Description of training loads using whole-body exercise during high-intensity interval training
Bibliographic record
Abstract
OBJECTIVES: To describe external training load and internal training load through sets of a single session of high-intensity interval training (HIIT) body work. METHODS: Twenty male individuals (24±3 years) performed a HIIT body work protocol consisting of a single bout of exercise with 1:1 stimuli. The exercises used were 30 min in duration with "all-out" intensity. The exercises included jumping jacks, mountain climbers, burpees and squat jumps, totaling 20 min of exercise. During exercise, total movement capacity, blood lactate measurement, ratings of perceived exertion and recovery, training load and intensity were monitored. RESULTS: The single bout examined showed a total of 382±89 movements. Differences (p<0.01) in the total amount of movement for each exercise were noted, reflecting the difficulty of maintaining exercise over time. Increases in lactate concentrations (before: 0.98±0.16, after: 14.10±1.66; mmol/L) were found postexercise. Significant differences (p<0.01) were found after the fifth set, and the values for movement capacity remained higher than the values of the first set, demonstrating high load in a single session. No differences in ratings of perceived exertion (RPE) during the sets were found. However, the ratings of perceived recuperation from the second set were significantly (p<0.01) lower than those from the first set. CONCLUSIONS: The exercise protocol used in this study was of high intensity and produced large values for stress during performance, with increases recorded for the internal load indicators.
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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) | 0.001 | 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) | 0.003 | 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".