A Behavioral Science-Informed Agentic Workflow for Personalized Nutrition Coaching: Development and Validation Study.

ABSTRACT:

Background

Effective management of cardiometabolic conditions requires sustained positive nutrition habits, often hindered by complex and individualized barriers. Direct human management is simply not scalable, and deterministic automated approaches to nutrition coaching may lack the personalization needed to address these diverse challenges.

Objective

We report the development and validation of a novel large language model (LLM)-powered agentic workflow designed to provide personalized nutrition coaching by directly identifying and mitigating patient-specific barriers.

Methods

We used behavioral science principles to create a comprehensive workflow that can map nutrition-related barriers to corresponding evidence-based strategies. First, a specialized LLM agent to in

SUBMITTER: Yang E 

PROVIDER: S-EPMC12460921 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature