The impact of menstrual cycle phase and symptoms on sleep, recovery, and stress in elite female basketball athletes: a longitudinal study.

Aim: This study aimed to examine the influence of menstrual cycle phases and symptom burden on sleep quality and recovery-stress states in elite female basketball players. Methods: Initially, twelve elite athletes participated in a 3-month observational study, which included psychometric screening using validated questionnaires and daily monitoring of menstrual symptoms, subjective sleep quality, sleep parameters, and recovery-stress states. The final analysis included eight athletes (26.75 ± 5.63 years, 178.62 ± 7.48 cm, 68.94 ± 7.13 kg, average cycle length of 29.00 ± 1.20 days, menstruation duration of 5.75 ± 0.71 days). In addition to self-reported data, objective menstrual cycle parameters were collected using the Ava fertility tracker. To verify cycle regularity, salivary hormone samples were collected twice weekly. Data analysis was conducted using linear mixed modeling to account for repeated measures and intra-individual variation. Results: Across both approaches, menstrual cycle phases showed only limited and inconsistent associations with sleep and recovery-stress states. In contrast, higher daily symptom burden and greater overall symptom severity were consistently associated with poorer sleep quality, reduced recovery, and elevated stress. Additionally, sleep behavior significantly influenced both sleep and recovery outcomes. Conclusion: Symptom burden appears to be a more relevant factor than menstrual phase in determining sleep and recovery-stress states in elite female athletes. These findings support individualized monitoring approaches that include menstrual symptoms tracking. Psychoeducation on sleep hygiene and menstrual health should be integrated into elite sports environments to strengthen athlete well-being. Overall, the study highlights the importance of multidimensional, athlete-centered strategies that combine behavioral, hormonal, and symptom-based data to optimize performance and recovery.