Digital technology provides an unparalleled opportunity to collect data in individuals’ everyday lives. This includes information derived from passive mobile sensing (e.g., activity, screen lock events, light and sound detectors, wifi connections, etc.), as well as assessments of psychological variables (behavior, emotions, cognitions) through smartphones and other wearables. Such data is may help remove barriers to monitoring and identification of significant mental health risk, such as suicidal ideation (SI).

The present project aims to use psychological theory to design digital indices and test the possibility to exploit digital technology to predict SI and psychiatric hospital readmission (PHR). We investigate this in one of the most vulnerable populations, psychiatric patients post-discharge from inpatient psychiatry stay, in collaboration with the Centre for Acute Psychiatric Disorders (CAPD) at the Psychiatric University Hospital. This period is clinically challenging and afflicted with high rates of suicidality, mood deterioration, frequent readmissions and thus also economically costly. During their inpatient stay (current mean duration: 17 days), eligible CAPD patients will be offered to take part in the study and those who agree to participate will have two apps installed on their personal phones. They will be pinged 5 times per day for 4 consecutive weeks following discharge to report on emotion, cognition, and behavior (App 1) and passive sensor data will be continually collected (App 2). It is aimed to consecutively include 150 patients over 12 months to test whether (i) implementation of the digital protocol is feasible, and (ii) SI and PHR outcome can be predicted using passive sensor information and psychological indices.

It is planned to develop a predictive algorithm for SI and PHR using various learning algorithms (e.g. random forest, support vector machines, recurrent neural networks, feedforward neural networks). The data will be split into training and test data sets to assess how derived algorithms generalize to new data. The training dataset will also be split into subsets, where we will apply k-fold cross-validation. Digital information collected on the basis of psychological theory may, in the future and based on our results, help reach vulnerable individuals early and provide links to just-in-time, cost-effective interventions or establish prompt mental health service contact. The current effort may thus lead to saving lives as well as to significant economic impact by reducing inpatient treatment and days lost to inability.

Kowatsch, T., Volland, D., Shih, I., Rüegger, D., Künzler, F., Barata, F., Filler, A., Büchter, D., Brogle, B., Heldt, K., Gindrat, P., Farpour-Lambert, N., l’Allemand, D. (2017) Design and Evaluation of a Mobile Chat App for the Open Source Behavioral Health Intervention Platform MobileCoach, In: Maedche A., vom Brocke J., Hevner A. (eds) Designing the Digital Transformation. DESRIST 2017. Lecture Notes in Computer Science, vol 10243. Springer: Berlin; Germany, 485-489. (Paper-PDF | Poster-PDF | Slide-PDF | Screencast)

Wahle, F., L. Bollhalder, T. Kowatsch and E. Fleisch (2017) Toward 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 19 (5), e191. (PDF)

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

Filler, A., Kowatsch, T., Haug, S., Wahle, F., Staake, T. & Fleisch, E. (2015) MobileCoach: A Novel Open Source Platform for the Design of Evidence-based, Scalable and Low-Cost Behavioral Health Interventions – Overview and Preliminary Evaluation in the Public Health Context. Wireless Telecommunications Symposium 2015 (WTS 2015), New York, USA. ***Outstanding Paper Award & Best Graduate Student Paper Award*** PDF

Larsen, M. E., Nicholas, J., & Christensen, H. (2016) A systematic assessment of smartphone tools for suicide prevention PLoS One, 11(4), e0152285.