Dr Huai Leng (Jessica) Pisaniello (nee Yong) 

Recipient: Dr Huai Leng (Jessica) Pisaniello (nee Yong)
Intended department: The Arthritis Research UK Centre for Epidemiology, Centre for Musculoskeletal Research, School of Biological Sciences, Faculty of Biology, Medicine Health- The University of Manchester

 

Project:

 

The Role of Mobile Health application in real time capture of self-reported symptoms and longitudinal activity, and its feasibility in patient-focused remote monitoring in musculoskeletal disorders.

 

Mobile health (mHealth) implementation in clinical care and research has great potential to capture real-time data longitudinally, when compared to traditional epidemiological methods. At present, self-reported symptoms or experience can be captured conveniently and stored digitally within mHealth technologies (e.g. smartphone, smartwatch) and mobile applications (apps). These data, although structurally heterogeneous, are rich sources of health-related information that could reflect a patient’s health journey.

In people with arthritis and other musculoskeletal diseases, tracking symptoms using mobile health apps can greatly improve our understanding of how these diseases affect people and how symptoms can change from day to day. The burden of chronic pain in arthritis is huge. We know that pain remains one of the most important, and yet, a challenging symptom to manage and treat in patients with musculoskeletal diseases. Chronic pain is a dynamic process and can be unpredictable, particularly for those with underlying inflammatory musculoskeletal diseases and concomitant chronic pain condition. Patients are often asked to summarise their overall pain severity since the last clinic visit, which could be weeks to months. This averaged pain severity may not necessarily reflect the overall pain severity over time and in particular, the fluctuation of pain over time. Under- or over-estimation of pain experience can have a huge impact on treatment decision, especially with biologic prescription and analgesic choice. Longitudinal capture of patient’s pain symptoms will help the clinicians in clarifying the definition of disease ‘flare’ and in guiding the trajectory of pain management. From the patients’ perspectives, being able to have better understanding of their pain patterns and levels of pain variability will allow greater sense of control in managing their pain. My research aims to understand how mobile health is implemented in collecting patient- generated health data, as well as how these temporally rich data are analysed. Specifically, my research aims to examine the long-term and short-term day-to-day pain variability over time in a chronic pain study cohort.

As the recipient of this prestigious overseas fellowship grant year 2018, I had the opportunity to work at the Centre for Epidemiology Versus Arthritis in Manchester, UK under the supervision of Professor William Dixon. This 15-month fellowship in Manchester has set off to an exceptionally invaluable foundation for my first year of PhD. I was working with

Professor Dixon and the Cloudy team on a large, UK nationwide, prospective mobile health study called Cloudy with a Chance of Pain. This study, which was conducted in January 2016, aimed to examine the association between weather and pain in people living with chronic pain. Although I was not involved in the setup of this project, I came to learn from many in the Cloudy team about the success of this study, in particular the patient and public involvement and recruitment strategies.

The process of preparing and analysing such a large data set has been a huge undertaking, only made possible with expertise input from many researchers with different background (rheumatologists, epidemiologists (national and international), meteorologist, statisticians, mathematician, PhD student with health informatics background and project manager). As a newly trained rheumatologist, doing this fellowship has certainly honed my skills and knowledge in applied epidemiology and statistics in musculoskeletal research.

In Manchester, as a clinical research fellow in the department, I managed to attend various in- house departmental courses, lectures and seminars. These include a 6-month course on epidemiology, genetic epidemiology and statistics in the first half of year, a 3-month course on

‘Statistical Modelling with Stata’ by Dr Mark Lunt, weekly departmental seminars, monthly applied epidemiology sessions, CfE scientific meetings and journal club. I was able to attend the first Digital Epidemiology Summer School course in July 2018, led by Professor Dixon. As a visiting postgraduate student with the University of Manchester, I was able to attend various postgraduate student-related lectures and courses. For my research, I also took the opportunity to learn programming using R for data preparation and analysis and this was made possible with great support and mentorship from Belay Birlie Yimer, a statistician colleague and Anna Beukenhorst, a PhD student with health informatics background. The IT service at the University of Manchester provides programming courses for the staff and students, which I attended to further improve my programming skills in R and Python.

For my research, using the Cloudy with a Chance of Pain dataset, I have the opportunity to examine long-term and short-term day-to-day pain variability in those with musculoskeletal diseases and to examine the driving factors behind the pain variability. First, I descriptively analysed the population-averaged pain severity and other pain symptoms (e.g. pain impact, mood, fatigue, sleep, waking up tired, morning stiffness, physical activity and wellbeing) over one-month period across different rheumatological conditions such as rheumatoid arthritis (RA), osteoarthritis (OA), spondyloarthropathy (SpA) and fibromyalgia (FM). I also performed similar analysis for participants with comorbid FM in RA, OA and SpA. Secondly, I analysed individual-level pain trajectory in terms of daily pain variability over time and change in pain state over time using different modelling approaches. These analyses are currently in progress and the final work will be disseminated in the form of publications and research presentations at rheumatology conferences. I was able to present the preliminary descriptive analyses of my work at the departmental seminar session in June 2019.

In addition, I am currently conducting a systematic literature review on pain trajectory and pain variability in musculoskeletal diseases and I intend to submit this work in the form of publication. All current and future output from this research will largely form my PhD thesis by publication.

I would like to take this opportunity to acknowledge and to thank Australian Rheumatology Association for this fellowship grant funding, and Arthritis Australia for the opportunity to apply for this fellowship within the National Research Program grants. I would also like to thank Professor Catherine Hill, Dr Samuel Whittle and Dr Rachel Black for their encouragement and support during the fellowship application process. This fellowship has been a rewarding and empowering lifetime experience for me as an early researcher, and I can never thank Professor William Dixon enough for his undivided supervision and mentorship, as well as the Cloudy team and colleagues in the department in Manchester.