (PL) (−6 %, d=−0·2, P=0·14). In males, T:C increased from 0 to 24 h of fasting with HMB-FA (+162 %, d=3·0, P=0·001), but not placebo (+13 %, d=0·4, P=0·60), due to reductions in cortisol. REE was higher at 24 h of fasting than 16 h of fasting independent of supplementation (+4·0 %, d=0·3, P=0·04). In conclusion, HMB-FA may affect cortisol responses, but not myofibrillar proteolysis, during acute 24-h fasting.