Other Titles
Exploring the Latent Trajectory of Depression in Arthritis Patients [Poster Title]
Abstract
Arthritis markedly diminishes the quality of life, compromises independence, and substantially escalates healthcare expenditures [1]. Numerous studies have identified a link between arthritis and depression. A meta-analysis indicated that depression is highly prevalent among individuals with arthritis and is linked to worse outcomes [2]. Additionally, some research suggests that individuals with depression have a higher likelihood of developing arthritis compared to controls [3]. However, there is a lack of research examining how depression progresses over time following an arthritis diagnosis and identifying the factors that influence these changes.
To investigate the trajectory of depression over time in patients diagnosed with arthritis and to identify the factors that contribute to changes in depressive symptoms.
We conducted a longitudinal study using data from the first to ninth waves of the Korean Longitudinal Study of Aging (KLoSA), which has surveyed Korea’s aging population biennially since 2006. To analyze depression patterns over 8 years following an arthritis diagnosis, we included participants who reported a new arthritis diagnosis without prior history in earlier waves. A latent growth model was used to identify depression patterns, comparing linear and quadratic models to select the best fit based on CFI, RMSEA, and SRMR.
The study included 769 participants (mean age 68.0±9.3, 78.7% female). The mean depression score rose before diagnosis to immediately after, then returned to eight years later, supporting a quadratic growth model. The intercept (baseline score) was 18.4 (SE = 0.19, P < .001), with a linear change rate of 0.14 (SE = 0.07, P = .060) and a quadratic change rate of -0.017 (SE = 0.007, P = .017). Age significantly affected the intercept (P < .001), while marital status and number of diseases impacted baseline scores. For the quadratic rate of change, final education (P = .042) and the number of diseases (P = .012) were significant association factors.
As patterns of depression reduction and the factors influencing this decrease are identified in individuals diagnosed with arthritis, healthcare providers should systematically monitor high-risk groups—such as older adults, unmarried individuals (including widowed or divorced), and those with multiple chronic conditions—to prevent an increase in depressive symptoms. This proactive monitoring may help prevent the occurrence of secondary conditions related to an arthritis diagnosis.
Notes
References:
1. Ke, C., Qiao, Y., Liu, S., Rui, Y., & Wu, Y. (2021). Longitudinal research on the bidirectional association between depression and arthritis. Social psychiatry and psychiatric epidemiology, 56, 1241-1247
2. Xue, Q., Pan, A., Gong, J., Wen, Y., Peng, X., Pan, J., & Pan, X. F. (2020). Association between arthritis and depression risk: a prospective study and meta-analysis. Journal of Affective Disorders, 273, 493-499.
3. Liu, R., Xin, Y., Shao, Y., Wu, B., & Liu, Y. (2024). Association of improvement and worsening of depressive symptoms with arthritis. BMC geriatrics, 24(1), 909.
Sigma Membership
Lambda Alpha at-Large
Type
Poster
Format Type
Text-based Document
Study Design/Type
Other
Research Approach
Other
Keywords:
Public and Community Health, Primary Care, Arthritis Patients, Quality of Life, Depression
Recommended Citation
Ji, Yoonjung and Baek, Wonhee, "Exploring the Latent Growth Trajectory of Depression in Arthritis Patients" (2025). International Nursing Research Congress (INRC). 179.
https://www.sigmarepository.org/inrc/2025/posters_2025/179
Conference Name
36th International Nursing Research Congress
Conference Host
Sigma Theta Tau International
Conference Location
Seattle, Washington, USA
Conference Year
2025
Rights Holder
All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record.
Review Type
Abstract Review Only: Reviewed by Event Host
Acquisition
Proxy-submission
Exploring the Latent Growth Trajectory of Depression in Arthritis Patients
Seattle, Washington, USA
Arthritis markedly diminishes the quality of life, compromises independence, and substantially escalates healthcare expenditures [1]. Numerous studies have identified a link between arthritis and depression. A meta-analysis indicated that depression is highly prevalent among individuals with arthritis and is linked to worse outcomes [2]. Additionally, some research suggests that individuals with depression have a higher likelihood of developing arthritis compared to controls [3]. However, there is a lack of research examining how depression progresses over time following an arthritis diagnosis and identifying the factors that influence these changes.
To investigate the trajectory of depression over time in patients diagnosed with arthritis and to identify the factors that contribute to changes in depressive symptoms.
We conducted a longitudinal study using data from the first to ninth waves of the Korean Longitudinal Study of Aging (KLoSA), which has surveyed Korea’s aging population biennially since 2006. To analyze depression patterns over 8 years following an arthritis diagnosis, we included participants who reported a new arthritis diagnosis without prior history in earlier waves. A latent growth model was used to identify depression patterns, comparing linear and quadratic models to select the best fit based on CFI, RMSEA, and SRMR.
The study included 769 participants (mean age 68.0±9.3, 78.7% female). The mean depression score rose before diagnosis to immediately after, then returned to eight years later, supporting a quadratic growth model. The intercept (baseline score) was 18.4 (SE = 0.19, P < .001), with a linear change rate of 0.14 (SE = 0.07, P = .060) and a quadratic change rate of -0.017 (SE = 0.007, P = .017). Age significantly affected the intercept (P < .001), while marital status and number of diseases impacted baseline scores. For the quadratic rate of change, final education (P = .042) and the number of diseases (P = .012) were significant association factors.
As patterns of depression reduction and the factors influencing this decrease are identified in individuals diagnosed with arthritis, healthcare providers should systematically monitor high-risk groups—such as older adults, unmarried individuals (including widowed or divorced), and those with multiple chronic conditions—to prevent an increase in depressive symptoms. This proactive monitoring may help prevent the occurrence of secondary conditions related to an arthritis diagnosis.
Description
This study aimed to explore the trajectory of depression over 8 years following an arthritis diagnosis and identify factors influencing depressive symptoms. The findings highlight the need for targeted monitoring and interventions for high-risk groups, such as older, unmarried individuals and those with multiple chronic conditions, to mitigate depressive symptoms and associated complications in arthritis patients.