1. Temporal associations among loneliness, anxiety, and depression during the COVID‐19 pandemic period.
- Author
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Wu, Jianfen, Wu, Yunpeng, and Tian, Yu
- Subjects
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STRUCTURAL equation modeling , *PSYCHOLOGY of college students , *TIME , *SEX distribution , *LONELINESS , *MENTAL depression , *DESCRIPTIVE statistics , *ANXIETY , *COVID-19 pandemic , *LONGITUDINAL method - Abstract
Numerous studies have reported that individuals' loneliness, anxiety, and depression levels increased during the COVID‐19 pandemic period. However, reciprocal associations among loneliness, anxiety, and depression, as well as gender differences in these associations, have not been investigated. Therefore, temporal associations among loneliness, anxiety, and depression and gender differences in these associations were examined in a longitudinal study during the COVID‐19 pandemic period. The loneliness, anxiety, and depression levels of 458 university students were evaluated at three timepoints (T1, T2, and T3) during the COVID‐19 pandemic period in China. The timepoints were separated by 1 month. Cross‐lagged panel designs were used to examine reciprocal associations among loneliness, anxiety, and depression as well as the stability and gender differences of these associations. Cross‐lagged panel analysis revealed that T1 depression positively predicted T2 anxiety and loneliness, T1 loneliness positively predicted T2 depression, T2 anxiety positively predicted T3 depression, T2 depression positively predicted T3 anxiety and loneliness, T2 loneliness positively predicted T3 depression, and T1 loneliness positively predicted T3 anxiety through the mediating role of T2 depression. No gender differences were observed in the cross‐lagged associations. During the COVID‐19 pandemic period, loneliness and depression predicted each other across time, and loneliness predicted anxiety across time, mediated by depression. No gender differences were observed in the cross‐lagged associations among loneliness, anxiety, and depression. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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