Methods Of Body Composition Assessment Research Articles

COMPARISON OF BODY COMPOSITION METHODS FOR ESTIMATING BODY FAT PERCENTAGE IN LOWER LIMB PROSTHESIS USERS

BACKGROUND: There is a dearth of literature evaluating the accuracy of Air Displacement Plethysmography (ADP) compared to Dual-energy X-ray Absorptiometry (DXA) for assessing body composition in individuals with lower limb amputations. Validity of ADP in persons with lower limb amputations must be established. OBJECTIVE: The objective of this study was to compare body composition in persons with lower limb amputations using the BOD POD® and DXA. METHODOLOGY: Body composition was performed on eleven lower limb prosthesis users (age 53.2±14.3 years, weight 81.9±22.3kg) using ADP and DXA with and without prosthesis. FINDINGS: Repeated measures ANOVA indicated no significant difference in body composition among and between trials, F(3,8)= 3.36, p= 0.075. There were no significant differences in Body Fat (BF) percentage with and without prostheses on the BOD POD (28.5±15.7% and 33.7±12.1%, respectively) nor the DXA (32.9±10.6% and 32.0±9.9%, respectively). Association between the BOD POD and DXA were greatest when prostheses were not worn compared to when they were worn. Bland-Altman plots indicate agreement between BOD POD® and DXA was greatest while wearing the prosthesis. CONCLUSION: This study is a first to compare total body fat percent between the BOD POD® and DXA in lower limb prosthesis users. BOD POD® report valid indices of BF%. Future work will utilize the BOD POD® in intervention studies for monitoring body composition changes across the continuum of rehabilitation. Layman's Abstract Measurement of body composition is helpful in understanding the health of persons with lower limb prosthesis. The gold standard method of body composition assessment is through Dual-energy X-ray Absorptiometry (DXA). This method can be costly and is less economical than Air Displacement Plethysmography (ADP). The aim of this research was to explore the accuracy of the ADP using a BOD POD® instrument in lower limb prosthesis wearers. Body composition measurements using the BOD POD® and DXA were administered. Assessments were performed while wearing and not wearing the prosthesis. Results indicate that no differences between the two body composition assessment methods either with or without prosthesis. The less costly ADP technology may be utilized for body composition in lower limb prosthesis users. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/41605/32165 How To Cite: Smith JD, Guerra G, Symons TB, Kwon EH, Yoon EJ. Comparison of body composition methods for estimating body fat percentage in lower limb prosthesis users. Canadian Prosthetics & Orthotics Journal. 2023; Volume 6, Issue 1, No.2. https://doi.org/10.33137/cpoj.v6i1.41605 Corresponding Author: Gary Guerra, PhD Department of Exercise and Sport Science, St. Mary’s University, San Antonio, Texas, USA.E-Mail: gguerra5@stmarytx.eduORCID ID: https://orcid.org/0000-0002-0161-4616

Infant body composition: A comprehensive overview of assessment techniques, nutrition factors, and health outcomes.

Body composition assessment is a valuable tool for clinical assessment and research that has implications for long-term health. Unlike traditional measurements such as anthropometrics or body mass index, body composition assessments provide more accurate measures of body fatness and lean mass. Moreover, depending on the technique, they can offer insight into regional body composition, bone mineral density, and brown adipose tissue. Various methods of body composition assessment exist, including air displacement plethysmography, dual-energy x-ray absorptiometry, bioelectrical impedance, magnetic resonance imaging, D3 creatine, ultrasound, and skinfold thickness, each with its own strengths and limitations. In infants, several feeding practices and nutrition factors are associated with body composition outcomes, such as breast milk vs formula feeding, protein intake, breast milk composition, and postdischarge formulas for preterm infants. Longitudinal studies suggest that body composition in infancy predicts later body composition, obesity, and other cardiometabolic outcomes in childhood, making it a useful early marker of cardiometabolic health in both term and preterm infants. Emerging evidence also suggests that body composition during infancy predicts neurodevelopmental outcomes, particularly in preterm infants at high risk of neurodevelopmental impairment. The purpose of this narrative review is to provide clinicians and researchers with a comprehensive overview of body composition assessment techniques, summarize the links between specific nutrition practices and body composition in infancy, and describe the neurodevelopmental and cardiometabolic outcomes associated with body composition patterns in term and preterm infants.

Understanding The Difference In Body Composition Assessment Methods In University Aged Canadian Students

Understanding The Difference In Body Composition Assessment Methods In University Aged Canadian Students

Detection of Sarcopenia in Patients with Liver Cirrhosis Using the Bioelectrical Impedance Analysis.

Bioelectrical impedance analysis (BIA) is a body composition assessment method. We aimed to determine its accuracy in the detection of sarcopenia in patients with liver cirrhosis (LC), using skeletal muscle index (SMI) at the level of third lumbar vertebra (L3-SMI) obtained using multislice computed tomography as the reference method. Patients with LC were enrolled in the period October 2019-March 2022 and follow-ups were conducted until January 2023. Their BIA parameters were compared against L3-SMI, and BIA cut-off values were proposed using AUROC analysis. Patients underwent outcome analysis based on obtained clinical characteristics. A total of 106 patients were included. We found a fair correlation between BIA parameters with the L3-SMI. We determined cut-off values of ≤11.1 kg/m2 for BIA-SMI (Se 73%, Sp 66%, AUROC 0.737, p < 0.001) and ≤5.05° for phase angle (PA) (Se 79%, Sp 60%, AUROC 0.762, p < 0.001) in the detection of sarcopenia. The relative risk of death was 2.2 times higher in patients with skeletal muscle mass (SMM) ≤ 36.5 kg. SMM was significantly associated with outcome in Kaplan-Meier analysis. This non-invasive and simple method that showed fair performances and a very good outcome prediction could provide for the unmet need for fast and affordable detection of sarcopenia in patients with LC and should be further evaluated.

Assessment of body composition in pediatric intestinal failure: A comparison study.

The objective of the study was to compare bioelectrical impedance analysis (BIA) and skinfolds with dual energy x-ray absorptiometry (DXA) in the assessment of body composition of children with intestinal failure. DXA is the reference method for body composition assessment in clinical settings. Children aged 1-18 years with intestinal failure whohave DXA as part of routine clinical monitoring were eligible. BIA measured total body water on the same day as DXA. Skinfold measurements were taken at four sites: triceps, biceps, subscapular, and suprailiac. Percentage of fat mass (%FM) and fat-free mass (%FFM) were derived from resistance and reactance measured by BIA by using age-specific equations. Percentage of FM was calculated from skinfold measures by using age-specific equations. Data on patient characteristics, intestinal failure-related factors, and feeding method were collected. Paired t test examined differences in %FM and %FFM and Bland-Altman analysis determined the agreement between BIA, skinfolds, and DXA. Marginal linear model assessed the effect of age, sex, and feeding method on the difference in body composition obtained between DXA and BIA and between DXA and skinfolds. Sixty-eight children with intestinal failure, mean age 8.9 ± 4.2 years, were studied. There was no difference between %FFM and %FM obtained by DXA and BIA (P = 0.26), with a mean bias (95% CI) of -0.69 (-1.9 to 0.5) for %FFM. Sex and age were individually and jointly associated with the bias observed between DXA and BIA (P < 0.05). Skinfold and DXA measurements were significantly different (P < 0.05). BIA is an acceptable clinical tool for assessing body composition in pediatric intestinal failure.

Evaluation of Changes in Hydration and Body Cell Mass with Bioelectrical Impedance Analysis after Exercise Program for Rheumatoid Arthritis Patients.

Rheumatoid arthritis (RA) is a debilitating disease that can result in complications such as rheumatoid cachexia. While physical exercise has shown benefits for RA patients, its impact on hydration and body cell mass remains uncertain. The presence of pain, inflammation, and joint changes often restrict activity and make traditional body composition assessments unreliable due to altered hydration levels. Bioelectrical impedance is a commonly used method for estimating body composition, but it has limitations since it was primarily developed for the general population and does not consider changes in body composition. On the other hand, bioelectrical impedance vectorial analysis (BIVA) offers a more comprehensive approach. BIVA involves graphically interpreting resistance (R) and reactance (Xc), adjusted for height, to provide valuable information about hydration status and the integrity of the cell mass. Twelve women with RA were included in this study. At the beginning of the study, hydration and body cell mass measurements were obtained using the BIVA method. Subsequently, the patients participated in a six-month dynamic exercise program encompassing cardiovascular capacity, strength, and coordination training. To evaluate changes in hydration and body cell mass, the differences in the R and Xc parameters, adjusted for height, were compared using BIVA confidence software. The results showed notable changes: resistance decreased after the exercise program, while reactance increased. BIVA, as a classification method, can effectively categorize patients into dehydration, overhydration, normal, athlete, thin, cachectic, and obese categories. This makes it a valuable tool for assessing RA patients, as it provides information independent of body weight or prediction equations. Overall, the implementation of BIVA in this study shed light on the effects of the exercise program on hydration and body cell mass in RA patients. Its advantages lie in its ability to provide comprehensive information and overcome the limitations of traditional body composition assessment methods.

The evolution of body composition assessment: from body mass index to body composition profiling

Obesity is currently an important medical and social problem due to the multiple associated conditions that worsen the health of the population. Thus, there is a need for the development of accurate and non-invasive methods of body composition assessment for the purposes of diagnosing and monitoring the treatment of this disease. According to the estimates of the World Obesity Federation, by 2025 the prevalence of this disease will reach 21% in women, and 18% in men. This literature review is dedicated to the subject of various methods for assessing the degree of obesity, as well as determining the composition of the body in the context of historical achievements and a critical assessment of new technologies.

Effect of Energy Intake on Percentage Body Fat as Measured by Bia and Cun-Bae Equation Formula: A Comparative Study of Body Fat Measurement Methods

Introduction: Body composition is becoming important to be taken into account, as it is not only an integral part of individual nutritional assessment but also as a biomarker in acute or chronic diseases. Percentage and distribution of body fat is clinically most interest as it play a significant role in determining cardiovascular risk. Various modern methods have been developed to determine percentage body fat for hospital setting, however, low-cost body composition technology for health surveillance is also required. The aim of this study was to determine the effect of energy intake on percentage body fat and to determine whether the CUN-BAE equation formula has an accuracy comparable to BIA, so that it can used as a low-cost body composition technology for health surveillance. Material and Method: This crossed sectional study is part of The Jember Body composition Study. This study invited 84 female dental students with an ideal range of Body Mass Index (21.06±0.17) to participate in this study. Calorie intake was calculated using the 24 hour recall method. Assessment of percentage body fat was performed using Bioelectrical Impedance Analyzer (BIA) and CUN-BAE equation formula. Result: Total energy intake was positively associated with total body fat in female dental students, higher energy intake was associated with higher percentage body fat as determined by CUN-BAE equation (95% CI 16.69, 16.94) as well as by BIA (95% CI 14.11, 15.38). Percentage body fat determine by the CUN-BAE equation method was highly correlated with that determine by the BIA method (95% CI -1.07, 0.33). The average results of FM measurements by the two measurement methods are also same. Conclusion: Higher energy intake is associated with higher percentage body fat. The CUN-BAE equation method has the same accuracy as the BIA method in calculating percentage fat mass. CUN-BAE equation formula is simple and suitable for low-cost body composition assessment method to assess percentage body fat in a population based study.

The association of body composition assessment with hospital length of stay in off-pump coronary artery bypass patients.

Bioelectrical impedance analysis is a widely available, non-invasive method for body composition assessment. To elucidate the perioperative body composition alterations and their prognostic utility for hospital length of stay (LOS) in low risk, off-pump coronary artery bypass (OPCAB) patients. Fifty patients undergoing elective OPCAB were included in the study. Body composition assessments were performed 1 day before the scheduled surgery and on the 6th postoperative day. Patients were grouped into < 9 days (n = 29, 58%) and ≥ 9 days (n = 21, 42%). Multivariate logistic regression analysis was performed to create a body composition-based screening panel for prolonged hospital stay. No significant differences in anthropometric measurements, clinical characteristics or occurrence of postoperative complications were detected between the study groups. Patients with longer hospitalization had significantly higher content of fat mass (FM%) and fat mass index (FMI), and significantly lower content of fat free mass (FFM%) baseline parameters (p = 0.011, p = 0.04 and p = 0.012, respectively). High FM% values had 15-fold, low FFM% values had 13-fold and high FMI values had 7-fold higher risk of experiencing longer stay in the hospital (p = 0.001, p = 0.001 and p = 0.005, respectively). The combined panel of three variables (higher FM%, lower FFM% and higher FMI) had 16-fold higher risk of longer hospitalization (adjusted OR = 16.40; 95% CI: 3.52-76.34; p = 0.0004). Preoperative high FM and low FFM content are independent predictors of prolonged hospital length of stay in normal- and increased-BMI patients after OPCAB.

Field-based adipose tissue quantification in sea turtles using bioelectrical impedance spectroscopy validated with CT scans and deep learning.

Loss of adipose tissue in vertebrate wildlife species is indicative of decreased nutritional and health status and is linked to environmental stress and diseases. Body condition indices (BCI) are commonly used in ecological studies to estimate adipose tissue mass across wildlife populations. However, these indices have poor predictive power, which poses the need for quantitative methods for improved population assessments. Here, we calibrate bioelectrical impedance spectroscopy (BIS) as an alternative approach for assessing the nutritional status of vertebrate wildlife in ecological studies. BIS is a portable technology that can estimate body composition from measurements of body impedance and is widely used in humans. BIS is a predictive technique that requires calibration using a reference body composition method. Using sea turtles as model organisms, we propose a calibration protocol using computed tomography (CT) scans, with the prediction equation being: adipose tissue mass (kg)=body mass - (-0.03 [intercept] -0.29 * length2/resistance at 50 kHz +1.07 * body mass -0.11 * time after capture). CT imaging allows for the quantification of body fat. However, processing the images manually is prohibitive due to the extensive time requirement. Using a form of artificial intelligence (AI), we trained a computer model to identify and quantify nonadipose tissue from the CT images, and adipose tissue was determined by the difference in body mass. This process enabled estimating adipose tissue mass from bioelectrical impedance measurements. The predictive performance of the model was built on 2/3 samples and tested against 1/3 samples. Prediction of adipose tissue percentage had greater accuracy when including impedance parameters (mean bias=0.11%-0.61%) as predictor variables, compared with using body mass alone (mean bias=6.35%). Our standardized BIS protocol improves on conventional body composition assessment methods (e.g., BCI) by quantifying adipose tissue mass. The protocol can be applied to other species for the validation of BIS and to provide robust information on the nutritional and health status of wildlife, which, in turn, can be used to inform conservation decisions at the management level.

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

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. A variety of body composition assessments were conducted across 184 healthy adult participants (114F, 70M) 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). Only %fat produced from HALO devices demonstrated equivalence with the 4C model although mean differences for HALO were <±1.0kg for FM and FFM. RMSEs ranged from 3.9% to 6.2% for %fat and 3.1-5.2kg 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 0.2-0.5kg for FM and FFM including assessments between smartphone types. 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.

A device-agnostic shape model for automated body composition estimates from 3D optical scans.

Many predictors of morbidity caused by metabolic disease are associated with body shape. 3D optical (3DO) scanning captures body shape and has been shown to accurately and precisely predict body composition variables associated with mortality risk. 3DO is safer, less expensive, and more accessible than criterion body composition assessment methods such as dual-energy X-ray absorptiometry (DXA). However, 3DO scanning has not been standardized across manufacturers for pose, mesh resolution, and post processing methods. We introduce a scanner-agnostic algorithm that automatically fits a topologically consistent human mesh to 3DO scanned point clouds and predicts clinically important body metrics using a standardized body shape model. Our models transform raw scans captured by any 3DO scanner into fixed topology meshes with anatomical consistency, standardizing the outputs of 3DO scans across manufacturers and allowing for the use of common prediction models across scanning devices. A fixed-topology body mesh template was automatically registered to 848 training scans from three different 3DO systems. Participants were between 18 and 89 years old with body mass index ranging from 14 to 52kg/m2 . Scans were registered by first performing a coarse nearest neighbor alignment between the template and the input scan with an anatomically constrained principal component analysis (PCA) domain deformation using a device and gender specific bootstrap basis trained on 70 seed scans each. The template mesh was then optimized to fit the target with a smooth per-vertex surface-to-surface deformation. A combined unified PCA model was created from the superset of all automatically fit training scans including all three devices. Body composition predictions to DXA measurements were learned from the training mesh PCA coefficients using linear regression. Using this final unified model, we tested the accuracy of our body composition models on a withheld sample of 562 scans by fitting a PCA parameterized template mesh to each raw scan and predicting the expected body composition metrics from the principal components using the learned regression model. We achieved coefficients of determination (R2 ) above 0.8 on all nine fat and lean predictions except female visceral fat (0.77). R2 was as high as 0.94 (total fat and lean, trunk fat), and all root-mean-squared errors were below 3.0kg. All predicted body composition variables were not significantly different from reference DXA measurements except for visceral fat and female trunk fat. Repeatability precision as measured by the coefficient of variation (%CV) was around 2-3x worse than DXA precision, with visceral fat %CV below 2x DXA %CV and female total fat mass at 5x. Our method provides an accurate, automated, and scanner agnostic framework for standardizing 3DO scans and a low cost, radiation-free alternative to criterion radiology imaging for body composition analysis. We published a web-app version of this work at https://shapeup.shepherdresearchlab.org/3do-bodycomp-analyzer/ that accepts mesh file uploads and returns templated meshes with body composition predictions for demo purposes.

Patient and Staff Perceptions on Using Bioelectrical Impedance Analysis in an Outpatient Haemodialysis Setting: A Qualitative Descriptive Study.

Bioelectrical impedance analysis (BIA) is an objective hydration and body composition assessment method recommended for use in haemodialysis patients. Limited research exists on the acceptability and utility of BIA in clinical practice. This qualitative study explored patient and staff acceptability and perceived value of BIA in an outpatient haemodialysis setting at a tertiary public hospital in Queensland, Australia. Participants included five patients receiving outpatient haemodialysis and 12 multidisciplinary clinical staff providing care to these patients. Semi-structured interviews were employed and data were analysed thematically. Patients were satisfied with the BIA measurement process and most thought the BIA data would be useful for monitoring changes in their nutrition status. Clinical staff valued BIA data for improving fluid management, assessing nutrition status and supporting patient care. Staff recommended targeting BIA use to patient groups who would benefit the most to improve its uptake in the haemodialysis setting. Conclusions: BIA use in the outpatient haemodialysis setting is acceptable and provides valuable objective data to support health-related behaviour changes in patients and enhance clinical practice. Implementation of BIA should be tailored to the local context and staff should be supported in its use.

Diffuse optical spectroscopic method for tissue and body composition assessment.

.SignificanceGrowing levels of obesity and metabolic syndrome have driven demand for more advanced forms of body composition assessment. While various techniques exist to measure body composition, devices are typically expensive and not portable, involve radiation [in the case of dual-energy x-ray absorptiometry (DXA)], and are limited to analysis of adiposity while metabolic information from blood supply and oxygenation are not considered.AimWe evaluate whether diffuse optical spectroscopic imaging (DOSI) can be used to predict site-specific adiposity and percent fat (whole body) while simultaneously providing information about local tissue hemoglobin levels and oxygenation.ApproachDOSI measures of tissue composition in gastrocnemius, quadriceps, abdomen, and biceps, DXA whole-body composition, and ultrasound-derived skin and adipose tissue thickness (SATT) in the quadriceps were obtained from 99 individuals aged 7 to 34 years old.ResultsVarious DOSI-derived parameters were correlated with SATT and an optical method is proposed for estimating SATT using a newly defined parameter, the optical fat fraction (OFF), which considers all parameters that correlate with SATT. Broadband absorption and scattering spectra from study participants with the thinnest () and thickest SATT (), representing best estimates for pure in vivo lean and fatty tissue, respectively, are reported. Finally, a trained prediction model is developed which allows DOSI assessment of OFF to predict DXA body-fat percentage, demonstrating that DOSI can be used to quantify body composition.ConclusionsThis study shows that DOSI can be used to assess the adiposity of specific tissues or the entire human body, and the OFF parameter is defined for corroboration and further evaluation in future research.

Type 1 diabetic body composition characterization by different methods: case report

Introduction: Several body composition formulae exist. Otherwise, no one is specific to type 1 diabetic which has been using in gyms and health places, but without scientifical comparison to analyze with gold standards. Objective: to characterize the body composition of an individual with DM1 and to compare it by different methods of body composition assessment. Methods : First day, after anamneses, it was made basic anthropometric measurements (weight, height, perimeters, and skinfolds) and Dual-energy X-ray absorptiometry (DXA). Three formula were used to compare with a gold standard (DXA). Descriptive values were used to analyze de comparisons . Results: DXA has shown 26.6%, 20.7 kg, and 57.2 kg to body fat percentage, Fat mass and Lean Mass, respectively. Deurenberg, Weststrate &amp; Seidell shown 22.0%, 17.8 kg and 63.2kg. Jackson &amp; Pollock, 35.9%, 29.0 kg and 52.0. Finally, Katch &amp; McArdle, 19.0%, 15.4 kg and 65.6 kg . Conclusion: The equation of Deurenberg, Weststrate, and Seidell (1991) presented the lowest delta of variation in relation to the body composition variables obtained by DXA. Thus, it is also possible to perform a body composition in people with type 1 diabetes through simpler techniques of assessment, such as equations based on body perimeter measurements and skinfolds.

Beyond the BMI: Validity and Practicality of Postpartum Body Composition Assessment Methods during Lactation: A Scoping Review.

The assessment of body composition during lactation is an important indicator of maternal nutritional status, which is central to the overall health of the mother and child. The lactating woman’s nutritional status potentially impacts on breastmilk composition and the process of lactation itself. The purpose of this scoping review was to synthesize comparative studies that sought to validate various body composition assessment techniques for use in lactating women in the postpartum period. Using the PRISMA-ScR guidelines, a comprehensive, systematic literature search was conducted using Scopus, Web of Science, and PubMed. Eight comparative studies were included in the review, with data from 320 postpartum women. The design methodologies varied substantially across studies, and included a range of simple techniques to advanced multi-compartment models for assessing body composition. The validity and reliability of measurement tools must be considered alongside issues of safety, practicality, and appropriateness to guide the research design when applied to lactating women.

Concordance between Laboratory and Field Methods for the Assessment of Body Fat in Olympic Combat Athletes: Analysis of the Influence of Adiposity.

Combat sports athletes competing in the same discipline exhibit notable and substantial differences in body weight, body composition (BC) and adiposity. No studies have considered the influence of adiposity levels in the agreement between different BC assessment methods. The aim of this study was to analyze the influence of adiposity in the agreement between different methods used to estimate relative body fat (%BF) in Olympic combat sport athletes. A total of 38 male athletes were evaluated using air displacement plethysmography and dual-energy X-ray absorptiometry (DXA) as laboratory methods, and bioelectrical impedance analysis (BIA), near-infrared interactance (NIR) and anthropometry as field methods. All methods were compared to DXA. Agreement analyses were performed by means of individual intraclass correlation coefficients (ICCs) for each method compared to DXA, Bland–Altman plots and paired Student t-tests. The ICCs for the different methods compared to DXA were analyzed, considering tertiles of %BF, tertiles of body weight and type of sport. For the whole group, individual ICCs oscillated between 0.806 for BIA and 0.942 for anthropometry. BIA showed a statistically significant underestimation of %BF when compared to DXA. The agreement between every method and DXA was not affected by %BF, but it was highest in athletes at the highest %BF tertile (>13%). The ICC between NIR and DXA was poor in 72–82 kg athletes. Our results indicate that field methods are useful for routine %BF analysis, and that anthropometry is particularly appropriate, as it showed the highest accuracy irrespective of the athletes’ adiposity.

Circumference-Based Predictions of Body Fat Revisited: Preliminary Results From a US Marine Corps Body Composition Survey.

Purpose: Body composition assessment methods are dependent on their underlying principles, and assumptions of each method may be affected by age and sex. This study compared an abdominal circumference-focused method of percent body fat estimation (AC %BF) to a criterion method of dual-energy x-ray absorptiometry (DXA), and a comparative assessment with bioelectrical impedance (BIA), in younger (≤30 years) and older (>age 30 years) physically fit (meeting/exceeding annual US Marine Corps fitness testing requirements) men and women. Methods: Fit healthy US Marines (430 men, 179 women; 18–57 years) were assessed for body composition by DXA (iDXA, GE Lunar), anthropometry, and BIA (Quantum IV, RJL Systems). Results: Compared to DXA %BF, male AC %BF underestimated for both ≤30 and >30 years age groups (bias, -2.6 ± 3.7 and -2.5 ± 3.7%); while female AC %BF overestimated for both ≤30 and >30 years age groups (2.3 ± 4.3 and 1.3 ± 4.8%). On an individual basis, lean men and women were overestimated and higher %BF individuals were underestimated. Predictions from BIA were more accurate and reflected less relationship to adiposity for each age and sex group (males: ≤30, 0.4 ± 3.2, >30 years, -0.5 ± 3.5; women: ≤30, 1.4 ± 3.1, >30 years, 0.0 ± 3.3). Total body water (hydration) and bone mineral content (BMC) as a proportion of fat-free mass (FFM) remained consistent across the age range; however, women had a higher proportion of %BMC/FFM than men. Older men and women (>age 30 years) were larger and carried more fat but had similar FFM compared to younger men and women. Conclusion: The AC %BF provides a field expedient method for the US Marine Corps to classify individuals for obesity prevention, but does not provide research-grade quantitative body composition data.

Official position of the Brazilian Association of Bone Assessment and Metabolism (ABRASSO) on the evaluation of body composition by densitometry: part I (technical aspects)\u2014general concepts, indications, acquisition, and analysis

ObjectiveTo review the technical aspects of body composition assessment by dual-energy X-ray absorptiometry (DXA) and other methods based on the most recent scientific evidence.Materials and methodsThis Official Position is a result of efforts by the Scientific Committee of the Brazilian Association of Bone Assessment and Metabolism (Associação Brasileira de Avaliação Óssea e Osteometabolismo, ABRASSO) and health care professionals with expertise in body composition assessment who were invited to contribute to the preparation of this document. The authors searched current databases for relevant publications. In this first part of the Official Position, the authors discuss the different methods and parameters used for body composition assessment, general principles of DXA, and aspects of the acquisition and analysis of DXA scans.ConclusionConsidering aspects of accuracy, precision, cost, duration, and ability to evaluate all three compartments, DXA is considered the gold-standard method for body composition assessment, particularly for the evaluation of fat mass. In order to ensure reliable, adequate, and reproducible DXA reports, great attention is required regarding quality control procedures, preparation, removal of external artifacts, imaging acquisition, and data analysis and interpretation.

Anthropometric Profile Assessed by Bioimpedance and Anthropometry Measures of Male and Female Rugby Players Competing in the Spanish National League

Different rugby positions make different demands on players. It therefore follows that optimum body composition may vary according to the position played. Using anthropometry and bioimpedance analysis (BIA) to assess body composition, the present study aimed to compare the effect of sex and position on body composition variables using anthropometry and BIA methods. A total of 100 competitive rugby players (35 women and 65 men) competing in the First Spanish National League were recruited voluntarily and for convenience for this study. In the laboratory, body composition was assessed by anthropometry, following the recommendations established by the International Society for the Advancement of Kinanthropometry (ISAK), and by direct segmental multi-frequency BIA, following the guidelines established by the Spanish Group of Kinanthropometry (GREC) of the Spanish Federation of Sports Medicine (FEMEDE). We found sex-related differences in height, weight, body mass index and body fat (%) by anthropometry and in body lean mass (%) by DSM-BIA, in 4 of the 6 skinfolds assessed (p &lt; 0.05). We also observed position-related differences in all the variables assessed (p &lt; 0.05) except for lean body mass, as measured by both methods of determining body composition, and front thigh skinfold. Body composition and ∑6skinfolds differs according to sex and playing position, backs (16.6 ± 3.8% and 92.3 ± 33.9 mm,) vs. forwards (20.0 ± 6.7 and 115.3 ± 37.6 mm), and the muscle-adipose (meso-endomorphic somatotype) development predominated in both sexes. Thus, forwards of both sexes are taller, heavier and fatter, possibly due to the specific demands of this position. In addition, body composition measurements vary according to the method used (DSM-BIA vs. anthropometry), indicating that anthropometry is probably the best body composition assessment method.