Submaximal Markers of Fatigue and Overreaching; Implications for Monitoring Athletes
Bibliographic record
Abstract
The regular monitoring of athletes is important to fine-tune training and detect early symptoms of overreaching. Therefore the aim of this study was to determine if a noninvasive submaximal running test could reflect a state of overreaching. 14 trained runners completed a noninvasive Lamberts Submaximal Running Test, one week before and 2 days after finishing an ultramarathon, and delayed onset of muscle soreness and the daily analysis of life demands for athletes questionnaire were also captured. After the ultramarathon, submaximal heart rate was lower at 70% (-3 beats) and 85% of peak treadmill running speed (P<0.01). Ratings of perceived exertion were higher at 60% (2 units) and 85% (one unit) of peak treadmill running speed, while 60-second heart rate recovery was significantly faster (7 beats, P<0.001). Delayed Onset of Muscle Soreness scores and the number of symptoms of stress (Daily Analysis of Life Demands for Athletes) were also higher after the ultramarathon (P<0.01). The current study shows that the Lamberts Submaximal Running Test is able to reflect early symptoms of overreaching. Responses to acute fatigue and overreaching were characterized by counterintuitive responses, such as lower submaximal heart rates and faster heart rate recovery, while ratings of perceived exertion were higher.
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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 | 0.002 |
| 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 | 0.001 | 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".