Can AI help with the hardest thing: pro health behavior change

Area of Science:

• Digital Health
• Behavioral Science
• Artificial Intelligence in Medicine

Background:

• Intensive lifestyle modification aids cardiovascular disease risk reduction, but sustained behavior change remains difficult.
• Existing interventions often fall short in promoting durable pro-health behaviors.
• The integration of technology in health behavior change requires careful consideration of efficacy and safety.

Purpose of the Study:

• To present an evidence-based framework for optimizing artificial intelligence (AI)-augmented pro-health behavior change interventions.
• To outline key areas for collaboration between AI, behavioral, and medical experts.
• To define essential user safety prerequisites for AI-driven health interventions.

Main Methods:

• Framework development involving collaboration across AI, behavioral science, and medical domains.
• Identification of critical intervention optimization stages: use-case development, real-time oversight, bias mitigation, and personalization.
• Definition of user safety requirements including autonomy, data transparency, explainability, and avoidance of neuromodulation.

Main Results:

• A structured framework for AI-augmented health behavior change interventions is proposed.
• Key collaborative processes for optimizing AI interventions are detailed.
• Essential user safety perquisites are identified to guide responsible AI implementation.

Conclusions:

• An AI-augmented framework, developed through multidisciplinary collaboration, can enhance pro-health behavior change interventions.
• Prioritizing user safety through autonomy, transparency, and trust-building is crucial for effective AI health solutions.
• This framework provides a roadmap for developing robust and safe AI-driven tools for disease management and prevention.