Excess post-exercise oxygen consumption in untrained males: effects of intermittent durations of arm ergometry
Bibliographic record
Abstract
The purpose of this study was to investigate excess post-exercise oxygen consumption (EPOC) following a continuous 30 min bout of upper-body exercise (UBE) compared with 3 consecutive 10 min bouts of UBE. Ten male subjects (age (mean +/- standard deviation), 25.7 +/- 5.83 years; arm VO(2) (peak), 2.2 +/- 0.25 L x min(-1), on separate days (48 h between trials) and in counterbalanced order, performed a continuous 30 min bout of arm exercise at 60% of arm VO2 peak and 3 separate 10 min bouts of arm exercise at 60% of arm VO(2) (peak). Subjects reported to the laboratory rested and after a 12 h fast. Each test was preceded by a 30 min baseline test to determine resting metabolic rate. Post-exercise VO2 was continuously monitored until baseline was re-established. Results showed that the combined magnitude of the EPOCs from the intermittent exercise sessions was significantly (p > .05) greater (4.47 +/- 1.58 L O2) than that elicited from the continuous exercise session (1.54 +/- 1.25 L O2). These data indicate that separating a continuous 30 min arm exercise into 3 equal 10 min arm exercises will elicit a small but significantly higher EPOC, and thus result in greater post-exercise energy expenditure. This could be beneficial for those unable to perform lower-body exercise (LBE), or for those with limited exercise capacities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".