Impact of holding a badminton racket on temporal and kinematic parameters during manual wheelchair propulsion based on forward and backward propulsion
Bibliographic record
Abstract
Introduction: This study evaluates the impact of a badminton racket on forward and backward propulsion in wheelchair badminton, aiming to discern if this impact varies between propulsion directions. Materials and Methods: Nineteen experienced wheelchair badminton players underwent propulsion tests with and without a badminton racket. Results: In forward propulsion, the badminton racket distinctly alters propulsion technique parameters depending on the propulsion direction. It increases sprint time by 4% to 5% and reduces mean, maximum, and peak velocities by 3% to 8% regardless of propulsion direction. Deceleration is also diminished by 9% to 11% with the racket in both directions, potentially decreasing overall performance. Notably, while the impact of the racket differs between propulsion directions, there is no significant difference in the effect between forward and backward propulsion. Conclusion: The use of a badminton racket influences propulsion technique parameters differently based on the propulsion direction and affects performance parameters such as velocity and deceleration consistently across both directions. However, the direction of propulsion does not amplify the racket's effect. These findings underscore the importance for wheelchair badminton players and coaches to consider equipment effects on performance in both forward and backward propulsion.
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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) | 0.001 | 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 | 0.001 |
| 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".