Psychological Profile of Trail Runners Associated With Running-Related Injuries: A Prospective Study
Bibliographic record
Abstract
Background: Trail running has increased in popularity due to the benefits of physical activity in nature. However, trail running has an inherent risk of running-related injuries (RRI). It is known that athletes with certain psychological traits have a greater tendency to suffer injuries; however, this is unknown in trail runners. The main objective of this study was to identify trail runners’ psychological profiles and to compare the proportion of RRI across these profiles. Hypothesis: Trail runners with psychological profiles of high stress, precompetitive anxiety, mental fatigue, competitiveness, and poor sleep quality are at increased risk of RRI. Study Design: Prospective cohort study. Level of Evidence: Level 2. Methods: A Gaussian mixture model cluster analysis was performed on 202 trail runners (55.5% male; aged 38.7 [33.4-46.2] years) with psychological stress, cognitive and somatic anxiety, self-confidence, mental fatigue, sleep quality, and competitiveness measured 4 weeks before participating in a race. The proportion of RRI during the race was recorded and compared across clusters. Results: Overall RRI proportion during competition was 11.3% (n = 24). The most common RRI were muscle (41.7%) and tendon/bursa (16.7%) injuries, affecting primarily the knee (33.3%) and lower leg (20.8%). Five psychological profiles were identified. Cluster 1 (competitive runners with moderate psychological stress and mental fatigue, low sleep quality and anxiety, and high self-confidence) had a higher RRI proportion than Cluster 3 (similar traits but lower psychological stress, mental fatigue, and higher self-confidence; 21.2% vs 3.2%; P = 0.02). Conclusion: Certain psychological profiles in trail runners were associated with higher RRI risk. Clinical Relevance: The medical team or trail running coaches should monitor runners with psychological profiles with higher psychological stress, mental fatigue, and cognitive anxiety, as well as lower self-confidence and sleep quality, to design strategies to reduce their risk of RRI.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".