Self-Reported Physical Activity, Injury, and Illness in Canadian Adolescent Ski Racers
Notice bibliographique
Résumé
Alpine ski racers spend a considerable amount of time on snow during the ski season and this may detract from other activities known to influence fundamental movement skills and overall health related outcomes. Parents of racers (n=52 F; n=44 M; age range 9.3-13.7 years) registered in an Alberta ski club in Canada were recruited to complete a baseline medical questionnaire at the start of preseason training in 2017. We describe the physical activity volume and sport participation level outside of physical education classes over the previous 12 months and report on the different injuries (fractures and concussion), medications (supplement use) and health care utilization. The mean number of activities participated over the previous 12 months was five (range of 1-14) and the top activities were cycling, hiking, and swimming. A cumulative mean of just under 400 hours of activity was reported, with different ranges between males (62-868.5) and females (27-1015). More males reported concussions over a lifetime, with alpine skiing accounting for 46% and mountain biking 15%. Over the past 12 months 18.4% of the athletes reported being injured and injury severity impacted return to sport with range of reported days missed from 1-60 days. Thirteen injuries were from alpine skiing and females (11.5%, 6/52) reporting more lower limb injuries than males (6.8% 3/44). Most athletes (85%) did not take medication on a regular basis and those that did had a medical diagnosis. The frequency of diagnosed respiratory conditions were 12.6% (12/95) with males reporting slightly more cases than females. No difference in the number of emergency visits (25%) between male and females occurred over the past 12 months however females reported more (n=102) allied health care, sport medicine visits and x-rays appointments when compared to males (n=65). In summary, the volume of activity reported was very high over the previous 12 months, therefore calculating total load should be considered since 10-14-year-old athletes are entering a growth period and may be at risk for overuse injuries. Employing wearable technology to quantify intensity and participation hours in combination with the self-report questionnaire would improve the accuracy of the data collected.
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 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 | 0,000 | 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».