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Predictive resting metabolic rate (RMR) equations are widely used to determine total daily energy expenditure (TDEE). However, it remains unclear whether these predictive RMR equations accurately predict TDEE in the athletic populations. The purpose of this study was to examine the accuracy of 10 commonly used RMR prediction equations (Cunningham, De Lorenzo, Freire, Harris-Benedict, Mifflin St. Jeor, Nelson, Owen, Tinsley, Watson, Schofield) in collegiate men and women athletes. One-hundred eighty-seven National Collegiate Athletic Association Division III men (n = 97) and women (n = 90) athletes were recruited to participate in one day of metabolic testing. RMR was measured using indirect calorimetry and body composition was analyzed using air displacement plethysmography. A repeated measures ANOVA with Bonferroni post hoc analyses was selected to determine mean differences between measured and predicted RMR. Linear regression analysis was used to assess the accuracy of each RMR prediction method (p<0.05). All prediction equations significantly underestimated RMR (p<0.001), although there was no difference between the De Lorenzo and Watson equations and measured RMR (p = 1.00) for women, only. In men, the Tinsley and Freire equations were the most agreeable formulas with the lowest root-mean-square prediction error value of 404 and 412 kcals, respectively. In women, the De Lorenzo and Watson equations were the most agreeable equations with the lowest root-mean-squared error value of 171 and 211 kcals, respectively. The results demonstrate that such RMR equations may underestimate actual energy requirements of athletes and thus, practitioners should interpret such values with caution. Highlights All prediction equations significantly underestimated RMR in men athletes. All prediction equations, except for the De Lorenzo and Watson equations, significantly underestimated RMR in women athletes. Although a significant underestimation of RMR in men athletes, the Freire and Tinsley equations were the most agreeable prediction equations. In women athletes, the De Lorenzo and Watson equations were the most agreeable prediction equations. [ABSTRACT FROM AUTHOR]

Jagim, AR, Camic, CL, Kisiolek, J, Luedke, J, Erickson, J, Jones, MT, and Oliver, JM. Accuracy of resting metabolic rate prediction equations in athletes. J Strength Cond Res 32(7): 1875–1881, 2018—The purpose of this study was to determine the accuracy of 5 different resting metabolic rate (RMR) prediction equations in male and female athletes. Twenty-two female (19.7 ± 1.4 years; 166.2 ± 5.5 cm; 63.5 ± 7.3 kg; 49.2 ± 4.3 kg of fat-free mass (FFM); 23.4 ± 4.4 body fat (BF) percent) and 28 male (20.2 ± 1.6 years; 181.9 ± 6.1 cm; 94.5 ± 16.2 kg; 79.1 ± 7.2 kg of FFM; 15.1 ± 8.5% BF) athletes were recruited to participate in 1 day of metabolic testing. Assessments comprised RMR measurements using indirect calorimetry, and body composition analyses using air displacement plethysmography. One-way repeated-measures analysis of variance with follow-up paired t tests were selected to determine differences between indirect calorimetry and 5 RMR prediction equations. Linear regression analysis was used to assess the accuracy of each RMR prediction method. An alpha level of p ≤ 0.05 was used to determine statistical significance. All the prediction equations significantly underestimated RMR while the Cunningham equation had the smallest mean difference (−165 kcals). In men, the Harris-Benedict equation was found to be the best prediction formula with the lowest root-mean-square prediction error value of 284 kcals. In women, the Cunningham equation was found to be the best prediction equation with the lowest root-mean-squared error value of 110 kcals. Resting metabolic rate prediction equations consistently seem to underestimate RMR in male and female athletes. The Harris-Benedict equation seems to be most accurate for male athletes, whereas the Cunningham equation may be better suited for female athletes. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of this study was to assess the effects of exercise on the aerobic power, serum lactate level, and cell blood count among active individuals in the environments with similar climatic characteristics differing in their level of air pollution. Methods: This trial comprised 20 volunteer students of Physical education in The University of Isfahan, Iran. Two places with the same climate (altitude, temperature, and humidity), but low and high level of air pollutants air were selected in Isfahan, Iran. Participants underwent a field Cooper test with a 12-minute run for fitness assessment. Then the aerobic power, serum lactate, and cell blood counts were measured and compared between the two areas. Results: The study participants had a mean (SD) age of 21.70 (2.10) years and body mass index (BMI) of 24.44 (2.32) Kg/m2. We found a significant decrease in mean Vo2 max, red blood cell count, hemoglobin, hematocrit, and mean corpuscular hemoglobin, as well as significant increase in mean lactate level, white blood cell count and mean corpuscular volume in the higher-polluted than in the lower-polluted area. No significant difference was documented for other parameters as platelet counts or maximum heart rate. Conclusions: Exercise in high-polluted air resulted in a significant reduction in the performance at submaximal levels of physical exertion. Therefore, the acute exposure to polluted air may cause a significant reduction in the performance of active individuals. The clinical importance of these findings should be assessed in longitudinal studies.

Objectives: The purpose of this study was to assess the effects of exercise on the aerobic power, serum lactate level, and cell blood count among active individuals in the environments with similar climatic characteristics differing in their level of air pollution. Methods: This trial comprised 20 volunteer students of Physical education in The University of Isfahan, Iran. Two places with the same climate (altitude, temperature, and humidity), but low and high level of air pollutants air were selected in Isfahan, Iran. Participants underwent a field Cooper test with a 12-minute run for fitness assessment. Then the aerobic power, serum lactate, and cell blood counts were measured and compared between the two areas. Results: The study participants had a mean (SD) age of 21.70 (2.10) years and body mass index (BMI) of 24.44 (2.32) Kg/m2. We found a significant decrease in mean Vo2 max, red blood cell count, hemoglobin, hematocrit, and mean corpuscular hemoglobin, as well as significant increase in mean lactate level, white blood cell count and mean corpuscular volume in the higherpolluted than in the lower-polluted area. No significant difference was documented for other parameters as platelet counts or maximum heart rate. Conclusions: Exercise in high-polluted air resulted in a significant reduction in the performance at submaximal levels of physical exertion. Therefore, the acute exposure to polluted air may cause a significant reduction in the performance of active individuals. The clinical importance of these findings should be assessed in longitudinal studies. [ABSTRACT FROM AUTHOR]