The Relationship between Type D Personality and Depression Symptoms in Stroke Patients: The Chain Mediating Effect of Self-Efficacy and Participation Preferences

D型人格与中风患者抑郁症状的关系:自我效能感和参与偏好的链式中介效应

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Abstract

BACKGROUND: Stroke significantly impacts population health, and post-stroke depressive symptoms are highly prevalent. Although depression symptoms are linked to Type D personality, self-efficacy, and participation preferences in discharge planning, the mechanisms underlying these interactions remain unclear. This study aimed to examine the chain mediation effects of self-efficacy and discharge planning participation preferences on the relationship between Type D personality and depression symptoms in stroke patients. METHODS: This study used a convenience sampling method to recruit 318 stroke patients from the Department of Neurology at the First People's Hospital of Changzhou. Participants were assessed using the Type D personality scale (DS14), stroke self-efficacy questionnaire (SSEQ), patient participation preferences assessment (PPPA), and self-rating depression scale (SDS). Statistical analyses included descriptive analysis, Spearman's rank correlation analysis, and chain mediation analysis; these analyses were performed using SPSS 25.0 and PROCESS v3.5 (Model 6). RESULTS: A total of 311 stroke patients were included in this study. Type D personality, self-efficacy, participation preferences, and depression symptoms are significantly correlated with each other (all P < 0.001). Self-efficacy and participation preferences acted as significant mediators between Type D personality and depression symptoms. The total indirect effect accounted for 46.51% of the total effect, and the chain pathway contributed 7.23% (chain indirect effect = 0.030, 95% CI 0.014-0.050). CONCLUSION: Type D personality indirectly influences depression symptoms through the chain mediation effects of self-efficacy and participation preferences in stroke patients. This study demonstrates the intrinsic mechanisms by which Type D personality contributes to depression symptoms, providing insights for healthcare professionals to prevent and clinically intervene in stroke patients with depression.

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