Supporting Adolescent Engagement with Artificial Intelligence-Driven Digital Health Behavior Change Interventions

Area of Science:

• Digital health
• Behavior change interventions
• Artificial intelligence in healthcare

Background:

• Adolescent engagement with behavior change interventions is crucial but challenging.
• Digital interventions offer vast process-level data for analysis.
• Optimizing engagement can increase intervention efficacy in youth.

Purpose of the Study:

• To propose a framework for using AI to understand and improve adolescent engagement in digital health behavior change interventions (DHBCIs).
• To outline AI applications for measuring, modeling, optimizing, and generating interventions for adolescent engagement.
• To address ethical considerations, particularly adolescent privacy, in AI implementation.

Main Methods:

• Leveraging process-level data from digital interventions.
• Applying artificial intelligence (AI) analytical capabilities.
• Utilizing the INSPIRE narrative-centered DHBCI as a case example.

Main Results:

• A proposed AI framework with four key goals: measurement, modeling, optimization, and generation of adolescent engagement.
• Identification of potential AI pitfalls, emphasizing adolescent privacy concerns.
• Highlighting numerous opportunities for future research in AI-driven adolescent interventions.

Conclusions:

• AI holds significant potential to enhance the engagement and efficacy of DHBCIs for adolescents.
• Ethical considerations, especially privacy, are paramount for successful AI implementation in youth-focused interventions.
• Further research is warranted to fully realize the benefits of AI in this domain.