Dr J McBeth, Prof W Dixon, Dr Sabine Van der Veer
No more applications being accepted
Competition Funded PhD Project (European/UK Students Only)
About the Project
Background
Globally, chronic pain is the leading contributor to disability. Currently assessments of pain in clinical settings and in pharmacological and non-pharmacological treatment trials rely on subjective reports requiring patients to recall and average their pain over hours, days, weeks and even months. This approach is susceptible to recall bias and does not capture the day to day pain variability that patients report. The increased use of wearable devices and the development of analysis methods to understand the large volumes of data collected provide unique opportunities to develop digital biomarkers of chronic pain.
Aims
This project will develop digital biomarkers of chronic pain that capture pain, pain severity and pain variability in real time. The specific objective(s) of the PhD will be agreed with the successful candidate but will likely include using data from completed and ongoing studies to:
Define a digital pain biomarker: Which combination of digital markers (sleep, physical activity, sedentary behaviour, and raw accelerometer data) can be used to evaluate pain? Can the digital pain biomarker be improved upon by the addition of other markers such as heart rate variability? What effect do individual (e.g. cognitive functioning) and contextual (e.g. economics) factors have on the digital pain biomarker?
Test validity and reliability: Using best practice in outcome measurement establish the reliability and validity of the digital pain biomarker.
Test sensitivity to change: How does the digital pain biomarker perform in clinical studies?
Academic background of candidates:
• Candidates are expected to hold (or be due to obtain) a minimum upper-second (or equivalent) class undergraduate degree in a clinical discipline, statistics, health informatics, psychology or a relevant subject, and will have strong statistical skills.
• A Masters degree in a related subject and/or relevant research experience is desirable.
• Experience of digital health studies or patient-generated health data is desirable.
For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (https://www.bmh.manchester.ac.uk/study/research/apply/). Informal enquiries may be made directly to the primary supervisor.
Funding Notes
This project is funded by Arthritis Research UK Centre for Epidemiology with RCUK stipend. Starting Sept 2019 for 3 years. If you are interested please make direct contact with the Supervisor to discuss the project . You MUST also submit an online application form - choose PhD Epidemiology.
As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.
References
Salathe M, Bengtsson L, Bodnar TJ, Brewer DD, Brownstein JS, Buckee C, et al. Digital epidemiology. PLoS computational biology. 2012;8(7):e1002616.
Druce KL, Cordingley L, Short V, et al. Quality of life, sleep and rheumatoid arthritis (QUASAR): a protocol for a prospective UK mHealth study to investigate the relationship between sleep and quality of life in adults with rheumatoid arthritis. BMJ Open. 2018;8(1):e018752. Published 2018 Jan 26. doi:10.1136/bmjopen-2017-018752
Druce KL, McBeth J, van der Veer SN, Selby DA, Vidgen B, Georgatzis K, Hellman B, Lakshminarayana R, Chowdhury A, Schultz DM, Sanders C, Sergeant JC, Dixon WG. Recruitment and Ongoing Engagement in a UK Smartphone Study Examining the Association between Weather and Pain: Cohort Study. JMIR Mhealth Uhealth. 2017 Nov 1;5(11):e168.
Beukenhorst AL, Parkes MJ, Cook L, Barnard R, van der Veer SN, Little MA, Howells K, Sanders C, Sergeant JC, O'Neill TW, McBeth J, Dixon WG. Protocol for a Feasibility Study Using Consumer Smartwatches to Assess Symptoms and Sensor Data: the Knee OsteoArthritis, Linking Activity and Pain Study
JMIR Research Protocols. (forthcoming/in press) DOI: 10.2196/10238
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