MOSS: Mobile Sensing and Support for People with Depression

KTI Moss

Towards a mental health information system that harnesses ubiquitous sensor information for depression screening and tailored interventions.

Limited personnel resources and costs have a negative impact on the health supply in mental health therapy and intervention programs. People with mental disorders such as dementia, major depression, substance abuse or mental retardation experience serious health-related consequences. For example, depression alone accounts for 4.3% of the global burden of disease and is among the largest single cause of disability worldwide (WHO 2013). As an economic consequence, the increasing prevalence of non-communicable diseases (i.e. non-infectious and non-transmissible among people) to which mental disorders contribute a major extent, is expected to account for a loss of US$ 47 trillion in 2030, i.e. approximately 75% of the global gross domestic product in 2010. A range of web-based mental health interventions emerged over the last couple of years to tackle the problem of health supply shortage. Unfortunately to date, if no extra personal guidance is utilized, they fail to deliver tailored and context sensitive support and therefore have limited potential. With MOSS we try to fill this gap. Using off the shelve smartphone sensors and state of the art machine learning techniques we aim at inferring a users mental state in order to deliver best practices that maximize intervention success.



Wahle, F., Bollhalder, L., Kowatsch, T., Fleisch, E. (2017) Towards the Design of Evidence-based Mental Health Information Systems for People with Depression: A Systematic Literature Review and Meta-Analysis, Journal of Medical Internet Research (JMIR) 19(5):e191. Link

Wahle, F., Kowatsch, T., Fleisch, E., Rufer, M., Weidt, S. (2016) Mobile Sensing and Support for People with Depression: A Pilot Trial in the Wild, JMIR Mhealth Uhealth, 4(3):e111. Link

Wahle, F., Kowatsch, T., Weidt, S. (2016) Mobile Sensing and Support for People with Depression, LATSIS Symposium 2016 ETH Zurich on personalized medicine. (PDF)

Weidt, S., Wahle, F., Rufer, M., Hörni, A., Kowatsch, T. (2015) MOSS: Mobile Sensing and Support – Mit einer App depressive Verstimmungen erkennen und Betroffenen helfen, Therapeutische Umschau 72 (9), 7.1-3. (PDF).

MOSS Press Release, March 2015. (PDF)

MOSS Flyer for recruitment of study participants, March 2015. (Formal Version) | (Informal Version)

Wahle, F., Kowatsch, T. (2014) Towards the Design of Evidence-based Mental Health Information Systems: A Preliminary Literature Review, International Conference on Information Systems (ICIS 2014), Auckland, New Zealand (PDF)


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Working Group

Fabian Wahle, Anna Hanulova, Anja Hörni, Jost Schweinfurther, Elgar Fleisch, Tobias Kowatsch


CHF 994.964


Apr 2014 – Oct 2015

UniversitätsSpital Zürich
Schweizerische Eidgenossenschaft