Five-component model validation of reference, laboratory and field methods of body composition assessment

External validation of a prediction model for estimating fat mass in Arab children and adolescents

BACKGROUND/AIMS

Current childhood fat mass (FM) assessment techniques are not suitable for clinical and population-level adiposity assessment. A prediction model, which accurately estimates childhood FM using predictor variables of weight, height, age, sex and ethnicity, requires validation in Arab populations. We evaluate the model's performance in Kuwaiti, Lebanese and Moroccan children/adolescents.

METHODS

Data from three cross-sectional studies on 471 individuals, aged 6-15 years, were obtained with complete information on predictors and the outcome of log transformed fat-free mass assessed by reference standard deuterium dilution (lnFFM). Country-specific predictive performance statistics of R2, calibration slope and calibration-in-the-large (measures the calibration/agreement between observed and predicted lnFFM with ideal values of 1 and 0, respectively) and root mean square error (RMSE) were quantified and pooled across countries via random-effects meta-analysis. FM estimates from bioimpedance were also available for Lebanese children and were compared to the reference standard.

RESULTS

The model showed strong predictive ability in all populations. Pooled R2 calibration slope and calibration-in-the-large values on the original lnFFM scale were 87.73% (95% CI: 77.20, 98.26%), 0.95 (95% CI: 0.83, 1.08) and -0.03 (95% CI: -0.16, 0.11), respectively. Model intercepts were recalibrated in each country to improve accuracy; updated country-specific equations are provided. After recalibration, RMSEs on the FM scale were 1.3, 1.6 and 2.8 kg in Kuwait, Lebanon and Morocco, respectively. The RMSE from the model was lower than bioimpedance (2.4 kg) amongst Lebanese children.

INTERPRETATION

The model explained a large proportion of the variance in FM, produced well-calibrated predictions and relatively low RMSEs in Arab settings. It predicted FM more accurately than bioimpedance, indicating its potential for implementation in clinical- and population-level settings, particularly in low- and middle-income countries.

A Decade of Progress in Ultrasound Assessments of Subcutaneous and Total Body Fat: A Scoping Review

Body composition assessment by ultrasonography is a vivid research field. Ultrasound (US) can be used to quantify subcutaneous and visceral fat, to evaluate the quantity and quality of skeletal muscle, and to infer intracellular fat content. This scoping review aimed to summarize recent advancements in subcutaneous fat estimation using US and related applications. A systematic search was conducted on PubMed, MEDLINE, Scopus, Google Scholar, and Web of Science to identify original articles published in English between 1 January 2014 and 20 December 2024. A total of 1869 articles were screened based on their titles and abstracts, and 283 were retrieved for full-text evaluation. Our search and selection strategy resulted in 89 eligible documents. The literature discussed in this review suggests that US is a reliable and valid technique for measuring subcutaneous fat thickness at selected anatomic locations. Standardized measurement protocols enabled accurate subcutaneous adipose tissue (SAT) patterning in various populations (e.g., athletes, children, adults, and patients with anorexia nervosa). Further research is warranted to establish clinically relevant cutoff values. US-derived SAT thicknesses can also provide whole-body fat estimates of fat mass (FM), fat-free mass (FFM), and body fat percentage (%BF). To this end, prediction formulas were developed to ensure agreement with criterion measures given by laboratory techniques, or multicompartment models based on combinations thereof. The resulting assessments of global adiposity were reliable but inaccurate in certain populations (e.g., overweight and obese). Nevertheless, due to its high reliability, US might be used to track changes in body fat content during nutritional and/or lifestyle interventions. Future investigations will be needed to evaluate its accuracy in this respect and to improve the validity of whole-body fat estimation compared to multicompartment models.

Body composition estimation from mobile phone three-dimensional imaging: evaluation of the USA army one-site method

Within the USA military, monitoring body composition is an essential component of predicting physical performance and establishing soldier readiness. The purpose of this study was to explore mobile phone three-dimensional optical imaging (3DO), a user-friendly technology capable of rapidly obtaining reliable anthropometric measurements and to determine the validity of the new Army one-site body fat equations using 3DO-derived abdominal circumference. Ninety-six participants (51 F, 45 M; age: 23·7 ± 6·5 years; BMI: 24·7 ± 4·1 kg/m2) were assessed using 3DO, dual-energy X-ray absorptiometry (DXA) and a 4-compartment model (4C). The validity of the Army equations using 3DO abdominal circumference was compared with 4C and DXA estimates. Compared with the 4C model, the Army equation overestimated BF% and fat mass (FM) by 1·3 ± 4·8 % and 0·9 ± 3·4 kg, respectively, while fat-free mass (FFM) was underestimated by 0·9 ± 3·4 kg (P < 0·01 for each). Values from DXA and Army equation were similar for BF%, FM and FFM (constant errors between -0·1 and 0·1 units; P ≥ 0·82 for each). In both comparisons, notable proportional bias was observed with slope coefficients of -0·08 to -0·43. Additionally, limits of agreement were 9·5-10·2 % for BF% and 6·8-7·8 kg for FM and FFM. Overall, while group-level performance of the one-site Army equation was acceptable, it exhibited notable proportional bias when compared with laboratory criterion methods and wide limits of agreement, indicating potential concerns when applied to individuals. 3DO may provide opportunities for the development of more advanced, automated digital anthropometric body fat estimation in military settings.

Smartphone three-dimensional imaging for body composition assessment using non-rigid avatar reconstruction

Background

Modern digital anthropometry applications utilize smartphone cameras to rapidly construct three-dimensional humanoid avatars, quantify relevant anthropometric variables, and estimate body composition.

Methods

In the present study, 131 participants ([73 M, 58 F] age 33.7 ± 16.0 y; BMI 27.3 ± 5.9 kg/m2, body fat 29.9 ± 9.9%) had their body composition assessed using dual-energy X-ray absorptiometry (DXA) and a smartphone 3D scanning application using non-rigid avatar reconstruction. The performance of two new body fat % estimation equations was evaluated through reliability and validity statistics, Bland-Altman analysis, and equivalence testing.

Results

In the reliability analysis, the technical error of the measurement and intraclass correlation coefficient were 0.5-0.7% and 0.996-0.997, respectively. Both estimation equations demonstrated statistical equivalence with DXA based on ±2% equivalence regions and strong linear relationships (Pearson's r 0.90; concordance correlation coefficient 0.89-0.90). Across equations, mean absolute error and standard error of the estimate values were ~ 3.5% and ~ 4.2%, respectively. No proportional bias was observed.

Conclusion

While continual advances are likely, smartphone-based 3D scanning may now be suitable for implementation for rapid and accessible body measurement in a variety of applications.

Validation of Bioelectrical Impedance Devices for the Determination of Body Fat Percentage in Firefighters

ABSTRACT

Jagim, AR, Luedke, J, Erickson, JL, Fields, JB, and Jones, MT. Validation of bioelectrical impedance devices for the determination of body fat percentage in firefighters. J Strength Cond Res 38(8): e448-e453, 2024-To cross-validate bioelectrical impedance devices for the determination of body fat percentage (BF%) in firefighters. Twenty-eight structural firefighters were evaluated (female, n = 2; male, n = 26 [mean ± SD] age: 38.2 ± 8.3 years; height: 180.2 ± 7.5 cm; body mass: 86.7 ± 20.8 kg; body mass index: 25.8 ± 7.8 kg·m-2) using multifrequency bioelectrical impedance analysis (MFBIA) hand-to-foot device, and single-frequency BIA foot scale (F2FBIA), and a single-frequency handheld BIA device (HHBIA). Dual X-ray absorptiometry served as the criterion. Validity metrics were examined to establish each method's performance. Body fat % values produced by MFBIA (r = 0.913), F2FBIA (r = 0.695), and HHBIA (r = 0.876) were strongly associated (p < 0.001) with criterion BF% measures. However, MFBIA, F2FBIA, and HHBIA all significantly (p < 0.001) underestimated BF% when compared with the criterion measure. Constant error ranged between 4.0 and 5.5% across all BIA devices. Despite strong associations between the BIA devices included in the current study and the criterion measure, all BIA devices underestimated BF%, which resulted in an overestimation of fat-free mass. In addition, proportional bias was observed in which BF% was overestimated at lower values and underestimated at higher values.

Mobile phone applications for 3-dimensional scanning and digital anthropometry: a precision comparison with traditional scanners

BACKGROUND

The precision of digital anthropometry through 3-dimensional (3D) scanning has been established for relatively large, expensive, non-portable systems. The comparative performance of modern mobile applications is unclear.

SUBJECTS/METHODS

Forty-six adults (age: 23.3 ± 5.3 y; BMI: 24.4 ± 4.1 kg/m2) were assessed in duplicate using: (1) a mobile phone application capturing two individual 2D images, (2) a mobile phone application capturing serial images collected during a subject's complete rotation, (3) a traditional scanner with a time of flight infrared sensor collecting visual data from a subject being rotated on a mechanical turntable, and (4) a commercial measuring booth with structured light technology using 20 infrared depth sensors positioned in the booth. The absolute and relative technical error of measurement (TEM) and intraclass correlation coefficient (ICC) for each method were established.

RESULTS

Averaged across circumferences, the absolute TEM, relative TEM, and ICC were (1) 0.9 cm, 1.5%, and 0.975; (2) 0.5 cm, 0.9%, and 0.986; (3) 0.8 cm, 1.5%, and 0.974; and (4) 0.6 cm, 1.1%, and 0.985. For total body volume, these values were (1) 2.2 L, 3.0%, and 0.978; (2) 0.8 L, 1.1%, and 0.997; (3) 0.7 L, 0.9%, and 0.998; and (4) 0.8 L, 1.1%, and 0.996, with segmental volumes demonstrating higher relative errors.

CONCLUSION

A 3D scanning mobile phone application involving full rotation of subjects in front of a smartphone camera exhibited similar reliability to larger, less portable, more expensive 3D scanners. In contrast, larger errors were observed for a mobile scanning application utilizing two 2D images, although the technical errors were acceptable for some applications.

Agreement between bioimpedance analysis and ultrasound scanning in body composition assessment

OBJECTIVES

This study aimed at evaluating the agreement between bioelectrical impedance analysis (BIA) using ABC-02 Medas and A-mode ultrasound (AUS) using BodyMetrix™ BX2000 for fat mass (FM), fat free mass (FFM), and body fat percentage (%BF) in females.

METHODS

The cross-sectional, single-center, observational study was performed in 206 female subjects aged 18-67 years. The examination program included measurements of body height and weight along with waist, hip circumferences, and body composition analysis. The measurements were performed by ultrasound scanner and bioimpedance analyzer.

RESULTS

We found that 20.9% of women were obese based on BMI (≥30 kg/m2), which was significantly lower when using a criterion based on body fat percentage (%BF ≥ 30%) measured with US (53.4%, p = .0056) or BIA (54.8%, p = .0051). At the group level, both methods were found interchangeable and showed practically negligible differences (0.1% for %BF, 0.5 kg for FM, and 0.4 kg for FFM). Agreement analysis conducted in the whole sample revealed a low level of agreement in estimating %BF (CCC = 0.72 0.77 0.82) and FFM (CCC = 0.81 0.84 0.86), and medium level of agreement in estimating FM (CCC = 0.91 0.93 0.94). The level of agreement in estimating %BF and FFM was improved to the medium level with the use of newly generated prediction equations.

CONCLUSION

Thus, the proposed equations can be used for conversion of body composition results obtained by AUS into the BIA data.

Tracking Body Composition Over a Competitive Season in Elite Soccer Players Using Laboratory- and Field-Based Assessment Methods

ABSTRACT

Bongiovanni, T, Lacome, M, Rodriguez, C, and Tinsley, GM. Tracking body composition over a competitive season in elite soccer players using laboratory- and field-based assessment methods. J Strength Cond Res 38(3): e104-e115, 2024-The purpose of this study was to describe body composition changes in professional soccer players over the course of a competitive playing season and compare the ability of different assessment methods to detect changes. Twenty-one elite male soccer players (age: 23.7 ± 4.8 years; height: 185.0 ± 5.2 cm; body mass: 80.7 ± 5.5 kg; body fat: 12.8 ± 2.2%) playing for an Italian national second league (Serie B) championship team were assessed at 4 time points throughout a competitive season: T0 (mid-October), T1 (mid-December), T2 (mid-February), and T3 (end of April). Dual-energy x-ray absorptiometry (DXA), skinfolds (SKF), and bioelectrical impedance analysis were performed at each time point, and multiple SKF-based equations were applied. A modified 4-compartment (4C) model was also produced. Data were analyzed using repeated measures analysis of variance, relevant post hoc tests, and Pearson's correlations. Dual-energy x-ray absorptiometry, 4C, and the SKF-based equations of Reilly and Civar detected differences in fat-free mass (FFM) between time points, with the most differences observed for DXA. Fat-free mass increased from T0 values to a peak at T2, followed by a decrease by T3, although FFM values remained higher than T0. Fat-free mass gain was primarily driven by increases in the lower limbs. Fat-free mass changes between all methods were significantly correlated, with correlation coefficients of 0.70-0.97. No significant differences between time points were observed for absolute fat mass or body fat percentage, although significant correlations between several methods for change values were observed. Select laboratory and field methods can detect changes in FFM over the course of a season in elite, professional soccer athletes, with a more limited ability to detect changes in adiposity-related variables. For SKF in this population, the equation of Reilly is recommended.

Deuterium oxide validation of bioimpedance total body water estimates in Hispanic adults

Background

To date, body composition assessments in Hispanics, computed via bioimpedance devices, have primarily focused on body fat percent, fat mass, and fat-free mass instead of total body water (TBW). Additionally, virtually no information is available on which type of bioimpedance device is preferred for TBW assessments in Hispanic populations.

Purpose

The purpose of this study was to validate two bioimpedance devices for the estimate of TBW in Hispanics adults when using a criterion deuterium oxide (D2O) technique.

Methods

One-hundred thirty individuals (males: n = 70; females: n = 60) of Hispanic descent had TBW estimated via D2O, single-frequency bioimpedance analysis ([SF-BIA] Quantum V, RJL Systems) and bioimpedance spectroscopy ([BIS] SFB7 Impedimed).

Results

The mean values for SF-BIA were significantly lower than D2O when evaluating the entire sample (37.4 L and 38.2 L, respectively; p < 0.05). In contrast, TBW values were not statistically significant when comparing D2O against BIS (38.4 L, p > 0.05). Bland-Altman analysis indicated no proportional bias when evaluating the entire sample for SF-BIA or BIS. The standard error of estimate and total error values were ≤ 2.3 L and Lin's concordance correlation coefficient were ≥ 0.96 for all comparisons.

Conclusion

The SF-BIA and BIS devices evaluated in the current study hold promise for accurate estimation of TBW in Hispanic adults. While both methods demonstrated relatively low errors relative to the D2O criterion, BIS exhibited a more consistent performance, particularly at the group level. These findings provide essential information for researchers and clinical nutrition practitioners assessing TBW in Hispanic adults.

Validation of skinfold equations and alternative methods for the determination of fat-free mass in young athletes

Intoduction

To cross-validate skinfold (SKF) equations, impedance devices, and air-displacement plethysmography (ADP) for the determination of fat-free mass (FFM).

Methods

Male and female youth athletes were evaluated (n = 91[mean ± SD] age: 18.19 ± 2.37 year; height: 172.1 ± 9.8 cm; body mass: 68.9 ± 14.5 kg; BMI: 23.15 ± 3.2 kg m-2; body fat: 19.59 ± 6.9%) using underwater weighing (UWW), ADP, and SKF assessments. A 3-compartment (3C) model (i.e., UWW and total body water) served as the criterion, and alternate body density (Db) estimates from ADP and multiple SKF equations were obtained. Validity metrics were examined to establish each method's performance. Bioelectrical impedance analysis (BIA), bioimpedance spectroscopy (BIS), and the SKF equations of Devrim-Lanpir, Durnin and Womersley, Jackson and Pollock (7-site), Katch, Loftin, Lohman, Slaughter, and Thorland differed from criterion.

Results

For females, Pearson's correlations between the 3C model and alternate methods ranged from 0.51 to 0.92, the Lin's concordance correlation coefficient (CCC) ranged from 0.41 to 0.89, with standard error of the estimate (SEE) ranges of 1.9-4.6 kg. For SKF, the Evans 7-site and J&P 3 Site equations performed best with CCC and SEE values of 0.82, 2.01 kg and 0.78, 2.21 kg, respectively. For males, Pearson's correlations between the 3C model and alternate methods ranged from 0.50 to 0.95, CCC ranges of 0.46-0.94, and SEE ranges of 3.3-7.6 kg. For SKF, the Evans 3-site equation performed best with a mean difference of 1.8 (3.56) kg and a CCC of 0.93.

Discussion

The Evans 7-site and 3-site SKF equations performed best for female and male athletes, respectively. The field 3C model can provide an alternative measure of FFM when necessary.

Evaluation of a Rapid Four-Compartment Model and Stand-Alone Methods in Hispanic Adults

BACKGROUND

A rapid 4-compartment (4C) model integrates dual-energy x-ray absorptiometry (DXA) and multi-frequency bioimpedance analysis (MFBIA), which may be useful for clinical and research settings seeking to employ a multi-compartment model.

OBJECTIVES

This study aimed to determine the added benefit of a rapid 4C model over stand-alone DXA and MFBIA when estimating body composition.

METHODS

One hundred and thirty participants (n = 60 male; n = 70 female) of Hispanic descent were included in the present analysis. A criterion 4C model that employed air displacement plethysmography (body volume), deuterium oxide (total body water), and DXA (bone mineral) was used to measure fat mass (FM), fat-free mass (FFM), and body fat percent (%BF). A rapid 4C model (DXA-derived body volume and bone mineral; MFBIA-derived total body water) and stand-alone DXA (GE Lunar Prodigy) and MFBIA (InBody 570) assessments were compared against the criterion 4C model.

RESULTS

Lin's concordance correlation coefficient values were >0.90 for all comparisons. The standard error of the estimates ranged from 1.3 to 2.0 kg, 1.6 to 2.2 kg, and 2.1 to 2.7% for FM, FFM, and %BF, respectively. The 95% limits of agreement ranged from ±3.0 to 4.2 kg, ±3.1 to 4.2 kg, and ±4.9 to 5.2% for FM, FFM, and %BF, respectively.

CONCLUSIONS

Results revealed that all 3 methods provided acceptable body composition results. The MFBIA device used in the current study may be a more economically friendly option than DXA or when there is a need to minimize radiation exposure. Nonetheless, clinics and laboratories that already have a DXA device in place or that value having the lowest individual error when conducting a test may consider continuing to use the machine. Lastly, a rapid 4C model may be useful for assessing body composition measures observed in the current study and those provided by a multi-compartment model (e.g., protein).

Learning Effects in Air Displacement Plethysmography

Air displacement plethysmography (ADP) is a widespread technique for assessing global obesity in both health and disease. The reliability of ADP has been demonstrated by studies focused on duplicate trials. The present study was purported to evaluate learning effects on the reliability of body composition assessment using the BOD POD system, the sole commercially available ADP instrument. To this end, quadruplicate trials were performed on a group of 105 subjects (51 women and 54 men). We estimated measurement error from pairs of consecutive trials-(1,2), (2,3), and (3,4)-to test the hypothesis that early measurements are subject to larger errors. Indeed, statistical analysis revealed that measures of reliability inferred from the first two trials were inferior to those computed for the other pairs of contiguous trials: for percent body fat (%BF), the standard error of measurement (SEM) was 1.04% for pair (1,2), 0.71% for pair (2,3), and 0.66% for pair (3,4); the two-way random effects model intraclass correlation coefficient (ICC) was 0.991 for pair (1,2), and 0.996 for pairs (2,3) and (3,4). Our findings suggest that, at least for novice subjects, the first ADP test should be regarded as a practice trial. When the remaining trials were pooled together, the reliability indices of single ADP tests were the following: ICC = 0.996, SEM = 0.70%, and minimum detectable change (MDC) = 1.93% for %BF, and ICC = 0.999, SEM = 0.49 kg, and MDC = 1.35 kg for fat-free mass (FFM). Thus, the present study pleads for eliminating learning effects to further increase the reliability of ADP.

Effects of time-restricted feeding (16/8) combined with a low-sugar diet on the management of non-alcoholic fatty liver disease: A randomized controlled trial

OBJECTIVES

Emerging studies have employed time-restricted feeding (TRF) and a low-sugar diet alone in the management of non-alcoholic fatty liver disease (NAFLD), but their combination has not been tested. The aim of this study was to investigate the effects of TRF combined with a low-sugar diet on NAFLD parameters, cardiometabolic and inflammatory biomarkers, and body composition in patients with NAFLD.

METHODS

A 12-wk randomized controlled trial was performed to compare the effects of TRF (16 h fasting/8 h feeding daily [16/8]) plus a low-sugar diet versus a control diet based on traditional meal distribution in patients with NAFLD. Changes in body composition, anthropometric indices, and liver and cardiometabolic markers were investigated.

RESULTS

TRF 16/8 with a low-sugar diet reduced body fat (26.7 ± 5.4 to 24.2 ± 4.9 kg), body weight (83.8 ± 12.7 to 80.5 ± 12.1 kg), waist circumference (104.59 ± 10.47 to 101.91 ± 7.42 cm), and body mass index (29.1 ± 2.6 to 28 ± 2.7 kg/m2), as well as circulating levels of fasting blood glucose and liver (alanine aminotransferase, 34 ± 13.9 to 21.2 ± 5.4 U/L; aspartate aminotransferase, 26.3 ± 6.2 to 20.50 ± 4 U/L; γ-glutamyl transpeptidase, 33 ± 15 to 23.2 ± 11.1 U/L; fibrosis score, 6.3 ± 1 to 5.2 ± 1.2 kPa; and controlled attenuation parameter, 322.9 ± 34.9 to 270.9 ± 36.2 dB/m), lipids (triacylglycerols, 201.5 ± 35.3 to 133.3 ± 48.7 mg/dL; total cholesterol, 190 ± 36.6 to 157.8 ± 33.6 mg/dL; and low-density lipoprotein cholesterol, 104.6 ± 27.3 to 84 ± 26.3 mg/dL), and inflammatory markers (high-sensitivity C-reactive protein, 3.1 ± 1.1 to 2 ± 0.9 mg/L; and cytokeratin-18, 1.35 ± 0.03 to 1.16 ± 0.03 ng/mL). These results were statistically significant (P < 0.05) compared with the control group.

CONCLUSIONS

TRF plus a low-sugar diet can reduce adiposity and improve liver, lipid, and inflammatory markers in patients with NAFLD.

Agreement and Precision of Deuterium Dilution for Total Body Water and Multicompartment Body Composition Assessment in Collegiate Athletes

BACKGROUND

Deuterium oxide (D2O) dilution is the criterion method for total body water (TBW) measurement, but results may vary depending on the specimen type, analysis method, and analyzing laboratory. Bioelectrical impedance (BIA) estimates TBW, but results may vary by device make and model.

OBJECTIVES

We investigated the accuracy and precision of TBW estimates and how measurement conditions affected the accuracy of body composition using multicompartment body composition models.

METHODS

Eighty collegiate athletes received duplicate TBW measures acquired from 3 BIA devices (S10, SFB7, and SOZO) and from unique D2O combinations of specimen type (saliva, urine), analysis methodology [Fourier transform infrared spectrophotometry (FTIR), isotope-ratio mass spectrometry (IRMS)], and 3 different laboratories. TBW measures were substituted into 2-compartment (2C) and 5-compartment (5C) body composition models. Criterion measures were compared using Lin's concordance correlation coefficient cutoff of poor (<0.90), moderate (0.90-0.95), substantial (0.95-0.99), and almost perfect (>0.99).

RESULTS

Fifty-one participants (26 female) completed the protocol. Using IRMS saliva as the criterion TBW, all other measures produced a substantial or almost perfect agreement, except for SFB7 (poor) and SOZO (moderate). The 2C body composition measures using D2O and BIA produced poor agreement except for moderate agreement for lab 3 FTIR saliva. The 5C body composition measures using D2O produced a substantial agreement, whereas the BIA device S10 and SOZO had a moderate agreement, while the SFB7 had a poor agreement to the criterion. Test-retest precision varied between techniques from 0.3% to 1.2% for TBW.

CONCLUSIONS

Small differences in TBW measurement led to significant differences in 2C models. The 5C models partially mitigate differences seen in 2C models when different TBW measures are used. Interchanging TBW measures in multicompartment models can be problematic and should be performed with these considerations.

Tracking changes in body composition: comparison of methods and influence of pre-assessment standardisation

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.

Effect of total body water estimates via bioimpedance on bod pod-based three-compartment body fat models

BACKGROUND/OBJECTIVES

Previous research has compared 2- and 3-compartment (2C and 3C, respectively) models against criterion 4-compartment (4C) models while utilizing the same body density (D_b) method for all measures. This design induces an inherent bias and obscures the added benefit of a 3C model over the simpler 2-compartment (2C) models.

PURPOSE

The purpose of this study was to determine the effect of total body water estimates via single-frequency (SF-BIA) and multi-frequency (MF-BIA) bioimpedance analysis on body fat estimates derived from air displacement plethysmography (ADP)-derived 3C models.

SUBJECTS/METHODS

A sample of 95 females and 82 males (n = 177) participated in this study. Underwater weighing, dual energy X-ray absorptiometry, and bioimpedance spectroscopy were used to calculate percent fat (%Fat) via a criterion 4C model (4C_CRITERION). %Fat was predicted via 3C_MFBIA (ADP and MF-BIA), 3C_SFBIA (ADP and SF-BIA), and a stand-alone 2-compartment (2C) model, based upon ADP, when using Siri and Brozek body density conversion formulas (2C_SIRI and 2C_BROZEK. respectively).

RESULTS

The standard error of estimate (SEE) was lowest for 3C_SFBIA when evaluated in females and males (2.72% and 2.31%, respectively) and highest for 2C_SIRI (3.98% and 3.84%, respectively). Similarly, the total error (TE) for females and males was lowest for 3C_SFBIA (3.21% and 2.67%, respectively) and highest for 2C_SIRI (4.58% and 4.48%, respectively) and 2C_BROZEK (4.65% and 4.33%, respectively).

CONCLUSIONS

Results suggest that SF-BIA and MF-BIA can improve the estimation of %Fat, beyond simpler 2C models, when integrated with ADP in a more advanced 3C model. Furthermore, the present study revealed that 3C_SFBIA was the best overall prediction model based upon TE values. The current study results support the integration of ADP and bioimpedance technology as part of a 3C model for the improvement of %Fat estimates over simpler 2C models.

Agreement Between A 2-Dimensional Digital Image-Based 3-Compartment Body Composition Model and Dual Energy X-Ray Absorptiometry for The Estimation of Relative Adiposity

The purpose of this study was to compare relative adiposity (%Fat) derived from a 2-dimensional image-based 3-component (3C) model (%Fat_3C-IMAGE) and dual-energy X-ray absorptiometry (DXA) (%Fat_DXA) against a 5-component (5C) laboratory criterion (%Fat_5C). 57 participants were included (63.2% male, 84.2% White/Caucasian, 22.5±4.7 yrs., 23.9±2.8 kg/m²). For each participant, body mass and standing height were measured to the nearest 0.1 kg and 0.1 cm, respectively. A digital image of each participant was taken using a 9.7 inch, 16g iPad Air 2 and analyzed using a commercially available application (version 1.1.2, made Health and Fitness, USA) for the estimation of body volume (BV) and inclusion in %Fat_3C-IMAGE . %Fat_3C-IMAGE and %Fat_5C included measures of total body water derived from bioimpedance spectroscopy. The criterion %Fat_5C included BV estimates derived from underwater weighing and bone mineral content measures via DXA. %Fat_DXA estimates were calculated from a whole-body DXA scan. A standardized mean effect size (ES) assessed the magnitude of differences between models with values of 0.2, 0.5, and 0.8 for small, moderate, and large differences, respectively. Data are presented as mean ± standard deviation. A strong correlation (r = 0.94, p <.001) and small mean difference (ES = 0.24, p <.001) was observed between %Fat_3C-IMAGE (19.20±5.80) and %Fat_5C (17.69±6.20) whereas a strong correlation (r = 0.87, p <.001) and moderate-large mean difference (ES = 0.70, p <.001) was observed between %Fat_DXA (22.01±6.81) and %Fat_5C. Furthermore, %Fat_3C-IMAGE (SEE = 2.20 %Fat, TE= 2.6) exhibited smaller SEE and TE than %Fat_DXA (SEE = 3.14 %Fat, TE = 5.5). The 3C image-based model performed slightly better in our sample of young adults than the DXA 3C model. Thus, the 2D image analysis program provides an accurate and non-invasive estimate of %Fat within a 3C model in young adults. Compared to DXA, the 3C image-based model allows for a more cost-effective and portable method of body composition assessment, potentially increasing accessibility to multi-component methods.

Development and validation of a simple anthropometric equation to predict appendicular skeletal muscle mass

BACKGROUND & AIMS

A limited number of studies have developed simple anthropometric equations that can be implemented for predicting muscle mass in the local community. Several studies have suggested calf circumference as a simple and accurate surrogate maker for muscle mass. We aimed to develop and cross-validate a simple anthropometric equation, which incorporates calf circumference, to predict appendicular skeletal muscle mass (ASM) using dual-energy X-ray absorptiometry (DXA). Furthermore, we conducted a comparative validity assessment of our equation with bioelectrical impedance analysis (BIA) and two previously reported equations using similar variables.

METHODS

ASM measurements were recorded for 1262 participants (837 men, 425 women) aged 40 years or older. Participants were randomly divided into the development or validation group. Stepwise multiple linear regression was applied to develop the DXA-measured ASM prediction equation. Parameters including age, sex, height, weight, waist circumference, and calf circumference were incorporated as predictor variables. Total error was calculated as the square root of the sum of the square of the difference between DXA-measured and predicted ASMs divided by the total number of individuals.

RESULTS

The most optimal ASM prediction equation developed was: ASM (kg) = 2.955 × sex (men = 1, women = 0) + 0.255 × weight (kg) - 0.130 × waist circumference (cm) + 0.308 × calf circumference (cm) + 0.081 × height (cm) - 11.897 (adjusted R² = 0.94, standard error of the estimate = 1.2 kg). Our equation had smaller total error and higher intraclass correlation coefficient (ICC) values than those for BIA and two previously reported equations, for both men and women (men, total error = 1.2 kg, ICC = 0.91; women, total error = 1.1 kg, ICC = 0.80). The correlation between DXA-measured ASM and predicted ASM by the present equation was not significantly different from the correlation between DXA-measured ASM and BIA-measured ASM.

CONCLUSIONS

The equation developed in this study can predict ASM more accurately as compared to equations where calf circumference is used as the sole variable and previously reported equations; it holds potential as a reliable and an effective substitute for estimating ASM.

Offseason Body Composition Changes Detected by Dual-Energy X-ray Absorptiometry versus Multifrequency Bioelectrical Impedance Analysis in Collegiate American Football Athletes

Tracking changes in body composition may provide key information about the effectiveness of training programs for athletes. This study reports on the agreement between bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) for tracking body composition changes during a seven-week offseason training program in 29 NCAA collegiate American football players. Body composition in subjects (mean ± SD; age: 19.7 ± 1.5 y; height: 179.8 ± 6.6 cm; body mass (BM: 96.1 ± 12.6 kg; DXA body fat: 20.9 ± 4.4%) was estimated using BIA (InBody 770) and DXA (Hologic Horizon) before and after the training intervention. Repeated measures ANOVA and post hoc comparisons were performed. Longitudinal agreement between methods was also examined by concordance correlation coefficient (CCC) and Bland-Altman analysis alongside linear regression to identify bias. Significant method by time interactions were observed for BM (DXA: 1.1 ± 2.4 kg; BIA: 1.4 ± 2.5 kg; p < 0.03), arms fat-free mass (FFM) (DXA: 0.4 ± 0.5 kg; BIA: 0.2 ± 0.4 kg; p < 0.03), and legs FFM (DXA: 0.6 ± 1.1 kg; BIA: 0.1 ± 0.6 kg; p < 0.01). Post hoc comparisons indicated that DXA-but not BIA-detected increases in FFM of the arms and legs. Time main effects, but no method by time interactions, were observed for total FFM (DXA: 1.6 ± 1.9 kg; BIA: 1.2 ± 2.1 kg; p = 0.004) and trunk FFM (DXA: 0.7 ± 1.3 kg; BIA: 0.5 ± 1.0 kg; p = 0.02). Changes in total BM (CCC = 0.96), FFM (CCC = 0.49), and fat mass (CCC = 0.50) were significantly correlated between BIA and DXA. DXA and BIA may similarly track increases in whole-body FFM in American collegiate football players; however, BIA may possess less sensitivity in detecting segmental FFM increases, particularly in the appendages.

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.