COVID-19 National Football League (NFL) Injury Analysis: Follow-Up Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".