A prospective study of running injuries: the Vancouver Sun Run “In Training” clinics
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Notice bibliographique
Résumé
OBJECTIVES: Seventeen running training clinics were investigated to determine the number of injuries that occur in a running programme designed to minimise the injury rate for athletes training for a 10 km race. The relative contributions of factors associated with injury were also reported. METHODS: A total of 844 primarily recreational runners were surveyed in three trials on the 4th, 8th, and 12th week of the 13 week programme of the "In Training" running clinics. Participants were classified as injured if they experienced at least a grade 1 injury-that is, pain only after running. Logistic regression modelling and odds ratio calculation were performed for each sex using the following predictor variables: age, body mass index (BMI), previous aerobic activity, running frequency, predominant running surface, arch height, running shoe age, and concurrent cross training. RESULTS: Age played an important part in injury in women: being over 50 years old was a risk factor for overall injury, and being less than 31 years was protective against new injury. Running only one day a week showed a non-significant trend for injury risk in men and was a significant risk factor in women and overall injury. A BMI of > 26 kg/m(2) was reported as protective for men. Running shoe age also significantly contributed to the injury model. Half of the participants who reported an injury had had a previous injury; 42% of these reported that they were not completely rehabilitated on starting the 13 week training programme. An injury rate of 29.5% was recorded across all training clinics surveyed. The knee was the most commonly injured site. CONCLUSIONS: Although age, BMI, running frequency (days a week), and running shoe age were associated with injury, these results do not take into account an adequate measure of exposure time to injury, running experience, or previous injury and should thus be viewed accordingly. In addition, the reason for the discrepancy in injury rate between these 17 clinics requires further study.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 découle