Neurophysiological Markers for Monitoring Exercise and Recovery Cycles in Endurance Sports
Bibliographic record
Abstract
The current study analyzes the suitability and reliability of selected neurophysiological and vegetative nervous system markers as biomarkers for exercise and recovery in endurance sport. Sixty-two healthy men and women, endurance trained and moderately trained, performed two identical acute endurance tests (running trial 1 and running trial 2) followed by a washout period of four weeks. Exercise protocol consisted of an acute running trial lasting 60 minutes. An intensity corresponding to 95% of the heart rate at individual anaerobic threshold for 40 minutes was followed by 20 minutes at 110%. At pre-exercise, post-exercise, three hours post-exercise and 24 hours post-exercise, experimental diagnostics on Brain-derived neurotrophic factor (BDNF), heart rate variability (HRV), Stroop Color and Word Test (SCWT), and Short-Form McGill Pain Questionnaire (SF-MPQ) were performed. Significant changes over time were found for all parameters (p < .05). Furthermore, there was an approached statistical significance in the interaction between gender and training status in BDNF regulation (F(3) = 2.43; p = 0.06), while gender differences were found only for LF/HF-ratio (3hPoEx, F(3) = 3.40; p = 0.002). Regarding the reliability, poor ICC-values (< 0.5) were found for BDNF, Stroop sensitivity and pNN50, while all other parameters showed moderate ICC-values (0.5-0.75). Plasma-BDNF, SCWT performance, pain perception and all HRV parameters are suitable exercise-sensitive markers after an acute endurance exercise. Moreover, pain perception, SCWT reaction time and all HRV parameters show a moderate reliability, others rather poor. In summary, a selected neurophysiological and vegetative marker panel can be used to determine exercise load and recovery in endurance sports, but its repeatability is limited due to its vaguely reliability.
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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 | 0.001 | 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".