The Effect of Different Musical Rhythms on Anaerobic Abilities in Taekwondo Athletes
Bibliographic record
Abstract
The aim of this study was to determine the effects of different musical rhythms on taekwondo athletes. The variance in athletes’ anaerobic abilities performed under slow and fast musical rhythms was tested in the study presented. Previous studies have demonstrated how music may cause physiological responses before, during, or after different types of exercises. The aim of this study was to identify the effect of music played in two different rhythms (slow 80 b/m, and fast 200 b/m) during anaerobic exercises by measuring four specific physiological variables: heart rate (HR), blood pressure (BP), blood lactate (BL), and rate of perceived exertion (RPE). Additionally, the peak power of each athlete was assessed. Ten black-belt taekwondo male athletes (average age 20.38±1.51) performed for 30 seconds at their maximum anaerobic power on a Monarch Ergonomic that was connected to the Wingate test. The RPE indicated significant differences with a probability value of 0.014 when measured two minutes after the testing. Measurements of heart rate, blood lactate, and diastolic blood pressure after exposure to slow and fast rhythms did not show significant differences. While it has been shown in previous research that the human body tends to synchronize with rhythmic elements of music, this only holds true for exposure to specific rhythms after a longer period of time. The study conducted was based on exposure to different rhythms for only 30 seconds, which may be why these variables did not differ significantly. Yet, results for systolic blood pressure proved significantly different for fast and slow musical rhythms with a probability value of 0.0004.
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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) | 0.001 | 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".