Spinal Cord Stimulation (SCS) is a well-established therapy for treating chronic pain. However, treatment response to SCS therapy may vary among people with chronic pain due to diverse pain perspectives amongst them. While, currently, chronic pain is evaluated by clinically validated patient reported outcomes (PROs) measures completed during a clinical assessment, monitoring improvement of the participant’s progress over time and comparison of PROs across participants remains subjective and challenging. Thus, objective measures to quantify pain are invaluable. This study aims to assess the feasibility of using digital biomarkers collected from wearables during SCS treatment to predict pain and PRO outcomes. Twenty participants with chronic pain were recruited and implanted with SCS. During the six months of the study, activity and physiological metrics were collected and data from 15 participants was used to develop a machine learning pipeline to objectively predict pain and PRO measures. We were able to reach the accuracy of 0.832 ± 0.012 in pain intensity prediction. The feature importance analysis showed that digital biomarker from the smart watch such as heart rate, heart rate variability, step count, and stand time can contribute to modeling different aspects of pain. The results of the study suggest that wearable biomarkers can be used to predict therapy outcomes in people with chronic pain and bring us another step closer towards objective evaluation of chronic pain.
