Effect of listening to preferred music at different frequencies during warmup on physical performance and psychophysiological responses in male athletes
Bibliographic record
Abstract
The present study assessed the effects of listening to preferred music during warm-up at different frequencies (440 Hz vs. 432 Hz) on the physical performance and psychophysiological responses in male athletes. In a double-blind crossover study design, twenty-two male athletes (Mean ± SD: age: 22.4 ± 1.6 years; body mass: 71.8 ± 8.3 kg; height: 1.83 ± 0.1 m; BMI: 22.7 ± 1.9 kg. m − 2 randomly performed the 3-min all out test (3MT), repeated sprint test (RST), squat jump (SJ) and countermovement jump (CMJ) tests after listening to preferred music at 440 Hz–432 Hz frequencies or without music during warm-up. Heart rate was measured during 3MT, Also, rating of perceived exertion (RPE) and feeling scale (FS) were measured just after 3MT and RST tests. Results showed that no music condition induced lower performance compared to listening to preferred music at 440 Hz (mean speed: p < 0.001, d = 0.82; peak speed: p < 0.001, d = 0.85; total distance covered: p < 0.001, d = 0.83; FS: p < 0.001, d = 1.21) and music 432 Hz (mean speed: p < 0.001, d = 0.75; peak speed: p < 0.001, d = 0.58; total distance covered: p < 0.001, d = 0.76, FS: p = 0.004, d = 0.77) during 3MT. In addition, a significant decrease in Total time and Fast time indices of RST, FS, SJ and CMJ values was recorded with music at 440 Hz compared to music 432 Hz (total time: p < 0.001, d = 0.65; fast time: p < 0.001, d = 0.53; FS: p = 0.018, d = 0.59; SJ: p = 0.031, d = 0.3; CMJ: p = 0.007, d = 0.41) and no music condition (total time: p < 0.001, d = 0.89; fast time: p < 0.001, d = 0.57; FS: p < 0.001, d = 1.21; SJ: p < 0.001, d = 0.64; CMJ: p < 0.001, d = 0.54). However, no significant change was observed in heart rate, fatigue index of the RST, or RPE. Listening to preferred music at a fast tempo and moderate volume during warm-up sessions improved physical performance and positive mood irrespective of whether it was tuned to 440 Hz–432 Hz. The benefits were more noticeable at 440 Hz.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".