Background & aims: Individual energy requirements of overweight and obese adults can often not be measured by indirect calorimetry, mainly due to the time-consuming procedure and the high costs. To analyze which resting energy expenditure (REE) predictive equation is the best alternative for indirect calorimetry in Belgian normal weight to morbid obese women.Methods: Predictive equations were included when based on weight, height, gender, age, fat free mass and fat mass. REE was measured with indirect calorimetry. Accuracy of equations was evaluated by the percentage of subjects predicted within 10% of REE measured, the root mean squared prediction error (RMSE) and the mean percentage difference (bias) between predicted and measured REE.Results: Twenty-seven predictive equations (of which 9 based on FFM) were included. Validation was based on 536 F (18–71 year). Most accurate and precise for the Belgian women were the Huang, Siervo, Muller (FFM), Harris–Benedict (HB), and the Mifflin equation with 71%, 71%, 70%, 69%, and 68% accurate predictions, respectively; bias −1.7, −0.5, +1.1, +2.2, and −1.8%, RMSE 168, 170, 163, 167, and 173 kcal/d. The equations of HB and Mifflin are most widely used in clinical practice and both provide accurate predictions across a wide range of BMI groups. In an already overweight group the underpredicting Mifflin equation might be preferred. Above BMI 45 kg/m2, the Siervo equation performed best, while the FAO/WHO/UNU or Schofield equation should not be used in this extremely obese group.Conclusions: In Belgian women, the original Harris–Benedict or the Mifflin equation is a reliable tool to predict REE across a wide variety of body weight (BMI 18.5–50). Estimations for the BMI range between 30 and 40 kg/m2, however, should be improved.
Background: When the resting energy expenditure (REE) of overweight and obese adolescents cannot be measured by indirect calorimetry, it has to be predicted with an equation. Objective: The aim of this study was to examine the validity of published equations for REE compared with indirect calorimetry in overweight and obese adolescents. Design: Predictive equations based on weight, height, sex, age, fatfree mass (FFM), and fat mass were compared with measured REE. REE was measured by indirect calorimetry, and body composition was measured by dual-energy X-ray absorptiometry. The accuracy of the REE equations was evaluated on the basis of the percentage of adolescents predicted within 10% of REE measured, the mean percentage difference between predicted and measured values (bias), and the root mean squared prediction error (RMSE). Results: Forty-three predictive equations (of which 12 were based on FFM) were included. Validation was based on 70 girls and 51 boys with a mean age of 14.5 y and a mean (6SD) body mass index SD score of 2.93 6 0.45. The percentage of adolescents with accurate predictions ranged from 74% to 12% depending on the equation used. The most accurate and precise equation for these adolescents was the Molnar equation (accurate predictions: 74%; bias: –1.2%; RMSE: 174 kcal/d). The often-used Schofield-weight equation for age 10–18 y was not accurate (accurate predictions: 50%; bias: +10.7%; RMSE: 276 kcal/d). Conclusions: Indirect calorimetry remains the method of choice for REE in overweight and obese adolescents. However, the sex-specific Molnar REE prediction equation appears to be the most accurate for overweight and obese adolescents aged 12–18 y. This trial was registered at www.trialregister.nl with the Netherlands Trial Register as ISRCTN27626398.
Since 2016, it is mandatory for all future students at the department of Media, Information and Communication, to participate in a study choice test (SCT), prior their enrollment. However, the outcome is not binding and students are still entitled to enter the first year after receiving negative advice. With the help of a structural model, built for my PhD research, the predictive value of the SCT is tested by comparing the time it takes the students to finish all first year exams, their average grade point and attrition, against the results of the SCT. By using the structural model, various background variables are also measured, such as engagement, effort and commitment are also measured. By using the normed fit index (NFI), the comparative fit index (CFI), the Tucker-Lewis Index (TLI) and the root mean square error of approximation (RMSEA), the fit of the model is established. In addition, a comparison of the direct and indirect influence of the SCT will provide more knowledge about the correlations between the different variables, the SCT and ultimately student success.
