We are seeking an excellent and motivated candidate for a Ph.D. position in the research and development of smartwatch-based intelligent user interfaces to support preventive healthcare applications. The Ph.D. candidate will leverage state-of-the-art AI techniques to explore opportunities for product and service design in preventive healthcare systems. These systems provide health-promoting services within people's living environments, rather than solely addressing disease symptoms in treatment facilities.
We conduct interviews in two waves, selecting on a first-come, first-serve basis. Initially, we will consider applicants who apply within the first 2 weeks. If the position remains unfilled, we will then interview candidates who apply between weeks 3 and 6.
The Ph.D. project will be mainly executed at the TU/e (TU Eindhoven) campus, where cross-disciplinary research is fostered, in cooperation with the Department of Psychology of Maastricht University (UM), Maastricht UMC+, and TU Berlin, co-funded by both ZonMw and NWO projects. The accepted Ph.D. candidate will have a unique opportunity to position themself at the forefront of future healthcare systems design.
In the first two years, the candidate will focus on the design case of experience sampling (ESM) for chronic pain management in women. Building on existing Experiencer (http://experiencer.eu/)
infrastructures, the candidate will develop a tailored smartwatch app for capturing momentary assessment (i.e., self-reports), activity logs (from the accelerometer), and physiological data. The candidate will design and implement a data-capturing system, which includes a smartwatch-based intelligent user interface that adapts to user preferences and behaviors, as well as an interactive dashboard providing intuitive visualizations for patients and caregivers. The candidate will also conduct usability tests of the user interface design, and handle and analyze data generated from the technical and user studies. The candidate will collaborate with the UM and TU Berlin engineers on connecting and synchronizing the smartwatch with supplementary physiological and activity-sensing devices where relevant. User-experience tests with patient samples should provide system feedback and lead to preliminary insights into the design principles that should guide the implementation of such systems.
In the last two years, the candidate will generalize the findings to multiple design case studies of preventive healthcare applications, such as cardiac care and obesity care, carry out further empirical studies, and collaborate in design activities to understand further design issues. The candidate will conclude the studies with recommendations for smartwatch-based ESM designs for preventive healthcare. The findings will be made more accessible and practical by translating them into design principles and guidelines that are both generative and evaluative for future human-centered preventive healthcare applications.
Throughout the four-year project, the candidate will conduct multiple iterations of development and evaluation of a smartwatch-based ESM tool that (i) supports longitudinal data collection pertaining to daily life activity, emotions, and other relevant aspects; (ii) ensures higher adherence in self-tracking through the application of behavior change techniques and gamification; (iii) provides insightful and actionable data visualization powered by state-of-the-art artificial intelligence tools and techniques; (iv) supports synchronization of multiple data sources, such as external physiological or activity sensing systems; and (v) makes measurements and data collection user-friendly at home.