CAREER: Platform for Characterizing Transdiagnostic Markers of Disease from Wearable and Mobile Technologies

该学院早期职业发展(Career)项目的目标是开发一个使用可穿戴传感器和手机的开源软件平台，以帮助诊断幼儿的内化性障碍。近五分之一的儿童会经历焦虑或抑郁等内化性障碍。这些障碍可能会阻碍成年后的发育。然而，对这些情况的客观和广泛的评估仍然是一个关键的未得到满足的需要。这样的评估可以在规模上进行有效诊断，同时促进对内在化障碍的理解，潜在地为未来的个性化干预提供信息。该项目的结果为改进对各种疾病的评估铺平了道路，例如老年人和患有神经障碍的个人的跌倒风险评估。该项目还将在佛蒙特州大学建立一个数字心理健康兴趣小组，将高中生、本科生和研究生与教职员工和行业专业人员聚集在一起。该小组将参与一年一度的数字精神健康黑客松，由密歇根大学生物医学创新中心主办，重点关注农村精神健康。这个NSF职业项目推动了儿童精神健康、数字医学、信号处理工程和从可穿戴传感器信号中提取意义的机器学习算法的工程。这些进展填补了关键的知识空白，并将在三个研究目标的背景下进行：1)开发和验证情景感知的多模式数字生物标记物，并使用它们来识别跨诊断潜在类别；2)开发、验证和优化儿童内在化障碍的多模式、多领域数字表型；以及3)开发一个开源平台，集成从可穿戴和移动技术中提取生物标记物、潜在类别和表型的算法。为该项目提供信息的数据将从内在化障碍风险较高的儿童那里收集。每个孩子将完成一系列简短的情绪诱导任务，在此过程中，将通过可穿戴设备和手机收集运动、语音、肌电、皮肤电活动和心电图数据。这些研究目标促进了对儿童内化障碍的生物标志物和表型、幼儿的跨诊断潜伏期以及这些疾病的生理标志物与其行为症状之间的关系的科学认识。这是第一个此类平台，将推进数字医学的开发和验证工作，并允许未来扩展到广泛的临床终点。该奖项反映了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。

The goal of this Faculty Early Career Development (CAREER) project is to develop an open-source software platform using wearable sensors and mobile phones to help diagnose internalizing disorders in young children. Nearly one in five children experience an internalizing disorder like anxiety or depression. These disorders can hinder development well into adulthood. Yet objective and widely available assessment for these conditions remains a key unmet need. Such an assessment could enable effective diagnosis at scale while advancing the understanding of internalizing disorders, potentially informing future personalized interventions. The results of this project pave the way for improved assessment of a variety of disorders such as fall risk assessment in older adults and in individuals with neurological disorders. This project will also establish a digital mental health interest group at the University of Vermont that will bring together high-school, undergraduate, and graduate students with faculty and industry professionals. This group will engage in an annual digital mental health hackathon, hosted by UVM’s Center for Biomedical Innovation, that focuses on rural mental healthcare.This NSF CAREER project advances the fields of childhood mental health, digital medicine, and the engineering of signal processing and machine learning algorithms for extracting meaning from wearable sensor signals. These advances fill key knowledge gaps, and will be made in the context of three research objectives: 1) developing and validating context-aware multi-modal digital biomarkers and using them to identify transdiagnostic latent classes, 2) developing, validating, and optimizing multi-modal, multi-domain digital phenotypes of childhood internalizing disorders, and 3) developing an open-source platform that integrates algorithms for extracting biomarkers, latent classes, and phenotypes from wearable and mobile technologies. Data to inform this project will be collected from children at elevated risk for internalizing disorders. Each child will complete a series of brief mood induction tasks during which movement, speech, electromyography, electrodermal activity, and electrocardiography data will be collected by wearables and mobile phones. These research objectives advance scientific knowledge of the biomarkers and phenotypes of childhood internalizing disorders, trans-diagnostic latent profiles in young children, and the relationship between physiological markers of these conditions and their behavioral symptoms. The first-of-its-kind platform will advance development and validation efforts in digital medicine and allow future extension to a wide array of clinical endpoints.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.