Theory-driven personalisation of recommendations for diabetes management

Current studies on diabetes recommender systems and apps mainly focus on the performance and personalisation of AI models and techniques, including machine learning and deep learning models that are trained on user data. These works often use a one-size-fits-all approach for presenting information to users. Yet, research shows that humans process information in different ways, and their attitudes towards an action depend on their attitude-function styles. The success of recommender apps for diabetes management to effectively influence behaviour heavily relies on considering social aspects of human behaviour, which have been largely overlooked in the literature. This represents a significant knowledge gap in this research area.