EFFECTS OF CLIMATIC CONDITIONS ON MOUNTAIN ULTRA-MARATHON RUNNERS’ HEART RATE VARIABILITY AND PARASYMPATHETIC ACTIVITY
Bibliographic record
Abstract
S. S. Sahota, P. K. Singh, I. J. Foster, A. L. Wookey, M. J. Rogers, C. Q. Malcolm, M.D. White Simon Fraser University, Burnaby, BC, Canada PURPOSE: Heart rate variability (HRV) can be employed to give an indication of parasympathetic activity as an index of fatigue. Following two 50 km mountain ultra-marathons, run on the same course but in different years and in differing climatic conditions, HRV was employed to assess fatigue of the runners. The root mean square of successive electrocardiogram R-R intervals (RMSSD), which reflects parasympathetic activity, was employed to give an index of fatigue, where a lower RMSSD value indicates greater fatigue. It was hypothesized that there would be a lower post-race RMSSD in 2014 when there was a greater heat stress relative to post-race RMSSD for the same race in 2015. METHODS: Five males volunteered for the study after an orientation session and completed a medical history, PAR-Q and informed consent forms for the study that was approved by the SFU Office of Research Ethics; one runner competed in both years. In both years, pre-race, and immediate post-race heart rate RMSSD was collected using chest heart rate straps and fitness computers. In 2014 the temperature was 22.3 ± 3.4°C (mean ±SD) and the ambient vapor pressure was 10.7 ± 0.3 mm Hg. For the 2015 race the temperature was 12.9 ± 4.5°C and ambient vapor pressure was 10.4 mm Hg. From each volunteer a 5 min section of the R-R data was analyzed using online software for HRV. The statistical analysis included a 2 factor non-repeated ANOVA with factors of Year (2014 and 2015) and Race Day Time (Pre-Race and Post-Race). The p-value set a 0.05. RESULTS: For RMSSD the main effect of Year (F=0.1, p=0.740) was not significant whereas there was a trend for an effect of Race Day Time (F=2.9, p=0.1). Pre-race RMSSD in 2014 was 34.7 ± 9.7 ms and the post-race RMSSD in 2014 was 19.3 ± 17.1 ms. In 2015, pre-race was 35.5 ± 18.1 ms, whereas the post-race value 23.9 ± 6.1 ms. CONCLUSION: The hypothesis that there would be a lower post-race RMSSD in 2014 when there was a greater heat stress compared to the same race in 2015 was not supported by the data. Supported by NSERC and CFI.
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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".