Physiological Determinants of Ultramarathon Trail-Running Performance
Bibliographic record
Abstract
CONTEXT: The physiological determinants of ultramarathon success have rarely been assessed and likely differ in their contributions to performance as race distance increases. PURPOSE: To examine predictors of performance in athletes who completed either a 50-, 80-, or 160-km trail race over a 20-km loop course on the same day. METHODS: Measures of running history, aerobic fitness, running economy, body mass loss, hematocrit alterations, age, and cardiovascular health were examined in relation to race-day performance. Performance was defined as the percentage difference from the winning time at a given race distance, with 0% representing the fastest possible time. RESULTS: In the 50-km race, training volumes, cardiovascular health, aerobic fitness, and a greater loss of body mass during the race were all related to better performance (all P < .05). Using multiple linear regression, peak velocity achieved in the maximal oxygen uptake test (β = -11.7, P = .002) and baseline blood pressure (β = 3.1, P = .007) were the best performance predictors for the men's 50-km race (r = .98, r2 = .96, P < .001), while peak velocity achieved in the maximal oxygen uptake test (β = -13.6, P = .001) and loss of body mass (β = 12.8, P = .03) were the best predictors for women (r = .94, r2 = .87, P = .001). In the 80-km race, only peak velocity achieved in the maximal oxygen uptake test predicted performance (β = -20.3, r = .88, r2 = .78, P < .001). In the 160-km race, there were no significant performance determinants. CONCLUSIONS: While classic determinants of running performance, including cardiovascular health and running fitness, predict 50-km trail-running success, performance in longer-distance races appears to be less influenced by such physiological parameters.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".