The temporal relationship between social adaptation and depression: based on cross-lagged network analysis
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Published on: February 22, 2018
Xiaowen Li1, Jia Zhang2, Jianhua Pan3
1Hefei College of Finance and Economics, Hefei, 230601, China.
Background:
Social adaptation (SA) and depression significantly challenge the older adults's psychological health. The complex interplay between these conditions, particularly in the context of varying different levels of social isolation (SI), is not well-understood. This study examines the association between SA and depression among older adults across varying levels of social isolation SI.
Methods:
This study used data from the China Longitudinal Aging Social Survey (CLASS) and dichotomized participants into low-SI and high-SI groups according to the sample median (16 points) of the composite social isolation (SI) score. Network analysis was used to examine the relationships between symptoms of SA and Depression at two distinct time points across different levels of SI. We then applied Cross-Lagged Panel Network (CLPN) analysis to assess the longitudinal predictive relationships between these symptoms.
Results:
Our findings reveal that, compared to the Low-SI group, the High-SI group exhibits more intricate relationships between social adaptation and depression, indicating a significant modulatory effect of SI on these correlations. Key symptoms identified as bridge items in the networks of SA and depression include "Not accepting new ideas," "Do something more," and "Sad." In the cross-lagged analysis, "Loss of appetite" showed the greatest impact in the Low-SI group, while "Good mood" was predominant in the High-SI group.
Conclusions:
This study provides valuable insights into the mechanisms underlying the interplay between SA and depression, elucidating the pivotal role of SI in this complex dynamic. The findings suggest that efforts to mitigate depression and enhance social adaptation among older adults should explicitly account for individuals' levels of social isolation in order to design more targeted and effective mental-health interventions.
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