Skip to main content

Resting metabolic rate: a comparison between different measurement methods used in male university students

Abstract

Introduction

Resting metabolic rate (RMR) is a widely used method to determine an individual's nutritional need. There are different methods of obtaining the RMR values to identify these needs.

Objective

This study aimed to compare the RMR values using two different indirect calorimetry (IC) devices, bioelectrical impedance analysis (BIA), and two predictive equations in a sample of male university students.

Methods

This cross-sectional study analyzed 65 male university students (aged 23.3 ± 2.8 years, body mass index 25.78 ± 3.98 kg/m2). RMR was obtained using two IC devices (VO2000® and Fitmate PRO®) and BIA (InBody 570®), and two predictive equations by Harris and Benedict (Proc Natl Acad Sci 4:370–373, 1918) and FAO/WHO/UNU (Food Agric Organ 1:103, 2001).

Results

The Bland–Altman (B&A) plot demonstrated considerable differences between the two gold standard methods, VO2000® and Fitmate PRO®, and BIA, with a mean difference of 7.8 and 5.7%, respectively. The B&A plot presented the lowest difference between the two IC devices with a mean difference of 2.1%. The B&A plot also showed a low difference between the two IC devices and predictive equations, with values ranging from −1.3 to −3.6%. In turn, the B&A plot between BIA and the predictive equations comprised approximately  − 9.2% of the mean difference. The analysis of variance (ANOVA) demonstrated no significant differences between the measurements (p > 0.05).

Conclusion

Based on the results, InBody 570® underestimates the reference values for RMR in two different IC devices as well as in the predictive equations. Although no significant differences were observed between ANOVA comparisons, the RMR values obtained using BIA should be analyzed with caution. The absence of sophisticated devices for RMR analysis indicates the need to use the FAO/WHO/UNU (Food Agric Organ 1:103, 2001) equation, which presented a lower difference compared with that of the gold standard measurements in male university students.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

References

  1. 1.

    Brunetto BC, Guedes DP, Brunetto AF (2010) Taxa metabólica basal emuniversitários: comparação entre valoresmedidos e preditos. Rev Nutr 23:369–377. https://doi.org/10.1590/S1415-52732010000300005

    Article  Google Scholar 

  2. 2.

    Redondo RB (2015) Resting energy expenditure; assessment methods and applications. Nutr Hosp 31:245–254. https://doi.org/10.3305/nh.2015.31.sup3.8772

    Article  Google Scholar 

  3. 3.

    Flack KD, Siders WA, Johnson L, Roemmich JN (2016) Cross-validation of resting metabolic rate prediction equations. J AcadNutr Diet 116:1413–1422. https://doi.org/10.1016/j.jand.2016.03.018

    Article  Google Scholar 

  4. 4.

    Steinberg A, Manlhiot C, Cordeiro K et al (2017) Determining the accuracy of predictive energy expenditure (PREE) equations in severely obese adolescents. ClinNutr 36:1158–1164. https://doi.org/10.1016/j.clnu.2016.08.006

    Article  Google Scholar 

  5. 5.

    Faria SL, Faria OP, Menezes CS et al (2012) Metabolic profile of clinically severe obese patients. ObesSurg 22:1257–1262. https://doi.org/10.1007/s11695-012-0651-y

    Article  Google Scholar 

  6. 6.

    Bedogni G, Bertoli S, Leone A et al (2019) External validation of equations to estimate resting energy expenditure in 14952 adults with overweight and obesity and 1948 adults with normal weight from Italy. ClinNutr 38:457–464. https://doi.org/10.1016/j.clnu.2017.11.011

    Article  Google Scholar 

  7. 7.

    PinheiroVolp AC, Esteves de Oliveira FC, Duarte Moreira Alves R et al (2011) Gastoenergético: componentes y métodos de evaluación. Nutr Hosp 26(3):430–440

    CAS  Google Scholar 

  8. 8.

    de Oliveira FCE, Alves RDM, Zuconi CP et al (2012) Agreement between different methods and predictive equations for resting energy expenditure in overweight and obese Brazilian men. J AcadNutr Diet 112:1415–1420. https://doi.org/10.1016/j.jand.2012.06.004

    Article  Google Scholar 

  9. 9.

    Compher C, Frankenfield D, Keim N, Roth-Yousey L (2006) Best practice methods to apply to measurement of resting metabolic rate in adults: a systematic review. J Am Diet Assoc 106:881–903. https://doi.org/10.1016/j.jada.2006.02.009

    Article  PubMed  Google Scholar 

  10. 10.

    Lustosa AMA, Bento APN, Barbosa FP et al (2013) Taxa metabólica basal de homensresidentesnacidade de Goiânia. Rev Bras Med do Esporte 19:96–98. https://doi.org/10.1590/s1517-86922013000200004

    Article  Google Scholar 

  11. 11.

    Wang X, Wang Y, Ding Z et al (2018) Relative validity of an indirect calorimetry device for measuring resting energy expenditure and respiratory quotient. Asia Pac J ClinNutr 27:72–77. https://doi.org/10.6133/apjcn.032017.02

    Article  Google Scholar 

  12. 12.

    Nieman DC, Austin MD, Benezra L et al (2006) Validation of Cosmed’sFitMateTM in measuring oxygen consumption and estimating resting metabolic rate. Res Sport Med 14:89–96. https://doi.org/10.1080/15438620600651512

    Article  Google Scholar 

  13. 13.

    Harris JA, Benedict FG (1918) A biometric study of human basal metabolism. Proc Natl AcadSci 4:370–373. https://doi.org/10.1073/pnas.4.12.370

    CAS  Article  Google Scholar 

  14. 14.

    Mifflin MD, St Jeor ST, Hill LA et al (1990) A new predictive equation for resting energy expenditure in healthy individuals. Am J ClinNutr 51:241–247. https://doi.org/10.1093/ajcn/51.2.241

    CAS  Article  Google Scholar 

  15. 15.

    United Nations University & WHO (2001) Human energy requirements: report of a joint FAO/WHO/UNU expert consultation. Food Agric Organ 1:103

    Google Scholar 

  16. 16.

    Krüger RL, Lopes AL, Gross JDS et al (2014) Validação de equações de predição da taxa metabólica basal emsujeitoseutróficos e obesos. Rev Bras Cineantropometria e Desempenho Hum 17:73–81. https://doi.org/10.5007/1980-0037.2015v17n1p73

    Article  Google Scholar 

  17. 17.

    Henry CJK, Rees DG (1991) New predictive equations for the estimation of basal metabolic rate in tropical peoples. Eur J ClinNutr 45(4):177–185

    CAS  Google Scholar 

  18. 18.

    Schofield WN (1985) Predicting basal metabolic rate, new standards and review of previous work. Hum NutrClinNutr 39(1):5–41

    Google Scholar 

  19. 19.

    Eickemberg M, Oliveira C, Roriz AK, Sampaio LR (2011) Bioimpedânciaelétrica e suaaplicaçãoemavaliaçãonutricional Bioelectric impedance analysis and its use for nutritional assessments. Rev Nutr 24:883–893. https://doi.org/10.1590/S1415-52732011000600009

    Article  Google Scholar 

  20. 20.

    Amaro-Gahete F, Jurado-Fasoli L, De-la-O A et al (2018) Accuracy and validity of resting energy expenditure predictive equations in middle-aged adults. Nutrients 10:1635–1648. https://doi.org/10.3390/nu10111635

    CAS  Article  PubMed Central  Google Scholar 

  21. 21.

    Luy SC, Dampil OA (2018) Comparison of the Harris–Benedict equation, bioelectrical impedance analysis, and indirect calorimetry for measurement of basal metabolic rate among adult obese Filipino patients with prediabetes or type 2 diabetes mellitus. J ASEAN Fed Endocr Soc 33:152–159. https://doi.org/10.15605/jafes.033.02.07

    Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Thomas J, Nelson J, Silverman S (2011) Explore four methods for collecting qualitative research. In: Research methods in physical activity, 6th edn. Human Kinetics, Champaign

  23. 23.

    Campbell B, Zito G, Colquhoun R et al (2014) Inter- and intra-day test-retest reliability of the CosmedFitmateProTM indirect calorimeter for resting metabolic rate. J IntSoc Sports Nutr 11:P46. https://doi.org/10.1186/1550-2783-11-S1-P46

    Article  Google Scholar 

  24. 24.

    Vandarakis D, Salacinski AJ, Broeder CE (2013) A comparison of cosmed metabolic systems for the determination of resting metabolic rate. Res Sport Med 21:187–194. https://doi.org/10.1080/15438627.2012.757226

    Article  Google Scholar 

  25. 25.

    Branco BHM, Bernuci MP, Marques DC et al (2018) Proposal of a normative table for body fat percentages of Brazilian young adults through bioimpedanciometry. J Exerc Rehabil 14:974–979. https://doi.org/10.12965/jer.1836400.200

    Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Guedes DP (2013) Procedimentosclínicosutilizados para análise da composição corporal. Rev Bras Cineantropometria e Desempenho Hum 15(1):113–129. https://doi.org/10.5007/1980-0037.2013v15n1p113

    Article  Google Scholar 

  27. 27.

    Heyward VH (1996) Evaluation of body composition. Sport Med 22:146–156. https://doi.org/10.2165/00007256-199622030-00002

    CAS  Article  Google Scholar 

  28. 28.

    de la Marcos SC, de SillerasMartín BM MAC et al (2015) Concordancia entre calorimetríaindirecta y modelospredictivosenunapoblaciónsanaespañola. NutrHosp 32:888–896. https://doi.org/10.3305/nh.2015.32.2.9162

    Article  Google Scholar 

  29. 29.

    Dettwyler KA (1993) Anthropometric standardization reference manual, abridged edition. Am J Phys Anthropol 92:239–241. https://doi.org/10.1002/ajpa.1330920214

    Article  Google Scholar 

  30. 30.

    Aliasgharzadeh S, Mahdavi R, AsghariJafarabadi M, Namazi N (2015) Comparison of indirect calorimetry and predictive equations in estimating resting metabolic rate in underweight females. Iran J Public Health 44:822–829

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Campbell B, Zito G, Colquhoun R et al (2014) Inter-and intra-day test-retest reliability of the CosmedFitmateProTM indirect calorimeter for resting metabolic rate. J IntSoc Sports Nutr 11(1):46. https://doi.org/10.1186/1550-2783-11-S1-P46

    Article  Google Scholar 

  32. 32.

    de Weir JBV (1949) New methods for calculating metabolic rate with special reference to protein metabolism. J Physiol 109:1–9. https://doi.org/10.1113/jphysiol.1949.sp004363

    Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Crouter SE, Antczak A, Hudak JR et al (2006) Accuracy and reliability of the ParvoMedicsTrueOne 2400 and MedGraphics VO2000 metabolic systems. Eur J ApplPhysiol 98:139–151. https://doi.org/10.1007/s00421-006-0255-0

    Article  Google Scholar 

  34. 34.

    Wahrlich V, Anjos LA, Going SB, Lohman TG (2006) Validation of the VO2000 calorimeter for measuring resting metabolic rate. ClinNutr 25:687–692. https://doi.org/10.1016/j.clnu.2006.01.002

    Article  Google Scholar 

  35. 35.

    Matsudo S, Araújo T, Matsudo V, Andrade D, Andrade E, Oliveira LC, Braggion G (2001) Questionário internacional de atividade física (IPAQ): estudo de validade e reprodutibilidade no Brasil. Rev Bras Atividade Física Saúde 6:5–18. https://doi.org/10.12820/rbafs.v.6n2p5-18

    Article  Google Scholar 

  36. 36.

    Giavarina D (2015) Understanding Bland Altman analysis. Biochem Med 25:141–151. https://doi.org/10.11613/BM.2015.015

    Article  Google Scholar 

  37. 37.

    Schober P, Schwarte LA (2018) Correlation coefficients: appropriate use and interpretation. AnesthAnalg 126:1763–1768. https://doi.org/10.1213/ANE.0000000000002864

    Article  Google Scholar 

  38. 38.

    Martin Bland J, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 327:307–310. https://doi.org/10.1016/S0140-6736(86)90837-8

    Article  Google Scholar 

  39. 39.

    Mukaka MM (2012) Statistics corner: a guide to appropriate use of correlation coefficient in medical research. Malawi Med J 24:69–71

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    McClave SA, Snider HL (2001) Dissecting the energy needs of the body. CurrOpinClinNutrMetab Care 4:143–147. https://doi.org/10.1097/00075197-200103000-00011

    CAS  Article  Google Scholar 

  41. 41.

    Branco BHM, Valladares D, de Oliveira FM et al (2019) Effects of the order of physical exercises on body composition, physical fitness, and cardiometabolic risk in adolescents participating in an interdisciplinary program focusing on the treatment of obesity. Front Physiol 10:1–11. https://doi.org/10.3389/fphys.2019.01013

    Article  Google Scholar 

  42. 42.

    Melzer K (2011) Carbohydrate and fat utilization during rest and physical activity. E SpenEur E J ClinNutrMetab 6:e45–e52. https://doi.org/10.1016/j.eclnm.2011.01.005

    Article  Google Scholar 

  43. 43.

    Andreato LV, Branco BHM (2016) Different sports, but the same physical and physiological profiles? Sport Med 46:1963–1965. https://doi.org/10.1007/s40279-016-0587-9

    Article  Google Scholar 

  44. 44.

    Marra M, Cioffi I, Sammarco R et al (2019) Are Raw BIA variables useful for predicting resting energy expenditure in adults with obesity? Nutrients 11:216. https://doi.org/10.3390/nu11020216

    CAS  Article  PubMed Central  Google Scholar 

  45. 45.

    Wahrlich V, dos Anjos LA (2001) Aspectoshistóricos e metodológicos da medição e estimativa da taxa metabólica basal: umarevisão da literatura. Cad SaudePublica 17:801–817. https://doi.org/10.1590/S0102-311X2001000400015

    CAS  Article  Google Scholar 

  46. 46.

    Jagim AR, Camic CL, Askow A et al (2019) Sex differences in resting metabolic rate among athletes. J Strength Cond Res 33:3008–3014. https://doi.org/10.1519/JSC.0000000000002813

    Article  PubMed  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Braulio Henrique Magnani Branco.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The study was approved by the Ethics and Local Research Committee (approval number: 3,404,860/2019). The study was conducted in accordance with the principles of the Declaration of Helsinki.

Informed consent

After approval, the study’s technical procedures were explained to the participants, and they were requested to provide written informed consent.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

de Souza Marques, D.C., Coelho, A.A., de Oliveira, F.M. et al. Resting metabolic rate: a comparison between different measurement methods used in male university students. Sport Sci Health 17, 449–457 (2021). https://doi.org/10.1007/s11332-020-00727-2

Download citation

Keywords

  • Anthropometry
  • Nutritional assessment
  • Metabolism