Presentation
Personalizing AI Technologies for Chronic Hypertension Health Management
SessionSF1: Student Research
DescriptionArtificial Intelligence (AI) has emerged as a promising tool to address limitations in healthcare; however, many AI interventions offer limited personalization. This study aims to assess the effect of personality traits on clinical outcomes during a mobile health (mHealth) digital health coaching (DHC) intervention for hypertension self-management and to propose an AI framework to guide the development of future automated interventions. Participants completed a 30-day mHealth hypertension program that involved daily blood pressure & weight monitoring, and also completed the Big Five Personality survey at baseline. A Generalized Linear Mixed Model (GLMM) was used to assess the relationship between personality traits and blood pressure changes over time. Results demonstrated that agreeableness influenced outcomes from the start of the intervention, while other personality traits showed more complex longitudinal effects. These findings suggest that participants may engage with and benefit from DHC interventions differently depending on personality characteristics. Based on these results, we propose an adaptive AI framework consisting of four components: (1) Trait Assessment, completing a personality survey; (2) a User Modeling Engine to personalize coaching approach; (3) an AI Adaptation Layer to dynamically tailor interactions and educational content; and (4) Behavioral Feedback Loop to continuously evaluate and refine intervention effectiveness.
Event Type
Lecture
TimeWednesday, October 21st1:30pm - 1:50pm PDT
Location