A study on the identification of factors related to depression in a population with an increasing number of chronic diseases in the short term in China based on a health ecology model.

Background: The rapid increase in the number of patients with chronic diseases and depression, as well as the rapid spread of their effects, have led to these two health problems gradually developing into major public health issues in China and around the world. Currently, many individuals with chronic diseases are experiencing depressive symptoms one after another. Therefore, it is imperative to conduct research on how to prevent depression in this growing population of individuals with chronic diseases in a timely manner.

Enhancing psychological well-being in college students: the mediating role of perceived social support and resilience in coping styles.

Background: The prevalence of depression among college students is higher than that of the general population. Although a growing body of research suggests that depression in college students and their potential risk factors, few studies have focused on the correlation between depression and risk factors. This study aims to explore the mediating role of perceived social support and resilience in the relationship between trait coping styles and depression among college students.

Relationship between social participation and depressive symptoms in patients with multimorbidity: the chained mediating role of cognitive function and activities of daily living.

Objective: The potential mechanisms linking social participation and depressive symptoms in Chinese individuals with multimorbidity are not yet fully understood. This study aims to explore how cognitive function and activities of daily living (ADLs) mediate the relationship between social participation and depressive symptoms in individuals with multimorbidity.

Methods: We selected 3782 participants with multimorbidity from the 2018 China Health and Retirement Longitudinal Study.

Classification of high-risk depressed mood groups in cancer patients based on Health Ecology Model.

Background: Depressed mood affects a significant number of patients with cancer, and can impair their quality of life and interfere with successful treatment. Our study aims to create a predictive model for identifying high-risk groups of depressed mood in cancer patients, offering a theoretical support for preventing depressed mood in these individuals.

Methods: The China Health and Retirement Longitudinal Study (CHARLS) provided the data for this research, which used CES-D as a tool to identify individuals with depressed mood.