The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Resting Energy Expenditure in Patients with Extreme Obesity: Comparison of the Harris–Benedict Equation with Indirect Calorimetry
Abstract
Background: The main objective of the work was the analysis and description of data on body composition and resting energy expenditure (REE) values of selected groups of patients with obesity whose REE measurement results using indirect calorimetry reached a level below 95% of the predicted REE calculated using the Harris–Benedict (H–B) equation. The sub-goals were to describe the dependence of body composition on the size of the REE and to find out if the deviations between the number of the total measured REE and the REE calculated using H–B in the adapted group (patients with altered REE values, lower than expected caused by long caloric restriction) are significant. Methods: For the research, 71 (39 women and 32 men) patients treated in obesitology were selected. Patients underwent the measurement of resting metabolism using indirect calorimetry (IC) and body composition measurement on the bioimpedance device and, at the same time, the value of resting metabolism was calculated for everyone using the H–B equation. The whole group was divided into five groups according to the deviation of the measurement using IC and the calculation of the H–B equation. Results: In the total set of examined individuals, there were 32.4% with a reduced REE value compared to the REE calculation according to the H–B equation, which corresponds to 23 individuals. In the adapted group, the average measured REE was 2242 ± 616 kcal compared to the H–B calculation of 2638 ± 713 kcal. Statistically, these results were not significant, but a high case-to-case variation was found. The highest deviation from the H–B predictive calculation was −42% and +43% in the whole research group. The amount of muscle tissue in the adapted group averaged 44.3 ± 11.9 kg and the amount of fat-free mass (FFM) 77.9 ± 20.1 kg. When statistically testing the dependence of REE on FFM and muscle tissue in the adapted group, a strong correlation was found. Conclusions: The H–B equation alone is not suitable for setting a suitable diet therapy for an individual with obesity. In order to select and characterize a group of adapted individuals, it will be necessary to use other methods or a larger research sample, and preferably examine and divide patients with specific comorbidities or include their health status.
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
“In addition, REE is also related to the percentage of muscle mass (17), due to the fact that muscle mass is more metabolically active when compared to fat mass.”
From Management of Acquired Hypothalamic Dysfunction and the Hypothalamic Syndrome; It Is More Than Obesity (2025) · cites this paper as [17]
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
The role of exercise and physical activity in weight loss and maintenance
Swift, D. L., Johannsen, N. M., Lavie, C. J., Earnest, C. P., & Church, T. S. (2014) · Progress in Cardiovascular Diseases · doi:10.1016/j.pcad.2013.09.012 · Cited by 855
Redman, L. M., Smith, S. R., Burton, J. H., Martin, C. K., Il'yasova, D., & Ravussin, E. (2018) · Cell Metabolism · doi:10.1016/j.cmet.2018.02.019 · Cited by 472
Indirect calorimetry in nutritional therapy. A position paper by the ICALIC study group
Oshima, T., Berger, M. M., De Waele, E., Guttormsen, A. B., Heidegger, C.-P., Hiesmayr, M., Singer, P., Wernerman, J., & Pichard, C. (2017) · Clinical Nutrition · doi:10.1016/j.clnu.2016.06.010 · Cited by 283
Zusman, O., Kagan, I., Bendavid, I., Theilla, M., Cohen, J., & Singer, P. (2019) · Clinical Nutrition · doi:10.1016/j.clnu.2018.04.020 · Cited by 143
Research works printed but not linked: 37
- Lobstein, 2020, Obesity: A Ghost at the Feast of the Sustainable Development Goals, Curr. Obes. Rep., 9, 470, 10.1007/s13679-020-00405-x · DOI
- Haththotuwa, R.N., Wijeyaratne, C.N., and Senarath, U. (2020). Worldwide epidemic of obesity. Obesity and Obstetrics, Elsevier. · DOI · Paper page
- Bischoff, 2020, Obesity therapy, Clin. Nutr. ESPEN, 38, 9, 10.1016/j.clnesp.2020.04.013 · DOI
- Kushner, 2014, Weight loss strategies for treatment of obesity, Prog. Cardiovasc. Dis., 56, 465, 10.1016/j.pcad.2013.09.005 · DOI · Paper page
- Westerterp, 2017, Control of energy expenditure in humans, Eur. J. Clin. Nutr., 71, 340, 10.1038/ejcn.2016.237 · DOI · Paper page
- Soares, 2018, Resting energy expenditure and body composition: Critical aspects for clinical nutrition, Eur. J. Clin. Nutr., 72, 1208, 10.1038/s41430-018-0220-0 · DOI
- Lam, 2017, Indirect calorimetry: An indispensable tool to understand and predict obesity, Eur. J. Clin. Nutr., 71, 318, 10.1038/ejcn.2016.220 · DOI
- Ndahimana, 2017, Measurement Methods for Physical Activity and Energy Expenditure: A Review, Clin. Nutr. Res., 6, 68, 10.7762/cnr.2017.6.2.68 · DOI · Paper page
- Pavlidou, E., Petridis, D., Tolia, M., Tsoukalas, N., Poultsidi, A., Fasoulas, A., Kyrgias, G., and Giaginis, C. (2018). Estimating the agreement between the metabolic rate calculated from prediction equations and from a portable indirect calorimetry device: An effort to develop a new equation for predicting resting metabolic rate. Nutr. Metab., 15. · DOI
- Macena, 2019, Agreement between the total energy expenditure calculated with accelerometry data and the BMR yielded by predictive equations v. the total energy expenditure obtained with doubly labelled water in low-income women with excess weight, Br. J. Nutr., 122, 1398, 10.1017/S0007114519002460 · DOI
- Marra, 2017, Prediction and evaluation of resting energy expenditure in a large group of obese outpatients, Int. J. Obes., 41, 697, 10.1038/ijo.2017.34 · DOI
- Carneiro, 2016, Is obesity associated with altered energy expenditure?, Adv. Nutr. Int. Rev. J., 7, 476, 10.3945/an.115.008755 · DOI · Paper page
- Löffler, M.C., Betz, M.J., Blondin, D.P., Augustin, R., Sharma, A.K., Tseng, Y.-H., Scheele, C., Zimdahl, H., Mark, M., and Hennige, A.M. (2021). Challenges in tackling energy expenditure as obesity therapy: From preclinical models to clinical application. Mol. Metab., 51. · DOI
- Most, 2017, Calorie restriction in humans: An update, Ageing Res. Rev., 39, 36, 10.1016/j.arr.2016.08.005 · DOI · Paper page
- Flack, 2016, Cross-Validation of Resting Metabolic Rate Prediction Equations, J. Acad. Nutr. Diet., 116, 1413, 10.1016/j.jand.2016.03.018 · DOI
- Madden, 2016, Estimation of energy expenditure using prediction equations in overweight and obese adults: A systematic review, J. Hum. Nutr. Diet., 29, 458, 10.1111/jhn.12355 · DOI
- Achamrah, 2021, Indirect calorimetry: The 6 main issues, Clin. Nutr., 40, 4, 10.1016/j.clnu.2020.06.024 · DOI
- Ridley, 2016, Prevalence of Underprescription or Overprescription of Energy Needs in Critically Ill Mechanically Ventilated Adults as Determined by Indirect Calorimetry, J. Parenter. Enter. Nutr., 40, 212, 10.1177/0148607114567898 · DOI · Paper page
- Most, J., and Redman, L.M. (2020). Impact of calorie restriction on energy metabolism in humans. Exp. Gerontol., 133. · DOI · Paper page
- Ramachandran, 2017, Indirect calorimetry: From bench to bedside, Indian J. Endocrinol. Metab., 21, 594, 10.4103/ijem.IJEM_484_16 · DOI
- Rattanachaiwong, 2019, Indirect calorimetry as point of care testing, Clin. Nutr., 38, 2531, 10.1016/j.clnu.2018.12.035 · DOI
- Harris, 1918, A biometric study of human basal metabolism, Proc. Natl. Acad. Sci. USA, 4, 370, 10.1073/pnas.4.12.370 · DOI · Paper page
- Delsoglio, M., Achamrah, N., Berger, M.M., and Pichard, C. (2019). Indirect calorimetry in Clinical Practice. J. Clin. Med., 8. · DOI · Paper page
- Jagim, 2018, Accuracy of resting metabolic rate prediction equations in athletes, J. Strength Cond. Res., 32, 1875, 10.1519/JSC.0000000000002111 · DOI
- Barcellos, 2021, Resting energy expenditure in cancer patients: Agreement between predictive equations and indirect calorimetry, Clin. Nutr. ESPEN, 42, 286, 10.1016/j.clnesp.2021.01.019 · DOI
- Anderson, 2014, Comparison of energy assessment methods in overweight individuals, J. Acad. Nutr. Diet., 114, 273, 10.1016/j.jand.2013.07.008 · DOI
- 2018, Validation of resting metabolic rate equations in obese and non-obese young healthy adults, Clin. Nutr. ESPEN, 26, 91, 10.1016/j.clnesp.2018.04.008 · DOI
- Kruizenga, H.M., Hofsteenge, G.H., and Weijs, P.J.M. (2016). Predicting resting energy expenditure in underweight, normal weight, overweight, and obese adult hospital patients. Nutr. Metab., 13. · DOI
- Macena, 2022, Estimates of resting energy expenditure and total energy expenditure using predictive equations in adults with overweight and obesity: A systematic review with meta-analysis, Nutr. Rev., 80, 2113, 10.1093/nutrit/nuac031 · DOI
- Poli, 2016, Resting energy expenditure in obese women: Comparison between measured and estimated values, Br. J. Nutr., 116, 1306, 10.1017/S0007114516003172 · DOI
- Trexler, E.T., Smith-Ryan, A.E., and Norton, L.E. (2014). Metabolic adaptation to weight loss: Implications for the athlete. J. Int. Soc. Sports Nutr., 11. · DOI · Paper page
- Hirsch, 2017, Influence of segmental body composition and adiposity hormones on resting metabolic rate and substrate utilization in overweight and obese adults, J. Endocrinol. Investig., 40, 635, 10.1007/s40618-017-0616-z · DOI
- Cancello, R., Soranna, D., Brunani, A., Scacchi, M., Tagliaferri, A., Mai, S., Marzullo, P., Zambon, A., and Invitti, C. (2018). Analysis of predictive equations for estimating resting energy expenditure in a large cohort of morbidly obese patients. Front. Endocrinol., 9. · DOI
- Rodrigues, 2018, Low validity of predictive equations for calculating resting energy expenditure in overweight and obese women with polycystic ovary syndrome, J. Hum. Nutr. Diet., 31, 266, 10.1111/jhn.12498 · DOI
- Popp, 2020, Evaluating steady-state resting energy expenditure using indirect calorimetry in adults with overweight and obesity, Clin. Nutr., 39, 2220, 10.1016/j.clnu.2019.10.002 · DOI
- Ocagli, H., Lanera, C., Azzolina, D., Piras, G., Soltanmohammadi, R., Gallipoli, S., Gafare, C.E., Cavion, M., Roccon, D., and Vedovelli, L. (2021). Resting energy expenditure in the elderly: Systematic review and comparison of equations in an experimental population. Nutrients, 13. · DOI
- Anthanont, 2016, Does basal metabolic rate predict weight gain?, Am. J. Clin. Nutr., 104, 959, 10.3945/ajcn.116.134965 · DOI
Cited by
van Santen, H. M., & Müller, H. L. (2025) · Endocrine Reviews · doi:10.1210/endrev/bnaf025 · Cited by 21
Metadata
- Authors
- Jílková, A., Lampová, B., Kádě, O., Kouřimská, L., Chrpová, D., Kaiserová, I., & Matoulek, M.
- Affiliations
- Department of Microbiology, Nutrition and Dietetics, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, 165 21 Prague, Czech Republic
- 3rd Internal Department of Endocrinology and Metabolism, General University Hospital, 1st Faculty of Medicine, Charles University, 128 08 Prague, Czech Republic
- Venue
- Journal of Clinical Medicine
- Publisher
- MDPI AG
- Published
- 2024-10-08
- Volume
- 13
- Issue
- 19
- Pages
- 5993
- ISSN
- 2077-0383
- Cited by
- 4
- References
- 4
- Language
- en
- Funders
- Co-operation Program, research area, “Metabolic Diseases—207037-1” (MH CZ—DRO-VFN64165, LM2023064)
- METROFOOD-CZ research infrastructure project (MH CZ—DRO-VFN64165, LM2023064)
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Resting Energy Expenditure in Patients with Extreme Obesity: Comparison of the Harris–Benedict Equation with Indirect Calorimetry. https://app.cochat.ai/papers/10.3390/jcm13195993
- The paper
- Jílková, A., Lampová, B., Kádě, O., Kouřimská, L., Chrpová, D., Kaiserová, I., & Matoulek, M. (2024). Resting Energy Expenditure in Patients with Extreme Obesity: Comparison of the Harris–Benedict Equation with Indirect Calorimetry. Journal of Clinical Medicine. https://doi.org/10.3390/jcm13195993
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.