Objective wearable measures and subjective questionnaires for predicting response to neurostimulation in people with chronic pain

Abstract

Background Neurostimulation is an effective therapy for treating and management of refractory chronic pain. However, the complex nature of pain and infrequent in-clinic visits, determining subject’s long-term response to the therapy remains difficult. Frequent measurement of pain in this population can help with early diagnosis, disease progression monitoring, and evaluating long-term therapeutic efficacy. This paper compares the utilization of the common subjective patient-reported outcomes with objective measures captured through a wearable device for predicting the response to neurostimulation therapy. Method Data is from the ongoing international prospective post-market REALITY clinical study, which collects long-term patient-reported outcomes from 557 subjects implanted by Spinal Cord Stimulator (SCS) or Dorsal Root Ganglia (DRG) neurostimulators. The REALITY sub-study was designed for collecting additional wearables data on a subset of 20 participants implanted with SCS devices for up to six months post implantation. We first implemented a combination of dimensionality reduction algorithms and correlation analyses to explore the mathematical relationships between objective wearable data and subjective patient-reported outcomes. We then developed machine learning models to predict therapy outcome based on the subject’s response to the numerical rating scale (NRS) or patient global impression of change (PGIC). Results Principal component analysis showed that psychological aspects of pain were associated with heart rate variability, while movement-related measures were strongly associated with patient-reported outcomes related to physical function and social role participation. Our machine learning models using objective wearable data predicted PGIC and NRS outcomes with high accuracy without subjective data. The prediction accuracy was higher for PGIC compared with the NRS using subjective-only measures primarily driven by the patient satisfaction feature. Similarly, the PGIC questions reflect an overall change since the study onset and could be a better predictor of long-term neurostimulation therapy outcome. Conclusions The significance of this study is to introduce a novel use of wearable data collected from a subset of patients to capture multi-dimensional aspects of pain and compare the prediction power with the subjective data from a larger data set. The discovery of pain digital biomarkers could result in a better understanding of the patient’s response to therapy and their general well-being.

Innovative Applications of Telemedicine and Other Digital Health Solutions in Pain Management: A Literature Review

“…Utilizing digital biomarkers in pain management, such as data collected from wearable devices or mobile apps, can provide objective measures of pain intensity, frequency, and impact on daily activities. They can supplement traditional subjective pain assessments and offer a more comprehensive view of the patient's condition [ 6 , 45 ]. Digital Therapeutics Digital therapeutics, such as mobile apps or online programs, can provide interactive tools for pain assessment and self-management.…”

“… Integration with Electronic Health Records (EHR) Digital health technologies can integrate with EHR systems to streamline pain assessment processes, ensure data accuracy, and facilitate communication between healthcare providers. By incorporating these digital health tools and technologies into pain assessment and diagnosis practices, healthcare providers can improve the accuracy, efficiency, and effectiveness of pain management for their patients [ 45 , 46 ]. Data-Driven Decision Making Digital health technologies provide healthcare providers with access to comprehensive data on patient pain symptoms, treatment history, and outcomes.…”

“…Recently, the US Food and Drug Administration (FDA) has approved an applied VR app called RelieVR for treating back pain [ 50 , 51 ]. Further research is needed to establish the long-term effectiveness of VR in managing chronic pain [ 45 ]. Conversational Artificial Intelligence Another category of potentially relevant digital health solutions for patients with chronic pain in the context of COVID-19 is artificial intelligence (AI)-based conversational applications [ 6 , 52 ].…”

“…Results from a pilot trial of an AI chat bot to promote self-management of chronic pain showed encouraging results [ 57 ]. This can help in predicting pain episodes, optimizing treatment strategies, and improving overall patient outcomes [ 45 ]. Multi-Agent Systems (MAS) MAS are well-established modalities to create communication and cooperation between agents.…”

“…Integration with Electronic Health Records (EHR) Digital health technologies can integrate with EHR systems to streamline pain assessment processes, ensure data accuracy, and facilitate communication between healthcare providers. By incorporating these digital health tools and technologies into pain assessment and diagnosis practices, healthcare providers can improve the accuracy, efficiency, and effectiveness of pain management for their patients [ 45 , 46 ].…”