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The present work explores how theories of motivation can be used to enhance video game research. Currently, Flow-Theory and Self-Determination Theory are the most common approaches in the field of Human-Computer Interaction. The dissertation provides an in-depth look into Motive Disposition Theory and how to utilize it to explain interindividual differences in motivation. Different players have different preferences and make different choices when playing games, and not every player experiences the same outcomes when playing the same game. I provide a short overview of the current state of the research on motivation to play video games. Next, Motive Disposition Theory is applied in the context of digital games in four different research papers, featuring seven studies, totaling 1197 participants. The constructs of explicit and implicit motives are explained in detail while focusing on the two social motives (i.e., affiliation and power). As dependent variables, behaviour, preferences, choices, and experiences are used in different game environments (i.e., Minecraft, League of Legends, and Pokémon). The four papers are followed by a general discussion about the seven studies and Motive Disposition Theory in general. Finally, a short overview is provided about other theories of motivation and how they could be used to further our understanding of the motivation to play digital games in the future. This thesis proposes that 1) Motive Disposition Theory represents a valuable approach to understand individual motivations within the context of digital games; 2) there is a variety of motivational theories that can and should be utilized by researchers in the field of Human-Computer Interaction to broaden the currently one-sided perspective on human motivation; 3) researchers should aim to align their choice of motivational theory with their research goals by choosing the theory that best describes the phenomenon in question and by carefully adjusting each study design to the theoretical assumptions of that theory.
There is no longer any doubt about the general effectiveness of psychotherapy. However, up to 40% of patients do not respond to treatment. Despite efforts to develop new treatments, overall effectiveness has not improved. Consequently, practice-oriented research has emerged to make research results more relevant to practitioners. Within this context, patient-focused research (PFR) focuses on the question of whether a particular treatment works for a specific patient. Finally, PFR gave rise to the precision mental health research movement that is trying to tailor treatments to individual patients by making data-driven and algorithm-based predictions. These predictions are intended to support therapists in their clinical decisions, such as the selection of treatment strategies and adaptation of treatment. The present work summarizes three studies that aim to generate different prediction models for treatment personalization that can be applied to practice. The goal of Study I was to develop a model for dropout prediction using data assessed prior to the first session (N = 2543). The usefulness of various machine learning (ML) algorithms and ensembles was assessed. The best model was an ensemble utilizing random forest and nearest neighbor modeling. It significantly outperformed generalized linear modeling, correctly identifying 63.4% of all cases and uncovering seven key predictors. The findings illustrated the potential of ML to enhance dropout predictions, but also highlighted that not all ML algorithms are equally suitable for this purpose. Study II utilized Study I’s findings to enhance the prediction of dropout rates. Data from the initial two sessions and observer ratings of therapist interventions and skills were employed to develop a model using an elastic net (EN) algorithm. The findings demonstrated that the model was significantly more effective at predicting dropout when using observer ratings with a Cohen’s d of up to .65 and more effective than the model in Study I, despite the smaller sample (N = 259). These results indicated that generating models could be improved by employing various data sources, which provide better foundations for model development. Finally, Study III generated a model to predict therapy outcome after a sudden gain (SG) in order to identify crucial predictors of the upward spiral. EN was used to generate the model using data from 794 cases that experienced a SG. A control group of the same size was also used to quantify and relativize the identified predictors by their general influence on therapy outcomes. The results indicated that there are seven key predictors that have varying effect sizes on therapy outcome, with Cohen's d ranging from 1.08 to 12.48. The findings suggested that a directive approach is more likely to lead to better outcomes after an SG, and that alliance ruptures can be effectively compensated for. However, these effects
were reversed in the control group. The results of the three studies are discussed regarding their usefulness to support clinical decision-making and their implications for the implementation of precision mental health.