乳腺癌
心理信息
抑郁症状
临床心理学
心理干预
医学
多项式logistic回归
癌症
萧条(经济学)
心理学
心理健康
评定量表
精神科
内科学
焦虑
梅德林
发展心理学
宏观经济学
经济
机器学习
政治学
计算机科学
法学
作者
Shiyi Chen,Minghong Tang,Zhihui Gu,Liu Li,Hui Wu,Mengyao Li
出处
期刊:Health Psychology
[American Psychological Association]
日期:2025-03-20
卷期号:44 (7): 677-685
摘要
Breast cancer patients suffer from depressive symptoms during treatments and may show different trajectories of depressive symptoms. The health ecology model provides an integrated perspective for explaining the factors influencing depressive symptoms. This study aimed to (a) analyze the trajectories of depressive symptoms that may occur in breast cancer patients and (b) explore their influencing factors by the health ecology model. A total of 236 participants (Mdnage = 55 years) finally completed three valid surveys. The patients answered a personal information sheet, the Self-rating Depression Scale, the Family Environment Scale, the Satisfaction With Life Scale, and the Fear of Progression Questionnaire-Short Form. Data were collected after surgery, 3 months after surgery, and 6 months after surgery. Latent Growth Mixture Modeling was used to identify distinct trajectories of depressive symptoms in patients. Influencing factors of trajectory memberships were identified using multinomial logistic regression. Three distinct trajectory groups ("slowly rising"; n = 210, 89%, "persistently low"; n = 13, 5.5%, and "fluctuating"; n = 13, 5.5%) were revealed for depressive symptoms. Life satisfaction, family environment, and fear of progression were associated with an increasing trend of depressive symptoms, and family environment was associated with a fluctuating trend. Our findings demonstrated the diversity of depressive symptoms changes, along with the impact of factors in psychological behaviors layer and interpersonal networks layer. It helps to identify breast cancer patients at higher risk of increasing or fluctuating depressive symptoms, thereby allowing for relevant psychological interventions. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
科研通智能强力驱动
Strongly Powered by AbleSci AI