96 A comparison of energy expenditure equations for basal-equivalent activities D. Bustos 1 , A. D. Lucena 2 , J. C. Guedes 3 1 Associated Laboratory for Energy, Transports, and Aeronautics (PROA/LAETA), University of Porto, PT (denissebustossandoval@gmail.com) ORCID 0000-0002-4942-7625, 2 Federal Rural University of Semiarid, BR (andrelucena@ufersa.edu.br) ORCID 000-0003-0181-4260, 3 Associated Laboratory for Energy, Transports, and Aeronautics (PROA/LAETA), University of Porto, PT (jccg@fe.up.pt) ORCID 0000-0003-2367-2187 https://doi.org/10.24840/978-972-752-260-6_0096-0102 Abstract Introduction: Resting energy expenditure (REE) represents the largest component of total energy expenditure and is a major contributor to energy balance. Over the past several decades, numerous REE equations have been developed targeted to different population groups. However, the generation of standardized equations for predicting energy expenditure, to be applied to every healthy individual, is still subject to research. Purpose: This study aims to test existing predictive equations for basal energy requirements and based on a comparison of their results and measured values, to determine the most appropriate to the characteristics of the studied group. Methodology: Thirty participants (age 30,37 ± 5,50) performed a sequence of five activities chosen to represent basal, light and moderate intensities. The included three basal- equivalent tasks were analyzed in this study. During each trial, oxygen consumption was measured by a portable metabolic system (K4b 2 ). From a previously developed literature research, equations were selected to estimate energy requirements. Calculations and values obtained from oximetry were compared. Results and Discussion: Retrieved predictive equations were filtered to 21 relevant equations from 15 authors. When observing general results, most participants showed the equation proposed by Korth (based on weight, height, sex, and age) to be the one predicting values with a better approximation to K4b 2 , followed by the Haaf&Weijs’ equation, based on fat-free mass (FFM). From the individual analysis, Korth’s equation proved to work well for men in most cases and poorly for women. Correspondingly, Haaf&Weijs equation gave better results for females. Specifically, better approximations were obtained within males participants. Finally, the associated deviations from measured values indicate more reliable results than a Level 1 (two with better accuracy than a Level 2) of the assessment approaches, for energy consumption while working, referred in the ISO 8996:2004 standard. Conclusions: Through this study, Korth (based on weight, height, sex, and age) and Haaf&Weijs (based on FFM) equations proved to be the most accurate. As a result, since body composition measurement is not always possible, the equation of Korth is advised for use in a young subjects’ sample with similar overall characteristics to the sample hereby presented. Future studies should be developed to test equations within bigger samples and propose a new regression model that better adapts to the studied population. Keywords: Resting energy expenditure, Energy requirements, Energy expenditure estimations. INTRODUCTION The accurate prediction of energy requirements for healthy individuals has many useful applications (Mifflin et al., 1990). Various studies associated with energy expenditure have been conducted within different contexts. From the occupational perspective, it has also been proven of great utility for ergonomics, safety, and health of workers (Lucena, Guedes, Vaz, & Silva, 2018). Specifically, resting energy expenditure (REE) contributes to 60-70% of daily energy requirements. REE is the maintenance energy cost of the body in rest under steady state conditions. This is different from the minimal energy cost. Energy expenditure can, for example, be lower during sleep or during undernutrition (ten Haaf & Weijs, 2014). REE can be measured through indirect calorimetry or estimated using predictive equations. The gold standard to determine the REE is the measurement by indirect calorimetry (ISO, 2004; Lucena et al., 2018). However, procedures for direct measurements are complex, expensive and not feasible for frequent and timely individual use (Sabounchi, Rahmandad, & Ammerman, 2013; ten Haaf & Weijs, 2014). As a result, several mathematical equations, mostly developed by regression methods, have been adopted as a major technique for this matter. Nevertheless, there is not an agreement on which equation is most suitable to which situation or to which population’s characteristics. Therefore, this study aims to validate existing resting energy expenditure