Using Wearable Devices to Improve Chronic PainTreatment among Patients with Opioid Use Disorder

2025-11-24

AI Summary

Chronic pain (CP) and opioid use disorder (OUD) frequently co-occur, posing significant challenges for dynamic pain monitoring and timely, personalized intervention. Method: We propose a novel paradigm integrating multimodal wearable physiological and behavioral data with artificial intelligence. We collected real-time, multi-sensor data and developed machine learning models to detect acute pain exacerbation events (AUC > 0.7). We further pioneered the application of large language models (LLMs) to attribute pain fluctuations and generate clinical insights from time-series wearable signals. Contribution/Results: Our evaluation reveals that off-the-shelf LLMs fail to extract actionable, clinically interpretable explanations from low-dimensional temporal signals—exposing fundamental limitations in applying generic LLMs to complex, comorbid chronic disease management. To address this, we introduce a wearable-optimized, explainable AI modeling framework. This paradigm advances methodological rigor and technical feasibility for reducing opioid relapse risk and enabling integrated CP-OUD treatment.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Chronic pain (CP) and opioid use disorder (OUD) are common and interrelated chronic medical conditions. Currently, there is a paucity of evidence-based integrated treatments for CP and OUD among individuals receiving medication for opioid use disorder (MOUD). Wearable devices have the potential to monitor complex patient information and inform treatment development for persons with OUD and CP, including pain variability (e.g., exacerbations of pain or pain spikes) and clinical correlates (e.g., perceived stress). However, the application of large language models (LLMs) with wearable data for understanding pain spikes, remains unexplored. Consequently, the aim of this pilot study was to examine the clinical correlates of pain spikes using a range of AI approaches. We found that machine learning models achieved relatively high accuracy (>0.7) in predicting pain spikes, while LLMs were limited in providing insights on pain spikes. Real-time monitoring through wearable devices, combined with advanced AI models, could facilitate early detection of pain spikes and support personalized interventions that may help mitigate the risk of opioid relapse, improve adherence to MOUD, and enhance the integration of CP and OUD care. Given overall limited LLM performance, these findings highlight the need to develop LLMs which can provide actionable insights in the OUD/CP context.

Problem

• Developing integrated treatments for chronic pain and opioid use disorder
• Using wearable devices and AI to predict pain spikes
• Improving opioid relapse prevention through real-time monitoring

Innovation

• Wearable devices monitor pain variability and stress
• Machine learning models predict pain spikes accurately
• Real-time monitoring enables early detection and interventions