Sleep Matters: Profiling Sleep Patterns to Predict Sports Injuries in Recreational Runners

Abstract

Running is one of the most popular recreational sports worldwide, yet it carries a high risk of sports injuries. While various risk factors have been identified, sleep has emerged as a potentially important but understudied contributor in recreational running. This study investigates whether distinct sleep profiles can predict sports injuries in recreational runners. A secondary analysis was conducted on survey data from 425 Dutch recreational runners. Latent profile analysis was applied to identify sleep risk profiles based on sleep duration, sleep quality, and sleep problems. Binary logistic regression tested the association between sleep profile membership and self-reported sports injuries, controlling for demographic and training variables. Findings revealed that four sleep profiles could be identified: Steady Sleepers, Poor Sleepers, Efficient Sleepers, and Fragmented Sleepers. Runners classified as Poor Sleepers were significantly more likely to report sports injuries than Steady Sleepers (OR = 1.78, 95% CI = 1.14–2.78; p = 0.01), with 68% injury probability. No significant differences were found for the other profiles. These findings underscore the importance of sleep as a multidimensional factor in injury prevention in recreational running, and suggest that interventions focusing on sleep duration and sleep quality may benefit running athletes’ health.