JMIR Publications

ABSTRACT

Background:

Digital interventions for mental health are pivotal for addressing barriers such as stigma, cost, and accessibility, particularly for underserved populations. While the effectiveness of digital interventions has been established, poor adherence and lack of engagement remain critical factors that undermine efficacy. Millions of individuals will never have access to a trained mental health practitioner, so there is substantial need for highly tailored and engaging self-guided resources. This study builds on a prior study that successfully leveraged behavioral economics (nudges and prompts) to enhance engagement. Furthering that research, this experiment will focus on developing a predictive analytics data set for a specific, geographically diverse population.

Objective:

Using the EvolutionHealth.care platform, this 6-arm randomized controlled trial (RCT) aims to analyze user engagement with randomized tips and to-do lists within a resiliency course tailored for Ukrainian refugees affected by the ongoing humanitarian crisis (Спільна Сила). Insights will inform the development of an AI-based personalization system to optimize engagement and address behavioral health challenges. Secondary objectives include identifying demographic and behavioral predictors of engagement and creating a scalable, culturally sensitive intervention model.

Methods:

Participants will be recruited through digital outreach, enrolled anonymously, and randomized into six groups to compare combinations of tips, nudges, and to-do lists. Engagement metrics (e.g., clicks, completion rates, session duration) and demographic data (e.g., age, gender) will be collected. Statistical analyses will include comparison between arms and interaction testing to evaluate the effectiveness of each intervention component. Ethical safeguards include IRB approval, informed consent, and strict data privacy standards.

Results:

The primary outcome is user engagement with randomized prompts. Secondary outcomes include correlations between engagement and demographic or behavioral characteristics. Findings will establish a robust dataset to train an AI-driven personalization engine for digital mental health interventions.

Conclusions:

This trial represents a novel approach to behavioral health research by integrating AI-ready datasets and randomized experiments to enhance engagement. By targeting a culturally sensitive and underserved population, the study contributes valuable insights into scalable, personalized digital health solutions. Clinical Trial: https://doi.org/10.17605/OSF.IO/34RMG