INTRODUCTION:

Resting energy expenditure or basal energy expenditure is the largest component of an individual's total energy expenditure, used in the development of an adequate nutritional plan and can be measured by indirect calorimetry, considered the gold standard for this determination. In its absence, this component can be estimated using predictive equations, whose application is practical, economic and easy to use, but with limitations and issues related to its accuracy.

OBJECTIVES:

It is intended to evaluate which equations are most applied, to identify the studies carried out in Portuguese populations and in studies carried out in adult populations without known pathologies, to assess the difference in results between methods.

METHODOLOGY:

A narrative literature review was carried out since 2011, looking for publications that compare Resting energy expenditure results obtained by indirect calorimetry and by different predictive equations, using the Web of Science and PubMed databases.

RESULTS:

311 publications were selected and after reading the abstracts, 201 met the inclusion criteria. 4 of these publications were applied to the Portuguese population. Of the 201 articles included, the century-old Harris-Benedict equation was the most applied (74%), followed by Food and Agriculture Organization of the United Nations/World Health Organization/United Nations University (49%) and in third place is the Mifflin-St. Jeor (45%). In studies applied to adult populations without known pathologies (n=37), 49% reported significant deviations in the results of the most common predictive equations compared to indirect calorimetry, and 64% showed better results applying the predictive equations developed specifically for this population.

CONCLUSIONS:

Half of the studies carried out in adult populations without known pathologies, included in this review, refered to significant differences between the Resting energy expenditure results applying the most common predictive equations and indirect calorimetry measurements. This fact leads to the growing development of new predictive equations adapted to specific populations and the application of new solutions in their development, such as artificial intelligence. The constant increase of studies on this topic proposing new predictive equations, makes it crucial to systematize this information and create guidelines that lead to the selection of the most appropriate method, adapted to different populations, namely the Portuguese population on which there is a scarcity of results.

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