Background
Visceral pain constitutes a third of all chronic pain, disproportionately affects women, and is disabling in 5% of the UK population. Moreover, it may frequently trigger unpredictable episodes of pain "flares" that may need hospital admission. Hitherto, visceral pain has not been systematically studied in terms of how pain connects to the underlying visceral disease, how it relates to other health problems, and how it affects people's physical and mental well- being.
In collaboration with colleagues from the University of Cambridge and UCL, we have secured a £4.1m grant (ADVANTAGE project, 2021 – 2026) to collect a UK-wide database of visceral pain patients to address the aforementioned key questions. In parallel, we have been working intensely at the University of Edinburgh on visceral pain, focusing specifically on building a database on endometriosis [1] which includes daily longitudinal self-reports (pain, physical and mental health, quality of life) and raw data from wearable sensors (actigraphy). Surprisingly, visceral pain conditions (including endometriosis) have not been studied in depth to understand day-to-day variability of pain symptoms and their effect on various physical activity and sleep variability patterns, also considering periodic lab test results.
Using standardised patient reported outcome measures (PROMs) can provide useful insights into patients’ self-perception of pain and diverse symptoms, and we have previously demonstrated >80% longitudinal adherence on daily self-reports over a full year across different cohorts [2].
The proliferation of new digital health technologies, including wearable sensors, has been gaining increasing momentum, and we have recently reviewed their use on top of stand-alone smartphone applications and PROMs [3]. The use of digital health technologies can provide additional continuous and passively collected data, which can be mined to obtain new insights complementing clinical reports, lab tests, and PROMs. Specifically, we have developed new signal processing algorithms towards assessing physical activity, sleep, and circadian rhythm variability to process actigraphy data, and demonstrated new insights that can be gained to complement and inform clinical assessments, for example in post-traumatic stress disorders [4]. We believe the framework we have developed combining self-reports and data from wearables may be useful and lead to new insights to other chronic conditions including visceral pain.
Aims
The recruited student will be working on developing novel signal processing algorithms and information fusion algorithms to mine the multimodal data collected (PROMs, lab-based results and clinical reports, data from wearables), and extending statistical machine learning algorithms to develop a robust user-friendly clinical decision support tool which could be deployed across visceral pain conditions. The primary testbed will be on endometriosis building on local expertise (AH and PS), with a view to also diversifying and applying the developed methodology across the different visceral pain conditions collected in ADVANTAGE.
We envisage this PhD project will capitalise on the rich data resources that are currently being collected and will provide novel insights across different visceral pain conditions such as better understanding triggers of pain flares, day-to-day pain variability patterns, and potentially enable patient stratification towards facilitating a more personalized treatment (precision medicine).
At the end of this PhD project, the student will have a unique and transferable skillset in signal processing, mining actigraphy data, and developing statistical machine learning methods, which is desirable in both academia and industry.
Training outcomes
-Practical understanding of the problems at the interface of clinical practice and data analytics, including the language barrier with niche terminology on both ends
-Developing expertise in actigraphy, time-series analysis, signal processing, information fusion, and statistical machine learning to tackle large-scale challenging problems
-Programming skills: transforming algorithmic concepts to software tools, and developing interfaces which can be used by experts and non-experts to facilitate data analysis.
Q&A Session
If you have any questions regarding this project, you are invited to attend a Q&A session hosted by the Supervisor(s) on 9th December at 10am via Microsoft Teams. Click here to join the meeting. If you get an error message when accessing the link, please try a different device.
About the Programme
This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.
All applications should be made via the University of Edinburgh, irrespective of project location. For those applying to a University of Glasgow project, your application along with any supporting documents will be shared with University of Glasgow.
Please note, you must apply to one of the projects and you must contact the primary supervisor prior to making your application. Additional information on the application process is available from the following link:
https://www.ed.ac.uk/usher/precision-medicine/app-process-eligibility-criteria
For more information about Precision Medicine visit:
http://www.ed.ac.uk/usher/precision-medicine
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