COVID-19 National Football League (NFL) Injury Analysis: Follow-Up Study
Notice bibliographique
Résumé
Background: In 2020, COVID-19 spread across the world and brought normal daily life to a halt, causing the shutdown of nearly everything in order to prevent its spread. The National Football League (NFL) similarly experienced shutdowns and the resulting effects, leaving athletes unable to train in some of the most advanced facilities with many of the best trainers in the world. A previous study, titled COVID-19 Return to Sport: NFL Injury Prevalence Analysis, determined that there was increased injury prevalence during the 2020 season, likely due to decreased physiological adaptations within athletes' bodies as a result of facility shutdowns. Understanding injury epidemiology is vital to the prevention of injuries and the development of return-to-play protocols. Objective: The objective of this study is to perform a follow-up study to COVID-19 Return to Sport: NFL Injury Prevalence Analysis in order to examine the longitudinal effects of the COVID-19 pandemic on injury epidemiology. This study examines if there was a recovery to baseline levels of injuries or if there are still lingering effects from the COVID-19 pandemic-induced spike in injuries. Methods: To determine if there was change in the number of injuries for each season, injury tallies collected from the 17-week-long 2018, 2019, and 2020 NFL regular seasons were compared with those from the 18-week-long 2021 and 2022 NFL regular seasons. A Kruskall-Wallis test with post hoc Dunn analysis was conducted to compare the rate of injuries per team per week between each of the 2018, 2019, 2020, 2021, and 2022 regular seasons. Results: The Kruskall-Wallis test revealed an H statistic of 32.61 (P<.001) for the comparison of the injury rates across the 5 seasons. The post hoc Dunn analysis showed that 2020 had a statistically significant difference when compared with each of the 2018 (P<.001), 2019 (P=.04), 2021 (P=.02), and 2022 (P=.048) seasons. The 2019 season showed no statistical significance when compared with the 2021 (P=.23) and 2022 (P=.13) seasons. Conclusions: The results of this follow-up study, combined with the previous study, show that extended training interruptions stemming from COVID-19 in 2020 induced detraining and led to increased injuries. Additionally, the results of this study show that retraining can occur, resulting in the development of injury protective factors, as injury rates returned to baseline levels after 2020. This is the first large-scale and long-term opportunity to demonstrate the effects of these principles and how they are important to understanding injury epidemiology.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| 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) | 0,001 | 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 ».