Disturbed eating tendencies, health-related behaviors, and depressive symptoms among university students in Korea

Abstract

Background & aims: There were few studies to investigate the related factors of depression among Korean students. Therefore, this study examined disturbed eating tendencies, health-related behaviors, and depressive symptoms among university students in Korea. Methods: We conducted a cross-sectional survey on a total of 637 students (279 men and 358 women), and the Korean version of the Beck depression rating scale (K-BDI) was used to evaluate the students' depression status. Results: Of the 637 students, 419 (65.8%) had no depressive symptoms (normal: K-BDI < 10), whereas 136 (21.4%: K-BDI 10 e16), 69 (10.8%: K-BDI 17e29), and 13 (2.0%: K-BDI ! 30) had mild, moderate, and severe depressive symptoms, respectively. Multivariable logistic regression showed that depressive symptoms (K-BDI ! 10) were associated with female gender (odds ratio [OR] ¼ 1.86, 95% confidence interval [CI] ¼ 1.26 to 2.76; p ¼ .002), high level of life stress (OR ¼ 4.37, 95% CI ¼ 2.23 to 8.55; p < .001), and disturbed eating behaviors (Korean version of Eating Attitude Test-26 ! 20; OR ¼ 5.14, 95% CI ¼ 2.52 to 10.5; p < .001). In contrast, depressive symptoms were inversely associated with a high body image satisfaction (OR ¼ 0.37, 95% CI ¼ 0.20 to 0.68; p ¼ .001) and self-esteem (self-esteem score ! 30) (OR ¼ 0.29, 95% CI ¼ 0.20 to 0.43; p < .001).

Salud mental y usos de la tecnología en el contexto universitario. Una revisión de la literatura

“…Las redes sociales han ejercido una creciente dependencia juvenil al punto de provocar diferentes problemas de salud manifiestos en alteraciones de la alimentación y el aumento del sedentarismo asociado a los cuadros depresivos (Seo & Je, 2018). La depresión depara comportamientos contrastantes con el grupo universitarios y dificultades en la inserción social.…”

Assessing diet in a university student population: a longitudinal food card transaction data approach

“…These transformed values were then standardised across each food type using z-scores. After transformation and standardisation, the k-means clustering algorithm was applied using a range of cluster numbers (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20). The appropriate number of clusters was selected using a scree plot to identify the inflexion point and through consideration of the number of students per cluster, to ensure approximately equal cluster sizes.…”

“…There are numerous studies that have investigated student diets across several countries. Most are of cross-sectional design and use self-report measures of diet including 24 h recalls or FFQ to track the diet of students (10,(12)(13)(14)(15)(16)(17) . Some also use proxy measures of diet, such as fruit and vegetable consumption (11,18) .…”