We are most fortunate to have recruited two talented students into our research group. Both received their undergraduate degrees here in Psychology and Neuroscience at the University of Glasgow.
Chiara Wilke received her degree in 2020 and then performed post graduate work last year in Germany. She has returned to perform a PhD on a collaborative project funded by the Scottish Graduate School of Social Science (SGSSS) entitled, “Developing and assessing a digital health app that implements the Situated Assessment Method to decrease distress and increase eustress.” Her supervisors are Lawrence Barsalou from the University of Glasgow (primary), Aleksandar Matic from Koa Health (secondary), and Esther Papies from University of Glasgow (secondary).
Project abstract. This project will build and evaluate a health app to help individuals learn about and regulate life stress. In previous work, we developed a new instrument for measuring an individual’s stress, the Situated Assessment Method (SAM2). Unlike other instruments that establish an overall measure of an individual’s stress level, SAM2 provides rich information about associated stress mechanisms. SAM2 is also novel in assessing both negative stress (distress) and positive stress (eustress), and typically explains 70-80% of the variance in distress and eustress, while establishing insight into associated stress mechanisms. Interestingly and significantly, performing the SAM2 assessment procedure across multiple timepoints induces learning about distress and eustress. Individuals increasingly understand how the distress and eustress they experience is related to specific stress mechanisms. In a series of longitudinal studies, we will build and assess a health app that implements the SAM2 procedure. Of particular interest is building an effective digital tool that promotes learning about distress and eustress to decrease distress and increase eustress (shifting the affect associated with stress from negative to positive). Besides collecting data that tracks distress and eustress longitudinally, the app will continually assess user learning and app engagement. The PhD student will become part of a large lab group at the University of Glasgow that focuses on health cognition and behaviour. The two Glasgow supervisors will provide training in health cognition, behaviour change, and research methods. The industry partner will serve as an equal third supervisor, providing training in app development, implementation, and assessment. The PhD project will play a foundational role for developing future collaborative projects that aim to develop increasingly powerful apps for decreasing distress and increasing eustress.
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.