Exploration of Persuasive System Design Features and Engagement in Digital Mental Health Interventions
Theme: Personalizing care
Type of research: Mixed-method
Description:
Digital Mental Health Interventions (DMHIs) are an effective way to deliver treatment and make it more accessible. However, they often suffer from high drop-out and non-adherence rates (Kelders, Kok, Ossebaard, & van Gemert-Pijnen, 2012). Factors that influence adherence and drop-outs can be the user's overall engagement or how persuasive system design (PSD) features are implemented. Engagement can be defined as users’ behavioral, cognitive, and affective involvement with the product (Kelders et al., 2020; O’Brien, 2016, 2008). PSD features are design elements built into software and information systems to reinforce, change, or shape user attitudes and behaviors. (Oinas-Kukkonen & Harjumaa, 2009). PSD features can potentially foster engagement with DMHIs, however, the relationship between the two constructs needs more research. Therefore, this project will aim to explore the relationship between PSD features and engagement.
To look at how different PSD features affect users’ interaction with user interface features, we will use basic eye-tracking analyses such as heatmaps. In addition to quantitative methods (eye-tracking and questionnaires), we expect students to perform short semi-structured interviews with participants to collect deeper insight into users’ experiences.
Literature Examples
Oinas-Kukkonen, H., & Harjumaa, M. (2009). Persuasive Systems Design: Key Issues, Process Model, and System Features. Communications of the Association for Information Systems, 24, pp-pp. https://doi.org/10.17705/1CAIS.02428
Kelders, S. M., Kip, H., & Greeff, J. (2020). Psychometric evaluation of the TWente Engagement with Ehealth Technologies Scale (TWEETS): evaluation study. Journal of medical internet research, 22(10), e17757.
Kelders, S. M., Van Zyl, L. E., & Ludden, G. D. (2020). The concept and components of engagement in different domains applied to eHealth: a systematic scoping review. Frontiers in psychology, 11, 926.
Alrefaei, D. et al. (2023). Using Eye Tracking to Measure User Engagement with a Decision Aid. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Augmented Cognition. HCII 2023. Lecture Notes in Computer Science(), vol 14019. Springer, Cham. https://doi.org/10.1007/978-3-031-35017-7_5