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Longitudinal analysis of resting energy expenditure and body mass composition in physically active children and adolescents

Abstract

Background

Monitoring body composition and changes in energy expenditure during maturation and growth is significant, as many components can influence body structure in adulthood. In the case of young players, when these changes can influence their strength and power, it seems to be equally important. Our aim was to examine whether resting energy expenditure (REE) and body composition would change after 10 months from baseline in physically active children and adolescents.

Methods

We obtained data from 80 children and adolescents aged 9 to 17 years at two measurement points: the baseline in September 2018 and after 10 months in July 2019. The study was carried out using a calorimeter (Fitmate MED, Cosmed, Rome, Italy), a device used to assess body composition using by the electrical bioimpedance method by means of a segment analyzer (TANITA MC-980). The Student’s t-test and linear regression analysis were used. Using the stepwise forward regression procedure, the selection of factors in a statistically significant way that describes the level of REE was made.

Results

We noticed that REE was not significantly different between baseline (1596.94 ± 273.01 kcal) and after 10 months (1625.38 ± 253.26 kcal). When analyzing the difference in REE between studies girls, we found body height as a significant predictor. The results of our study show a negative relationship between growth and REE. Differences between sexes and age in REE between baseline and after 10 months were not significant.

Conclusions

Our study involving physically active children and adolescents, which used repeated objective measures and longitudinal statistical modeling to analyze them, was unable to demonstrate any interaction between body weight change, body composition measurements, and REE.

Peer Review reports

Background

According to experts from the European Food Safety Authority (EFSA), Food and Agriculture Organization of the United Nations (FAO), World Health Organization (WHO), United Nations University (UNU) and the Institute of Medicine in the United States, the basis for determining energy demand is total energy expenditure (TEE) including all its components, i.e. energy expenditure related to resting energy expenditure (REE), physical activity, the thermal effect of food as well as tissue building and synthesis [1]. Furthermore, the energy expenditure associated with body development and intensive physical activity also increases demand. The measurement of REE is usually the first step in determining the energy demand for people training in various sports disciplines. REE becomes a significant contribution to total energy expenditure even for active people, such as those training for endurance sports (marathon, swimming, rowing, etc.). For most people, REE accounts for almost 60 to 70% of total energy demand [2]. Therefore, the definition of REE can serve as a valuable tool in the development of food rations or nutrition plans to improve athletic performance and prevent body weight loss in physically active children and adolescents.

A very widespread method of determining REE is indirect calorimetry, based on the principle of the body using the energy obtained from the oxidation of nutrients, which is associated with the consumption of oxygen and the release of carbon dioxide in quantities proportional to the expenditure of energy, and therefore the relationship between the rate of oxygen absorption of the body and the amount of energy released in the oxidation processes [3, 4].

Physical activity (PA) refers to any type of musculoskeletal activity which raises energy expenditure above basal values. It is considered to be the component that shows the greatest variability relative to total energy expenditure (TEE). Monitoring body composition and changes in energy expenditure during maturation is significant, as many components can influence body structures in adulthood. Furthermore, it is important for young football players that these changes can influence their strength and power [5]. The strong relationship between REE and muscle mass has been the subject of research by many authors. The literature shows fat-free mass (FFM) as the strongest indicator affecting REE [6,7,8,9]. It has been shown that FFM can have a strong impact on energy requirements. Age is also a parameter that has been confirmed in numerous studies to influence REE [10, 11]. This may be associated with pubertal spurt and a greater amount of FFM in this group in older children and adolescents who train longer and thus have more muscle fibers. The literature also indicates the effect of puberty on REE [12]. Research in soccer players indicates that REE increases by approximately 400 kcal/day from the chronological ages of 10 to 13 [13].

Indirect calorimetry has been implemented by Japanese researchers to precisely estimate the REE in children and adolescents. The study was carried out among 221 children aged 6 to 17. The researchers applied multiple regression analysis using a combination of age, sex, body weight, and body height, or a combination of age, sex, lean mass, and fat mass. The research showed that REE of Japanese children increased with age, both in boys and girls, and there was a significant gender difference in the age group 12 to 17 [14]. Furthermore, Broadney et al. showed that the differences in REE of the children studied result from differences in body composition [15]. They demonstrated the age dependence of REE in Caucasian American children. There are very few reports in the literature regarding the determination of REE in the same group in two different time moments using indirect calorimetry in a population of children and adolescents who play football.

Our study is one of the few conducted in Poland using indirect calorimetry to calculate REE in physically active children and adolescents. The determination of the resting energy expenditure in a group of children and adolescents, especially those who play sports, is the basis for a precise determination of the energy demand on food intake, which is directly related to the health and physical condition of the respondents [4]. When evaluating REE of children and adolescents, factors that can influence REE should be considered to ensure appropriate interpretation. Age, sex, body size, and body composition, specifically fat-free mass (FFM), have been identified as the most significant factors, with the population group and PA as possible contributing factors [16, 17]. However, there is little information on the effect of long-term weight gain and growth on REE in children and adolescents, and only a few studies have tracked longitudinal changes in REE during childhood, particularly during puberty. Previous studies have been limited by their cross-sectional design [11, 18] or small sample size [19]. After REE, PA is the second largest and most variable component that contributes to total energy expenditure. It refers to any voluntary and involuntary bodily movements produced by muscle contraction [20]. Apart from the direct effect of PA on total energy expenditure, evidence exists [21, 22] that PA may influence REE, and its effects can last for hours or days (referred to as the excess post-exercise O2 consumption [EPOC]) [20, 22, 23].

Therefore, the purpose of our study was a longitudinal analysis of resting energy expenditure and body composition in physically active children and adolescents. We hypothesized that: 1) the REE would increase during the follow-up period; 2) there would be a difference in body composition and change in body weight over time between the sexes.

Methods

Participants

A study was carried out in the 2018/2019 school year in a randomly selected (from 7 sports schools in this region) sports school in Rzeszów (Poland) and involved healthy children and adolescents aged 9 to 17 years. All data were obtained at two moments: baseline (T0)– September 2018 and after 10 months (T1) – June, July 2019. The inclusion criteria were age 9–18 years, training football about three times a day and playing a game once a week, and consent from the parent or guardian to participate in the study.

The study group consisted of 80 students (17 girls and 63 boys) aged 9 to 17. The study methodology has been published in detail [24].

Study participants and their legal guardians received verbal and written information on the objectives, risks, and benefits of the study. Both guardians and participants gave their informed written consent to participate in the study.

Assessments

Research was carried out at the Laboratory for Innovative Research in Dietetics (Centre for Innovative Research in Medical and Natural Sciences, University of Rzeszow, Rzeszow, Poland). Body weight, body height, and REE assessments of the study sample have been published elsewhere [24]. Body height was measured 3 times with an accuracy of 0.1 cm (by a portable Seca 213 stadiometer). REE was measured by the indirect calorimetry method using an indirect calorimeter (Fitmate MED, Cosmed, Rome, Italy). The Fitmate Med device was validated and showed a very high reliability of the measurements obtained [25]. The results obtained using Fitmate Med are comparable to those obtained with the Douglas bag system, which uses a sensor to measure VCO2 [26]. A study by Campbell et al. examined the validity and reliability of the Fitmate device. On the first day, two 15 minute tests were performed, then on the second day (within a week after performing 1 test) another test was carried out. To assess the reliability of the test, intraclass correlation coefficients (ICC) and standard error measurement (SEM) were used, while Anova analyzed systematic error. Relative consistency was accepted with the SEM and ICC values (0.981 and 0.946, responding during the day and between). Moreover, no systematic error was found between the measurements [25]. In order to properly use the device in the pediatric population, a request for guidance was sent to the manufacturer. According to the instructions, it was recommended to use disposable antibacterial filters with rubber mouthpieces to improve mouth grip and limit the risk of air leakage by using a reusable mask (a petite/pediatric size).

All recommendations concerning preparations for the study were outlined during the meeting with participants and parents / guardians, including rest, refraining from eating meals 12 hours before the test, refraining from drinking beverages with caffeine content for the last 48 hours before the test, as well as refraining from participating in physical activity for the previous 12 hours.

Body composition and body mass index

Body composition was measured using the electrical bioimpedance method (6.25 kHz, 50 kHz, 90 μA) using a calibrated segment analyzer (Tanita MC-980 PLUS MA, Tokyo, Japan) with an accuracy of 0.1 kg/ 0.1%. Tanita MC 980 has approvals for medical use and meets the NAWI and CLASS III standards and the MDD 93/42/EEC directive, as well as the CE0122 EU certificate [27]. The results obtained using the Tanita Analyzer for studies involving children are consistent with those obtained from Dual Energy X-ray Absorptiometry (DXA) [13, 28,29,30]. The analyzer is equipped with 8 electrodes, 4 of which are built into the platform, while the others are in holders. Participants were asked to remove their footwear and socks, then the skin on their feet was cleaned so that the measurement was carried out correctly. All test participants were in their underwear, stood still on the platform, in the designated places. According to the Tanita MC980 PLUS MA manual, the machine was set as vertically as possible to ensure accurate measurement. The device was set and adjusted so that the level indicator was in the center of the level meter. Participants stood on the platform barefoot, upright, with straight legs, placing their feet so that they touched the front and rear electrodes, making sure that the weight of the body was evenly distributed between both feet. In their hands, the examined person held handles positioned away from the body at an angle of 35 °-40 °. A person’s measurement is taken while in a standing position with the elecrodes in contact with bare feet and hands. The device automatically measures body weight and then impedance. The Commuter software (a microprocessor) imbedded in the product uses the measured impedance, the participant’s sex, body height, fitness, age, and the weight to determine body fat percentage based on equation formulas. The Tanita reference method is DXA. Through multiple regression analysis, Tanta has derived standard formulas to determine the percentage of body fat. The Tanita equations are generalized for standard adults, athletes, and children.

Body mass index (BMI) was calculated as body weight (kg)/ height (m)2. The definitions of body mass deficiency, normal body weight, overweight, and obesity were based on the recommendations of the Centers for Disease Control and Prevention [31].

Arterial blood pressure

Blood pressure was measured three times according to the recommendations of the National High Blood Pressure Education Program Working Group in Children and Adolescents (NHBPEP) [32], using a Welch Allyn 4200B-E2 blood pressure meter (Aston Abbotts, UK) with cuffs sized to fit the shoulders of the participants. The average of three measurements was calculated for each person tested.

Statistical analysis

The results of the study were obtained using descriptive statistics: number (n), average, Me - median and standard deviation (SD). Both parametric and non-parametric tests were used to analyze the variables. The choice of the parametric test depended on fulfilling its basic assumptions, i.e., the conformity of the tested variable with normal distribution, which was verified by the Kolmogorov–Smirnov test. The Student’s t-test was used for normally distributed variables. In addition, linear regression analysis was used. Using the stepwise forward regression procedure, the selection of factors in a statistically significant way that describes the level of REE was made. Statistical significance was established as a p value less than 0.05. Calculations were performed with the Statistica 10.0 tool (StatSoft, Inc.Tulsa, Oklahoma, United States).

Causal framework

The multivariate models were adjusted for a set of a priori–determined covariates that included age, body height, BMI, fat mass, FFM, total body water, hip circumference, waist circumference, systolic pressure, and diastolic pressure.

Because age and sex are a strong determinant of REE, we controlled for age and sex in a basic model. Potential confounders were first selected based on previous studies, as well as a literature search. Confounding variables were included in the final model if the covariate was associated with exposure at p < 0.05 or a priori (if there was a strong theoretical or clinical reason to keep them in the model). As there were too many variables, a stepwise procedure was employed to include potential confounding variables that have a detectable effect on the association of interest while retaining the above-mentioned variables in the model. We also performed a formal sensitivity analysis, as described by Lin et al., to assess the potential effect of unmeasured confounding on our results. It is also possible that residual confounders remained after inaccurate measurement of physical activity, smoking status, or blood pressure.

Results

Characteristics of the study group

A total of 80 respondents aged 9 to 17 years were surveyed twice, 21.3% of whom were girls (N = 17), and 78.7% were boys (N = 63). The mean age of the respondents at baseline was 12.04 ± 2.26 years and after 10 months - 12.32 ± 2.32 years. The mean age of the girls at baseline and after 10 months was significantly higher (p < 0.05) than the mean age of the boys (Table 1).

Table 1 Characteristics of the study group by age and sex in T0 and T1

The findings

In Tables 2 and 3 the differences between the groups are presented.

Table 2 Between-group differences from T0 to T1 by age
Table 3 Between-group differences from T0 to T1 by age and sex

We noticed that REE was not significantly different between baseline (1596.94 ± 273.01 kcal) and after 10 months (1625.38 ± 253.26 kcal). When divided into two groups: children (9–12 years) and adolescents (13–17 years), in both groups there were no significant differences in REE values. Furthermore, both in girls and boys, there were no significant differences in REE.

A significant difference in body weight was observed in younger children (37.51 ± 7.60 kg vs 39.31 ± 7.86 kg), and older children (55.22 ± 8.47 kg vs 57.56 ± 8.38 kg) children, but body height was significantly higher only in younger children (145.84 ± 8.48 cm vs 148.22 ± 8.57 cm). Additionally, both at baseline and after 10 months, the increase in fat free mass was observed (p < 0.05). However, fat mass (%) increases significantly only in adolescents (20.70 ± 5.86% vs. 21.72% ± 5.67%, p = 0.0461).

Significant differences in hip circumference were observed between the two measurement points (p = 0.0009). The value has increased in both younger and older children (p < 0.05).

In adolescents aged 13–17 years, an increase in systolic blood pressure was observed from 112.03 ± 13.63 mmHg to 117.38 ± 9.80 mmHg (p = 0.0412).

In Table 3 the differences between the sexes and age are presented. We found significant differences in body weight, BMI, hip circumference, and systolic blood pressure in girls (p < 0.05). In boys, significant differences were the same in the younger (9–12 years) and older (13–17 years) groups, and differences have been observed in body height, body weight, BMI, FFM, total body water, and hip circumference.

The differences between girls and boys are presented in Table 4.

Table 4 The differences in variables between girls and boys in T0 and T1

Due to the large number of statistically significant differences between girls and boys, stepwise linear regression was performed separately for both sexes (Table 5).

Table 5 The result of the general regression model for the selected parameters (independent variables were selected by the stepwise forward regression procedure)

We noticed that at baseline, REE was influenced by FFM and diastolic blood pressure in girls (higher FFM and pressure = higher REE). In the baseline group of boys, REE was influenced by TBW (higher TBW, higher REE) and age (higher age, lower REE).

After 10 months, REE was influenced by body height in girls (higher height means higher REE, β = 0.499). In the group of boys, the REE was influenced by TBW (positive, that is, higher TBW, higher REE results) and diastolic pressure (but it was negative, that is, higher diastolic pressure, lower REE (β = − 0.199).

When analyzing the difference in REE between the studies in girls, we only had one significant predictor, which turned out to be the difference in body height. The greater the difference in body height, the greater the difference in REE between T0 and T1. In the group of boys, there was no significant predictor that would influence the change in REE (i.e., the difference in REE between T0 and T1).

Discussion

This is the first longitudinal analysis to examine changes in REE and body composition in healthy children and adolescents who play sports regularly in Poland. This is a very important issue, as changes in body and energy expenditure with growth and age are relevant in the population of young football players. Longitudinal studies of REE are rare, particularly in children and adolescents, due to the high costs associated with repeated examinations of REE. The main purpose of our study was to check if with the age and increase of FFM (the greatest predictor of REE change), body height and body weight gain, the REE value will also change.

Resting energy expenditure

Data show that body weight gain before puberty is associated with an increase in REE and that the increase is greater than predicted from changes in body composition [33]. Our results showed that age was not related to the measured REE in the total sample. Furthermore, when the study group was divided into two sub-groups: children (9–12 years) and adolescents (13–17 years), the REE also did not increase significantly from baseline. This is consistent with existing evidence [34] and is true for children in middle school through age and sex categories and the population groups we examined. Therefore, we have received support for our hypothesis that body weight gain and changes in body composition in children and adolescents elicit adaptive changes in REE.

Body composition

Furthermore, there was no association between body mass gain or change in body composition and REE in girls and boys. Therefore, our hypothesis that REE has a significant impact on changes in body weight or composition is refuted. The results of the literature in both adults [35, 36] and children [37,38,39] have been inconsistent, mainly due to different age ranges and populations. In the study by Broadney et al., the lower REE in African American children was likely due to a lower trunk lean mass and a greater appendicular lean mass. In addition, they noticed that differences in the distribution of lean mass may largely explain the observed lower REE in African-American children compared to Caucasian-American children [15]. However, it was not a longitudinal study. Sun et al. took a sample of children and prospectively monitored body composition and REE throughout puberty. Unlike the Broadney study, they did not identify attenuation in REE racial differences by adjusting for compartment-specific lean mass [40].

In the study by Hosking et al. relative to changes in body composition, there were little or no significant changes in REE prior to age 9 to 10 years [41]. There were only a few longitudinal studies with changes in REE in children [19, 36, 39, 42]. In different studies, the TEE adjusted for FFM did not differ significantly from 10.4 to 12.8 years [36]. In addition, Spadano et al., similar to our study, noticed that the mean REE in children 12 years of age did not differ significantly from the REE in 15 years of age [42]. However, we found significant increases during growth in both FFM, the most important determinant of REE [43] and FM [44, 45] in older children, an independent contributor to REE. These mentioned studies have shown a dependence on age and body composition.

Main predictors

When analyzing the results with stepwise linear regression, we found predictors of REE in boys and girls. Body height turned out to be a predictor of the change in the REE difference between studies. However, this result was only significant in girls. The greater the difference in body height, the greater the difference in REE between baseline and after 10 months. A taller person differs from a shorter person of the same body weight in their relative amounts of adipose tissue, muscle, and other organs and tissues. Therefore, body mass alone is an inadequate phenotypic marker of the body size of an adult human and therefore its REE. People who are tall, given the same age and level of fat (%) will weigh more than people who are short [46].

Limitations

The study has some limitations. Limitations of resources required the use of BIA instead of a gold standard measure, such as dual energy X-ray absorptiometry (DXA) for analysis of body composition, and a portable device (Fitmate Med). This may lead to errors compared to the gold standard. Despite not measuring CO2 production, it is very convenient in the clinical setting to assume a minimal analysis error. In addition, environmental factors, such as food intake and physical activity, could help regulate the overall regulation of energy balance. Young soccer players are grouped by chronological age to reduce the effects of developmental differences. However, young athletes of the same chronological age can vary in their maturity status (stage of puberty, skeletal age, maturity timing) therefore another limitation of our study was not measuring the Tanner stages to directly indicate the stage of puberty, which may influence on the results. We had got large age ranges, numerically small files, and did not take into account maturation. Finally, there were many environmental and epigenetic influences that could affect the results (health status, children’s morbidity, injuries, and nutrition).

Conclusions

In conclusion, the results of our study show a negative relationship between growth, age, and REE. The differences between sexes and age in REE between baseline and 10 months after were not significant. Although lean body mass appears to be the largest predictor of REE in physically active people, in our study, despite a significant increase in FFM, REE did not increase significantly. To summarize, a study involving physically active children and adolescents, which used repeated objective measures and longitudinal statistical modeling to analyze them, was unable to demonstrate any interaction between body weight change, body composition measurements, and REE after 10 months. In Poland, actual REE measurement is not feasible in most clinical and research settings. The importance of REE lies in its potential to influence weight gain, and although the role of REE in future body weight change remains controversial, we have been unable to support the hypothesis that it increases with chronological age.

Availability of data and materials

The dataset supporting the conclusions of this article is included within the article (and its additional file).

Abbreviations

BMI:

Body mass index

EFSA:

European Food Safety Authority

FAO:

Food and Agriculture Organization of the United Nations

FFM:

fat-free mass

NHBPEP:

National High Blood Pressure Education Program Working Group in Children and Adolescents

PA:

Physical activity

REE:

Resting energy expenditure

TBW:

Total body water

TEE:

Total energy expenditure

UNU:

United Nations University

WHO:

World Health Organization

References

  1. Jarosz M. Nutrition standards for the polish population: Instytut Żywności i Żywienia; 2017. p. 21–5. https://ncez.pl/upload/normy-net-1.pdf (Accessed: 24 Jan 2021)

    Google Scholar 

  2. Siervo M, Boschi V, Falconi C. Which REE prediction equation should we use in normal-weight, overweight and obese women? Clin Nutr. 2003;22:193–204.

    CAS  PubMed  Article  Google Scholar 

  3. Achamrah N, Oshima T, Genton L. Innovations in energy expenditure assessment. Curr Opin Clin Nutr Metab Care. 2018;21(5):321–8.

    PubMed  Article  Google Scholar 

  4. Delsoglio M, Achamrah N, Berger MM, Pichard C. Indirect Calorimetry in clinical practice. J Clin Med. 2019;8(9):1387.

    CAS  PubMed Central  Article  Google Scholar 

  5. Guo SS, Wu W, Chumlea WC, Roche AF. Predicting overweight and obesity in adulthood from body mass index values in childhood and adolescence. Am J Clin Nutr. 2002;76(3):653–8.

    CAS  PubMed  Article  Google Scholar 

  6. Weinsier RL, Schutz Y, Bracco D. Reexamination of the relationship of resting metabolic rate to fat-free mass and to the metabolically active components of fat-free mass in humans. Am J Clin Nutr. 1992;55(4):790–4.

    CAS  PubMed  Article  Google Scholar 

  7. Cunningham JJ. Body composition as a determinant of energy expenditure: a synthetic review and a proposed general prediction equation. Am J Clin Nutr. 1991;54(6):963–9.

    CAS  PubMed  Article  Google Scholar 

  8. Fukagawa NK, Bandini LG, Young JB. Effect of age on body composition and resting metabolic rate. Am J Phys. 1990;259(2 Pt 1):E233–8.

    CAS  Google Scholar 

  9. Karhunen L, Franssila-Kallunki A, Rissanen A, Kervinen K, Kesäniemi YA, Uusitupa M. Determinants of resting energy expenditure in obese non-diabetic caucasian women. Int J Obes Relat Metab Disord. 1997;21(3):197–202.

    CAS  PubMed  Article  Google Scholar 

  10. Maffeis C, Schutz Y, Micciolo R, Zoccante L, Pinelli L. Resting metabolic rate in six- to ten-year-old obese and nonobese children. J Pediatr. 1993;122(4):556–62.

    CAS  PubMed  Article  Google Scholar 

  11. Molnár D, Schutz Y. The effect of obesity, age, puberty and gender on resting metabolic rate in children and adolescents. Eur J Pediatr. 1997;156(5):376–81.

    PubMed  Article  Google Scholar 

  12. Griffiths M, Payne PR, Stunkard AJ, Rivers JP, Cox M. Metabolic rate and physical development in children at risk of obesity. Lancet. 1990;336(8707):76–8.

    CAS  PubMed  Article  Google Scholar 

  13. Meredith-Jones KA, Williams SM, Taylor RW. Bioelectrical impedance as a measure of change in body composition in young children. Pediatr Obes. 2015;10(4):252–9.

    CAS  PubMed  Article  Google Scholar 

  14. Kaneko K, Ito C, Koizumi K, Watanabe S, Umeda Y, Ishikawa-Takata K. Resting energy expenditure (REE) in six- to seventeen-year-old Japanese children and adolescents. J Nutr Sci Vitaminol (Tokyo). 2013;59(4):299–309.

    CAS  Article  Google Scholar 

  15. Broadney MM, Shareef F, Marwitz SE, et al. Evaluating the contribution of differences in lean mass compartments for resting energy expenditure in African American and Caucasian American children. Pediatr Obes. 2018;13(7):413–20.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. De Lany JP, Bray GAH, Volaufova DW, J. Energy expenditure in preadolescent African American and white boys and girls: the Baton Rouge Children’s study. Am J Clin Nutr. 2002;75:705–13.

    Article  Google Scholar 

  17. Jakicic JM, Wing RR. Differences in resting energy expenditure in African-American vs Caucasian overweight females. Int J Obes. 1998;22:236–42.

    CAS  Article  Google Scholar 

  18. Rodríguez G, Moreno LA, Sarría A, et al. Determinants of resting energy expenditure in obese and non-obese children and adolescents. J Physiol Biochem. 2002;58(1):9–15.

    PubMed  Article  Google Scholar 

  19. Goran MI, Gower BA, Nagy TR, Johnson RK. Developmental changes in energy expenditure and physical activity in children: evidence for a decline in physical activity in girls before puberty. Pediatrics. 1998;101(5):887–91.

    CAS  PubMed  Article  Google Scholar 

  20. Institute of Medicine. Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein, and amino acids. Washington, DC: The National Academies Press; 2005.

    Google Scholar 

  21. Shook RP, Hand GA, Wang X, Paluch AE, Moran R, Hebert JR, et al. Low fitness partially explains resting metabolic rate differences between African American and white women. Am J Med. 2014;127:436–42.

    PubMed  Article  Google Scholar 

  22. Speakman JR, Selman C. Physical activity and resting metabolic rate. Proc Nutr Soc. 2003;62:621–34.

    PubMed  Article  Google Scholar 

  23. Frankenfield DC, Coleman A. Recovery to resting metabolic state after walking. J Am Diet Assoc. 2009;109:1914–6.

    PubMed  Article  Google Scholar 

  24. Łuszczki E, Bartosiewicz A, Dereń K, et al. The diagnostic-measurement method-resting energy expenditure assessment of polish children practicing football. Diagnostics (Basel). 2021;11(2):340.

    Article  Google Scholar 

  25. Campbell B, Zito G, Colquhoun R, Martinez N, St Louis C, Johnson M, et al. Inter- and intra-day test-retest reliability of the Cosmed Fitmate ProTM indirect calorimeter for resting metabolic rate. J Int Soc Sports Nutr. 2014;11:46.

    Article  Google Scholar 

  26. Nieman DC, Austin MD, Benezra L, Pearce S, McInnis T, Unick J, et al. Validation of Cosmed’s FitMate in measuring oxygen consumption and estimating resting metabolic rate. Res Sports Med. 2006;14:89–96.

    PubMed  Article  Google Scholar 

  27. Tanita. Professional Product Guide https://tanita.eu/media/wysiwyg/catalogue/tanita_pro_product-guide_april_2017.pdf (Access 17 Feb 2022).

  28. Pietrobelli A, Rubiano FS, Onge MP, Heymsfield SB. New bioimpedance analysis system: improved phenotyping with whole-body analysis. Eur J Clin Nutr. 2004;58:1479–84.

    CAS  PubMed  Article  Google Scholar 

  29. Pietrobelli A, Rubiano F, Wang J, Wang Z, Heymsfield SM. Validation of contact electrode bioimpedance analysis in a pediatric population. Obes Rev. 2005;6(S1):P132 (abstract).

    Google Scholar 

  30. Kabiri LS, Hernandez DC, Mitchell K. Reliability, validity, and diagnostic value of a pediatric bioelectrical impedance analysis scale. Child Obes. 2015;11:650–5.

    PubMed  Article  Google Scholar 

  31. Barlow SE, Expert Committee. Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatrics. 2007;120(Suppl 4):S164–92.

    PubMed  Article  Google Scholar 

  32. National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents. The fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents. Pediatrics. 2004;114(2 Suppl 4th Report):555–76.

    Article  Google Scholar 

  33. Pretorius A, Wood P, Becker P, Wenhold F. Resting energy expenditure and related factors in 6- to 9-year-old southern African children of diverse population groups. Nutrients. 2021;13(6):1983.

    PubMed  PubMed Central  Article  Google Scholar 

  34. Goran MI, Kaskoun M, Johnson R. Determinants of resting energy expenditure in young children. J Pediatr. 1994;125(3):362–7.

    CAS  PubMed  Article  Google Scholar 

  35. Buscemi S, Verga S, Caimi G, Cerasola G. Low relative resting metabolic rate and body weight gain in adult Caucasian Italians. Int J Obes. 2005;29(3):287–91.

    CAS  Article  Google Scholar 

  36. Tataranni PA, Harper IT, Snitker S, et al. Body weight gain in free-living Pima Indians: effect of energy intake vs expenditure. Int J Obes Relat Metab Disord. 2003;27(12):1578–83.

    CAS  PubMed  Article  Google Scholar 

  37. DeLany JP, Bray GA, Harsha DW, Volaufova J. Energy expenditure and substrate oxidation predict changes in body fat in children. Am J Clin Nutr. 2006;84(4):862–70.

    CAS  PubMed  Article  Google Scholar 

  38. Johnson MS, Figueroa-Colon R, Herd SL, et al. Aerobic fitness, not energy expenditure, influences subsequent increase in adiposity in black and white children. Pediatrics. 2000;106(4):E50.

    CAS  PubMed  Article  Google Scholar 

  39. Salbe AD, Weyer C, Harper I, Lindsay RS, Ravussin E, Tataranni PA. Assessing risk factors for obesity between childhood and adolescence: II. Energy metabolism and physical activity. Pediatrics. 2002;110(2 Pt 1):307–14.

    PubMed  Article  Google Scholar 

  40. Sun M, Gower BA, Bartolucci AA, Hunter GR, Figueroa-Colon R, Goran MI. A longitudinal study of resting energy expenditure relative to body composition during puberty in African American and white children. Am J Clin Nutr. 2001;73(2):308–15.

    CAS  PubMed  Article  Google Scholar 

  41. Hosking J, Metcalf BS, Jeffery AN, Voss LD, Wilkin TJ. Little impact of resting energy expenditure on childhood weight and body composition: a longitudinal study (EarlyBird 47). Nutr Res. 2011;31(1):9–13.

    CAS  PubMed  Article  Google Scholar 

  42. Bitar A, Vernet J, Coudert J, Vermorel M. Longitudinal changes in body composition, physical capacities and energy expenditure in boys and girls during the onset of puberty. Eur J Nutr. 2000;39(4):157–63.

    CAS  PubMed  Article  Google Scholar 

  43. Ravussin E, Bogardus C. Relationship of genetics, age, and physical fitness to daily energy expenditure and fuel utilization. Am J Clin Nutr. 1989;49(5 Suppl):968–75.

    CAS  PubMed  Article  Google Scholar 

  44. Bandini LG, Must A, Cyr H, Anderson SE, Spadano JL, Dietz WH. Longitudinal changes in the accuracy of reported energy intake in girls 10-15 y of age. Am J Clin Nutr. 2003;78(3):480–4.

    CAS  PubMed  Article  Google Scholar 

  45. Tershakovec AM, Kuppler KM, Zemel B, Stallings VA. Age, sex, ethnicity, body composition, and resting energy expenditure of obese African American and white children and adolescents. Am J Clin Nutr. 2002;75(5):867–71.

    CAS  PubMed  Article  Google Scholar 

  46. Heymsfield SB, Pietrobelli A. Body size and human energy requirements: reduced mass-specific total energy expenditure in tall adults. Am J Hum Biol. 2010;22(3):301–9.

    PubMed  Article  Google Scholar 

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E.Ł., M.K., A.M. - development of the concept of research / scientific work, A.B., K.D., − data compilation, E.Ł., A.B., K.D.- analysis and interpretation of data, E.Ł., A.B. - writing an article, A.M., − substantive review article, E.Ł., A.M. - overseeing the final article. All authors reviewed the manuscript. The author(s) read and approved the final manuscript.

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Correspondence to Edyta Łuszczki.

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The study participants and their legal guardians received verbal and written information about the objectives, risks, and benefits of the study. Both the guardians and the participants gave their informed written consent to participate in the study. This research project was carried out in accordance with the Helsinki Declaration. The study was approved by the institutional Bioethics Committee at the University of Rzeszow (Resolution No. 2/01/2019) and by all appropriate administrative bodies.

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Łuszczki, E., Bartosiewicz, A., Kuchciak, M. et al. Longitudinal analysis of resting energy expenditure and body mass composition in physically active children and adolescents. BMC Pediatr 22, 260 (2022). https://doi.org/10.1186/s12887-022-03326-x

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  • DOI: https://doi.org/10.1186/s12887-022-03326-x

Keywords

  • Children and adolescents
  • Indirect calorimetry
  • Metabolism
  • Physical activity
  • Resting energy expenditure