Leveraging expectation effects to improve outcomes in the context of digital mental health interventions

Abstract

Patients' expectations about treatment benefits are robust predictors of clinical outcomes across medical and psychological contexts. Recent evidence suggests that expectancy effects in digital mental health interventions (DMHIs) are comparable in magnitude to those observed in face-to-face treatments. Despite their relevance, systematic guidance on how expectations can be shaped and optimized within DMHIs is limited. This narrative review summarizes theoretical and empirical work on expectations, including placebo and nocebo mechanisms, and digital intervention design to outline how expectation principles may be integrated into DMHIs. We describe three overarching principles - proactive expectation management, warmth and competence, and observational learning - and discuss how these mechanisms can be translated into practice across different stages and components of DMHI, including recruitment and onboarding, content, guidance, and interface design. We further highlight opportunities for expectation monitoring, automated feedback, and just-in-time adaptive interventions to support expectations. While modifications to single components may yield limited effects, coordinated, expectation-informed optimization across multiple DMHI components may have the potential to meaningfully enhance engagement and clinical outcomes. We conclude by discussing conceptual and methodological considerations and outline promising future directions for integrating the expectation lens within digital mental health.