MINDER: Wearable sensor-based detection of digital biomarkers of adherence to medications for opioid use disorder

MINDER：基于可穿戴传感器的数字生物标记检测，用于检测阿片类药物使用障碍药物的依从性

• 批准号: 10656796
• 负责人: STEPHANIE P CARREIRO
• 金额: $ 68.42万
• 依托单位: UNIV OF MASSACHUSETTS MED SCH WORCESTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-06 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10656796
• 关键词: Accelerometer; Adherence; Agonist; Algorithms; Artificial Intelligence; Behavior Therapy; Biometry; Buprenorphine; Clinical; Communities; Companions; Custom; Data; Data Set; Detection; Development; Device or Instrument Development; Digital biomarker; Drug Screening; Event; Foundations; Health Personnel; Health Technology; Heart Rate; Human; Individual; Infrastructure; Ingestion; Intervention; Laboratories; Machine Learning; Measures; Medical Device; Medicine; Methodology; Methods; Modeling; Monitor; Morbidity - disease rate; Motion; Nature; Opioid; Opioid agonist; Optics; Outcome; Overdose reduction; Patient Self-Report; Patients; Persons; Physiologic Monitoring; Physiological; Process; Public Health; Records; Recovery; Research; Research Personnel; Risk; Skin Temperature; Support System; System; Systems Development; Testing; Time; Toxicology; Upper arm; Urine; addiction; buprenorphine treatment; clinical practice; computer human interaction; data communication; data ingestion; design; effective therapy; experience; improved; improved outcome; innovation; iterative design; mHealth; machine learning algorithm; machine learning model; medication for opioid use disorder; mobile application; mortality; novel; open source; opioid misuse; opioid mortality; opioid use; opioid use disorder; overdose death; precision medicine; preference; prevent; prototype; real time monitoring; sensor; success; usability; wearable sensor technology

PROJECT SUMMARY/ABSRACT Medications for opioid use disorder (MOUD), including the partial opioid agonist buprenorphine, provide a treatment option for opioid use disorder (OUD) that significantly reduces morbidity and mortality. Even with successful buprenorphine initiation, however, adherence is paramount to prevent return to non-medical opioid use and its associated risks. Current methods of determining buprenorphine adherence are limited by their retrospective nature and recall bias. We propose to develop a novel artificial intelligence-assisted wearable sensor system, MINDER, which will continuously monitor physiologic changes, and will use machine learning algorithms to accurately identify buprenorphine use. The MINDER system will be comprised of a custom wearable sensor (MINDER-band), a companion mobile app and a clinician facing portal. The MINDER-band, which is a low profile, upper arm band with a user-driven design, continuously records physiologic data. We will use the band to curate a high-quality dataset of MOUD ingestions and subsequently use machine learning to evaluate the ability of the sensor to detect MOUD (specifically buprenorphine) ingestion events. Finally, we will deploy the MINDER system in real-world MOUD treatment settings to understand usability factors. The investigative team brings together complementary expertise in toxicology/addiction medicine, mobile health (Carreiro, Smelson), machine learning, human computer interaction (Venkatasubramanian), novel on-body wearable sensors, and medical device development (Mankodiya, Solanki). The specific aims of the project are to: 1) Understand the requirements, barriers, and facilitators for an ML driven buprenorphine adherence support system, 2) Develop and test a novel wearable sensing system, MINDER, designed for individuals in buprenorphine treatment, 3) Curate a high quality annotated dataset for machine learning-based modeling of buprenorphine adherence, 4) Model the buprenorphine ingestion data collected from the MINDER-band to build the ML algorithms infrastructure for the MINDER system. Upon completion, the MINDER system will be ready for clinical deployment. This study will lay the groundwork for novel just-in-time adaptive behavioral interventions to personalize OUD treatment, improve buprenorphine adherence and its success, and ultimately reduce morbidity and mortality from OUD.

项目概要/摘要 阿片类药物使用障碍（MOUD）的药物，包括部分阿片类激动剂丁丙诺啡，提供了一个 阿片类药物使用障碍（OUD）的治疗选择，可显著降低发病率和死亡率。即使有 然而，成功地开始丁丙诺啡，坚持对于防止返回非医用阿片类药物至关重要 使用及其相关风险。目前确定丁丙诺啡依从性的方法受到它们的限制。 回溯性和回忆偏差。我们建议开发一种新型的人工智能辅助可穿戴设备， 传感器系统MINDER将持续监测生理变化，并将使用机器学习 准确识别丁丙诺啡使用的算法。MINDER系统将由一个定制的 可穿戴传感器（MINDER带）、配套移动的应用程序和面向临床医生的门户网站。MINDER乐队， 其是具有用户驱动设计的低轮廓上臂带，连续记录生理数据。我们将 使用该乐队来策划一个高质量的MOUD数据集，然后使用机器学习来 评估传感器检测MOUD（特别是丁丙诺啡）摄入事件的能力。最后我们将 在真实的MOUD治疗环境中部署MINDER系统，以了解可用性因素。的 调查小组汇集了毒理学/成瘾医学、移动的健康 （Carreiro、Smelson）、机器学习、人机交互（Venkatasubramanian）、小说体上 可穿戴传感器和医疗设备开发（Mankodiya，Solanki）。该项目的具体目标是 目的：1）了解ML驱动的丁丙诺啡依从性支持的要求、障碍和促进因素 系统，2）开发和测试一种新的可穿戴传感系统，MINDER，为个人设计， 丁丙诺啡治疗，3）为基于机器学习的建模策划高质量的注释数据集， 丁丙诺啡依从性，4）对从MINDER带收集的丁丙诺啡摄入数据进行建模， 为MINDER系统构建ML算法基础设施。完成后，MINDER系统将 准备临床部署。这项研究将为新型的即时适应行为奠定基础 干预措施，以个性化OUD治疗，提高丁丙诺啡的依从性及其成功率，并最终 降低OUD的发病率和死亡率。