Abstracts Category : Other

Mobile Mental Health Information System for People with Depression

by Fabian Wahle

Mental disorders belong to a significant and serious disease pattern with an increasing prevalence worldwide. Due to limited health personnel and financial resources, scalability of mental health services and tailored care of individuals are two key barriers for successful treatment. Scalable mental health information systems (MHISs) are expected to address these barriers. Thus, from an information system design perspective, it is of utmost importance to understand the underlying mechanisms that determine the composition of system components of evidence-based MHIS interventions. While existing research on MHIS postulates a variety of components that show an impact on utilization of mental health information systems and treatment outcomes, there largely remains uncertainty with regard to which technical design choices guide the development of successful MHIS. In investigating this question, the present thesis presents a comprehensive overview of the state of the art of technology-mediated MHIS for patients with depression in the form of a systematic review and meta-analysis and identifies and discusses relevant system components by means of Oinas- Kukkonens' Persuasive System Design framework for future work on the design of evidence-based MHIS interventions. In total, 6,387 studies were screened of which 45 were included for analysis. Results suggest, that technology-mediated MHISs for the treatment of depression have a consistent positive overall effect compared to controls. Adding to the debate on which system components should be considered for future MHIS design, a new set of system components was identified and qualitatively analysed with respect to its therapeutic gain.A particularly promising subset of MHISs relates to those delivering support via smartphones. Smartphones are ubiquitous and have a large complement of sensors that can potentially be useful in monitoring behavioural patterns and providing context-based interventions. This leads to the exploration of the question whether mobile sensors can be utilized for context-aware just-in-time interventions for people suffering from depression. In an explorative approach, a smartphone app, Mobile Sensing and Support (MOSS), is designed from the ground up. In a first proof of concept, the developed app is tested in a nonrandomized, uncontrolled single-arm clinical trial, with respect to implementation feasibility, acceptability and, to a limited degree, efficacy. The app utilizes a novel intervention recommendation system, extending the concept of a just-in-time adaptive intervention by utilizing a holistic view on the users behavioural context derived from mobile sensor data. Over 80 interactive micro interventions stemming from cognitive behavioural therapy (CBT) are implemented for administration. Depression symptom progression is tracked using the patient health questionnaire (PHQ-9) to conduct non-parametric statistical hypothesis tests to compare scores before and after intervention. Correlation analysis is used to test the relationship betweenAdvisors/Committee Members: Fleisch, Elgar, Schmid, Petra, Kowatsch, Tobias.