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Activity Number:
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407
- Emerging Methods in Sports Injury Research: Strategies for Shifting the Paradigm
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 5, 2020 : 1:00 PM to 2:50 PM
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Sponsor:
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Section on Statistics in Sports
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Abstract #313523
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Title:
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Reconciling the Dynamics Between Sports-Related Injury Severity and Recovery Using Time Loss
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Author(s):
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Avinash Chandran* and Loretta DiPietro and Heather Young and Angelo Elmi
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Companies:
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Datalys Center for Sports Injury Research and Prevention and The George Washington University and The George Washington University and The George Washington University
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Keywords:
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Sports injury;
Time loss;
Recovery;
Injury severity;
Mixed effects;
Injury surveillance
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Abstract:
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Time loss (TL) is commonly synonymized with injury severity in sports medicine. However, injury severity is inherently latent, and TL may be better utilized as a reflection of the recovery process by incorporating injury severity in analyses. We propose two approaches for applying the aforementioned framework where injury severity is represented using a random effect term in analyses of TL. Using sample data collected within the NCAA Injury Surveillance Program, we fit random effects Poisson and Weibull AFT Regression models to perform ‘severity-adjusted’ analyses of TL and make inferences regarding injury recovery. In practice, the incorporation of a random effect term attenuated associations between observable covariates and TL. Model fit was also improved in random effects models (AICPoisson= 51870.06; AICWeibull-AFT= 51291.00) compared with fixed effects models (AICPoisson= 160695.20; AICWeibull-AFT= 53179.48). We present a framework for reconciling the dynamics between sports-related injury severity and recovery. This approach can be used in the future to inform nuanced injury recovery and rehabilitation strategies.
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Authors who are presenting talks have a * after their name.
