A clinically actionable framework for personalizing iCBT to improve depression outcomes

Internet-based cognitive behavioral therapy (iCBT) is effective for depression, but its impact is constrained by low engagement and modest response rates. Personalization may address these limitations, yet a gap remains between research evidence and clinically actionable implementation. This narrative review synthesizes evidence on personalization in iCBT for depression using a three-stage framework: pre-implementation optimization, stratifying treatment based on patient characteristics, and dynamically adapting therapy using progress monitoring. Evidence was evaluated using principles of evidence grading, with attention to the volume, consistency, and directness of findings for each stage. The strongest evidence supports pre-implementation optimization of engagement and stratification of initial support based on baseline severity, treatment history, and related clinical characteristics. Evidence for dynamic adaptation is promising but less developed, with support for early identification of nonresponse and adjustment of treatment intensity, but limited iCBT-specific trials testing adaptive treatment strategies in depression. Across stages, engagement and clinical outcomes are related but distinct targets for personalization. Emerging research on responsiveness, progress monitoring, and digital biomarkers offers future opportunities for more precise and scalable personalization. More rigorous depression-specific iCBT studies are needed to determine when, how, and for whom personalized interventions improve engagement and clinical outcomes.