An injury profile of amateur and semi-professional KwaZulu-Natal triathletes
Notice bibliographique
Résumé
Background: A triathlon comprises of a combination of swimming, cycling and running. Triathlons are usually classified as sprint distance, Olympic distance, and Ironman or ultra-distance. Triathlon was popularized in the 2000 Summer Olympic Games and, despite this, there is insufficient data relating to injuries in the South African context. This study aimed to determine the injury profile of amateur and semi-professional Kwa-Zulu Natal triathletes. Methods: This Institutional Research Board approved, cross sectional study, included 80 active members of the Kwa-Zulu Natal Triathlon Association. All triathletes completed a questionnaire on training and injury profiles; with only those having had a musculoskeletal complaint additionally completing a clinical consultation. In order to assess associations between presence of injury and explanatory variables, binary logistic regression using backward selection based on likelihood ratios was used. Data was described using frequency tables for categorical data and summary statistics for continuous data. Odds ratios was reported and a p value <0.05 was considered statistically significant. For triathletes reporting injuries, linear regression was used for factors associated with injury severity. Results: Fifty seven triathletes responded giving a response rate of 71% (68% male, 32% female). The point and period (year) prevalence of triathlon-related musculoskeletal pain was 17.5% and 68.4% respectively. The ranking of the most common site of injury in the last 12 months included the knee (64%), low back (21%) and thigh (18%); with females having had a significantly higher risk of injury than males (p=.019). Additionally, injury risk also increased with weight (p=.055), number of triathlons undertaken in the previous year (p=.031), number of triathlons in the last 4 months (p=.009) and running distance during competition times (p=.011). Injury risk decreased with increasing distance of cycling (p=.061) and swimming (p=.030) in a competition, and length of training in- and off-season (p=.105 and p=.043 respectively). Strong trends were demonstrated between injury severity and long-slow training distance (p=.006) and weight (p=.006). By contrast to risk of injury, injury severity was negatively associated with weight, while a long-slow distance was positively associated with the severity of the injury. Of all the health professions, chiropractic was the most utilized health profession. Conclusion and recommendations: The results concur with previous research, but add insights into factors predisposing triathletes to injury. The most common injuries require investigation to develop preventative interventions to reduce injuries in triathletes. Health professionals require education about triathlon-related injuries to improve preventative and curative interventions.
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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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».