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

项目总结/摘要 这个指导性的、以患者为导向的研究职业发展奖的目标有两个方面：1）描述 自主神经系统与应激反应相关，无论是在实验室还是在门诊环境中， 为了告知生物传感器驱动的压力检测分类器算法的开发， 市售智能手表，以及2）将主要研究者确定为独立研究人员 在马萨诸塞州总医院-哈佛医学院。这项研究的具体目标是 通过一项创新的研究，利用传统的实验室为基础的优势， 心理生理学研究，前沿，在自然监测压力和压力'自主 相关性使用的影响，和流动的生态瞬时评估相结合， 心电图监测这项研究将为压力检测算法的开发提供信息， 将在市面上销售的智能手表上运行。实时压力的临床和健康应用 检测方法很多，但这项技术对早期康复的人来说特别有希望。 酒精使用障碍，对他们来说，未经检查的压力会增加酒精使用和参与其他活动的风险 适应不良的应对行为在此开发的智能手表嵌入式应力检测算法 研究最终将与现有的基于智能手机的复发预防应用程序相关联， 实时指导患者减轻酒精使用风险。主要研究者职业生涯的目标 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）获得Matlab编程的便利，重点是信号 分析和心理生理模型的建立，3）扩大心血管波形的专业知识， 区间分析，特别强调人工制品管理，以及4）获得开发技能 和应用基于移动健康的临床干预措施。这些目标将通过培训计划实现 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信息将通过所开展的研究得到补充。John Kelly博士、Paolo Bonato博士、Gari博士 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健康治疗开发。 马萨诸塞州总医院-哈佛医学院提供了一个特殊的环境， 开展这项培训和研究。到5年奖励期结束时，目标是有一个工作 压力检测分类器算法准备用于R 01测试，并为主要研究者建立 独立调查员该奖项与NIH的目标一致，即增加和保持一个强大的 研究人员的队列，以满足国家的临床研究需求。

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.