Interrelationships between stress, eating attitudes, and metabolism in endurance athletes across a competitive season
Bibliographic record
Abstract
A competitive sport season represents a multidimensional stressor where physical and psychological stress may render an athlete susceptible to energy deficiency (ED). Downstream effects of ED can include a reduction in measured-to-predicted resting metabolic rate (RMRratio), indicating metabolic compensation. A pathway linking stress, eating attitudes, and metabolic compensation has not been explored. To test if sport-specific stress is associated with eating attitudes and metabolism in endurance athletes (18–22 years) at different phases of a competitive season, we assessed two groups of athletes: 26 swimmers (15 female and 11 male) during peak season (PEAK), and 26 runners (female) across pre- (PRE) and off-season (OFF). Stress (RESTQ-52), eating attitudes (cognitive restraint (CR), drive for muscularity (DM), and body dissatisfaction), and metabolism (RMRratio) were assessed. In PRE, sport-specific stress and CR were negatively correlated with RMRratio ( R = −0.58; p < 0.05, and R = −0.57; p < 0.05, respectively). In PEAK, sport-specific stress and DM were negatively correlated with RMRratio ( R = −0.64; p < 0.05; R = −0.40; p < 0.05, respectively). DM was positively related to sport-specific stress ( R = 0.55; p < 0.05). During OFF, there was no relation between RMRratio and sport-specific stress. In runners, there was a change in stress from PRE-to-OFF with highest reported stress during PRE ( p < 0.05) versus OFF. Regression analyses revealed that sport-specific stress and CR were significant predictors of RMRratio during PRE and PEAK ( p < 0.05), but not OFF ( p > 0.05). Associations between stress, eating attitudes, and metabolic compensation in endurance athletes during PRE and PEAK season suggest that during heavier training, metabolic compensation may be linked to upstream eating attitudes associated with sport-stressors.
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 | 0.001 |
| 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 | 0.000 |
| 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".