Pain is a difficult outcome to measure due to its multifaceted and subjective nature. The need for selecting proper outcome measures is high because of the increasing demand for scientifically valid demonstrations of treatment efficacy. It is a complex and subjective experience that poses a number of measurement challenges. However, in the current culture of evidence-based medicine, it is important that clinicians and researchers utilise sensitive and accurate pain outcome measures. A dolorimeter is an instrument used to measure pain threshold and pain tolerance. Dolorimetry has been defined as “the measurement of pain sensitivity”.
A professor of mechanical and industrial engineering at Northeastern University, Yingzi Lin is working on a solution to help doctors and patients gauge pain more effectively. Lin runs the Intelligent Human-Machine Systems lab at Northeastern, where she tracks people’s physiological responses to painful sensations, such as changes in brain activity, sweat glands, pupil dilation, heart rate, and facial expressions. The lab she runs at Northeastern has been studying people’s reactions under stimulus involving small (and harmless) amounts of pain, such as dipping their hand in ice cold water.
Lin tracks body signals and running all those tests as she asks people to rate pain using the zero-to-10 scale. Her system, the Continuous Objective Multimodal Pain Assessment Sensing System, received a National Science Foundation grant that she is using to conduct pilot studies at Brigham and Women’s Hospital, testing different ways to measure body signals that can help create a system to evaluate painful sensations more accurately.
That kind of system will be a giant leap toward accurately estimating pain levels on the spot, in minutes, and when patients need it, Lin says. And, she says, it’s what doctors need when treating patients who are unable to give their own rating. However, people’s rankings can be shaped by their sex, age, and different psychological influences. Now targeting that pain, Lin’s system works with technology she has used in her lab to detect people’s emotional states in situations involving machines so that the data is a bit more reliable.