Rapport Matters: Enhancing HIV mHealth Communication through Linguistic Analysis and Large Language Models

In HIV care, a strong rapport between patient and provider is essential for strengthening trust, enhancing therapy adherence, and ultimately leading to improved health outcomes. As the adoption of digital interactions in HIV care via mobile health (mHealth) tools is emerging, maintaining rapport in these asynchronous text-based communications becomes a critical yet challenging task. In this paper, we analyze 1,740 messages from an mHealth platform, categorized by experienced clinicians as either 'rapport-building' or 'information-only.' We utilize linguistic analysis to uncover key attributes of rapport-building communication. This led to a set of machine learning (ML) models and Large Language Models (LLMs) capable of classifying these communication styles. Further, we propose the application of LLMs not only to identify but also to actively rewrite 'information only' messages into versions that enhance rapport building without compromising information integrity. Our research demonstrates potential advancements in HIV mHealth communication by integrating linguistic analysis with language models, leading to more effective patient-provider interactions.