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Longitudinal monitoring of Parkinson’s disease symptom progression using patient reported outcome measures, clinical assessments, and non-invasive sensors

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

About This PhD Project

Project Description

Accurate quantification of Parkinson’s Disease (PD) symptom severity is critical to assist neurologists optimize treatment and mitigate PD symptoms. In practice, clinical assessments are sparse (typically twice a year) under-representing the true time scale of PD fluctuations. Moreover, clinicians reach conclusions on the short period the patient physically visits the clinic, relying on their patients’ retrospective description of symptom severity. Ultimately, this may lead to a fragmented picture of daily aspects in understanding the extent of PD symptoms, which leads to suboptimal treatment, whilst these is no monitoring between intervening patient visits.
We have previously shown that speech can be used to replicate the Unified Parkinson’s Disease Rating Scale (UPDRS) [1], which provides a holistic clinical impression of symptom severity, and also to assess rehabilitation [2]. Similarly, we demonstrated Patient Reported Outcome Measures (PROMs) provide a reliable additional dimensionality in understanding daily symptom severity in mental disorders cohorts, which the participants found useful and engaging [3]. The increasing availability of sensors in the form of smartwatches and smartphones may assist in quantifying symptom severity objectively, for example to quantify depression symptoms [4], and also to provide links between sleep and symptom severity [5].
The aim of this PhD project is to analyse longitudinal PROMs’ variability, and develop functional relationships between PROMs, clinical assessments (UPDRS), and sensor-based measurements. The student will explore links between actigraphy, speech, and sleep with clinical instruments (PROMs and UPDRS), with the ultimate goal of providing better longitudinal quantitative insights into PD symptom progression.
How is the project collaborative?
The supervisors bring a diverse set of skills and will be supporting the project from both a clinical perspective (Esther, Gordon), and also from a data analytics perspective (Thanasis). We plan to work collaboratively exchanging knowledge and expertise throughout the project from the data collection phase, to data analysis and interpretation of key findings. Thanasis will advise on the equipment and data collection methodology in close collaboration with Esther and Gordon who will engage with the local PD communities to collect data. Similarly, Esther and Gordon will be continuously updated on the data analysis progress, and will be assisting in posing key questions that are useful from a practitioner’s perspective, and also in interpreting findings. Physical visits to Dundee and Edinburgh by the supervisors and the recruited student will help ensure the student gets clinical input (Esther, Gordon) to develop a solid background in this domain and keep on coming with interesting new clinical questions, and also to master the analytical techniques required to analyse the data (Thanasis). We aim to keep this process iterative as we plan to use this project as a baseline for a longer-term fruitful collaboration.
We strongly believe that this interdisciplinary approach and the synergies it creates will create a unique environment to train a non-clinical PhD student, and also has the potential to lead to key breakthroughs and ultimately lead to better clinical outcomes which will benefit patients.

To apply please send a cover letter, curriculum vitae and two references to:


[1] A. Tsanas, M.A. Little, P.E. McSharry, L.O. Ramig: Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson’s disease symptom severity, Journal of the Royal Society Interface, Vol. 8, pp. 842-855, 2011
[2] A. Tsanas, M.A. Little, C. Fox, L.O. Ramig: Objective automatic assessment of rehabilitative speech treatment in Parkinson’s disease, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 22, pp. 181-190, 2014
[3] A. Tsanas, K.E.A. Saunders, A.C. Bilderbeck, N. Palmius, M. Osipov, G.D. Clifford, G.M. Goodwin, M. De Vos: Daily longitudinal self-monitoring of mood variability in bipolar disorder and borderline personality disorder, Journal of Affective Disorders, Vol. 205, pp. 225-233, 2016
[4] N. Palmius, A. Tsanas, K.E.A. Saunders, A.C. Bilderbeck, J.R. Geddes, G.M. Goodwin, M. De Vos: Detecting bipolar depression from geographic location data, IEEE Transactions on Biomedical Engineering, Vol. 64, No. 8, pp. 1761-1771, 2017
[5] B. Sheaves, K. Porcheret, A. Tsanas, C. Espie, R. Foster, D. Freeman, P.J. Harrison, K. Wulff, G.M. Goodwin: Insomnia, nightmares, and chronotype as markers of risk for severe mental illness: results from a student population, Sleep, Vol. 39(1), pp. 173-181, 2016

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