Can Trained Runners Effectively Attenuate Impact Acceleration During Repeated High-Intensity Running Bouts?
Bibliographic record
Abstract
The purpose of this study was to investigate the effects of prolonged high-intensity running on impact accelerations in trained runners. Thirteen male distance runners completed two 20-minute treadmill runs at speeds corresponding to 95% of onset of blood lactate accumulation. Leg and head accelerations were collected for 20 s every fourth minute. Rating of perceived exertion (RPE) scores were recorded during the third and last minute of each run. RPE responses increased (P < .001) from the start (11.8 ± 0.9, moderate intensity) of the first run to the end (17.7 ± 1.5, very hard) of the second run. Runners maintained their leg impact acceleration, impact attenuation, stride length, and stride frequency characteristics with prolonged run duration. However, a small (0.11-0.14g) but significant increase (P < .001) in head impact accelerations were observed at the end of both first and second runs. It was concluded that trained runners are able to control leg impact accelerations during sustained high-intensity running. Alongside the substantial increases in perceived exertion levels, running mechanics and frequency domain impact attenuation levels remained constant. This suggests that the present trained runners are able to cope from a mechanical perspective despite an increased physiological demand.
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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 | 0.001 |
| 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 | 0.001 | 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".