Im Hippocampus leben keine Nilpferde: Generation PSY macht wild auf Forscherkarriere
Mittwoch, 19. September 2018
11.00 – 12.30 Uhr
FB Informatik und Informationswissenschaft
Prof. Dr. Jakob Eyvind Bardram, Technical University of Denmark, Department of Applied Mathematics and Computer Science
Recently, an increasing amount of research has been trying to find ways to detect changes in depressive symptoms by monitoring the patient's behaviour using mobile and wearable technology. For example, a number of studies have shown a significant correlation between mobility patterns as tracked by GPS and the level of depression. Similarly, correlations between sleep patterns and depressive symptoms have been shown.
However, many of these studies have been non-conclusive and even contradictory.
A recent systematic review of more than 3,500 scientific publications has investigated whether these many studies agree on the relationship between depressive symptoms and patient behaviour collected from wearable devices. The study shows that some behaviour is strongly correlated to changes in depressive symptoms .
In this talk, I will go over the results from this study and discuss how -- more generally -- behavioural data can be collected from mobile sensing technology. This includes presenting the ongoing work in CACHET on building technology for this kind of research, including the work on open international data standards for mobile health (mHealth) .
1. Rohani DA, Faurholt-Jepsen M, Kessing LV, Bardram JE. Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review. JMIR Mhealth Uhealth 2018;6(8):e165. DOI: 10.2196/mhealth.9691. PMID: 30104184