Search Results

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.

Purpose: 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. Methods: 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. Results: 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. Conclusion: 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.

Purpose: To investigate the association between player wellness and injury in elite junior Australian football players over 1 competitive season. Methods: Prospective cohort study. Elite junior Australian football players (N = 196, average age = 17.7 y, range = 16–18 y) were recruited in the under-18 state league competition in Victoria, Australia. They recorded their wellness (sleep, fatigue, soreness, stress, and mood) according to a 5-point Likert scale 3 times weekly, with injuries (missed match/training session) entered into an online sport-injury surveillance system. A logistic generalized estimating equation was used to examine the association (expressed as odds ratio [OR]) between wellness and injury (yes/no). Results: Soreness was associated with injury at each time point across the week, with the strongest association evident for soreness reported 6 d postmatch (OR = 1.30; 95% confidence interval [CI], 1.17–1.44; P < .001). Stress and injury were associated with injury for average stress values across the week, as well as specifically on day 1 postmatch (OR = 1.10; 95% CI, 1.01–1.21; P = .038). Mood reported in the middle of the week (3 d postmatch) was associated with injury (OR = 0.87; 95% CI, 0.78–0.97; P = .014), as was fatigue (OR = 1.10; 95% CI, 1.00–1.22; P = .044). Conclusions: This study demonstrates key associations between wellness and injury in elite junior Australian football, specifically soreness, stress, fatigue, and mood. Monitoring strategies help identify injury-risk profiles, which can help decision makers (coaches or medical staff) intervene when relevant to reduce injury risk.

Purpose: To investigate associations between load (training and competition) and wellness in elite junior Australian Football players across 1 competitive season. Methods: A prospective cohort study was conducted during the 2014 playing season in 562 players from 9 teams. Players recorded their training and match intensities according to the session-rating-of-perceived-exertion (sRPE) method. Based on sRPE player loads, a number of load variables were quantified, including cumulative load and the change in load across different periods of time (including the acute-to-chronic load ratio). Wellness was quantified using a wellness index including sleep, fatigue, soreness, stress, and mood on a Likert scale from 1 to 5. Results: Players spent an average of 85 (21) min in each match and 65 (31) min per training session. Average match loads were 637 (232) arbitrary units, and average training loads were 352 (233) arbitrary units. Over the 24 wk of the 2014 season, overall wellness had a significant linear negative association with 1-wk load (B = −0.152; 95% confidence interval, −0.261 to −0.043; P = .006) and an inverse U-curve relationship with session load (B = −0.078; 95% confidence interval, 0.143 to 0.014; P = .018). Mood, stress, and soreness were all found to have associations with load. Conclusions: This study demonstrates that load (within a session and across the week) is important in managing the wellness of elite junior Australian Football players. Quantifying loads and wellness at this level will help optimize player management and has the potential to reduce the risk of adverse events such as injury.