Smart Measures of Social Isolation of Parkinson's Disease

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  • Competition Funded Project (Students Worldwide)
This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.

Project description

Parkinson's disease (PD) is a neurodegenerative disease affecting motor, cognitive, and emotional functions. Monitoring and managing the symptoms of this multifaceted progressive disease is extremely challenging as it is limited by snap-shot clinical measures or patient self-reports approximately once every six months. Critical treatment decisions are made assuming that these snap-shots reflect a person's typical day-to-day functioning.

Yet, PD symptoms are highly idiosyncratic and fluctuate hourly and daily, and can be difficult for a patient to convey to their clinician. Building on initial work carried out in our multidisciplinary team, this PhD project will begin the development a smartphone application to regularly monitor social isolation using combinatorial measures captured as a Smart phone application. This application and the predictive model to be developed will enable a better individualised measure of PD progression in terms of social isolation. Our hypothesis is that this will be more effective than standard methods.

This project is to investigate Social Isolation in relation to Parkinson's Disease. We will develop Smart mobile technology to gauge Parkinson's progression in terms of social isolation, longitudinally and 'in the wild'. And relate this measure to the Unified Parkinson's Disease Rating Scale (Non-motor aspects of Experiences of Daily Living). In this way, our work will be ecologically valid and grounded in existing practice. We take a combinatorial approach using image analysis of facial expressions, auditory analysis of vocalisations, and frequency and repetition of encountered Bluetooth signals. The science and novelty for this project is in the combined delivery of pre-existing image, auditory, and Bluetooth algorithms and the building of a statistically predictive model to assist health care professionals in understand the onset of social isolation, so that interventions can be made to address this onset earlier than usual.

This project will focus on combining the different domains of data, developing the software and algorithms to do this. A prototype app will be deployed for use by a group of people with Parkinson's (N=6-10) for 12-18 months, which will be refined via an interactive process with input from the supervisors, the wider collaborative team, representatives with Parkinson's and clinicians. The views of the participants will be obtained via direct questions on their smartphone and a focus group(s) carried out at the end.

Potential Outcomes/Impact: This will ensure effective patient-to-clinician communication, individualised cost-effective treatment, and enhance the patient's physical and mental wellbeing.

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