Understanding the particular mechanisms by which vulnerability and capability factors influence cognitive reactivity (CR) can contribute to an enhanced capacity to adequately react to depression. However, few studies have explored the CR model. The main aim of the present study was to develop a model that specifies the predictive effects of CR for depression among individuals at high risk for first-episode and recurrent depression. A national cross-sectional, online study using convenience sampling was conducted among 587 vulnerable healthy individuals and 224 depressed patients in China. A battery of indices, including measures of CR, social support, resilience, self-compassion, life events, neuroticism, sleep condition, and negative emotion, were collected. A structural equation model was applied to analyse the data. The final first-episode and recurrent depressive symptoms of the CR models showed good model fit. According to the models, 45%-52% of the variance in depressive symptom was predicted by CR. Social support, self-compassion, resilience, and positive life events directly influenced CR, with β values ranging from -0.18 to -0.24 (P < 0.01). Neuroticism, negative emotion, poor sleep conditions, and negative life events also directly and positively influenced CR (P < 0.01). The relationship between these negative or positive contributing factors and depression was also indirectly influenced by CR (P < 0.01). Our findings demonstrate the role of CR in the prevention and treatment of depression. The first-episode and recurrent depressive symptoms of the CR models considering both vulnerabilities and capabilities of CR in the psychopathology of depression provide a theoretical basis for interventions that reduce CR in high-risk populations.
Keywords: cognitive reactivity; depression; high risk; structural equation model; survey.
© 2020 Australian College of Mental Health Nurses Inc.