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

项目摘要/摘要 这个有指导、以患者为导向的研究职业发展奖的目标有两个：1)特征 自主神经系统与应激反应有关，在实验室和门诊环境中都是如此。 为了通知开发一种生物传感器驱动的压力检测分类器算法，该算法可以在 商用智能手表，以及2)将首席研究员确立为独立研究员 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 通过一项创新的研究完成，该研究利用了传统实验室、 心理生理学研究和前沿，在自然压力和应激自主神经监测中 使用影响的生态瞬时评估和动态的组合进行关联 心电监护。这项研究将为应力检测算法的开发提供信息 将使用市面上出售的智能手表运行。实时应激在临床和健康中的应用 检测的方法很多，但这项技术对早期康复的个人来说尤其有希望。 酒精使用障碍，无节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。在此基础上开发的嵌入智能手表的应力检测算法 研究最终将链接到现有的基于智能手机的复发预防应用程序，这些应用程序将提示 对患者进行实时辅导，以降低饮酒风险。首席调查员的职业目标 发展和培训计划包括：1)学习机器学习的基本原理 重视生物传感器技术，2)使用MatLab编程，重点是信号 分析和心理生理学模型建立，3)拓宽心血管波形和 特别强调人工制品管理的区间分析，以及4)在开发过程中获得技能 以及基于移动健康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因开展的研究而得到加强。约翰·凯利博士，保罗·博纳托，加里 Clifford和Bettina Hoeppner将担任该奖项的导师，并将在以下方面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mHealth治疗开发。 马萨诸塞州综合医院-哈佛医学院提供了一个特殊的环境，在这里 进行这种培训和研究。到五年奖励期结束时，目标是有一个工作 为R01测试做好准备的应力检测分类器算法，以及将建立的主要调查者为 一名独立调查员。这一奖项与NIH增加和保持强劲的 一批研究人员，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.