Functional versus dysfunctional coping with physical and emotional pain.

Functional versus dysfunctional coping with physical and emotional pain: Utilizing machine learning for their ambulatory assessment, experimental evaluation, and prediction.

The overall aim of this project is to conduct a series of experiments that will isolate signals of effective vs. ineffective coping during laboratory tasks and also utilize Ecological Momentary Assessment (EMA) methodology to examine this in the persons’ environment. Specifically, this project will examine the way that participants manage unwanted sensations and emotions during a physical pain challenge (i.e., cold pressor task) and during an undesirable emotion induction task (e.g., by recalling an event that for them was painful) both at a physiological and self-report level. Further, EMA will be utilized to assess participants in their lives for three days post lab experiment so as to examine transference of coping techniques to their everyday life, while also assessing with a wearable device their psychophysiological and self-reports. Information collected will be fed into machine learning algorithms to determine which combination ofphysiological signals is associated with different types of coping and use this information to train the algorithms for real-time prediction of coping. The potential implications of the findings are enormous, identifying and predicting, in real time, whether a person will proceed with avoidance vs. acceptance coping with physical and emotional pain, can lead to a personalized medicine approach with immediate corrective feedback and management approaches for patients with chronic debilitating pain conditions.