Puzzle away the Puzzledness: Action-Design Study of an Educational Escape Room for Intervention in SMEs’ Perception towards ICT Adoption

Digital transformation initiatives in small and medium-sized enterprises (SME) are often hampered by individual practitioners’ perceptions of information and communication technology (ICT). This research employs an educational escape room (ER) game for an intervention towards informed decision-making on ICT adoption in SMEs. ER design and implementation are elaborated and consequently tested with SME practitioners, all embedded in an action-design study based on a qualitative research methodology. The result highlights a trade-off between creating immersive game experiences and achieving learning objectives. Still, the outcome implies an impact on players’ perception of ICT integrated in the ER. The findings contribute to the emerging field of serious games for learning and shed light on the potential of game-based interventions for SMEs

A systematic evaluation of machine learning-based biomarkers for major depressive disorder across modalities

Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy. Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression. Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification. Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research