Author(s): CECCANTI, S., SCHMID, M.J., ERLACHER, D., FARAGUNA, U., VORSTER, A.P.A., Institution: UNIVERSITY OF VERONA, Country: ITALY, Abstract-ID: 902

INTRODUCTION:
Sleep health is multidimensional. Focusing on a single sleep parameter like duration or efficiency fails to capture the complexity of sleep-related health, particularly in athletic populations where sleep health may be shaped by training-related stressors and behavioral psychosocial factors. Despite growing evidence linking sleep to recovery and performance, the role of sleep health in preventing injuries and illnesses among athletes remains insufficiently addressed.
In this study we analyzed the association between sleep health and annual injury and illness load in a representative sample of >2000 Swiss elite athletes, considering as well stress and substance related predictors of sleep health.
METHODS:
A total of 2293 athletes (46.4% female; age 22.1 ± 7.5 years) completed a questionnaire that contained baseline demographics, sleep health and habits (Bernese Sleep Health Questionnaire), and sport characteristics. Sleep health was quantified using a composite sleep health score (SHS; range 0–8), integrating sleep duration, efficiency, timing, daytime sleepiness, restfulness, regularity, snoring, and sleep-related disorders. Annual injury and illness load were expressed as days per year. A linear regression model was applied to identify determinants of sleep health. Associations between sleep health and injury and illness load were examined using negative binomial regression models adjusted for perceived stress, training hours per week, travel, performance level, sport category, age, and gender.
RESULTS:
Sleep health showed a strong association with illness load: each 1-point increase in SHS was associated with a 14% reduction in illness days per year (IRR = 0.86, p < .001). Moreover, each 1-point increase in the sleep health score was associated with a 5% reduction in injury days per year (IRR = 0.95, p = .018). Additionally, stress (IRR = 1.07, p = .04), training volume (IRR = 1.01, p < .001), and travel exposure (IRR = 1.07, p = .04) were also associated with injuries. In general, poorer sleep health was associated with higher stress, together with unfavorable sleep behavior like greater evening caffeine, alcohol, nicotine, and sleeping pill use (all p < .001).
CONCLUSION:
Sleep health is inversely associated with both illness and injuries in athletes, pointing to a central role of sleep in physiological recovery and immune function. Sleep health represents a modifiable and monitorable parameter that is closely linked to stress and substance-related behaviors. Therefore, measuring and improving sleep health in athletes through screening and counseling may potentially be a potent approach to reduce illness days and to support athletes' health and injury-prevention strategies.