Influence of circadian preference, sleep inertia and their interaction on marathon completion time: A retrospective, cross‐sectional investigation of a large mass‐participation city marathon
Bibliographic record
Abstract
Summary Burgeoning interest in marathons necessitates an understanding of performance determinants. Research has highlighted the importance of diet, training and sleep, yet relations of circadian preference and sleep inertia with marathon performance remain largely unexplored. Because marathons generally start early‐to‐mid morning, these characteristics may have relevant impact. This study investigates relationships of circadian preference, sleep inertia and their interaction with marathon completion time. Consenting participants in a 2016 large mass‐participation city marathon completed self‐report questionnaires capturing circadian preference and sleep inertia, along with demographics and other characteristics. Circadian preference and sleep inertia were described across subgroups. Analyses examined the associations and interactions of circadian preference and sleep inertia with marathon completion times, with adjusted analyses accounting for age, sex and sleep health. Participants were marathon finishers ( n = 936; 64.5% male; 66.3% young‐adults), with a majority reporting morningness tendencies (60.8%). Results supported a linear association between increasing eveningness preference with slower marathon times ( p = 0.003; p adjusted = 0.002), while some support was provided for a linear relationship between greater sleep inertia and slower marathon times ( p = 0.04; p adjusted = 0.07). A significant interaction was observed ( p = 0.02; p adjusted = 0.01), with the directionality suggesting that the circadian preference relationship weakened when sleep inertia severity increased, and vice‐versa. Our results suggest deleterious associations of increasing eveningness preference and greater sleep inertia with marathon completion time. These features may aid identifying marathoners who could be at a disadvantage, while also serving as modifiable targets for personalized training regimens preceding competition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".