Reliability of methods to measure energy expenditure during and after resistance exercise
Bibliographic record
Abstract
The primary purpose of this study was to assess the reliability of 3 methods estimating energy expenditure (EE) during and in response to resistance exercise. Ten males (aged 29.4 ± 10.2 years) with ≥2 months resistance training (RT) experience performed 3 training sessions incorporating the bench press and back-squat; sessions were separated by 48 to 72 h. Total energy expenditure (TEE) was estimated using a Suunto T6D Heart Rate Monitor and 2 methods (named "Scott" and "Magosso") that used oxygen uptake and blood lactate measurements to determine aerobic and anaerobic energy expenditure (AnEE). For TEE, relative reliability for both the Scott and Magosso methods remained "nearly perfect" across all testing days for the bench press and back-squat; with interclass correlations (ICC) > 0.93 and percentage of the typical error measurement (TEM%) below 5.8%. The heart rate method showed moderate variability between testing days for both exercises; ICCs ranged between 0.66-0.92 with TEM% between 18%-37% during the bench press and 11%-17% during the back-squat. The estimation of AnEE showed that the Scott and Magosso methods had "strong" to "very strong" relative reliability for both exercises; however, a low absolute reliability was observed. Mean EE was significantly higher in the Scott and Magosso methods during the bench press >912 kJ and back-squat >1170 kJ, with the heart rate method estimating 358 kJ and 416 kJ. The Scott and Magosso methods showed a high degree of reliability between testing days when measuring EE. Heart rate methods may significantly underestimate EE during and in response to RT.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".