Collaborative Research: SCH: Psychophysiological sensing to enhance mindfulness-based interventions for self-regulation of opioid cravings

阿片类药物使用障碍（OUD）是一种慢性疾病，也是美国主要的公共卫生问题。在一段时间的戒断后，过量使用阿片类药物的风险特别高，导致与药物相关的死亡。OUD包括身体依赖和大脑回路中的奖励和动机、自我调节和应激反应中的神经适应，这些症状在停药后可能持续数年。物质渴求是OUD患者复发的主要原因之一。研究表明，压力、焦虑和兴奋等心理暗示会促使药物渴望的培养。研究发现，以正念为基础的策略可以减少渴望，减少心理暗示，防止复发。正念干预（MBIs）通过认知行为技能的发展，给生理觉醒、应激和成瘾行为带来临床相关的改变。该项目专注于开发和测试创新技术，通过可穿戴式和家庭生理监测，以及自适应、个性化和即时mbi的产生，帮助OUD的可持续恢复。虽然研究的重点是OUD，但其原理和结果可以扩展到包括其他物质使用障碍。该项目包括若干教育和外联活动，如为医疗专业人员开设的机器学习课程和为中学女生举办的年度讲习班。本研究的重点是阿片类药物使用障碍（OUD），相关认知和行为与a)奖励，b)自我调节，c)应激反应，d)阿片类药物渴望，e)身体阿片类药物戒断症状和mbi已知受OUD和急性阿片类药物戒断影响。特别是，研究任务集中在这两个领域。首先，有效的生理特征识别和提取，以检测在大型OUD人群中具有普遍性的渴望，并考虑年龄、性别、吸毒习惯等外部因素。其次，开发一种有效的多模态传感集成方法，从声学和生理传感的结合中捕捉心理渴望线索（例如，压力、唤醒）。这将包括新的基于多实例（MIL）多任务学习的分类技术，这些分类技术具有接近实时的可扩展性能。该研究将解决室内渴望相关传感的基本缺陷，即只有一小部分长信号可能传达与目标情绪状态/类别相关的信息。最后一项任务将包括开发一个渴望上下文感知的MBI推荐系统，该系统模拟OUD受试者的渴望干预和反馈的动态特性。该系统将进行正式验证，以确保安全，防止不良后果。该研究的成功实施，将开始测试将被动感知、自适应人工智能（AI）和正念干预相结合对调节药物渴望的有效性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Opioid use disorder (OUD) is a chronic condition and a leading public health problem in the U.S. The risk of overdose is particularly high following a period of abstinence leading to drug-related deaths. OUD includes physical dependency and neural adaptations in brain circuits of reward and motivation, self-regulation, and stress reactivity that can persist years after drug discontinuation. Substance craving is one of the primary causes of OUD patient's relapse. Studies have shown psychological cues such as stress, anxiety, and arousal can precipitate the cultivation of drug craving. Research has found that mindfulness-based strategies reduce cravings, psychological cues and prevent relapse. Mindfulness-based interventions (MBIs) bring about clinically relevant changes to physiological arousal, stress, and addictive behavior through cognitive behavioral skill development. This project focuses on developing and testing innovative technologies to aid sustainable recovery of OUD with wearable and in-home physiological monitoring and generation of adaptive, personalized, and just-in-time MBIs. While the research is focused on OUD, the principle and the outcomes can be expanded to include other substance use disorders. The project includes several education and outreach activities such as machine learning course for medical professionals and annual workshops for middle school girls.This study focuses on opioid use disorder (OUD), related cognition, and behaviors associated with a) reward, b) self-regulation, c) stress reactivity, d) opioid craving, e) physical opioid withdrawal symptoms and MBIs known to be impacted by OUD and post-acute withdrawal from opioids. In particular, the research tasks focus on there areas. First, effective physiological feature identification and extraction to detect craving that is generalizable across large OUD populations and consider the external factors such as age, gender, drug use habits, etc. Second, development of an effective multi-modal sensing integration approach to capture psychological craving cues (e.g., stress, arousal) from a combination of acoustic and physiological sensing. This will include novel multiple instance (MIL) multitask learning based classification techniques that are scalable with near real-time performance. The study will address the fundamental gaps of indoor craving-relevant sensing where only a small fraction of a long signal may convey information relevant to the targeted emotional state/class. The last task will include development of a craving context-aware MBI recommender system that models the dynamic nature of OUD subjects craving-interventions and feedbacks. The system will be formally validated to ensure safety against adverse outcomes. Successful execution of the research will begin to test the effectiveness of integrating passive sensing, adaptive artificial intelligence (AI), and mindfulness interventions on regulating drug craving.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.