CDT student Shazmin Majid passes viva

CDT student Shazmin Majid passes viva

We proudly announce that Horizon CDT has reached its 90th student to successfully pass their PhD viva subject to minor corrections.

Supervised by Dr Stuart Reeves, Professor Richard Morriss and Dr Grazziela Figueredo, Shazmin Majid (2018) successfully defended their PhD thesis entitled ‘Integrating the use of sensing technology to detect early warning signs of relapse for those with lived experience of bipolar disorder’.

Shaz has accepted a position at EPAM Systems in London as Head of User Research and Insights (UK).

Congratulations all round to Shaz!




In 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, currently this technology is being developed without reference to clinical guidelines, best practice principles and with lack of high-quality research evidence. This doctoral research drew upon participatory involvement approaches from Human-Computer Interaction and Patient and Public Involvement to highly involve the user to develop a self-tracking tool which uses passive and active sensing 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 utility of the tool 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 highly accepted by users, with constructive suggestions for improvement.

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