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)在美国是一种慢性疾病，也是一个主要的公共卫生问题。在一段时间的戒毒导致与药物有关的死亡后，过量服药的风险特别高。OUD包括身体依赖和大脑中奖励和动机的神经适应、自我调节和压力反应，这些都可以在停药数年后持续存在。物质渴求是OUD患者复发的主要原因之一。研究表明，压力、焦虑和性唤醒等心理暗示会加速对毒品的渴求。研究发现，以正念为基础的策略可以减少欲望、心理暗示，并防止复发。基于正念的干预(MBIS)通过认知行为技能的发展带来生理唤醒、压力和成瘾行为的临床相关变化。该项目的重点是开发和测试创新技术，通过可穿戴和家庭生理监测以及自适应、个性化和及时的mbi的生成来帮助可持续的OUD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。该项目包括几个教育和推广活动，如医疗专业人员的机器学习课程和中学女孩的年度研讨会。这项研究集中在阿片使用障碍(OUD)、相关认知和与a)奖励、b)自我调节、c)应激反应、d)阿片类药物渴望、e)物理阿片类药物戒断症状和已知受OUD和急性阿片类药物戒断后影响的行为相关的行为。特别是，研究任务集中在这三个方面。第一，有效的生理特征识别和提取，以检测可在大范围人群中推广的渴望，并考虑诸如年龄、性别、吸毒习惯等外部因素。第二，开发一种有效的多模式感知整合方法，从声音和生理感知的组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，这些技术可扩展，具有接近实时的性能。这项研究将解决与室内渴望相关的感知的根本差距，在这种情况下，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情境感知的MBI推荐系统，该系统将模拟OUD受试者渴望的动态性质-干预和反馈。该系统将经过正式验证，以确保安全，不受不利后果的影响。这项研究的成功实施将开始测试整合被动感知、自适应人工智能(AI)和正念干预措施控制毒瘾的有效性。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.