Comorbid anxiety predicts lower odds of MDD improvement in a trial of smartphone-delivered interventions

Abstract

Comorbid anxiety disorders are common among patients with major depressive disorder (MDD), but their impact on outcomes of digital and smartphone-delivered interventions is not well understood. This study is a secondary analysis of a randomized controlled effectiveness trial (n=638) that assessed three smartphone-delivered interventions: Project EVO (a cognitive training app), iPST (a problem-solving therapy app), and Health Tips (an active control). We applied classical machine learning models (logistic regression, support vector machines, decision trees, random forests, and k-nearest-neighbors) to identify baseline predictors of MDD improvement at 4 weeks after trial enrollment. Our analysis produced a decision tree model indicating that a baseline GAD-7 questionnaire score of 11 or higher, a threshold consistent with at least moderate anxiety, strongly predicts lower odds of MDD improvement in this trial. Our exploratory findings suggest that depressed individuals with comorbid anxiety have reduced odds of substantial improvement in the context of smartphone-delivered interventions, as the association was observed across all three intervention groups. Our work highlights a methodology that can identify interpretable clinical thresholds, which, if validated, could predict symptom trajectories and inform treatment selection and intensity.³.

Keywords: Anxiety disorders; Comorbidity; Machine learning; Major depressive disorder; Mental health; Mood disorders.