The Functional Movement Profile Of Mountain Ultramarathoners
Bibliographic record
Abstract
Mountain ultramarathon running involves balance and coordination over varying terrain. The Functional Movement ScreenTM (FMS) is an easy-to-administer and reliable test of an athlete’s capability to complete movements relevant to athletic performance. Low FMS scores are associated with a higher risk of injury. Recreational runners have previously been described as having low FMS scores. However, the functional movement capabilities of ultramarathon runners have not been investigated. Given the increased popularity of ultramarathon running, it is necessary to assess the movement capabilities of ultramarathoners and determine if there is an injury risk or areas of improvement for training. PURPOSE: To describe the FMS capabilities of ultramarathon runners and the relationship of FMS scores to race completion. METHODS: Runners were examined before ultra-endurance races of 65 (n = 4), 80 (n = 7), 112 (n = 7), and 193 (n = 43) km. An FMS test was administered prior to the race, assessing on a scale of 0-3 the capability to complete seven basic athletic movements. A two-way ANOVA (race length and finisher/non-finisher) was conducted. RESULTS: Ultramarathon runners exhibited moderate overall FMS scores (15.9 ± 2.1 AU). No significant difference existed for total FMS between finishers (n = 41) and non-finishers (n = 20) (16.2 ± 2.0 vs. 15.3 ± 2.0 AU, respectively, p = 0.22). For subscores of the FMS, the squat score (Finishers = 2.0 ± 0.6, Non-Finishers =1.7 ± 0.7 AU) was the lowest score and involved significant compensation patterns or difficulties in balance. No significant differences were present between race distance and the total FMS score (40 km = 16.3 ± 0.3, 40 km = 15.9 ± 2.2, 100 km = 17.0 ± 0.9, 120 km =15.7 ± 2.1, p = 0.50). CONCLUSIONS: Ultramarathon runners display moderate coordination and stability. Ultra-endurance mountain runners appear to have higher FMS scores compared to recreational runners perhaps as a result of more frequent training on uneven surfaces and technical terrain. Enhanced FMS scores do not appear to predict the capability to complete an ultramarathon. Training for improved squat mobility and stability may be warranted in ultramarathon runners. 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 | 0.001 |
| 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) | 0.002 | 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".