Depression, particularly in early adulthood, presents a significant mental health challenge with far-reaching implications. Innovative approaches to address and alleviate depressive symptoms are of paramount importance in this context. One such approach involves the utilization of technology, specifically chatbot-based programs, to target specific cognitive biases associated with depression.

The central objective is to empirically examine whether this program can effectively influence depressive mood and negative cognition in individuals grappling with depressive symptoms.

To ascertain the program’s efficacy, participants were divided into two groups: the CBM-I group (n=20), which underwent interpretation bias modification training, and the Mood Check group(n=20), which served as a control and engaged in a simple mood-checking exercise. A battery of psychological measures was employed, including assessments of depression, interpretation bias, suicidal ideation, resilience, and attention control.

Analysis results showed that the CBM-I group had a significant reduction in depression (PHQ-9, CES-D) compared to the Mood Check group in the post-measurement. Moreover, resilience (CD-RISC) and attention control (ACQ) significantly improved in the CBM-I group.

This research serves as a stepping stone towards a deeper understanding of how chatbot-based interventions can contribute to the management of early adulthood depression, offering new perspectives and possibilities in the realm of mental health support and treatment.

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