Published Predictive Equations Overestimate Measured Resting Metabolic Rate in Young, Healthy Females
Bibliographic record
Abstract
OBJECTIVE: To compare resting metabolic rate (RMR) measured by indirect calorimetry versus RMR predicted by several published formulas in a sample of healthy young women. METHODS: RMR was measured using indirect calorimetry and predicted using 6 commonly used equations (Nelson, 1992; Mifflin, 1990; Owen, 1986; Schofield(Weight), 1985; Schofield(Weight and Height), 1985; Harris-Benedict, 1919) in 47 reportedly healthy young females (age = 22.8 ± 2.9 years; body mass index = 21.8 ± 2.1 kg/m(2)). Comparisons between measured versus predicted RMR were conducted using paired t tests, and agreement using Pearson's correlation coefficient, analysis of variance, and the method of Bland-Altman. RESULTS: All 6 equations overestimated measured RMR by 140-738 kcal/d (all p < 0.001). The proportion of subjects for whom measured versus predicted RMR differed by ±10% ranged from 74% (Nelson) to 100% (Harris-Benedict). The adjusted coefficients of determination (R(2)) between measured and predicted RMR ranged from 0.13 to 0.19 (all p < 0.05). Bland-Altman analysis R(2) values ranged from 0.03 (p = 0.233; Harris-Benedict) to 0.72 (p = 0.000; Owen). Given its continued popularity, we modified the Harris-Benedict equation (RMR(modified Harris-Benedict) (kcal/d) = 738 / (RMR(Harris-Benedict) - 738)). Doing so reduced the mean difference between measured and predicted RMR from +738 kcal/d to -0.53 kcal/d (p = 0.984). CONCLUSION: No equation performed well, and none should be used interchangeably with measured RMR. We recommend that a new equation be validated for, and prospectively tested in, young women. In the interim, RMR should be measured in this population or predicted using the modified Harris-Benedict equation that we developed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".