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.

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. METHODS: A total of 1262 college students completed questionnaires including the Trait Coping Styles Questionnaire (TCSQ), the Patient Health Questionnaire-9 (PHQ-9), the Perceived Social Support Scale (PSSS), and the Resilience Scale-14 (RS-14). Common method bias tests and spearman were conducted, then regressions and bootstrap tests were used to examine the mediating effects. RESULTS: In college students, there was a negative correlation between perceived control PC and depression, with a significant direct predictive effect on depression (ß = -0.067, P < 0.01); in contrast, negative control NC showed the opposite relationship (ß = 0.057, P < 0.01). PC significantly positively predicted perceived social support (ß = 0.575, P < 0.01) and psychological resilience (ß = 1.363, P < 0.01); conversely, NC exerted a significant negative impact. Perceived social support could positively predict psychological resilience (ß = 0.303, P < 0.01), and both factors had a significant negative predictive effect on depression. Additionally, Perceived social support and resilience played a significant mediating role in the relationship between trait coping styles and depression among college students, with three mediating paths: PC/NC → perceived social support → depression among college students (-0.049/0.033), PC/NC→ resilience → depression among college students (-0.122/-0.021), and PC/NC → perceived social support → resilience → depression among college students (-0.016/0.026). CONCLUSION: The results indicate that trait coping styles among college students not only directly predict lower depression but also indirectly influence them through perceived social support and resilience. This suggests that guiding students to confront and solve problems can alleviate their depression.

Grey-Theory-Based Optimization Model of Emergency Logistics Considering Time Uncertainty.

Natural disasters occur frequently in recent years, causing huge casualties and property losses. Nowadays, people pay more and more attention to the emergency logistics problems. This paper studies the emergency logistics problem with multi-center, multi-commodity, and single-affected-point. Considering that the path near the disaster point may be damaged, the information of the state of the paths is not complete, and the travel time is uncertainty, we establish the nonlinear programming model that objective function is the maximization of time-satisfaction degree. To overcome these drawbacks: the incomplete information and uncertain time, this paper firstly evaluates the multiple roads of transportation network based on grey theory and selects the reliable and optimal path. Then simplify the original model under the scenario that the vehicle only follows the optimal path from the emergency logistics center to the affected point, and use Lingo software to solve it. The numerical experiments are presented to show the feasibility and effectiveness of the proposed method.