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.

翻译：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医学病症。目前，在接受阿片类药物使用障碍药物治疗（MOUD）的个体中，针对CP与OUD的循证综合治疗方案仍十分缺乏。可穿戴设备具备监测复杂患者信息、并为OUD与CP患者（包括疼痛变异性，如疼痛加剧或疼痛峰值）及临床相关因素（如感知压力）的治疗方案开发提供依据的潜力。然而，利用大型语言模型（LLMs）结合可穿戴数据来理解疼痛峰值的应用尚未得到探索。因此，本试点研究旨在运用一系列人工智能方法，探究疼痛峰值的临床相关因素。我们发现，机器学习模型在预测疼痛峰值方面取得了相对较高的准确率（>0.7），而LLMs在提供疼痛峰值相关洞见方面则存在局限。通过可穿戴设备进行实时监测，并结合先进的人工智能模型，可促进疼痛峰值的早期检测，并支持个性化干预措施，这可能有助于降低阿片类药物复吸风险、提高对MOUD的依从性，并加强CP与OUD护理的整合。鉴于LLMs整体表现有限，这些发现凸显了开发能够在OUD/CP情境下提供可操作见解的LLMs的必要性。