Influence of music on maximal self-paced running performance and passive post-exercise recovery rate.
Bibliographic record
Abstract
AIM: The purpose of this study was to examine the influence of fast tempo music (FM) on self--paced running performance (heart rate, running speed, ratings of perceived exertion), and slow tempo music (SM) on post--exercise heart rate and blood lactate recovery rates. METHODS: Twelve participants (5 Women) completed three randomly assigned conditions: static noise (control), FM and SM. Each condition consisted of self--paced treadmill running, and supine post--exercise recovery periods (20 min each). Average running speed, heart rate (HR) and ratings of perceived exertion (RPE) were measured during the treadmill running period, while HR and blood lactate were measured during the recovery period. RESULTS: Listening to FM during exercise resulted in a faster self--selected running speed (10.8 ± 1.7 vs. 9.9 ± 1.4 km•hour--1, p<0.001) and higher peak HR (184 ± 12 vs. 177 ± 17 beats•min--1, p< 0.01) without a corresponding difference in peak RPE (FM, 16.8 ± 1.8 vs. SM 15.7 ± 1.9, p= 0.10). Listening to SM during the post--exercise period reduced HR throughout (main effect p<0.001) and blood lactate at the end of recovery (2.8 ± 0.4 vs. 4.7 ± 0.8 mmol•L--1, p<0.05). CONCLUSION: Listening to FM during exercise can increase self--paced intensity without altering perceived exertion levels while listening to SM after exercise can accelerate the recovery rate back to resting levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".