The Effect of Neuromuscular Fatigue on the Spatiotemporal Coordination of Rowing
Bibliographic record
Abstract
Within rowing, lower back disorders (LBDs) are common, but the mechanisms underpinning LBDs are poorly understood. Considering this, it is essential to understand how coordination and motor control change under different constraints such as ergometer rowing and fatigue. This can help better inform movement features linked to LBDs. Measurement of the continuous relative phase (CRP) is a method used to quantify body segment and joint coordination, as CRP measures the spatiotemporal control of multi-joint movement. The purpose of this study was twofold: to examine the general spatiotemporal coordination aspects of ergometer rowing in an unfatigued state, and to quantify how the spatiotemporal coordination of a rowing movement changes in response to a fatigue-inducing rowing trial. Wearable IMUs monitored 20 participants’ movement during a 2000 m ergometer row. The Borg-10 Rating of Perceived Exertion (RPE) scale was used to quantify perceived fatigue. Despite significant RPE increases across all athletes, the spatiotemporal coordination of rowing revealed prevailing strategies for the lumbar spine and lower extremity but no significant effects (α = 0.05) of fatigue on CRP outcomes (MARP, DP), cross-correlation lag (RXY), or range of motion. These findings provide further insight into rowing movements and support the idea that heterogeneous responses to fatigue may exist, requiring further study.
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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 | 0.001 | 0.003 |
| 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".