Dual energy X-ray absorptiometry produces larger measurement error in non-Hispanic Caucasians than Hispanics.

BACKGROUND: Dual energy X-ray absorptiometry (DXA) is often used as a criterion measure in body composition research and in clinical settings for the estimate of body fat percent (%Fat). The accuracy of DXA for predicting %Fat has primarily been conducted in non-Hispanic populations. AIM: The purpose of this study was to determine the agreement of DXA-derived %Fat in Hispanic and non-Hispanic Caucasian adults. METHODS: The sample consisted of Hispanic males (n = 96) and females (n = 102) and non-Hispanic Caucasian males (n = 145) and females (n = 161). The %Fat of a whole-body DXA scan was compared against a criterion 4-compartment (4C) model via constant error (CE = DXA - 4C model) and 95% limits of agreement. Also, a 2 × 2 factorial ANOVA, using CE as a dependent variable, was conducted to examine the main and interaction effects of sex and ethnicity. RESULTS: When compared to the 4C model, DXA overestimated %Fat by 4.0% in Hispanics and 5.5% in non-Hispanic Caucasians (all p < 0.05). The 95% limits of agreement ranged from ±5.5% to ±5.9% for all group comparisons. The 2 × 2 factorial ANOVA indicated the CE was greater in non-Hispanic Caucasians than Hispanics (CE difference = 1.5%; p < 0.05). CONCLUSION: Our findings revealed that DXA significantly overestimates %Fat in both populations (Hispanics and non-Hispanic Caucasians), when compared to a 4C model, regardless of sex (male or female). However, the error is more profound in non-Hispanic Caucasian adults. It is worth nothing that DXA may be useful for tracking changes in body composition that occur throughout a lifestyle intervention. Nonetheless, practitioners should be aware that the estimate of %Fat from DXA may be larger than the actual values obtained from a 4C model.

Generalized Equations for Predicting Percent Body Fat from Anthropometric Measures Using a Criterion Five-Compartment Model.

INTRODUCTION: Anthropometric-based equations are used to estimate percent body fat (%BF) when laboratory methods are impractical or not available. However, because these equations are often derived from two-compartment models, they are prone to error because of the assumptions regarding fat-free mass composition. The purpose of this study was to develop a new anthropometric-based equation for the prediction of %BF, using a five-compartment (5C) model as the criterion measure. METHODS: A sample of healthy adults (52.2% female; age, 18 to 69 yr; body mass index, 15.7 to 49.5 kg·m-2) completed hydrostatic weighing, dual-energy x-ray absorptiometry, and bioimpedance spectroscopy measurements for calculation of 5C %BF (%BF5C), as well as skinfolds and circumferences. %BF5C was regressed on anthropometric measures using hierarchical variable selection in a random sample of subjects (n = 279). The resulting equation was cross-validated in the remaining participants (n = 78). New model performance was also compared with several common anthropometric-based equations. RESULTS: The new equation [%BFNew = 6.083 + (0.143 × SSnew) - (12.058 × sex) - (0.150 × age) - (0.233 × body mass index) + (0.256 × waist) + (0.162 × sex × age)] explained a significant proportion of variance in %BF5C (R2 = 0.775, SEE = 4.0%). Predictors included sum of skinfolds (SSnew, midaxillary, triceps, and thigh) and waist circumference. The new equation cross-validated well against %BF5C when compared with other existing equations, producing a large intraclass correlation coefficient (0.90), small mean bias and limits of agreement (0.4% ± 8.6%), and small measures of error (SEE = 2.5%). CONCLUSIONS: %BFNew improved on previous anthropometric-based equations, providing better overall agreement and less error in %BF estimation. The equation described in this study may provide an accurate estimate of %BF5C in healthy adults when measurement is not practical.

Prediction of underwater residual lung volume in healthy men and women.

Regression equations are commonly used to predict residual lung volume (RV) during underwater weighing when measurement is not practical. However, the equations currently available were derived from on-land measures of RV and may account for changes in lung capacity during submersion, thus leading to inaccuracies in assessment of percent body fat (%BF). The purpose of this study was to (1) develop a new equation (RVNEW ) for the prediction of underwater RV, (2) cross-validate RVNEW and compare it to existing RV equations, and (3) compare the effects of RVNEW and existing equations on underwater %BF. One-hundred seventy-five healthy adults were recruited to complete simultaneous hydrostatic weighing and RV measurements. The sample was randomly divided into development (n = 131) and cross-validation (n = 44) cohorts. Regression analysis in the development cohort resulted in the following equation: underwater RV = -3·419 + 0·026 × height (cm) + 0·019 × age (y) (p < 0·001; R2  = 0·53; SEE = 0·26). In the cross-validation cohort, Bland-Altman analysis revealed that the new equation provided the best overall agreement with underwater RV (bias ± 1·96 SD, 0·07 ± 0·5 L), while existing equations produced significantly different values from measured RV and wider limits of agreement. When used to calculate %BF, the new RV equation produced the strongest agreement with underwater %BF (-0·5% ± 3·8%), although all equations produced strong correlations (all r > 0·95) and limits of agreement ≤4·7%. The results of this study suggest that RVNEW may be more appropriate for RV estimation during hydrostatic weighing than existing equations. However, its applicability to populations outside the current study needs to be examined.

Age-Based Prediction of Maximal Heart Rate in Children and Adolescents: A Systematic Review and Meta-Analysis.

Purpose: Maximal heart rate (MHR) is an important physiologic tool for prescribing and monitoring exercise in both clinical and athletic settings. However, prediction equations developed in adults may have limited accuracy in youth. The purpose of this study was to systematically review and analyze the available evidence regarding the validity of commonly used age-based MHR prediction equations among children and adolescents. Methods: Included articles were peer-reviewed, published in English, and compared measured to predicted MHR in male and female participants <18 years old. The standardized mean difference effect size (ES) was used to quantify the accuracy of age-predicted MHR values and a priori moderators were examined to identify potential sources of variability. Results: The cumulative results of 20 effects obtained from seven articles revealed that prediction equations did not accurately estimate MHR (ES= 0.44, p < .05) by 6.3 bpm (bpm). Subgroup analyses indicated that the Fox equation (MHR = 220-age) overestimated MHR by 12.4 bpm (ES = 0.95, p < .05), whereas the Tanaka equation (MHR = 208-0.7*age) underestimated MHR by 2.7 bpm (ES = -0.34, p < .05). Conclusions: Age-based MHR equations derived from adult populations are not applicable to children. However, if the use of age-based equations cannot be avoided, we recommend using the Tanaka equation, keeping in mind the range of error reported in this study. Future research should control for potential pubertal influences on sympathetic modulation during exercise to facilitate the development of more age-appropriate methods for prescribing exercise intensity.

The Accuracy of Acquiring Heart Rate Variability from Portable Devices: A Systematic Review and Meta-Analysis.

BACKGROUND: Advancements in wearable technology have provided practitioners and researchers with the ability to conveniently measure various health and/or fitness indices. Specifically, portable devices have been devised for convenient recordings of heart rate variability (HRV). Yet, their accuracies remain questionable. OBJECTIVE: The aim was to quantify the accuracy of portable devices compared to electrocardiography (ECG) for measuring a multitude of HRV metrics and to identify potential moderators of this effect. METHODS: This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Articles published before July 29, 2017 were located via four electronic databases using a combination of the terms related to HRV and validity. Separate effect sizes (ESs), defined as the absolute standardized difference between the HRV value recorded using the portable device compared to ECG, were generated for each HRV metric (ten metrics analyzed in total). A multivariate, multi-level model, incorporating random-effects assumptions, was utilized to quantify the mean ES and 95% confidence interval (CI) and explore potential moderators. RESULTS: Twenty-three studies yielded 301 effects and revealed that HRV measurements acquired from portable devices differed from those obtained from ECG (ES = 0.23, 95% CI 0.05-0.42), although this effect was small and highly heterogeneous (I2 = 78.6%, 95% CI 76.2-80.7). Moderator analysis revealed that HRV metric (p <0.001), position (p = 0.033), and biological sex (ß = 0.45, 95% CI 0.30-0.61; p <0.001), but not portable device, modulated the degree of absolute error. Within metric, absolute error was significantly higher when expressed as standard deviation of all normal-normal (R-R) intervals (SDNN) (ES = 0.44) compared to any other metric, but was no longer significantly different after a sensitivity analysis removed outliers. Likewise, the error associated with the tilt/recovery position was significantly higher than any other position and remained significantly different without outliers in the model. CONCLUSIONS: Our results suggest that HRV measurements acquired using portable devices demonstrate a small amount of absolute error when compared to ECG. However, this small error is acceptable when considering the improved practicality and compliance of HRV measurements acquired through portable devices in the field setting. Practitioners and researchers should consider the cost-benefit along with the simplicity of the measurement when attempting to increase compliance in acquiring HRV measurements.

Field-Based Performance Tests Are Related to Body Fat Percentage and Fat-Free Mass, But Not Body Mass Index, in Youth Soccer Players.

The primary aim of this study was to determine the association between body composition and performance outcomes in youth soccer players. Twenty-five competitive male youth soccer players (age = 13.7 ± 0.8 years, height = 167.4 ± 9.7 cm, weight = 57.6 ± 12.1 kg) volunteered to participate in this study. Height and weight were used to calculate body mass index (BMI). Body fat percentage (BF%) and fat-free mass (FFM) were determined with dual-energy X-ray absorptiometry. Each athlete performed the Pacer test, vertical jump, and t-test drill. Participants were predominantly normal weight (20.4 ± 2.7 kg·m-2). The body composition results were 20.3 ± 4.9% for BF% and 46.5 ± 8.7 kg for FFM. The results of the performance tests indicated a mean ± standard deviation (SD) of 1418 ± 332 m for Pacer, 57.2 ± 7.4 cm for vertical jump, 11.6 ± 0.7 s for t-test. Body mass index was not associated with any performance measure (r = 0.02 to -0.21, all p > 0.05). Body fat percentage was associated with the Pacer, vertical jump, and t-test (r = -0.62, -0.57, 0.61, respectively; all p < 0.01) and remained after accounting for BMI. Fat-free mass was only significantly related to t-test (r = -0.43, p < 0.01). However, after controlling for BMI, FFM was related to all three performance tests. Body fat percentage and FFM are associated with performance in youth soccer players, with stronger relationships reported in the former metric. The findings highlight the need for accurate body composition measurements as part of an assessment battery in young athletes.

Age-Predicted Maximal Heart Rate Equations Are Inaccurate for Use in Youth Male Soccer Players.

PURPOSE: The purpose of this study was to compare the differences between measured (MHRobt) and predicted (MHRpred) maximal heart rate (MHR) in youth athletes. METHODS: In total, 30 male soccer players [14.6 (0.6) y] volunteered to participate in this study. MHRobt was determined via maximal-effort graded exercise test. Age-predicted MHR (MHRpred) was calculated for each participant using equations by Fox, Tanaka, Shargal, and Nikolaidis. Mean differences were compared using Friedman's 2-way analysis of variance and post hoc pairwise comparisons. Agreement between MHRobt and MHRpred values was calculated using the Bland-Altman method. RESULTS: There were no significant differences between MHRobt and MHRpred from the Fox (P = .777) and Nikolaidis (P = .037) equations. The Tanaka and Shargal equations significantly underestimated MHRobt (P < .001). All 4 equations produced 95% limits of agreement of ±15.0 beats per minute around the constant error. CONCLUSIONS: The results show that the Fox and Nikolaidis equations produced the smallest mean difference in predicting MHRobt. However, the wide limits of agreement suggests that none of the equations adequately account for individual variability in MHRobt. Practitioners should avoid applying these equations in youth athletes and utilize a lab or field testing protocol to obtain MHR.