Biomechanical determinants of elite rowing technique and performance
Bibliographic record
Abstract
In rowing, the parameters of injury, performance, and technique are all interrelated and in dynamic equilibrium. Whilst rowing requires extreme physical strength and endurance, a high level of skill and technique is essential to enable an effective transfer of power through the rowing sequence. This study aimed to determine discrete aspects of rowing technique, which strongly influence foot force production and asymmetries at the foot-stretchers, as these are biomechanical parameters often associated with performance and injury risk. Twenty elite female rowers performed an incremental rowing test on an instrumented rowing ergometer, which measured force at the handle and foot-stretchers, while three-dimensional kinematic recordings of the ankle, knee, hip, and lumbar-pelvic joints were made. Multiple regression analyses identified hip kinematics as a key predictor of foot force output (R(2) = 0.48), whereas knee and lumbar-pelvic kinematics were the main determinants in optimizing the horizontal foot force component (R(2) = .41). Bilateral asymmetries of the foot-stretchers were also seen to significantly influence lumbar-pelvic kinematics (R(2) = 0.43) and pelvic twisting (R(2) = 0.32) during the rowing stroke. These results provide biomechanical evidence toward aspects of technique that can be modified to optimize force output and performance, which can be of direct benefit to coaches and athletes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".