Lower Limb Power And Reactive Strength In Ultramarathon Running: Comparing Finishers And Non-finishers
Bibliographic record
Abstract
Mountainous ultramarathon racing involves high degrees of neuromuscular fatigue and muscular damage. Previous research has identified that greater muscular power or strength differentiates elite ultramarathon performers from road runners; however, the relationship of muscular strength and power to the capability to finish an ultramarathon is unclear. PURPOSE: To describe the muscular strength and power profiles of mountain ultramarathoners and the relationship of these variables to race performance. METHODS: Runners were examined before and immediately after ultra-endurance mountain races of 65, 80, 112, and 193 km. Neuromuscular coordination and muscular power were assessed by five countermovement jumps on a dual-force plate set-up. Jump Height and reactive strength (jump height * time to take-off-1) were recorded as assessments of power and coordination, respectively. Finishers of the race and non-finishers were compared (two-way ANOVA for sex and finish) (Male Finishers = 17, Male Non-Finishers = 8, Female Finishers = 7, Female Non-Finishers = 2). RESULTS: Ultramarathon runners exhibited low jump heights (Male runners = 25.0 ± 5.7 cm, Female = 16.0 ± 4.1 cm), and low reactive strength (Male = 0.25 ± 0.09 AU, Female = 0.17 ± 004 AU) at baseline. There was no difference in jump height (in both sexes) between finishers and non-finishers of an ultra-marathon (Male = 24.6 ± 4.8 vs. 26.0 ± 7.9 cm, Female = 16.8 ± 4.7 vs. 13.2 ± 0.72 cm, respectively). CONCLUSIONS: While ultramarathon runners display low jump heights and low reactive strength, greater muscular power or reactive strength does not appear to affect the capability to complete an ultramarathon. Training for ultramarathons should focus on other determinants of performance, such as aerobic capacity. Natural Sciences and Engineering Research Council of Canada (NSERC-RGPIN-2018-1502)
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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 | 0.000 |
| 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) | 0.003 | 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".