用可穿戴设备和AI预测阿片类药物成瘾患者的疼痛突增，助力个性化干预。
From Wearables to Warnings: Predicting Pain Spikes in Patients with Opioid Use Disorder
- 结合可穿戴数据与机器学习模型预测疼痛突增。
- 机器学习准确率超0.7，而大语言模型表现有限。
- 适合临床研究者及数字健康开发者参考。
慢性疼痛（CP）与阿片类药物使用障碍（OUD）是常见且相互关联的慢性疾病。目前针对接受阿片类药物维持治疗（MOUD）患者中同时存在CP和OUD的整合性证据基础治疗仍匮乏。可穿戴设备有潜力监测复杂患者信息，支持针对OUD和CP患者开发治疗方案，包括疼痛变异性（如疼痛突增）及其临床相关因素（如主观压力）。然而，将大语言模型（LLMs）与可穿戴数据结合以理解疼痛突增的应用尚未探索。因此，本初步研究旨在通过多种AI方法分析疼痛突增的临床相关因素。结果发现，机器学习模型在预测疼痛突增方面达到较高准确率（>0.7），而大语言模型在提供洞察方面表现受限。实时可穿戴设备监测结合先进AI模型，可能实现疼痛突增的早期识别，并支持个性化干预，有助于降低阿片类药物复吸风险、提高MOUD依从性，并促进CP与OUD治疗的整合。鉴于大语言模型整体表现有限，这些发现凸显了需开发能在此情境下提供可操作洞察的大语言模型。
原文摘要 · Abstract (English)
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.
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