Cognitive Triad and Depression: A Meta-Analysis of Cross-Sectional and Longitudinal Studies.

A large body of research has examined the association between the cognitive triad (negative views of the self, the world and the future) and depression; however, findings regarding its strength and direction remain inconsistent. The present meta-analysis systematically synthesized existing empirical evidence to evaluate both cross-sectional and longitudinal associations between the cognitive triad and depression. A three-level random-effects model was used to pool correlation coefficients (r) for cross-sectional associations, and cross-lagged regression models were applied to test prospective relations. Fifty-nine cross-sectional studies (132 effect sizes) were included, demonstrating a strong overall association between the cognitive triad and depressive symptoms (r = 0.596, 95% confidence interval, CI [0.546, 0.622]). Depressive symptoms were most strongly associated with a negative view of the self (r = 0.593), followed by a negative view of the world (r = 0.558) and the future (r = 0.538). Exploratory cross-lagged analyses were conducted across eight longitudinal studies. Preliminary results suggested that depressive symptoms at Time 1 predicted more negative cognitive triad scores at Time 2 (β = 0.160, 95% CI [0.100, 0.220]). The prospective effect of the cognitive triad on later depressive symptoms appeared to be moderated by age group; this effect was significant among participants aged 15 years or older (β = 0.187, 95% CI [0.112, 0.261]) but not among those below 15 years (β = -0.035, 95% CI [-0.122, 0.051]). The findings indicate a strong association between the cognitive triad and depressive symptoms. Furthermore, exploratory longitudinal analyses provide preliminary evidence for a prospective effect of depressive symptoms on subsequent negative cognitions and suggest a potential developmental shift in cognitive vulnerability to depression from late adolescence into adulthood.
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Huang Huang, Zhu Zhu, Feng Feng, Cui Cui, Wang Wang, Fu Fu, Tang Tang
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