• Yaqi Wang, Xueying Xu, Yuan He, Jingwen Liu, Qingyun Lv, Hairong Chang, Li Fu, Yue Zhao, Xiaonan Zhang, and Xiaoying Zang.
• School of Nursing, Tianjin Medical University, Tianjin, China.
• Prev Med. 2024 Oct 1; 187: 108122108122.

ObjectivesAs populations age, the issue of social participation among older adults has gained prominence. Studies indicate variability in social participation trajectories among this demographic, yet the transition patterns and their effects on depression remain unclear. This longitudinal study aims to explore the latent classes and transition patterns in social participation among older adults and to evaluate their effects on depression.MethodsData from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) in 2014 (T1) and 2018 (T2) were analyzed, including 2293 older adults. Latent class analysis (LCA) and latent transition analysis (LTA) were employed to identify latent classes of social participation at T1 and T2, as well as the transition probabilities between these classes. Multinomial logistic regression was used to examine predictors of transitions, and depression levels at T2 were compared across transition patterns.ResultsThe LCA results supported a 3-class model labeled as low, moderate, and high social participation. The probabilities of remaining stable and transitioning to other classes were similar across the three classes (ranging from 0.50 to 0.54). Age, gender, and other baseline characteristics emerged as significant predictors of transition patterns. Older adults experiencing positive transitions exhibited reduced depression compared to those in their original class over time, while those with negative transitions showed increased depression.ConclusionsThis research prompts a deep understanding of social participation dynamics in older adults and their effects on depression. Identifying social participation classes and transition patterns could inform interventions to enhance social participation and reduce depression among older adults.Copyright © 2024 Elsevier Inc. All rights reserved.

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