Sprint performance and force application of tennis players during manual wheelchair propulsion with and without holding a tennis racket
Bibliographic record
Abstract
The objective of this exploratory research is to study the impact of holding a tennis racket while propelling a wheelchair on kinetic and temporal parameters in a field-based environment. 13 experienced wheelchair tennis players with disabilities (36.1 ± 8.2 years, 76.8 ± 15.3 kg, 174.8 ± 17.1 cm) classified between 30/8 and first series performed two 20 m sprints in a straight line, on a tennis court: one while holding a tennis racket and the second without a tennis racket. They used their own sports wheelchair. Potential participants were excluded if they had injuries or pain that impaired propulsion. Maximal total force, maximal propulsive moment, rate of rise, maximal power output, push and cycle times and maximal velocity were measured. Sprinting while holding a tennis racket increased the cycle time by 0,051 s and push time by 0,011s. Sprinting while holding a tennis racket decreased the maximal propulsive moment, maximal power output, rate of rise and maximal velocity during propulsion by 6.713 N/m, 151.108 W, 672.500 N/s and 0.429 m/s, respectively. Our results suggest that the biomechanical changes observed associated with racket propulsion are generally in a direction that would be beneficial for the risk of injury. But sprinting holding a racket seems to decrease players propulsion performance. Working on forward accelerations with a tennis racket would be a line of work for coaches.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".