Juliane Kloidt

Juliane Kloidt received her degree in 2021 and then joined the Center for Doctoral Training in Socially Intelligent Artificial Agents here on a project, entitled, “Improving engagement with mobile health apps by understanding (mis)alignment between design elements and personal characteristics.”  Her supervisors are Lawrence Barsalou from the University of Glasgow (primary) and Aleksandar Matic from Koa Health (secondary).

Project abstract.  This project will deepen understanding of how to personalise mobile health apps to user personal characteristics aiming to improve the engagement and ultimately intervention effectiveness. The main objectives will be the following:  (1) Identify specific links between personal characteristics and service design elements that predict engagement and/or drop-outs, (2) Explore if the engagement with mobile health apps can be improved by avoiding misaligned (reinforcing aligned) design elements with personal characteristics that pre-dominantly drive drop-outs (engagement), (3) Deliver a set of takeaways for designing socially intelligent interfaces aware of personal characteristics

Extensive literature research will be first conducted to characterise design elements, and to identify the links to personal characteristics that can influence engagement. This will result in a set of hypotheses on the relationship between different personal characteristics and the engagement mechanisms. Sequentially, one or more studies will be conducted to capture personal traits of the users who have already used a selected set of relevant mobile health apps. By applying standard statistical methods as well as machine learning (to unpack more complex interplay between personal characteristics and design elements), this data will be used to identify engagement/drop-out predictors. The learnings will be used to design and test personalisation in a real-world scenario.