Sally Sophie Kindermann*, German, Lueneburg, Germany Kai Kossow, German, Lueneburg, Germany Sharlina Spiering, German, Lueneburg, Germany Andreas Maier, German, Lueneburg, Germany Jörn Moock, German, Lueneburg, Germany Guido Barbian, German, Lueneburg, Germany Wulf Roessler, German, Lueneburg, Germany Track: Research Presentation Topic: Mobile & Tablet Health Applications Presentation Type: Poster presentation Submission Type: Single Presentation Last modified: 2013-09-25

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Background and Objectives: Bipolar affective disorder is characterized by extreme mood swings varying from manic to depressive. During both phases there is a greater risk of suicide attempts for patients. Timely detection of behavioral modifications could help to intervene early and therefore prevent suicidal tendencies. Mobile sensor technology in modern smart phones provides a wide variety of data and could therefore deliver a comprehensive picture of the user’s habits and behaviors. These features can be optimally used for monitoring activity and behavior patterns of patients with bipolar disorder. Nowadays, many people are already familiar with the latest mobile technology and use internet-enabled mobile devices, so that a good acceptance and integration of smart phones as measuring instruments in patients’ daily lives is expected. For this reason we developed “SIMBA” (Social Information Monitoring for Patients with Bipolar Affective Disorder), a new sensor-based application (app) for Android smart phones that acquires movement data and information on social behavior and interaction. Our aim is to investigate if SIMBA is useful in terms of detecting indicators for upcoming mood changes and creating rest-activity-profiles of patients with bipolar disorder for an earlier prediction of phase transitions.
Methods: In the first stage, we conduct a feasibility study under medical supervision with patients of a psychiatric outpatient clinic and a healthy control group for a period of 12 months. In addition to the use of SIMBA, self-reports completed by participants on the smart phone and regular clinical ratings of the patients’ symptoms are collected in order to validate sensor data. Sensor data will be modeled to make it accessible for an evaluation. Statistical analyses of all data will be performed by univariate and multivariate methods for small sample sizes.
Discussion and Outlook: By detecting mood changes in patients with bipolar disorder at an early stage, medical practitioners and the patients themselves can be alerted in advance. Based on this, we will discuss further potential benefits and forward-looking implementation possibilities of SIMBA for patients, practitioners and the healthcare system. In the future, SIMBA - an innovative measuring method- could be an essential early warning system for upcoming phase transitions. Simultaneously to occurring mood changes and its measuring, medical professionals and patients themselves could be automatically informed by the system. SIMBA could therefore enhance knowledge about the course of disease that can lead to an optimized clinical treatment and support the individual clinical management of patients.