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

合作研究：SCH：心理生理学传感，以增强基于正念的干预措施，以自我调节阿片类药物的渴望

• 批准号: 2320678
• 负责人: Tauhidur Rahman
• 金额: $ 33.75万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2320678&HistoricalAwards=false
• 关键词: Collaborative; Research; SCH; Psychophysiological; sensing

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.

阿片类药物使用障碍 (OUD) 是一种慢性疾病，也是美国的一个主要公共卫生问题。在戒断一段时间后，过量服用药物的风险特别高，导致与药物相关的死亡。 OUD 包括奖励和动机、自我调节和压力反应的大脑回路中的身体依赖性和神经适应，这些适应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加速药物渴望的培养。研究发现，基于正念的策略可以减少渴望、心理暗示并防止旧病复发。基于正念的干预措施 (MBI) 通过认知行为技能的发展，对生理唤醒、压力和成瘾行为带来临床相关的变化。该项目重点开发和测试创新技术，通过可穿戴和家用生理监测以及生成自适应、个性化和即时 MBI 来帮助 OUD 可持续恢复。虽然该研究的重点是 OUD，但其原理和结果可以扩展到其他物质使用障碍。该项目包括多项教育和外展活动，例如针对医疗专业人员的机器学习课程和针对中学生的年度研讨会。本研究重点关注阿片类药物使用障碍 (OUD)、相关认知以及与 a) 奖励、b) 自我相关的行为。调节，c）应激反应性，d）阿片类药物渴望，e）身体阿片类药物戒断症状和已知受 OUD 和阿片类药物急性戒断影响的 MBI。特别是，研究任务集中在这些领域。首先，有效的生理特征识别和提取，以检测大量 OUD 人群中普遍存在的渴望，并考虑年龄、性别、吸毒习惯等外部因素。其次，开发有效的多模式传感集成方法来捕获心理渴望来自声学和生理传感相结合的线索（例如压力、唤醒）。这将包括基于新型多实例（MIL）多任务学习的分类技术，这些技术可扩展且具有近乎实时的性能。该研究将解决室内渴望相关感知的基本差距，即长信号中只有一小部分可以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，该系统可以模拟 OUD 受试者渴望干预和反馈的动态性质。该系统将经过正式验证，以确保安全，防止不良后果。该研究的成功执行将开始测试整合被动传感、自适应人工智能 (AI) 和正念干预措施对调节药物渴望的有效性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力评估进行评估，认为值得支持。优点和更广泛的影响审查标准。

项目成果

"Reading Between the Heat": Co-Teaching Body Thermal Signatures for Non-intrusive Stress Detection

“阅读热之间”：共同教授用于非侵入式压力检测的身体热特征

• 发表时间: 2023
• 期刊: Wearable and Ubiquitous Technologies
• 作者: Yi Xiao, Harshit Sharma

Building MechanoBeat: Instrumenting Mechanical "Heartbeats" on Everyday Objects for User Interaction

构建 MechanoBeat：在日常物体上检测机械“心跳”以进行用户交互

• DOI: 10.1145/3583571.3583573
• 发表时间: 2023
• 期刊: GetMobile: Mobile Computing and Communications
• 作者: Oshim, Md. Farhan;Killingback, Julian;Follette, Dave;Peng, Huaishu;Rahman, Tauhidur
• 通讯作者: Rahman, Tauhidur

Towards Accurate and Scalable Mental Health Screening Technologies for Young Children

• DOI: 10.1145/3594739.3610763
• 发表时间: 2023-10
• 期刊: Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing
• 作者: Manasa Kalanadhabhatta;Deepak Ganesan;Tauhidur Rahman

Neuromorphic High-Frequency 3D Dancing Pose Estimation in Dynamic Environment

• DOI: 10.1016/j.neucom.2023.126388
• 发表时间: 2023-01
• 期刊: Neurocomputing
• 影响因子: 6
• 作者: Zhongyang Zhang;Kaidong Chai;Haowen Yu;Ramzi M Majaj;Francesca Walsh;Edward Wang;U. Mahbub;H. Siegelmann;Donghyun Kim-;Tauhidur Rahman

Spike-based Neuromorphic Computing for Next-Generation Computer Vision

• DOI: 10.48550/arxiv.2310.09692
• 发表时间: 2023-10
• 期刊: ArXiv
• 作者: Md. Sakib Hasan;Catherine D. Schuman;Zhongyang Zhang;Tauhidur Rahman;Garrett S. Rose