Аннотация
BACKGROUND: Fatigue is a familiar problem in medical training, and a growing body of work has linked it to worse academic performance and lower psycho-emotional well-being. Few data are available for Central Asian student cohorts. We set out to measure the prevalence of fatigue in a single fifth-year cohort and to test how it relates to body mass index (BMI), physical activity, and self-reported quality of life (QoL). MATERIALS AND METHODS: Cross-sectional, single-center survey conducted in the middle of the academic semester at a medical faculty in Kazakhstan, on fifth-year students only. Two hundred and sixty-nine fifth-year medical students completed the survey. We measured fatigue with the fatigue assessment scale (FAS) and the fatigue severity scale (FSS), and quality of life with the Dartmouth COOP Functional Health Assessment Charts/World Organization of Family Doctors (COOP-WONCA) charts. Body mass index and physical activity (short international physical activity questionnaire [IPAQ] adaptation) were collected by self-report. Descriptive statistics, one-way analysis of variance (ANOVA), the Chi-square test, and multinomial logistic regression were run in IBM SPSS Statistics 23.0. Two-tailed P < 0.05. RESULTS: Clinically significant fatigue (FSS ≥ 4) was found in 27.7% of the cohort. The composite COOP-WONCA score split students into three groups (good, moderate, poor quality of life). Physical inactivity was the strongest independent predictor of falling into the poor-QoL group (OR ≈ 3.4–3.6, P < 0.05), while higher FSS scores independently predicted moderate (vs good) QoL (OR = 1.05 per point, P = 0.048). BMI was not an independent predictor. CONCLUSIONS: Roughly 1 in 4 fifth-year medical students reach the clinical threshold for fatigue by mid-semester, and this is associated with measurable losses in daily and social functioning. Activity-level interventions look like the most promising entry point. To our knowledge, this is the first Central Asian study to combine FAS, FSS, and COOP-WONCA in one cohort and use multinomial logistic regression to identify independent predictors of poor QoL in medical students.