s12986-018-0278-7.pdf 879,68KB
1000 Titel
  • Estimating the agreement between the metabolic rate calculated from prediction equations and from a portable indirect calorimetry device: an effort to develop a new equation for predicting resting metabolic rate
1000 Autor/in
  1. Pavlidou, Eleni |
  2. Petridis, Dimitris |
  3. Tolia, Maria |
  4. Tsoukalas, Nikolaos |
  5. Poultsidi, Antigoni |
  6. Fasoulas, Aristeidis |
  7. Kyrgias, George |
  8. Giaginis, Constantinos |
1000 Erscheinungsjahr 2018
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2018-06-15
1000 Erschienen in
1000 Quellenangabe
  • 15:41
1000 Copyrightjahr
  • 2018
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1000 Abstract/Summary
  • BACKGROUND: Many studies have been performed over time in order to determine the reliability of metabolic rate prediction equations. PURPOSE: To evaluate the agreement, in terms of bias, absolute bias and accuracy between metabolic rate prediction equations and measured metabolic rate using indirect calorimetry system (IC), investigating also the factors affecting this agreement. METHODS: The anthropometric features of 383 Caucasian participants of all Body Mass Index (BMI) classes were recorded and Resting Metabolic Rate (RMR) was measured by using the IC Fitmate portable device. The resulting values were compared with the predictive values of Harris & Benedict, Schofield, Owen, FAO-WHO-UNU, Mifflin and Harrington equations. RESULTS: A closer approximation in agreement was obtained using the Harrington equation (based on BMI, age and gender). The equations using variables, such as weight, height, age and gender demonstrated higher agreement than the equations using merely weight and gender. Higher educational level was associated with normal weight, while higher calorific ratio was found in the class of normal-weighted individuals. An inverse relationship between ΒΜΙ and RMR was also observed and a logarithmic equation for calculating RMR was created, which was differentiated in relation to BMI classes, using the weight and gender variables. CONCLUSION: A better measurement agreement between RMR prediction equations and IC may be achieved due to BMI consideration. The present findings contributed to a better understanding of the measured parameters, confirming the inverse relationship between BMI and RMR. Age group and gender variables may also exert significant role on the bias response of some RMR equations.
1000 Sacherschließung
lokal Predictive equation
lokal Resting metabolic rate
lokal Resting energy expenditure
lokal Basal metabolic rate
lokal Indirect calorimetry
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