Predicting the resting metabolic rate of young Australian males.

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) cross-validate 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 (alpha = 0.05, power = 0.80) statistically and physiologically significant differences of 8% between our predicted/measured RMRs and those predicted from the equations of other investigators. SUBJECTS: Thirty-eight males (chi +/- s.d.: 24.3+/-3.3y; 85.04+/-13.82 kg; 180.6+/-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 kJ/day. 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 kJ/day) and that predicted from skinfold thicknesses (R = 0.886, SEE = 440 kJ/day). The regression equations of Harris & Benedict (1919) and Schofield (1985) all overestimated the mean RMR of our subjects by 518 - 600 kJ/day (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 significant 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-y-old 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.

Distractors--can they be biased too?

Numerous work has been done on item bias and differential item functioning. Although there is some research on distractor analysis, no detailed study has been attempted to examine the way distractors in an item function, with regards to comparing distractor performance. This paper examines how distractors function differentially and compares various methods for identifying this. The Pearson chi-square, likelihood ratio chi-square and Neyman weighted least squares chi-square tests are some of these methods. Possible causes of distractor bias are discussed with illustrations from a physics problem-solving scale.

Body composition changes in elite male bodybuilders during preparation for competition.

Anthropometric profiles together with a 4 compartment criterion model of body composition analysis (total body water, bone mineral, fat and residual masses via a combination of deuterium dilution, dual-energy x-ray absorptiometry and hydrodensitometry) were conducted on 3 elite male bodybuilders 10 wk and then 5 d before competition. A mean body mass reduction from 99.70 (Quetelet's Index = 31.6 kg/m2) to 92.79 kg (Quetelet's Index = 29.2 kg/m2) was accompanied by a decline in the sum of 8 skinfold thicknesses (triceps, subscapular, biceps, iliac crest, supraspinale, abdominal, front thigh and medial calf) from 51.1 to 36.7 mm. The 4 compartment body composition model indicated that there were reductions of: percent body fat (%BF) from 9.1 to 5.0%, fat free mass (FFM) from 90.60 to 88.14 kg and fat mass (FM) from 9.10 to 4.65 kg. Sixty-four percent of the 6.91 kg loss in body mass therefore came from the FM. The 2 compartment hydrodensitometric model yielded higher %BFs (initial = 11.2; final = 7.1) than the 4 compartment model (initial = 9.1; final = 5.0) which is theoretically more valid because it controls for biological variability in the percentages of water and bone mineral in the FFM. Nevertheless, both models registered decreases of 4.1%BF.

Relationship between maximum aerobic power and resting metabolic rate in young adult women.

The literature is inconclusive as to the chronic effect of aerobic exercise on resting metabolic rate (RMR), and furthermore there is a scarcity of data on young women. Thirty-four young women exhibiting a wide range of aerobic fitness [maximum aerobic power (VO2max) = 32.3-64.8 ml.kg-1.min-1] were accordingly measured for RMR by the Douglas bag method, treadmill VO2max, and fat-free mass (FFM) by using Siri's three-compartment model. The interclass correlation (n = 34) between RMR (kJ/h) and VO2max (ml.kg-1.min-1) was significant (r = 0.39, P < 0.05). However, this relationship lost statistical significance when RMR was indexed to FFM and when partial correlation analysis was used to control for FFM differences. Furthermore, multiple linear-regression analysis indicated that only FFM emerged as a significant predictor of RMR (kJ/h). When high- (n = 12) and low-fitness (n = 12) groups were extracted from the cohort on the basis of VO2max scores, independent t-tests revealed significant between-group differences (P < 0.05) for RMR (kJ.kg-1.h-1) and VO2max (ml.kg-1.min-1) but not for RMR (kJ/h), RMR (kJ.kg FFM-1.h-1), and FFM. Analysis of covariance of RMR (kJ/h) with FFM as the covariate also showed no significant difference (P = 0.56) between high- and low-fitness groups. Thus the results suggest that 1) FFM accounts for most of the differences in RMR between subjects of varying VO2max values and 2) the RMR per unit of FFM in young healthy women is unrelated to VO2max.