Archive Number: 4.937
Status of Thesis: Open
Date of start: 2022-08-16
Background: An increasing number of digital interventions have been developed to provide treatment and resources for mental health improvement. Following the general trend toward using elements borrowed from video game designs in utilitarian systems, many digital mental health interventions (DMHIs) have begun to incorporate more and more game design elements (GDEs). However, different GDEs yield different effects. For instance, goals lead to self-regulation processes within users, while leaderboards trigger social comparison between users. Moreover, extant research has argued that when the selection of GDEs is unsuitable for the respective application context or neglects users' preferences (e.g., by developing one-size-fits-all solutions), gamification can fail to achieve the desired motivational effects or even lead to unintended negative consequences. Accordingly, when designing gamification for a novel context, established methods have underlined the importance of exploring users' context-specific GDE preferences for gaining a first understanding of which GDEs may be suitable to evoke meaningful engagement in this context. Besides identifying highly valued GDEs that are particularly promising for effective gamification design, investigating users' GDE preferences also helps to explore rejected GDEs that call for examining alternative designs. Several studies have provided valuable information on users' GDE preferences in different contexts, such as healthy nutrition (Berger & Jung, 2021), physical activity (Schmidt-Kraepelin et al., 2019), and learning management systems (Schöbel et al., 2016). Nevertheless, the insights on user preferences in these contexts are not easily transferable for the effective design of gamified DMHIs as the context has some special requirements regarding user-system interactions.
Objectives: Reveal users' GDE preferences in DMHIs and the rationales for their GDE choices (the specific DMHI will be selected during the first meeting)
Methods: Online survey based on a best-worst-scaling (BWS) approach with potential users of a selected DMHI (e.g., stress management app or sleep tracker app).
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