Sleep Characteristics of Elite Youth Athletes: A Clustering Approach to Optimize Sleep Support Strategies.

Abstract

Elite athletes experience chronic sleep insufficiency due to training and competition schedules. However, there is little research on sleep and caffeine use of elite youth athletes and a need for a more nuanced understanding of their sleep difficulties. This study aimed to (1) examine the differences in sleep characteristics of elite youth athletes by individual and team sports, (2) study the associations between behavioral risk factors associated with obstructive sleep apnea and caffeine use with sleep quality, and (3) characterize the latent sleep profiles of elite youth athletes to optimize the sleep support strategy. A group (N = 135) of elite national youth athletes completed a self-administered questionnaire consisting of the Pittsburgh Sleep Quality Index (PSQI)and questions pertaining to obstructive sleep apnea, napping behavior, and caffeine use. K-means clustering was used to characterize unique sleep characteristic subgroups based on PSQI components. Athletes reported 7.0 (SD = 1.2) hours of sleep. Out of the total group,45.2% of the athletes had poor quality sleep (PSQI global >5), with team-sport athletes reporting significantly poorer sleep quality than individual-sport athletes. Multiple logistic regression analysis indicated that sport type significantly correlated with poor sleep quality. The K-means clustering algorithm classified athletes' underlying sleep characteristics into 4 clusters to efficiently identify athletes with similar underlying sleep issues to enhance interventional strategies. These findings suggest that elite youth team-sport athletes are more susceptible to poorer sleep quality than individual-sport athletes. Clustering methods can help practitioners characterize sleep-related problems and develop efficient athlete support strategies.