Deliberate recovery: Exploring the relationship between expertise and sleep quantity in athletes
Bibliographic record
Abstract
The development of sporting skill requires an extended commitment to intensive practice, the accumulation of which must be balanced with adequate rest. In contrast to practice habits, the patterns and characteristics of recovery among athletes have been relatively understudied. This study sought to explore how the 'deliberate' use of recovery varies according to athlete expertise through an examination of sleep, a biologically necessary and universally accessible form of recovery with established effects on performance and learning. Individual endurance sport athletes (n = 43) recorded daily information on sleep timing and duration, as well as training load, over a 14-day period, and a follow-up questionnaire assessed sleep chronotype and categorized athletes into three skill groups. Elite and pre-elite athletes reported sleeping significantly longer than non-elite athletes (F(2, 555) = 7.18, p = .001, partial ?2 = .025). Similarly, elite and pre-elite athletes attempted to sleep significantly earlier in the night (F(2, 556) = 4.96, p = .007, partial ?2 = .018) and napped significantly longer during the day (F(2, 105) = 6.04, p = .003, partial ?2 = .103). Training load may contribute to these differences, while in this sample sleep chronotype did not. This study suggests athletes engage in recovery activities differently according to their skill level, with higher-level athletes making better use of sleep for recovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".