A novel method of utilizing skinfolds and bioimpedance for determining body fat percentage via a field-based three-compartment model

Body composition estimates using a 2D image analysis system across different environmental conditions: An agreement study

PURPOSE

This study examined the agreement between %Fat measurements using a smartphone-based application (IMAGE) across different environmental conditions.

METHODS

A single reference image was obtained using an 8 MP smartphone camera under Ambient Light in front of a white background. Additional photos were obtained using a 0.7 MP, 5 MP, and 12 MP smartphone cameras; low-, moderate-, and bright-lighting conditions; and various color backgrounds including black, green, orange, and gray.

RESULTS

%Fat measured using the 0.7 MP camera (27.8 ± 6.2 %Fat) was higher than the reference (26.8 ± 6.1 %Fat) (p < 0.001). The black (32.0 ± 12.0 %Fat), green (27.5 ± 6.3 %Fat), and gray (27.8 ± 6.3 %Fat) backgrounds yielded higher %Fat than the white (p = 0.03, 0.01, and 0.001). All camera, lighting, and background conditions were strongly correlated with the reference (all intraclass correlation coefficient [ICC] >0.98, all standard error of the estimate [SEE] <1.5 %Fat, all p < 0.001), except the black background which yielded poorer agreement with the white background (ICC = 0.69, SEE = 4.5%, p < 0.001).

CONCLUSION

%Fat from IMAGE were strongly correlated across various environmental conditions.

Agreement between bioimpedance analysis and ultrasound scanning in body composition assessment

OBJECTIVES

This study aimed at evaluating the agreement between bioelectrical impedance analysis (BIA) using ABC-02 Medas and A-mode ultrasound (AUS) using BodyMetrix™ BX2000 for fat mass (FM), fat free mass (FFM), and body fat percentage (%BF) in females.

METHODS

The cross-sectional, single-center, observational study was performed in 206 female subjects aged 18-67 years. The examination program included measurements of body height and weight along with waist, hip circumferences, and body composition analysis. The measurements were performed by ultrasound scanner and bioimpedance analyzer.

RESULTS

We found that 20.9% of women were obese based on BMI (≥30 kg/m2), which was significantly lower when using a criterion based on body fat percentage (%BF ≥ 30%) measured with US (53.4%, p = .0056) or BIA (54.8%, p = .0051). At the group level, both methods were found interchangeable and showed practically negligible differences (0.1% for %BF, 0.5 kg for FM, and 0.4 kg for FFM). Agreement analysis conducted in the whole sample revealed a low level of agreement in estimating %BF (CCC = 0.72 0.77 0.82) and FFM (CCC = 0.81 0.84 0.86), and medium level of agreement in estimating FM (CCC = 0.91 0.93 0.94). The level of agreement in estimating %BF and FFM was improved to the medium level with the use of newly generated prediction equations.

CONCLUSION

Thus, the proposed equations can be used for conversion of body composition results obtained by AUS into the BIA data.

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.

Validation of skinfold equations and alternative methods for the determination of fat-free mass in young athletes

Intoduction

To cross-validate skinfold (SKF) equations, impedance devices, and air-displacement plethysmography (ADP) for the determination of fat-free mass (FFM).

Methods

Male and female youth athletes were evaluated (n = 91[mean ± SD] age: 18.19 ± 2.37 year; height: 172.1 ± 9.8 cm; body mass: 68.9 ± 14.5 kg; BMI: 23.15 ± 3.2 kg m-2; body fat: 19.59 ± 6.9%) using underwater weighing (UWW), ADP, and SKF assessments. A 3-compartment (3C) model (i.e., UWW and total body water) served as the criterion, and alternate body density (Db) estimates from ADP and multiple SKF equations were obtained. Validity metrics were examined to establish each method's performance. Bioelectrical impedance analysis (BIA), bioimpedance spectroscopy (BIS), and the SKF equations of Devrim-Lanpir, Durnin and Womersley, Jackson and Pollock (7-site), Katch, Loftin, Lohman, Slaughter, and Thorland differed from criterion.

Results

For females, Pearson's correlations between the 3C model and alternate methods ranged from 0.51 to 0.92, the Lin's concordance correlation coefficient (CCC) ranged from 0.41 to 0.89, with standard error of the estimate (SEE) ranges of 1.9-4.6 kg. For SKF, the Evans 7-site and J&P 3 Site equations performed best with CCC and SEE values of 0.82, 2.01 kg and 0.78, 2.21 kg, respectively. For males, Pearson's correlations between the 3C model and alternate methods ranged from 0.50 to 0.95, CCC ranges of 0.46-0.94, and SEE ranges of 3.3-7.6 kg. For SKF, the Evans 3-site equation performed best with a mean difference of 1.8 (3.56) kg and a CCC of 0.93.

Discussion

The Evans 7-site and 3-site SKF equations performed best for female and male athletes, respectively. The field 3C model can provide an alternative measure of FFM when necessary.

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 (SFT_NICKERSON). SFT-based %Fat was estimated using a body fat equation from González-Agüero (SFT_G-A) and body density conversion formulas from Siri (SFT_SIRI) and Brozek (SFT_BROZEK). Criterion %Fat was measured via DXA. SFT_G-A, SFT_SIRI, and SFT_BROZEK 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% (SFT_BROZEK) to 8.60% (SFT_G-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 SFT_NICKERSON = 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 SFT_G-A, SFT_SIRI, and SFT_BROZEK erroneously place an individual with excessive adiposity in a normal healthy range. Accordingly, the current study developed a new equation (SFT_NICKERSON) that can easily be administered in people with DS in a quick and efficient time frame. However, further research is warranted in this area.

Effects of creatine monohydrate timing on resistance training adaptations and body composition after 8 weeks in male and female collegiate athletes

Background

Limited research is available on the potential impact of creatine monohydrate administration before or after workouts among athletes. This study aimed to investigate the effects of pre- vs. post-exercise creatine monohydrate supplementation on resistance training adaptations and body composition.

Methods

In a randomized, double-blind, placebo-controlled, parallel design, 34 healthy resistance-trained male and female athletes were randomly assigned and matched according to fat free mass to consume a placebo, or 5-g dose of creatine monohydrate within 1 h before training, or within 1 h after training for 8 weeks, while completing a weekly resistance training program. Participants co-ingested 25-gram doses of both whey protein isolate and maltodextrin along with each assigned supplement dose. Body composition, muscular strength, and endurance, along with isometric mid-thigh pull were assessed before and after the 8-week supplementation period. A 3 × 2 mixed factorial (group x time) ANOVA with repeated measures on time were used to evaluate differences.

Results

All groups experienced similar and statistically significant increases in fat free mass (+1.34 ± 3.48 kg, p = 0.04), upper (+2.21 ± 5.69 kg, p = 0.04) and lower body strength (+7.32 ± 10.01 kg, p &lt; 0.001), and decreases in body mass (−1.09 ± 2.71 kg, p = 0.03), fat mass (−2.64 ± 4.16 kg, p = 0.001), and percent body fat (−2.85 ± 4.39 kg, p &lt; 0.001).

Conclusions

The timing of creatine monohydrate did not exert any additional influence over the measured outcomes.

Breast cancer classification application based on QGA-SVM

Early diagnosis of breast cancer plays an important role in improving survival rate. Physiological changes of breast tissue can be observed and measured through medical electrical impedance, and the results can be used as a preliminary diagnosis by doctors before treatment. In this paper, quantum genetic algorithm (QGA) and support vector machine (SVM) were combined to classify breast tissues to help clinicians in diagnosis. The algorithm uses QGA to optimize the parameters of SVM and improve the classification performance of SVM. In this experiment, the electrical impedance data measured from breast tissue provided by UCI [58] was used as the data set. Objectively speaking, the data volume of the data set is small and the representativeness is not strong enough. However, the experimental results show that QGA-SVM shows better classification performance, and it is better than SVM.

Longitudinal analyses of serum neurofilament light and associations with obesity indices and bioelectrical impedance parameters

Neurofilament light is a constituent of the neuronal cytoskeleton and released into the blood following neuro-axonal damage. It has previously been reported that NfL measured in blood serum is inversely related to body mass index. However, no reports exist with regard to body composition assessed using bioelectrical impedance analysis or other indicators of obesity beyond BMI. We analyzed the relationship between sNfL and body composition according to the three compartment model. Additionally, associations between sNfL, body shape index, waist-to-height ratio, and BMI were examined. The sample consisted of 769 participants assessed during the baseline examination and 693 participants examined in the course of the follow-up of the BiDirect Study. Associations between sNfL, BMI, BSI, and WtHR were separately analyzed using linear mixed models. Body compartments operationalized as fat mass, extracellular cell mass, and body cell mass were derived using BIA and the relationship with sNfL was analyzed with a linear mixed model. Lastly, we also analyzed the association between total body water and sNfL. We found significant inverse associations of sNfL with BMI and WtHR. The analysis of the three compartment model yielded significant inverse associations between sNfL, body cell mass and body fat mass, but not extracellular mass. Furthermore, total body water was also inversely related to sNfL. A potential mechanism could involve body cell mass and body fat mass as highly adaptive body constituents that either directly absorb sNfL, or promote the formation of new vasculature and thereby increase blood volume.

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 (%Fat_3C-IMAGE) and dual-energy X-ray absorptiometry (DXA) (%Fat_DXA) against a 5-component (5C) laboratory criterion (%Fat_5C). 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 %Fat_3C-IMAGE . %Fat_3C-IMAGE and %Fat_5C included measures of total body water derived from bioimpedance spectroscopy. The criterion %Fat_5C included BV estimates derived from underwater weighing and bone mineral content measures via DXA. %Fat_DXA 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 %Fat_3C-IMAGE (19.20±5.80) and %Fat_5C (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 %Fat_DXA (22.01±6.81) and %Fat_5C. Furthermore, %Fat_3C-IMAGE (SEE = 2.20 %Fat, TE= 2.6) exhibited smaller SEE and TE than %Fat_DXA (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.

Validity of a 3-compartment body composition model using body volume derived from a novel 2-dimensional image analysis program

BACKGROUND/OBJECTIVES

The purpose of this study was: (1) to compare body volume (BV) estimated from a 2-dimensional (2D) image analysis program (BV_IMAGE), and a dual-energy x-ray absorptiometry (DXA) equation (BV_{DXA-Smith-Ryan}) to an underwater weighing (UWW) criterion (BV_UWW); (2) to compare relative adiposity (%Fat) derived from a 3-compartment (3C) model using BV_IMAGE (%Fat_3C-IMAGE), and a 4-compartment (4C) model using BV_{DXA-Smith-Ryan} (%Fat_{4C-DXA-Smith-Ryan}) to a 4C criterion model using BV_UWW (%Fat_4C-UWW).

SUBJECT/METHODS

Forty-eight participants were included (60% male, 22.9 ± 5.0 years, 24.2 ± 2.6 kg/m²). BV_IMAGE was derived using a single digital image of each participant taken from the rear/posterior view. DXA-derived BV was calculated according to Smith-Ryan et al. Bioimpedance spectroscopy and DXA were used to measure total body water and bone mineral content, respectively, in the 3C and 4C models. 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.

RESULTS

Near-perfect correlation (r = 0.998, p < 0.001) and no mean differences (p = 0.267) were observed between BV_IMAGE (69.6 ± 11.5 L) and BV_UWW (69.5 ± 11.4 L). No mean differences were observed between %Fat_{4C-DXA-Smith-Ryan} and the %Fat_4C-UWW criterion (p = 0.988). Small mean differences were observed between %Fat_3C-IMAGE and %Fat_4C-UWW (ES = 0.2, p < 0.001). %Fat_3C-IMAGE exhibited smaller SEE and TE, and tighter limits of agreement than %Fat_{4C-DXA-Smith-Ryan}.

CONCLUSIONS

The 2D image analysis program provided an accurate and non-invasive estimate of BV, and subsequently %Fat within a 3C model in generally healthy, young adults.

Body composition assessment in children with inflammatory bowel disease: A comparison of different methods

AIM

To assess different techniques to measure body composition in paediatric patients with inflammatory bowel disease using dual energy X-ray absorptiometry as a reference method. We hypothesised that a three-compartment model may demonstrate superiority over other methods as skinfold thickness equations and bioelectrical impedance analysis.

METHODS

Body composition was assessed using skinfold thickness equations, bioelectrical impedance analysis and the three-compartment model. Data obtained with these methods were compared to the results obtained by dual energy X-ray absorptiometry. Statistical analysis was performed using Spearman's correlation and Bland-Altman's limits of agreement method.

RESULTS

Twenty-one paediatric patients with inflammatory bowel disease were included: 11 females and 10 males; mean age for the entire group: 14.3 years, range 12-16 years. In children with inflammatory bowel disease, skinfold thickness equations, bioelectrical impedance analysis and the three-compartment model showed reliable measurements with small differences in the percentage of total body fat and good limits of agreements.

CONCLUSION

The assessment of body composition using bioelectrical impedance analysis provides a valid and accurate method in children with inflammatory bowel disease as compared to dual energy X-ray absorptiometry. In the future, superiority of 3-compartment model in research and clinical settings of nutritional intervention and disease status in children with inflammatory bowel disease remains to be demonstrated.

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 due to 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 y; body mass index [BMI], 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 to several common anthropometric-based equations.

RESULTS

The new equation [%BFNew = 6.083 + (0.143 × SSnew) - (12.058 × Sex) - (0.150 × Age) - (0.233 × BMI) + (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 to other existing equations, producing a large intraclass correlation coefficient (ICC = 0.90), small mean bias and limits of agreement (0.4 ± 8.6%), and small measures of error (SEE = 2.5%).

CONCLUSION

%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.

Combining Anthropometry and Bioelectrical Impedance to Predict Body Fat in Female Athletes

CONTEXT

Accurate methods for predicting percent body fat in female athletes are needed for those who lose weight for competition. Methods mandated by sports-governing bodies for minimal weight determination in such athletes lack validation.

OBJECTIVE

To determine whether combining anthropometry (skinfolds, SF) and bioelectrical impedance analysis (BIA) in a 3 component model (3C) would improve the prediction of percentage body fat (%Fat) in female athletes. Secondarily, the Slaughter skinfold equation was evaluated. We hypothesized that compared to outcomes for SF or BIA alone, 3C-determined %Fat would not differ from our criterion method (accuracy) and would be a stronger predictor (higher r2) of the criterion.

DESIGN

Cross sectional.

SETTING

Laboratory-based study during the pre-season for collegiate sport.

PARTICIPANTS

Female athletes (n=18 D1 NCAA) recruited from swim and gymnastic teams.

MAIN OUTCOME VARIABLES

%Fat based on a four-compartment (4C) criterion incorporating body density (air displacement plethysmography), total body water (D2O dilution), and bone mineral mass (DEXA) compared to predicted %Fat using SF alone (Slaughter equation), bioelectrical impedance analysis (single frequency for TBW estimate) and combined skinfolds and BIA (3C).

RESULTS

Regression revealed that for %Fat using the criterion 4C, the highest adjusted coefficient of determination and lowest prediction error (r2 ±standard error of estimate) was 3C (r2=0.87 ±2.8%) followed by BIA (r2=0.80 ±3.5%) and SF (r2=0.76 ± 3.8%) (for all, p<0.05). Means differed for %Fat determined using BIA (26.6 ±7.5) and 3C (25.5 ±7.2) vs. the 4C (23.5 ±7.4) (ANOVA and post hoc p<0.05). The SF estimate (24.0 +7.8) did not differ from the 4C value.

CONCLUSIONS

Combining SF and BIA might improve the prediction and lower the prediction error for determining %Fat in female athletes compared to SF or BIA separately. Regardless, the Slaughter skinfold equation appeared accurate for determining the mean %Fat in these female athletes.

Accuracy of a Mobile 2D Imaging System for Body Volume and Subsequent Composition Estimates in a Three-Compartment Model

PURPOSE

The purpose of the study was to compare a single two-dimensional image processing system (IMAGE) to underwater weighing (UWW) for measuring body volume (BV) and subsequently estimating body fat percentage (%Fat), fat mass (FM), and fat-free mass (FFM) via a 3-compartment (3C) model.

METHODS

A sample of participants age 18-39 yr was recruited for this study (n = 67, 47.8% female). BV was measured with UWW and predicted via the IMAGE software. The BV estimates from UWW (3CUWW) and IMAGE (3CIMAGE) were separately combined with constant total body water and body mass values for 3C model calculation of %Fat, FM, and FFM.

RESULTS

BV obtained from the IMAGE was 67.76 ± 12.19 and 67.72 ± 12.04 L from UWW, which was not significantly different (P = 0.578) and very largely correlated (r = 0.99, P < 0.001). When converted to %Fat (3CUWW = 21.01% ± 7.30%, 3CIMAGE = 21.08% ± 7.04%, P = 0.775), FM (3CUWW = 14.68 ± 5.15 kg, 3CIMAGE = 14.78 ± 5.08 kg, P = 0.578), and FFM (3CUWW = 57.00 ± 13.20 kg, 3CIMAGE = 56.90 ± 12.84 kg, P = 0.578) with the 3C model, no significant mean differences and very large correlations (r values ranged from 0.96 to 0.99) were observed. In addition, the standard error of estimate, total error, and 95% limits of agreement for all three metrics were small and considered acceptable.

CONCLUSIONS

An IMAGE system provides valid estimates of BV that accurately estimates body composition in a 3C model.

A Field-based Three-Compartment Model Derived from Ultrasonography and Bioimpedance for Estimating Body Composition Changes

PURPOSE

The purpose of this study was to assess the agreement between a field-based three-compartment (3CFIELD) model and a laboratory-based three-compartment (3CLAB) model for tracking body composition changes over time.

METHODS

Resistance-trained males completed a supervised nutrition and resistance training intervention. Before and after the intervention, assessments were performed via air displacement plethysmography (ADP), bioimpedance spectroscopy (BIS), portable ultrasonography (US), and bioelectrical impedance analysis (BIA). ADP body density and BIS body water were used within the reference 3CLAB model, whereas US-derived body density and BIA body water were used within the 3CFIELD model. Two-compartment model body composition estimates provided by US and BIA were also examined. Changes in fat-free mass and fat mass were analyzed using repeated-measures ANOVA, equivalence testing, Bland-Altman analysis, linear regression, and related validity analyses.

RESULTS

Significant increases in fat-free mass (3CLAB, 4.0 ± 4.5 kg; 3CFIELD, 3.9 ± 4.2 kg; US, 3.2 ± 4.3 kg; BIA, 3.9 ± 4.2 kg) and fat mass (3CLAB, 1.3 ± 2.2 kg; 3CFIELD, 1.4 ± 2.2 kg; US, 2.1 ± 2.6 kg; BIA, 1.4 ± 2.9 kg) were detected by all methods. However, only the 3CFIELD model demonstrated equivalence with the 3CLAB model. In addition, the 3CFIELD model exhibited superior performance to US and BIA individually, as indicated by the total error (3CFIELD, 1.0 kg; US, 1.8 kg; BIA, 1.6 kg), 95% limits of agreement (3CFIELD, ±2.1 kg; US, ±3.3 kg; BIA, ±3.1 kg), correlation coefficients (3CFIELD, 0.79-0.82; US, 0.49-0.55; BIA, 0.61-0.72), and additional metrics.

CONCLUSIONS

The present study demonstrated the potential usefulness of a 3CFIELD model incorporating US and BIA data for tracking body composition changes over time, as well as its superiority to US or BIA individually. As such, this accessible multicompartment model may be suitable for implementation in field or limited-resource settings.

Agreement Between 2 Segmental Bioimpedance Devices, BOD POD, and DXA in Obese Adults

This study examined the agreement between 2 segmental bioimpedance analysis (BIA) devices, air displacement plethysmography (BOD POD), and dual energy X-ray absorptiometry (DXA) for estimating body composition in obese adults. Fifty obese adults (25 men and 25 women; age = 34.20 ± 11.19 years; BMI = 36.14 ± 5.33 kg/m²) had their body fat percentage (BF%) and fat-free mass (FFM) evaluated with 2 segmental BIA devices (InBody 230 and InBody 720), BOD POD, and DXA (Lunar iDXA). Body composition via the BOD POD was determined using the Siri equation whereas manufacturer-based equations generated metrics (ie, BF% and FFM) for the InBody devices. The effect size of the mean differences for all BF% and FFM comparisons were trivial (Cohen's d < 0.20). The standard error of estimate (SEE), total error (TE), and 95% limits of agreement (LOAs) were low for both segmental BIA devices when compared to DXA (SEE < 2.26% and 2.35 kg; TE < 2.58% and 2.66 kg; 95% LOAs < ± 4.94% and 4.86kg). The error for BOD POD was also low when compared to DXA (SEE = 2.39% and 2.57 kg; TE = 2.34% and 2.56 kg; 95% LOAs = 4.63% and 5.06 kg). Validity statistics were slightly higher, but considered acceptable, when comparing the segmental BIA devices against BOD POD (SEE < 3.37% and 3.63 kg; TE < 3.44% and 3.79 kg; 95% LOAs < ± 6.62% and 7.19 kg). Lastly, the 2 segmental BIA devices produced nearly identical validity statistics when compared to each other. However, both BIA devices revealed proportional bias for BF% and FFM when compared to the BOD POD and DXA (all p < 0.05). The current study's findings indicate the InBody 230 is interchangeable with the InBody 720 in obese adults. Also, the trivial effect size, when compared against the BOD POD and DXA, suggest the InBody devices could be used for estimating group BF% and FFM. In contrast, the significant proportional bias demonstrates the BIA devices are not acceptable for individual estimates of body composition in an obese clinical population.