Integrating the use of sensing technology to detect early warning signs of relapse for those with lived experience of bipolar disorderTools Majid, Shazmin (2023) Integrating the use of sensing technology to detect early warning signs of relapse for those with lived experience of bipolar disorder. PhD thesis, University of Nottingham.
AbstractIn the world of pervasive mobile technology, it is inevitable that novel technological solutions have been leveraged to understand symptoms of bipolar disorder (BD). Increasingly, these technologies use a combination of passive and active sensing techniques. BD is a complex condition where the sense of self is consistently in “flux”. There are questions of how much this sense of self is currently reflected in self-tracking technology for BD. Upon investigating this, we found that user involvement in self-tracking technology is variable, where high-level involvement is seldom seen in the literature. Furthermore, this technology is being developed without reference to clinical guidelines, best practice principles and with a lack of high-quality research evidence. Using a combination of participatory design methods from healthcare and human-computer interaction, the overall aim of this doctoral research is to bring the user’s personal and lived experience of BD to the forefront in order to design and assess a mobile self-tracking tool which uses passive and active sensing techniques to understand early warning signs (EWS) in BD – a clinically validated framework in understanding relapse. The research was organised into three work packages: Concept Generation and Ideation, Prototype Design and Deployment and Evaluation. In the first work package, the everyday practices of self-tracking were explored in two user-led workshops (n=18 users). The findings revealed a high degree of complexity and individual variability in self-tracking where over 50 methods of tracking were described. In the next phase, the findings were built upon using follow-up interviews (n=10) to guide the redesign of a self-tracking tool to be closely aligned to users’ needs and preferences. During the Evaluation phase, the final prototype was enrolled for a 6-month beta test in a real-world context with eight users. The findings revealed that the tool was useful in understanding EWS from both a subjective (i.e., user led) and statistical viewpoint. Frequencies in the passive data were connected to EWS via the active data, however, there were inconsistencies in how users interpreted the data compared to our statistical analysis - proving that “no one size fits all” in technology for BD. Overall, the tool was demonstrated good usability and acceptability from users, with constructive suggestions for improvement.
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