How do elite endurance athletes self-regulate their recovery around hard training? An experience sampling study
Bibliographic record
Abstract
Effective training for high-performance sport requires recovery, yet little research has examined the psychology of how athletes shape their recovery between training sessions. Although qualitative research has described how elite endurance athletes implement recovery through self-regulatory processes, there is a need to explore how this evidence transfers to more ecologically valid contexts. This study employed experience sampling methods to describe how elite endurance athletes self-regulated their recovery around two important workouts, 2–3 days apart in their planned training. Twenty-two endurance athletes, (7 males, 15 females) training within Canadian National cycling and triathlon teams (able-bodied and parasport) reported on processes of recovery self-regulation and perceived recovery and stress states using their smartphone, up to eight times daily for five days. We described indices and patterns for how the athletes employed four recovery self-regulation processes (i.e., awareness, checking-in, interpreting, adjusting), as well as when in relation to their key workouts and recovery-stress states.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".