Multi-scale markers of circadian rhythm changes for monitoring of mental health

Almost one quarter of adults currently experience some form of mental health disorder in the UK, costing the healthcare system an estimated £77 billion each year. However, there exists very little objective or real-time monitoring of sufferers of mental health issues. This pilot project will investigate the development of a novel data fusion framework that will be suitable for combining many observations of a patient's behaviour to allow accurate mental health monitoring in any environment. Recent studies have shown that certain types of physical behaviour, daily cycles (circadian rhythms) and social networking activity can be indicative of an individual's state of mental health. However, recording the necessary data to make a diagnosis is difficult, both due to the nature of the health issues and because of the instrumentation needed. Recent developments in commercially available equipment (including smart phones) mean that we now have the opportunity to cheaply and routinely record human behaviour as well as daily patterns of physiology (such as sleep and cardiac activity). By then applying advanced pattern recognition and data fusion techniques, we intend to provide daily feedback of mental well-being to both the patient and care providers. This could facilitate early interventions in deteriorating individuals, thereby lowering costs of health care and reducing the severity of the illness. We also intend to begin to answer the more fundamental question about how circadian rhythms change as mental health deteriorates.
The developed of a user-friendly and user-controlled monitoring system, together with a suite of suitable algorithms, will be an important step towards a larger integration of the ever increasing multi-dimensional biometric data we are beginning to collect. This includes signals such as location, body temperature, speech patterns and social interaction behaviours. The potential to fuse data from many different sensors, and many different algorithms, will provide a platform for intelligible interpretation of the vast quantities of data that are beginning to confront researchers in biomedical applications. It will also help to improve the accuracy of monitoring systems and provide the doctor with more objective assessments of patient behaviour, which could lead to more accurate and timely diagnoses.

To maximise the impact of this project we will work closely with our clinical collaborators (Prof. Guy Goodwin at the Warneford Hospital, Oxford, UK), who specialize in mental health disorders. We also have begun to collaborate with Prof Russell Foster and his Circadian and Visual Neuroscience group in the Department of Ophthalmology at the University of Oxford, to help understand the mechanistic pathways involved in changes in mental health. This grant will help to consolidate our collaboration with these groups and provide a dedicated full-time postdoctoral researcher to work closely with all three teams. The Centre for Doctoral Training in Healthcare Innovation, of which the PI is the Associate Director, will also provide a catchment pool from which to recruit high quality, fully funded students to work with our team.

We will engage the wider clinical community and the NHS about the impact of these technologies in improving health care provision and reducing costs in the NHS and how they can be widely integrated into clinical workflow. We are currently in discussion with Prof. Digby Quested and Prof. John Geddes (at the Department of Psychiatry in Oxford) concerning clinical trials and mood monitoring programmes such as 'True Colours'. The research detailed in this proposal will have direct applicability to such systems and could be integrated into conventional clinical pathways through our clinical partners at the Warneford Hospital.

The dissemination of the key research findings will be to leading international journals such as IEEE Transactions on Biomedical Engineering, Annals of Biomedical Engineering, IOP Physiological Measurement, and Journal of the Royal Society Interface, and to international conferences such as the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) and IEEE Engineering in Medicine and Biology.

The research is likely to have direct commercial applicability on a broad level, ranging from close commercial partners to the sleep and health monitoring industry in general. There has been significant direct industrial interest in this research. Potential partners, who have already shared data and signed confidentiality agreements, include Proteus Biomed, who manufacture monitoring patches and edible digital tags for medication, which we intend to incorporate into our monitoring process. We will continue to work with them and explore the potential for commercialisation of the developed algorithms. The University of Oxford has a dedicated technology transfer company, ISIS Innovation, which has a long experience in the commercialisation of intellectual property including patenting and licensing of research findings, and the foundation of spin-out companies. We will involve ISIS Innovation in developing an appropriate IP protection strategy for the algorithms developed. The results of the studies may also have an impact on government policies to do with management of mental health, since the proposed technology will allow us to assess the response of an individual to a particular drug or therapy.

Finally, the proposed research complements, but could also augment the recently funded Wellcome Trust 'Sleep and Circadian Neuroscience Institute' at the University of Oxford. We expect that the proposed research will translate to the ongoing research activities in this Institute, and further strengthen the efforts. Our long term funding goal at the end of this 1-year grant is to extend the findings of this work, drawing on additional funding and resources from the BRC, Wellcome Trust and commercial partners.

We expect to be able to deliver a prototype into the NHS to begin trials by the end of the 1-year grant and begin commercial partnerships in the following year, with an expectation of scaling to a randomised clinical trial over the following three years.