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. Data related to social participation, cognitive function, ADLs, and depressive symptoms were extracted. Regression and Bootstrap analyses were used to explore the sequential mediating effects of social participation, cognitive function, ADLs, and depressive symptoms. RESULTS: (1) There was a significant correlation between social participation, cognitive function, activities of daily living, and depressive symptoms (p < 0.01). (2) Social participation directly affected depressive symptoms (ß = -0.205, p < 0.05). (3) Cognitive function (ß = -0.070, p < 0.01) and activities of daily living (ß = -0.058, p < 0.01) played separate mediating roles in the effect of social participation on depressive symptoms. (4) Cognitive function and activities of daily living had a chain-mediated role in the relationship between social participation and depressive symptoms in patients with multimorbidity (ß = -0.020, p < 0.01). CONCLUSION: A chained mediating effect was found between cognitive function, ADLs, social participation, and depressive symptoms in patients with multimorbidity. Social participation was found to improve the cognitive function of patients with multimorbidity, which in turn enhanced their daily life activities and ultimately alleviated their depressive symptoms.

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. Influencing factors of depressed mood in cancer patients was analyzed using a binary logistic regression model. Using the Harvard Cancer Index, we classified the high-risk patients for depressed mood. RESULTS: In present study, 52.96 % of cancer patients met criteria for depressed mood based on the CES-D. Significant correlations were found between depressed mood and factors such as gender, self-rated health, sleep duration, exercise, satisfaction with family, residence, education, life satisfaction, and medical insurance. Utilizing the Harvard Cancer Index, we classified patients into five risk levels for depressed mood, revealing a significant variation in the number of depressive patients across these levels (x2=99.82, P < 0.05). Notably, the incidence of depressed mood increased with the risk level among cancer patients (x2=103.40, P < 0.05). LIMITATIONS: Lack of data on tumor typing and subgroups makes it unlikely to explore the specifics of depressed mood in patients with various types of cancer. CONCLUSION: The determinants of depressed mood in cancer patients are multi-dimensional. The Harvard Cancer Index may be helpful in identifying high-risk populations.