The delta concept does not effectively normalise exercise responses to exhaustive interval training
Bibliographic record
Abstract
OBJECTIVES: ) are taken into account to normalise relative exercise intensity. DESIGN: Repeated-measures, within-subjects design with mixed-effects modelling. METHODS: as prescription benchmarks. On separate occasions, participants then completed four high-intensity interval training sessions of identical intensity (70 %Δ) and format (4-min on, 2-min off); all performed to exhaustion. Acute high-intensity interval training responses were modelled with participant as a random effect to provide estimates of inter- and intra-individual variability. RESULTS: Greater variability was generally observed at the between- compared with the within-individual level, ranging from 50 % to 89 % and from 11 % to 50 % of the total variability, respectively. For the group mean time to exhaustion of 20.3 min, inter- and intra-individual standard deviations reached 9.3 min (coefficient of variation = 46 %) and 4.5 min (coefficient of variation = 22 %), respectively. CONCLUSIONS: Due to the high variability observed, the %Δ method does not effectively normalise the relative intensity of exhaustive high-intensity interval training across individuals. The generally larger inter- versus intra-individual variability suggests that day-to-day biological fluctuations and/or measurement errors cannot explain the identified shortcoming of the method.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".