Action Mechanism and Optimization Strategy of Artificial Intelligence in Health Behavior Intervention

Traditional diet and exercise interventions are increasingly constrained by persistent challenges, including low user adherence, delayed or lagging health feedback, and a lack of sufficiently personalized guidance. This study examines artificial intelligence in health behavior intervention and constructs a closed-loop model of “perception-cognition-decision.” The perceptual layer realizes the accurate quantification of multimodal health data with the help of computer vision technology. The cognitive layer integrates the digital nudge theory and optimizes the user selection environment through visual presentation and anthropomorphic interaction. The decision-making layer introduces the big language model to upgrade the traditional imperative intervention into a generative dialogue with contextual understanding. The dynamic threshold model of just-in-time adaptive intervention solves the problem of inaccurate intervention timing. The results show that the intervention model can delay the exponential decline of user compliance, which provides a reference for realizing high-precision personalized health management.