Chronic fatigue syndrome (CFS) in medical students is caused by extreme academic and professional loads, disturbances of circadian rhythms and chronic stress, leads to somatic and psychosomatic complications. Screening tools (e.g., Chalder Fatigue Scale) are not adapted to medical education because such factors as early professional load and imbalance between study and work are ignored. Objective. To investigate the indicators of chronic fatigue syndrome and develop a specialized questionnaire for comprehensive assessment of its development risks in medical students. Materials and methods. A prospective cohort study with cross-sectional analysis of the data of 203 students (mean age 22 years; 53 men, 150 women) from the 2nd—6th courses of the Samara State Medical University was performed. Inclusion criteria: age 18—25 years, informed voluntary consent. MORI questionnaire (20 items, 4 domains: emotional, cognitive, physical exhaustion; somatic symptoms) was developed. Data were collected anonymously through Yandex Forms. Statistical analysis included the determination of Cronbach’s α (reliability), exploratory factor analysis (construct validity), Pearson correlations and χ² test (association of symptoms with risk of CFS). Results. A clinically significant risk of CFS development has been revealed in 70.9% (n=144) of the students (moderate — 46.3%, high — 24.6%, very high — 1.5%). Key predictors: Workload ≥1.0 rate (OR=2.9; 95% CI 1.7—5.1); Emotional exhaustion (r=0.56, p<0.01); Sleep disturbances (OR=4.8 for high risk; 95% CI 2.9—8.1). Somatic symptoms are associated with CFS severity: drops in blood pressure level (25.1%), frequent acute respiratory viral infections (16.7%), changes in weight (p<0.05). High reliability (α=0.89) of the MORI questionnaire and three-factor structure explaining 45% dispersion have been shown. Conclusion. High prevalence of chronic fatigue syndrome in medical students means the need to implement specialized tools of screening (MORI questionnaire) and comprehensive prevention, including load optimization, sleep schedule correction, psychological support and digital technologies (mobile applications for monitoring).
Zolotovskaya et al. (2026) studied this question.