Rowing and pain: does rowing lead to exercise-induced hypoalgesia?
Bibliographic record
Abstract
Physical activity acutely alters pain processing known as exercise-induced hypoalgesia (EIH). This randomized controlled crossover study investigated the effects of two different rowing exercises on EIH and to explore whether possible EIH effects are related to individual rowing specific performance. Fifty male experienced rowers conducted two rowing sessions (submaximal: 30 min of moderate rowing (70% of maximum heart rate); maximal: 350 m in an all-out fashion) and a control session. Pre and post exercise pain sensitivity was measured bilaterally using pressure pain thresholds (PPT; Newton (N)) at the elbow, knee, ankle, sternum, and forehead. Individual performance was determined as maximum watt/kg and was tested for correlations with changes in PPT. Higher PPT values were observed after maximal exercise at all landmarks with a mean change ranging from 2.5 ± 7.8 N (right elbow; p = 0.027; dz = 0.323) to 10.0 ± 12.2 N (left knee; p ≤ 0.001; dz = 0.818). The submaximal (range from −1.6 ± 8.8 N (Sternum; p = 0.205; dz = 0.182) to 2.0 ± 10.3 N (right ankle; p = 0.176; dz = 0.194)) and control session (range from −0.5 ± 7.6 N (left elbow; p = 0.627; dz = 0.069) to 2.6 ± 9.1 N (right ankle; p = 0.054; dz = 0.279)) did not induce changes. Relative performance levels were not correlated to EIH (range from: r = −0.129 ( p = 0.373) at sternum to r = 0.176 ( p = 0.221) at left knee). EIH occurred globally after a short maximal rowing exercise while no effects occurred after rowing for 30 min at submaximal intensity. EIH cannot be explained by rowing specific performance levels in experienced rowers. However, the sample may lack sufficient heterogeneity in performance levels to draw final conclusions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".