Designing and Personalising Hybrid Health Explanations for Lay Users

Recommender systems are increasingly used in mobile health interventions, such as managing Chronic Musculoskeletal Pain (CMP). While researchers have highlighted the importance of explaining health-related recommendations to lay users, with benefits such as increased trust and a higher tendency to follow up on these recommendations, how to design explanations for lay users in critical contexts such as health remains largely unexplored. To address this gap, we develop a mobile health application to support users with CMP through coaching and personalised health recommendations delivered via a conversational rule-based recommender system. This article describes the three-phase iterative development of the RS, involving health experts and end users. In the first iteration, we conduct a preliminary validation study with \(N=282\) participants to ensure the app’s validity and improve the initial set of health recommendations. Next, two user studies are conducted centred around designing effective and understandable explanations for these recommendations. First, we design six explanation modalities tailored towards lay users, and through a qualitative study ( \(N=11\) ), extract initial design guidelines for explaining health recommendations, finding a strong preference towards feature importance explanations and identifying issues with modalities that highlight negative emotions. Given these results, we explore whether extending feature importance explanations with textual information into a ‘hybrid’ explanation could benefit end users, and whether these benefits depend on a user’s personal characteristics (need for cognition and ease-of-satisfaction). Through a mixed-methods study with \(N=262\) participants, we find that the hybrid modality significantly increased user trust, transparency, persuasiveness, usefulness and satisfaction compared to unimodal explanations. However, users with a higher need for cognition rate unimodal explanations more positively than hybrid ones.