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Developing a Behavioral Phenotyping Layer for Artificial Intelligence-Driven Predictive Analytics in a Digital

Trevor van Mierlo1, Rachel Fournier1, Siu Kit Yeung2

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Summary
This summary is machine-generated.

This study uses randomized tips and to-do lists to improve engagement in digital mental health for Ukrainian refugees, aiming to build AI-driven personalization for better adherence and accessibility.

Keywords:
AI-driven personalizationartificial intelligenceattritionbehavioral economicsdigital mental healthdigital phenotypingengagementmachine learningself-guided therapy

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Area of Science:

  • Digital mental health interventions
  • Behavioral economics in health
  • Artificial intelligence in healthcare

Background:

  • Digital mental health tools face challenges with user engagement and adherence.
  • Millions lack access to mental health practitioners, necessitating scalable self-guided resources.
  • Prior research successfully used behavioral economics (nudges) to boost engagement.

Purpose of the Study:

  • To analyze user engagement with randomized tips and to-do lists in a resiliency course for Ukrainian refugees.
  • To inform the development of an AI-based personalization system for digital mental health.
  • To identify predictors of engagement and create a scalable, culturally sensitive intervention model.

Main Methods:

  • A 6-arm randomized controlled trial comparing combinations of tips, nudges, and to-do lists.
  • Recruitment via digital outreach, anonymous enrollment, and data collection on engagement metrics and demographics.
  • Statistical analysis including between-arm comparisons and interaction testing for intervention components.

Main Results:

  • The study protocol was designed in January 2025.
  • Alpha and beta testing are scheduled for July 2025, with a soft launch in August 2025.
  • The experiment will run until sample size requirements are met, with ongoing data monitoring.

Conclusions:

  • This trial pioneers AI-ready behavioral datasets through randomized experimentation.
  • It targets an underserved, culturally sensitive population, offering insights for scalable digital mental health.
  • Findings aim to enhance engagement, accessibility, and long-term adherence in digital health interventions.