The Relationship Between Depressive Symptoms and Self-Neglect in Chinese Older Adults Living Alone: A Latent Profile Analysis
Year: 2025
Type: article
Abstract: Objectives: To clarify the latent profile of depressive symptoms in Chinese older adults living alone and to explore the relationship between this profile and self-neglect. Methods: Data from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS) were utilized to conduct a latent profile analysis for the identification of depressive symptoms. Logistic regression was employed to analyze the related factors. Generalized linear modeling was used to assess the impact of different profiles of depressive symptoms on self-neglect. Results: A total of 1822 older adults living alone, with a mean age of (83.60 ± 9.15) years, were included in the study. Three categories of depression were identified: the C1 mild depression–sleep disturbance group (29.36%), the C2 moderate depression–forcefulness group (55.22%), and the C3 major depression–loneliness loss group (15.42%). Logistic regression analysis indicated that gender, place of residence, annual household income, educational level, reason for living alone, self-rated health status, cognitive function, and emotional support were significant influencing factors (p < 0.05). The risk of experiencing higher levels of self-neglect in the C2 was 1.264 times greater than in the C1. Furthermore, the risk of higher levels of self-neglect in the C3 was 2.040 times greater than in the C1. Conclusions: Heterogeneity in depressive symptoms is evident among Chinese older adults living alone, with variations in self-neglect across different potential categories of these individuals. The focus should be on older adults in the C2 and C3 profiles. This study proposes targeted intervention strategies from family, community, and policy development perspectives to help improve self-neglect in older adults.
Source: Healthcare
Authors: Yali Hu, Miaomiao Wu, Yan Zhang, Lunfang Xie
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,258
Funder: Anhui Medical University
OpenAlex ID: W4408669124
