Measurement agreement in percent body fat estimates among laboratory and field assessments in college students: Use of equivalence testing

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.

The Relationship between Body Composition and ECG Ventricular Activity in Young Adults

This study aimed to determine the correlation between body composition (measured as weight, body mass index, and body fat percentage (BFP)) and electrocardiographic ventricular parameters (the QT and TQ intervals and the ratios between the electrical diastole and electrical systole (TQ/QT) and between the cardiac cycle and electrical diastole (RR/TQ), both for uncorrected and corrected intervals) in a sample of 50 healthy subjects (age interval 19-23 years, mean age 21.27 ± 1.41 years, 33 women and 17 men). Subjects' measurements were performed with a bioimpedancemetry body composition analyzer and a portable ECG monitor with six leads. Starting from the correlations obtained between the investigated continuous variables, we performed a standard linear regression analysis between the body composition parameters and the ECG ones. Our results revealed that some of our regression models are statistically significant (p < 0.001). Thus, a specific part of the variability of the dependent variables (ECG ventricular activity parameters for corrected QT intervals) is explained by the independent variable BFP. Therefore, body composition influences ventricular electrical activity in young adults, which implies a differentiated interpretation of the electrocardiogram in these situations.

Oxygen uptake efficiency slope in healthy normal weight young males: an applicable framework for calculation and interpretation

Background

The oxygen uptake efficiency slope (OUES) is considered a reliable indicator of cardiorespiratory fitness in young and clinical populations who cannot achieve maximal effort during a graded exercise test. However, OUES accuracy depends on the data points used for its calculation and it is still not clear if the submaximal OUES can accurately assess CRF in healthy young males.

Objective

We investigated the association between peak oxygen uptake and peak and submaximal OUES, and the agreement between submaximal OUES and peak OUES in male adolescents and young adults.

Methods

In this cross-sectional, observational study, fifty normal weight healthy participants (age 14-22 years, peak oxygen uptake 43.8 ± 7.3 mL·min^-1·kg^-1) performed a graded exercise test on a cycle ergometer and pulmonary gas exchange was assessed using breath-by-breath analysis. Peak oxygen uptake, and oxygen consumption at the aerobic and at the anaerobic threshold were determined as the 30-s average of the oxygen consumption values. Peak OUES (up to peak) and submaximal OUES (up to the aerobic and anaerobic thresholds) were calculated from the logarithmic relation between oxygen consumption and pulmonary ventilation.

Results

Very strong correlations were observed between peak oxygen uptake and peak OUES (r = 0.80-0.88) while fair-to-very strong correlations were observed between the peak oxygen uptake and the two submaximal OUES (r = 0.32-0.81). The level of agreement between peak OUES and OUES up to the anaerobic threshold (r = 0.89-0.93; Typical percentage error 6%; Intraclass correlation coefficient = 0.89-0.93) was greater than the one between the peak oxygen uptake with OUES up to the aerobic threshold (r = 0.39-0.56; Typical percentage error 15%; Intraclass correlation coefficient = 0.38-0.56).

Conclusions

. The peak OUES is a better indicator of aerobic fitness than the OUES up to the anaerobic threshold in healthy, young males. The OUES up to the anaerobic threshold is a valid alternative to peak OUES.

Analysis of Total and Segmental Body Composition Relative to Fitness Performance Measures in Law Enforcement Recruits

Law enforcement agencies often test the fitness performance and body composition of incoming recruits. This study investigated the relationships between whole and segmental body composition, and fitness tests in law enforcement recruits. A retrospective analysis of 72 male and 11 female recruits was performed. Bioelectrical impedance analysis (BIA) variables were: lean mass (LM), upper-extremity lean mass (UELM), trunk LM, lower-extremity lean mass (LELM), fat mass (FM), upper-extremity fat mass (UEFM), trunk FM, and lower-extremity fat mass (LEFM). Fitness tests included: vertical jump (VJ), peak anaerobic power (PAPw), 75-yard pursuit run (75PR), push-ups, sit-ups, 2-kg medicine ball throw (MBT), and the multi-stage fitness test (MSFT). Partial correlations and ANCOVAs between quartiles assessed relationships between body composition and performance. Significant moderate-to-large relationships were found; LM, UELM, trunk LM, LELM all related to PAPw (r = 0.500-0.558) and MBT (r = 0.494-0.526). FM, UEFM, trunk FM, LEFM all related to VJ (r = -0.481 to -0.493), 75PR (r = 0.533-0.557), push-ups (r = -0.484 to -0.503), sit-ups (r = -0.435 to -0.449), and MSFT (r = -0.371 to -0.423). The highest LM quartile (4) had superior PAPw and MBT than LM quartiles 1-3. Higher FM quartiles performed poorer in VJ, push-ups, and sit-ups. The 75PR quartiles 2, 3, and 4 were slower than quartile 1, and MSFT quartile 4 completed less shuttles. Total and segmental measures of LM and FM shared the same relationships; lower FM and higher LM related to better performance. Monitoring body composition could help guide training to optimize performance.

A pose-independent method for accurate and precise body composition from 3D optical scans

OBJECTIVE

The aim of this study was to investigate whether digitally re-posing three-dimensional optical (3DO) whole-body scans to a standardized pose would improve body composition accuracy and precision regardless of the initial pose.

METHODS

Healthy adults (n = 540), stratified by sex, BMI, and age, completed whole-body 3DO and dual-energy X-ray absorptiometry (DXA) scans in the Shape Up! Adults study. The 3DO mesh vertices were represented with standardized templates and a low-dimensional space by principal component analysis (stratified by sex). The total sample was split into a training (80%) and test (20%) set for both males and females. Stepwise linear regression was used to build prediction models for body composition and anthropometry outputs using 3DO principal components (PCs).

RESULTS

The analysis included 472 participants after exclusions. After re-posing, three PCs described 95% of the shape variance in the male and female training sets. 3DO body composition accuracy compared with DXA was as follows: fat mass R2 = 0.91 male, 0.94 female; fat-free mass R2 = 0.95 male, 0.92 female; visceral fat mass R2 = 0.77 male, 0.79 female.

CONCLUSIONS

Re-posed 3DO body shape PCs produced more accurate and precise body composition models that may be used in clinical or nonclinical settings when DXA is unavailable or when frequent ionizing radiation exposure is unwanted.

The Association Between Obesity and Key Health or Psychosocial Outcomes Among Autistic Adults: A Systematic Review

Obesity is linked with health and psychosocial outcomes among many populations. However, it is unclear the extent to which obesity is linked with these outcomes among autistic adults. We searched seven research databases for articles examining the association between obesity and autistic adults' health and psychosocial outcomes. Three studies found that obesity was associated with health outcomes, including: in-hospital mortality, risk of type II diabetes, cardiovascular disease, and number of co-occurring medical conditions. One study found no significant association between autism diagnosis, mental health conditions, and body mass index. Obesity increases the risk of in-hospital mortality and some chronic conditions among autistic adults, highlighting the need for clinicians trained to promote weight management among autistic adults.

Skeletal Muscle Mass and Fat Mass Relationships With Physical Fitness Test Performance in Law Enforcement Recruits Before Academy

ABSTRACT

Lockie, RG, Carlock, BN, Ruvalcaba, TJ, Dulla, JM, Orr, RM, Dawes, JJ, and McGuire, MB. Skeletal muscle mass and fat mass relationships with physical fitness test performance in law enforcement recruits before academy. J Strength Cond Res 35(5): 1287-1295, 2021-The purpose of this study was to analyze relationships between skeletal muscle mass percentage (SMM%) and fat mass percentage (FM%) relative to fitness test performance in law enforcement recruits. Retrospective analysis was conducted on 338 recruits (271 men and 67 women) from 4 academy classes. Skeletal muscle mass percentage and FM% were measured using cost-effective and practical bioelectrical impedance analysis (BIA) equipment that used hand and foot placement. The fitness tests included grip strength; vertical jump; 75-yard pursuit run; 2-kg medicine ball throw (MBT); push-ups and sit-ups completed in 60 seconds; and the 20-m multistage fitness test. Partial correlations controlling for sex-derived relationships between SMM%, FM%, and the tests. Recruits were split into quartile groups for SMM% and FM% (group 1 had the lowest SMM% or highest FM% and group 4 the highest SMM% or lowest FM%). A 1-way multivariate analysis of variance (MANOVA), with sex as a covariate and Bonferroni post-hoc, compared between-group results. Skeletal muscle mass percentage correlated with all fitness tests expect for MBT; FM% with all but grip strength and MBT (r = ±0.107-0.293). Greater SMM% or lesser FM% tended to relate to better fitness test performance. The MANOVA data indicated groups 3 and 4 (better SMM% or FM% profiles) exhibited superior fitness than group 1 (poorest SMM% or FM% profile) (p ≤ 0.048). Recruits should ideally increase SMM% and decrease FM% before academy to optimize fitness training and testing performance, although specific guidelines should be agency specific. Staff could use BIA to monitor body composition during academy to indicate how recruits are tolerating training.

New Multisite Bioelectrical Impedance Device Compared to Hydrostatic Weighing and Skinfold Body Fat Methods

The purpose of this study was to compare the Skulpt Chisel™ to seven-site skinfold (SKF) and hydrostatic weighing (HW) body fat percentage (%BF) estimates. Twenty-six participants (aged 24 ± 4 years; BMI 23.1 ± 3.5 kg·m^-2) were assessed. Significant differences in %BF estimates were found for all methodological pairings; p < 0.05. The SKF method underestimated %BF compared to HW (-2.52 ± 3.42 %BF). The Skulpt Chisel™ overestimated %BF compared to both HW (3.38 ± 6.10 %BF) and SKF (5.90 ± 5.26 %BF). Limits of agreement comparing HW to Skulpt Chisel™ indicated a difference between 95% confidence interval bounds (Upper bound: 5.84 %BF, Lower bound 0.92 %BF) and for HW to SKF (Upper bound: -1.14 %BF, Lower bound: -3.91 %BF). Regression analysis showed no significant bias for any methodological pairing; (p > 0.05). In conclusion, the Skulpt Chisel™ method should be used with caution when evaluating %BF of adults with similar demographics reported in this study.

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

BACKGROUND & AIMS

Body composition assessment via 3-dimensional optical (3DO) scanning has emerged as a rapid and simple evaluation method. The aim of this study was to establish the precision of body composition estimates from four commercially available 3DO scanners and evaluate their validity as compared to a reference 4-component (4C) model.

METHODS

The body composition of 171 participants was assessed using four commercially-available 3DO scanners (FIT3D®, Naked Labs®, Size Stream®, and Styku®) and a 4C model utilizing data from dual-energy x-ray absorptiometry, air displacement plethysmography, and bioimpedance spectroscopy. Body composition estimates were compared via equivalence testing, Deming regression, Bland-Altman analysis, concordance correlation coefficients (CCC), root mean square error (RMSE), and related metrics. Precision metrics, including the root mean square coefficient of variation (RMS-%CV), precision error, and intraclass correlation coefficient, were generated for duplicate scans in 139 participants.

RESULTS

All scanners produced reasonably reliable estimates, with RMS-%CV of 2.3-4.3% for body fat percentage (BF%), 2.5-4.3% for fat mass (FM), and 0.7-1.4% for fat-free mass (FFM). ICC values ranged from 0.975 to 0.996 for BF% and 0.990 to 0.999 for FM and FFM. All scanners except Styku® demonstrated equivalence with 4C, using 5% equivalence regions, and constant errors of <1% for BF% and ≤0.5 kg for FM and FFM. However, the slopes of regression lines differed from the line of identity for most scanners and variables. CCC values ranged from 0.74 to 0.90 for BF%, 0.85 to 0.95 for FM, and 0.93 to 0.97 for FFM. RMSE values ranged from 3.7 to 6.1% for BF% and 2.8-4.6 kg for FM and FFM. Bland-Altman analysis indicated proportional bias of varying magnitudes was present for all scanners.

CONCLUSIONS

Commercially available 3DO scanners produce relatively reliable body composition estimates. Three out of four scanners demonstrated equivalence with a 4C model for assessments of BF%, FM, and FFM, although other metrics of validity varied among scanners, and proportional bias was present for all scanners.

Classification Accuracy of Body Mass Index for Excessive Body Fatness in Kuwaiti Adolescent Girls and Young Adult Women

PURPOSE

Adolescent obesity, as defined by BMI, is amongst the highest in the world in Kuwait. This study aimed to determine the extent to which BMI might be underestimating obesity as defined by excessive fatness in Kuwaiti female adolescents and young adults.

METHODS

A total of 400 apparently healthy Kuwaiti female university students (mean age 18.0 years, SD 0.6) were recruited. Excessive fatness was defined as body fat percentage ≥30, measured using the Tanita model TBF-310 Bio-impedance system with the manufacturer's equation. Obesity was defined as recommended by the WHO in adult participants - those aged ≥19.1 years - as BMI≥30 kg/m². In the adolescent participants (age <19.1 years) obesity was defined as recommended by the WHO as a BMI-for-age Z score of ≥2.00. The accuracy of BMI-defined obesity to identify excessively fat individuals was determined by estimating the prevalence of obesity using high BMI and prevalence of excessive fatness, and by calculating sensitivity and specificity and predictive values.

RESULTS

Median BMI was 27.8 kg/m² (range 15.1-51.2) and median body fat percentage was 32.0 (range 5.0-54.0). The prevalence of excessive fatness was 62% (247/400 individuals were excessively fat), while the prevalence of obesity according to BMI was 42% (169/400 individuals were obese according to their BMI). The sensitivity of BMI to identify the excessively fat individuals was moderate (66%) but specificity was high (96%). The positive predictive value of BMI was 96% and the negative predictive value was 64%.

CONCLUSION

BMI-based measures substantially underestimate the prevalence of excessive fatness in Kuwaiti adolescent females. Obesity is even more prevalent, and requires more urgent attention, than is apparent from BMI-based measures used in most research and national surveys. BMI may also be too crude for use as an exposure or outcome variable in many epidemiological studies of Arab adolescent girls and adult women.