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

The applicability of a commercial 3DO body scanner in measuring body composition in Chinese adults with overweight and obesity: a secondary analysis based on a weight-loss clinical trial

BACKGROUND

A commercial three-dimensional optical (3DO) scanning system was reported to be used in body composition assessment. However, the applicability in Chinese adults has yet to be well-studied.

METHODS

This secondary analysis was based on a 16-week weight-loss clinical trial with an optional extension to 24 weeks. Waist and hip circumference and body composition were measured by 3DO scanning at each follow-up visit during the study. Bioelectrical impedance analysis (BIA) was also performed to confirm the reliability of 3DO scanning at each visit. We used Lin's concordance correlation coefficients (CCC) to evaluate the correlation between the two methods above-mentioned. Bland-Altman analysis was also performed to evaluate the agreement and potential bias between different methods.

RESULTS

A total number of 70 Chinese adults overweight and obese (23 men and 47 women, aged 31.8 ± 5.8 years) were included in the analysis, which resulted in 350 3DO scans and corresponding 350 BIA measurements. The percent body fat, fat mass, and fat-free mass were 33.9 ± 5.4%, 26.7 ± 4.6 kg, and 50.3 ± 8.7 kg before the trial by 3DO scanning. And they were 30.5 ± 5.8%, 22.5 ± 4.7 kg, and 49.4 ± 8.3 kg after 16 weeks of the trial. Compared with BIA, 3DO scanning performed best in the assessment of fat-free mass (CCC = 0.89, 95%CI: 0.86, 0.90), then followed by fat mass (CCC = 0.76, 95%CI: 0.71, 0.80) and percent body fat (CCC = 0.70, 95%CI: 0.64, 0.75). Subgroup analysis showed that 3DO scanning and BIA correlated better in women than that in men, and correlated better in measuring fat-free mass in participants with larger body weight (BMI ≥28.0 kg/m2) than those with smaller body weight (<28.0 kg/m2).

CONCLUSIONS

3DO scanning is an effective technology to monitor changes in body composition in Chinese adults overweight and obese. However its accuracy and reliability in different ethnicities needs further exploration.

Prediction of total and regional body composition from 3D body shape

Accurate assessment of body composition is essential for evaluating the risk of chronic disease. 3D body shape, obtainable using smartphones, correlates strongly with body composition. We present a novel method that fits a 3D body mesh to a dual-energy X-ray absorptiometry (DXA) silhouette (emulating a single photograph) paired with anthropometric traits, and apply it to the multi-phase Fenland study comprising 12,435 adults. Using baseline data, we derive models predicting total and regional body composition metrics from these meshes. In Fenland follow-up data, all metrics were predicted with high correlations (r > 0.86). We also evaluate a smartphone app which reconstructs a 3D mesh from phone images to predict body composition metrics; this analysis also showed strong correlations (r > 0.84) for all metrics. The 3D body shape approach is a valid alternative to medical imaging that could offer accessible health parameters for monitoring the efficacy of lifestyle intervention programmes.

Various modalities of resistance exercise promote similar acute cognitive improvements and hemodynamic increases in young, healthy adults

The aim was to examine the effects of modalities of acute resistance exercise (RE) on cognition and hemodynamics including internal carotid artery (ICA) blood flow (BF). Twenty adults completed familiarization and experimental visits. One-repetition maximum (1RM) for bilateral leg extension was quantified, and baseline executive functioning was determined from three run-in visits. Subsequent visits included three randomized, volume-equated, acute exercise bouts of 30 %1RM+blood flow restriction (BFR), 30 %1RM, and 70 %1RM. Both 30 %1RM trials completed four sets of exercise (1 × 30, 3 × 15), and the 70 %1RM condition completed four sets of 8 repetitions. BFR was induced with 40 % of the pressure to occlude the femoral arteries. 11 min following each exercise, participants completed the Stroop and Shifting Attention Tests. Baseline and post-exercise values were used to calculate change scores. The resulting mean change scores were evaluated with mixed factorial ANOVAs. A p≤0.05 was considered significant. All measured outcome variables increased in response to exercise. The ANOVAs for cognitive scores indicated no significant (p>0.05) interactions. For cognitive flexibility and executive function index, there were main effects of Sex. Change scores of the females were significantly greater than the males for cognitive flexibility (7.6 ± 5.9 vs. -2.6 ± 8.4 au; p=0.007) and executive function index (7.4 ± 4.6 vs. -2.5 ± 6.5 au; p=0.001). For ICA BF, there was no significant interaction or any main effect. The females exhibited a smaller exercise-induced increase in blood pressure compared to the males (17.7 ± 5.9 vs. 11.0 ± 4.1 mmHg; p=0.010). Each RE modality yielded acute improvements in cognition, but only for females. There were no cognitive improvements related to BFR such that each RE bout yielded similar results.

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.

Myofascial release induces declines in heart rate and changes to microvascular reactivity in young healthy adults

OBJECTIVES

The purpose of this study was to compare physiological responses to myofascial release (MFR) and passive limb movement (PLM).

DESIGN

Nineteen (23 ± 2.6yrs) adults (10 men and 9 women) completed two experiments on separate days: MFR and PLM. Participation included collecting ultrasound images, blood pressure, and heart rate (HR) as well as performing a vascular occlusion test (VOT). The VOT assessed muscle tissue oxygenation (StO2) with near-infrared spectroscopy. Experiments consisted of moving the upper limb to release subtle barriers of resistance in the muscle/fascia (MFR) and passive, assisted range of motion (PLM).

RESULTS

There was a significantly (p = 0.012) greater decrease in HR following MFR (-7.3 ± 5.2 BPM) than PLM (-1.3 ± 0.9 BPM). There was an equivalent change in brachial blood flow (-17.3 ± 23.0 vs. -11.9 ± 14.9 mL min-1; p = 0.37) and vascular conductance (-19.3 ± 31.1 vs. -12.4 ± 15.3 mL min-1 mmHg-1; p = 0.38). Microvascular responses differed between the experiments such that MFR exhibited greater area under the curve (AUC, 1503 ± 499.1%∙s-1 vs. 1203 ± 411.1%∙s-1; p = 0.021) and time to maximum StO2 (40.0 ± 8.4s vs. 35.8 ± 7.3s; p = 0.009).

CONCLUSIONS

As evidenced by HR, MFR induced greater parasympathetic activity than PLM. The greater AUC and time to StO2max following MFR suggested a spillover effect to induce prolonged hyper-saturation. These results may be of interest to those investigating possible MFR-related rehabilitative benefits.

Development and cross-validation of a circumference-based predictive equation to estimate body fat in an active population

Objective

The U.S. Army uses sex-specific circumference-based prediction equations to estimate percent body fat (%BF) to evaluate adherence to body composition standards. The equations are periodically evaluated to ensure that they continue to accurately assess %BF in a diverse population. The objective of this study was to develop and validate alternative field expedient equations that may improve upon the current Army Regulation (AR) body fat (%BF) equations.

Methods

Body size and composition were evaluated in a representatively sampled cohort of 1904 active-duty Soldiers (1261 Males, 643 Females), using dual-energy X-ray absorptiometry (%BFDXA), and circumferences obtained with 3D imaging and manual measurements. Sex stratified linear prediction equations for %BF were constructed using internal cross validation with %BFDXA as the criterion measure. Prediction equations were evaluated for accuracy and precision using root mean squared error, bias, and intraclass correlations. Equations were externally validated in a convenient sample of 1073 Soldiers.

Results

Three new equations were developed using one to three circumference sites. The predictive values of waist, abdomen, hip circumference, weight and height were evaluated. Changing from a 3-site model to a 1-site model had minimal impact on measurements of model accuracy and performance. Male-specific equations demonstrated larger gains in accuracy, whereas female-specific equations resulted in minor improvements in accuracy compared to existing AR equations. Equations performed similarly in the second external validation cohort.

Conclusions

The equations developed improved upon the current AR equation while demonstrating robust and consistent results within an external population. The 1-site waist circumference-based equation utilized the abdominal measurement, which aligns with associated obesity related health outcomes. This could be used to identify individuals at risk for negative health outcomes for earlier intervention.

Patient perceptions of three-dimensional (3D) surface imaging technology and traditional methods used to assess anthropometry

Background

Obesity and overweight are commonplace, yet attrition rates in weight management clinics are high. Traditional methods of body measurement may be a deterrent due to invasive and time-consuming measurements and negative experiences of how data are presented back to individuals. Emerging new technologies, such as three-dimensional (3D) surface imaging technology, might provide a suitable alternative. This study aimed to understand acceptability of traditional and 3D surface imaging-based body measures, and whether perceptions differ between population groups.

Methods

This study used a questionnaire to explore body image, body measurement and shape, followed by a qualitative semi-structured interview and first-hand experience of traditional and 3D surface imaging-based body measures.

Results

49 participants responded to the questionnaire and 26 participants attended for the body measurements and interview over a 2-month period. There were 3 main themes from the qualitative data 1) Use of technology, 2) Participant experience, expectations and perceptions and 3) Perceived benefits and uses.

Conclusion

From this study, 3D-surface imaging appeared to be acceptable to patients as a method for anthropometric measurements, which may reduce anxiety and improve attrition rates in some populations. Further work is required to understand the scalability, and the role and implications of these technologies in weight management practice. (University Research Ethics Committee reference number ER41719941).

3D Convolutional Deep Learning for Nonlinear Estimation of Body Composition from Whole-Body Morphology

Total and regional body composition are strongly correlated with metabolic syndrome and have been estimated non-invasively from 3D optical scans using linear parameterizations of body shape and linear regression models. Prior works produced accurate and precise predictions on many, but not all, body composition targets relative to the reference dual X-Ray absorptiometry (DXA) measurement. Here, we report the effects of replacing linear models with nonlinear parameterization and regression models on the precision and accuracy of body composition estimation in a novel application of deep 3D convolutional graph networks to human body composition modeling. We assembled an ensemble dataset of 4286 topologically standardized 3D optical scans from four different human body shape databases, DFAUST, CAESAR, Shape Up! Adults, and Shape Up! Kids and trained a parameterized shape model using a graph convolutional 3D autoencoder (3DAE) in lieu of linear PCA. We trained a nonlinear Gaussian process regression (GPR) on the 3DAE parameter space to predict body composition via correlations to paired DXA reference measurements from the Shape Up! scan subset. We tested our model on a set of 424 randomly withheld test meshes and compared the effects of nonlinear computation against prior linear models. Nonlinear GPR produced up to 20% reduction in prediction error and up to 30% increase in precision over linear regression for both sexes in 10 tested body composition variables. Deep shape features produced 6-8% reduction in prediction error over linear PCA features for males only and a 4-14% reduction in precision error for both sexes. Our best performing nonlinear model predicting body composition from deep features outperformed prior work using linear methods on all tested body composition prediction metrics in both precision and accuracy. All coefficients of determination (R2) for all predicted variables were above 0.86. We show that GPR is a more precise and accurate method for modeling body composition mappings from body shape features than linear regression. Deep 3D features learned by a graph convolutional autoencoder only improved male body composition accuracy but improved precision in both sexes. Our work achieved lower estimation RMSEs than all previous work on 10 metrics of body composition.

Skeletal muscle estimation using magnetic-resonance-imaging-based equations for dual-energy X-ray absorptiometry and bioelectrical impedance analysis

BACKGROUND/OBJECTIVES

Skeletal muscle mass (SMM) estimation is important but challenging in clinical settings. Criterion methods, such as magnetic resonance imaging (MRI), are often inaccessible. However, surrogate methods, such as dual-energy X-ray absorptiometry (DXA) and multi-frequency bioelectrical impedance analysis (MFBIA), can use MRI-based equations to estimate SMM, although the agreement between these methods is unclear.

SUBJECTS/METHODS

Total and segmental SMM were estimated with DXA and MFBIA using MRI-based equations in 313 healthy adults (120 M, 193 F; age 30.2 ± 13.0 y; BMI 24.6 ± 4.0 kg/m2). DXA total SMM was estimated using the Kim and McCarthy equations, and segmental SMM was estimated using the McCarthy equations. Relationships between DXA and MFBIA SMM were examined using Deming regression, Lin's concordance correlation coefficient (CCC), equivalence testing, Bland-Altman analysis, and related tests.

RESULTS

Strong linear relationships were observed for total (R2 0.95, CCC 0.96-0.97), leg (R2 0.90, CCC 0.85) and arm (R2 0.93, CCC 0.93) SMM in the entire sample. Kim equation SMM demonstrated statistical equivalence with MFBIA for total SMM, but the Deming regression slope differed from 1 and proportional bias was present. McCarthy equation total SMM exhibited a regression slope that did not differ from 1, and no proportional bias was present in the entire sample. However, equivalence with MFBIA was not observed. Systematically higher leg and arm SMM values were observed with DXA as compared to MFBIA.

CONCLUSIONS

While DXA and MFBIA total SMM generally exhibited strong agreement, higher appendicular SMM by DXA highlights technical differences between methods.

Associations between 3D surface scanner derived anthropometric measurements and body composition in a cross-sectional study

BACKGROUND

3D laser-based photonic scanners are increasingly used in health studies to estimate body composition. However, too little is known about whether various 3D body scan measures estimate body composition better than single standard anthropometric measures, and which body scans best estimate it. Furthermore, little is known about differences by sex and age.

METHODS

105 men and 96 women aged between 18 and 90 years were analysed. Bioelectrical Impedance Analysis was used to estimate whole relative fat mass (RFM), visceral adipose tissue (VAT) and skeletal muscle mass index (SMI). An Anthroscan VITUSbodyscan was used to obtain 3D body scans (e.g. volumes, circumferences, lengths). To reduce the number of possible predictors that could predict RFM, VAT and SMI backward elimination was performed. With these selected predictors linear regression on the respective body compositions was performed and the explained variations were compared with models using standard anthropometric measurements (Body Mass Index (BMI), waist circumference (WC) and waist-to-height-ratio (WHtR)).

RESULTS

Among the models based on standard anthropometric measures, WC performed better than BMI and WHtR in estimating body composition in men and women. The explained variations in models including body scan variables are consistently higher than those from standard anthropometrics models, with an increase in explained variations between 5% (RFM for men) and 10% (SMI for men). Furthermore, the explained variation of body composition was additionally increased when age and lifestyle variables were added. For each of the body composition variables, the number of predictors differed between men and women, but included mostly volumes and circumferences in the central waist/chest/hip area and the thighs.

CONCLUSIONS

3D scan models performed better than standard anthropometric measures models to predict body composition. Therefore, it is an advantage for larger health studies to look at body composition more holistically using 3D full body surface scans.

Accuracy and Precision of 3-dimensional Optical Imaging for Body Composition by Age, BMI, and Ethnicity

BACKGROUND

The obesity epidemic brought a need for accessible methods to monitor body composition, as excess adiposity has been associated with cardiovascular disease, metabolic disorders, and some cancers. Recent 3-dimensional optical (3DO) imaging advancements have provided opportunities for assessing body composition. However, the accuracy and precision of an overall 3DO body composition model in specific subgroups are unknown.

OBJECTIVES

This study aimed to evaluate 3DO's accuracy and precision by subgroups of age, body mass index, and ethnicity.

METHODS

A cross-sectional analysis was performed using data from the Shape Up! Adults study. Each participant received duplicate 3DO and dual-energy X-ray absorptiometry (DXA) scans. 3DO meshes were digitally registered and reposed using Meshcapade. Principal component analysis was performed on 3DO meshes. The resulting principal components estimated DXA whole-body and regional body composition using stepwise forward linear regression with 5-fold cross-validation. Duplicate 3DO and DXA scans were used for test-retest precision. Student's t tests were performed between 3DO and DXA by subgroup to determine significant differences.

RESULTS

Six hundred thirty-four participants (females = 346) had completed the study at the time of the analysis. 3DO total fat mass in the entire sample achieved R2 of 0.94 with root mean squared error (RMSE) of 2.91 kg compared to DXA in females and similarly in males. 3DO total fat mass achieved a % coefficient of variation (RMSE) of 1.76% (0.44 kg), whereas DXA was 0.98% (0.24 kg) in females and similarly in males. There were no mean differences for total fat, fat-free, percent fat, or visceral adipose tissue by age group (P > 0.068). However, there were mean differences for underweight, Asian, and Black females as well as Native Hawaiian or other Pacific Islanders (P < 0.038).

CONCLUSIONS

A single 3DO body composition model produced accurate and precise body composition estimates that can be used on diverse populations. However, adjustments to specific subgroups may be warranted to improve the accuracy in those that had significant differences. This trial was registered at clinicaltrials.gov as NCT03637855 (Shape Up! Adults).

Automated body composition estimation from device-agnostic 3D optical scans in pediatric populations

BACKGROUND

Excess adiposity in children is strongly correlated with obesity-related metabolic disease in adulthood, including diabetes, cardiovascular disease, and 13 types of cancer. Despite the many long-term health risks of childhood obesity, body mass index (BMI) Z-score is typically the only adiposity marker used in pediatric studies and clinical applications. The effects of regional adiposity are not captured in a single scalar measurement, and their effects on short- and long-term metabolic health are largely unknown. However, clinicians and researchers rarely deploy gold-standard methods for measuring compartmental fat such as magnetic resonance imaging (MRI) and dual X-ray absorptiometry (DXA) on children and adolescents due to cost or radiation concerns. Three-dimensional optical (3DO) scans are relatively inexpensive to obtain and use non-invasive and radiation-free imaging techniques to capture the external surface geometry of a patient's body. This 3D shape contains cues about the body composition that can be learned from a structured correlation between 3D body shape parameters and reference DXA scans obtained on a sample population.

STUDY AIM

This study seeks to introduce a radiation-free, automated 3D optical imaging solution for monitoring body shape and composition in children aged 5-17.

METHODS

We introduce an automated, linear learning method to predict total and regional body composition of children aged 5-17 from 3DO scans. We collected 145 male and 206 female 3DO scans on children between the ages of 5 and 17 with three scanners from independent manufacturers. We used an automated shape templating method first introduced on an adult population to fit a topologically consistent 60,000 vertex (60 k) mesh to 3DO scans of arbitrary scanning source and mesh topology. We constructed a parameterized body shape space using principal component analysis (PCA) and estimated a regression matrix between the shape parameters and their associated DXA measurements. We automatically fit scans of 30 male and 38 female participants from a held-out test set and predicted 12 body composition measurements.

RESULTS

The coefficient of determination (R2) between 3DO predicted body composition and DXA measurements was at least 0.85 for all measurements with the exception of visceral fat on 3D scan predictions. Precision error was 1-4 times larger than that of DXA. No predicted variable was significantly different from DXA measurement except for male trunk lean mass.

CONCLUSION

Optical imaging can quickly, safely, and inexpensively estimate regional body composition in children aged 5-17. Frequent repeat measurements can be taken to chart changes in body adiposity over time without risk of radiation overexposure.

Beyond BMI

This review examined the origins of the concept of the BMI in the work of Quetelet in the 19th century and its subsequent adoption and use in tracking the course of the pandemic of obesity during the 20th century. In this respect, it has provided a valuable international epidemiological tool that should be retained. However, as noted in this review, the BMI is deficient in at least three ways. First, it does not measure body fat distribution, which is probably a more important guide to the risk of excess adiposity than the BMI itself. Second, it is not a very good measure of body fat, and thus its application to the diagnosis of obesity or excess adiposity in the individual patient is limited. Finally, the BMI does not provide any insights into the heterogeneity of obesity or its genetic, metabolic, physiological or psychological origins. Some of these mechanisms are traced in this review.

Effects of Sex and Cuff Pressure on Physiological Responses during Blood Flow Restriction Resistance Exercise in Young Adults

PURPOSE

The purpose of this study was to examine the physiological responses resulting from an acute blood flow restriction resistance exercise bout with two different cuff pressures in young, healthy men and women.

METHODS

Thirty adults (18-30 yr) completed a bilateral leg extension blood flow restriction bout consisting of four sets (30-15-15-15 repetitions), with cuffs applied at pressures corresponding to 40% and 60% of the minimum arterial occlusion pressure (AOP) needed to completely collapse the femoral arteries. During each of these conditions (40% and 60% AOP), physiological measures of near-infrared spectroscopy (NIRS) and EMG amplitude (EMG AMP) were collected from the dominant or nondominant vastus lateralis. After each set, ratings of perceived exertion (RPE) were collected, whereas only at baseline and at the end of the bout, mean arterial pressure (MAP) was assessed. Separate mixed-factorial ANOVA models were used to examine mean differences in the change in EMG AMP and NIRS parameters during each set. The absolute RPE and MAP values were also examined with separate ANOVAs. A P value ≤0.05 was considered statistically significant.

RESULTS

Regardless of sex or cuff pressure, the change in EMG AMP was lower in set 1 (14.8%) compared with the remaining sets (22.6%-27.0%). The 40% AOP condition elicited the greatest changes in oxy[heme] and deoxy[heme], while also providing lower RPEs. For MAP, there was an effect for time such that MAP increased from preexercise (87.5 ± 4.3 mm Hg) to postexercise (104.5 ± 4.1 mm Hg).

CONCLUSIONS

The major findings suggested that the 40% AOP condition permitted the greatest amount of recovery during the interset rest. In addition, there did not seem to be any meaningful sex-related difference in this sample of young healthy adults.

Total and regional appendicular skeletal muscle mass prediction from dual-energy X-ray absorptiometry body composition models

Sarcopenia, sarcopenic obesity, frailty, and cachexia have in common skeletal muscle (SM) as a main component of their pathophysiology. The reference method for SM mass measurement is whole-body magnetic resonance imaging (MRI), although dual-energy X-ray absorptiometry (DXA) appendicular lean mass (ALM) serves as an affordable and practical SM surrogate. Empirical equations, developed on relatively small and diverse samples, are now used to predict total body SM from ALM and other covariates; prediction models for extremity SM mass are lacking. The aim of the current study was to develop and validate total body, arm, and leg SM mass prediction equations based on a large sample (N = 475) of adults evaluated with whole-body MRI and DXA for SM and ALM, respectively. Initial models were fit using ordinary least squares stepwise selection procedures; covariates beyond extremity lean mass made only small contributions to the final models that were developed using Deming regression. All three developed final models (total, arm, and leg) had high R2s (0.88-0.93; all p < 0.001) and small root-mean square errors (1.74, 0.41, and 0.95 kg) with no bias in the validation sample (N = 95). The new total body SM prediction model (SM = 1.12 × ALM - 0.63) showed good performance, with some bias, against previously reported DXA-ALM prediction models. These new total body and extremity SM prediction models, developed and validated in a large sample, afford an important and practical opportunity to evaluate SM mass in research and clinical settings.

Body surface area equations for physically active men and women

OBJECTIVES

To improve predictive formulae for estimating body surface area (BSA) in healthy men and women using a modern three-dimensional scanner technology.

METHODS

Body surface areas were obtained from a convenience sample of 1267 US Marines (464 women and 803 men) using a whole body surface scanner (Size Stream SS20). The reliability of SS20 measures of total and regional BSA within participants was compared across triplicate scans. We then derived a series of formulae to estimate SS20-measured BSA using various combinations of sex, height, and mass. We also assessed relationships between percent body fat measured by dual-energy x-ray absorptiometry and sex-specific formulae errors in Marines.

RESULTS

Body surface areas recorded by the SS20 were highly reliable whether measured for the total body or by region (ICC ≥ .962). Formulae estimates of BSA from sex, height, and mass were precise (root-mean-square deviation, 0.031 m² ). Errors from the Marine Corps formulae were positively associated with percent body fat for men (p = .001) but not women (p = .843).

CONCLUSIONS

Clinicians, military leaders, and researchers can use the newly developed BSA formulae for precise estimates in healthy physically active men and women. Users should be aware that height- and mass-based BSA estimates are less accurate for individuals with extremely low or high percent body fat.

Digital Anthropometry: A Systematic Review on Precision, Reliability and Accuracy of Most Popular Existing Technologies

Digital anthropometry (DA) has been recently developed for body composition evaluation and for postural analysis. The aims of this review are to examine the current state of DA technology, as well as to verify the methods for identifying the best technology to be used in the field of DA by evaluating the reliability and accuracy of the available technologies on the market, and lay the groundwork for future technological developments. A literature search was performed and 28 studies met the inclusion criteria. The reliability and accuracy of DA was high in most studies, especially in the assessment of patients with obesity, although they varied according to the technology used; a good correlation was found between DA and conventional anthropometry (CA) and body composition estimates. DA is less time-consuming and less expensive and could be used as a screening tool before more expensive imaging techniques or as an alternative to other less affordable techniques. At present, DA could be useful in clinical practice, but the heterogeneity of the available studies (different devices used, laser technologies, population examined, etc.) necessitates caution in the interpretation of the obtained results. Furthermore, the need to develop integrated technologies for analyzing body composition according to multi-compartmental models is increasingly evident.

Physiological biomarker monitoring during arduous military training: Maintaining readiness and performance

OBJECTIVES

Physiological and psychological stressors can degrade soldiers' readiness and performance during military training and operational environments. Integrative and holistic assessments of biomarkers across diverse human performance optimization domains during multistressor training can be leveraged to provide actionable insight to military leadership regarding service member health and readiness.

DESIGN/METHOD

A broad categorization of biomarkers, to include biochemical measures, bone and body composition, psychometric assessments, movement screening, and physiological load can be incorporated into robust analytical pipelines for understanding the complex factors that impact military human performance.

RESULTS

In this perspective commentary we overview the rationale, selection, and methodologies for monitoring biomarker domains that are relevant to military research and specifically highlight methods that have been incorporated in a research program funded by the Office of Naval Research, Code 34 Biological and Physiological Monitoring and Modeling of Warfighter Performance.

CONCLUSIONS

The integration of screening and continuous monitoring methodologies via robust analytical approaches will provide novel insight for military leaders regarding health, performance, and readiness outcomes during multistressor military training.

The Influence of Full-Time Holistic Support Delivered by a Sports Nutritionist on Within-Day Macronutrient Distribution in New Zealand Provincial Academy Rugby Union Players

Dietary intake is an important consideration for rugby union ('rugby') players to ensure substrate provision for optimal performance and facilitate recovery. Within-day meal distribution is especially important for athletes, particularly those with congested schedules and multiple daily training sessions. In the present study, 10 provincial academy rugby players engaged in a holistic support protocol informed by behaviour-change techniques led by a full-time sports nutritionist. Dietary intake was estimated during a 4-week monitoring and 4-week intervention period using the remote food photography method on one high-volume training day (two training sessions) and two low-volume training days (≤1 training session) per week. Lean body mass did not change significantly in response to the intervention. Significant increases were observed for protein on both low-volume (breakfast, AM snack, evening snack) and high-volume (post-gym, AM snack, evening snack) training days. Carbohydrate intake post-intervention was significantly greater at the pre-gym eating occasion but lower at PM snack and dinner eating occasions on high-volume days. These data suggest that incorporating a holistic support protocol led by a sports nutritionist can influence within-day nutrient intake in rugby players; however, no change to lean body mass was observed, and the influence of these changes in nutrient intake on performance and recovery warrants further investigation.

Military Body Composition Standards and Physical Performance: Historical Perspectives and Future Directions

ABSTRACT

Harty, PS, Friedl, KE, Nindl, BC, Harry, JR, Vellers, HL, and Tinsley, GM. Military body composition standards and physical performance: historical perspectives and future directions. J Strength Cond Res 36(12): 3551-3561, 2022-US military physique and body composition standards have been formally used for more than 100 years. These metrics promote appropriate physical fitness, trim appearance, and long-term health habits in soldiers, although many specific aspects of these standards have evolved as evidence-based changes have emerged. Body composition variables have been shown to be related to many physical performance outcomes including aerobic capacity, muscular endurance, strength and power production, and specialized occupational tasks involving heavy lifting and load carriage. Although all these attributes are relevant, individuals seeking to improve military performance should consider emphasizing strength, hypertrophy, and power production as primary training goals, as these traits appear vital to success in the new Army Combat Fitness Test introduced in 2020. This fundamental change in physical training may require an adjustment in body composition standards and methods of measurement as physique changes in modern male and female soldiers. Current research in the field of digital anthropometry (i.e., 3-D body scanning) has the potential to dramatically improve performance prediction algorithms and potentially could be used to inform training interventions. Similarly, height-adjusted body composition metrics such as fat-free mass index might serve to identify normal weight personnel with inadequate muscle mass, allowing for effective targeted nutritional and training interventions. This review provides an overview of the origin and evolution of current US military body composition standards in relation to military physical readiness, summarizes current evidence relating body composition parameters to aspects of physical performance, and discusses issues relevant to the emerging modern male and female warrior.

Visual body composition assessment methods: A 4-compartment model comparison of smartphone-based artificial intelligence for body composition estimation in healthy adults

BACKGROUND & AIMS

Visual body composition (VBC) estimates produced from smartphone-based artificial intelligence represent a user-friendly and convenient way to automate body composition remotely and without the inherent geographical and monetary restrictions of other body composition methods. However, there are limited studies that have assessed the reliability and agreement of this method and thus, the aim of this study was to evaluate VBC estimates compared to a 4-compartment (4C) criterion model.

METHODS

A variety of body composition assessments were conducted across 184 healthy adult participants (114 F, 70 M) including dual-energy X-ray absorptiometry and bioimpedance spectroscopy for utilization in the 4C model and automated assessments produced from two smartphone applications (Amazon Halo®, HALO; and myBVI®) using either Apple® or Samsung® phones. Body composition components were compared to a 4C model using equivalence testing, root mean square error (RMSE), and Bland-Altman analysis. Separate analyses by sex and racial/ethnic groups were conducted. Precision metrics were conducted for 183 participants using intraclass correlation coefficients (ICC), root mean squared coefficients of variation (RMS-%CV) and precision error (PE).

RESULTS

Only %fat produced from HALO devices demonstrated equivalence with the 4C model although mean differences for HALO were <±1.0 kg for FM and FFM. RMSEs ranged from 3.9% to 6.2% for %fat and 3.1-5.2 kg for FM and FFM. Proportional bias was apparent for %fat across all VBC applications but varied for FM and FFM. Validity metrics by sex and specific racial/ethnic groups varied across applications. All VBC applications were reliable for %fat, fat mass (FM), and fat-free mass (FFM) with ICCs ≥0.99, RMS-%CV between 0.7% and 4.3%, and PEs between 0.3% and 0.6% for %fat and 0.2-0.5 kg for FM and FFM including assessments between smartphone types.

CONCLUSIONS

Smartphone-based VBC estimates produce reliable body composition estimates but their equivalence with a 4C model varies by the body composition component being estimated and the VBC being employed. VBC estimates produced by HALO appear to have the lowest error, but proportional bias and estimates by sex and race vary across applications.

New Equations for Hydrostatic Weighing without Head Submersion

New equations were derived to predict the density of the body (DB) by hydrostatic weighing with the head above water (HWHAW). Hydrostatic weighing with the head below water (HWHBW) was the criterion for DB measurement in 90 subjects (44 M, 46 F). Head volume by immersion (HVIMM) was determined by subtracting the mass in water with the head below water (MWHBW) from the mass in water with the head above water (MWHAW), with subjects at residual lung volume. Equations were derived for head volume prediction (HVPRED) from head measurements and used to correct DB by HWHAW. Equations were also derived for HWHAW using direct regression of DB from uncorrected density (with MWHAW in place of MWHBW). Prediction equations were validated in 45 additional subjects (21 M, 24 F). Results were evaluated using equivalence testing, linear regression, Bland−Altman plots, and paired t-tests. Head girth, face girth, and body mass produced the smallest errors for HVPRED. In both M and F validation groups, equivalence (±2% fat by weight) was demonstrated between body fat percent (BF%) by HWHBW and BF% by HWHAW with HVPRED. Variance in computer-averaged samples of MWHAW was significantly less (p < 0.05) than MWHBW. Prediction error was smaller for BF% by HWHAW with HVPRED than for alternative methods. Conclusions: Equivalence between BF% by HWHBW and BF% by HWHAW with HVPRED was demonstrated and differences were not statistically significant. Weight fluctuations were smaller for HWHAW than HWHBW.

Physical and Physiological Characterization of Female Elite Warfighters

INTRODUCTION

This study characterized a sample of the first women to complete elite United States (US) military training.

METHODS

Twelve female graduates of the US Army Ranger Course and one of the first Marine Corps Infantry Officers Course graduates participated in 3 d of laboratory testing including serum endocrine profiles, aerobic capacity, standing broad jump, common soldiering tasks, Army Combat Fitness Test, and body composition (dual-energy x-ray absorptiometry, three-dimensional body surface scans, and anthropometry).

RESULTS

The women were 6 months to 4 yr postcourse graduation, 30 ± 6 yr (mean ± SD); height, 1.67 ± 0.07 m; body mass, 69.4 ± 8.2 kg; body mass index, 25.0 ± 2.3 kg·m -2 . Dual-energy x-ray absorptiometry relative fat was 20.0% ± 2.0%; fat-free mass, 53.0 ± 5.9 kg; fat-free mass index, 20.0 ± 1.7 kg·m -2 ; bone mineral content, 2.75 ± 0.28 kg; bone mineral density, 1.24 ± 0.07 g·cm -2 ; aerobic capacity, 48.2 ± 4.8 mL·kg -1 ·min -1 ; total Army Combat Fitness Test score 505 ± 27; standing broad jump 2.0 ± 0.2 m; 123 kg casualty drag 0.70 ± 0.20 m·s -1 , and 4 mile 47 kg ruck march 64 ± 6 min. All women were within normal healthy female range for circulating androgens. Physique from three-dimensional scan demonstrated greater circumferences at eight of the 11 sites compared with the standard military female.

CONCLUSIONS

These pioneering women possessed high strength and aerobic capacity, low %BF; high fat-free mass, fat-free mass index, and bone mass and density; and they were not virilized based on endocrine measures as compared with other reference groups. This group is larger in body size and leaner than the average Army woman. These elite physical performers seem most comparable to female competitive strength athletes.

Silhouette images enable estimation of body fat distribution and associated cardiometabolic risk

Inter-individual variation in fat distribution is increasingly recognized as clinically important but is not routinely assessed in clinical practice, in part because medical imaging has not been practical to deploy at scale for this task. Here, we report a deep learning model trained on an individual’s body shape outline—or “silhouette” —that enables accurate estimation of specific fat depots of interest, including visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes, and VAT/ASAT ratio. Two-dimensional coronal and sagittal silhouettes are constructed from whole-body magnetic resonance images in 40,032 participants of the UK Biobank and used as inputs for a convolutional neural network to predict each of these quantities. Mean age of the study participants is 65 years and 51% are female. A cross-validated deep learning model trained on silhouettes enables accurate estimation of VAT, ASAT, and GFAT volumes (R²: 0.88, 0.93, and 0.93, respectively), outperforming a comparator model combining anthropometric and bioimpedance measures (ΔR² = 0.05–0.13). Next, we study VAT/ASAT ratio, a nearly body-mass index (BMI)—and waist circumference-independent marker of metabolically unhealthy fat distribution. While the comparator model poorly predicts VAT/ASAT ratio (R²: 0.17–0.26), a silhouette-based model enables significant improvement (R²: 0.50–0.55). Increased silhouette-predicted VAT/ASAT ratio is associated with increased risk of prevalent and incident type 2 diabetes and coronary artery disease independent of BMI and waist circumference. These results demonstrate that body silhouette images can estimate important measures of fat distribution, laying the scientific foundation for scalable population-based assessment.

Smartphone camera based assessment of adiposity: a validation study

Body composition is a key component of health in both individuals and populations, and excess adiposity is associated with an increased risk of developing chronic diseases. Body mass index (BMI) and other clinical or commercially available tools for quantifying body fat (BF) such as DXA, MRI, CT, and photonic scanners (3DPS) are often inaccurate, cost prohibitive, or cumbersome to use. The aim of the current study was to evaluate the performance of a novel automated computer vision method, visual body composition (VBC), that uses two-dimensional photographs captured via a conventional smartphone camera to estimate percentage total body fat (%BF). The VBC algorithm is based on a state-of-the-art convolutional neural network (CNN). The hypothesis is that VBC yields better accuracy than other consumer-grade fat measurements devices. 134 healthy adults ranging in age (21–76 years), sex (61.2% women), race (60.4% White; 23.9% Black), and body mass index (BMI, 18.5–51.6 kg/m²) were evaluated at two clinical sites (N = 64 at MGH, N = 70 at PBRC). Each participant had %BF measured with VBC, three consumer and two professional bioimpedance analysis (BIA) systems. The PBRC participants also had air displacement plethysmography (ADP) measured. %BF measured by dual-energy x-ray absorptiometry (DXA) was set as the reference against which all other %BF measurements were compared. To test our scientific hypothesis we run multiple, pair-wise Wilcoxon signed rank tests where we compare each competing measurement tool (VBC, BIA, …) with respect to the same ground-truth (DXA). Relative to DXA, VBC had the lowest mean absolute error and standard deviation (2.16 ± 1.54%) compared to all of the other evaluated methods (p < 0.05 for all comparisons). %BF measured by VBC also had good concordance with DXA (Lin’s concordance correlation coefficient, CCC: all 0.96; women 0.93; men 0.94), whereas BMI had very poor concordance (CCC: all 0.45; women 0.40; men 0.74). Bland-Altman analysis of VBC revealed the tightest limits of agreement (LOA) and absence of significant bias relative to DXA (bias −0.42%, R² = 0.03; p = 0.062; LOA −5.5% to +4.7%), whereas all other evaluated methods had significant (p < 0.01) bias and wider limits of agreement. Bias in Bland-Altman analyses is defined as the discordance between the y = 0 axis and the regressed line computed from the data in the plot. In this first validation study of a novel, accessible, and easy-to-use system, VBC body fat estimates were accurate and without significant bias compared to DXA as the reference; VBC performance exceeded those of all other BIA and ADP methods evaluated. The wide availability of smartphones suggests that the VBC method for evaluating %BF could play an important role in quantifying adiposity levels in a wide range of settings.

Trial registration: ClinicalTrials.gov Identifier: NCT04854421.

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.

Digital Anthropometry for Body Circumference Measurements: European Phenotypic Variations throughout the Decades

This review summarizes body circumference-based anthropometrics that are in common use for research and in some cases clinical application. These include waist and hip circumference-based central body indices to predict cardiometabolic risk: waist circumference, waist-to-hip ratio, waist-to-height ratio, waist-to-thigh ratio, body adiposity index, a body shape index (ABSI), hip index (HI), and body roundness index (BRI). Limb circumference measurements are most often used to assess sarcopenia and include: thigh circumference, calf circumference, and mid-arm circumference. Additionally, this review presents fascinating recent developments in optic-based imaging technologies that have elucidated changes over the last decades in average body size and shape in European populations. The classical apple and pear shape concepts of body shape difference remain useful, but novel and exciting 3-D optical “e-taper” measurements provide a potentially powerful new future vista in anthropometrics.

Digital anthropometric evaluation of young children: comparison to results acquired with conventional anthropometry

OBJECTIVE

Three-dimensional optical (3DO) imaging devices for acquiring anthropometric measurements are proliferating in healthcare facilities, although applicability in young children has not been evaluated; small body size and movement may limit device accuracy. The current study aim was to critically test three commercial 3DO devices in young children.

METHODS

The number of successful scans and circumference measurements at six anatomic sites were quantified with the 3DO devices in 64 children, ages 5-8 years. Of the scans available for processing, 3DO and flexible tape-measure measurements made by a trained anthropometrist were compared.

RESULTS

Sixty of 181 scans (33.1%) could not be processed for technical reasons. Of processed scans, mean 3DO-tape circumference differences tended to be small (~1-9%) and varied across systems; correlations and bias estimates also varied in strength across anatomic sites and systems (e.g., regression R2s, 0.54-0.97, all p < 0.01). Overall findings differed across devices; best results were for a multi-camera stationary system and less so for two rotating single- or dual-camera systems.

CONCLUSIONS

Available 3DO devices for quantifying anthropometric dimensions in adults vary in applicability in young children according to instrument design. These findings suggest the need for 3DO devices designed specifically for small and/or young children.

Assessment of clinical measures of total and regional body composition from a commercial 3-dimensional optical body scanner

BACKGROUND

The accurate assessment of total body and regional body circumferences, volumes, and compositions are critical to monitor physical activity and dietary interventions, as well as accurate disease classifications including obesity, metabolic syndrome, sarcopenia, and lymphedema. We assessed body composition and anthropometry estimates provided by a commercial 3-dimensional optical (3DO) imaging system compared to criterion measures.

METHODS

Participants of the Shape Up! Adults study were recruited for similar sized stratifications by sex, age (18-40, 40-60, >60 years), BMI (under, normal, overweight, obese), and across five ethnicities (non-Hispanic [NH] Black, NH White, Hispanic, Asian, Native Hawaiian/Pacific Islander). All participants received manual anthropometry assessments, duplicate whole-body 3DO (Styku S100), and dual-energy X-ray absorptiometry (DXA) scans. 3DO estimates provided by the manufacturer for anthropometry and body composition were compared to the criterion measures using concordance correlation coefficient (CCC) and Bland-Altman analysis. Test-retest precision was assessed by root mean square error (RMSE) and coefficient of variation.

RESULTS

A total of 188 (102 female) participants were included. The overall fat free mass (FFM) as measured by DXA (54.1 ± 15.2 kg) and 3DO (55.3 ± 15.0 kg) showed a small mean difference of 1.2 ± 3.4 kg (95% limits of agreement -7.0 to +5.6) and the CCC was 0.97 (95% CI: 0.96-0.98). The CCC for FM was 0.95 (95% CI: 0.94-0.97) and the mean difference of 1.3 ± 3.4 kg (95% CI: -5.5 to +8.1) reflected the difference in FFM measures. 3DO anthropometry and body composition measurements showed high test-retest precision for whole body volume (1.1 L), fat mass (0.41 kg), percent fat (0.60%), arm and leg volumes, (0.11 and 0.21 L, respectively), and waist and hip circumferences (all <0.60 cm). No group differences were observed when stratified by body mass index, sex, or race/ethnicity.

CONCLUSIONS

The anthropometric and body composition estimates provided by the 3DO scanner are precise and accurate to criterion methods if offsets are considered. This method offers a rapid, broadly available, and automated method of body composition assessment regardless of body size. Further studies are recommended to examine the relationship between measurements obtained by 3DO scans and metabolic health in healthy and clinical populations.

The Utility of Body Composition Assessment in Nutrition and Clinical Practice: An Overview of Current Methodology

Body composition is a key component for maintaining good general health and longevity. It can be influenced by a variety of factors, including genetics, environment, and lifestyle choices. The assessment of body composition is an essential tool for nutrition specialists to effectively evaluate nutritional status and monitor progression during dietary interventions. As humans age, there is a natural increase in fat mass coupled with a gradual decline in lean mass, specifically in bone and muscle mass. Individuals with a high body fat percentage are at a greater risk of cardiovascular diseases, type 2 diabetes, several types of cancer, and early mortality. Significant decreases in bone mineral density signify osteopenia and osteoporosis, while reductions in skeletal muscle mass increase the risk of developing sarcopenia. Moreover, undernutrition exacerbates the effects of many medical conditions and is important to address. Though weight tracking and calculation of BMI are used commonly by clinicians and dietitians, these measures do not provide insight on the relative contributions of fat mass and fat-free mass or the changes in these compartments that may reflect disease risk. Therefore, it is important that healthcare professionals have a critical understanding of body composition assessment and the strengths and limitations of the methods available.

Anthropometric Indicators as a Tool for Diagnosis of Obesity and Other Health Risk Factors: A Literature Review

Obesity is characterized by the accumulation of an excessive amount of fat mass (FM) in the adipose tissue, subcutaneous, or inside certain organs. The risk does not lie so much in the amount of fat accumulated as in its distribution. Abdominal obesity (central or visceral) is an important risk factor for cardiovascular diseases, diabetes, and cancer, having an important role in the so-called metabolic syndrome. Therefore, it is necessary to prevent, detect, and appropriately treat obesity. The diagnosis is based on anthropometric indices that have been associated with adiposity and its distribution. Indices themselves, or a combination of some of them, conform to a big picture with different values to establish risk. Anthropometric indices can be used for risk identification, intervention, or impact evaluation on nutritional status or health; therefore, they will be called anthropometric health indicators (AHIs). We have found 17 AHIs that can be obtained or estimated from 3D human shapes, being a noninvasive alternative compared to X-ray-based systems, and more accessible than high-cost equipment. A literature review has been conducted to analyze the following information for each indicator: definition; main calculation or obtaining methods used; health aspects associated with the indicator (among others, obesity, metabolic syndrome, or diabetes); criteria to classify the population by means of percentiles or cutoff points, and based on variables such as sex, age, ethnicity, or geographic area, and limitations.

Antimicrobial Dosing in Specific Populations and Novel Clinical Methodologies: Obesity

Obesity and its related comorbidities can negatively influence the outcomes of certain infectious diseases. Specific dosing recommendations are often lacking in the product label for patients with obesity that leads to unclear guidance in practice. Higher rates of therapeutic failure have been reported with some fixed dose antibiotics and pragmatic approaches to dose modification are limited for orally administered agents. For i.v. antimicrobials dosed on weight, alternate body size descriptors (ABSDs) have been used to reduce the risk of overdosing. These ABSDs are mathematical transformations of height and weight that represent fat-free weight and follow the same principles as body surface area (BSA)-based dosing of cancer chemotherapy. However, ABSDs are rarely studied in pivotal phase III studies and so can risk the underdosing of antimicrobials in patients with obesity when incorrectly applied in the real-world setting. Specific case examples are presented to highlight these risks. Although general principles may be considered by clinicians, a universal approach to dose modification in obesity is unlikely. Studies that can better distinguish human body phenotypes may help reduce our reliance on height and weight to define dosing. Simple and complex technologies exist to quantify individual body composition that could improve upon our current approach. Early evidence suggests that body composition parameters repurposed from medical imaging data may improve upon height and weight as covariates of drug clearance and distribution. Clinical trials that can integrate human body phenotyping may help us identify new approaches to optimal dose selection of antimicrobials in patients with obesity.

Come Back Skinfolds, All Is Forgiven: A Narrative Review of the Efficacy of Common Body Composition Methods in Applied Sports Practice

Whilst the assessment of body composition is routine practice in sport, there remains considerable debate on the best tools available, with the chosen technique often based upon convenience rather than understanding the method and its limitations. The aim of this manuscript was threefold: (1) provide an overview of the common methodologies used within sport to measure body composition, specifically hydro-densitometry, air displacement plethysmography, bioelectrical impedance analysis and spectroscopy, ultra-sound, three-dimensional scanning, dual-energy X-ray absorptiometry (DXA) and skinfold thickness; (2) compare the efficacy of what are widely believed to be the most accurate (DXA) and practical (skinfold thickness) assessment tools and (3) provide a framework to help select the most appropriate assessment in applied sports practice including insights from the authors' experiences working in elite sport. Traditionally, skinfold thickness has been the most popular method of body composition but the use of DXA has increased in recent years, with a wide held belief that it is the criterion standard. When bone mineral content needs to be assessed, and/or when it is necessary to take limb-specific estimations of fat and fat-free mass, then DXA appears to be the preferred method, although it is crucial to be aware of the logistical constraints required to produce reliable data, including controlling food intake, prior exercise and hydration status. However, given the need for simplicity and after considering the evidence across all assessment methods, skinfolds appear to be the least affected by day-to-day variability, leading to the conclusion 'come back skinfolds, all is forgiven'.

Validity of a 3-dimensional body scanner: comparison against a 4-compartment model and dual energy X-ray absorptiometry

Three-dimensional (3D) body scanner technology for body composition assessment is expanding. The aim of this study was to assess the validity of a 3D body scanner. One hundred and ninety-four participants (43% male; age: 23.52 ± 5.47 years; body mass index: 23.98 ± 3.24 kg·m^-2) were measured using 3D scanner and a 4-compartment (4C) model utilizing dual-energy X-ray absorptiometry (DXA), air displacement plethysmography, and bioelectrical impedance spectroscopy. Dependent t tests, validity statistics including total error (TE), standard error of the estimate, constant error, and Bland-Altman analyses were utilized. Compared with 4C, 3D scanner fat mass (FM) [mean difference (MD; 3D-4C): 2.66 kg ± 3.32 kg] and percent body fat (%BF) (MD: 4.13% ± 5.36%) were significantly (p < 0.001) over-predicted; fat free mass (FFM) was significantly underpredicted (MD: -3.15 kg ± 4.75 kg; p < 0.001). 3D demonstrated poor validity indicated by TE (%BF: 5.61%; FM: 4.50 kg; FFM: 5.69 kg). In contrast, there were no significant differences between 3D and DXA measures; 3D scanner demonstrated acceptable measurement for %BF (TE: 4.25%), FM (TE: 2.92 kg), and lean mass (TE: 3.86 kg). Compared with the 4C criterion, high TE values indicated 3D estimates were not valid. In contrast, 3D estimates produced acceptable measurement agreement when compared with DXA; an average overestimation of %BF by 5.31% (vs. 4C) and 4.20% (vs. DXA) may be expected. Novelty: 3D body composition estimates are not valid compared with the 4-C criterion model. 3D estimates appeared to be more valid in females, compared with males. When compared with DXA, 3D estimates were acceptable.

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

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.

Novel body fat estimation using machine learning and 3-dimensional optical imaging

Estimates of body composition have been derived using 3-dimensional optical imaging (3DO), but no equations to date have been calibrated using a 4-component (4C) model criterion. This investigation reports the development of a novel body fat prediction formula using anthropometric data from 3DO imaging and a 4C model. Anthropometric characteristics and body composition of 179 participants were measured via 3DO (Size Stream^® SS20) and a 4C model. Machine learning was used to identify significant anthropometric predictors of body fat (BF%), and stepwise/lasso regression analyses were employed to develop new 3DO-derived BF% prediction equations. The combined equation was externally cross-validated using paired 3DO and DXA assessments (n=158), producing a R² value of 0.78 and a constant error of (X±SD) 0.8±4.5%. 3DO BF% estimates demonstrated equivalence with DXA based on equivalence testing with no proportional bias in the Bland-Altman analysis. Machine learning methods may hold potential for enhancing 3DO-derived BF% estimates.