No abstract

Predictive resting metabolic rate (RMR) equations are widely used to determine athletes’ resting energy expenditure (REE). However, it remains unclear whether these predictive RMR equations accurately predict REE in the athletic populations. The purpose of the study was to compare 12 prediction equations (Harris-Benedict, Mifflin, Schofield, Cunningham, Owen, Liu’s, De Lorenzo) with measured RMR in Turkish national team athletes and sedentary controls. A total of 97 participants, 49 athletes (24 females, 25 males), and 48 sedentary (28 females, 20 males), were recruited from Turkey National Olympic Teams at the Ministry of Youth and Sports. RMR was measured using a Fitmate GS (Cosmed, Italy). The results of each 12 prediction formulas were compared with the measured RMR using paired t-test. The Bland-Altman plot was performed to determine the mean bias and limits of agreement between measured and predicted RMRs. Stratification according to sex, the measured RMR was greater in athletes compared to controls. The closest equation to the RMR measured by Fitmate GS was the Harris-Benedict equation in male athletes (mean difference -8.9 (SD 257.5) kcal/day), and Liu’s equation [mean difference -16.7 (SD 195.0) kcal/day] in female athletes. However, the intra-class coefficient (ICC) results indicated that all equations, including Harris-Benedict for male athletes (ICC = 0.524) and Liu’s for female athletes (ICC = 0.575), had a moderate reliability compared to the measured RMR. In sedentary subjects, the closest equation to the measured RMR is the Nelson equation in males, with the lowest RMSE value of 118 kcal/day [mean difference: 10.1 (SD 117.2) kJ/day], whereas, in females, all equations differ significantly from the measured RMR. While Nelson (ICC = 0.790) had good and Owen (ICC = 0.722) and Mifflin (calculated using fat-free mass) (ICC = 0.700) had moderate reliability in males, all predictive equations showed poor reliability in females. The results indicate that the predictive RMR equations failed to accurately predict RMR levels in the participants. Therefore, it may not suitable to use them in determining total energy expenditure.

No abstract

In square contingency tables, analysis of agreement between the row and column classifications is of interest. In such tables, kappa or weighted kappa coefficients are used to summarize the degree of agreement between two raters. In addition to investigate the agreement between raters for square contingency tables, category distinguishability should be considered. Because the kappa coefficient is insufficient to measure the category distinguishability, the degree of distinguishability is suggested to use. In practice, some problems have occurred with regards to the use of the degree of distinguishability. The aim of this study is to assess the agreement coefficient and degree of distinguishability in square contingency tables together. In this study, the adjusted degree of distinguishability is suggested to solve the problem of calculating the degree of distinguishability falls outside the defined range. A simulation study is performed to compare the proposed adjusted degree of distinguishability and the classical degree of distinguishability. Furthermore, interpretation levels for the degree of distinguishability are determined based on a simulation study. The results are discussed over numerical examples and simulation.

Log-linear models are used to analyze the contingency tables. If the variables are ordinal or interval, because the score values affect both the model significance and parameter estimates, selection of score values has importance. Sometimes an interval variable contains open-ended categories as the first or last category. While the variable has openended classes, estimates of the lowermost and/or uppermost values of distribution must be handled carefully. In that case, the unknown values of first and last classes can be estimated firstly, and then the score values can be calculated. In the previous studies, the unknown boundaries were estimated by using interquartile range (IQR). In this study, we suggested interdecile range (IDR), interpercentile range (IPR), and the mid-distance range (MDR) as alternatives to IQR to detect the effects of score values on model parameters.