Influence of foot-stretcher height on rowing technique and performance
Bibliographic record
Abstract
Strength, technique, and coordination are crucial to rowing performance, but external interventions such as foot-stretcher set-up can fine-tune technique and optimise power output. For the same resultant force, raising the height of foot-stretchers on a rowing ergometer theoretically alters the orientation of the resultant force vector in favour of the horizontal component. This study modified foot-stretcher heights and examined their instantaneous effect on foot forces and rowing technique. Ten male participants rowed at four foot-stretcher heights on an ergometer that measured handle force, stroke length, and vertical and horizontal foot forces. Rowers were instrumented with motion sensors to measure ankle, knee, hip, and lumbar-pelvic kinematics. Key resultant effects of increased foot-stretcher heights included progressive reductions in horizontal foot force, stroke length, and pelvis range of motion. Raising foot-stretcher height did not increase the horizontal component of foot force as previously speculated. The reduced ability to anteriorly rotate the pelvis at the front of the stroke may be a key obstacle in gaining benefits from raised foot-stretcher heights. This study shows that small changes in athlete set-up can influence ergometer rowing technique, and rowers must individually fine-tune their foot-stretcher height to optimise power transfer through the rowing stroke on an ergometer.
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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) | 0.000 | 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) | 0.002 | 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".