# Sex-Specific Interrelationship Between Sleep Quality and Daytime Sleepiness in Predicting Injury Occurrence in Physically Active University Students

## TL;DR

The study finds that sleep quality is linked to injury risk in physically active university students, with differences between males and females.

## Contribution

The paper introduces sex-specific insights into how sleep quality and daytime sleepiness interact to predict injury occurrence in young adults.

## Key findings

- Poor sleep quality was significantly associated with injury occurrence, especially in females.
- Sleep factors explained only a small percentage of injury variance, with limited predictive value.
- Interaction patterns between sleep quality and daytime sleepiness differed between males and females.

## Abstract

Background/Objectives: Sleep quality and daytime sleepiness influence vigilance and motor control, but their joint contribution to injury risk in physically active young adults remains unclear. This study examined sex-specific associations between sleep quality, daytime sleepiness, and injury occurrence in university students. Methods: A cross-sectional sample of 418 students (199 males, 219 females) was analyzed. Sleep quality (PSQI), daytime sleepiness (ESS), and 12-month injury occurrence were assessed with validated questionnaires. Bivariate χ2 tests examined individual associations. Sex-stratified log-linear models evaluated classical (multiplicative) interactions between sleep quality (SQ), excessive daytime sleepiness (EDS), and injury (INJ). Additive interaction was assessed using Poisson-derived risk ratios (RR10, RR01, RR11) and synergy indices (RERI, AP, S). Results: Poor sleep quality was significantly associated with injury occurrence (χ2 = 4.76, p = 0.029; OR = 1.60, 95% CI: 1.05–2.45), driven primarily by females (χ2 = 5.39, p = 0.020; OR = 1.98). In males, interaction plots showed non-parallel slopes and log-linear modeling supported significant two-way dependencies (ΔG2 = 18.37, p < 0.001), but the three-way interaction was not significant (p = 0.119). In females, relationships were fully additive (ΔG2 = 0.011, p = 0.917). Additive interaction metrics indicated no synergy in males, whereas females showed a mild supra-additive pattern (RR11 = 1.61). Importantly, logistic regression models showed that sleep factors explained only 0.6–1.2% of variance in males and up to 4.3% in females, indicating limited overall predictive value. Poor sleep quality contributed modestly to injury occurrence, while daytime sleepiness added minimal explanatory improvement. Conclusions: Sleep–injury relationships were sex-specific. Poor sleep quality was the most consistent predictor of injury—especially among females—while interaction patterns differed between sexes. Sleep factors contributed modestly to injury risk and should be interpreted within a broader framework of intrinsic determinants in physically active young adults.