The purpose of this PhD is to seek to find an objective way of measuring trends in mood over a period of time, in such a way that it doesn’t interfere with daily life or influence the measure itself. The hypothesis is that by prototyping novel forms of wearable technology, to gather and classify data about posture and movement from the upper body, that mood may be predicted from this data. Laban Movement Analysis will form the basis of the interpretation of posture and movement data into mood data. Such a technology could present a viable alternative to mood measurement by questionnaire, potentially improving the outcomes for the long-term management of mental health conditions by helping the wearer to reflect upon trends in their mood and consider how they are affected by different interventions.
The independent Mental Health Taskforce to the NHS, reported in February 2016 that one in four adults will experience a diagnosable mental health condition every year. This increasing pressure on mental health services means that many people do not seek or are unable to access professional support. Those people that do qualify for help may face long waiting lists for treatment, and once in treatment, still yet, face uncertainty in relation to the efficacy of the treatment they receive. Mental health conditions are most commonly diagnosed and monitored using questionnaires administered by mental health professionals. The use of questionnaires for this purpose is popular as a form of measurement-based care, which is at the core of evidence-based mental health practice. Unfortunately, despite being best practice, questionnaires still have a number of inherent limitations. Questionnaires can only provide a snapshot view each time they are administered, they are subjective, and they rely on accurate self-report.
The literature search for this PhD will encompass three broad themes: understanding the mind-body connection, the rise of the quantified-self movement and designing wearable technology. The recent rise in the quantified-self using everyday wearables such as fitness trackers and smart watches, suggests that given appropriate functionality and good design, then the time might be right for acceptance of wearable mood measurement technology.
This author is supported by the Horizon Centre for Doctoral Training at the University of Nottingham (RCUK Grant No. EP/L015463/1) and Nottingham Biomedical Research Centre (Mental Health Theme).