Bringing real-time stress detection to scale: Development of a biosensor driven, stress detection classifier for smartwatches

大规模实现实时压力检测：为智能手表开发生物传感器驱动的压力检测分类器

• 批准号: 9891764
• 负责人: DAVID EDDIE
• 金额: $ 18.41万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-05 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9891764
• 关键词: Acute; Address; Affect; Alcohol consumption; Algorithms; Arousal; Autonomic nervous system; Award; Awareness; Bayesian Modeling; Biosensor; Cardiovascular system; Cellular Phone; Chronic; Clinical; Clinical Research; Clinical Sciences; Code; Cognitive; Complement; Coping Behavior; Custom; Detection; Development; Devices; Disease; Disease remission; Ecological momentary assessment; Electrocardiogram; Engineering; Ensure; Environment; General Hospitals; Goals; Health Sciences; Impairment; Individual; Intervention; Knowledge; Laboratories; Language; Learning; Life; Link; Machine Learning; Manuscripts; Massachusetts; Measures; Mentored Patient-Oriented Research Career Development Award; Mentors; Mentorship; Mobile Health Application; Monitor; Morphologic artifacts; Motivation; Notification; Participant; Patients; Physiologic Monitoring; Physiological; Preparation; Principal Investigator; Psychophysiology; Recovery; Relapse; Research; Research Personnel; Risk; Running; Sensitivity and Specificity; Series; Signal Transduction; Stress; Targeted Research; Technology; Testing; Time; Training; Translating; Translations; United States National Institutes of Health; Universities; acute stress; alcohol risk; alcohol use disorder; base; career development; classification algorithm; classifier algorithm; clinical application; cohort; deter alcohol use; disorder later incidence prevention; early alcohol use; experience; fitness; health application; heart rate variability; improved; indexing; innovation; mHealth; medical schools; medical specialties; model building; negative affect; novel; programs; real time monitoring; reduced alcohol use; relapse risk; skills; smart watch; smartphone Application; societal costs; stress management; stress reactivity; stress state; stressor; symposium; therapy development; time use; wearable sensor technology

PROJECT SUMMARY/ABSTRACT The goals of this mentored, patient-oriented, research career development award are two-fold: 1) Characterize the autonomic nervous system correlates of stress-reactivity, both in laboratory and ambulatory contexts in order to inform the development of a biosensor driven, stress-detection classifier algorithm that can run on commercially available smartwatches, and 2) establish the principal investigator as an independent researcher at Massachusetts General Hospital - Harvard Medical School. The specific aims of this research will be accomplished through an innovative study leveraging the strengths of traditional laboratory-based, psychophysiological research, and cutting-edge, in natura monitoring of stress and stress’ autonomic correlates using a combination of ecological momentary assessment of affect, and ambulatory electrocardiogram monitoring. This research will inform the development of a stress-detection algorithm that will run on commercially available smartwatches. The clinical and health applications for real-time stress detection are numerous, but this technology holds particular promise for individuals in early recovery from alcohol use disorder for whom unchecked stress heightens risk for alcohol use and engagement in other maladaptive coping behaviors. The smartwatch-embedded stress detection algorithm developed in this research will ultimately be linked to existing smartphone-based relapse prevention apps that will prompt patients with real-time coaching to mitigate alcohol use risk. Aims of the principal investigator’s career development and training plan include, 1) learning fundamental principles of machine learning with an emphasis on biosensor technologies, 2) gaining facility with Matlab programming, with an emphasis on signal analysis and psychophysiological model building, 3) broadening expertise in cardiovascular waveform and interval analysis with particular emphasis on artefact management, and 4) acquiring skills in the development and application of mHealth-based clinical interventions. These goals will be achieved through a training plan comprised of mentorship, formal coursework, seminars, conferences, and manuscript preparation. Knowledge gained via the training plan will be augmented by the research undertaken. Drs. John Kelly, Paolo Bonato, Gari Clifford, and Bettina Hoeppner will serve as mentors on this award, and will provide targeted expertise in machine learning approaches, Matlab programing, artefact management, and mHealth treatment development. Massachusetts General Hospital - Harvard Medical School provides an exceptional environment in which to conduct this training and research. By the end of the 5-year award period, the goals are to have a working stress-detection classifier algorithm ready for R01 testing, and for the principal investigator to be established as an independent investigator. This award is consistent with NIH's goal of increasing and maintaining a strong cohort of investigators to address the nation's clinical research needs.

项目概要/摘要 这个以患者为导向、以患者为导向的研究职业发展奖的目标有两个：1) 描述 在实验室和流动环境中，自主神经系统与应激反应相关 为了为生物传感器驱动的压力检测分类器算法的开发提供信息，该算法可以在 市售智能手表，以及 2) 将首席研究员确定为独立研究员 在马萨诸塞州总医院 - 哈佛医学院。这项研究的具体目标将是 通过利用传统实验室优势的创新研究来完成， 心理生理学研究，以及压力自然监测和压力自主神经的前沿研究 使用生态瞬时情感评估和动态评估相结合进行关联 心电图监测。这项研究将为压力检测算法的开发提供信息 将在市售智能手表上运行。实时压力的临床和健康应用 检测方法有很多，但这项技术对早期康复的个人特别有希望 对于患有酒精使用障碍的人来说，不受控制的压力会增加饮酒和参与其他活动的风险 适应不良的应对行为。本次开发的智能手表嵌入式压力检测算法 研究最终将与现有的基于智能手机的复发预防应用程序联系起来，这将促使 为患者提供实时指导，以降低饮酒风险。首席研究员的职业目标 开发和培训计划包括：1）学习机器学习的基本原理 重点是生物传感器技术，2) 熟悉 Matlab 编程，重点是信号 分析和心理生理学模型构建，3）扩大心血管波形和 区间分析，特别强调工件管理，以及 4) 获取开发技能 基于移动医疗的临床干预措施的应用。这些目标将通过培训计划来实现 包括指导、正式课程、研讨会、会议和手稿准备。知识 通过培训计划获得的成果将通过开展的研究得到加强。博士。约翰·凯利、保罗·博纳托、加里 Clifford 和 Bettina Hoeppner 将担任该奖项的导师，并将提供有针对性的专业知识 机器学习方法、Matlab 编程、人工制品管理和移动医疗治疗开发。 马萨诸塞州总医院 - 哈佛医学院提供了一个特殊的环境 进行此项培训和研究。到 5 年奖励期结束时，目标是建立一个可行的 压力检测分类器算法已准备好进行 R01 测试，并可将主要研究者确定为 独立调查员。该奖项与 NIH 增加和维持强大的目标是一致的。 一组研究人员来满足国家的临床研究需求。