Resting Energy Expenditure in CrossFit® Participants: Predictive Equations versus Indirect Calorimetry

Maraline Santos Sena, Marcio Leandro Ribeiro de Souza, Valden Luis Matos Capistrano Junior

Abstract


Background: CrossFit® involves high-intensity functional movements and research has shown that the program increases metabolic rates in participants. Objective: To measure resting energy expenditure (REE) in CrossFit® participants using indirect calorimetry (IC) and to verify the most appropriate predictive equation to estimate REE. Methods: Overall, 142 CrossFit® participants (18–59 years; 91 [64.1%], women) underwent weight, height, waist circumference, and body mass index (BMI) measurements. Body composition was evaluated using a portable ultrasound system (BodyMetrix®). REEs were measured (mREE) by IC and predicted by six different equations (pREE): Harris-Benedict, World Health Organization (WHO), Henry and Rees, Cunningham (1980 and 1991), and Mifflin–St. Jeor. Results: The mean age was 33.0 (6.3) years, with no significant difference between men and women; mean mREE, 1583.2(404.4) kcal/d; and pREE, 1455.5(230.9) to 1711.3(285.5) kcal/d. The best REE predictive equations for this population were Cunningham (1991) (P=0.338), WHO (P=0.494), and Harris-Benedict (P=0.705) equations. The Harris-Benedict equation presented a smaller difference compared with IC [12.9(307.6) kcal], the Cunningham (1991) equation showed improved adequacy (102.5%), and the WHO equation presented highest accuracy (59.9%). The equations that were closest to the mREE were the Harris-Benedict for women and the WHO equation for men. Conclusion: Therefore, for CrossFit® participants, the REE can accurately be predicted with the Cunningham (1991), WHO, and Harris-Benedict equations.

Keywords


Basal Metabolism, Energy Expenditure, Indirect Calorimetry, Athletes, Body Composition

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References


Cavedon, V., Milanese, C., Marchi, A., & Zancanaro, C. (2020). Different amount of training affects body composition and performance in High-Intensity Functional Training participants. PLoS One, 15(8), e0237887. doi: 10.1371/journal.pone.0237887.

Claudino, J.V., Gabbett, T.J., Bourgeois, F., Souza, H.S., Miranda, R.C., Mezencio. B., … & Serrão, J.C. (2018). Crossfit Overview: Systematic Review and Meta-Analysis. Sports Medicine Open, 4(1), p.11. doi: 10.1186/s40798-018-0124-5.

Compher, C., Frankenfield, D., Keim, N., Roth-Yousey, L., & Evidence Analysis Working Group. (2006). Best practice methods to apply to measurement of resting metabolic rate in adults: a systematic review. Journal of the American Dietetic Association, 106(6), p.881-903. doi: 10.1016/j.jada.2006.02.009.

Cunningham, J.J. (1980). A reanalysis of the factors influencing basal metabolic rate in normal adults. The American Journal of Clinical Nutrition, 33(11), p.2372-2374. doi: 10.1093/ajcn/33.11.2372.

Cunningham, J.J. (1991). Body composition as a determinant of energy expenditure: a synthetic review and a proposed general prediction equation. The American Journal of Clinical Nutrition, 54(6), p.963-969. doi: 10.1093/ajcn/54.6.963.

Delsoglio, M., Achamrah, N., Berger, M.M., & Pichard, C. (2019). Indirect calorimetry in clinical practice. Journal of Clinical Medicine, 8(9), p.1387. doi: 10.3390/jcm8091387.

Frankenfield, D.C., Rowe, W.A., Smith, J.S., & Cooney, R.N. (2003). Validation of several established equations for resting metabolic rate in obese and nonobese people. Journal of the American Dietetic Association, 103(9), p.1152-1159. doi: 10.1016/s0002-8223(03)00982-9.

Haaf, T.T., & Weijs, P.J.M. (2014). Resting energy expenditure prediction in recreational athletes of 18-35 years: confirmation of Cunningham equation and an improved weight-based alternative. PLoS One, 9(10), e108460. doi: 10.1371/journal.pone.0108460.

Harris, J.A., & Benedict, F.G. (1918). A biometric study of human basal metabolism. Proceedings of the National Academy of Sciences of the USA, 4(12), p.370-373. doi: 10.1073/pnas.4.12.370.

Haugen, H.A., Chan, L.N., & Li, F. (2007). Indirect calorimetry: a practical guide for clinicians. Nutrition in Clinical Practice, 22(4), p.377-388. doi: 10.1177/0115426507022004377.

Henry, C.J., & Rees, D.G. (1991). New predictive equations for the estimation of basal metabolic rate in tropical peoples. European Journal of Clinical Nutrition, 45(4), p.177-185.

Henry, C.J.K. (2005). Basal metabolic rate studies in humans: measurement and development of new equations. Public Health Nutrition, 8(7A), p.1133- 1152. doi: 10.1079/phn2005801.

Jackson, A.S., & Pollock, M.L. (1978). Generalized equations for predicting body density of men. The British Journal of Nutrition, 40(3), p.497-504. doi: 10.1079/bjn19780152.

Jackson, A.S., Pollock, M.L., & Ward, A. (1980). Generalized equations for predicting body density of women. Medicine and Science in Sports and Exercise, 12(3), p. 175-181.

Levine, J.A. (2005). Measurement of energy expenditure. Public Health Nutrition, 8(7A), p.1123-1132. doi: 10.1079/phn2005800

Mangine, G.T., Stratton, M.T., Almeda, C.G., Roberts, M.D., Esmat, T.A., VanDusseldorp, T.A., & Feito, Y. (2020). Physiological differences between advanced Crossfit athletes, recreational Crossfit participants, and physically-active adults. PLoS One, 15(4), e0223548. doi: 10.1371/journal.pone.0223548.

Mangine, G.T., Tankersley, J.E., McDougle, J.M., Velazquez, N., Roberts, M.D., Esmat, T.A., VanDusseldorp, T.A., & Feito, Y. (2020). Predictors of Crossfit Open Performance. Sports (Basel), 8(7), p.102. doi: 10.3390/sports8070102.

Mifflin, M.D., St Jeor, S.T., Hill, L.A., Scott, B.J., Daugherty, S.A., & Koh, Y.O. (1990). A new predictive equation for resting energy expenditure in healthy individuals. The American Journal of Clinical Nutrition, 51(2), p.241-247. doi: 10.1093/ajcn/51.2.241.

Muller, M.J., & Soares, M.J. (2018). Resting energy expenditure and body composition: critical aspects for clinical nutrition. European Journal of Clinical Nutrition, 72(9), p.1208-1214. doi: 10.1038/s41430-018-0220-0.

Nattiv, A. (2000). Stress fractures and bone health in track and field athletes. Journal of Science and Medicine in Sport, 3(3), p.268-279. doi: 10.1016/s1440-2440(00)80036-5.

Oshima, T., Berger, M.M., Waele, E., Guttormsen, A.B., Heidegger, C.P., Hiesmayr, M., … & Pichard, C. (2017). Indirect calorimetry in nutritional therapy. A position paper by the ICALIC study group. Clinical Nutrition, 36(3), p.651-662. doi: 10.1016/j.clnu.2016.06.010

Pereira, M.P., Rocha, G.T., Santos, L.G.M., Viana, G.C.G., & Navarro, A.C. (2008). Avaliação das equações de predição da taxa metabólica basal em homens e mulheres ativos residentes em Brasília, DF, Brasil. Revista Brasileira de Nutrição Esportiva, 2(8), p.67-75.

Psota, T., & Chen, K.Y. (2013). Measuring energy expenditure in clinical populations: rewards and challenges. European Journal of Clinical Nutrition, 67(5), p.436-442. doi: 10.1038/ejcn.2013.38.

Redondo, R.B. (2015). Resting energy expenditure; assessment methods and applications. Nutricion Hospitalaria, 31(Suppl.3), p.245-254. doi: 10.3305/nh.2015.31.sup3.8772.

Souza, R.A.S., Silva, A.G., Souza, M.F., Souza, L.K.F., Roschel, H., Silva, S.F., & Saunders, B. (2021). A systematic review of CrossFit® workouts and dietary and supplementation interventions to guide nutritional strategies and future research in CrossFit®. International Journal of Sport Nutrition and Exercise Metabolism, epub ahead of print. doi: 10.1123/ijsnem.2020-0223.

Stewart, A., Marfell-Jones, M., Olds, T., & Ridder, H. (2011). International standards for anthropometric assessment. Lower Hutt: ISAK, 2011.

Thein-Nissenbaum, J.M., Rauh, M.J., Carr, K.E., Loud, K.J., & McGuine, T.A. (2011). Associations between disordered eating, menstrual dysfunction, and musculoskeletal injury among high school athletes. The Journal of Orthopaedic and Sports Physical Therapy, 41(2), p.60–69. doi: 10.2519/jospt.2011.3312.

Thomas, D.T., Erdman, K.E., & Burke, L.M. (2016). American College of Sports Medicine Joint Position Statement. Nutrition and Athletic Performance. Medicine and Science in Sports and Exercise, 48(3), p.543-568. doi: 10.1249/MSS.0000000000000852.

Thompson, J., & Manore, M. (1996). Predicted and measured resting metabolic rate of male and female endurance athletes. Journal of the American Dietetic Association, 96(1), p.30-34. doi: 10.1016/S0002-8223(96)00010-7.

Tibana, R.A., Sousa, N.M.F., Cunha, G.V., & Prestes, J. (2017). Correlações das variáveis antropométricas e fisiológicas com o desempenho no Crossfit®. Revista Brasileira de Prescrição e Fisiologia do Exercício, 11(70), p.880-887.

Warlich, V., & Anjos, L.A. (2001). Aspectos históricos e metodológicos da medição e estimativa da taxa metabólica basal: uma revisão de literatura. Caderno de Saúde Pública, 17(4), p.801-817. doi: 10.1590/s0102-311x2001000400015.

Weir, J.V.B. (1949). New methods for calculating metabolic rate with special reference to protein metabolism. The Journal of Physiology, 109(1-2), p.1-9. doi: 10.1113/jphysiol.1949.sp004363.

Wentz, L., Liu, P., Llich, J.Z., & Haymes, E.M. (2012). Dietary and training predictors of stress fractures in female runners. International Journal of Sport Nutrition and Exercise Metabolism, 22(5), p.374-382. doi: 10.1123/ijsnem.22.5.374.

World Health Organization (WHO). (1985). Energy and protein requirements. Report of a joint FAO/WHO/UNU Expert Consultation. Genebra, 206p. (Technical Report Series, No.724).

World Health Organization (WHO). (2000). Obesity – preventing and managing the global epidemic. Report of a WHO consultation on obesity. Genebra, 253p. (Technical Report Series, No.894).




DOI: http://dx.doi.org/10.7575/aiac.ijkss.v.9n.2p.7

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