Quantifying bipolar disorder for technology-assisted self-management

Amor, J. D., Svobodova, Martina ORCID: https://orcid.org/0000-0001-7949-4039, Jones, I. and James, C. J. 2015. Quantifying bipolar disorder for technology-assisted self-management. Presented at: World Congress on Medical Physics and Biomedical Engineering, Toronto, ON, Canada, 7-12 June 2015. Published in: Jaffray, D. A. ed. World Congress on Medical Physics and Biomedical Engineering, June 7-12, 2015, Toronto, Canada. IFMBE Proceedings. , vol.51 Springer, pp. 1397-1400. 10.1007/978-3-319-19387-8_340

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Abstract

Bipolar Disorder (BD) is a serious mental health condition that is characterized by recurring affective episodes, interspersed with periods of remission. Current treatment for BD focuses on pharmacological and therapeutic techniques to control the condition. In addition to this, a significant number of people with BD adopt self-management techniques in order to help maintain a stable life pattern and control their condition. There are two significant drawbacks to current selfmanagement in that current systems tend to be paper based and reliant on user self-insight, which is frequently lost in the run up to an affective episode. These two shortcomings can be addressed with a technology-enabled solution. Through the use of specific sensors and an electronic mood diary, the important indicators of relapse in BD can be monitored and users alerted to potential indicators of relapse in a timely manner so that they can take appropriate action. This paper presents work that has been carried out in the development of the Auto-Motive system on the quantification of indicator metrics in BD for use in a technology-enabled selfmanagement system. We present the methodology and results from a series of focus groups that have been carried out with users and professionals to identify the important indicators in BD and the way in which the can be quantified for use in a decision support system. Results from the focus groups are in line with the literature and professional insight and point to sleep (hours of sleep and sleep quality), physical activity, time at home and at work and medication compliance as being particularly important indicators to quantify and use in the Auto-Motive system.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Medicine
Publisher:	Springer
ISSN:	1680-0737
Last Modified:	02 Nov 2022 11:18
URI:	https://orca.cardiff.ac.uk/id/eprint/101523

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