Motivational Music During Resistance Training Improves Strength Endurance
Bibliographic record
Abstract
Benefits of music to endurance performance, isometric strength, and running strength endurance but not to anaerobic performance have been reported. Weight training facilities commonly play music, but it is unknown if music improves the outcomes of dynamic resistance training. PURPOSE: To determine if listening to motivational music during resistance training improves strength endurance. METHODS: 40 healthy young adults (28 women) performed a 10 repetition maximum (10RM) leg press test and were randomly assigned to either music or no music training groups. Subjects then performed 4 weeks of a resistance training protocol for their legs. During training, subjects in the music group listened to individually selected motivational music while those in the no music group wore ear plugs to avoid ambient music. Final testing consisted of measuring the maximum number of repetitions completed at the previously determined 10RM weight. All subjects performed their tests without music. RESULTS: The resistance training resulted in greater leg press strength endurance in both the music (+16.5 repetitions; p<0.001) and no music groups (+10.6 repetitions; p<0.001). The music group performed more repetitions than the no music group (p=0.08). Greater improvements were measured in subjects who did not otherwise listen to music while exercising (p<0.05), especially in the music group (p<0.001). CONCLUSIONS: Listening to self-selected motivational music during resistance training resulted in greater improvements in strength endurance in the lower limbs of healthy young adults. Further study could elucidate whether this is a sustainable or transit effect and applicable to other populations.
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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".