COVID-19 Return to Sport: NFL Injury Prevalence Analysis

  1. Troy B Puga
  2. Joshua Schafer
  3. Prince N Agbedanu
  4. Kevin Treffer

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Abstract

Sport injuries have been common among athletes across the globe for decades and have the potential to disrupt athletic careers, performance, and psyche. Many health professionals and organizations have undertaken injury mitigation strategies to prevent sport injuries through protective equipment, training protocols, and a host of other evidence-based practices. Many of these specialized training methods were disrupted due to protocols to mitigate the spread of COVID-19. This research examines the effects of the COVID-19 pandemic in relation to the prevalence of athletic injuries in the National Football League (NFL).

Objective

During the COVID-19 pandemic, NFL teams and athletes across all levels of sport were reported to have reduced training in preparation for their seasons due to protocols to mitigate the spread of COVID-19. This study compares the prevalence of injury during the 2018, 2019, and 2020 NFL seasons, with the aim to determine the potential causes of the differences in injury prevalence.

Methods

Official injury reports from each team were counted during the 17-week regular season of each year (2018, 2019, and 2020). The data were analyzed using an unpaired t test to compare the injury prevalence between each of the three seasons.

Results

The 2018 season produced a total of 1561 injuries and a mean of 48.8 injuries per team. The 2019 season produced a total of 1897 injuries and a mean of 59.3 injuries per team, while the 2020 season produced a total of 2484 injuries and a mean of 77.6 injuries per team. An unpaired t test was performed using the data to compare the mean number of injuries per team during each of the seasons. Comparison of the 2020 season against the 2019 season showed a statistically significant difference (P<.001); comparison of the 2020 season to the 2018 season found a statistically significant difference (P<.001); and comparison between the 2019 and the 2018 seasons found a statistically significant difference (P=.03).

Conclusions

Although the 2019 and 2018 seasons showed a statistically significant difference (P=.03), this difference is not as large when we compare the 2020 seasons versus the 2019 (P<.001) and 2018 (P<.001) seasons. The astronomical increase in injury prevalence during the 2020 season over the previous years raises the possibility that there was a reduced physiological adaptation to stress, due to the limited amount of training as a result of the closure of practice facilities in order to slow the spread of COVID-19.

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  1. Version published to 10.2196/35862
  2. ScreenIT

    SciScore for 10.1101/2021.12.16.21267811: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    We admit that there may also be limitations to the study in that it may not have accounted for other injuries such as preseason, injured reserve, and unreported injuries. The difference in training time must also be determined between the seasons. There must be more work done at both the professional and amateur levels to determine the level that physiological adaption plays in injury rates. Potential follow-up studies could include examining the prevalence of injuries for individual teams with COVID-19 outbreaks and examining how geographic COVID-19 hotspots impacted injuries for the teams.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.2196/preprints.35862
  4. Version published to 10.1101/2021.12.16.21267811v1 on medRxiv