748 FO31 – Evaluating an injury prevention program (Prep-to-Play) in 2713 women and girls playing community Australian football: a hybrid implementation-effectiveness, stepped-wedge cluster randomised controlled trial
Notice bibliographique
Résumé
Background Prep-to-Play, an injury prevention program, was co-designed with the Australian Football League for women’s Australian Football. Online resources were distributed to coaches in 2019, but coaches reported low confidence to use Prep-to-Play. A supported implementation strategy was devised, to enhance Prep-to-Play use. Objective To compare Prep-to-Play use and injury rates between unsupported and supported implementation. Design Hybrid implementation-effectiveness (type-III) stepped-wedge, cluster randomised controlled trial. Setting Community women’s Australian Football. Participants 165 teams [under-16 to senior, 2713 players]. Interventions Ten geographically-separated clusters (≥14 teams) began the unsupported phase and transitioned to the supported phase in 2021/2022 (random allocation to one-of-five dates). In the supported phase, 56 trained physiotherapists provided a workshop and two support visits for coaches/team leaders (Fig1). Outcomes Team representatives reported weekly Prep-to-Play use, knee injuries and head impacts. Prep-to-Play ‘use’ was defined as completing ≥75% of program elements (≥6/8 warm-up, ≥2/3 strength, ≥1 skills, Fig1), in ≥two-thirds of sessions. Anterior cruciate ligament (ACL) injuries were medically confirmed, and concussions were confirmed medically (60%) or via physiotherapist phone assessment. Prep-to-Play use and injury rates were compared between supported and unsupported phases (adjusted for clustering, time, age-group, region, competition-level). Results 134 teams received the workshop and two support visits. Weekly Prep-to-Play use increased from 15% (95%CI: 11% to 19%) to 35% (30% to 41%) following supported implementation (Odds Ratio 3.1, 2.1 to 4.7). Accounting for background time trends not associated with the intervention, injury (ACL, knee, concussion) incidence reduced per additional week spent in the supported phase, with a relative-risk reductions of ~3% per week (Table1). Estimated reductions were not statistically significant. Based on findings from descriptive analysis, we considered first-order fractional polynomials to model background trends in outcomes over time that were not associated with intervention exposure. Fractional polynomials provide a simple but flexible way to model trends in continuous variables and include a log transformation and a linear trend as special cases. For all injury types, we used the Akaike Information Criterion (AIC) to choose the best fractional polynomial fit for modelling the background time trend. Injury models were adjusted for team (random effect), time (study week as continuous variable), age-group (reference: senior, versus junior), region (reference: metropolitan, vs regional) competition-level (reference: top division, versus other divisions). Cluster-week and team-week random effects as per the primary outcome were trialed to capture within-period correlation. However, models failed to converge due to the low number of injuries reported. Conclusion A physiotherapist-led supported implementation resulted in a three-fold increase in odds of Prep-to-Play use. The sport-specific injury prevention program including warm-up, strength, and contact activities may reduce ACL and concussion injuries in community women’s Australian Football.
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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 | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 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 ».