Comparison between Measured and Predicted Basal Metabolic Rate in Indonesian Adolescent Female Basketball Players
Perbedaan Basal Metabolic Rate Berdasarkan Pengukuran dan Formula pada Atlet Bola Basket Remaja Putri Indonesia
Downloads
Background: Accurate estimation of energy requirement is significantly crucial for athletes to support performance. Meanwhile, Basal Metabolic Rate (BMR) constitutes the largest component of Total Energy Expenditure (TEE) and is commonly assessed using estimation formulas.
Objectives: This study aimed to compare measured and predicted BMR using Body Impedance Analysis (BIA) and estimation formulas respectively among adolescent female basketball players in the Youth Sports Training Center (PPOP) Special Capital Region (DKI) Jakarta.
Methods: A total of 12 adolescent female basketball players aged 14-18 years were subjected to BIA measurements to obtain BMR and body composition. BMR was compared with 24 formulas using paired t-tests, while mean differences and effect size were analyzed to determine the best predictive formula.
Results: The results showed significant differences between measured (1473.6±201.2 kcal) and the majority of all predicted BMR (p-value<0.05), except for Cunningham (1459.0±102.1 kcal), Harris-Benedict (1441.7±87.0 kcal), IMNA (1398.7±91.1 kcal), and Kim (1384.3±69.6 kcal). The smallest differences between measured and predicted BMR were observed in Cunningham (14.7±113.3 kcal) and Harris-Benedict (31.9±116.2 kcal). Effect size analyses showed large differences in the majority of formulas (>1), while Cunningham (0.129) and Harris-Benedict (0.274) had the smallest effect sizes.
Conclusions: Cunningham and Harris-Benedict may serve as alternative estimations for BMR aside from using BIA in adolescent female basketball players in PPOP DKI Jakarta. Future studies should consider indirect calorimetry methods to enhance BMR measurement accuracy. Similar studies should also be performed on various athletes in Indonesia with larger sample sizes.
Martinho, D., Naughton, R., Faria, A., Rebelo, A. & Sarmento, H. Predicting resting energy expenditure among athletes: a systematic review. Biol. Sport 40, 787–804 (2023). Doi: 10.5114/biolsport.2023.119986
Loucks, A. B., Kiens, B. & Wright, H. H. Energy availability in athletes. Journal of Sports Sciences 29, S7–S15 (2011). Doi: 10.1080/02640414.2011.588958
Balci, A. et al. Current predictive resting metabolic rate equations are not sufficient to determine proper resting energy expenditure in olympic young adult national team athletes. Front. Physiol. 12, 625370 (2021). Doi: 10.3389/fphys.2021.625370
Mountjoy, M. et al. International Olympic Committee (IOC) consensus statement on Relative Energy Deficiency in Sport (RED-S): 2018 Update. International Journal of Sport Nutrition and Exercise Metabolism 28, 316–331 (2018). Doi: 10.1123/ijsnem.2018-0136
Marra, M. et al. Accuracy of predictive equations for estimating resting energy expenditure in obese adolescents. The Journal of Pediatrics 166, 1390-1396.e1 (2015). Doi: 10.1016/j.jpeds.2015.03.013
Staal, S., Sjödin, A., Fahrenholtz, I., Bonnesen, K. & Melin, A. K. Low RMRratio as a surrogate marker for energy deficiency, the choice of predictive equation vital for correctly identifying male and female ballet dancers at risk. International Journal of Sport Nutrition and Exercise Metabolism 28, 412–418 (2018). Doi: 10.1123/ijsnem.2017-0327
Westerterp, K. R. Physical activity and physical activity induced energy expenditure in humans: measurement, determinants, and effects. Front. Physiol. 4, (2013). Doi: 10.3389/fphys.2013.00090
Pinheiro Volp, A. C., Esteves de Oliveira, F. C., Duarte Moreira Alves, R., Esteves, E. A. & Bressan, J. Energy expenditure: components and evaluation methods. Nutr Hosp 26, 430–440 (2011). Doi: 10.1590/S0212-16112011000300002
Sabounchi, N. S., Rahmandad, H. & Ammerman, A. Best-fitting prediction equations for basal metabolic rate: informing obesity interventions in diverse populations. Int J Obes 37, 1364–1370 (2013). Doi: 10.1038/ijo.2012.218
Delsoglio, M., Achamrah, N., Berger, M. M. & Pichard, C. Indirect calorimetry in clinical practice. JCM 8, 1387 (2019). Doi: 10.3390/jcm8091387
Ndahimana, D. & Kim, E.-K. Measurement methods for physical activity and energy expenditure: a review. Clin Nutr Res 6, 68 (2017). Doi: 10.7762/cnr.2017.6.2.68
Frankenfield, D. C., Rowe, W. A., Smith, J. S. & Cooney, R. N. Validation of several established equations for resting metabolic rate in obese and nonobese people. Journal of the American Dietetic Association 103, 1152–1159 (2003). Doi: 10.1016/s0002-8223(03)00982-9
Harris, J. A. & Benedict, F. G. A Biometric study of human basal metabolism. Proc. Natl. Acad. Sci. U.S.A. 4, 370–373 (1918). Doi: 10.1073/pnas.4.12.370
Finan, K., Larson, D. E. & Goran, M. I. Cross-validation of prediction equations for resting energy expenditure in young, healthy children. Journal of the American Dietetic Association 97, 140–145 (1997). Doi: 10.1016/S0002-8223(97)00039-4
Cunningham, J. J. A reanalysis of the factors influencing basal metabolic rate in normal adults. The American Journal of Clinical Nutrition 33, 2372–2374 (1980). 10.1093/ajcn/33.11.2372
Bernstein, R. et al. Prediction of the resting metabolic rate in obese patients. The American Journal of Clinical Nutrition 37, 595–602 (1983). Doi: 10.1093/ajcn/37.4.595
Roza, A. M. & Shizgal, H. M. The Harris Benedict equation reevaluated: resting energy requirements and the body cell mass. The American Journal of Clinical Nutrition 40, 168–182 (1984). Doi: 10.1093/ajcn/40.1.168
Owen, O. et al. A reappraisal of caloric requirements in healthy women. The American Journal of Clinical Nutrition 44, 1–19 (1986). Doi: 10.1093/ajcn/44.1.1
Mifflin, M. et al. A new predictive equation for resting energy expenditure in healthy individuals. The American Journal of Clinical Nutrition 51, 241–247 (1990). Doi: 10.1093/ajcn/51.2.241
Molnár, D., Jeges, S., Erhardt, E. & Schutz, Y. Measured and predicted resting metabolic rate in obese and nonobese adolescents. The Journal of Pediatrics 127, 571–577 (1995). Doi: 10.1016/s0022-3476(95)70114-1
Henry, C. J. & Rees, D. G. New predictive equations for the estimation of basal metabolic rate in tropical peoples. Eur J Clin Nutr 45, 177–185 (1991).
Nelson, K., Weinsier, R., Long, C. & Schutz, Y. Prediction of resting energy expenditure from fat-free mass and fat mass. The American Journal of Clinical Nutrition 56, 848–856 (1992). Doi: 10.1093/ajcn/56.5.848
Maffeis, C., Schutz, Y., Micciolo, R., Zoccante, L. & Pinelli, L. Resting metabolic rate in six- to ten-year-old obese and nonobese children. The Journal of Pediatrics 122, 556–562 (1993). Doi: 10.1016/s0022-3476(05)83535-8
Liu, H.-Y., Lu, Y.-F. & Chen, W.-J. Predictive equations for basal metabolic rate in chinese adults. Journal of the American Dietetic Association 95, 1403–1408 (1995). Doi: 10.1016/S0002-8223(95)00369-X
De Lorenzo, A. et al. A new predictive equation to calculate resting metabolic rate in athletes. J Sports Med Phys Fitness 39, 213–219 (1999).
Wang, Z. et al. Resting energy expenditure-fat-free mass relationship: new insights provided by body composition modeling. American Journal of Physiology-Endocrinology and Metabolism 279, E539–E545 (2000). Doi: 10.1152/ajpendo.2000.279.3.E539
Trumbo, P., Schlicker, S., Yates, A. A., Poos, M., & Food and nutrition board of the institute of medicine, the national academies. dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein and amino acids. J Am Diet Assoc 102, 1621–1630 (2002). Doi: 10.1016/s0002-8223(02)90346-9
FAO/WHO/UNU. Human energy requirements: report of a joint FAO/ WHO/UNU expert consultation. Food Nutr Bull 26, 166 (2005).
Müller, M. J. et al. World Health Organization equations have shortcomings for predicting resting energy expenditure in persons from a modern, affluent population: generation of a new reference standard from a retrospective analysis of a German database of resting energy expenditure. Am J Clin Nutr 80, 1379–1390 (2004). Doi: 10.1093/ajcn/80.5.1379
Johnstone, A. M., Rance, K. A., Murison, S. D., Duncan, J. S. & Speakman, J. R. Additional anthropometric measures may improve the predictability of basal metabolic rate in adult subjects. Eur J Clin Nutr 60, 1437–1444 (2006). Doi: 10.1038/sj.ejcn.1602477
Taguchi, M., Ishikawa-Takata, K., Ouchi, S. & Higuchi, M. Validity of prediction equation of basal metabolic rate based on fat0free mass in Japanese female athletes. Jpn. J. Phys. Fitness Sports Med. 60, 423–432 (2011). Doi: 10.7600/jspfsm.60.423
Kim, J.-H., Kim, M.-H., Kim, G.-S., Park, J.-S. & Kim, E.-K. Accuracy of predictive equations for resting metabolic rate in Korean athletic and non-athletic adolescents. Nutr Res Pract 9, 370–378 (2015). Doi: 10.4162/nrp.2015.9.4.370
Jagim, A. R. et al. Accuracy of resting metabolic rate prediction equations in athletes. J Strength Cond Res 32, 1875–1881 (2018). Doi: 10.1519/JSC.0000000000002111
Schofield, K. L., Thorpe, H. & Sims, S. T. Resting metabolic rate prediction equations and the validity to assess energy deficiency in the athlete population. Exp Physiol 104, 469–475 (2019). Doi: 10.1113/EP087512
Watson, A. D. et al. Determining a resting metabolic rate prediction equation for collegiate female athletes. J Strength Cond Res 33, 2426–2432 (2019). Doi: 10.1519/JSC.0000000000002856
Devrim Lanpir, A., Kocahan, T., Deliceoğlu, G., Tortu, E. & Bilgic, P. Is there any predictive equation to determine resting metabolic rate in ultra-endurance athletes? Progress in Nutrition 21, 25–33 (2019). Doi: 10.23751/pn.v21i1.8052
Carlsohn, A., Scharhag-Rosenberger, F., Cassel, M. & Mayer, F. Resting metabolic rate in elite rowers and canoeists: difference between indirect calorimetry and prediction. Ann Nutr Metab 58, 239–244 (2011). Doi: 10.1159/000330119
Reneau, J., Obi, B., Moosreiner, A. & Kidambi, S. Do we need race-specific resting metabolic rate prediction equations? Nutr Diabetes 9, 21 (2019). Doi: 10.1038/s41387-019-0087-8
Tortu, E., Birol, A. & Aksarı, M. Evaluation of different equations for resting metabolic rate prediction in female combat sports athletes. Monten. J. Sports Sci. Med. 12, 41–48 (2023). Doi: 10.26773/mjssm.230906
Frings-Meuthen, P. et al. Resting energy expenditure of master athletes: accuracy of predictive equations and primary determinants. Front. Physiol. 12, 641455 (2021). Doi: 10.3389/fphys.2021.641455
Łuszczki, E. et al. Resting energy expenditure of physically active boys in southeastern poland—the accuracy and validity of predictive equations. Metabolites 10, 493 (2020). Doi: 10.3390/metabo10120493
Oliveira, T. M. et al. Predictive equations for resting metabolic rate are not appropriate to use in Brazilian male adolescent football athletes. PLoS ONE 16, e0244970 (2021). Doi: 10.1371/journal.pone.0244970
Lee, S., Moto, K., Oh, T. & Taguchi, M. Comparison between predicted and measured resting energy expenditures in Korean male collegiate soccer players. Phys Act Nutr 26, 025–031 (2022). Doi: 10.20463/pan.2022.0015
Thomas, D. T., Erdman, K. A. & Burke, L. M. Position of the academy of nutrition and dietetics, dietitians of Canada, and the American College of Sports Medicine: nutrition and athletic performance. Journal of the Academy of Nutrition and Dietetics 116, 501–528 (2016). Doi: 10.1016/j.jand.2015.12.006
Thompson, J. & Manore, M. M. Predicted and measured resting metabolic rate of male and female endurance athletes. Journal of the American Dietetic Association 96, 30–34 (1996). Doi: 10.1016/S0002-8223(96)00010-7
Ten Haaf, T. & Weijs, P. J. M. Resting energy expenditure prediction in recreational athletes of 18–35 years: confirmation of cunningham equation and an improved weight-based alternative. PLoS ONE 9, e108460 (2014). Doi: 10.1371/journal.pone.0108460
Heymsfield, S. B. et al. Body-size dependence of resting energy expenditure can be attributed to nonenergetic homogeneity of fat-free mass. Am J Physiol Endocrinol Metab 282, E132-138 (2002). Doi: 10.1152/ajpendo.2002.282.1.E132
Cunningham, J. J. Body composition as a determinant of energy expenditure: a synthetic review and a proposed general prediction equation. Am J Clin Nutr 54, 963–969 (1991). Doi: 10.1093/ajcn/54.6.963
de Oliveira, E. P., Orsatti, F. L., Teixeira, O., Maestá, N. & Burini, R. C. Comparison of predictive equations for resting energy expenditure in overweight and obese adults. J Obes 2011, 534714 (2011). Doi: 10.1155/2011/534714
Sun, G. et al. Comparison of multifrequency bioelectrical impedance analysis with dual-energy X-ray absorptiometry for assessment of percentage body fat in a large, healthy population. The American Journal of Clinical Nutrition 81, 74–78 (2005). Doi: 10.1093/ajcn/81.1.74
Ugras, S. Evaluating of altered hydration status on effectiveness of body composition analysis using bioelectric impedance analysis. Libyan Journal of Medicine 15, 1741904 (2020). Doi: 10.1080/19932820.2020.1741904
Madzima, T. A., Panton, L. B., Fretti, S. K., Kinsey, A. W. & Ormsbee, M. J. Night-time consumption of protein or carbohydrate results in increased morning resting energy expenditure in active college-aged men. Br J Nutr 111, 71–77 (2014). Doi: 10.1017/S000711451300192X.
Copyright (c) 2024 Amerta Nutrition
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
AMERTA NUTR by Unair is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
1. The journal allows the author to hold the copyright of the article without restrictions.
2. The journal allows the author(s) to retain publishing rights without restrictions
3. The legal formal aspect of journal publication accessibility refers to Creative Commons Attribution Share-Alike (CC BY-SA).
4. The Creative Commons Attribution Share-Alike (CC BY-SA) license allows re-distribution and re-use of a licensed work on the conditions that the creator is appropriately credited and that any derivative work is made available under "the same, similar or a compatible license”. Other than the conditions mentioned above, the editorial board is not responsible for copyright violation.