Development and validation of new predictive equations for resting energy expenditure in physically active boys

Development and validation of new predictive equations for resting energy expenditure in physically active boys | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Development and validation of new predictive equations for resting energy expenditure in physically active boys Edyta Łuszczki, Paweł Jagielski, Anna Bartosiewicz, Katarzyna Dereń, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2181853/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Mar, 2023 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract Measurement or estimation of resting energy expenditure (REE) should be the first step in determining energy demand in physically active boys. The purpose of this study was to develop and validate new equations for resting energy expenditure in male children and adolescents practicing football. The study was carried out among 184 boys in the validation group and 148 boys in the cross-validation group (mean age 13.20 years and 13.24 years, respectively). The calorimeter and device for assessing body composition by bioelectrical impedance analysis (BIA) were used. Model of multiple regression showed that REE can be predicted in this population with equations 1 or 2. Predictive Eq. 1 had an average error of 51 ± 199 kcal and predictive Eq. 2–39 ± 193 kcal. Cohen's d coefficient was 0.2, which confirms the small difference. The bias was 4.7% and 3.9%, respectively. The accuracy was 61.2% in the population for predictive Eqs. 1 and 66.2% for predictive Eq. 2. Therefore, the new formulas developed and validated in this study are recommended for the estimation of REE in physically active boys, when the use of in-direct calorimetry is not feasible or available. Health sciences/Health care/Nutrition Health sciences/Health care/Public health/Epidemiology Figures Figure 1 Introduction The energy expenditure values among young people were developed in 2008, and the compilation provides values of metabolic equivalents (MET) for each type of activity, averaging them for age, sex, and other characteristics [ 1 ]. In some cases, adult values have been considered [ 2 ]. Nevertheless, children's physical and mental characteristics vary compared to adults. Physical activity (PA) is crucial for both the health and correct development of children and adolescents. Therefore, from a public health perspective, increasing PA is one of the most important ways to improve overall health [ 3 ]. Higher levels of physical fitness in childhood and adolescence have been associated with a healthier cardiovascular profile in adulthood, as well as a lower risk of premature death [ 4 ]. The proportions of the body compartments: fat mass (FM) and fat free mass (FFM) change during growth and development. Consequently, these components can be determinant when analyzing the physical fitness of children and adolescents [ 5 ]. In this context, physical fitness has been documented as a key determinant of a healthy lifestyle [ 6 ]. Research shows that of the top ten extracurricular sports among children and adolescents, the most popular is football. It is followed by swimming, running, and cycling [ 7 ], and boys and girls are very different in this respect. In Poland and Europe, the popularity of sports schools for the young generation is growing. Students in sports championship schools attend at least 16 hours of sports activities per week. Many sports championship schools, which educate in team disciplines, such as football or volleyball, field teams of outstanding students in league competitions. Appropriate energy intake in the diet is a key for the population of young athletes. Most of these children eat at school, some live in a boarding school, and eat all their meals there. The measurement or estimation of resting energy expenditure (REE) should be the first step in determining the energy demand of these children and adolescents [ 8 ]. REE constitutes 60–70% of the total energy requirement for most people [ 9 ]. However, the REE level should be set accordingly as a valuable tool in the development of food rations, the menu in schools, and nutrition plans. It improves athletic performance and prevents weight loss in children and adolescents. Energy balance is integral for boys to sustain optimal growth and development, with additional nutritional intake required to offset the increased energy cost of training. Studies have investigated the nutritional intake in adult professional football players [ 10 – 11 ]. However, a relatively limited number of studies have investigated the nutritional intake of children and adolescents football players [ 12 ]. The findings of these studies have presented suboptimal energy intake relative to estimates of energy expenditure [ 12 ]. In one study investigating the dietary and activity regimes of adolescent soccer players in the UK, the mean daily energy deficit of -3299 ± 729 kJ was observed [ 13 ]. Therefore, correctly indicating the REE and then increasing it by the expenditure of physical activity will correctly estimate the total daily energy demand. Despite REE can be calculated very precisely using laboratory methods such as indirect calorimetry (IC), for children and adolescents in sports predictive equations from the literature are also used. There are many equations created for children in the literature, however, these patterns were not formulated for physically active boys or young athletes. In addition, the method for evaluating REE is time-consuming, cost-prohibited, and requires sophisticated tools; therefore, sports trainers, teachers, or people related to sports use the predictive equations developed. The creation of new predictive equations will be very useful for small sports clubs where, due to lack of funds and equipment, the use of IC is not possible. The researchers noticed that the comparison of ready-to-use formulas to calculate REE in physically active people with IC shows that several of these equations underestimate or overestimate REE by up to 300 kcal [ 14 ]. The literature shows that this bias may be caused by differences in the metabolic activity of FM and FFM because most of these predictive equations are not based on body composition, but on total body weight [ 15 ]. It is widely known that the accuracy of REE predictive formulas is characteristic of the population for which they were formulated, and should not be used for groups different from what was originally intended [ 16 ]. The results of the literature agree that REE is elevated with excess weight [ 17 , 18 ]. Observation presents the FFM as the strongest indicator that affects the REE [ 19 ]. FM is also a component of body composition, which could affect REE, but the research is inconclusive [ 20 , 21 ]. In 2020, we conducted a study that compares the precision of REE known from previous publications in the literature with the values derived from IC measurement among boys who play football. Our results showed that most ready-made formulas underestimate REE, which can be a problem, especially if we define the total energy demand of children, who, due to intensive physiological development and regular training, require more calories per day. In conclusion, to date, the best predictive equation has not been created for boys activity undertaking intense physical activity [ 22 ]. As a result of the still low availability and high cost of IC devices, further research was needed that could allow the formation of a special formula for physically active boys. In 2020, while testing the accuracy of commonly used formulas, we developed new predictive equations. However, it was not published due to a lack of validation. Therefore, the objective of this study was to validate the new predictive equations for active male children and adolescents training football. Results Characteristics of the study group 184 boys partake in the study when the new predictive equations were developed and 148 subjects in 2022 to validate the new formulas. The descriptive characteristics of the study group are presented in Table 1 . Table 1 Anthropometric characteristics of the study participants. Me – median; SD – standard deviation; p – t Student test; BMI – body mass index; FFM- fat free mass. Variables Validation group (2020 year) N = 184 Cross- validation group (2022 year) N = 148 Mean SD Me Min Max Mean SD Me Min Max p Age [years] 13.20 2.16 13.00 10.00 16.00 13.24 1.75 13.24 10.01 16.43 0.8289 Height [cm] 162.91 14.90 166.00 132.00 191.00 161.01 13.51 160.50 128.00 193.00 0.2310 Weight [kg] 52.37 14.44 53.85 24.80 102.20 50.84 13.38 49.65 26.92 87.88 0.3233 BMI [kg/m 2 ] 19.27 2.57 19.05 13.60 28.00 19.23 2.42 19.11 13.79 26.53 0.8259 Fat % 17.58 3.76 16.90 8.80 34.00 18.38 4.03 17.68 10.40 40.80 0.0597 FFM [kg] 43.17 12.02 44.40 20.60 79.60 41.52 11.21 39.70 22.70 72.40 0.2017 Me – median; SD – standard deviation; p – t Student test; BMI – body mass index; FFM- fat free mass. The findings Model of multiple regression showed that REE can be predicted in this population with equations 1 or 2, as follows: Predictive Eq. 1: REE (kcal/d) = -196.49 + 9.25 * Height (cm) + 10.20 * Weight (kg) (R = 0.84, p < 0.001; bias = 0, p = 0.93) Predictive Eq. 2: REE (kcal/d) = 359.45–23.69* Age (years) + 5.64 * Height (cm) + 20.36 * FFM (kg)* (R = 0.86, p < 0.001; bias = 0, p = 0.9992) For the first, the following data were used: height and weight (basic anthropometric data), and for the second, age, height, and fat free mass (body mass composition data). The R correlation coefficient indicates that there is a strong correlation between the results obtained from the developed predictive equation and the results measured by the indirect calorimetry method. REE calculated by indirect calorimetry in the first group averaged 1844 ± 328 kcal and in the second group 1760 ± 357 kcal. The cross-validation group consisted of 148 boys. The results showed that both Eq. 1 and Eq. 2 had a very low error in relation to the REE measured with the IC. Predictive Eq. 1 had an average error of 51 ± 199 kcal and predictive Eq. 2–39 ± 193 kcal. Cohen's d coefficient was 0.2, which confirms the small difference. The bias was 4.7% and 3.9%, respectively. These values are a limit value that does not exceed 10% of the error; therefore, predictive Eqs. 1 and 2 seem to be appropriate to be used in young sports people. The accuracy of the prediction was on a very high level in both two equations. The accuracy was 61.2% in the population for predictive Eqs. 1 and 66.2% for predictive Eq. 2. This means that both equations are consistent (with an error of ± 10%) for more than 60% of the population. Accuracy of the predictive models is critical as it determines the quality of their predictions that form the scientific evidence. In our study 61.2% and 66.2% represent a high accuracy (Table 2 ). Table 2 Validity of the resting energy expenditure. REE (kcal/d) T - test Bias kcal/d LLA kcal/d ULA kcal/d Bias (%) r p-value R 2 p-value (linear regression) CCC Prediction Mean SD p-value Mean SD Mean SD (correlation) Accurate % Under % Over % REE validation group 1844 328 100 Harris Benedict 1513 256 < 0.0001 -332 175 -675 11 -17.5 8.0 0.84 < 0.0001 0.72 < 0.0001 0.50 14.7 84.8 0.5 Formula 1 Formula 2 1844 1844 279 167 0.9345 0.9992 0 0 172 167 -337 -327 337 327 0.8 0.8 9.0 9.1 0.84 0.86 < 0.0001 < 0.0001 0.73 0.74 < 0.0001 < 0.0001 0.84 0.85 73.4 76.0 10.9 10.4 15.7 13.7 REE cross-validation group 1760 357 100 Harris Benedict 1477 236 < 0.0001 -283 204 -117 684 -14.9 9.7 0.84 < 0.0001 0.70 < 0.0001 0.53 27.2 71.4 1.4 Formula 1 Formula 2 1811 1799 256 266 0.0022* 0.0154** 51 39 199 193 -441 -417 339 339 4.7 3.9 12.5 12.1 0.84 0.85 < 0.0001 < 0.0001 0.70 0.72 < 0.0001 < 0.0001 0.78 0.81 61.2 66.2 8.8 8.8 29.9 25.0 REE - measured resting metabolic rate by indirect calorimetry; SD – standard deviation; Bias - difference between the predicted value and the measured REE; LLA - lower limit of agreement; ULA - upper limit of agreement; bias% - bias in %; CCC - concordance correlation coefficient; Accurate % - percentage of subjects in which the error of the predictive equation was within 10% of the measured value; Under % - percentage of subjects underestimated by the predictive equation with an error > 10% of the measured value; Over % - percentage of subjects overestimated by the predictive equation with an error > 10% of the measured value; *Cohen's d = 0.26; ** Cohen's d = 0.20 The mean value of the differences between measured and predicted REE (bias) is indicated by the solid line in the Fig. 1 . The dashed lines delimit the 95% confidence interval. All regression lines were statistically significant at p < 0.0001, indicating a systematic bias. When considering the Harris-Benedict predictive equation, it appeared to underestimate the energy demand in two groups. In the first group, the error is an average of -332 ± 175 kcal per day (-17.5% ± 8.0 bias). In the second group, it was − 283 ± 204 kcal (-14.9% ± 9.7 bias), respectively. Discussion In the present study, two new specific equations were developed and validated to predict REE in physically active male children and adolescents. A correct estimate of resting energy expenditure is essential for nutritional management and calculates total energy. When IC measurement is not feasible or available, ready formulas became a valuable tool for estimating REE. However, the optimal predictive accuracy of these equations is obtained when used in subjects with the same characteristics as those in whom the equations were developed [ 23 ]. To our knowledge, this is the first study available in the literature on the development and validation of new predictive equations to estimate REE in healthy boys who practice sports regularly. This is a very important issue because optimizing energy consumption is of fundamental importance in the population of young athletes for whom, in addition to energy expenditure associated with the development of the body, the energy demand due to intense physical activity also increases. The mean REE value for physically active boys was 1844 kcal / day at the start of the study and 1760 kcal in the cross-validation group. It seems to be representative of other active male children and adults examined so far. The literature presented that among active men, REE values were 1858 kcal/day, 1788 kcal/day, 1808 kcal/day [ 24 – 26 ] and 1834 kcal/day among 10 footballers [ 27 ]. According to our previous study, we found that the predictive equation for boys who are physically active regularly has not been developed to date. Although the use of the Institute of Medicine of the National Academies (IMNA) predictive equation gave the smallest error in the REE estimates, this equation and all the predictive formulas used in these studies underestimated the REE of children and adolescents. The mean error ranged from 477 kcal / d for the Maffeis predictive equation to 182 kcal / d for the IMNA predictive equation [ 22 ]. Due to the still low availability of IC devices, we decided that further research is needed that will allow us to generate special equations for physically active boys. In our study, we identified appropriate predictive equations for the population of physically active boys who play male football. A linear regression analysis was performed using a formula to predict new resting metabolism, thus obtaining two new predictive equations for REE in the study group. It is well known that predictive formulas cannot replace REE measurements by indirect calorimetry, but the large, homogeneous group in our study allows us to conclude that these predictive equations can be used among young male football players. The results of the study can be applied with caution to similar groups of the population, taking into account the limitations of this study and the factors that affect the REE of athletes. The first prediction equation, based on anthropometric parameters (body weight and height), is easier to use by dietitians and physicians during medical examination because it only needs a typical scale and growth meter/stadiometer. The second equation, based on body composition (FFM), age and height, is more population-specific than the predictive Eq. 1 because in most studies FFM was the main significant determinant of REE in the population [ 28 – 30 ]. However, it involves specific equipment and more time to measure body composition. Nevertheless, in a similar random sample of 148 boys (cross-validation group), both new equations predicted REE with a bias of 4.7% and 3.9% and were accurate for 61.2% (Eqs. 1) and 66.2% of the population. Furthermore, when an external validation was performed in an independent group of physically active boys, the REE estimated by both equations was significantly different from the measured REE, but the prediction was very high and similar to different studies that validated new predictive equations [ 31 ]. In the validation cohort, the two new equations present the same small mean difference and a similar SD of the differences between measured and predictive REE, although FFM has been shown to be the best predictor of REE in many studies. In the literature, it is observed that FFM explains REE better compared to body weight. Differences in REE are known to be related to FFM, but genetics could also explain the difference in REE between populations. Therefore, the predictive equations for REE based on body composition are generally population-specific and therefore should be more appropriate [ 32 ]. Together with age and gender, physical activity has a significant influence on FFM. The issue of body composition and the influences of physical activity is quite different from REE. However, there is limited evidence on the evaluation of body composition and its dynamic changes in children and adolescents who train sports regularly [ 33 ]. Strengths and Limitations The study was the first known to develop and validate REE in physically active boys and represents two population groups, the first (for which two prediction equations are developed) and the cross-validation group. This is the first known study to create new equations to estimate REE in boys who play football regularly. Groups 1 and 2 were relatively large and representative. The results should be considered to estimate REE only in a specific population of football, boys, Caucasian race, and at a specific age of 10–16 years. The group consisted of young boys who played sport regularly; therefore, our results could be confounded by overweight, obesity, and unintentional weight gain, therefore, our findings could not be generalized. The age range of the subjects in the experiment is from 10.01–16.43 years old, and their hydration status are always changing with their maturations, and it could influence on fat free mass measured by BIA. Conclusions Our study allowed us to collect data to develop new predictive equations for male children and adolescents who play football. This is very important because REE is an essential element in evaluating and determining the diet of boys with high physical activity in the context of balancing their daily energy expenditure. It affects not only the maintenance of normal body weight composition and is related to the proper development and maintenance of health, but also the maintenance of optimal physical fitness in boys. Most ready-made predictive equations underestimate REE, which can be a problem, especially if we define the total energy demand of children, who due to intensive physiological development and regular training, require more calories per day. Therefore, the new predictive equations could be more appropriate for predicting REE in this population. These predictive equations can be very useful for small sports clubs where, due to lack of funds and equipment, the use of IC is not possible. Methods Subjects and new predictive equations A detailed description of the group and the methodology has already been published [ 22 ]. In September 2021, with the start of school classes without restrictions and online classes, invitations were sent to all Sports Championship School directors (13 schools) in the Podkarpackie Voivodeship (south-eastern Poland). Of the six schools that agreed, an invitation was sent to all parents or guardians of boys attending these schools. The inclusion criteria were as follows: male, playing football, regularly training, age between 9 and 16, training for minimum 2 years, training 3 times a day / match once a week, and with the consent of parents / guardians to participate in the study. 183 parents/guardians agreed to participate in the study. Of these, 35 children did not complete the study due to injury or discontinued the study for any reason. All subjects were healthy. In the last 6 months, no weight loss or infection with increased fever were reported. They did not use any drugs. Parents / guardians and participants gave their informed consent to participate in the study. Finally, the study group consisted of 148 boys aged 9 to 16 years. Assessments All examinations were conducted in the laboratories of Rzeszow University by experienced researchers between January and May 2022. Participants came forward to the laboratory from 7:00 to 10:00 a.m. at controlled temperature. The temperature at the location of the measurements was controlled (22–25 C). Anthropometric Measurements, Body Composition Before taking the measurements, the participants received precise information about the course of the study. To minimize the risk of bias in body composition analysis, the bladder was emptied. Height measurement was made with a height meter (Seca 213) with an accuracy of 0.5 cm. The boys took off their shoes and stood with their backs to the stadiometer in an upright position. The average of three measurements was used for analysis. Body composition was measured using bioelectrical impedance analysis (BIA, 6.25 kHz/50 kHz, 90 µA). The TANITA MC-980 MA (Tanita, Tokyo, Japan) was used. The analyzer is equipped with 8 electrodes, of which 4 are built into the platform, while the others are placed in the handles. Participants were asked to remove footwear and socks. Measurements were made in underwear, standing in designated places on the platform. According to the Tanita MC-980 PLUS MA manual, accurate measurement requires setting up the machine as level as possible. The adjustable feet were rotated in 4 positions so that the bubble of the level indicator was in the middle. Participants stood upright on the platform with their legs extended, placing their feet so that they touched the front and rear electrodes, ensuring that the weight was evenly distributed on both feet. The person examined held handles in their hands that were taken from the body at an angle of 35–40. Resting Energy Expenditure Resting energy expenditure (kcal/day) was measured using a Cosmed Quark RMR indirect calorimeter (Rome, Italy) with a ventilated canopy hood and a disposable antibacterial filter. Full service of all measurement instruments was performed prior to the study, and daily calibration was performed according to the manufacturer's instructions. The evidence-based protocol for measurement of resting energy expenditure by indirect calorimetry was adopted in the study and clearly discussed (Table 3 ). Table 3 Evidence-based guidelines for measurement of resting metabolic rate with indirect calorimetry Criteria Guidelines for measurement Study group recommendation Fasting (thermic effect of food) Minimum fast 5 hours after meals or snacks (Grade II), 4 hours after small meal if longer fast is clinically inappropriate (Grade II) All recommendations concerning preparations for the study were outlined, including: having rest minimum for 20 minutes, abstention from nicotine for minimum 2 hours, refraining from the consumption of meals 12 hours before the test, refraining from drinking beverages with caffeine and alcohol content for the last 48 hours before the test, as well as refraining from participation in a physical activity for the previous 14 hours. The method of conducting the study was explained in detail and each study participant had the opportunity to visit the test rooms beforehand and familiarize themselves with the equipment so that it did not raise concerns or cause anxiety in the researched group. Alcohol ingestion Minimum abstention from alcohol for 2 hours (Grade III) Nicotine ingestion Minimum abstention from nicotine for 2 hours (Grade II) Caffeine ingestion Minimum abstention from caffeine for 4 hours (Grade II) Rest periods Rest 10–20 minutes (Grade III) Physical activity restriction Minimum abstention from moderate aerobic or anaerobic exercise for 2 hours before test (Grade II), for vigorous resistance exercise abstention of at least 14 hours (Grade III) Environmental conditions Allow a room temperature of 20°C-25°C (68°F-77°F) (Grade III) Ensure each individual is physically comfortable with measurement position during the test and repeated measures are in the same reclined position (Grade V) The rooms had a controlled temperature between 22 to 25°C. In addition, each participant had the opportunity to acclimatize in the environment by lying flat for 30 minutes. Gas collection devices Use rigorous adherence to prevent air leaks (Grade III) Further studies comparing modern gas collection devices are needed in healthy and clinical populations (Grade V) With these devices, exhaled gas was captured by a canopy (ventilated hood system) or a face mask connected to oxygen and carbon dioxide analyzers mounted on a metabolic cart. This is essential for correct measurement. Steady-state conditions and measurement interval Discard initial 5 minutes. Then achieve a 5-minute period with 10% CV b for VO 2 c and VCO 2 d (Grade II) We use a 20-minute protocol in which the first 5 minutes of data are discarded and the remaining 15 minutes of data have a coefficient of variation of no more than 10%. No. of measures/24 hours Achieve steady state and one measure is adequate; if not, two to three nonconsecutive measures improve accuracy (Grade II) 1 measure/24 hours Repeated measures (daily to monthly variation) Repeated measures vary 3%-5% over 24 hours (Grade II) and vary up to 10% over weeks to months (Grade II) - Respiratory quotient (RQ) RQ measures 0.70 or 1 suggest protocol violations or inaccurate gas measurement (Grade II) The Quark RMR is a state-of-the-art metabolic system designed for accurate measurement of Resting Energy Expenditure (REE) and respiratory ratio (R), in a non-invasive way, through the measurement of oxygen consumption (VO2) and carbon dioxide production (VCO2) together with other ventilatory parameters. RQ was between 0.7 and 1.0. a Grade I - strong, consistent evidence; Grade II - somewhat weaker evidence and disagreement among authors may exist; Grade III - limited design quality; Grade IV - professional opinion only, no clinical trials; Grade V - no available studies. b CV - coefficient of variation (standard deviation [mean of individual replicate measures] x 100). c VO 2 - oxygen consumption. d VCO 2 - carbon dioxide production. The following statistical methods were used: Descriptive statistics are presented in Table 1 (number (n), Me - median and standard deviation (SD)). Shapiro-Wilk test, allowed to test the compliance of the tested variable with the normal distribution and the Student's t-test or Mann-Whitney U test was used to check the differences between both groups for the analyzed quantitative or ordinal variables. The bias was calculated as the mean difference between the predicted value and the measured REE. The agreement between measured and estimated REE values was evaluated by determining the bias in absolute values and as a percentage of the measured value and the corresponding limit of agreement (upper limit of agreement (ULA) = bias + 1.96 × SD; lower limit of agreement (LLA) = bias − 1.96 × SD). Additionally, Pearson’s product-moment correlation coefficient (r) and the coefficient of determination (R2) were calculated. The concordance correlation coefficient (CCC) as a measure of agreement was also determined. The percentage of participants whose predicted REE value was within ± 10% of the measured REE was used as a precision measure. The heteroscedasticity was tested using the Bland–Altman method. The plots presented the difference between predicted and measured REE versus the mean of predicted and measured REE. The PS IMAGO PRO 8.0 software (IBM SPSS STA-TISTICS 28) and the MedCalc software were used. The adopted level of statistical significance was p < 0.05. Ethical consent This research project obtains acceptance of Institutional Bioethics Committee at the University of Rzeszów (Resolution No. 2/01/2019). Declarations Author Contributions: Conceptualization, Edyta Łuszczki and Paweł Jagielski; Data curation, Maciej Kuchciak, Łukasz Oleksy and Paweł Jagielski; Formal analysis, Łukasz Oleksy, Artur Stolarczyk, Piotr Matłosz and Paweł Jagielski; Investigation, Edyta Łuszczki, Anna Bartosiewicz, Katarzyna Dereń, Piotr Matłosz and Maciej Kuchciak; Methodology, Edyta Łuszczki, Anna Bartosiewicz, Piotr Matłosz and Paweł Jagielski; Project administration, Edyta Łuszczki; Resources, Artur Stolarczyk and Katarzyna Dereń; Supervision, Edyta Łuszczki; Writing – original draft, Edyta Łuszczki, Anna Bartosiewicz and Piotr Matłosz; Writing – review & editing, Edyta Łuszczki and Artur Mazur. Funding: The study was carried out as part of the project supported by the National Science Centre in Poland: “MINIATURA 5” (No. 2021/05/X/NZ7/01187; to E.Ł.). Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Rzeszów University Bioethics Commission (No. 2/01/2019). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are not publicly available due to confidentiality reasons. These data are available on request from the corresponding author. Conflicts of Interest: The authors declare that they have no conflict of interest. References Ridley, K., Ainsworth, B.E., Olds, T.S. Development of a Compendium of Energy Expenditures for Youth: A second update of codes and MET values. Int. J. Behav. Nutr. Phys. Act. 5 , 45 (2008). Ainsworth, B. et al. Compendium of Physical Activities. Med. Sci. Sports Exerc. 43 , 1575–1581 (2011). Herbert, J. et al. Objectively Assessed Physical Activity of Preschool-Aged Children from Urban Areas. Int. J. Environ. Res. 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Prediction of resting energy expenditure from fat-free mass and fat mass. Am. J. Clin. Nutr. 56 , 848–856 (1992). Segal, K.R., Gutin, B., Albu, J., Pi-Sunyer, F.X. Thermal effects of food and exercise in lean and obese men of similar lean body mass. Am. J. Physiol, 252 , E110–E117 (1987). Łuszczki, E. et al. Resting Energy Expenditure of Physically Active Boys in Southeastern Poland—The Accuracy and Validity of Predictive Equations. Metabolites, 10 , 493; https://doi.org/10.3390/metabo10120493 (2020). Frankenfield, D., Roth-Yousey, L., Compher, C. Comparison of predictive equations for resting metabolic rate in healthy nonobese and obese adults: a systematic review. J. Am. Diet. Assoc. 105 , 775e89 (2005). Poehlman, E.T., Melby, C.L., Badylak, S.F. Resting metabolic rate and postprandial thermogenesis in highly trained and un-trained males. Am. J. Clin. Nutr, 47 , 793–798 (1988). Horton, T.J., Geissler, C. Effect of habitual exercise on daily energy expenditure and metabolic rate during standardized activity. Am. J. Clin. Nutr. 59 , 13–19 (1994). Schulz, L.O., Nyomba, B.L., Alger, S., Anderson, T.E., Ravussin, E. Effect of endurance training on sedentary energy expenditure measured in a respiratory chamber. Am. J. Physiol. Metab, 260 , E257–E261 (1991). Lawrence, J., Lee, H-M., Kim, J-H., Kim, E-K. Variability in results from predicted resting energy needs as compared to measured resting energy expenditure in Korean children. Nutr. Res. 29 , 777–783 (2009). Goran, M.I., Kaskoun, M., Johnson, R. Determinants of resting energy expenditure in young children. J. Pediatr, 125 , 362–367 (1994). DeLany, J.P., Bray, G.A., Harsha, D.W., Volaufova, J. Energy expenditure in preadolescent African American and white boys and girls: the Baton Rouge Children’s Study. Am. J. Clin. Nutr. 75 , 705–713 (2002). McDuffie, J.R. et al. Prediction equations for resting energy expenditure in overweight and normal-weight black and white children. Am. J. Clin. Nutr. 80 , 365–373 (2004). Lazzer, S., Agosti, F., De Col, A., Sartorio, A. Development and cross-validation of prediction equations for estimating resting energy expenditure in severely obese Caucasian children and adolescents. Br. J. Nutr. 96 ( 5 ), 973–9 (2006). EFSA Panel on Dietetic Products: Scientific opinion on dietary reference values for energy. EFSA J. 11 , 17–19 (2011). Meleleo, D. et al. Evaluation of body composition with bioimpedence. A comparison between athletic and non-athletic children. Eur. J. Sport Sci. 17 , 710–719 (2017). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Mar, 2023 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Major revision 06 Jan, 2023 Reviews received at journal 19 Dec, 2022 Reviewers agreed at journal 09 Dec, 2022 Reviewers agreed at journal 09 Dec, 2022 Reviewers invited by journal 09 Dec, 2022 Editor assigned by journal 09 Dec, 2022 Editor invited by journal 20 Oct, 2022 Submission checks completed at journal 20 Oct, 2022 First submitted to journal 19 Oct, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2181853","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":145691261,"identity":"b1b7311a-ee1c-42e0-a7a2-c8d878ac151c","order_by":0,"name":"Edyta Łuszczki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIie3QMQrCMBSA4VcK6fKwa0r1DpFAlha8Srvo4iaIg8iTgo6ubh6iF6gEOnkUh7o5iQ3SwSXWTSQ/PEIgHyQBcLl+seC1CBaQWX0zZCd+R7Dqtl5PAjzrSUIf5e2+3shBdFUNrJKc4q2dRAUqzmumWDyXR7jMchqe7URoVDAmTA0Bb6dz4rmdTDTKpj2WsujSkkcPInwU/ExCMY4toR6Ea7aMqM4kw+kCsnomd5/eEu6L0vzY+LTXJTTrZHSIi8pK3svaYfwL0N31e+JyuVz/3ROXYjqhnDyP+QAAAABJRU5ErkJggg==","orcid":"","institution":"Medical College of Rzeszów University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Edyta","middleName":"","lastName":"Łuszczki","suffix":""},{"id":145691262,"identity":"c33ee1c2-09cc-4114-bc6a-6095b46fef3e","order_by":1,"name":"Paweł Jagielski","email":"","orcid":"","institution":"Jagiellonian University Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Paweł","middleName":"","lastName":"Jagielski","suffix":""},{"id":145691263,"identity":"477b9462-1e70-4c63-957a-0a06d516b679","order_by":2,"name":"Anna Bartosiewicz","email":"","orcid":"","institution":"Medical College of Rzeszów University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Bartosiewicz","suffix":""},{"id":145691264,"identity":"d76ef7cd-6c22-4247-a2b6-5ed4f829423a","order_by":3,"name":"Katarzyna Dereń","email":"","orcid":"","institution":"Medical College of Rzeszów University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Katarzyna","middleName":"","lastName":"Dereń","suffix":""},{"id":145691265,"identity":"7d1fe6ae-1762-4207-b04b-980ac50d5fd7","order_by":4,"name":"Piotr Matłosz","email":"","orcid":"","institution":"Medical College of Rzeszów University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Piotr","middleName":"","lastName":"Matłosz","suffix":""},{"id":145691267,"identity":"4e8b001e-c945-4da6-93e7-5ced14ec5425","order_by":5,"name":"Maciej Kuchciak","email":"","orcid":"","institution":"Medical College of Rzeszów University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Maciej","middleName":"","lastName":"Kuchciak","suffix":""},{"id":145691268,"identity":"8973a6e6-0ca1-405b-92a8-723aeb0a53ae","order_by":6,"name":"Łukasz Oleksy","email":"","orcid":"","institution":"Jagiellonian University Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Łukasz","middleName":"","lastName":"Oleksy","suffix":""},{"id":145691271,"identity":"eeba14fe-b7d8-445d-b64e-94794cac1c3c","order_by":7,"name":"Artur Stolarczyk","email":"","orcid":"","institution":"Medical University of Warsaw","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Artur","middleName":"","lastName":"Stolarczyk","suffix":""},{"id":145691274,"identity":"055bcb20-3b61-4052-b9df-46bac716f8e8","order_by":8,"name":"Artur Mazur","email":"","orcid":"","institution":"Medical College of Rzeszow University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Artur","middleName":"","lastName":"Mazur","suffix":""}],"badges":[],"createdAt":"2022-10-19 08:14:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2181853/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2181853/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-023-31661-1","type":"published","date":"2023-03-20T20:05:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":28245219,"identity":"84b15dd4-1ef4-4ab1-ba98-60276263edbe","added_by":"auto","created_at":"2022-10-25 19:06:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":130565,"visible":true,"origin":"","legend":"\u003cp\u003eAgreement between measured and predicted resting energy expenditure (REE) using the equations of (A) Harris Benedict, (B) Formula 1, (C) Formula 2 – Bland Altman plot.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2181853/v1/e7b17154ac795525c34098b5.png"},{"id":44723125,"identity":"3f9b8a3c-c8ed-4fdb-b50e-97cc806f8330","added_by":"auto","created_at":"2023-10-16 20:13:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":515547,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2181853/v1/3cd2a6c9-fe44-4830-bca2-20556c713ece.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of new predictive equations for resting energy expenditure in physically active boys","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe energy expenditure values among young people were developed in 2008, and the compilation provides values of metabolic equivalents (MET) for each type of activity, averaging them for age, sex, and other characteristics [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In some cases, adult values have been considered [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Nevertheless, children's physical and mental characteristics vary compared to adults. Physical activity (PA) is crucial for both the health and correct development of children and adolescents. Therefore, from a public health perspective, increasing PA is one of the most important ways to improve overall health [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Higher levels of physical fitness in childhood and adolescence have been associated with a healthier cardiovascular profile in adulthood, as well as a lower risk of premature death [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe proportions of the body compartments: fat mass (FM) and fat free mass (FFM) change during growth and development. Consequently, these components can be determinant when analyzing the physical fitness of children and adolescents [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In this context, physical fitness has been documented as a key determinant of a healthy lifestyle [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Research shows that of the top ten extracurricular sports among children and adolescents, the most popular is football. It is followed by swimming, running, and cycling [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and boys and girls are very different in this respect.\u003c/p\u003e \u003cp\u003eIn Poland and Europe, the popularity of sports schools for the young generation is growing. Students in sports championship schools attend at least 16 hours of sports activities per week. Many sports championship schools, which educate in team disciplines, such as football or volleyball, field teams of outstanding students in league competitions. Appropriate energy intake in the diet is a key for the population of young athletes. Most of these children eat at school, some live in a boarding school, and eat all their meals there. The measurement or estimation of resting energy expenditure (REE) should be the first step in determining the energy demand of these children and adolescents [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. REE constitutes 60\u0026ndash;70% of the total energy requirement for most people [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the REE level should be set accordingly as a valuable tool in the development of food rations, the menu in schools, and nutrition plans. It improves athletic performance and prevents weight loss in children and adolescents.\u003c/p\u003e \u003cp\u003eEnergy balance is integral for boys to sustain optimal growth and development, with additional nutritional intake required to offset the increased energy cost of training. Studies have investigated the nutritional intake in adult professional football players [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, a relatively limited number of studies have investigated the nutritional intake of children and adolescents football players [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The findings of these studies have presented suboptimal energy intake relative to estimates of energy expenditure [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In one study investigating the dietary and activity regimes of adolescent soccer players in the UK, the mean daily energy deficit of -3299\u0026thinsp;\u0026plusmn;\u0026thinsp;729 kJ was observed [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, correctly indicating the REE and then increasing it by the expenditure of physical activity will correctly estimate the total daily energy demand.\u003c/p\u003e \u003cp\u003eDespite REE can be calculated very precisely using laboratory methods such as indirect calorimetry (IC), for children and adolescents in sports predictive equations from the literature are also used. There are many equations created for children in the literature, however, these patterns were not formulated for physically active boys or young athletes. In addition, the method for evaluating REE is time-consuming, cost-prohibited, and requires sophisticated tools; therefore, sports trainers, teachers, or people related to sports use the predictive equations developed. The creation of new predictive equations will be very useful for small sports clubs where, due to lack of funds and equipment, the use of IC is not possible. The researchers noticed that the comparison of ready-to-use formulas to calculate REE in physically active people with IC shows that several of these equations underestimate or overestimate REE by up to 300 kcal [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The literature shows that this bias may be caused by differences in the metabolic activity of FM and FFM because most of these predictive equations are not based on body composition, but on total body weight [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It is widely known that the accuracy of REE predictive formulas is characteristic of the population for which they were formulated, and should not be used for groups different from what was originally intended [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The results of the literature agree that REE is elevated with excess weight [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Observation presents the FFM as the strongest indicator that affects the REE [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. FM is also a component of body composition, which could affect REE, but the research is inconclusive [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn 2020, we conducted a study that compares the precision of REE known from previous publications in the literature with the values derived from IC measurement among boys who play football. Our results showed that most ready-made formulas underestimate REE, which can be a problem, especially if we define the total energy demand of children, who, due to intensive physiological development and regular training, require more calories per day. In conclusion, to date, the best predictive equation has not been created for boys activity undertaking intense physical activity [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. As a result of the still low availability and high cost of IC devices, further research was needed that could allow the formation of a special formula for physically active boys. In 2020, while testing the accuracy of commonly used formulas, we developed new predictive equations. However, it was not published due to a lack of validation. Therefore, the objective of this study was to validate the new predictive equations for active male children and adolescents training football.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the study group\u003c/h2\u003e \u003cp\u003e184 boys partake in the study when the new predictive equations were developed and 148 subjects in 2022 to validate the new formulas. The descriptive characteristics of the study group are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnthropometric characteristics of the study participants. Me \u0026ndash; median; SD \u0026ndash; standard deviation; p \u0026ndash; t Student test; BMI \u0026ndash; body mass index; FFM- fat free mass.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eValidation group\u003c/p\u003e \u003cp\u003e(2020 year)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;184\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c12\" namest=\"c7\"\u003e \u003cp\u003eCross- validation group\u003c/p\u003e \u003cp\u003e(2022 year)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;148\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge [years]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e16.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.8289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeight [cm]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e162.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e166.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e132.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e191.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e161.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e160.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e128.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e193.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.2310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeight [kg]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e102.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e49.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e26.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e87.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.3233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI [kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e19.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e13.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e26.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.8259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFat %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e17.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e40.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.0597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFFM [kg]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e79.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e39.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e22.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e72.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eMe \u0026ndash; median; SD \u0026ndash; standard deviation; p \u0026ndash; t Student test; BMI \u0026ndash; body mass index; FFM- fat free mass.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eThe findings\u003c/h2\u003e \u003cp\u003eModel of multiple regression showed that REE can be predicted in this population with equations 1 or 2, as follows:\u003c/p\u003e \u003cp\u003ePredictive Eq.\u0026nbsp;1:\u003c/p\u003e \u003cp\u003eREE (kcal/d) = -196.49\u0026thinsp;+\u0026thinsp;9.25 * Height (cm)\u0026thinsp;+\u0026thinsp;10.20 * Weight (kg)\u003c/p\u003e \u003cp\u003e(R\u0026thinsp;=\u0026thinsp;0.84, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; bias\u0026thinsp;=\u0026thinsp;0, p\u0026thinsp;=\u0026thinsp;0.93)\u003c/p\u003e \u003cp\u003ePredictive Eq.\u0026nbsp;2:\u003c/p\u003e \u003cp\u003eREE (kcal/d)\u0026thinsp;=\u0026thinsp;359.45\u0026ndash;23.69* Age (years)\u0026thinsp;+\u0026thinsp;5.64 * Height (cm)\u0026thinsp;+\u0026thinsp;20.36 * FFM (kg)*\u003c/p\u003e \u003cp\u003e(R\u0026thinsp;=\u0026thinsp;0.86, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; bias\u0026thinsp;=\u0026thinsp;0, p\u0026thinsp;=\u0026thinsp;0.9992)\u003c/p\u003e \u003cp\u003eFor the first, the following data were used: height and weight (basic anthropometric data), and for the second, age, height, and fat free mass (body mass composition data). The R correlation coefficient indicates that there is a strong correlation between the results obtained from the developed predictive equation and the results measured by the indirect calorimetry method.\u003c/p\u003e \u003cp\u003eREE calculated by indirect calorimetry in the first group averaged 1844\u0026thinsp;\u0026plusmn;\u0026thinsp;328 kcal and in the second group 1760\u0026thinsp;\u0026plusmn;\u0026thinsp;357 kcal.\u003c/p\u003e \u003cp\u003eThe cross-validation group consisted of 148 boys. The results showed that both Eq.\u0026nbsp;1 and Eq.\u0026nbsp;2 had a very low error in relation to the REE measured with the IC. Predictive Eq.\u0026nbsp;1 had an average error of 51\u0026thinsp;\u0026plusmn;\u0026thinsp;199 kcal and predictive Eq.\u0026nbsp;2\u0026ndash;39\u0026thinsp;\u0026plusmn;\u0026thinsp;193 kcal. Cohen's d coefficient was 0.2, which confirms the small difference. The bias was 4.7% and 3.9%, respectively. These values are a limit value that does not exceed 10% of the error; therefore, predictive Eqs.\u0026nbsp;1 and 2 seem to be appropriate to be used in young sports people. The accuracy of the prediction was on a very high level in both two equations. The accuracy was 61.2% in the population for predictive Eqs.\u0026nbsp;1 and 66.2% for predictive Eq.\u0026nbsp;2. This means that both equations are consistent (with an error of \u0026plusmn;\u0026thinsp;10%) for more than 60% of the population. Accuracy of the predictive models is critical as it determines the quality of their predictions that form the scientific evidence. In our study 61.2% and 66.2% represent a high accuracy (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValidity of the resting energy expenditure.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"18\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eREE (kcal/d)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT - test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eBias kcal/d\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLLA\u003c/p\u003e \u003cp\u003ekcal/d\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eULA\u003c/p\u003e \u003cp\u003ekcal/d\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eBias (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003ep-value (linear regression)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eCCC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c18\" namest=\"c16\"\u003e \u003cp\u003ePrediction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(correlation)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eAccurate %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003eUnder %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003eOver\u003c/p\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREE validation group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHarris Benedict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e84.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormula 1\u003c/p\u003e \u003cp\u003eFormula 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1844\u003c/p\u003e \u003cp\u003e1844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e279\u003c/p\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9345\u003c/p\u003e \u003cp\u003e0.9992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e172\u003c/p\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-337\u003c/p\u003e \u003cp\u003e-327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e337\u003c/p\u003e \u003cp\u003e327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e73.4\u003c/p\u003e \u003cp\u003e76.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREE cross-validation group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHarris Benedict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e27.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e71.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormula 1\u003c/p\u003e \u003cp\u003eFormula 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1811\u003c/p\u003e \u003cp\u003e1799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e256\u003c/p\u003e \u003cp\u003e266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0022*\u003c/p\u003e \u003cp\u003e0.0154**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e199\u003c/p\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-441\u003c/p\u003e \u003cp\u003e-417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e339\u003c/p\u003e \u003cp\u003e339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e61.2\u003c/p\u003e \u003cp\u003e66.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e29.9\u003c/p\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"18\"\u003eREE - measured resting metabolic rate by indirect calorimetry;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"18\"\u003eSD \u0026ndash; standard deviation;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"18\"\u003eBias - difference between the predicted value and the measured REE;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"18\"\u003eLLA - lower limit of agreement;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"18\"\u003eULA - upper limit of agreement; bias% - bias in %;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"18\"\u003eCCC - concordance correlation coefficient;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"18\"\u003eAccurate % - percentage of subjects in which the error of the predictive equation was within 10% of the measured value;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"18\"\u003eUnder % - percentage of subjects underestimated by the predictive equation with an error\u0026thinsp;\u0026gt;\u0026thinsp;10% of the measured value;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"18\"\u003eOver % - percentage of subjects overestimated by the predictive equation with an error\u0026thinsp;\u0026gt;\u0026thinsp;10% of the measured value;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"18\"\u003e*Cohen's d\u0026thinsp;=\u0026thinsp;0.26;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"18\"\u003e** Cohen's d\u0026thinsp;=\u0026thinsp;0.20\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe mean value of the differences between measured and predicted REE (bias) is indicated by the solid line in the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The dashed lines delimit the 95% confidence interval. All regression lines were statistically significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, indicating a systematic bias.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen considering the Harris-Benedict predictive equation, it appeared to underestimate the energy demand in two groups. In the first group, the error is an average of -332\u0026thinsp;\u0026plusmn;\u0026thinsp;175 kcal per day (-17.5% \u0026plusmn; 8.0 bias). In the second group, it was \u0026minus;\u0026thinsp;283\u0026thinsp;\u0026plusmn;\u0026thinsp;204 kcal (-14.9% \u0026plusmn; 9.7 bias), respectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, two new specific equations were developed and validated to predict REE in physically active male children and adolescents. A correct estimate of resting energy expenditure is essential for nutritional management and calculates total energy. When IC measurement is not feasible or available, ready formulas became a valuable tool for estimating REE. However, the optimal predictive accuracy of these equations is obtained when used in subjects with the same characteristics as those in whom the equations were developed [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first study available in the literature on the development and validation of new predictive equations to estimate REE in healthy boys who practice sports regularly. This is a very important issue because optimizing energy consumption is of fundamental importance in the population of young athletes for whom, in addition to energy expenditure associated with the development of the body, the energy demand due to intense physical activity also increases.\u003c/p\u003e \u003cp\u003eThe mean REE value for physically active boys was 1844 kcal / day at the start of the study and 1760 kcal in the cross-validation group. It seems to be representative of other active male children and adults examined so far. The literature presented that among active men, REE values were 1858 kcal/day, 1788 kcal/day, 1808 kcal/day [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and 1834 kcal/day among 10 footballers [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to our previous study, we found that the predictive equation for boys who are physically active regularly has not been developed to date. Although the use of the Institute of Medicine of the National Academies (IMNA) predictive equation gave the smallest error in the REE estimates, this equation and all the predictive formulas used in these studies underestimated the REE of children and adolescents. The mean error ranged from 477 kcal / d for the Maffeis predictive equation to 182 kcal / d for the IMNA predictive equation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Due to the still low availability of IC devices, we decided that further research is needed that will allow us to generate special equations for physically active boys.\u003c/p\u003e \u003cp\u003eIn our study, we identified appropriate predictive equations for the population of physically active boys who play male football. A linear regression analysis was performed using a formula to predict new resting metabolism, thus obtaining two new predictive equations for REE in the study group. It is well known that predictive formulas cannot replace REE measurements by indirect calorimetry, but the large, homogeneous group in our study allows us to conclude that these predictive equations can be used among young male football players. The results of the study can be applied with caution to similar groups of the population, taking into account the limitations of this study and the factors that affect the REE of athletes.\u003c/p\u003e \u003cp\u003eThe first prediction equation, based on anthropometric parameters (body weight and height), is easier to use by dietitians and physicians during medical examination because it only needs a typical scale and growth meter/stadiometer. The second equation, based on body composition (FFM), age and height, is more population-specific than the predictive Eq.\u0026nbsp;1 because in most studies FFM was the main significant determinant of REE in the population [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, it involves specific equipment and more time to measure body composition.\u003c/p\u003e \u003cp\u003eNevertheless, in a similar random sample of 148 boys (cross-validation group), both new equations predicted REE with a bias of 4.7% and 3.9% and were accurate for 61.2% (Eqs.\u0026nbsp;1) and 66.2% of the population. Furthermore, when an external validation was performed in an independent group of physically active boys, the REE estimated by both equations was significantly different from the measured REE, but the prediction was very high and similar to different studies that validated new predictive equations [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In the validation cohort, the two new equations present the same small mean difference and a similar SD of the differences between measured and predictive REE, although FFM has been shown to be the best predictor of REE in many studies.\u003c/p\u003e \u003cp\u003eIn the literature, it is observed that FFM explains REE better compared to body weight. Differences in REE are known to be related to FFM, but genetics could also explain the difference in REE between populations. Therefore, the predictive equations for REE based on body composition are generally population-specific and therefore should be more appropriate [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Together with age and gender, physical activity has a significant influence on FFM. The issue of body composition and the influences of physical activity is quite different from REE. However, there is limited evidence on the evaluation of body composition and its dynamic changes in children and adolescents who train sports regularly [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eThe study was the first known to develop and validate REE in physically active boys and represents two population groups, the first (for which two prediction equations are developed) and the cross-validation group. This is the first known study to create new equations to estimate REE in boys who play football regularly. Groups 1 and 2 were relatively large and representative.\u003c/p\u003e \u003cp\u003eThe results should be considered to estimate REE only in a specific population of football, boys, Caucasian race, and at a specific age of 10\u0026ndash;16 years. The group consisted of young boys who played sport regularly; therefore, our results could be confounded by overweight, obesity, and unintentional weight gain, therefore, our findings could not be generalized. The age range of the subjects in the experiment is from 10.01\u0026ndash;16.43 years old, and their hydration status are always changing with their maturations, and it could influence on fat free mass measured by BIA.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study allowed us to collect data to develop new predictive equations for male children and adolescents who play football. This is very important because REE is an essential element in evaluating and determining the diet of boys with high physical activity in the context of balancing their daily energy expenditure. It affects not only the maintenance of normal body weight composition and is related to the proper development and maintenance of health, but also the maintenance of optimal physical fitness in boys. Most ready-made predictive equations underestimate REE, which can be a problem, especially if we define the total energy demand of children, who due to intensive physiological development and regular training, require more calories per day. Therefore, the new predictive equations could be more appropriate for predicting REE in this population. These predictive equations can be very useful for small sports clubs where, due to lack of funds and equipment, the use of IC is not possible.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSubjects and new predictive equations\u003c/h2\u003e \u003cp\u003eA detailed description of the group and the methodology has already been published [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn September 2021, with the start of school classes without restrictions and online classes, invitations were sent to all Sports Championship School directors (13 schools) in the Podkarpackie Voivodeship (south-eastern Poland). Of the six schools that agreed, an invitation was sent to all parents or guardians of boys attending these schools. The inclusion criteria were as follows: male, playing football, regularly training, age between 9 and 16, training for minimum 2 years, training 3 times a day / match once a week, and with the consent of parents / guardians to participate in the study. 183 parents/guardians agreed to participate in the study. Of these, 35 children did not complete the study due to injury or discontinued the study for any reason. All subjects were healthy. In the last 6 months, no weight loss or infection with increased fever were reported. They did not use any drugs. Parents / guardians and participants gave their informed consent to participate in the study. Finally, the study group consisted of 148 boys aged 9 to 16 years.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eAssessments\u003c/h2\u003e \u003cp\u003eAll examinations were conducted in the laboratories of Rzeszow University by experienced researchers between January and May 2022. Participants came forward to the laboratory from 7:00 to 10:00 a.m. at controlled temperature. The temperature at the location of the measurements was controlled (22\u0026ndash;25 C).\u003c/p\u003e \u003cp\u003eAnthropometric Measurements, Body Composition\u003c/p\u003e \u003cp\u003eBefore taking the measurements, the participants received precise information about the course of the study. To minimize the risk of bias in body composition analysis, the bladder was emptied. Height measurement was made with a height meter (Seca 213) with an accuracy of 0.5 cm. The boys took off their shoes and stood with their backs to the stadiometer in an upright position. The average of three measurements was used for analysis.\u003c/p\u003e \u003cp\u003eBody composition was measured using bioelectrical impedance analysis (BIA, 6.25 kHz/50 kHz, 90 \u0026micro;A). The TANITA MC-980 MA (Tanita, Tokyo, Japan) was used. The analyzer is equipped with 8 electrodes, of which 4 are built into the platform, while the others are placed in the handles. Participants were asked to remove footwear and socks. Measurements were made in underwear, standing in designated places on the platform. According to the Tanita MC-980 PLUS MA manual, accurate measurement requires setting up the machine as level as possible. The adjustable feet were rotated in 4 positions so that the bubble of the level indicator was in the middle. Participants stood upright on the platform with their legs extended, placing their feet so that they touched the front and rear electrodes, ensuring that the weight was evenly distributed on both feet. The person examined held handles in their hands that were taken from the body at an angle of 35\u0026ndash;40.\u003c/p\u003e \u003cp\u003eResting Energy Expenditure\u003c/p\u003e \u003cp\u003eResting energy expenditure (kcal/day) was measured using a Cosmed Quark RMR indirect calorimeter (Rome, Italy) with a ventilated canopy hood and a disposable antibacterial filter. Full service of all measurement instruments was performed prior to the study, and daily calibration was performed according to the manufacturer's instructions. The evidence-based protocol for measurement of resting energy expenditure by indirect calorimetry was adopted in the study and clearly discussed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEvidence-based guidelines for measurement of resting metabolic rate with indirect calorimetry\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGuidelines for measurement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudy group recommendation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting\u003c/p\u003e \u003cp\u003e(thermic effect of food)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimum fast 5 hours after meals or snacks (Grade II), 4 hours after small meal if longer fast is clinically inappropriate (Grade II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eAll recommendations concerning preparations for the study were outlined, including: having rest minimum for 20 minutes, abstention from nicotine for minimum 2 hours, refraining from the consumption of meals 12 hours before the test, refraining from drinking beverages with caffeine and alcohol content for the last 48 hours before the test, as well as refraining from participation in a physical activity for the previous 14 hours.\u003c/p\u003e \u003cp\u003eThe method of conducting the study was explained in detail and each study participant had the opportunity to visit the test rooms beforehand and familiarize themselves with the equipment so that it did not raise concerns or cause anxiety in the researched group.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol ingestion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimum abstention from alcohol for 2 hours (Grade III)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNicotine ingestion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimum abstention from nicotine for 2 hours (Grade II)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaffeine ingestion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimum abstention from caffeine for 4 hours (Grade II)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRest periods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRest 10\u0026ndash;20 minutes (Grade III)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity restriction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimum abstention from moderate aerobic or anaerobic exercise for 2 hours before test (Grade II), for vigorous resistance exercise abstention of at least 14 hours (Grade III)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAllow a room temperature of 20\u0026deg;C-25\u0026deg;C (68\u0026deg;F-77\u0026deg;F) (Grade III) Ensure each individual is physically comfortable with measurement position during the test and repeated measures are in the same reclined position (Grade V)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe rooms had a controlled temperature between 22 to 25\u0026deg;C.\u003c/p\u003e \u003cp\u003eIn addition, each participant had the opportunity to acclimatize in the environment by lying flat for 30 minutes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGas collection devices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUse rigorous adherence to prevent air leaks (Grade III) Further studies comparing modern gas collection devices are needed in healthy and clinical populations (Grade V)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWith these devices, exhaled gas was captured by a canopy (ventilated hood system) or a face mask connected to oxygen and carbon dioxide analyzers mounted on a metabolic cart. This is essential for correct measurement.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSteady-state conditions and measurement interval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiscard initial 5 minutes. Then achieve a 5-minute period with 10% CV \u003csup\u003eb\u003c/sup\u003e for VO\u003csub\u003e2\u003c/sub\u003e \u003csup\u003ec\u003c/sup\u003e and VCO\u003csub\u003e2\u003c/sub\u003e \u003csup\u003ed\u003c/sup\u003e (Grade II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWe use a 20-minute protocol in\u003c/p\u003e \u003cp\u003ewhich the first 5 minutes of data are discarded and the remaining 15 minutes of data have a coefficient of variation of no more than 10%.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of measures/24 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAchieve steady state and one measure is adequate; if not, two to three nonconsecutive measures improve accuracy (Grade II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 measure/24 hours\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRepeated measures\u003c/p\u003e \u003cp\u003e(daily to monthly variation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRepeated measures vary 3%-5% over 24 hours (Grade II) and vary up to 10% over weeks to months (Grade II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory quotient (RQ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRQ measures 0.70 or 1 suggest protocol violations or inaccurate gas measurement (Grade II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe Quark RMR is a state-of-the-art metabolic system designed for accurate measurement of Resting Energy Expenditure (REE) and respiratory ratio (R), in a non-invasive way, through the measurement of oxygen consumption (VO2) and carbon dioxide production (VCO2) together with other ventilatory parameters. RQ was between 0.7 and 1.0.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Grade I - strong, consistent evidence; Grade II - somewhat weaker evidence and disagreement among authors may exist; Grade III - limited design quality; Grade IV - professional opinion only, no clinical trials; Grade V - no available studies.\u003c/p\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e CV - coefficient of variation (standard deviation [mean of individual replicate measures] x 100).\u003c/p\u003e \u003cp\u003e\u003csup\u003ec\u003c/sup\u003e VO\u003csub\u003e2\u003c/sub\u003e - oxygen consumption.\u003c/p\u003e \u003cp\u003e\u003csup\u003ed\u003c/sup\u003e VCO\u003csub\u003e2\u003c/sub\u003e - carbon dioxide production.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe following statistical methods were used:\u003c/p\u003e \u003cp\u003eDescriptive statistics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (number (n), Me - median and standard deviation (SD)). Shapiro-Wilk test, allowed to test the compliance of the tested variable with the normal distribution and the Student's t-test or Mann-Whitney U test was used to check the differences between both groups for the analyzed quantitative or ordinal variables. The bias was calculated as the mean difference between the predicted value and the measured REE.\u003c/p\u003e \u003cp\u003eThe agreement between measured and estimated REE values was evaluated by determining the bias in absolute values and as a percentage of the measured value and the corresponding limit of agreement (upper limit of agreement (ULA)\u0026thinsp;=\u0026thinsp;bias\u0026thinsp;+\u0026thinsp;1.96 \u0026times; SD; lower limit of agreement (LLA)\u0026thinsp;=\u0026thinsp;bias\u0026thinsp;\u0026minus;\u0026thinsp;1.96 \u0026times; SD). Additionally, Pearson\u0026rsquo;s product-moment correlation coefficient (r) and the coefficient of determination (R2) were calculated. The concordance correlation coefficient (CCC) as a measure of agreement was also determined.\u003c/p\u003e \u003cp\u003eThe percentage of participants whose predicted REE value was within \u0026plusmn;\u0026thinsp;10% of the measured REE was used as a precision measure. The heteroscedasticity was tested using the Bland\u0026ndash;Altman method. The plots presented the difference between predicted and measured REE versus the mean of predicted and measured REE.\u003c/p\u003e \u003cp\u003eThe PS IMAGO PRO 8.0 software (IBM SPSS STA-TISTICS 28) and the MedCalc software were used.\u003c/p\u003e \u003cp\u003eThe adopted level of statistical significance was p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eEthical consent\u003c/p\u003e \u003cp\u003e This research project obtains acceptance of Institutional Bioethics Committee at the University of Rzesz\u0026oacute;w (Resolution No. 2/01/2019).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Conceptualization, Edyta Łuszczki and Paweł Jagielski; Data curation, Maciej Kuchciak, Łukasz Oleksy and Paweł Jagielski; Formal analysis, Łukasz Oleksy, Artur Stolarczyk, Piotr Matłosz and Paweł Jagielski; Investigation, Edyta Łuszczki, Anna Bartosiewicz, Katarzyna Dereń, Piotr Matłosz and Maciej Kuchciak; Methodology, Edyta Łuszczki, Anna Bartosiewicz, Piotr Matłosz and Paweł Jagielski; Project administration, Edyta Łuszczki; Resources, Artur Stolarczyk and Katarzyna Dereń; Supervision, Edyta Łuszczki; Writing \u0026ndash; original draft, Edyta Łuszczki, Anna Bartosiewicz and Piotr Matłosz; Writing \u0026ndash; review \u0026amp; editing, Edyta Łuszczki and Artur Mazur.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The study was carried out as part of the project supported by the National Science Centre in Poland: \u0026ldquo;MINIATURA 5\u0026rdquo; (No. 2021/05/X/NZ7/01187; to E.Ł.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u003c/strong\u003e The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Rzesz\u0026oacute;w University Bioethics Commission (No. 2/01/2019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u003c/strong\u003e Informed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The data presented in this study are not publicly available due to confidentiality reasons. These data are available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRidley, K., Ainsworth, B.E., Olds, T.S. Development of a Compendium of Energy Expenditures for Youth: A second update of codes and MET values. Int. J. Behav. Nutr. Phys. Act. \u003cb\u003e5\u003c/b\u003e, 45 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAinsworth, B. \u003cem\u003eet al.\u003c/em\u003e Compendium of Physical Activities. Med. Sci. Sports Exerc. \u003cb\u003e43\u003c/b\u003e, 1575\u0026ndash;1581 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerbert, J. \u003cem\u003eet al.\u003c/em\u003e Objectively Assessed Physical Activity of Preschool-Aged Children from Urban Areas. Int. J. Environ. Res. 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Res. \u003cb\u003e10\u003c/b\u003e, 969\u0026ndash;977 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDa Rocha, E.E., Alves, V.G., Silva, M.H., Chiesa, C.A., da Fonseca, R.B. Can measured resting energy expenditure be estimated by formulae in daily clinical nutrition practice? Curr. Opin. Clin. Nutr. Metab. Care, \u003cb\u003e8\u003c/b\u003e, 319\u0026ndash;328 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmelzle, H., Schr\u0026ouml;der, C., Armbrust, S., Unverzagt, S., Fusch, C. Resting energy expenditure in obese children aged 4 to 15 years: Measured versus predicted data. Acta Paediatr. \u003cb\u003e93\u003c/b\u003e, 739\u0026ndash;746 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarpenter, A., Pencharz, P., Mouzaki, M. Accurate Estimation of Energy Requirements of Young Patients. J. Pediatr. Gas-troenterol. 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Physiol, \u003cb\u003e252\u003c/b\u003e, E110\u0026ndash;E117 (1987).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eŁuszczki, E. \u003cem\u003eet al.\u003c/em\u003e Resting Energy Expenditure of Physically Active Boys in Southeastern Poland\u0026mdash;The Accuracy and Validity of Predictive Equations. Metabolites, \u003cb\u003e10\u003c/b\u003e, 493; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/metabo10120493\u003c/span\u003e\u003cspan address=\"10.3390/metabo10120493\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrankenfield, D., Roth-Yousey, L., Compher, C. Comparison of predictive equations for resting metabolic rate in healthy nonobese and obese adults: a systematic review. J. Am. Diet. 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Nutr. \u003cb\u003e75\u003c/b\u003e, 705\u0026ndash;713 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcDuffie, J.R. \u003cem\u003eet al.\u003c/em\u003e Prediction equations for resting energy expenditure in overweight and normal-weight black and white children. Am. J. Clin. Nutr. \u003cb\u003e80\u003c/b\u003e, 365\u0026ndash;373 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLazzer, S., Agosti, F., De Col, A., Sartorio, A. Development and cross-validation of prediction equations for estimating resting energy expenditure in severely obese Caucasian children and adolescents. Br. J. Nutr. \u003cb\u003e96\u003c/b\u003e(\u003cb\u003e5\u003c/b\u003e), 973\u0026ndash;9 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEFSA Panel on Dietetic Products: Scientific opinion on dietary reference values for energy. EFSA J. \u003cb\u003e11\u003c/b\u003e, 17\u0026ndash;19 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeleleo, D. \u003cem\u003eet al.\u003c/em\u003e Evaluation of body composition with bioimpedence. A comparison between athletic and non-athletic children. Eur. J. Sport Sci. \u003cb\u003e17\u003c/b\u003e, 710\u0026ndash;719 (2017).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-2181853/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2181853/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMeasurement or estimation of resting energy expenditure (REE) should be the first step in determining energy demand in physically active boys. The purpose of this study was to develop and validate new equations for resting energy expenditure in male children and adolescents practicing football. The study was carried out among 184 boys in the validation group and 148 boys in the cross-validation group (mean age 13.20 years and 13.24 years, respectively). The calorimeter and device for assessing body composition by bioelectrical impedance analysis (BIA) were used. Model of multiple regression showed that REE can be predicted in this population with equations 1 or 2. Predictive Eq.\u0026nbsp;1 had an average error of 51\u0026thinsp;\u0026plusmn;\u0026thinsp;199 kcal and predictive Eq.\u0026nbsp;2\u0026ndash;39\u0026thinsp;\u0026plusmn;\u0026thinsp;193 kcal. Cohen's d coefficient was 0.2, which confirms the small difference. The bias was 4.7% and 3.9%, respectively. The accuracy was 61.2% in the population for predictive Eqs.\u0026nbsp;1 and 66.2% for predictive Eq.\u0026nbsp;2. Therefore, the new formulas developed and validated in this study are recommended for the estimation of REE in physically active boys, when the use of in-direct calorimetry is not feasible or available.\u003c/p\u003e","manuscriptTitle":"Development and validation of new predictive equations for resting energy expenditure in physically active boys","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-10-25 19:06:14","doi":"10.21203/rs.3.rs-2181853/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-01-06T12:40:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-12-19T17:37:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"eb479166-1395-43cd-8537-b06ef2cf7678","date":"2022-12-09T20:20:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"ef80ad6c-077c-4470-82a4-0a47362d3a25","date":"2022-12-09T20:20:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-12-09T20:18:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-12-09T20:13:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-10-20T08:58:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-10-20T08:40:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2022-10-19T08:04:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3a8379ba-d019-4a32-afba-f967ae1fa42a","owner":[],"postedDate":"October 25th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":16381477,"name":"Health sciences/Health care/Nutrition"},{"id":16381478,"name":"Health sciences/Health care/Public health/Epidemiology"}],"tags":[],"updatedAt":"2023-10-16T20:09:45+00:00","versionOfRecord":{"articleIdentity":"rs-2181853","link":"https://doi.org/10.1038/s41598-023-31661-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2023-03-20 20:05:49","publishedOnDateReadable":"March 20th, 2023"},"versionCreatedAt":"2022-10-25 19:06:14","video":"","vorDoi":"10.1038/s41598-023-31661-1","vorDoiUrl":"https://doi.org/10.1038/s41598-023-31661-1","workflowStages":[]},"version":"v1","identity":"rs-2181853","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2181853","identity":"rs-2181853","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}