A Beginner's Guide to Applying Large Language Models in Behavioral Interventions.

Area of Science:

• Behavioral Science
• Artificial Intelligence
• Digital Health

Background:

• Digital behavioral interventions (DBIs) are crucial for chronic disease self-management.
• Current DBIs often lack personalization, limiting sustained engagement.
• Large language models (LLMs) present an opportunity for more adaptive conversational support.

Purpose of the Study:

• To provide a structured introduction to LLMs for behavioral scientists.
• To outline the integration of LLMs into behavioral interventions.
• To guide researchers in developing and implementing LLM-based interventions.

Main Methods:

• Description of natural language processing and transformer architectures.
• Explanation of LLM system components: prompting, context management, retrieval-augmented generation, and guardrails.
• Case example: integrating a proprietary LLM into a mobile intervention for systemic sclerosis self-management.

Main Results:

• A phased design workflow for early-stage development and responsible implementation of LLM-based interventions.
• A decision framework for researchers navigating trade-offs between proprietary and alternative LLM models.
• Considerations informed by formative implementation efforts.

Conclusions:

• LLMs offer significant potential for personalized and scalable behavioral interventions.
• Careful architectural, methodological, and ethical planning is critical for successful LLM integration.
• Interdisciplinary collaboration and rigorous evaluation are vital for ensuring safety, scientific rigor, and improved patient outcomes.