# From Wearables to Warnings: Predicting Pain Spikes in Patients with Opioid Use Disorder

arXiv: https://arxiv.org/abs/2511.19577 · HTML: https://mlsanity.com/paper/2511.19577/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 51 references that are in the map.
- First posted on arXiv 24 Nov 2025.

## Builds on (most cited first, 8 of 8)

- [Diet-ODIN: A Novel Framework for Opioid Misuse Detection with Interpretable Dietary Patterns](https://mlsanity.com/paper/2403.08820/) (2024)
- [Agentic AI framework for End-to-End Medical Data Inference](https://mlsanity.com/paper/2507.18115/) (2025)
- [ChatGPT and Bard Responses to Polarizing Questions](https://mlsanity.com/paper/2307.12402/) (2023)
- [Leveraging Large Language Models to Extract Information on Substance Use Disorder Severity from Clinical Notes: A Zero-shot Learning Approach](https://mlsanity.com/paper/2403.12297/) (2024)
- [One VLM, Two Roles: Stage-Wise Routing and Specialty-Level Deployment for Clinical Workflows](https://mlsanity.com/paper/2508.16839/) (2025)
- [Predicting Opioid Use Outcomes in Minoritized Communities](https://mlsanity.com/paper/2307.03083/) (2023)
- [How is Fatherhood Framed Online in Singapore?](https://mlsanity.com/paper/2307.04053/) (2023)
- [User Archetypes and Information Dynamics on Telegram: COVID-19 and Climate Change Discourse in Singapore](https://mlsanity.com/paper/2406.06717/) (2024)

## Abstract (arXiv)

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.