Elite handcycling: a qualitative analysis of recumbent handbike configuration for optimal sports performance
Bibliographic record
Abstract
Our understanding of handbike configuration is limited, yet it can be a key determinant of performance in handcycling. This study explored how 14 handcycling experts (elite handcyclists, coaches, support staff, and manufacturers) perceived aspects of recumbent handbike configuration to impact upon endurance performance via semi-structured interviews. Optimising the handbike for comfort, stability, and power production was identified as key themes. Comfort and stability were identified to be the foundations of endurance performance and were primarily influenced by the seat, backrest, headrest, and their associated padding. Power production was determined by the relationship between the athletes' shoulder and abdomen and the trajectories of the handgrips, which were determined by the crank axis position, crank arm length, and handgrip width. Future studies should focus on quantifying the configuration of recumbent handbikes before determining the effects that crank arm length, handgrip width, and crank position have on endurance performance. Practitioner Summary: To gain a greater understanding of the impact of handbike configurations on endurance performance, the perceptions of expert handcyclists were explored qualitatively. Optimising the handbike for comfort and stability, primarily via backrest padding and power production, the position of the shoulders relative to handgrips and crank axis, were critical.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".