Sensorbasiertes System zur Therapieunterstützung und Management von Depressionen

Sensor-based System for Therapy Support and Management of Depression

Teilvorhaben 13GWO162 A: Methoden und Algorithmen für die Analyse und Modellierung multimodaler mobiler Daten zum personalisierten Monitoring und zur Vorhersage depressiver Episoden

Project Part 13GWO162 A: Methods and algorithms for the analysis and modeling of multimodal mobile data for personalized monitoring and prediction of depressive episodes

Teilvorhaben 13GWO162 E: Verfahren zur hardwarenahen Erfassung und zum Ressourcen effizientes Handling mobiler Sensordaten

Project Part 13GWO162 E: Methods for the embedded acquisition and resource-efficient handling of mobile sensor data

BMBF_CMYK_Gef_L_300dpi_mit_FKZ_AE    Steady_final


Smartphones are an essential part of our daily life. The small devices connect us to the rest of the world and are equipped with several sensors that permanently collect data about the user. In addition, the spectrum of biosensors (activity trackers, wearables etc.) constantly increases. Thus, patients become collectors and owners of manifold, exponentially growing, longitudinal data. This development raises the question how this immense amount of biological and self-monitoring data may be utilized for the benefit of the patient and how it can be systematically integrated into care processes. Providing, of course, that data protection guidelines are met.


The main objective of the STEADY project is the development of a digital platform that allows patients with an affective disorder to easily collect biological and self-monitoring data (in terms of Ecological Momentary Assessment) for years to come. In a pilot study, the effect of this platform on the self-management of affective disorders and the efficiency of medical care will be investigated. To this end, the STEADY demonstrator will be used that will be developed within the course of the project. Affective disorders are especially well suited to evaluate the present approach due to their recurring and chronic course, their frequency of occurrence and their severity as well as the currently existing deficits and shortages in care. The STEADY system measures diverse biological parameters (like heart rate, blood pressure or skin conductance), environmental parameters (like light intensity or geo-localization), behavioral parameters (like sleep, speech, activity or device usages), and self-ratings concerning the current mood of the patient. With the help of specific processing methods and algorithms that will be developed within the project, these parameters will be analyzed and returned to the patient in an intuitive and educational manner. In addition, specific patterns will be identified for each individual patient via time series analyses that capture the severity and the nature of the affective symptoms. The gaol is to enhance the patient’s awareness for symptom changes (e.g., the beginning of a depressive episode) that is currently dominated by self-perception. If specific patterns in the biological data precede affective alterations and suggest causal factors, this information can be utilized for prophylactic interventions and an optimized self-management. Within the STEADY system, the patient shall remain the owner of his or her data at any point in time. However, he or she may share his or her data via specific interfaces with health insurances, physicians or research facilities to increase the efficiency of medical care. Besides these opportunities, the project also aims to systematically assess the risks and disadvantages of such self-measurements.


The extensive self-monitoring of patients will provoke profound changes in the health care system. The role of the patient will be strengthened considerably (empowerment), because self-management is supported and optimized. In addition, the patient becomes the producer and owner of his or her own data. Since specific data patterns are identified for each individual patient that provide important information about an improved management of the disorder, longitudinal, patient-centered analyses will lead to a truly personalized medicine.


adesso, University of Leipzig, the German Research Network on Depression and Suicidality