The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Current Predictive Resting Metabolic Rate Equations Are Not Sufficient to Determine Proper Resting Energy Expenditure in Olympic Young Adult National Team Athletes
Abstract
Predictive resting metabolic rate (RMR) equations are widely used to determine athletes’ resting energy expenditure (REE). However, it remains unclear whether these predictive RMR equations accurately predict REE in the athletic populations. The purpose of the study was to compare 12 prediction equations (Harris-Benedict, Mifflin, Schofield, Cunningham, Owen, Liu’s, De Lorenzo) with measured RMR in Turkish national team athletes and sedentary controls. A total of 97 participants, 49 athletes (24 females, 25 males), and 48 sedentary (28 females, 20 males), were recruited from Turkey National Olympic Teams at the Ministry of Youth and Sports. RMR was measured using a Fitmate GS (Cosmed, Italy). The results of each 12 prediction formulas were compared with the measured RMR using paired t-test. The Bland-Altman plot was performed to determine the mean bias and limits of agreement between measured and predicted RMRs. Stratification according to sex, the measured RMR was greater in athletes compared to controls. The closest equation to the RMR measured by Fitmate GS was the Harris-Benedict equation in male athletes (mean difference -8.9 (SD 257.5) kcal/day), and Liu’s equation [mean difference -16.7 (SD 195.0) kcal/day] in female athletes. However, the intra-class coefficient (ICC) results indicated that all equations, including Harris-Benedict for male athletes (ICC = 0.524) and Liu’s for female athletes (ICC = 0.575), had a moderate reliability compared to the measured RMR. In sedentary subjects, the closest equation to the measured RMR is the Nelson equation in males, with the lowest RMSE value of 118 kcal/day [mean difference: 10.1 (SD 117.2) kJ/day], whereas, in females, all equations differ significantly from the measured RMR. While Nelson (ICC = 0.790) had good and Owen (ICC = 0.722) and Mifflin (calculated using fat-free mass) (ICC = 0.700) had moderate reliability in males, all predictive equations showed poor reliability in females. The results indicate that the predictive RMR equations failed to accurately predict RMR levels in the participants. Therefore, it may not suitable to use them in determining total energy expenditure.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 [10]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A guideline of selecting and reporting intraclass correlation coefficients for reliability research
Koo, T. K., & Li, M. Y. (2016) · Journal of Chiropractic Medicine · doi:10.1016/j.jcm.2016.02.012 · Cited by 31,709
Lee, P. H., Macfarlane, D. J., Lam, T., & Stewart, S. M. (2011) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/1479-5868-8-115 · Cited by 3,370
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
Self-citationMountjoy, M., Sundgot-Borgen, J., Burke, L., Carter, S., Constantini, N., Lebrun, C., Meyer, N., Sherman, R., Steffen, K., Budgett, R., et al. (2014) · British Journal of Sports Medicine · doi:10.1136/bjsports-2014-093502 · Cited by 1,469 · cited 2×
“This underestimation of energy needs can create a huge energy deficit in this athlete’s diet prescription, resulting in loss of body weight and relative energy deficiency syndrome with various critical symptoms including metabolic, physiological, immunological issues (Mountjoy et al., 2014, 2018).”
BackgroundThomas, D. T., Erdman, K. A., & Burke, L. M. (2016) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2015.12.006 · Cited by 1,359
Compher, C., Frankenfield, D., Keim, N., & Roth-Yousey, L. (2006) · Journal of the American Dietetic Association · doi:10.1016/j.jada.2006.02.009 · Cited by 923
The Harris Benedict equation reevaluated: resting energy requirements and the body cell mass
Roza, A. M., & Shizgal, H. M. (1984) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/40.1.168 · Cited by 824
A simple formula for the calculation of sample size in pilot studies
Viechtbauer, W., Smits, L., Kotz, D., Budé, L., Spigt, M., Serroyen, J., & Crutzen, R. (2015) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2015.04.014 · Cited by 612
Cunningham, J. (1991) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/54.6.963 · Cited by 607
Johnstone, A. M., Murison, S. D., Duncan, J. S., Rance, K. A., & Speakman, J. R. (2005) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/82.5.941 · Cited by 514
Mountjoy, M., Sundgot-Borgen, J., Burke, L., Ackerman, K. E., Blauwet, C., Constantini, N., Lebrun, C., Lundy, B., Melin, A., Meyer, N., et al. (2018) · International Journal of Sport Nutrition and Exercise Metabolism · doi:10.1123/ijsnem.2018-0136 · Cited by 447
Lee, I.-M., & Shiroma, E. J. (2014) · British Journal of Sports Medicine · doi:10.1136/bjsports-2013-093154 · Cited by 415
Prediction of resting energy expenditure from fat-free mass and fat mass
Nelson, K., Weinsier, R., Long, C., & Schutz, Y. (1992) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/56.5.848 · Cited by 371
Qualitative Attributes and Measurement Properties of Physical Activity Questionnaires: A Checklist
Terwee, C. B., Mokkink, L. B., van Poppel, M. N. M., Chinapaw, M. J. M., van Mechelen, W., & de Vet, H. C. W. (2010) · Sports Medicine · doi:10.2165/11531370-000000000-00000 · Cited by 269 · cited 1×
“Since there is no gold standard criterion for measuring physical activity level (Terwee et al., 2010) and motion sensors, such as triaxial accelerometers, are practically not feasible due to their high cost and timeconsuming features (Lee and Shiroma, 2014), it is widely preferred to determine the level of physical ac…”
BackgroundFullmer, 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
New strategies in sport nutrition to increase exercise performance
Close, G. L., Hamilton, D. L., Philp, A., Burke, L. M., & Morton, J. P. (2016) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2016.01.016 · Cited by 221
Drinking Strategies: Planned Drinking Versus Drinking to Thirst
Kenefick, R. W. (2018) · Sports Medicine · doi:10.1007/s40279-017-0844-6 · Cited by 142
Leskinen, T., Rinnankoski-Tuikka, R., Rintala, M., Seppänen-Laakso, T., Pöllänen, E., Alen, M., Sipilä, S., Kaprio, J., Kovanen, V., Rahkila, P., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012609 · Cited by 75
Research works printed but not linked: 37
- Bernstein, 1983, Prediction of the resting metabolic rate in obese patients., Am. J. Clin. Nutr., 37, 595, 10.1093/ajcn/37.4.595 · DOI
- Black, 2018, Low energy availability, plasma lipids, and hormonal profiles of recreational athletes., J. Strength Cond. Res., 32, 2816, 10.1519/JSC.0000000000002540 · DOI · Paper page
- Blundell, 2012, Body composition and appetite: fat-free mass (but not fat mass or BMI) is positively associated with self-determined meal size and daily energy intake in humans., Br. J. Nutr., 107, 445, 10.1017/S0007114511003138 · DOI · Paper page
- Carlsohn, 2011, Resting metabolic rate in elite rowers and canoeists: difference between indirect calorimetry and prediction., Ann. Nutr. Metab., 58, 239, 10.1159/000330119 · DOI
- Compher, 2005, Comparison between Medgem and Deltatrac resting metabolic rate measurements., Eur. J. Clin. Nutr., 59, 1136, 10.1038/sj.ejcn.1602223 · DOI
- Craig, 2003, International physical activity questionnaire: 12-Country reliability and validity., Med. Sci. Sports Exerc., 35, 1381, 10.1249/01.MSS.0000078924.61453.FB · DOI · Paper page
- Da Rocha, 2006, Indirect calorimetry: methodology, instruments and clinical application., Curr. Opin. Clin. Nutr. Metab. Care, 9, 247, 10.1097/01.mco.0000222107.15548.f5 · DOI
- De Jonge, 2012, Poor sleep quality and sleep apnea are associated with higher resting energy expenditure in obese individuals with short sleep duration., J. Clin. Endocrinol. Metab., 97, 2881, 10.1210/jc.2011-2858 · DOI
- De Lorenzo, 1999, A new predictive equation to calculate resting metabolic rate in athletes., J. Sports Med. Phys. Fitness, 39, 213
- Delsoglio, 2019, Indirect calorimetry in clinical practice., J. Clin. Med., 8, 10.3390/jcm8091387 · DOI
- Devrim-Lanpir, 2019, Is there any predictive equation to determine resting metabolic rate in ultra-endurance athletes?, Prog. Nutr., 21, 25, 10.23751/pn.v21i1.8052 · DOI
- Harris, 1918, A biometric study of human basal metabolism., Proc. Natl. Acad. Sci. U.S.A., 4, 370, 10.1073/pnas.4.12.370 · DOI · Paper page
- 2005, Panel on Dietary Reference. (Intakes)for Electrolytes, & Water. DRI, Dietary Reference Intakes for Water, Potassium, Sodium, Chloride, and Sulfate.
- Jagim, 2018, Accuracy of resting metabolic rate prediction equations in athletes., J. Strength Cond. Res., 32, 1875, 10.1519/JSC.0000000000002111 · DOI
- Johnstone, 2006, Additional anthropometric measures may improve the predictability of basal metabolic rate in adult subjects., Eur. J. Clin. Nutr., 60, 1437, 10.1038/sj.ejcn.1602477 · DOI
- 1985, Energy and Protein Requirements.
- Joseph, 2017, Are predictive equations for estimating resting energy expenditure accurate in Asian Indian male weightlifters?, Indian J. Endocrinol. Metab., 21, 515, 10.4103/ijem.IJEM_563_16 · DOI
- Juzwiak, 2016, Comparison of measured and predictive values of basal metabolic rate in brazilian paralympic track and field athletes., Int. J. Sport Nutr. Exerc. Metab., 26, 330, 10.1123/ijsnem.2015-2015 · DOI
- Kyle, 2004, Bioelectrical impedance analysis—part II: utilization in clinical practice., Clin. Nutr., 23, 1430, 10.1016/j.clnu.2004.09.012 · DOI · Paper page
- Liu, 1995, Predictive equations for basal metabolic rate in chinese adults. A cross-validation study., J. Am. Diet. Assoc., 95, 1403, 10.1016/S0002-8223(95)00369-X · DOI
- Loucks, 2011, Energy availability in athletes., J. Sports Sci., 29, S7, 10.1080/02640414.2011.588958 · DOI · Paper page
- Miller, 2013, Common prediction equations overestimate measured resting metabolic rate in young hispanic women., Top. Clin. Nutr., 28, 120, 10.1097/TIN.0b013e31828d7a1b · DOI
- Mountjoy, 2015, Relative energy deficiency in sport (RED-S) clinical assessment tool (CAT)., Br. J. Sports Med., 49, 421, 10.1136/bjsports-2014-094559 · DOI
- Nieman, 2006, Validation of cosmed’s FitMateTM in measuring oxygen consumption and estimating resting metabolic rate., Res. Sport. Med., 14, 89, 10.1080/15438620600651512 · DOI
- Nieman, 2007, Validation of Cosmed’s FitMate in measuring exercise metabolism., Res. Sports. Med., 15, 67, 10.1080/15438620601184380 · DOI
- Park, 2019, Using physical activity levels to estimate energy requirements of female athletes., J. Exerc. Nutr. Biochem., 23, 1, 10.20463/jenb.2019.0024 · DOI
- Reale, 2020, Metabolic rate in adolescent athletes: the development and validation of new equations, and comparison to previous models., Int. J. Sport Nutr. Exerc. Metab., 30, 249, 10.1123/ijsnem.2019-0323 · DOI · Paper page
- Romieu, 2017, Energy balance and obesity: what are the main drivers?, Cancer Causes Control, 28, 247, 10.1007/s10552-017-0869-z · DOI · Paper page
- Saglam, 2010, International physical activity questionnaire: reliability and validity of the Turkish version., Percept. Mot. Skills, 111, 278, 10.2466/06.08.PMS.111.4.278-284 · DOI · Paper page
- Schofield, 2019, Resting metabolic rate prediction equations and the validity to assess energy deficiency in the athlete population., Exp. Physiol., 104, 469, 10.1113/EP087512 · DOI
- Spiegelman, 2001, Obesity and the regulation of energy balance., Cell, 104, 531, 10.1016/S0092-8674(01)00240-9 · DOI · Paper page
- ten Haaf, 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, 10.1371/journal.pone.0108460 · DOI
- Thompson, 1996, Predicted and measured resting metabolic rate of male and female endurance athletes., J. Am. Diet. Assoc., 96, 30, 10.1016/S0002-8223(96)00010-7 · DOI
- Vandarakis, 2013, A comparison of cosmed metabolic systems for the determination of resting metabolic rate., Res. Sport. Med., 21, 187, 10.1080/15438627.2012.757226 · DOI
- Verney, 2015, Comparisons of a multi-frequency bioelectrical impedance analysis to the dual-energy X-Ray absorptiometry scan in healthy young adults depending on their physical activity level., J. Hum. Kinet., 14, 73, 10.1515/hukin-2015-0063 · DOI
- Weir, 1949, New methods for calculating metabolic rate with special reference to protein metabolism., J. Physiol., 109, 1, 10.1113/jphysiol.1949.sp004363 · DOI · Paper page
- Yeung, 2020, The use of a portable metabolic monitoring device for measuring RMR in healthy adults., Br. J. Nutr., 124, 1229, 10.1017/S0007114520001014 · DOI
Cited by
Stellingwerff, T., Mountjoy, M., McCluskey, W. T., Ackerman, K. E., Verhagen, E., & Heikura, I. A. (2023) · British Journal of Sports Medicine · doi:10.1136/bjsports-2023-106914 · Cited by 119
Everett, S. (2025) · Nutrients · doi:10.3390/nu17172792 · Cited by 37
Metadata
- Authors
- Balci, A., Badem, E. A., Yılmaz, A. E., Devrim-Lanpir, A., Akınoğlu, B., Kocahan, T., Hasanoğlu, A., Hill, L., Rosemann, T., & Knechtle, B.
- Venue
- Frontiers in Physiology
- Publisher
- Frontiers Media SA
- Published
- 2021-02-04
- Volume
- 12
- ISSN
- 1664-042X
- Cited by
- 22
- References
- 18
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.