Boston Marathon Prospective Study: Medical And Performance Outcomes Associated With Indicators Of Low Energy Availability
Bibliographic record
Abstract
PURPOSE: To evaluate association between indicators of problematic Low Energy Availability (LEA), i.e. LEA of long duration and/or severity, and race performance and medical outcomes of athletes participating in the 2022 Boston Marathon. METHODS: Registered entrants (age ≥ 18 yr) for the 2022 Boston Marathon were recruited to complete an encrypted electronic pre-race survey (1-4 weeks pre-event), with de-identified responses related to training, nutrition, and performance history. Questionnaire data was linked to official race timing and medical encounter data from the marathon. Female LEA indicators included: total EDE-Q scores (>2.3), and/or a total LEAF-Q score (≥8), and/or self-report of diagnosed Eating Disorder/Disordered Eating (ED/DE). Male LEA indicators included: total EDE-Q scores (>1.68), and/or self-report of diagnosed ED/DE, and/or for male participants <50 yrs of age: a low sex drive score based on 3 questions from the LEAM-Q subsection on reproductive dysfunction. RESULTS: Of 1,030 study participants (F: 546, M: 484), (ages F: 43.5 ± 10.3 yrs, M: 51.5 ± 10.7 yr; mean ± SDs), 30.4% (42.5% of females and 17.6% of males) were identified with problematic LEA indicators based on validated scoring criteria. Both females and males with LEA indicators finished with significantly worse (mean age- and sex-matched) Boston Marathon division place rankings compared to healthy controls (Females: 1377.4 ± 82.9 vs. 948.9 ± 57.6, p < 0.001; Males: 1262.4 ± 103.3 vs. 794.6 ± 41.0, p < 0.001 respectively). Overall, athletes with indicators of problematic LEA had a 1.95-fold greater relative risk (CI: 1.13-3.36, p = 0.017) of requiring medical support for any condition compared to healthy controls, and a 3.55-fold greater relative risk (CI: 1.17-10.76, p = 0.025) of a significant medical event (i.e., did not finish requiring intra-event medical transportation or transfer to a local hospital from a course medical tent, or transport to a hospital from the post-finish medical area). CONCLUSIONS: This is the first large questionnaire-based study on problematic LEA where the outcomes could be directly linked to real-world performance and medical consequences in a specific competition. Our novel findings confirm the performance and health implications of problematic LEA in both female and male athletes.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".