The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Metabolic Rate in Adolescent Athletes: The Development and Validation of New Equations, and Comparison to Previous Models
Abstract
We sought to assess the accuracy of current or developing new prediction equations for resting metabolic rate (RMR) in adolescent athletes. RMR was assessed via indirect calorimetry, alongside known predictors (body composition via dual-energy X-ray absorptiometry, height, age, and sex) and hypothesized predictors (race and maturation status assessed via years to peak height velocity), in a diverse cohort of adolescent athletes (n = 126, 77% male, body mass = 72.8 ± 16.6 kg, height = 176.2 ± 10.5 cm, age = 16.5 ± 1.4 years). Predictive equations were produced and cross-validated using repeated k-fold cross-validation by stepwise multiple linear regression (10 folds, 100 repeats). Performance of the developed equations was compared with several published equations. Seven of the eight published equations examined performed poorly, underestimating RMR in >75% to >90% of cases. Root mean square error of the six equations ranged from 176 to 373, mean absolute error ranged from 115 to 373 kcal, and mean absolute error SD ranged from 103 to 185 kcal. Only the Schofield equation performed reasonably well, underestimating RMR in 51% of cases. A one- and two-compartment model were developed, both r2 of .83, root mean square error of 147, and mean absolute error of 114 ± 26 and 117 ± 25 kcal for the one- and two-compartment model, respectively. Based on the models’ performance, as well as visual inspection of residual plots, the following model predicts RMR in adolescent athletes with better precision than previous models; RMR = 11.1 × body mass (kg) + 8.4 × height (cm) − (340 male or 537 female).
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“To address the limitations of traditional equations a predictive model based on data from 126 adolescent athletes (average age 16.5 years) has been developed [11].”
From Optimizing Performance Nutrition for Adolescent Athletes: A Review of Dietary Needs, Risks, and Practical Strategies (2025) · cites this paper as [11]
In the citing paper
“This equation may better estimate baseline energy needs for adolescent athletes in clinical and sports nutrition settings.”
From Optimizing Performance Nutrition for Adolescent Athletes: A Review of Dietary Needs, Risks, and Practical Strategies (2025) · cites this paper as [11]
In the citing paper
“Traditional predictive equations (e.g., Harris-Benedict, Schofield, Cunningham) were developed for adults and may not be reliable measures of resting metabolic rate (RMR) in adolescents [10,11].”
From Optimizing Performance Nutrition for Adolescent Athletes: A Review of Dietary Needs, Risks, and Practical Strategies (2025) · cites this paper as [11]
In the citing paper
“Recently, new predictive resting metabolism equations have been developed from a cohort of male and female junior athletes (n = 126) who each undertook an indirect calorimetry assessment of RMR under standardised conditions. The cohort included athletes from a range of sports with an average age of 16.5 years (range 13.1–19.7) [24].”
From Youth Athlete Development and Nutrition (2021) · cites this paper as [24]
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Citations
Cites
A new predictive equation for resting energy expenditure in healthy individuals
Mifflin, M., St Jeor, S., Hill, L., Scott, B., Daugherty, S., & Koh, Y. (1990) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/51.2.241 · Cited by 2,544
Cunningham, J. (1991) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/54.6.963 · Cited by 607
Fullmer, S., Benson-Davies, S., Earthman, C. P., Frankenfield, D. C., Gradwell, E., Lee, P. S. P., Piemonte, T., & Trabulsi, J. (2015) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2015.04.003 · Cited by 228
Research works printed but not linked: 21
- Cunningham, J.J. (1980). A reanalysis of the factors influencing basal metabolic rate in normal adults. American Journal of Clinical Nutrition, 33(11), 2372–2374. PubMed ID: 7435418 doi:10.1093/ajcn/33.11.237210.1093/ajcn/33.11.2372 · DOI · Paper page
- De Lorenzo, A., Bertini, I., Candeloro, N., Piccinelli, R., Innocente, I., & Brancati, A. (1999). A new predictive equation to calculate resting metabolic rate in athletes. Journal of Sports Medicine and Physical Fitness, 39(3), 213–219. PubMed ID: 10573663
- Granados, A., Gebremariam, A., & Lee, J.M. (2015). Relationship between timing of peak height velocity and pubertal staging in boys and girls. Journal of Clinical Research in Pediatric Endocrinology, 7(3), 235–237. PubMed ID: 26831559 doi:10.4274/jcrpe.200710.4274/jcrpe.2007 · DOI
- Harris, J.A., & Benedict, F.G. (1918). A biometric study of human basal metabolism. Proceeding of the National Academy of Sciences of the United States of America, 4(12), 370–373. doi:10.1073/pnas.4.12.37010.1073/pnas.4.12.370 · DOI · Paper page
- Henry, C.J., Dyer, S., & Ghusain-Choueiri, A. (1999). New equations to estimate basal metabolic rate in children aged 10–15 years. European Journal of Clinical Nutrition, 53(2), 134–142. PubMed ID: 10099947 doi:10.1038/sj.ejcn.160069010.1038/sj.ejcn.1600690 · DOI
- Jagim, A.R., Camic, C.L., Kisiolek, J., Luedke, J., Erickson, J., Jones, M.T., & Oliver, J.M. (2018). Accuracy of resting metabolic rate prediction equations in athletes. Journal of Strength and Conditioning Research, 32(7), 1875–1881. PubMed ID: 28682934 doi:10.1519/JSC.000000000000211110.1519/JSC.0000000000002111 · DOI
- Kim, J.H., Kim, M.H., Kim, G.S., Park, J.S., & Kim, E.K. (2015). Accuracy of predictive equations for resting metabolic rate in Korean athletic and non-athletic adolescents. Nutrition Research and Practice, 9(4), 370–378. PubMed ID: 26244075 doi:10.4162/nrp.2015.9.4.37010.4162/nrp.2015.9.4.370 · DOI
- Koziel, S.M., & Malina, R.M. (2018). Modified maturity offset prediction equations: Validation in independent longitudinal samples of boys and girls. Sports Medicine, 48(1), 221–236. PubMed ID: 28608181 doi:10.1007/s40279-017-0750-y10.1007/s40279-017-0750-y · DOI
- Lazzer, S., Agosti, F., De Col, A., & Sartorio, A. (2006). Development and cross-validation of prediction equations for estimating resting energy expenditure in severely obese Caucasian children and adolescents. British Journal of Nutrition, 96(5), 973–979. PubMed ID: 17092390 doi:10.1017/BJN2006194110.1017/BJN20061941 · DOI
- Lazzer, S., Patrizi, A., De Col, A., Saezza, A., & Sartorio, A. (2014). Prediction of basal metabolic rate in obese children and adolescents considering pubertal stages and anthropometric characteristics or body composition. European Journal of Clinical Nutrition, 68(6), 695–699. PubMed ID: 24595222 doi:10.1038/ejcn.2014.2610.1038/ejcn.2014.26 · DOI
- Loureiro, L.L., Fonseca, S., Jr., Castro, N.G., Dos Passos, R.B., Porto, C.P., & Pierucci, A.P. (2015). Basal metabolic rate of adolescent modern pentathlon athletes: Agreement between indirect calorimetry and predictive equations and the correlation with body parameters. PLoS One, 10(11), e0142859. PubMed ID: 26569101 doi:10.1371/journal.pone.014285910.1371/journal.pone.0142859 · DOI · Paper page
- Mahan, L.K., Escott-Stump, S., Raymond, J.L., & Krause, M.V. (2012). Krause’s food and the nutrition care process. St. Louis, MO: Elsevier Health Sciences.
- Marshall, W.A., & Tanner, J.M. (1969). Variations in pattern of pubertal changes in girls. Archives of Disease in Childhood, 44(235), 291–303. PubMed ID: 5785179 doi:10.1136/adc.44.235.29110.1136/adc.44.235.291 · DOI · Paper page
- Marshall, W.A., & Tanner, J.M. (1970). Variations in the pattern of pubertal changes in boys. Archives of Disease in Childhood, 45(239), 13–23. PubMed ID: 5440182 doi:10.1136/adc.45.239.1310.1136/adc.45.239.13 · DOI · Paper page
- Mirwald, R.L., Baxter-Jones, A.D., Bailey, D.A., & Beunen, G.P. (2002). An assessment of maturity from anthropometric measurements. Medicine and Science in Sports and Exercise, 34(4), 689–694. PubMed ID: 11932580
- Mountjoy, M., Sundgot-Borgen, J.K., Burke, L.M., Ackerman, K.E., Blauwet, C., Constantini, N., … Budgett, R. (2018). IOC consensus statement on relative energy deficiency in sport (RED-S): 2018 update. British Journal of Sports Medicine, 52(11), 687–697. PubMed ID: 29773536 doi:10.1136/bjsports-2018-09919310.1136/bjsports-2018-099193 · DOI · Paper page
- Nana, A., Slater, G.J., Stewart, A.D., & Burke, L.M. (2015). Methodology review: Using dual-energy X-ray absorptiometry (DXA) for the assessment of body composition in athletes and active people. International Journal of Sport Nutrition and Exercise Metabolism, 25(2), 198–215. PubMed ID: 25029265 doi:10.1123/ijsnem.2013-022810.1123/ijsnem.2013-0228 · DOI · Paper page
- Rogol, A.D., Clark, P.A., & Roemmich, J.N. (2000). Growth and pubertal development in children and adolescents: Effects of diet and physical activity. American Journal of Clinical Nutrition, 72(2 Suppl), 521S–528S. PubMed ID: 10919954 doi:10.1093/ajcn/72.2.521S10.1093/ajcn/72.2.521S · DOI · Paper page
- Schofield, K.L., Thorpe, H., & Sims, S.T. (2019). Resting metabolic rate prediction equations and the validity to assess energy deficiency in the athlete population. Experimental Physiology, 104(4), 469–475. PubMed ID: 30758869 doi:10.1113/EP08751210.1113/EP087512 · DOI
- Schofield, W.N. (1985). Predicting basal metabolic rate, new standards and review of previous work. Human Nutrition: Clinical Nutrition, 39(Suppl 1), 5–41.
- Yamashita, T., Yamashita, K., & Kamimura, R. (2007). A stepwise AIC method for variable selection in linear regression. Communications in Statistics–Theory and Methods, 36(13), 2395–2403. doi:10.1080/0361092070121563910.1080/03610920701215639 · DOI · Paper page
Cited by
Youth Athlete Development and Nutrition
Desbrow, B. (2021) · Sports Medicine · doi:10.1007/s40279-021-01534-6 · Cited by 159
Ackerman, K. E., Rogers, M. A., Heikura, I. A., Burke, L. M., Stellingwerff, T., Hackney, A. C., Verhagen, E., Schley, S., Saville, G. H., Mountjoy, M., et al. (2023) · British Journal of Sports Medicine · doi:10.1136/bjsports-2023-107359 · Cited by 43
Everett, S. (2025) · Nutrients · doi:10.3390/nu17172792 · Cited by 37
Alcock, R., Hislop, M., Vidgen, H. A., & Desbrow, B. (2024) · Journal of Functional Morphology and Kinesiology · doi:10.3390/jfmk9040221 · Cited by 33
Metadata
- Authors
- Reale, R. J., Roberts, T. J., Lee, K. A., Bonsignore, J. L., & Anderson, M. L.
- Affiliations
- Gatorade Sports Science Institute, PepsiCo, Inc.
- Venue
- International Journal of Sport Nutrition and Exercise Metabolism
- Publisher
- Human Kinetics
- Published
- 2020-07-01
- Volume
- 30
- Issue
- 4
- Pages
- 249-257
- ISSN
- 1526-484X
- 1543-2742
- Cited by
- 22
- References
- 3
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