Movement Patterns and Injury Incidence in Cross-country Skiers: A Prospective Cohort Study
Bibliographic record
Abstract
Purpose of the Work This 12-month prospective study describes the characteristics of a group of elite cross-country skiers using subject demographics; intake physical measurements (Movement Competency Screen—MCS, hamstring length, and trunk muscle endurance); and monthly injury, training, and racing reports. The primary hypothesis is that new injury is associated with poor movement competency. Secondary hypotheses are that new injury is associated with (a) a history of injury, (b) a long career in cross-country skiing, (c) high training hours, (d) high running training hours, (e) high roller ski training hours, (f) poor trunk muscle endurance, and (g) reduced active straight leg raise (ASLR). Mean injury incidence will be used to examine differences between the injury incidence rates of (a) the ski season and off-season, (b) traumatic and nontraumatic injuries, and (c) injuries by anatomic location. Introduction Cross-country ski injury incidence studies have employed variable methodologies, using retrospective injury and training surveillance. Standardised injury incidence measures will improve the understanding of cross-country ski injury incidence. Studying the relationship between movement patterns and new injury may identify risk factors for future injury, and eventually reduce injury rates with appropriate intervention strategies. Methods At enrolment, 71 professional or collegiate cross-country skiers (35 men, 36 women) provided demographics and injury history, then performed the Movement Competency Screen (MCS), hamstring length, and trunk muscle endurance tests. Self-report electronic injury and training surveillance occurred monthly for 12 months. Spearman’s correlation determined the relationship between new injury and MCS score, past injury, total training time, and run training time. A t-test compared injury incidence (the mean number of injuries per subject per 1,000 training/exposure hours) between anatomic regions, type of injuries, and seasons. Results/Main Points The study was completed by 58% of subjects (18 men, 23 women). There were 3.18 injuries per subject per 1,000 training/exposure hours. New injury was not correlated with MCS score, but was correlated with previous injury (p < .05). New injury did increase as the time spent running increased, although not significantly (p = .08). New injury was not correlated with any other variable. Risk factor analysis found previous injury was a significant predictor of new injury when accounting for overall training time, run time, and MCS score. Lower-extremity injury incidence (2.13) was significantly higher than upper extremity (0.46) or trunk injury incidence (0.22). Nontraumatic/overuse injury incidence (2.76) was significantly higher than acute injury incidence (1.05) (p < .05). Off-season injury incidence (5.25) was higher than ski season (2.27), although not significantly (p = .07). Conclusion This is the first examination of the relationship between MCS score and new injury in cross-country skiers. New injury positively correlated with previous injury, but not with MCS score, hamstring length, trunk endurance ratio, or training/exposure hours. Lower-extremity and nontraumatic/overuse injuries had the highest incidence rates. Previously injured skiers are at greater risk for further injury. The results lay the foundation for further movement and injury studies and future injury prevention strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".