Objectives: To illustrate why the research question determines whether and how sport medicine investigators should adjust for workload when interested in interventions or causal risk factors for injury. Design: Theoretical conceptualization. Methods: We use current concepts of causal inference to demonstrate the advantages and disadvantages of adjusting for workload through different analytic approaches when evaluating causal effects on injury risk. Results: When a risk factor of interest changes workload, including workload in the regression will cause bias. When workload represents time-at-risk (e.g. games played, minutes run), including workload as an offset in Poisson regression provides a comparison of injury rates (injuries per unit time). This is equivalent to including log(workload) as an independent variable with the coefficient fixed to 1. If workload is included as an independent variable instead of an offset, using log(workload) rather than workload is more consistent with theory. This practice is similar to the principles of allometric scaling. When workload represents a combination of both time-at-risk and intensity, such as with session ratings of perceived exertion, the optimal analytical strategy may require modeling time-at-risk and intensity separately rather than as one factor. Conclusions: Whether to account for recent workload or not, and how to account for recent workload, depends on the research question and the causal assumptions, both of which should be explicitly stated.
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© 2022 Sports Medicine Australia
- Causal inference
- Injury rate
- Poisson regression
PubMed: MeSH publication types
- Journal Article