Heart Rate, Mean Arterial Blood Pressure, and Pulmonary Function Changes Associated With an Ultraendurance Triathlon
Bibliographic record
Abstract
OBJECTIVE: We studied 8 triathletes competing in the 3-day World Ultraman Championships (day 1: 10-km swim, 165-km bike; day 2: 261-km bike; day 3: 85-km run) to determine the effect of extreme physical exertion on heart rate (HR), mean arterial blood pressure (MAP) and pulmonary function, and their relation to overall athletic performance. METHODS: Baseline HR, MAP and pulmonary function measurements were made 2 days before the start of competition. During the competition, HR and MAP measurements were made less than 30 minutes before the start and 10 minutes after the finish each race day. Pulmonary function was measured within 5 minutes of the finish each race day. RESULTS: Forced vital capacity and forced expiratory volume in 1 second (FEV1.0) were reduced from baseline at the end of each race day. Peak expiratory flow (PEF) was reduced from baseline on days 1 and 3. Lower baseline resting HR was correlated (r = 0.77, P = .021) with faster total race times. The decreases in FEV1.0 and PEF over the whole race also correlated with performance (r = 0.77 and 0.93). Multiple regression analysis of baseline data indicated that HR and MAP had the strongest association with total race time prediction (54% and 19% of total). However, when declines in pulmonary function over the total race were also included, PEF was found to be associated with 87% of the total race time prediction. CONCLUSIONS: The strong association in the decline in PEF to race time, though just correlative, suggests a link between pulmonary function and ultratriathlon performance.
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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 | 0.001 |
| 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".