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Validation of air displacement plethysmography for assessing body composition


Medicine & Science in Sports & Exercise: July 2000 - Volume 32 - Issue 7 - p 1339-1344
Special Communications: Methods

WAGNER, D. R., V. H. HEYWARD, and A. L. GIBSON. Validation of air displacement plethysmography for assessing body composition. Med. Sci. Sports Exerc., Vol. 32, No. 7, pp. 1339–1344, 2000.

Purpose The purpose of this study was to verify the validity of an air displacement plethysmography device (Bod Pod®) for estimating body density (Db).

Methods The Db from the Bod Pod (DbBP) was compared with the Db from hydrostatic weighing (DbHW) at residual lung volume in a heterogeneous sample of 30 black men who varied in age (32.0 ± 7.7 yr), height (180.3 ± 7.5 cm), body mass (84.2 ± 15.0 kg), body fatness (16.1 ± 7.5%), and self-reported physical activity level and socioeconomic status. The Db for each method was converted to relative body fat (%BF) using race-specific conversion formulas and subsequently compared with %BF obtained from dual-energy x-ray absorptiometry (%BFDXA).

Results Linear regression, using DbHW as the dependent variable and DbBP as the predictor, produced an R2 = 0.84 and SEE = 0.00721 g·cc1. However, the mean difference between the two methods (0.00450 ± 0.00718 g·cc1) was significant (P < 0.01). The Bod Pod underestimated the Db of 73% of the sample. The %BF estimates from the Bod Pod, HW, and DXA differed significantly (P < 0.01). The average %BFBP (17.7 ± 7.4%) was significantly greater than %BFHW (15.8 ± 7.5%) and %BFDXA (16.1 ± 7.5%); however, there was no significant difference between %BFHW and %BFDXA.

Conclusion The Bod Pod significantly and systematically underestimated Db, resulting in an overestimation of %BF. More cross-validation research is needed before recommending the Bod Pod as a reference method.

Exercise and Sports Science Department, Vanguard University of Southern California, Costa Mesa, CA 92626; and Center for Exercise and Applied Human Physiology, University of New Mexico, Albuquerque, NM 87131

Submitted for publication May 1999.

Accepted for publication September 1999.

Address for correspondence: Dale R. Wagner, Ph.D., Exer-cise and Sports Science Department, Vanguard University of Southern California, 55 Fair Dr., Costa Mesa, CA 92626; E-mail: DWagner@

Multicomponent models, combining the body density (Db), total body water, and total body mineral data obtained from several different laboratory procedures, are frequently used to derive criterion measures of body composition. However, this approach is costly, time consuming, and often not practical. Thus, many researchers and clinicians continue to rely on a two-component model that simply separates the body into fat and fat-free components to assess body composition. Commonly, the two-component model involves measuring Db and then using a conversion formula to estimate relative body fat (%BF). Regardless of which body composition model is used, multicomponent or two-component, an accurate measurement of Db is critical to obtain valid and reliable estimates of %BF.

Db is the ratio of body mass to body volume (Vb). Traditionally, hydrostatic weighing (HW) has been used to determine Vb. This technique, which was pioneered by Behnke et al (2)., is based on Archimedes principle—a body immersed in a fluid is buoyed by a force equal to the weight of the displaced fluid.

The HW procedure requires the subject to perform the maneuver of maximally exhaling while completely submerged under water. Additionally, the subject needs to remain relatively motionless underwater in order for a trained technician to get an accurate reading of the subject’s underwater weight. This procedure, often considered unpleasant by some subjects, must be repeated multiple times for a valid and reliable reading. Furthermore, to ensure an accurate measure of Vb from HW, residual lung volume (VR) must also be measured. Thus, HW is time-consuming, requires a skilled technician, and is difficult, and sometimes impossible, for some subjects to perform.

An alternative to HW is the Bod Pod® (Life Measurement Instruments, Concord, CA), a large, egg-shaped, fiberglass chamber. Based on air displacement plethysmography, the Bod Pod uses a pressure-volume relationship to derive Vb for a subject seated inside the chamber. The Vb is equal to the volume of air in an empty chamber minus the volume of air remaining in the chamber after the subject enters the chamber. The physical design and the operating principles of the Bod Pod have been described in detail elsewhere (5).

The Bod Pod method is much easier and more accommodating for subjects than HW, thereby potentially reducing subject error. Furthermore, it is a faster, more convenient, and easier test to administer, thereby reducing potential error associated with technician skill. Thus, the Bod Pod appears to be a promising tool for the determination of Vb and Db, and a method that could potentially replace HW.

Although this device has been extensively advertised and commercially available for several years, there remains a paucity of research on the Bod Pod. The published research is limited to only one study that demonstrated its validity and reliability for measuring the volume of inanimate objects (5), and one study that verified that it was a reliable and valid tool for estimating %BF in adult humans (15). Thus, the purpose of this study was to cross-validate Db measured by the Bod Pod to that obtained from HW. Additionally, the %BF values estimated from the Bod Pod and HW were compared with those obtained from dual-energy x-ray absorptiometry (DXA), a method that estimates %BF independent of Db.

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Thirty black men, ages 19–45 yr, volunteered to participate in this study. The sample was limited to black men as this was a substudy of a larger research study done to cross-validate the body composition formulas available for black men (23). Subjects were recruited from the Albuquerque area via word of mouth, newspaper advertisements, and flyers distributed throughout the university and to health/fitness clubs, major corporations, churches, community centers, and at public events. The subject pool was heterogeneous with respect to age, height, body mass, body fatness, physical activity level, and socioeconomic status. All subjects underwent a physical examination by a physician and were deemed to be in good health. The subjects stayed overnight in the clinical research ward of the hospital to control for the influence of physical activity and food and drug intake on body composition measures. The participants were informed of the purpose, procedures, risks, and benefits of the study before signing an informed consent. The Human Research Review Committees of the University Hospital and the University of New Mexico approved this study.

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Body mass and height.

With the men in briefs only, body mass was measured twice to the nearest 0.01 kg on a calibrated electronic scale (Bod Pod, Life Measurement Instruments, Concord, CA), and the average of the two values was used for subsequent calculations. Height was measured twice to the nearest 0.1 cm using a stadiometer (Holtain Ltd., Crymych, Dyfed Wales), with the average of the two measurements recorded. This measurement was taken with subjects at mid-inspiration, standing erect, and arms hanging freely at the sides.

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Dual-energy x-ray absorptiometry.

Relative body fat was determined by scanning the subjects with DXA (Lunar DPX, Lunar Radiation Corp., Madison, WI). The DXA was calibrated daily with the manufacturer’s “standard block” bone simulating substance of known composition and attenuating capacity and weekly with an aluminum spine phantom. A licensed radiological technologist performed all scans. From a supine position, the men were scanned using the medium speed scan mode (20 min) unless their body thickness exceeded 27 cm. In such cases, the slow speed scan (40 min) was used. Lunar software (version 3.6Z) provided an estimate of %BF based on an extrapolation of fatness from the ratio of soft tissue attenuation of two x-ray energies in pixels not containing bone (14).

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Bod Pod.

Before each test, the Bod Pod was calibrated according to the manufacturer’s instructions with the chamber empty using a cylinder of known volume (50 L). The subject, in briefs and swim cap only, then entered and sat in the fiberglass chamber. The Bod Pod was sealed, and the subject breathed normally for 20 s while Vb was measured. After this, the subject was connected to a breathing tube internal to the system to measure thoracic gas volume (VTG). The subject resumed tidal breathing through the tube. After two or three breathing cycles, a valve in the circuit momentarily occluded the airway. At this point, the subject gently “puffed” by alternately contracting and relaxing the diaphragm. This effort produced small pressure fluctuations in the airway and chamber that were used to determine VTG. This value was used to correct Vb for VTG. This was the same method used by previous Bod Pod researchers (15,16).

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The Vb and Db were measured using HW at VR. The VR was measured via closed-circuit spirometry with a 9-L Collins Helium Analyzer (Warren E. Collins, Inc., Braintree, MA) using a helium dilution technique (17) with the participant seated out of the water. A minimum of three trials was done with the two closest readings within 100 mL being averaged and used to correct Vb measured from HW. To minimize the error, the same experienced technician conducted all VR tests.

A load cell system (Precision Biomedical, State College, PA), integrated to an analog signal acquisition system (Biopac Systems, Inc., Goleta, CA) and a personal computer, was used to measure underwater weight. The analog signal was acquired at 200 Hz and processed using commercially available software (AcqKnowledge; Biopac Systems, Inc., Goleta, CA). For this assessment, the subject assumed a hands and knees position on the load cell platform. A forced expiratory reserve volume maneuver was performed as the subject lowered his head below the surface of the water to become completely submerged. The underwater weight was obtained by averaging the flattest region of the waveform over approximately 1 s after complete expiration. Three trials within 100 g were averaged and used as the underwater weight for calculation of Vb and Db (4).

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Statistical analysis.

The Db from the Bod Pod was compared to the Db from HW at VR using cross-validation criteria developed by Lohman (13). The criteria for accepting the predictive accuracy of the Bod Pod included: (a) no significant difference between average DbBP and DbHW, (b) a substantial correlation (ry,y′ ≥ 0.80) and shared variance (r2) between DbBP and DbHW, (c) a standard error of estimate (SEE) and total error (E) of ≤ 0.0080 g·cc1, (d) a nonsignificant correlation (ry,res) between the DbHW and the residual scores (e.g., DbHW – DbBP), and (e) the slope and intercept of the lines of best fit not differing significantly from the line of identity. Additionally, residual scores (e.g., DbHW –DbBP) were analyzed using the Bland and Altman (3) method to determine the percentage of subjects whose Bod Pod-derived Db was estimated within ± 0.0080 g·cc1 of DbHW. For this analysis, the Db from both methods was averaged and plotted against each subject’s residual score.

After the analysis of Db, the Db data were converted to %BF using the race-specific conversion formulas of Schutte et al. (20) and Wagner (23) so that the densitometric methods could be compared with DXA. The Schutte et al. formula has customarily been used to convert Db to %BF for black men. However, Wagner recently showed that the fat-free body density of black men was 1.10570 g·cc1 rather than the 1.113 g·cc1 estimated by Schutte et al. Thus, %BF was estimated from both conversion formulas and compared with %BFDXA using a one-way repeated measures ANOVA. The alpha level for significance was set at P ≤ 0.05. After a significant main effect, Tukey’s post hoc tests were used to determine which methods differed significantly.

The statistical analyses employed in this study assume that all the variables are normally distributed. Extreme cases will have a deleterious effect on regression solutions, and cross-validation procedures and should be excluded from the analysis (22). Therefore, before data analysis, the normality of the distributions was examined by inspecting the skewness and kurtosis of the variables. Statistical outliers were defined as individual scores exceeding ± 3.29 SD from the mean for that variable (22). All variables were normally distributed with no potential outliers (z-scores < 3.29); therefore, all subjects were included in subsequent analyses. Data were analyzed using the Statistical Package for the Social Sciences (SPSS version 8.0 for Windows).

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Descriptive characteristics.

The physical characteristics of the sample (N = 30) are presented in Table 1. As evidenced by the data in the table, the sample was diverse.

Table 1

Table 1

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Db comparison.

The Db from the Bod Pod and HW were highly correlated (r = 0.91, P < 0.01). This relationship is depicted in Figure 1. Linear regression produced an R2 = 0.84, SEE = 0.00721 g·cc1, and E = 0.00837 g·cc1. The correlation between the reference measure (DbHW) and residual scores (DbHW –DbBP) was not significant (ry,res = 0.25, P > 0.05). The slope did not differ significantly from one (0.93, P > 0.05), and the intercept was not significantly different from zero (0.08, P > 0.05).

Figure 1

Figure 1

Although the mean difference in Db between HW and the Bod Pod was only 0.00450 g·cc1, the paired t-test revealed that the DbBP (1.06294 ± 0.0171 g·cc1) was significantly less (P < 0.01) than DbHW (1.06744 ± 0.0174 g·cc1). Furthermore, the Bland and Altman plot of residual scores showed that the Bod Pod accurately estimated the Db of 67% of the subjects within ± 0.0080 g·cc1 of HW; however, 73% of the subjects had a DbBP that was less than DbHW (Fig. 2).

Figure 2

Figure 2

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%BF comparison.

The Schutte et al. (20) and Wagner (23) conversion formulas produced significantly different (P < 0.01) estimates of %BF. However, regardless of which formula (Schutte et al. (20) or Wagner (23)) was used to convert Db to %BF, the ANOVA indicated a significant overall main effect for testing method (P < 0.01). Although average %BFHW (Wagner = 15.8 ± 7.5%BF; Schutte et al. = 17.1 ± 6.8%BF) did not differ significantly (P > 0.05) from %BFDXA (16.1 ± 7.5%BF), the mean %BFBP (Wagner = 17.7 ± 7.4%BF; Schutte et al. = 18.8 ± 6.7%BF) was significantly greater than %BF estimates from HW and DXA (P < 0.01). The observed statistical power for the ANOVA was 95.6% with an eta-squared value of 22.5%. The relationship between the %BFDXA and the %BF from HW and the Bod Pod is represented in Figure 3.

Figure 3

Figure 3

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Although the correlation between DbHW and DbBP was high and the regression data (SEE, slope, and intercept) met the cross-validation criteria, the Bod Pod underestimated the average Db of this sample by a small, but significant, amount (0.00450 ± 0.00718 g·cc1). To date, there has only been one published study comparing human subjects in the Bod Pod with HW (15). In their study, Db data were not reported, rather it was converted to %BF using the Siri formula (21); therefore, data were expressed in terms of %BF rather than Db. Using the Siri formula (21) and the mean %BF values reported for their entire sample, we estimated the mean difference between DbHW and DbBP in the McCrory et al. (15) study to be only 0.00044 g·cc1.

We converted the Db obtained in our study to %BF using the race-specific conversion formulas of Schutte et al. (20) and Wagner (23). Regardless of which conversion formula was used, the Bod Pod significantly overestimated the mean %BFHW of this sample (1.73% BF using Schutte et al. and 1.92% BF using Wagner). This result differs greatly from the small, nonsignificant mean underestimation of 0.3% BF for the Bod Pod compared with HW reported in the McCrory et al. (15) study.

McCrory et al. (15) reported that the Bod Pod estimated the %BF of 75% of their subjects within ± 2.0% BF. The plot of the residual scores in our study (Fig. 2) also found good individual agreement between methods with the Db of two-thirds of our sample estimated within ± 0.0080 g·cc1. However, unlike the results of McCrory et al., close inspection of our Bland and Altman (3) plot revealed that 73% of the residual scores were positive; thus, there was a systematic tendency for the Bod Pod to consistently underestimate Db in the present study.

HW is not an error-free reference measure, and the procedure is certainly more difficult to perform than what is required to obtain Bod Pod measurements. Therefore, it could be argued that the difference between methods in measuring Db might be due more to measurement errors associated with the HW procedure than errors from the Bod Pod. In fact, McCrory et al. (15) reported a smaller, although not statistically significant, between-trial coefficient of variation for the Bod Pod (1.7 ± 1.1%) compared with HW (2.3 ± 1.9%).

To test which method (HW or Bod Pod) had greater validity, the Dbs from both methods were converted to %BF and compared with %BFDXA. DXA was selected as the comparison method because it yields an estimate of %BF that is completely independent of Db. DXA has been shown to be an accurate method to estimate %BF (8,9,12,18). On average, the difference between %BFDXA and %BFHW was only 0.25% BF (P > 0.05) compared with −1.67% BF for the Bod Pod (P < 0.01). Additionally, the shared variance (r2 = 0.90) was greater and the standard error of estimate was lower (SEE = 2.47% BF) for %BFHW than for %BFBP (r2 = 0.86, SEE = 2.84% BF) when the two methods were regressed on %BFDXA. These data suggest that HW provides a more accurate measure of Db and subsequent estimation of %BF than the Bod Pod.

There are several potential reasons why the Bod Pod underestimated average Db in this sample. Close scrutiny of the Bod Pod formula for estimating Db is one item to consider when searching for potential sources of error:MATH where BM is body mass in air, Vbraw is the raw, uncorrected body volume, and SAA is the surface area artifact (5). With proper calibration, errors in BM and Vbraw should be negligible. Thus, if there is an error in the Bod Pod measurement, it most likely resides in the estimates and assumptions associated with the SAA and VTG.

The SAA is the product of body surface area, as estimated by the DuBois formula (6), and a constant derived from the testing of plastic films and aluminum foil (5). Surfaces produce apparent negative volumes when measured in the Bod Pod due to the fact that air under isothermal conditions is more compressible than air under adiabatic conditions; thus, SAA must be considered. The constant in the SAA formula was derived from a regression equation created from measuring the surface area of aluminum or plastic films and then recording the apparent volume measured by the Bod Pod (personal communication, Life Measurement Instruments).

The VTG, which includes the volume of air in the lungs and any air trapped in the thorax, is measured when the subject puffs against a closed airway. This maneuver produces pressure changes that, when correlated to tidal breathing, yields VTG. It is assumed that Vb needs to be increased by 40% of VTG to account for the difference between isothermal air in the thorax and adiabatic air in the Bod Pod chamber (5).

When measured at the end of an exhalation, VTG should be equal to functional residual capacity (FRC) (19). FRC is the sum of expiratory reserve volume (ERV) and VR. Thus, the VTG from the Bod Pod should equal the sum of the ERV and VR from gas dilution. One potential explanation for the overestimation of %BFBP seen in this study is that the Bod Pod overestimated VTG. Unfortunately, the Bod Pod is programmed to occlude the airway and signal the subject to commence the puffing maneuver at the midpoint of exhalation rather than at the end of an exhalation, making it difficult to compare VTG from the Bod Pod to FRC from gas dilution.

Given that VTG values are more than double VR values, one might assume that VTG errors could produce large errors in the determination of Db. However, as McCrory et al. (16) pointed out, only 40% of VTG enters the Bod Pod formula used to calculate Db, whereas 100% of VR enters into the calculation of Db by HW. The largest source of error in measuring Db via HW is the measurement of VR (1). Thus, an error in the VR measurement during the original validation of the Bod Pod must also be considered as a potential reason for the differences in DbHW and DbBP observed in the present study.

An underestimation of VTG will result in an overestimation of Db and an underestimation of %BF. Conversely, an underestimation of VR will yield an underestimation of Db and an overestimation of %BF. The subjects in this study were of similar age (32.0 ± 7.7 yr) and stature (180.3 ± 7.5 cm) to the men in the McCrory et al. (15) Bod Pod validation study (38.6 ± 9.1 y and 179.7 ± 5.3 cm). The average VTG was also similar (8.7% difference; 3.99 ± 0.87 L to 3.67 ± 0.86 L), but there was a greater difference (15%) in the average VR (1.76 ± 0.41 L to 1.53 ± 0.35 L). Given the age and stature of the subjects, the mean VR value for the men in the McCrory et al. study seems low and is considerably less than what would be estimated from a VR prediction equation (11). Thus, if the VR was underestimated in the McCrory et al. validation study, then the %BF from HW would have been overestimated in that study. Increasing the VR in the McCrory et al. study would result in lowering their %BF from HW and produce an outcome consistent with our results: a small but systematic overestimation of %BF from the Bod Pod.

Variations in the measurement of VR could potentially affect the results. For example, Girandola et al. (10) reported that there was an increase of 6.7% in VR, resulting in a 0.6% reduction in %BF when VR was measured in the water instead of outside the tank. However, both the present study and that of McCrory et al. (15) measured VR outside of the tank, making this a nonissue for this comparison. Also, McCrory et al. (15) used oxygen dilution to measure VR, whereas we used a helium dilution procedure, but research shows that both techniques produce comparable estimates of VR (7,17).

Because the Bod Pod is easy to use, requires little effort on the part of the subject, and is a quick and convenient method of estimating Db and %BF, it is tempting to consider this method as a replacement for the cumbersome HW method. Previous research (15) found the Bod Pod to be highly reliable and valid compared with HW; thus, it has been widely marketed as an alternative to HW. However, our results showed a small, but significant, underestimation of Db, resulting in an overestimation of %BF using this device. Furthermore, the underestimation of Db was systematic. Using DbHW as the criterion, we recommend the following correction to DbBP: 0.932 (DbBP) + 0.077. Thus, although the Bod Pod may be a promising replacement for HW in the future, the results from this study raise some concern about its validity and use as a reference method. Certainly, more research needs to be conducted before the Bod Pod can be accepted as a valid reference method.

We speculate that an overestimation of VTG by the Bod Pod and/or an underestimation of VR during the validation study of McCrory et al. (15) might have contributed to the overestimation of %BFBP seen in this study. If the Bod Pod could be reprogrammed to signal the puffing maneuver and occlude the airway at the end of an exhalation instead of during mid-exhalation and if researchers record ERV as well as VR, comparisons between FRC from gas dilution to VTG from the Bod Pod can be made. This may help to quantify and clarify small differences between the Bod Pod and HW. We also recommend taking the VR measurement with the subject seated out of the water to minimize differences to the original validation study of McCrory et al. (15). Finally, because DXA estimates %BF independent of Db, we recommend including it as an “external check” when comparing the Bod Pod and HW as was done in this study.

This research was supported by the General Clinical Research Center at the University of New Mexico (NIH NCRR Grant 5 M01 RR00997), the Office of Graduate Studies (RPT Grant), and the Graduate and Professional Students Association (SRAC Grant). We thank Dr. David James for conducting prescreening physicals, and the GCRC and Clinical Nutrition Laboratory personnel for their assistance in data collection.

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