Validation of air displacement plethysmography for assessing body composition

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 x cc(-1). However, the mean difference between the two methods (0.00450 +/- 0.00718 g x cc(-1) 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.     

Skinfold prediction equation for athletes developed using a four-component model

INTRODUCTION: Skinfold (SKF) equations exist to predict percent body fat (%BF) in athletes; however, none have been derived from multicomponent model reference measures. PURPOSE: To develop and cross-validate a %BF prediction equation based on SKF in athletes using a four-component model as the reference measure. METHODS: Subjects were 132 collegiate athletes (20.7 +/- 2.0 yr; 78 males: 28 black, 50 white; 54 females: 10 black, 44 white). Four-component model estimates of %BF (%BF4C) included measures of total body water from deuterium dilution, bone mineral by dual- energy x-ray absorptiometry (DXA), and body density by densitometry using underwater weighing. SKF measures included subscapular, triceps, chest, midaxillary, suprailiac, abdominal, and thigh sites (7SKF). A prediction equation was developed on 102 athletes using 7SKF, race, and gender as predictor variables. Cross-validation was performed on a representative holdout sample of 30 athletes. RESULTS: The equation cross-validated well (slope and intercept both not different (P > 0.05) from the line of identity (LOI); r(YY') = 0.85, total error (TE) = 3.76%BF) and was better than the existing athlete SKF equations (intercept and slope both different from LOI (P < 0.01); r(YY') = 0.76, TE = 4.51%BF). Notably, a prediction equation developed using 3SKF sites (abdomen, thigh, and triceps) produced a similar accuracy (intercept and slope both not different from LOI (P > 0.05); r(YY') = 0.85, TE = 3.66%BF). CONCLUSIONS: The new 7SKF equation improved on SKF equations developed using densitometry. The final equation based on the whole sample was %BF' = 10.566 + 0.12077*(7SKF) - 8.057*(gender) - 2.545*(race). Moreover, a 3SKF equation was comparable in accuracy to the 7SKF equation: BF' = 8.997 + 0.24658*(3SKF) - 6.343*(gender) - 1.998*(race).     

Validity of Futrex-5000 for body composition determination.

Underwater weighing (UWW), skinfolds (SKF), and the Futrex-5000 (FTX) were compared by using UWW as the criterion measure of body fat in 30 male and 31 female Caucasians. Estimates of body fat (% fat) were obtained using The Y's Way to Fitness SKF equations and the standard FTX technique with near-infrared interactance (NIR) measured at the biceps, plus six sites for men and five sites for women. SKF correlated significantly higher with UWW than did FTX with UWW for males (0.95 vs 0.80), females (0.88 vs 0.63), and the whole group (0.94 vs 0.81). Fewer subjects (52%) were within +/- 4% of the UWW value using FTX, compared with 87% with SKF. FTX overestimated body fat in lean subjects with less than 8% fat and underestimated it in subjects with greater than 30% fat. Measuring NIR at additional sites did not improve the predicted variance. Partial F-tests indicate that using body mass index, instead of height and weight, in the FTX equation improved body fat prediction for females. Biceps NIR predicted additional variance in body fat beyond height, weight, frame size, and activity level but little variance above that predicted by these four variables plus SKF (2% more in males and less than 1% in females). Thus, SKF give more information and more accurately predict body fat, especially at the extremes of the body fat continuum.     

Body composition in paraplegic male athletes

The body composition and anthropometric characteristics of male paraplegic athletes (PARA, N = 22) were contrasted to an able-bodied ectomorphic (N = 22) and mesomorphic (N = 31) comparison group of moderately and highly trained male subjects. The validity of 12 body composition [density (Db)] prediction equations reported in the literature, 4 generalized, were determined (tested) on this special group of athletes (PARA). On the whole, the prediction equations over-predicted Db in PARA by 0.0039 to 0.0166 g X cm-3 (under-predicted relative fat by 1.8 to 7.4%). Five diameter, 11 circumference, and 7 skinfold measures were used in a SAS-STEPWISE multiple regression procedure with hydrostatically determined Db to develop several suitable Db prediction equations for the paraplegic athlete. Diameters were poor predictors (r = 0.60, SEE = 0.0164), while skinfolds, circumferences, or a combination of measures were acceptable, with the combined equation being best (r = 0.95, SEE = 0.0064). The findings of this study suggest that even generalized equations do not adequately predict Db in PARA and that paraplegic specific equations are presently best suited for predicting Db in paraplegic athletes. The results further indicate that although these equations meet many of the criteria of Lohman, the SEE and total error values are unusually high and make prediction of body composition using anthropometry in a heterogeneous group of PARA athletes slightly unreliable.     

A new nonexercise-based VO2(max) equation for aerobically trained females

PURPOSE: The purposes of the present study were to (a) modify previously published VO2(max) equations using the constant error (CE) values for aerobically trained females, (b) cross-validate the modified equations to determine their accuracy for estimating VO2(max) in aerobically trained females, (c) derive a new nonexercise-based equation for estimating VO2(max) in aerobically trained females if the modified equations are found to be inaccurate, and (d) cross-validate the new VO2(max) equation using the PRESS statistic and an independent sample of aerobically trained females. METHODS: A total of 115 aerobically trained females (mean +/- SD: age = 38.5 +/- 9.4 yr) performed a maximal incremental test on a cycle ergometer to determine actual VO2(max). The predicted VO2(max) values from nine published equations were compared with actual VO2(max) by examining the CE, standard error of estimate (SEE), validity coefficient (r), and total error (TE). RESULTS: Cross-validation of the modified nonexercise-based equations on a random subsample of 50 subjects resulted in a %TE > or = 13% of the mean of actual VO2(max). Therefore, the following nonexercise-based VO2(max) equation was derived on a random subsample of 80 subjects: VO2(max) (mL x min(-1)) = 18.528 (weight in kg) + 11.993 (height in cm) - 17.197(age in yr) + 23.522 (h x wk(-1) of training) + 62.118 (intensity of training using the Borg 6-20) + 278.262 (natural log of years of training) - 1375.878 (R = 0.83, R2 adjusted = 0.67, and SEE = 259 mL x min(-1)). Cross-validation of this equation on the remaining sample of 35 subjects resulted in a %TE of 10%. CONCLUSIONS: The nonexercise equation presented here is recommended over previously published equations for estimating VO2(max) in aerobically trained females.     

Body composition estimates from multicomponent models using BIA to determine body water

PURPOSE: The purpose of this study was to compare estimates of body fat (%BF) from three- and four-component models with total body water (TBW) determined by single-frequency bioelectrical impedance analysis (BIA; %BF3C-BIA and %BF4C-BIA) to %BF estimates from densitometry (%BF2C-D) and from three- and four-component models with TBW determined using deuterium dilution (%BF3C-D2O and %BF4C-D2O), the criterion methods. METHODS: Measures of body density by hydrostatic weighing, TBW by BIA and D2O dilution, and bone mineral by dual energy x-ray absorptiometry (DXA) were obtained in 40 men and 93 women, 18-42 yr. TBW was estimated from BIA resistance (RJL analyzer) using an equation developed and cross-validated in two independent samples. Body fat was estimated using the three-component model of Siri (1961) and a four-component model modified from Lohman (1986). RESULTS: There was a strong relation and no significant difference between TBW estimated by BIA and D2O [r = 0.94, SEE = 2.4; xDiff = 0.0 +/- 2.4 L (SD), P > 0.05]. There were strong relations between methods for estimating %BF, with deviations from %BF4C-D2O (errors) for %BF3C-BIA [r = 0.99, SEE = 2.4% BF, xDiff = -0.4 +/- 2.4% BF (SD)] and %BF4C-BIA [r = 0.99, SEE = 2.3% BF, xDiff = 0.2 +/- 2.3% BF (SD)] being nonsignificant (P > 0.05) although greater than for %BF3C-D2O [r = 1.00, SEE = 0.5% BF, xDiff = -0.6 +/- 0.5% BF (SD)], and comparable or slightly worse than for %BF2C-D [r = 0.99, SEE = 2.3% BF, xDiff = 0.4 +/- 2.3% BF (SD)]. CONCLUSIONS: We conclude that because estimates of %BF from multicomponent models with TBW estimated from BIA are not more accurate than from body density alone using a two-component model, estimates of %BF from three- and four-component models using TBWBIA are not acceptable substitutes for estimates from the same models using TBWD2O.     

Hydrostatic weighing without head submersion: description of a method

Hydrostatic weighing (HW) was performed at residual volume (RV) and total lung capacity without head submersion (TLCNS). Ninety-five males (25.6 +/- 4.9 yr) and 87 females (22.6 +/- 5.2 yr) were studied at two laboratory sites using identical protocols. Twenty males and 20 females were separated from the original group and randomly assigned to cross-validation groups. RVs were determined by the oxygen dilution method. Vital capacity was determined with the subject submerged in water to the shoulders. Underwater weight was determined using 10 trials at RV and 5 trials at TLCNS, with the order of methods randomly assigned. Regression analysis provided an equation to predict body density (pDb) at RV from body density (Db) at TLCNS. The equation for males was pDb (HW at RV) = 0.5829 (DbHW at TLCNS) + 0.4059, r = 0.88, SEE = 0.0067. The equation for females was pDb (HW at RV) = 0.4745 (DbHW at TLCNS) + 0.5173, r = 0.85, SEE = 0.0061. Cross-validation showed no significant differences using Db from HW at RV (males = 1.0626 g.ml-1, females = 1.0493 g.ml-1 and pDb from HW at TLCNS (males = 1.0625 g.ml-1, females = 1.0479 g.ml-1). The correlation coefficient SEE and total error for males were r = 0.95, 0.0043, and 0.0041, respectively and for females r = 0.82, 0.0084, and 0.0085, respectively. Mean percent fat for RV and TLCNS was identical for males and differed by 0.7% for females. Test-re-test data indicated the TLCNS procedure was reliable (r = 0.98).(ABSTRACT TRUNCATED AT 250 WORDS)     

Effect of mode selection when using leg-to-leg BIA to estimate body fat in collegiate wrestlers

AIM: When using leg-to-leg bioelectrical impedance analysis (LBIA) to examine body composition, a computer-programmed mode (i.e., standard STD or athletic ATH) must be selected prior to assessment. This study examined the effect of LBIA mode selection on the estimated %BF of collegiate wrestlers. METHODS: Forty hydrated (Usg <1.02) wrestlers had %BF estimated using the ATH mode, which was then compared to the STD mode and hydrostatic weighing (HW), used as the reference method. Mean difference from HW (MD), standard error of estimate (SEE), and pure error (PE) values were calculated for the entire sample and three data subsets according to body mass index (BMI, kg/m(2)): <25 (n=16), 25-29.9 (n=18), and >30 (n=6). RESULTS: The %BF (mean+/-SD) was underestimated by the ATH (12.1+/-4.7) and overestimated by the STD (17.1+/-5.2) mode when compared to HW (14.1+/-6.3) for the entire sample (P<0.05). When examined relative to BMI, the ATH mode accurately estimated %BF in the BMI <25 group (MD=-1.2%, SEE=2.7%, PE=2.8%) and the STD mode accurately estimated %BF in the BMI >30 group (MD=1.5%, SEE=2.4%, PE=2.8%). Both modes inaccurately assessed %BF in the BMI 25-29.9 group with predictive errors >3.5%BF. CONCLUSIONS: The ATH mode is not appropriate for all individuals meeting the definition of athletic. However, the predictive accuracy of LBIA may be improved by selecting the ATH mode when BMI <25 and the STD mode when BMI >30, even when testing athletes.     

Validity of bioelectrical impedance equations for estimating fat-free weight in lean males.

The present study examined the validity of bioelectrical impedance (BIA) equations for estimating fat-free weight (FFW) in lean males (X +/- SD = 9.1 +/- 2.2% fat) by comparing the estimates with values obtained from underwater weighing. Sixty-eight Caucasian male volunteers served as subjects. Cross-validation analyses included examination of the constant error (CE), standard error of the estimate (SEE), r, and total error (TE). The results indicated that the equations of Oppliger et al. (16), which resulted in small TE (1.70 kg) and CE (-0.02 kg) values, most accurately estimated FFW. Simple linear regression showed that FFW was more highly correlated with body weight (BW) (r = 0.98, P < 0.0001) and resulted in a lower SEE (1.68 kg) than either height2/resistance (Ht2/R) (r = 0.81, P < 0.0001; SEE = 5.12 kg) or the independent variable (weight x resistance)/height2 [WR/Ht2] utilized by the manufacturer of the BIA analyzer (r = 0.15, P > 0.05; SEE = 8.59 kg). Multiple regression showed that when WR/Ht2, Ht2/R, resistance, body mass index, Ht2, and/or Ht was added to the prediction equation, which utilized BW alone, they accounted for less than 1% additional variance and reduced the SEE by < or = 0.16 kg. The results indicated that BW alone estimated FFW as accurately as any of the BIA equations in lean males.     

Evaluation of the BOD POD for assessing body fat in collegiate football players.

PURPOSE: The purpose of this investigation was to evaluate the accuracy of a new air displacement plethysmograph, BOD POD Body Composition System, for determining %fat in collegiate football players. METHODS: Body fatness was estimated from body density (Db), which was measured on the same day using the BOD POD and hydrostatic weighing (HW) in 69 Division IA football players. In addition, 20 subjects were whole body scanned using dual-energy x-ray absorptiometry, DXA (Lunar DPX-L) to assess total body mineral content and %fat. Mineral content and HW determined Db were used to compute %fat from a three-component model (3C; fat, mineral, and residual). RESULTS: Test-retest reliability for assessing %fat using the BOD POD (N = 15) was 0.994 with a technical error of measurement of 0.448%. Mean (+/- SEM) Db measured with the BOD POD (1.064 +/- 0.002 g x cc(-1) was significantly greater (P < 0.05) than HW (1.060 +/- 0.002 g x cc(-1)), thus resulting in a lower %fat for the BOD POD (15.1 +/- 0.8%) compared with HW (17.0 +/- 0.8%). Similar results (N = 20) were found for DXA (12.9 +/- 1.2%) and the 3C (12.7 +/- 0.8%) where %fat scores were significantly higher (P < 0.05) than scores determined using the BOD POD (10.9 +/- 1.0%). CONCLUSIONS: Db measured with the BOD POD was higher than the criterion HW, thus yielding lower %fat scores for the BOD POD. In addition, BOD POD determined %fat was lower than DXA and 3C determined values in a subgroup of subjects. Assessment of %fat using the BOD POD is reliable and requires minimal technical expertise; however, in this study of collegiate football players, %fat values were underpredicted when compared to HW, DXA, and the 3C model.     

Anthropometry and bioelectrical impedance inconsistently predicts fatness in women with regional adiposity.

PURPOSE: This investigation examined the accuracy of several generalizable anthropometric (ANTHRO) and bioelectrical impedance (BIA) regression equations to estimate % body fat (%BF) in women with either upper body (UB) or lower body (LB) fat distribution patterns. METHODS: Thirty-six premenopausal women were individually matched for age (X = 38.6 +/- 6.6 yr), BMI (X = 25.5 +/- 4.2 kg x m(-2)) and %BF (30.3 +/- 8.1%; hydrostatic, [UWW]) and placed by waist to hip ratio (WHR) into two distinct groups: LB (N = 18; WHR < or = 0.73) and UB (N = 18; WHR > or = 0.80). Equations tested were ANTHRO: Jackson et al. (JPW-7 and 3 site), 1980; Durnin and Womersley (DW), 1974; Tran and Weltman (TW), 1989; and Vogel et al. (V), 1988; BIA: Lohman (L), 1992; Gray et al. (G), 1989; and VanLoan and Mayclin (VLM), 1987. Circumference and skinfold measures were made by a trained technician. BIA (Vallhalla, 1990B) measures were taken 4 h postprandially under controlled conditions of water intake and exercise. %BF by UWW (criterion) was not different between groups (UB = 30.8 +/- 8.2%; LB = 29.7 +/- 8%). RESULTS: In the UB group, three of five ANTHRO equations significantly overestimated %BF by approximately 6% (range = 3-8%) as compared with UWW. BIA overestimated %BF in UB by 5% using G and in both groups by about 6% using VLM, whereas L underestimated %BF in LB by about 4%. CONCLUSION: We conclude that ANTHRO and some BIA equations are accurate for predicting %BF in LB fat "shaped" women but are not appropriate for women with primarily abdominal fat patterning.     

The influence of somatotype on anthropometric prediction of body composition in young women

The influence of somatotype on the validity of anthropometric prediction of body density (Db) in young women (N = 92) was investigated. Three groups of predominantly endomorph (N = 27), mesomorph (N = 35), and ectomorph (N = 30) women were identified by the Heath-Carter and Sheldon somatotyping methods. Discriminant analysis revealed a 100% accuracy in somatotype group determination. Thirteen diameters, 26 girths, and 8 skinfolds were measured and used in a STEPWISE regression analysis to derive somatotype-specific regression equations to predict body density. Combining all the measures provided very good prediction accuracy in all three groups with multiple correlation coefficients (R) of 0.98, 0.90, and 0.90, and the standard error of estimate (SEE) of 0.003, 0.005, and 0.005 for Db (gm X cc-1) in the endomorphs, mesomorphs, and ectomorphs, respectively. A cross-validation study confirmed the accuracy of the somatotype-specific regression equations and demonstrated an inherent weakness in using some generalized equations on specific somatotypes. The use of non-somatotype-specific equations resulted in mean Db prediction errors ranging from -0.018 to +0.023 gm X cc-1 (8.5 to -10.7% Fat). Although all equations tested demonstrated specific weaknesses in one or more of the somatotype groups when predicting Db, the Jackson et al. (1980) equation performed better than most of the non-somatotype-specific prediction equations. These findings suggest that the anthropometric estimation of Db may not be sample specific in the same manner as had been previously thought and that greater accuracy may be achieved by using regression equations which have been generated on a previously somatotyped population sample.     

Impact of total body water fluctuations on estimation of body fat from body density

The purpose was to investigate the possibility that variability in body weight in females due to water retention causes differences in body density (Db) values determined by hydrostatic weighing (HW). Determination of total body water (TBW) and Db were concurrently measured in seven females who experienced considerable fluctuations in body weight (1.5-4.5 kg) and seven males, ages 19-24. Females were measured when they felt they were at their lowest (LO) and highest (HI) body weights (BW) during a menstrual cycle. Males were randomly tested approximately 3 wk apart. Mean values of selected variables were compared in the LO vs HI testing sessions by paired t-tests. Significant mean differences were found in the females (P less than 0.01) for the following variables: BW (kg) (LO = 58.9, HI = 61.1), Db (g.cc-1) (LO = 1.0430, HI = 1.037), and percent body fat (%BF) as determined by HW alone (LO = 24.8%, HI = 27.6%). Variables significant at the P less than 0.05 level were TBW(l) (LO = 33.6, HI = 35.1) and %TBW of the fat-free body (LO = 74.5, HI = 75.9). However, changes in TBW could not entirely account for observed changes in Db. Only mean BW (kg) was significant (P less than 0.01) in the males (LO = 74.3, HI = 74.6). It is concluded that changes in TBW can in part result in significantly different Db values obtained from HW in females who did experience perceptible changes in BW during a menstrual cycle. The remaining differences may be due to changes in fat and protein content or methodological errors.     

Nonexercise models for estimating VO2max with waist girth, percent fat, or BMI

Previously published nonexercise models using either percent fat or body mass index (BMI) as body composition measures provided valid estimates of VO2max. PURPOSE: This study was conducted to investigate the use of waist girth (WG) as a body composition surrogate in the nonexercise models and to compare the accuracy of nonexercise models that include WG, %fat, or BMI. METHODS: A total of 2417 men and 384 women were measured for VO2max by indirect calorimetry (RER > 1.1); age (yr); gender by M = 1, W = 0; self-report activity habit by the 11-point (0-10) NASA physical activity status scale (PASS); WG at the apex of the umbilicus; %fat by skinfolds; and BMI by weight (kg) divided by height squared (m). RESULTS: Three models were developed by multiple regression to estimate VO2max from age, gender, PASS, and either WG (R = 0.81, standard error of estimate (SEE) = 4.80 mL.kg.min), %fat (R = 0.82, SEE = 4.72 mL x kg(-1) x min(-1)), or BMI (R = 0.80, SEE = 4.90 mL x kg(-1) x min(-1)). Cross-validation by the PRESS technique confirmed these statistics. Accuracy of the models for predicting VO2max of subsamples was supported by constant errors (CE) < 1 mL.kg.min for subgroups of gender, age, PASS, and VO2max between 30 and 50 mL x kg(-1) x min(-1) (70% of the sample). CE were > 1 mL x kg(-1) x min(-1) for VO2max < 30 and > 50 mL x kg(-1) x min(-1). CONCLUSIONS: Waist girth is an acceptable surrogate for body composition in the nonexercise models. All models were similar in accuracy and valid for estimating VO2max of most adults, but with reduced accuracy at the extremes of fitness (VO2max < 30 and >50 mL x kg(-1) x min(-1)).     

A comparison of the effects of measured, predicted, estimated and constant residual volumes on the body density of male athletes

The aim of this study was to use the measured residual volume (RV) of male athletes (n = 207) as a criterion and assess the error in their RV, body density (BD) and relative body fat (%BF) associated with using RVs predicted from regression equations, RVs estimated from vital capacity (VC) and an assumed constant RV of 1300 ml. The ventilated residual volume (RV) was determined both before and after the underwater weighing by helium dilution with the subject immersed to neck level. The mean of the absolute differences Idl and SEE between the 2 RV trials were 66 and 89 ml, respectively. These increased to values ranging 195-747 and 259-308 ml, respectively, when the means of the 2 RV trials for each subject were compared with the RVs predicted via regression equations, estimated from the VC and assumed to be a constant of 1300 ml. A similar trend emerged with variation of only the RV in the BD formula for each subject. The 2 RV trials resulted in a Idl and SEE of .00109 (.5% BF) and .00145 g.cm-3 (.6% BF), respectively, but these increased to values ranging .00306 (1.3% BF)-.01207 (5.1% BF) and .00394 (1.7% BF)-.00441 g.cm-3 (1.9% BF), respectively, for predicted, estimated and assumed constant RVs. In all cases the lowest Idl and SEE were associated with the RVs predicted by a multiple regression equation (R = .616; SEE = 259 ml) which was generated on our sample.(ABSTRACT TRUNCATED AT 250 WORDS)     

Influence of testing protocol on ventilatory thresholds and cycling performance

PURPOSE: To compare the ventilatory response of two incremental exercise tests and determine their predictive validity on 40-km cycle time trial (40K) mean power output (40Kavgwatts). METHODS: Fifteen male cyclists performed two incremental exercise tests (T50x3:100 W +50 W x 3(-1) min, T25x1:20 W + 25 W x min(-1)) and a 40K over an 8-d period. Key variable was power at ventilatory threshold (VT). For VT determination during each test we used: VE/VO2 method, first clear breakpoint on the VE/VCO2 plot, V-slope method, RER = 1, and RER = 0.95. RESULTS: VO2max during T50x3 and T25x1 was not different (66.6 vs 67.6 mL x kg(-1) x min(-1)), although T25x1 peak power output (MaxT25x1; 402 W) was significantly higher than MaxT50x3 (363 W). T50x3 and T25x1 VT power outputs indicated that the power output at T25x1:RER = 1 and T25x1:RER = 0.95 were significantly higher compared with T50x3 (324 vs 304 W and 282 vs 264 W, respectively). Regression analyses between T50x3 variables and 40Kavgwatts were significant for T50x3:V-slope (R2 = 0.37; SEE 20.2 W), T50x3:VE/VO2 (R2 = 0.64; SEE 15.3 W), T50x3:RER = 0.95 (R2 = 0.42; SEE 19.4 W), T50x3:RER = 1 (R2 = 0.45; SEE 18.8 W), and MaxT50x3 (R2 = 0.51; SEE 17.8 W). Regression analyses between T25x1 variables and 40Kavgwatts were significant for T25x1:V-slope (R2 = 0.63; SEE 15.4 W), T25x1:VE/VO2 (R2 = 0.64; SEE 15.2 W), T25x1:RER = 0.95 (R2 = 0.53; SEE 17.4 W), T25x1:RER = 1 (R2 = 0.57; SEE 16.7 W), and MaxT25x1 (R2 = 0.65; SEE 15.0 W). There was no significant difference between 40Kavgwatts (282 W) and power outputs at T50x3:VE/VO2 (277 W), T50x3:V-slope (289 W), T25x1:VE/VO2 (276 W), and T25x1:RER = 0.95 (282 W). CONCLUSION: Generally, T25x1 based VT variables were superior to T50x3 variables regarding the prediction of 40Kavgwatts. We conclude that the VE/VO2 method is protocol independent and a valid 40Kavgwatts predictor.     

Validity of VO2max equations for aerobically trained males and females

PURPOSE: The purpose of this investigation was to cross-validate existing VO2max prediction equations on samples of aerobically trained males and females. METHODS: A total of 142 aerobically trained males (mean +/- SD; 39.0 +/- 11.1 yr, N = 93) and females (39.7 +/- 10.1 yr, N = 49) performed a maximal incremental test to determine actual VO2max on a cycle ergometer. The predicted VO2max values from 18 equations (nine for each gender) were compared with actual VO2max by examining the constant error (CE), standard error of estimate (SEE), correlation coefficient (r), and total error (TE). RESULTS: The results of this investigation indicated that all of the equations resulted in significant (P < 0.006) CE values ranging from -216 to 1415 mL x min(-1) for the males and 132 to 1037 mL x min(-1) for the females. In addition the SEE, r, and TE values ranged from 266 to 609 mL x min(-1), 0.36 to 0.88, and 317 to 1535 mL x min(-1), respectively. Furthermore, the lowest TE values for the males and females represented 10% and 12% of the mean actual VO2max values, respectively. CONCLUSIONS: The results of the analysis indicated that the two equations using age, body weight, and the power output achieved at VO2 as predictor variables had the lowest SEE (7.7-9.8% of actual VO2max) and TE (10-12% of actual VO2max) values and are recommended for estimating VO2max in aerobically trained males and females. The magnitude of the TE values (>or= 20% of actual VO2max) associated with the remaining 16 equations, however, were too large to be of practical value for estimating VO2max.     

Comparison of the BOD POD with the four-compartment model in adult females

PURPOSE: This study was designed to compare the accuracy and bias in estimates of total body density (Db) by hydrostatic weighing (HW) and the BOD POD, and percent body fat (%fat) by the BOD POD with the four-compartment model (4C model) in 42 adult females. Furthermore, the role of the aqueous and mineral fractions in the estimation of body fat by the BOD POD was examined. METHODS: Total body water was determined by isotope dilution ((2)H(2)0) and bone mineral was determined by dual-energy x-ray absorptiometry. Db and %fat were determined by the BOD POD and HW. The 4C model of Baumgartner was used as the criterion measure of body fat. RESULTS: HW Db (1.0352 g x cm(-3)) was not statistically different (P = 0.35) from BOD POD Db (1.0349 g x cm(-3)). The regression between Db by HW and the BOD POD significantly deviated from the line of identity (Db by HW = 0.90 x Db by BOD POD + 0.099; R(2) = 0.94). BOD POD %fat (28.8%) was significantly lower (P < 0.01) than %fat by the 4C model (30.6%). The regression between %fat by the 4C model and the BOD POD significantly deviated from the line of identity (%fat by 4C model = 0.88 x %fat by BOD POD + 5.41%; R(2) = 0.92). BOD POD Db and %fat showed no bias across the range of fatness. Only the aqueous fraction of the fat-free mass (FFM) had a significant correlation with the difference in %fat between the 4C model and the BOD POD. CONCLUSION: These data indicate that the BOD POD underpredicted body fat as compared with the 4C model, and the aqueous fraction of the FFM had a significant effect on estimates of %fat by the BOD POD.     

Prediction of claudication pain from clinical measurements obtained at rest.

The ability to predict claudication pain during single-stage (S) and progressive (P) treadmill protocols from clinical measurements obtained at rest was examined. Peripheral hemodynamic measurements from the more severely diseased lower limb and medical history data were obtained from 56 claudicant patients during supine rest immediately preceding S (1.5 mph and 7.5% grade) and P (2 mph, 0% grade with 2% increase every 2 min) treadmill protocols. Distance walked to onset of claudication pain (CPD) and to maximal pain (MPD) during both protocols were recorded. The claudication distances during the S protocol were not correlated with either the peripheral hemodynamic or medical history variables. In contrast, CPD and MPD during the P protocol were predicted (P less than 0.05) by ankle/brachial systolic blood pressure index (ABI) (quadratic relationship), laterality of claudication pain (1 = unilateral, 2 = bilateral), and gender (1 = female, 2 = male) from the following regression equations: CDP (m) = 159.9 - (321.8 x ABI) + (445.6 x ABI2) - (93.5 x laterality) + (99.0 x gender), R = 0.74, R2 = 0.55, adjusted R2 = 0.53, SEE = 110.5, P less than 0.0001; and MPD (m) = 83.1 + (195.0 x ABI) + (174.0 x ABI2) - (76.4 x laterality) + (114.2 x gender), R = 0.76, R2 = 0.58, adjusted R2 = 0.55, SEE = 138.3, P less than 0.0001. It is concluded that the regression equations for the prediction of CPD and MPD may be used to quickly estimate the functional severity of peripheral vascular occlusive disease in clinical settings where treadmill testing is not feasible or is impractical.     

Single-breathhold, four-dimensional, quantitative assessment of LV and RV function using triggered, real-time, steady-state free precession MRI in heart failure patients

PURPOSE: To validate a novel, real-time, steady-state free precession (SSFP), single-breathhold technique for the assessment of left ventricular (LV) and right ventricular (RV) function in heart failure patients. MATERIALS AND METHODS: A total of 20 heart failure patients (mean age 59 +/- 17 years) underwent scanning with our new, real-time, spiral SSFP sequence in which each cardiac phase was acquired in 118 msec at a resolution of 1.8 x 1.8 mm. Each cardiac slice (1-cm thick) was automatically advanced based on a cardiac trigger, allowing complete coverage of the heart in a single breathhold. The patients also underwent LV and RV assessment with the gold standard: multiple breathhold, cardiac-gated, segmented k-space strategy. LV and RV end-systolic volume (ESV) and end-diastolic volume (EDV) and LV mass were compared between the two imaging techniques. RESULTS: The new real-time strategy was highly concordant with the gold standard technique in the assessment of LVEDV (r = 0.98), LVESV (r = 0.98), RVESV (r = 0.86), RVEDV (r = 0.91), LVMASS (r = 0.95), RVEF (r = 0.70), and LVEF (r = 0.94). The mean bias (95% confidence interval [CI]) for each parameter is LVEDV: 10.6 cc (cm(3)) (3.8-17.4 cc), LVESV: -0.8 cc (-5.3 to 3.7 cc), RVEDV: 3.7 cc (-5.6 to 13.2 cc), RVESV: -3.1 cc (-11.1 to 4.9 cc), LVMASS: 26 g (12.4-39.8 g), RVEF: -2.9% (1.3 to -7.2 %), LVEF: 1.9% (5 to -1.1%). In addition, data acquisition was only nine +/- two seconds with the real-time strategy vs. 312 +/- 41 seconds for the standard technique. CONCLUSION: In patients with heart failure, real-time, spiral SSFP allows rapid and accurate assessment of RV and LV function in a single-breath hold. Using the same strategy, increased temporal resolution will allow real-time assessment of cardiac wall motion during stress studies.