Background: Depression among older adults is an important public health concern associated with increased risk of suicide and decreased physical, cognitive, and social functioning. This study identified trajectories of depressive symptoms and investigated predictive variables of group-based trajectory modeling among Korean community-dwelling older adults. Methods: Participants comprised 2016 community-dwelling Korean adults over 65 years. Data from the years 2006–2016 of the Korean Longitudinal Study of Aging, a nationally representative panel survey that has been conducted every two years since 2006, were used. We employed a group-based trajectory modeling analysis to identify depressive symptom trajectories. Multinomial logistic regression analysis was conducted to identify predictors of each class of depressive symptoms. Results: Five depressive symptom trajectory groups were identified: Group 1, “None” (28.9%); Group 2, “Slowly worsening” (24.3%); Group 3, “Rapidly worsening” (17.5%); Group 4 “Improving” (12.4%); and Group 5, “Persistently severe” (16.9%). Older adults followed five distinct depressive symptom trajectories over 10 years. Mini-Mental State Examination scores, number of chronic diseases, educational level, and social activity were predictors associated with increasing depressive symptoms. Conclusions: This study showed that many older adults living in the community have depressive symptoms. To prevent and treat depression and aid successful mental health aging among older adults, the development of interventions should be tailored to target specific needs for each symptom trajectory. It is necessary to develop community-based interventions and strategies to identify and prevent depressive symptom trajectories among older adults.
|Publication status||Published - 2022 Dec|
Bibliographical noteFunding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2020R1A6A1A03041989).
We sincerely thank Dr. Chang Gi Park, a senior statistician at the College of Nursing, University of Illinois of Chicago, for statistical consultation.
© 2022, The Author(s).
All Science Journal Classification (ASJC) codes
- Psychiatry and Mental health