Digital Health Platforms & Patient-Generated Health Data: Advancing a PGHD Platform for Mental Health Care
Thesis (MA)
Advisor(s): Luca Kittelmann (luca.kittelmann@tum.de)
CONTEXT
Wearable-based interventions are increasingly discussed as a promising way to support healthcare through the continuous collection of patient-generated health data (PGHD) (Shapiro et al., 2012). In contrast to episodic data collection during consultations, wearable devices such as smart rings or smartwatches can provide longitudinal information on sleep, physical activity, heart rate, heart rate variability, and related behavioral patterns through passive data collection (Reindl-Spanner et al., 2023). Especially in the context of mental health care, such data is considered valuable because it may help complement self-reports and clinical consultations with more continuous and everyday-life-oriented insights (Matcham et al., 2019; Wuttke et al., 2023; Pedrelli et al., 2020).
Within an ongoing research project at TUM, a mobile mental health platform has been developed to support the care of patients with depression by integrating wearable-based interventions and other forms of PGHD into a shared digital platform. The system combines a mobile application for patients, a web-based interface for healthcare professionals, and multiple backend services for data integration, analysis, and visualization. In its current state, the platform already supports questionnaire-based data collection as well as the integration of wearable sensor data, including Oura-based data streams, and provides clinicians with access to relevant patient information through a professional dashboard.
Initial deployments of the platform in a study context have generated feedback from both patients and physicians regarding usability, functionality, and integration into clinical workflows. Based on this feedback, the platform is now being further developed to improve its technical architecture, feature set, and clinical usability.
This thesis focuses on the technical and conceptual advancement of the existing platform, with a particular emphasis on system requirements and feature development derived from real-world user feedback.
TASK(S)
- Analysis and structuring of requirements for the further development of a PGHD platform based on feedback from patients and healthcare professionals
- Design and implementation of new platform features
- Conceptual or technical refinement of the system architecture
- Development and prototyping of selected platform components
- Evaluation of the developed concepts or features
- Derivation of technical design principles and requirements for PGHD platforms in healthcare
REQUIREMENTS
- High degree of autonomy and responsibility
- Strong interest in digital health technologies and healthcare information systems
- Experience in software development, data analysis, or system design
- Structured, independent, and reliable work style
- Interest in translating user feedback into technical system requirements
FURTHER INFORMATION
The thesis can be written in English or German. If you have further questions, please do not hesitate to contact me directly. Please send your application including our specific application form, a current transcript of records and your CV to luca.kittelmann@tum.de. Please also note that we can only consider applications with complete documents.
References
Matcham, F., Barattieri di San Pietro, C., Bulgari, V., De Girolamo, G., Dobson, R., Eriksson, H., et al. (2019). Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD): A multi-centre prospective cohort study protocol. BMC Psychiatry, 19(1), 72.
Wuttke, A., Steinmetz, A., Endres, K., Simon, P., Fellgiebel, A., & Haller, N. (2023). Behavioral activation by wearable devices in patients with late-life depression. GeroPsych.
Pedrelli, P., Fedor, S., Ghandeharioun, A., Howe, E., Ionescu, D. F., Bhathena, D., et al. (2020). Monitoring changes in depression severity using wearable and mobile sensors. Frontiers in Psychiatry, 11, 584711.
Reindl-Spanner, P., Prommegger, B., Gensichen, J., & Krcmar, H. (2022). Insights on patient-generated health data in healthcare: A literature review. PACIS 2022 Proceedings.
Shapiro, M., Johnston, D., Wald, J., & Mon, D. (2012). Patient-generated health data. White paper. RTI International. Prepared for Office of Policy and Planning, Office of the National Coordinator for Health Information Technology.