The Effect of Energy Status on Distance Running Performance Across a Competitive Season in Female Cross-country Runners
Bibliographic record
Abstract
Female cross-country runners are at a higher risk for chronic energy deficiency (ED). Cross-sectional evidence demonstrates the negative effects of ED on surrogate markers of performance. The association between chronic ED and distance running performance in female cross-country runners across a season is unclear. PURPOSE: To examine competitive 5km time trial performance across a season in female cross-country runners categorized by energy status. METHODS: 18 female cross-country runners (19.5 ± 0.3 yrs, 54.6 ± 1.4 kg, 61.2 ± 1.7 mL/kg/min) participated in this prospective observational study across a 10-12 week competitive season. Outcome measures were assessed pre and post-season. Resting energy expenditure (REE) was measured using indirect calorimetry and compared to the Harris Benedict predicted REE (REE/pREE) as a marker of ED. Performance was assessed during an outdoor 5km time trial. Fasting blood samples were assayed for total triiodothyronine (TT3). Repeated measures ANOVA were performed to compare log TT3 and 5km time trial outcomes across the season among participants grouped by tertiles (n=6 each group) of pre-season REE/pREE (severe ED (SED; REE/pREE<0.84), moderate ED (MED; 0.84≤REE/pREE<0.90), and energy replete (ER; REE/pREE≥0.90)). RESULTS: There was a significant group effect (p=0.004), however no time or interaction effects (p>0.49) were observed for the 5km time trial. Pre-season the SED group was 4.1s (p=0.005) slower vs the MED group, while no differences were observed between the ER and the SED or MED groups (p>0.1). Post-season the SED group was 3.7s (p=0.002) and 2.6s (p=0.023) slower vs the MED and ER groups, respectively. For log TT3 there were no significant time or group effects (p>0.15), however a trend toward a significant interaction effect (p=0.097) was observed. Pre-season log TT3 was lower (p=0.043) in the SED vs MED groups, a trend to significance (p=0.067) was observed between the SED and ER groups, with no difference between the ER and MED groups. Log TT3 was not different between groups post-season (p=0.36). CONCLUSIONS: We provide evidence of decrements in 5km time trial performance in severely ED female cross-country runners. Early detection of severe ED utilizing REE may be a strategy for preventing suboptimal distance running performance across a competitive season.
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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 | 0.000 | 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".