The Effect Of Music Loudness On Anaerobic Performance And Muscular Endurance
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to investigate the effect of music loudness on average anaerobic power, bench press muscular endurance, and leg press muscular endurance in regularly active 20 to 22 year old females. METHODS: At each testing session, participants were randomly assigned to 1 of 4 music loudness levels: zero decibels (Db), 20 Db lower than preferred volume, 20 Db higher than preferred volume, and preferred volume. Leg press repetitions to fatigue, bench press repetitions to fatigue, and average power (watts/kilogram) on a 30-second Wingate test were measured for each participant at every music loudness level. RESULTS: Wingate data revealed that soft music resulted in significantly higher average power than no music (p=0.035), preferred music resulted in significantly higher average power than no music (p=0.009), and loud music resulted in significantly higher average power than no music (p=0.005). Bench press data revealed that preferred music resulted in significantly higher repetitions to fatigue than no music (p=0.004), loud music resulted in significantly higher repetitions to fatigue than no music (p=0.031), and loud music resulted in significantly higher repetitions to fatigue than then soft music (p=0.009). Leg press data revealed that loud music resulted in significantly higher repetitions than no music (p=0.011), and loud music resulted in significantly higher repetitions tha soft music (p=0.041). CONCLUSIONS: Music had a positive effect on performance, as measured in all exercises, with preferred and loud music conditions having the greatest impact.
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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 | 0.002 |
| 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) | 0.003 | 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".