Comorbid anxiety predicts lower odds of depression improvement during smartphone-delivered psychotherapy

원문 초록

Comorbid anxiety disorders are common among patients with major depressive disorder (MDD), and numerous studies have identified an association between comorbid anxiety and resistance to pharmacological depression treatment. However, the impact of anxiety on the effectiveness of non-pharmacological interventions for MDD is not as well understood. In this study, we applied machine learning techniques to predict treatment responses in a large-scale clinical trial (n=493) of individuals with MDD, who were recruited online and randomly assigned to one of three smartphone-based interventions. Our analysis reveals that a baseline GAD-7 questionnaire score in the moderate to severe range (>10) predicts reduced probability of recovery from MDD. Our findings suggest that depressed individuals with comorbid anxiety face lower odds of substantial improvement in the context of smartphone-based therapeutic interventions for depression. Our work highlights a methodology that can identify simple, clinically useful "rules of thumb" for treatment response prediction using interpretable machine learning models.