Interactions between running volume and running pace on injury occurrence in recreational runners: A secondary analysis

Context: The combination of excessive increases in running pace and volume is essential to consider when investigating associations between running and running-related injury. Objectives: To complete a secondary analysis, using a dataset from a randomized trial, to evaluate the interactions between relative or absolute weekly changes in running volume and running pace on the occurrence of running injuries among a cohort of injury-free recreational runners in Denmark. Design: Prospective cohort study. Setting: Running volume and pace were collected during a 24-week follow-up using global positioning systems data. Training data were used to calculate relative and absolute weekly changes in running volume and pace. Patients or Other Participants: A total of 586 recreational runners were included in the analysis. All participants were injury free at baseline. Main Outcome Measure(s): Running-related injury was the outcome. Injury data were collected weekly using a modified version of the Oslo Sports Trauma Research Centre questionnaire. Risk difference (RD) was the measure of injury risk. Results: A total of 133 runners sustained running-related injuries. A relative weekly change of progression >10% in running volume and progression in running pace (RD = 8.1%, 95% CI = 9.3%, 25.6%) and an absolute weekly change of progression >5 km in running volume and progression in running pace (RD = 5.2%, 95% CI = 12.0%, 22.5%) were not associated with a statistically significant positive interaction. Conclusions: Given that coaches, clinicians, and athletes may agree that excessive increases in running pace and running volume are important contributors to injury development, we analyzed the interaction between them. Although we did not identify a statistically significant positive interaction on an additive scale in runners who progressed both running pace and running volume, readers should be aware that an interaction is an important analytical approach that could be applied to other datasets in future publications.