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| Hauptverfasser: | , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2511.19577 |
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| _version_ | 1866908758533210112 |
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| author | Goyal, Abhay Kumar, Navin DiMeola, Kimberly Trujillo, Rafael Shimgekar, Soorya Ram Poellabauer, Christian Zonooz, Pi Gjoni-Markaj, Ermonda Barry, Declan Madden, Lynn |
| author_facet | Goyal, Abhay Kumar, Navin DiMeola, Kimberly Trujillo, Rafael Shimgekar, Soorya Ram Poellabauer, Christian Zonooz, Pi Gjoni-Markaj, Ermonda Barry, Declan Madden, Lynn |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19577 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Wearables to Warnings: Predicting Pain Spikes in Patients with Opioid Use Disorder Goyal, Abhay Kumar, Navin DiMeola, Kimberly Trujillo, Rafael Shimgekar, Soorya Ram Poellabauer, Christian Zonooz, Pi Gjoni-Markaj, Ermonda Barry, Declan Madden, Lynn Artificial Intelligence Human-Computer Interaction 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. |
| title | From Wearables to Warnings: Predicting Pain Spikes in Patients with Opioid Use Disorder |
| topic | Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2511.19577 |