Impact of Sprinting and Dribbling on Shoulder Joint and Pushrim Kinetics in Wheelchair Basketball Athletes
Bibliographic record
Abstract
Background: While wheelchair basketball is one of the most popular Paralympic sports, it eventually causes shoulder problems and pain in many athletes. However, shoulder kinetics has never been assessed during propulsion in wheelchair basketball. This study analyzes the impact of sprinting and dribbling on pushrim and shoulder kinetics in terms of external forces and net muscular moments. Methods: A group of 10 experienced wheelchair basketball athletes with various classifications performed four, 9-m sprints on a basketball court using classic synchronous propulsion, and four sprints while dribbling forward. Pushrim and shoulder kinetics were calculated by inverse dynamics, using a motion capture device and instrumented wheels. Findings: Sprinting was associated to peak shoulder load from 13 to 346% higher than in previous studies on standard wheelchair propulsion in most force/moment components. Compared to sprinting without a ball, dribbling reduced the speed, the peak external forces in the anterior and medial direction at the shoulder, and the peak net shoulder moment of internal rotation. Interpretation: The high shoulder load calculated during both sprinting and dribbling should be considered during training sessions to avoid overloading the shoulder. Dribbling generally reduced the shoulder load, which suggests that propelling while dribbling does not put the shoulder at more risk of musculoskeletal disorders than sprinting.
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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 | 0.001 |
| 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".