Affective responses to high-intensity interval training with continuous and respite music
Bibliographic record
Abstract
Music has been shown to enhance affective responses to continuous exercise, but the most effective application of music during interval exercise is poorly understood. This study examined two contrasting applications of music designed to assuage the decline in pleasure often experienced during high-intensity interval training (HIIT). In a repeated measures crossover design, 18 recreationally active participants (10 men and 8 women; Mage = 25.1 ± 5.1 years; MBMI = 23.08 ± 2.01 kg/m 2; M VO2max = 38.82 ± 10.73 ml/kg/min) completed three HIIT sessions (10 x 60 s efforts at 100% Wmax, separated by 75 s recovery) on a cycle ergometer. Participants completed two experimental conditions: respite music (applied only during the recovery periods), continuous music (applied throughout the entire HIIT session); and a no-music control condition. Results indicated that music did not influence affective valence during the work bouts or recovery periods of the HIIT sessions (ps >.05), but that listening to music continuously elicited greater post-task enjoyment (p =.032, d = 0.66) and remembered pleasure (p =.044, d = 0.5). This study is the first to investigate the application of music during a practical HIIT protocol and to compare the effects of respite versus continuous music during interval exercise.
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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) | 0.000 | 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) | 0.002 | 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".