York University researchers have collaborated on a groundbreaking study that will help shape the future of pain management.
Prediction models that identify and forecast changes in pain experiences of users of the pain management app Manage My Pain were published in the industry-leading Journal of Medical Internet Research (JMIR) and are the first to define pain volatility in this manner.
Until now, no research that attempts to define pain volatility in this way, let alone predict it, has been published. ManagingLife, the app’s developer, collaborated with experts in pain, mental health and data science to apply data science techniques that are unprecedented in health care and pain management.
The analysis involved three groups of York U researchers, including Professor and Canada Research Chair in Health Psychology Joel Katz (Human Pain Mechanisms Lab); Professor and York Research Chair (Tier II) Jane Heffernan (Centre for Disease Modelling, Mathematics and Statistics); and Professor Paul Ritvo (Health Behaviour Change Lab).
Data for this analysis came from users of the Manage My Pain app, created for the millions of people with chronic pain who want to better understand their conditions and are looking to better communicate with their doctors. Researchers used data mining techniques to define pain volatility – a new measure to describe how the severity of pain changes over time.
Machine learning techniques were then used to predict users’ pain volatility levels six months in the future, based on the information entered into the app in the first month. Prior to this research, the data required to conduct such an analysis have been previously limited to pain outcomes collected by traditional methods of assessing pain, such as paper-based questionnaires.
The researchers believe this innovative, data-driven approach to analysis could help shape future treatments of pain.
“With the significant increase in data available and by applying machine learning methods, we can better understand how pain experiences change and better understand how that might evolve in the future,” said Katz, one of the lead authors of the study. “This study may help to identify risk factors for heightened volatility and, therefore, to potentially prevent the development of high pain volatility through effective interventions.”
The study used data from 782 users who collectively recorded more than 329,000 data points. The study reported that its model predicts whether users experience low or high levels of pain volatility six months in the future, with approximately 70 per cent accuracy.
The study, titled “Defining and Predicting Pain Volatility in Users of the Manage My Pain App: Analysis Using Data Mining and Machine Learning Methods,” was published on Nov. 15.
Read about previous studies on the Manage My Pain app: York researchers map patterns of user engagement for Manage My Pain app; York U researchers to look for patterns in patient data from ManagingLife’s pain diary app.
ManagingLife is a privately held corporation based in Toronto that has developed a digital solution for pain management that combines patient self-management, remote monitoring and analytics to help chronic pain sufferers and practitioners learn more about their condition and better communicate with each other. With its award-winning app Manage My Pain, ManagingLife works with disability carriers, health plans, pain clinics, and clinical trials to help health-care professionals to better measure and manage their patients’ pain and medications.