A Strategy to Inform Athlete Sleep Support From Questionnaire Data and Its Application in an Elite Athlete Cohort.

PURPOSE

Information from the Pittsburgh Sleep Quality Index (PSQI) and Athlete Sleep Behavior Questionnaire (ASBQ) provide the ability to identify the sleep disturbances experienced by athletes and their associated athlete-specific challenges that cause these disturbances. However, determining the appropriate support strategy to optimize the sleep habits and characteristics of large groups of athletes can be time-consuming and resource-intensive. The purpose of this study was to characterize the sleep profiles of elite athletes to optimize sleep-support strategies and present a novel R package, AthSlpBehaviouR, to aid practitioners with athlete sleep monitoring and support efforts.

METHODS

PSQI and ASBQ data were collected from a cohort of 412 elite athletes across 27 sports through an electronic survey. A k-means cluster analysis was employed to characterize the unique sleep-characteristic typologies based on PSQI and ASBQ component scores.

RESULTS

Three unique clusters were identified and qualitatively labeled based on the z scores of the PSQI components and ASBQ components: cluster 1, "high-priority; poor overall sleep characteristics + behavioral-focused support"; cluster 2, "medium-priority, sleep disturbances + routine/environment-focused support"; and cluster 3, "low-priority; acceptable sleep characteristics + general support."

CONCLUSIONS

The findings of this study highlight the practical utility of an unsupervised learning approach to perform clustering on questionnaire data to inform athlete sleep-support recommendations. Practitioners can consider using the AthSlpBehaviouR package to adopt a similar approach in athlete sleep screening and support provision.