Is the change in longitudinal cognitive function in older adults with diabetes affected by trajectory classes of depressive symptoms?
Published In: Public Health Nursing, 2024, v. 41, n. 5. P. 1006 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Kang, Gyeong A; Yoon, Ju Young 3 of 3
Abstract
Objective: This study aims to identify classes based on the trajectory of depressive symptoms and to examine the impact of trajectory classes of depressive symptoms on longitudinal changes in cognitive function in older adults with diabetes. Methods: This is a secondary data analysis of 572 older adults with diabetes using data from the 5th (2014) to 8th (2020) wave of the Korean longitudinal study of aging. Analysis of latent class growth and the effect of trajectories of depressive symptoms on cognitive function was examined using a latent growth curve model. This analysis has been found to be functional in change trajectories and in describing the direction of the trajectory. Results: The trajectory of depressive symptoms was classified into four classes: low‐stable (36.89%), high‐decreasing (20.28%), low‐increasing (18.71%), and high‐persistent (24.13%). Compared with the high‐persistent class, higher initial levels of cognitive function were observed in the high‐decreasing and low‐stable classes. Compared with the high‐persistent class, a slower rate of cognitive decline was observed in the low‐stable class (B = 0.410, p =.021). Conclusions: Continuous monitoring of depressive symptoms and early management of depressive symptoms for community‐dwelling older adults with diabetes can help prevent the cognitive decline and delay the deterioration of cognitive function. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Public Health Nursing. 2024/09, Vol. 41, Issue 5, p1006
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2024
- ISSN:0737-1209
- DOI:10.1111/phn.13372
- Accession Number:179392358
- Copyright Statement:Copyright of Public Health Nursing is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.