“But will they do it?” Challenging assumptions and incivility in the academic discourse on high-intensity interval training
Bibliographic record
Abstract
Debate over whether to promote high-intensity interval training (HIIT) in public-health contexts has centred on assumptions that people will have negative psychological responses to HIIT, leading to poor adoption and adherence. We challenge these assumptions through reviews of (1) studies that have measured psychological responses to HIIT and (2) studies that have measured adherence to HIIT protocols in supervised or unsupervised settings. Overall, the evidence suggests that HIIT is just as enjoyable as moderate-intensity continuous training (MICT). In supervised situations, on average, adherence is similarly high for HIIT and MICT (>89%). In unsupervised situations, adherence is similarly lower for both HIIT and MICT (<69%). Based on these findings, we recommend that attention be directed toward improving behaviour-change and maintenance for all types of exercise. Resources are better spent addressing fundamental questions about exercise initiation and adherence, than perpetuating a vitriolic and uncivil debate over the value of HIIT versus MICT. We discuss how debate, incivility, and bullying undermine scientific progress and we issue a call for respectful, civil dialogue in academic HIIT discussions. We conclude with recommendations that can be used by all members of the scientific community to practice, champion, and defend civil discourse.
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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 | 0.027 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.011 |
| 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".