Music Enhances Performance and Perceived Enjoyment of Sprint Interval Exercise
Bibliographic record
Abstract
INTRODUCTION: Interval exercise training can elicit physiological adaptations similar to those of traditional endurance training, but with reduced time. However, the intense nature of specific protocols, particularly the "all-out" efforts characteristic of sprint interval training (SIT), may be perceived as being aversive. The purpose of this study was to determine whether listening to self-selected music can reduce the potential aversiveness of an acute session of SIT by improving affect, motivation, and enjoyment, and to examine the effects of music on performance. METHODS: Twenty moderately active adults (22 ± 4 yr) unfamiliar with interval exercise completed an acute session of SIT under two different conditions: music and no music. The exercise consisted of four 30-s "all-out" Wingate Anaerobic Test bouts on a cycle ergometer, separated by 4 min of rest. Peak and mean power output, RPE, affect, task motivation, and perceived enjoyment of the exercise were measured. Mixed-effects models were used to evaluate changes in dependent measures over time and between the two conditions. RESULTS: Peak and mean power over the course of the exercise session were higher in the music condition (coefficient = 49.72 [SE = 13.55] and coefficient = 23.65 [SE = 11.30]; P < 0.05). A significant time by condition effect emerged for peak power (coefficient = -12.31 [SE = 4.95]; P < 0.05). There were no between-condition differences in RPE, affect, or task motivation. Perceived enjoyment increased over time and was consistently higher in the music condition (coefficient = 7.00 [SE = 3.05]; P < 0.05). CONCLUSION: Music enhances in-task performance and enjoyment of an acute bout of SIT. Listening to music during intense interval exercise may be an effective strategy for facilitating participation in, and adherence to, this form of training.
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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".