Generative AI for Enhanced Engagement in Digital Wellness Programs: A Predictive Approach to Health Outcomes

Abstract

Users often abandon digital wellness programs due to a lack of system responses that are engaging, personally relevant, and supportive, thus impeding success in achieving desired health outcomes. In response, AI is increasingly being explored as a means to improve user interaction and engagement, ultimately leading to superior health outcomes. A crucial question often left unanswered is how AI should be integrated within health promotion and disease prevention programs to achieve lasting behavior change and improved health outcomes. We anticipate that program effectiveness will be enhanced by taking a predictive approach, wherein AI is used as a generative tool to inform future health states and program adaptations on an individual and group level. Consequently, in this essay, we exploit both qualitative and quantitative research methodologies to explore the alignment between AI and user interaction within a more client-centered, micro, inclusively well-being view: a digital wellness framework built on predictive, generative AI. We suggest several implications for and alliances between different healthcare stakeholders, as well as identify salient areas for future research. This research aims to provide a strategic vision of the role of generative AI within user-based interactions for both digital wellness platforms and the health sector more broadly. The purpose of this study was to showcase the potential and expectations associated with incorporating AI generative tools in the digital health and wellness promotion settings of the future and to explore the impact of this approach on user engagement and health outcomes. To investigate these links, a mixed-methods case was used: the influence of AI within digital health platforms.