This study compared body composition by dual-energy X-ray absorptiometry (DEXA; Lunar DPX-L) with that via a four-compartment (4C; water, bone mineral mass, fat, and residual) model. Relative body fat was determined for 152 healthy adults [30.0 +/- 11.1 (SD) yr; 75.10 +/- 14.88 kg; 176.3 +/- 8.7 cm] aged from 18 to 59 yr. The 4C approach [20.7% body fat (%BF)] resulted in a significantly (P < 0.001) higher mean %BF compared with DEXA (18.9% BF), with intraindividual variations ranging from -2.6 to 7.3% BF. Linear regression and a Bland and Altman plot demonstrated the tendency for DEXA to progressively underestimate the %BF of leaner individuals compared with the criterion 4C model (4C %BF = 0.862 x DEXA %BF + 4.417; r(2) = 0.952, standard error of estimate = 1.6% BF). This bias was not attributable to variations in fat-free mass hydration but may have been due to beam-hardening errors that resulted from differences in anterior-posterior tissue thickness.

Objective: To determine anthropometric and body composition changes in female bodybuilders during preparation for competition. Design: There was an attempt to match subjects in the control and experimental groups for height and percentage body fat (%BF) for the initial test of this longitudinal study. Subjects: Five competitive bodybuilders (X AE s.d.: 35.3 AE 5.7 y; 167.3 AE 3.7 cm; 66.38 AE 6.30 kg; 18.3 AE 3.5 %BF) and ®ve athletic females (X AE s.d.: 30.9AE 13.0 y; 166.9 AE 3.9 cm; 55.94 AE 3.59 kg; 19.1 AE 3.3 %BF) were recruited from advertisements in a bodybuilding newsletter and placed on sports centre noticeboards. Interventions: The following measurements were conducted 12 weeks, 6 weeks and 3 ± 5 d before the bodybuilders' competitions: anthropometric pro®le, body density by underwater weighing, total body water via deuterium dilution and bone mineral mass from a dual-energy X-ray absorptiometry scan. A combination of the last three measurements enabled the %BF to the determined by a four compartment model. Results: A signi®cant (P 0.001) 5.80 kg body mass loss by the bodybuilders as they prepared for competition was primarily due to a reduction in fat mass (FM; À4.42 kg; 76.2%) as opposed to fat-free mass (FFM; À1.38 kg; 23.8%). The decreases in body mass and FM over the ®nal 6 weeks were greater than those over the ®rst 6 weeks. Their %BF decreased (P`0.001) from 18.3 to 12.7, whereas the values for the control group remained essentially unchanged at 19.1 ± 19.6 %BF. These body composition changes by the bodybuilders were accompanied by a signi®cant decline (P`0.001) of 25.5 mm (76.3 ± 50.8 mm) in the sum of eight skinfold thicknesses (triceps subscapular biceps iliac crest supraspinale abdominal front thigh medial calf). Conclusions: Although the bodybuilders presented with low %BFs at the start of the experiment, they still signi®cantly decreased their body mass during the 12 week preparation for competition and most of this loss was due to a reduction in FM as opposed to FFM. Sponsorship: Australian Research Council (small grants scheme). Descriptors: four-compartment body composition model; dual energy X-ray absorptiometry; somatotype

Objective: To generate equations for the prediction of percent body fat (% BF) via a four-compartment criterion body composition model from anthropometric variables and age. Design: Multiple regression analyses were used to predict % BF from the best-weighted combinations of independent variables. Subjects: In all 79 healthy males ( X X7s.d.: 35.0712.2 y; 84.24712.53 kg; 179.876.8 cm) aged 19-59 y were recruited from advertisements placed in a university newsletter and on community centres' noticeboards. Interventions: The following measurements were conducted: % BF using a four-compartment (water, bone mineral mass, fat and residual) model and a restricted anthropometric profile (nine skinfolds, five girths and two bone breadths). Results: Stepwise multiple regression selected six (subscapular, biceps, abdominal, thigh, calf and mid-axilla) of the nine skinfold measurements to predict % BF and using the sum of these six produced a quadratic equation with a standard error of estimate (SEE) and R 2 of 2.5% BF and 0.89, respectively. The inclusion of age as a predictor further improved the equationHowever, the best equation used only the sum of three skinfold thicknesses (mid-axilla, calf and thigh) and age but also included waist girth and biepicondylar femur breadth as predictors (% BF ¼ À0.00258 Â ( P 3SF) 2 þ 0.558 Â P 3SF þ 0.118 Â age þ 0.282 Â waist girth -2.100 Â femur breadth -2.34; SEE ¼ 1.8% BF, R 2 ¼ 0.94). Analyses of two age groups, o30 and Z30 y, demonstrated that for the same % BF, the former exhibited a higher sum of skinfold thicknesses. Conclusions: Equations were generated for the prediction of % BF via the four-compartment criterion body composition model from anthropometric variables and age. Agewise differences for the sum of skinfold thicknesses may be related to an increase in internal fat for the older subjects. Sponsorship: Australian Research Council (small grants scheme).

The aims of this study were to determine the most appropriate duration for the measurement of the maximal accumulated O2 deficit (MAOD), which is analogous to the anaerobic capacity, to ascertain the effects of mass, fat free mass (FFM), leg volume (Vleg) and lower body volume (V1b) on anaerobic test performance, to examine the reproducibility for peak power output (Wpeak) or maximal anaerobic power using an air-braked cycle ergometer and to produce approximations for the percentages of aerobic and anaerobic metabolism during exercise of short duration but high intensity. A group of 12 endurance trained cyclists [mean age 25.1 (SD 4.6) years; mean body mass 73.43 (SD 7.12) kg; mean maximal oxygen consumption 5.12 (SD 0.35) l.min-1; mean body fat 12.5 (SD 4.1) %] accordingly performed four counterbalanced treatments of 45, 60, 75 and 90 s of maximal cycling on an air-braked ergometer. The mean O2 deficit of 3.52 l for the 45-s treatment was significantly less (P < 0.01) than those for the 60 (3.75 l), 75 (3.80 l) and 90-s (3.75 l) treatments. These data therefore indicate that in predominantly aerobically trained subjects the O2 deficit attains a plateau after 60 s of maximal cycling on an air-braked ergometer. Statistically significant interclass correlation coefficients (P < 0.05) between the anthropometric variables (mass, FFM, Vleg and Vlb) and Wpeak or maximal anaerobic power (0.624-0.748) and MAOD (ml) or anaerobic capacity (0.666-0.772) furthermore would suggest the relevance of taking into account muscle mass during anaerobic tests.(ABSTRACT TRUNCATED AT 250 WORDS)

Participation in at least 30 min of moderate intensity activity on most days is assumed to confer health benefits. This study accordingly determined whether the more vigorous household and garden tasks (sweeping, window cleaning, vacuuming and lawn mowing) are performed by middle-aged men at a moderate intensity of 3-6 metabolic equivalents (METs) in the laboratory and at home. Measured energy expenditure during self-perceived moderate-paced walking was used as a marker of exercise intensity. Energy expenditure was also predicted via indirect methods. Thirty-six males [ X (SD): 40.0 (3.3) years; 179.5 (6.9) cm; 83.4 (14.0) kg] were measured for resting metabolic rate (RMR) and oxygen consumption ( VO(2)) during the five activities using the Douglas bag method. Heart rate, respiratory frequency, CSA (Computer Science Applications) movement counts, Borg scale ratings of perceived exertion and Quetelet's index were also recorded as potential predictors of exercise intensity. Except for vacuuming in the laboratory, which was not significantly different from 3.0 METs ( P=0.98), the MET means in the laboratory and home were all significantly greater than 3.0 ( P/=3.0 METs.

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Objectives: The aims of this study were: (a) to generate regression equations for predicting the resting metabolic rate (RMR) of 18 to 30-y-old Australian males from age, height, mass and fat-free mass (FFM); and (b) crossvalidate RMR prediction equations, which are frequently used in Australia, against our measured and predicted values.Design: A power analysis demonstrated that 38 subjects would enable us to detect (a 0.05, power 0.80) statistically and physiologically signi®cant differences of 8% between our predictedameasured RMRs and those predicted from the equations of other investigators. Subjects: Thirty-eight males (X AE s.d.: 24.3 AE 3.3 y; 85.04 AE 13.82 kg; 180.6 AE 8.3 cm) were recruited from advertisements placed in a university newsletter and on community centre noticeboards. Interventions: The following measurements were conducted: skinfold thicknesses, RMR using open circuit indirect calorimetry and FFM via a four-compartment (fat mass, total body water, bone mineral mass and residual) body composition model. Results: A multiple regression equation using the easily measured predictors of mass, height and age correlated 0.841 with RMR and the SEE was 521 kJaday. Inclusion of FFM as a predictor increased both the R and the precision of prediction, but there was virtually no difference between FFM via the four-compartment model (R 0.893, SEE 433 kJaday) and that predicted from skinfold thicknesses (R 0.886, SEE 440 kJaday). The regression equations of Harris & Benedict (1919) and Scho®eld (1985) all overestimated the mean RMR of our subjects by 518 ± 600 kJaday (P`0.001) and these errors were relatively constant across the range of measured RMR. The equations of Hayter & Henry (1994) and Piers et al (1997) only produced physiologically signi®cant errors at the lower end of our range of measurement. Conclusions: Equations need to be generated from a large database for the prediction of the RMR of 18 to 30-yold Australian males and FFM estimated from the regression of the sum of skinfold thicknesses on FFM via the four compartment body composition model needs to be further explored as an expedient RMR predictor.

Unfortunately, this paper contains the following error: The CSA arm coefficient for lawn mowing in Table 4 (p. 66) should be 4.5x10-4 .