Conventional methods to prescribe exercise intensity are ineffective for exhaustive interval training
Bibliographic record
Abstract
Abstract Purpose To compare methods of relative intensity prescription for their ability to normalise performance (i.e., time to exhaustion), physiological, and perceptual responses to high-intensity interval training (HIIT) between individuals. Methods Sixteen male and two female cyclists (age: 38 ± 11 years, height: 177 ± 7 cm, body mass: 71.6 ± 7.9 kg, maximal oxygen uptake ( $$ \dot{\text{V}} $$ V ˙ O 2max ): 54.3 ± 8.9 ml·kg −1 min −1 ) initially undertook an incremental test to exhaustion, a 3 min all-out test, and a 20 min time-trial to determine prescription benchmarks. Then, four HIIT sessions (4 min on, 2 min off) were each performed to exhaustion at: the work rate associated with the gas exchange threshold ( $$ \dot{\text{W}} $$ W ˙ GET ) plus 70% of the difference between $$ \dot{\text{W}} $$ W ˙ GET and the work rate associated with $$ \dot{\text{V}} $$ V ˙ O 2max ; 85% of the maximal work rate of the incremental test (85% $$ \dot{\text{W}} $$ W ˙ max ); 120% of the mean work rate of the 20 min time-trial (120%TT); and the work rate predicted to expend, in 4 min, 80% of the work capacity above critical power. Acute HIIT responses were modelled with participant as a random effect to provide estimates of inter-individual variability. Results For all dependent variables, the magnitude of inter-individual variability was high, and confidence intervals overlapped substantially, indicating that the relative intensity normalisation methods were similarly poor. Inter-individual coefficients of variation for time to exhaustion varied from 44.2% (85% $$ \dot{\text{W}} $$ W ˙ max ) to 59.1% (120%TT), making it difficult to predict acute HIIT responses for an individual. Conclusion The present study suggests that the methods of intensity prescription investigated do not normalise acute responses to HIIT between individuals.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".