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Development of an Emotion-Sensitive mHealth Approach for Mood-State Recognition in Bipolar Disorder

Daus, Henning; Bloecher, Timon; Egeler, Ronny; De Klerk, Richard; Stork, Wilhelm 1; Backenstrass, Matthias
1 Institut für Technik der Informationsverarbeitung (ITIV), Karlsruher Institut für Technologie (KIT)


Internet- and mobile-based approaches have become increasingly significant to psychological research in the field of bipolar disorders. While research suggests that emotional aspects of bipolar disorders are substantially related to the social and global functioning or the suicidality of patients, these aspects have so far not sufficiently been considered within the context of mobile-based disease management approaches. As a multiprofessional research team, we have developed a new and emotion-sensitive assistance system, which we have adapted to the needs of patients with bipolar disorder. Next to the analysis of self-assessments, third-party assessments, and sensor data, the new assistance system analyzes audio and video data of these patients regarding their emotional content or the presence of emotional cues. In this viewpoint, we describe the theoretical and technological basis of our emotion-sensitive approach and do not present empirical data or a proof of concept. To our knowledge, the new assistance system incorporates the first mobile-based approach to analyze emotional expressions of patients with bipolar disorder. As a next step, the validity and feasibility of our emotion-sensitive approach must be evaluated. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000124650
Veröffentlicht am 14.10.2020
DOI: 10.2196/14267
Web of Science
Zitationen: 3
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Technik der Informationsverarbeitung (ITIV)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2368-7959
KITopen-ID: 1000124650
Erschienen in JMIR mental health
Verlag JMIR Publications
Band 7
Heft 7
Seiten Art.-Nr.: e14267
Vorab online veröffentlicht am 03.07.2020
Schlagwörter bipolar disorder; mood recognition; emotion recognition; monitoring; mobile apps; assistance system; mHealth
Nachgewiesen in Dimensions
Web of Science
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