scholarly journals World Health Organization equations have shortcomings for predicting resting energy expenditure in persons from a modern, affluent population: generation of a new reference standard from a retrospective analysis of a German database of resting energy expenditure

2004 ◽  
Vol 80 (5) ◽  
pp. 1379-1390 ◽  
Author(s):  
Manfred J Müller ◽  
Anja Bosy-Westphal ◽  
Susanne Klaus ◽  
Georg Kreymann ◽  
Petra M Lührmann ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
World Health Organization ◽  
Retrospective Analysis ◽  
Resting Energy Expenditure ◽  
World Health ◽  
Reference Standard ◽  
Health Organization ◽  
Resting Energy

The validity of resting energy expenditure predictive equations in adults with central obesity: A sub-sample of the RaNCD cohort study

2019 ◽  
Vol 25 (3) ◽  
pp. 217-224 ◽  
Author(s):  
Yahya Pasdar ◽  
Shima Moradi ◽  
Behrooz Hamzeh ◽  
Farid Najafi ◽  
Seyed Mostafa Nachvak ◽  
...  
Keyword(s):  
Cohort Study ◽  
Energy Expenditure ◽  
Central Obesity ◽  
Pearson Correlation ◽  
Resting Energy Expenditure ◽  
World Health ◽  
Predictive Equations ◽  
Significant Difference ◽  
Health Organization ◽  
Resting Energy

Background: There are different equations for estimating Resting Energy Expenditure (REE). However, these equations were mainly developed based on populations of western countries. Aim: The present study was conducted to determine the validity of REE predictive equations in adults with central obesity. Methods: This study was conducted with 129 adults with central obesity aged 35–65 years, a sub-sample from a large cohort study (Western Iran), Kurdish population. REE was measured by indirect calorimetry (IC) and REE predictive equations. Data were analysed using Pearson correlation, paired t-test, concordance correlation coefficient (CCC), mean squared deviation (MSD), level of agreement (LOA) and Bland-Altman plot. Results: All REE predictive equations had low CCC and high LOA. Although there was no statistically significant difference in the REE measured with IC and the REE predicted with the Food and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/UNU), FAO/WHO/UNU (Height), Muller and revised Harris-Benedict equations ( P = 0.874, 0.113, 0.619, 0.143 and P = 0.121), other equations had statistically significant differences with IC ( P<0.001). In addition, the highest correlation was found between the IC (r = 0.682). The least difference was related to the FAO/WHO/UNU equation, with an agreement limit of -507.96 to 500.79 Kcal/day, with a 95% confidence interval. Conclusions: The results of this study showed that the FAO/WHO/UNU, Muller, revised Harris-Benedict equations and Mifflin St Jeor equations are relatively acceptable for estimating REE. However, these prediction equations are not good at predicting REE; more precise equations are needed to apply for different ethnic groups.

scholarly journals Resting Energy Expenditure in CrossFit® Participants: Predictive Equations versus Indirect Calorimetry

2021 ◽  
Vol 9 (2) ◽  
pp. 7
Author(s):  
Maraline Santos Sena ◽  
Marcio Leandro Ribeiro de Souza ◽  
Valden Luis Matos Capistrano Junior
Keyword(s):  
Energy Expenditure ◽  
Indirect Calorimetry ◽  
Resting Energy Expenditure ◽  
World Health ◽  
Predictive Equations ◽  
Portable Ultrasound ◽  
Significant Difference ◽  
Health Organization ◽  
Functional Movements ◽  
Resting Energy

Background: CrossFit® involves high-intensity functional movements and research has shown that the program increases metabolic rates in participants. Objective: To measure resting energy expenditure (REE) in CrossFit® participants using indirect calorimetry (IC) and to verify the most appropriate predictive equation to estimate REE. Methods: Overall, 142 CrossFit® participants (18–59 years; 91 [64.1%], women) underwent weight, height, waist circumference, and body mass index (BMI) measurements. Body composition was evaluated using a portable ultrasound system (BodyMetrix®). REEs were measured (mREE) by IC and predicted by six different equations (pREE): Harris-Benedict, World Health Organization (WHO), Henry and Rees, Cunningham (1980 and 1991), and Mifflin–St. Jeor. Results: The mean age was 33.0 (6.3) years, with no significant difference between men and women; mean mREE, 1583.2(404.4) kcal/d; and pREE, 1455.5(230.9) to 1711.3(285.5) kcal/d. The best REE predictive equations for this population were Cunningham (1991) (P=0.338), WHO (P=0.494), and Harris-Benedict (P=0.705) equations. The Harris-Benedict equation presented a smaller difference compared with IC [12.9(307.6) kcal], the Cunningham (1991) equation showed improved adequacy (102.5%), and the WHO equation presented highest accuracy (59.9%). The equations that were closest to the mREE were the Harris-Benedict for women and the WHO equation for men. Conclusion: Therefore, for CrossFit® participants, the REE can accurately be predicted with the Cunningham (1991), WHO, and Harris-Benedict equations.

scholarly journals COVID-19: Anatomy of a Pandemic

2021 ◽  
Vol 3 (4) ◽  
pp. 84-89
Author(s):  
Hassan Naji
Keyword(s):  
World Health Organization ◽  
Retrospective Analysis ◽  
World Health ◽  
Human History ◽  
The World ◽  
Health Organization

SARS-CoV-2, the virus that causes COVID-19, has more than 82% genome similarity with SARS-CoV and more than 89% similarity with two bat coronaviruses, bat-SL-CoVZXC21 and bat-SL-CoVZC45. The virus went and caused the most recent pandemic in human history with fatality totaling more than 3 million deaths, and cases rising up to 176 million worldwide according to the World Health Organization (WHO). In this paper, a retrospective analysis of the emergence and spread of SARS-CoV-2 around the world are presented.

scholarly journals Prediction of Resting Energy Expenditure in Children: May Artificial Neural Networks Improve Our Accuracy?

2020 ◽  
Vol 9 (4) ◽  
pp. 1026 ◽  
Author(s):  
Valentina De Cosmi ◽  
Alessandra Mazzocchi ◽  
Gregorio Paolo Milani ◽  
Edoardo Calderini ◽  
Silvia Scaglioni ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Absolute Error ◽  
World Health ◽  
Lower Grade ◽  
Healthy Children ◽  
Artificial Neural ◽  
Resting Energy

The inaccuracy of resting energy expenditure (REE) prediction formulae to calculate energy metabolism in children may lead to either under- or overestimated real caloric needs with clinical consequences. The aim of this paper was to apply artificial neural networks algorithms (ANNs) to REE prediction. We enrolled 561 healthy children (2–17 years). Nutritional status was classified according to World Health Organization (WHO) criteria, and 113 were obese. REE was measured using indirect calorimetry and estimated with WHO, Harris–Benedict, Schofield, and Oxford formulae. The ANNs considered specific anthropometric data to model REE. The mean absolute error (mean ± SD) of the prediction was 95.8 ± 80.8 and was strongly correlated with REE values (R2 = 0.88). The performance of ANNs was higher in the subgroup of obese children (101 ± 91.8) with a lower grade of imprecision (5.4%). ANNs as a novel approach may give valuable information regarding energy requirements and weight management in children.

scholarly journals Resting Energy Expenditure of Master Athletes: Accuracy of Predictive Equations and Primary Determinants

2021 ◽  
Vol 12 ◽  
Author(s):  
Petra Frings-Meuthen ◽  
Sara Henkel ◽  
Michael Boschmann ◽  
Philip D. Chilibeck ◽  
José Ramón Alvero Cruz ◽  
...  
Keyword(s):  
Physical Activity ◽  
Body Weight ◽  
General Population ◽  
Energy Expenditure ◽  
Ambient Temperature ◽  
Resting Energy Expenditure ◽  
Fat Free Mass ◽  
World Health ◽  
Master Athletes ◽  
Resting Energy

Resting energy expenditure (REE) is determined mainly by fat-free mass (FFM). FFM depends also on daily physical activity. REE normally decreases with increased age due to decreases in FFM and physical activity. Measuring REE is essential for estimating total energy expenditure. As such, there are a number of different equations in use to predict REE. In recent years, an increasing number of older adults continue to participate in competitive sports creating the surge of master athletes. It is currently unclear if these equations developed primarily for the general population are also valid for highly active, older master athletes. Therefore, we tested the validity of six commonly-used equations for predicting REE in master athletes. In conjunction with the World Masters Athletic Championship in Malaga, Spain, we measured REE in 113 master athletes by indirect calorimetry. The most commonly used equations to predict REE [Harris &amp; Benedict (H&amp;B), World Health Organization (WHO), Müller (MÜL), Müller-FFM (MÜL-FFM), Cunningham (CUN), and De Lorenzo (LOR)] were tested for their accuracies. The influences of age, sex, height, body weight, FFM, training hours per week, phase angle, ambient temperature, and athletic specialization on REE were determined. All estimated REEs for the general population differed significantly from the measured ones (H&amp;B, WHO, MÜL, MÜL-FFM, CUN, all p &lt; 0.005). The equation put forward by De Lorenzo provided the most accurate prediction of REE for master athletes, closely followed by FFM-based Cunningham’s equation. The accuracy of the remaining commonly-used prediction equations to estimate REE in master athletes are less accurate. Body weight (p &lt; 0.001), FFM (p &lt; 0.001), FM (p = 0.007), sex (p = 0.045) and interestingly temperature (p = 0.004) are the significant predictors of REE. We conclude that REE in master athletes is primarily determined by body composition and ambient temperature. Our study provides a first estimate of energy requirements for master athletes in order to cover adequately athletes’ energy and nutrient requirements to maintain their health status and physical performance.

Sign in / Sign up

Export Citation Format

Share Document