Physiological and psychological adaptations during taper in competitive swimmers
Bibliographic record
Abstract
An effective taper in training load is essential for increased performance outcomes at competition, however optimal taper individualization remains elusive. Monitoring psychological and physical adaptations that occur during the taper may help guide the optimization of training during a taper resulting in improvements in performance. We monitored physical and psychological variables as well as competition performance of 10 elite swimmers before and during a 21-day taper in preparation for the Canadian National Championships or World Championships. Mood and recovery were assessed using the Brunel Mood Scale and the Recovery-Stress Questionnaire. Resting muscle tension, skin conductance, and respiration rate were measured using biofeedback technology. Speed and heart rate were assessed with a 2 × 200 m submaximal swim test. Resting HR was measured using the Rusko test. We hypothesized that psychological and physiological measurements would change during the taper phase and that these changes would be associated with improved competition performance. Results showed that the Brunel Mood Scale, Recovery-Stress Questionnaire, 2 × 200 m submaximal test, and the Rusko test changed significantly throughout the taper period. Variables from the Brunel Mood Scale and the Recovery-Stress Questionnaire showed a significant relationship with improved performance, indicating that these questionnaires show good utility for assessing progress during a taper in the future.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| 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".