Open AccessIncidence rate and clinical characteristics of seasonal affective disorders in senior medical students
Author(s) -
I. I. Ukraintsev,
Г. Г. Симуткин,
Н. А. Бохан
Publication year - 2021
Publication title -
bûlletenʹ sibirskoj mediciny
Language(s) - English
Resource type - Journals
eISSN - 1819-3684
pISSN - 1682-0363
DOI - 10.20538/1682-0363-2021-3-112-119
Subject(s) - logistic regression , incidence (geometry) , demography , mann–whitney u test , psychology , rank correlation , seasonality , spearman's rank correlation coefficient , test (biology) , medicine , clinical psychology , statistics , mathematics , paleontology , geometry , sociology , biology
Aim. To study the incidence rate, clinical features, and prognosis of seasonal affective disorder (SAD) in senior (6th-year) medical students. Materials and methods. SAD screening using the Seasonal Pattern Assessment Questionnaire (SPAQ, 1987) included 119 undergraduate medical students. 78 students were females (65.5%) and 41 – males (34.5%) ( p = 0.001). The average age of women was 23 (22; 23) years, the average age of men – 23 (22; 24) years. Statistical processing was performed using the Mann – Whitney U-test, Pearson’s χ2 test, and Spearman’s rank correlation coefficient ( r s ). Results. The data on the prevalence of affective disorders with a seasonal pattern in medical students were obtained: SAD – 9.2%, sub-SAD – 13.5%, psychological undulation of season perception (PUSP) – 16.8%. The number of students who did not exhibit seasonal undulation of the six main characteristics recorded by the SPAQ was 72 (60.5%) ( p = 0.001). There were statistically significant differences in the higher median Global Seasonality Score of the SPAQ for SAD compared with PUSP, both with and without account of the gender factor ( p = 0.001). The use of a binary logistic regression model made it possible to identify groups of students with or without SAD according to the SPAQ. The data obtained determined the contribution of the following factors: gender, seasonality, body weight, and the number of sleep hours per day in spring. Conclusion. The study made it possible to obtain a logistic regression model that allowed to predict the greatest likelihood of developing SAD.