Modelling inter-individual variability in acute and adaptive responses to interval training: insights into exercise intensity normalisation
Bibliographic record
Abstract
Abstract Purpose To investigate the influence of exercise intensity normalisation on intra- and inter-individual acute and adaptive responses to an interval training programme. Methods Nineteen cyclists were split in two groups differing (only) in how exercise intensity was normalised: 80% of the maximal work rate achieved in an incremental test (% $$\dot{\text{W}}$$ W ˙ max) vs. maximal sustainable work rate in a self-paced interval training session (% $$\dot{\text{W}}$$ W ˙ max-SP). Testing duplicates were conducted before and after an initial control phase, during the training intervention, and at the end, enabling the estimation of inter-individual variability in adaptive responses devoid of intra-individual variability. Results Due to premature exhaustion, the median training completion rate was 88.8% for the % $$\dot{\text{W}}$$ W ˙ max group, but 100% for the % $$\dot{\text{W}}$$ W ˙ max-SP the group. Ratings of perceived exertion and heart rates were not sensitive to how intensity was normalised, manifesting similar inter-individual variability, although intra-individual variability was minimised for the % $$\dot{\text{W}}$$ W ˙ max-SP group. Amongst six adaptive response variables, there was evidence of individual response for only maximal oxygen uptake (standard deviation: 0.027 L·min−1·week−1) and self-paced interval training performance (standard deviation: 1.451 W·week−1). However, inter-individual variability magnitudes were similar between groups. Average adaptive responses were also similar between groups across all variables. Conclusions To normalise completion rates of interval training, % $$\dot{\text{W}}$$ W ˙ max-SP should be used to prescribe relative intensity. However, the variability in adaptive responses to training may not reflect how exercise intensity is normalised, underlining the complexity of the exercise dose–adaptation relationship. True inter-individual variability in adaptive responses cannot always be identified when intra-individual variability is accounted for.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".