The Effect of Timing of Sports Massage on 60-m Sprint Performance in Competitive Athletes
Bibliographic record
Abstract
Background: Sports massage is considered to improve muscle tone and flexibility and thus, overall, contribute to the improvement of athletes' performance. It is not yet clear whether pre- or post-warm-up sports massage can enhance athletes' performance. Additionally, while essential oils are believed to enhance the effects of massage, empirical evidence remains limited. Purpose: The present study aimed to evaluate the timing effect of pre-competition sports massage, using two different massage oils, on sprint performance. Methods: A total of 40 competitive male young sprint and multiple-sprint sport athletes were randomly divided into two groups-group 1: sports massage applied after the warm-up followed by a max 60-m sprint trial, group 2: sports massage before the warm-up followed by a max 60-m sprint trial. All participants were assessed in three different massage conditions: (i) control: usual warm-up, no massage; (ii) AEO: sports massage using activation essential oil; (iii) BO: sports massage using baby oil. Results: The results demonstrated a statistically significant main effect of massage on sprint performance, by reducing 60-m sprint time (F(2,78) = 5.304, p ≤ 0.005). Specifically, sprint performance improved (3.91%, p = 0.008) when the sport massage session took place after the athletes' warm-up (group 1) and when the AEO was applied (p = 0.004). Conclusion: A brief pre-competition sports massage, especially after the warm-up session and when AEO is applied, could be used as a complementary approach to help improve sprint performance.
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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".