Project 458590

After surgery, some patients experience pain that lasts for a long time, known as chronic post-surgical pain (CPSP). People who have this type of pain often report that it negatively affects aspects of their life, like their mood and ability to work. There are some ways to predict if a patient will likely develop CPSP after surgery. But, because most ways of collecting data are limited by people having to collect data manually, like by telephone-based interviews, we do not have studies with large enough numbers of patients included to help us know for certain what factors put people at risk for CPSP. The aim of my research is to design and test a technology system that will automatically check on patients' pain, daily, in order to see if their pain after surgery is under control and what medications they are using. The aim of doing this is to help build better models to predict who is at risk for CPSP and prevent it. This work will be built into the current VISION-2 study, led by my supervisors' group. VISION-2 is a study of 20,000 surgery patients. Patients in this study wear a new monitoring device made by Cloud DX, that sticks to the chest, which can measure a person's vital signs continually (every second). The VISION-2 Study will look for patterns in patients' vital signs that could lead to serious complications after surgery, including CPSP. As a part of this work, my study will include three stages. First, with patients who have had surgery, I will design the software application (app.) to measure patients' pain and medication use after surgery. Next, I will work with patients to complete detailed testing so that we are sure the app is understandable and acceptable to patients. I will then use the app on a Cloud DX tablet to collect the pain data on 1000 patients for the first 30 days after their surgery. We will use advanced data analyses methods to understand how patients' pain and medication use after surgery may contribute to the development of CPSP.