Self-regulating recovery: Athlete perspectives on implementing recovery from elite endurance training
Bibliographic record
Abstract
Recovery is the process of restoring performance capabilities between training sessions. Recovery strategies must be implemented to meet contextual demands, yet this process is not well-understood from an athlete perspective. Drawing on previous descriptions of self-regulation in learning, this study aimed to describe the process of implementing recovery from the perspective of endurance athletes. Twelve elite Canadian endurance athletes (6 women, 6 men; 25–31 years-old; each had competed in multiple Olympics or World Championships) participated in two semi-structured interviews, separated by 1 week of recording their thoughts, feelings, and activities of recovery in an activity journal. Through reflexive thematic analysis, we found that recovery was athlete-led; they used self-regulatory processes pertaining to self-knowledge and planning (the theme of “Knowing my body”), self-awareness and interpretation (“Listening to my body”), and self-control and adjustment (“Respecting my body”). The athletes’ described their recovery as integrated with their environment; various people and places supplemented, facilitated, and provided aspects for their recovery. Our results suggest that recovery can be effectively understood as a series of athlete-led skills, supported and enhanced through specific environmental interactions, which we summarize in a novel heuristic called the Athlete Recovery Regulation Cycle. These findings advance a new perspective on recovery as a product of skills that athletes can develop to hone the effectiveness of their recovery from training. Keywords: self-regulated learning; sport performance; sport practice; mental performance.Lay summary: We asked elite endurance athletes to describe their process of implementing recovery around training. This process was primarily athlete-led, involving a set of self-regulatory skills, integrated with the support of people and places in their environment. Recovery should be considered in terms of skills that athletes can develop and for which they can take ownership.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".