Comparisons between dual-energy X-ray absorptiometry and bioimpedance devices for appendicular lean mass and muscle quality in Hispanic adults.

The purpose of this study was to compare single- and multi-frequency bioimpedance (BIA) devices against dual-energy X-ray absorptiometry (DXA) for appendicular lean mass (ALM) and muscle quality index (MQI) metrics in Hispanic adults. One hundred thirty-one Hispanic adults (18-55 years) participated in this study. ALM was measured with single-frequency bioimpedance analysis (SFBIA), multi-frequency bioimpedance analysis (MFBIA) and DXA. ALMTOTAL (left arm + right arm + left leg + right leg) and ALMARMS (left arm + right arm) were computed for all three devices. Handgrip strength (HGS) was measured using a dynamometer. The average HGS was used for all MQI models (highest left hand + highest right hand)/2. MQIARMS was defined as the ratio between HGS and ALMARMS. MQITOTAL was established as the ratio between HGS and ALMTOTAL. SFBIA and MFBIA had strong correlations with DXA for all ALM and MQI metrics (Lin's concordance correlation coefficient values ranged from 0·86 (MQIMFBIA-ARMS) to 0·97 (Arms LMSFBIA); all P < 0·001). Equivalence testing varied between methods (e.g. SFBIA v. DXA) when examining the different metrics (i.e. ALMTOTAL, ALMARMS, MQITOTAL and MQIARMS). MQIARMS was the only metric that did not differ from the line of identity and had no proportional bias when comparing all the devices against each other. The current study findings demonstrate good overall agreement between SFBIA, MFBIA and DXA for ALMTOTAL and ALMARMS in a Hispanic population. However, SFBIA and MFBIA have better agreement with DXA when used to compute MQIARMS than MQITOTAL.

Emergence of imaging technology beyond the clinical setting: Utilization of mobile health tools for at-home testing.

Body composition assessment plays a pivotal role in understanding health, disease risk, and treatment efficacy. This narrative review explores two primary aspects: imaging techniques, namely ultrasound (US) and dual-energy x-ray absorptiometry (DXA), and the emergence of artificial intelligence (AI) and mobile health apps in telehealth for body composition. Although US is valuable for assessing subcutaneous fat and muscle thickness, DXA accurately quantifies bone mineral content, fat mass, and lean mass. Despite their effectiveness, accessibility and cost remain barriers to widespread adoption. The integration of AI-powered image analysis may help explain tissue differentiation, whereas mobile health apps offer real-time metabolic monitoring and personalized feedback. New apps such as MeThreeSixty and Made Health and Fitness offer the advantages of clinic-based imaging techniques from the comfort of home. These innovations hold the potential for individualizing strategies and interventions, optimizing clinical outcomes, and empowering informed decision-making for both healthcare professionals and patients/clients. Navigating the intricacies of these emerging tools, critically assessing their validity and reliability, and ensuring inclusivity across diverse populations and conditions will be crucial in harnessing their full potential. By integrating advancements in body composition assessment, healthcare can move beyond the limitations of traditional methods and deliver truly personalized, data-driven care to optimize well-being.

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

Background: To date, body composition assessments in Hispanics, computed via bioimpedance devices, have primarily focused on body fat percent, fat mass, and fat-free mass instead of total body water (TBW). Additionally, virtually no information is available on which type of bioimpedance device is preferred for TBW assessments in Hispanic populations. Purpose: The purpose of this study was to validate two bioimpedance devices for the estimate of TBW in Hispanics adults when using a criterion deuterium oxide (D2O) technique. Methods: One-hundred thirty individuals (males: n = 70; females: n = 60) of Hispanic descent had TBW estimated via D2O, single-frequency bioimpedance analysis ([SF-BIA] Quantum V, RJL Systems) and bioimpedance spectroscopy ([BIS] SFB7 Impedimed). Results: The mean values for SF-BIA were significantly lower than D2O when evaluating the entire sample (37.4 L and 38.2 L, respectively; p < 0.05). In contrast, TBW values were not statistically significant when comparing D2O against BIS (38.4 L, p > 0.05). Bland-Altman analysis indicated no proportional bias when evaluating the entire sample for SF-BIA or BIS. The standard error of estimate and total error values were ≤ 2.3 L and Lin's concordance correlation coefficient were ≥ 0.96 for all comparisons. Conclusion: The SF-BIA and BIS devices evaluated in the current study hold promise for accurate estimation of TBW in Hispanic adults. While both methods demonstrated relatively low errors relative to the D2O criterion, BIS exhibited a more consistent performance, particularly at the group level. These findings provide essential information for researchers and clinical nutrition practitioners assessing TBW in Hispanic adults.

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

BACKGROUND: A rapid 4-compartment (4C) model integrates dual-energy x-ray absorptiometry (DXA) and multi-frequency bioimpedance analysis (MFBIA), which may be useful for clinical and research settings seeking to employ a multi-compartment model. OBJECTIVES: This study aimed to determine the added benefit of a rapid 4C model over stand-alone DXA and MFBIA when estimating body composition. METHODS: One hundred and thirty participants (n = 60 male; n = 70 female) of Hispanic descent were included in the present analysis. A criterion 4C model that employed air displacement plethysmography (body volume), deuterium oxide (total body water), and DXA (bone mineral) was used to measure fat mass (FM), fat-free mass (FFM), and body fat percent (%BF). A rapid 4C model (DXA-derived body volume and bone mineral; MFBIA-derived total body water) and stand-alone DXA (GE Lunar Prodigy) and MFBIA (InBody 570) assessments were compared against the criterion 4C model. RESULTS: Lin's concordance correlation coefficient values were >0.90 for all comparisons. The standard error of the estimates ranged from 1.3 to 2.0 kg, 1.6 to 2.2 kg, and 2.1 to 2.7% for FM, FFM, and %BF, respectively. The 95% limits of agreement ranged from ±3.0 to 4.2 kg, ±3.1 to 4.2 kg, and ±4.9 to 5.2% for FM, FFM, and %BF, respectively. CONCLUSIONS: Results revealed that all 3 methods provided acceptable body composition results. The MFBIA device used in the current study may be a more economically friendly option than DXA or when there is a need to minimize radiation exposure. Nonetheless, clinics and laboratories that already have a DXA device in place or that value having the lowest individual error when conducting a test may consider continuing to use the machine. Lastly, a rapid 4C model may be useful for assessing body composition measures observed in the current study and those provided by a multi-compartment model (e.g., protein).

Evaluation of Skinfold Techniques in People with Down Syndrome: Development of a New Equation.

The primary aim of this study was to evaluate the accuracy of skinfold thickness (SFT) measurements for the estimation of %Fat when compared to dual energy X-ray absorptiometry (DXA) in individuals with Down syndrome (DS). The secondary aim was to develop a new SFT-based body fat equation (SFTNICKERSON). SFT-based %Fat was estimated using a body fat equation from González-Agüero (SFTG-A) and body density conversion formulas from Siri (SFTSIRI) and Brozek (SFTBROZEK). Criterion %Fat was measured via DXA. SFTG-A, SFTSIRI, and SFTBROZEK were significantly lower than DXA (mean differences ranged from -7.59 to -13.51%; all p < 0.001). The SEE values ranged from 3.47% (SFTBROZEK) to 8.60% (SFTG-A). The 95% limits of agreement were greater than ±10% for all comparisons. Mid-axilla and suprailium were significant predictors of %Fat (both p < 0.05). %Fat SFTNICKERSON = 10.323 + (0.661 × mid-axilla) + (0.712 × suprailium). Age and all other skinfold sites were not statically significant in the regression model (all p > 0.05). Current findings indicate that SFTG-A, SFTSIRI, and SFTBROZEK erroneously place an individual with excessive adiposity in a normal healthy range. Accordingly, the current study developed a new equation (SFTNICKERSON) that can easily be administered in people with DS in a quick and efficient time frame. However, further research is warranted in this area.

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.

Proportional bias of multifrequency bioimpedance analysis is larger in Hispanic females than males.

Body composition algorithms are typically validated using multiethnic populations without accounting for ethnicity. This might be problematic when using multifrequency bioimpedance analysis (MF-BIA) for Hispanics. Group error (i.e., constant error [CE]), individual error (i.e., 95% limits of agreement [LOAs]), and proportional bias of MF-BIA were determined in Hispanic men and women (n = 84 and 97, respectively) when using dual energy X-ray absorptiometry (DXA) as a reference method. Because of the lack of an ethnic-specific impedance equation for Hispanics, it was hypothesized that MF-BIA would be biased when compared with DXA. For body fat percent, MF-BIA displayed similar CE ± 95% LOA for the sample (-3.17 ± 5.45%), males (-3.2 ± 5.5%), and females (-3.2 ± 5.4%) compared with DXA. However, moderate proportional bias was present for females (r = 0.48). The sample (r = 0.22) and males (r = -0.04) had trivial to no-proportional bias. Regarding fat mass, MF-BIA exhibited CE ± 95% LOA values of -1.4 ± 4.2 kg for the sample, -1.9 ± 4.6 kg for males, and -0.9 ± 3.6 kg for females. There was strong proportional bias for females (0.68) and moderate bias for the sample (r = 0.36). No proportional bias was observed for males (r = -0.02). For fat-free mass, males demonstrated the largest CE ± 95% LOA (1.6 ± 4.6), compared with the sample (1.2 ± 3.9 kg) and females (0.9 ± 3.4 kg) when MF-BIA was compared with DXA. No proportional biases existed for the sample (r = -0.01) or males (r = -0.10). However, females exhibited a moderate, negative bias (r = -0.38). Because of the observed moderate-to-strong proportional biases within body composition estimates, the need for ethnic-specific algorithms is warranted, particularly for the Hispanic female population.

Aerobic exercise is an independent determinant of levels of inflammation and oxidative stress in middle-aged obese females.

The purpose of this study was to investigate the effects of a 4-week moderate-intensity aerobic exercise on changes of body composition and markers of inflammation and oxidative stress independent from weight loss in middle-aged obese females. Thity-five obese females were randomly assigned to either an exercise (EX, N=16) or control (CON, N=19) group. The EX performed moderate intensity aerobic exercise on the treadmill for 60 min at 55% of maximal oxygen consumption (VO2max) for 4 weeks (3 days/wk). Body composition measurement with dual-energy x-ray absorptiometry and blood collection were conducted before and after the 4-weeks intervention. Blood samples were used to measure levels of tumor necrosis factor-alpha (TNF-α), C-reactive protein, adiponectin, total antioxidant status (TAS), and 8-hydroxydeoxyguanosine. Four weeks of aerobic exercise intervention significantly increased VO2max in EX (P<0.001). EX also observed a decrease in TNF-α (P=0.033) and an increase in TAS (P=0.028) without changes in body weight and fat mass after 4 weeks of aerobic exercise training. No changes were observed in CON after the intervention. Results of this study indicate that moderate aerobic exercises may contribute, at least a part, to reductions of inflammation and oxidative stress independently from fat loss. Therefore, it may reduce risks of obesity-associated disorders in middle-aged obese females.

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

BACKGROUND/OBJECTIVES: Previous research has compared 2- and 3-compartment (2C and 3C, respectively) models against criterion 4-compartment (4C) models while utilizing the same body density (Db) method for all measures. This design induces an inherent bias and obscures the added benefit of a 3C model over the simpler 2-compartment (2C) models. PURPOSE: The purpose of this study was to determine the effect of total body water estimates via single-frequency (SF-BIA) and multi-frequency (MF-BIA) bioimpedance analysis on body fat estimates derived from air displacement plethysmography (ADP)-derived 3C models. SUBJECTS/METHODS: A sample of 95 females and 82 males (n = 177) participated in this study. Underwater weighing, dual energy X-ray absorptiometry, and bioimpedance spectroscopy were used to calculate percent fat (%Fat) via a criterion 4C model (4CCRITERION). %Fat was predicted via 3CMFBIA (ADP and MF-BIA), 3CSFBIA (ADP and SF-BIA), and a stand-alone 2-compartment (2C) model, based upon ADP, when using Siri and Brozek body density conversion formulas (2CSIRI and 2CBROZEK. respectively). RESULTS: The standard error of estimate (SEE) was lowest for 3CSFBIA when evaluated in females and males (2.72% and 2.31%, respectively) and highest for 2CSIRI (3.98% and 3.84%, respectively). Similarly, the total error (TE) for females and males was lowest for 3CSFBIA (3.21% and 2.67%, respectively) and highest for 2CSIRI (4.58% and 4.48%, respectively) and 2CBROZEK (4.65% and 4.33%, respectively). CONCLUSIONS: Results suggest that SF-BIA and MF-BIA can improve the estimation of %Fat, beyond simpler 2C models, when integrated with ADP in a more advanced 3C model. Furthermore, the present study revealed that 3CSFBIA was the best overall prediction model based upon TE values. The current study results support the integration of ADP and bioimpedance technology as part of a 3C model for the improvement of %Fat estimates over simpler 2C models.

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

The purpose of this study was to compare relative adiposity (%Fat) derived from a 2-dimensional image-based 3-component (3C) model (%Fat3C-IMAGE) and dual-energy X-ray absorptiometry (DXA) (%FatDXA) against a 5-component (5C) laboratory criterion (%Fat5C). 57 participants were included (63.2% male, 84.2% White/Caucasian, 22.5±4.7 yrs., 23.9±2.8 kg/m2). For each participant, body mass and standing height were measured to the nearest 0.1 kg and 0.1 cm, respectively. A digital image of each participant was taken using a 9.7 inch, 16g iPad Air 2 and analyzed using a commercially available application (version 1.1.2, made Health and Fitness, USA) for the estimation of body volume (BV) and inclusion in %Fat3C-IMAGE . %Fat3C-IMAGE and %Fat5C included measures of total body water derived from bioimpedance spectroscopy. The criterion %Fat5C included BV estimates derived from underwater weighing and bone mineral content measures via DXA. %FatDXA estimates were calculated from a whole-body DXA scan. A standardized mean effect size (ES) assessed the magnitude of differences between models with values of 0.2, 0.5, and 0.8 for small, moderate, and large differences, respectively. Data are presented as mean ± standard deviation. A strong correlation (r = 0.94, p <.001) and small mean difference (ES = 0.24, p <.001) was observed between %Fat3C-IMAGE (19.20±5.80) and %Fat5C (17.69±6.20) whereas a strong correlation (r = 0.87, p <.001) and moderate-large mean difference (ES = 0.70, p <.001) was observed between %FatDXA (22.01±6.81) and %Fat5C. Furthermore, %Fat3C-IMAGE (SEE = 2.20 %Fat, TE= 2.6) exhibited smaller SEE and TE than %FatDXA (SEE = 3.14 %Fat, TE = 5.5). The 3C image-based model performed slightly better in our sample of young adults than the DXA 3C model. Thus, the 2D image analysis program provides an accurate and non-invasive estimate of %Fat within a 3C model in young adults. Compared to DXA, the 3C image-based model allows for a more cost-effective and portable method of body composition assessment, potentially increasing accessibility to multi-component methods.

Evaluation of Load Velocity Profiles with Varying Warm-up Sets and Relative Intensities.

The purpose of this study was to determine whether the number of warm-up sets and relative intensity impacts the prediction of velocity-based one-repetition maximum (1RM) values. Twenty resistance-trained individuals (males: n = 10, females: n = 10) participated in this study. Warm-up sets consisted of subject's bench-pressing loads at 50 (five-repetitions), 70 (three-repetitions), and 90% (one-repetition) of estimated 1RM. A maximum of four attempts were performed to determine 1RM, while recording mean concentric velocity (MCV) using a linear position transducer during warmup and 1RM trials in order to develop load-velocity profiles. Specifically, four different velocity-based 1RM equations (EQ) were developed from the warm-up sets of 50, 70, and 90% (MCV-EQ1), 50 and 90% (MCV-EQ2), 70 and 90% (MCV-EQ3), and 50 and 70% (MCV-EQ4). Constant error (CE) for the MCV prediction equations were not statistically significant for any comparisons (CEs = 0.80 to 2.96kg, all p > 0.05). Correlation coefficients between the MCV prediction methods and measured 1RM were near perfect for all comparisons (r ≥ 0.98, all p < 0.001). The standard error of estimate (SEE) and 95% limits of agreement (LOAs) were lowest for MCV-EQ1 (7.86 kg and ± 15.00 kg, respectively) and slightly higher for MCV-EQ3 (9.24 kg and 17.74 kg, respectively). Nonetheless, SEEs and 95% LOAs for MCV-EQ2 (8.10 kg and ± 15.55kg, respectively) and MCV-EQ4 (8.38 kg and ± 16.08 kg, respectively) were similar as MCV-EQ1. Current study results indicated that an additional warm-up set only slightly increases the accuracy of velocity-based 1RM estimations. Furthermore, larger differences in relative intensity will help produce slightly more accurate 1RM values.

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.

Impact of Spotter Sex on One Repetition Maximum Bench Press Performance.

ABSTRACT: Nickerson, BS, Salinas, G, Garza, JM, Cho, S, and Snarr, RL. Impact of spotter sex on one repetition maximum bench press performance. J Strength Cond Res 35(9): 2397-2400, 2021-Resistance exercise is popular because of favorable health outcomes associated with increased muscular fitness. For these reasons, 1 repetition maximum (1RM), mean velocity (MV), and peak power (PP) are of interest during the bench press. However, research has yet to evaluate whether spotter sex impacts bench press performance. Therefore, the purpose of this study was to determine the impact of spotter sex on bench press performance during a 1RM testing protocol. Twenty resistance-trained individuals (10 men and 10 women) visited the laboratory on 2 separate occasions. Estimated 1RM was self-reported by subjects before the 1RM protocol. During their visits, subjects had their 1RM (kg), MV (m·s-1), and PP (W) determined on a bench press 1RM protocol while using a male or female spotter. Deception was used by telling subjects the intent of the study was to determine the reliability of a linear position transducer for measuring MV and PP during the 1RM trials. The main findings revealed that measured 1RM values for male weight lifters were significantly higher than estimated 1RM values when using both a male (p = 0.01) and female spotter (p < 0.01). In addition, results revealed MV and PP were significantly higher for the 1RM trials when male weight lifters had a male spotter (both p < 0.01). Alternatively, there were no significant differences in estimated vs. measured 1RM values for women as well as no effect of spotter sex on bench press strength (all p > 0.05). Practitioners should note that sex of a spotter does not seem to impact measured 1RM. However, notable influences may be observed within MV and PP.

Examining Race-Related Error in Two-Compartment Models of Body Composition Assessment: A Systematic Review and Meta-Analysis.

BACKGROUND: The Brozek and Siri formulas estimate relative adiposity (%Fat) from total body density (Db) using a 2-compartment (2C) model. Racial/ethnic differences in Db have been reported, along with subsequent errors in estimated %Fat. OBJECTIVE: The primary aim of this systematic review and meta-analysis was to examine potential race/ethnic differences in the accuracy of the Brozek and Siri 2C formulas using aggregate-level data. METHODS: Peer-reviewed studies available in English that provided 2C and 4C estimates of %Fat were located using searches of the PubMed (n = 150), Scopus (n = 170), and Web of Science (n = 138) online electronic databases. Random-effects models were used to determine potential differences between racial groups using a mean ES and 95% confidence intervals. RESULTS: The cumulative results from 78 effects indicate that the relative accuracy of the Brozek equation did not vary between racial groups (between group p = 0.053). In contrast, the Siri equation slightly underestimated %Fat for Asian adults (ESWMD = -1.40%, 95%CI -2.33% to -0.46%; p = 0.004) and Black adults (ESWMD = -1.10%, 95%CI -2.11% to -0.08%; p = 0.034), with no significant differences observed in Hispanic adults (ESWMD = 0.64%, 95%CI -1.02% to 2.31%; p = 0.448) and White adults (ESWMD = 0.08%, 95%CI -0.42% to 0.57%; p = 0.766) (between group p = 0.019). CONCLUSION: Small, but statistically significant, error was found between racial groups when estimating %Fat using the 2C Siri equation when compared to 4C models. However, the observed error due to race/ethnicity appears to be of little clinical or practical significance when using either equation.

Evaluation of Obesity Cutoff Values in Hispanic Adults: Derivation of New Standards.

The diagnostic accuracy of clinical-based body composition methods such as body mass index (BMI), waist circumference (WC), bioimpedance analysis (BIA), and dual energy X-ray absorptiometry (DXA) has yet to be evaluated in Hispanic adults. Moreover, it has also been suggested that previously established obesity cutoff values may need adjusting. PURPOSE: The primary aim of this study was to investigate the diagnostic accuracy of BMI, WC, BIA, and DXA for obesity classification in Hispanic adults. The secondary aim was to internally derive obesity cutoff values producing equal sensitivity and specificity for the respective tests. METHODS: Hispanic females (n = 101) and males (n = 90) volunteered to participate in this study (18-45 years). Body fat percentage was estimated with BIA, DXA, and a 4-compartment (4C) model. Obesity-defined criteria was employed as follows: (Body fat percentage ≥ 25% and 35% and WC ≥ 102cm and 88cm for males and females, respectively; BMI ≥ 30 kg/m2). A 4C model was used as a criterion to evaluate BMI, WC, DXA, and BIA. RESULTS: Sensitivity of DXA and BIA (74.1%-96.9%) was greater than BMI and WC (25.8%-51.9%) when using previously established standards. However, specificity was poor for DXA (<70%), but considered good to excellent for BMI, WC, and BIA (83.1%-96.6%) when using previously established standards. Internally derived cutoff values improved sensitivity for BMI and WC (74.2%-81.5%) and improved specificity for DXA (>80.0%). CONCLUSION: The internally derived cutoff values, producing identical sensitivity, and specificity, were developed and shown to improve the diagnostic performance of the body composition methods compared to previously established obesity cutoff standards. Consequently, the internally derived obesity cutoff values are recommended for use by allied health professionals in clinical practice when equal sensitivity and specificity is desired.

Inter-device reliability of wearable technology for quantifying jump height in collegiate athletes.

The purpose of this study was to evaluate the inter-device reliability of three VERT devices (Mayfonk Athletic, Florida, USA) when worn on the waist (W), left-hip (LH), and right-hip (RH) during single- and double-leg counter movement jumps (CMJ) in collegiate athletes. Thirty-two female and twenty-eight male NCAA Division II athletes (n = 60) participated in the present study. Jump height (JH) values for double-leg CMJs were analyzed by each device using a one-way repeated measures ANOVA whereas a 2 (jump leg) x 3 (wear location) repeated measures ANOVA was employed to evaluate single-leg CMJs. Reliability of the VERT devices were based upon intraclass correlation coefficients (ICC). Double-leg CMJs revealed an excellent ICC between all three VERT devices (ICC = 0.969). However, JH for RH and LH (45.69 ± 9.84 and 45.82 ± 10.45 cm, respectively) were on average lower than W (50.44 ± 12.37cm; both p < 0.001). The ICCs were excellent for right- and left-leg CMJs (ICC = 0.939 and 0.941, respectively). However, an interaction was observed (p < 0.001). No differences existed for left- or right-leg when VERT was worn on the waist. However, JH was higher when VERT devices were worn on the opposite hip of the jump leg (i.e., LH>RH for right-leg CMJs; RH>LH for leftleg CMJs; all p < 0.001). Results suggest that LH and RH are interchangeable for double-leg CMJs, but not with waist despite excellent reliability. In addition, all wear locations provided excellent ICCs for single-leg CMJs. However, waist provides more consistent JH values for right- and left-leg CMJs while RH and LH show more variability.

Development of a Body Mass Index-based Body Fat Equation: Effect of Handgrip Strength.

Body mass index (BMI)-based body fat equations from Womersley (BMIWOMERSLEY), Jackson (BMIJACKSON), Deurenberg (BMIDEURENBERG), and Gallagher (BMIGALLAGHER) are practical in clinical and field settings. However, research has shown these prediction equations produce large error, which may be due to the inability of BMI to account for differences in fat mass and fat-free mass. Thus, accounting for variations in muscular strength via relative handgrip (RHG) strength could help enhance the accuracy of a BMI-based body fat equation. PURPOSE: The purpose of the current study was twofold: 1) to develop a new BMI-based body fat equation that includes the measurement of RHG (BMINICKERSON) and 2) to cross-validate BMINICKERSON, BMIWOMERSLEY, BMIJACKSON, BMIDEURENBERG, and BMIGALLAGHER against a four-compartment criterion. METHODS: The development and cross-validation samples consisted of 230 and 110 participants, respectively. Criterion body fat percent was determined with a four-compartment model. RHG was calculated by summing the max of each handgrip strength measurement and dividing by body mass. BMI (kg·m), RHG (kg·kg), age (yr), ethnicity (Hispanic or non-Hispanic White), and sex (male or female) were entered into a stepwise regression to calculate BMINICKERSON. RESULTS: BMINICKERSON was calculated as follows: body fat percent = 21.504 - (12.484 × RHG) - (7.998 × sex) + (0.722 × BMI). In the cross-validation sample, BMINICKERSON produced lower constant error (CE) and total error (TE) values (CE = -0.11%, TE = 4.28%) than all other BMI-based body fat equations (CE = 0.89%-1.90%, TE = 5.71%-6.87%). Furthermore, the 95% limits of agreement were lower for BMINICKERSON ± 8.47% than previous BMI-based body fat equations (95% limits of agreement = ±11.14% to 13.33%). CONCLUSION: Current study results confirm that previous BMI-based body fat equations produce large error in Hispanics and non-Hispanic Whites but can be improved by accounting for RHG. Allied health professionals are encouraged to use BMINICKERSON in clinical and field settings for adiposity assessments.

Fat-free mass characteristics vary based on sex, race, and weight status in US adults.

Common body composition estimation techniques necessitate assumptions of uniform fat-free mass (FFM) characteristics, although variation due to sex, race, and body characteristics may occur. National Health and Nutrition Examination Survey data from 1999 to 2004, during which paired dual-energy x-ray absorptiometry (DXA) and bioimpedance spectroscopy assessments were performed, were used to estimate FFM characteristics in a sample of 4619 US adults. Calculated FFM characteristics included the density and water, bone mineral, and residual content of FFM. A rapid 4-component model was also produced using DXA and bioimpedance spectroscopy data. Study variables were compared across sex, race/ethnicity, body mass index (BMI), and age categories using multiple pairwise comparisons. A general linear model was used to estimate body composition after controlling for other variables. Statistical analyses accounted for 6-year sampling weights and complex sampling design of the National Health and Nutrition Examination Survey and were based on 5 multiply imputed datasets. Differences in FFM characteristics across sex, race, and BMI were observed, with notable dissimilarities between men and women for all outcome variables. In racial/ethnic comparisons, non-Hispanic blacks most commonly presented distinct FFM characteristics relative to other groups, including greater FFM density and proportion of bone mineral. Body composition errors between DXA and the 4-component model were significantly influenced by sex, age, race, and BMI. In conclusion, FFM characteristics, which are often assumed in body composition estimation methods, vary due to sex, race/ethnicity, and weight status. The variation of FFM characteristics in diverse populations should be considered when body composition is evaluated.

Evaluation of Load-Velocity Relationships and Repetitions-to-Failure Equations in the Presence of Male and Female Spotters.

Nickerson, BS, Williams, TD, Snarr, RL, Garza, JM, and Salinas, G. Evaluation of load-velocity relationships and repetitions-to-failure equations in the presence of male and female spotters. J Strength Cond Res 34(9): 2427-2433, 2020-The purpose of this study was 2 fold: (a) to determine whether differences in mean concentric velocity (MCV), repetitions-to-failure (RTF), measured 1 repetition maximum (1RM), and 1RM prediction methods vary between lifter and spotter sex and (b) determine the accuracy of velocity-based 1RM (MCV1RM) and repetitions-to-failure-based 1RM (RTF1RM) prediction equations in the presence of either a male or female spotter. Twenty resistance-trained individuals (50% men) participated in this study. The initial 2 visits involved measuring 1RM for the bench press with a male or female spotter. Visits 3 and 4 required subjects to lift loads at 30 (5-repetitions), 50 (5-repetitions), and 70% 1RM (RTF) in the presence of a male or female spotter. Velocity-based 1RM was determined through individual regression equations using the submaximal loads (MCV30, MCV50, and MCV70). Repetitions-to-failure-based 1RM was determined through the RTF at 70% 1RM using Wathen (Wathen1RM), Mayhew (Mayhew1RM), and Epley (Epley1RM) equations. There were significant interactions when assessing Wathen1RM and Mayhew1RM (p < 0.05). Female lifters produced significantly higher estimated 1RM values during the male spotter condition using Wathen1RM and Mayhew1RM than the female spotter condition (p = 0.032 and 0.033, respectively). MCV1RM and Epley1RM produced smaller mean differences than Wathen1RM and Mayhew1RM when compared with measured 1RM. However, MCV1RM produced the largest standard error of estimate, whereas Epley1RM produced the lowest values. Epley1RM should be used over MCV1RM, Wathen1RM, and Mayhew1RM when loads up to 70% 1RM are implemented. Also, spotter sex only seems to impact female lifters when using the RTF1RM prediction equations of Wathen1RM and Mayhew1RM.