Movement Patterns and Injury Incidence in Cross-country Skiers: A Prospective Cohort Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».