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

职业：通过可穿戴和移动技术表征疾病跨诊断标志物的平台

• 批准号: 2046440
• 负责人: Ryan McGinnis
• 金额: $ 50万
• 依托单位: University of Vermont & State Agricultural College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046440&HistoricalAwards=false
• 关键词: CAREER; Platform; Characterizing; Transdiagnostic; Markers

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.

这个教师早期职业发展（Career）项目的目标是开发一个开源软件平台，使用可穿戴传感器和手机来帮助诊断幼儿的内化障碍。近五分之一的儿童患有焦虑或抑郁等内化障碍。这些障碍会阻碍发育直至成年。然而，对这些情况的客观和广泛的评估仍然是一个未得到满足的关键需求。这样的评估可以实现大规模的有效诊断，同时促进对内化障碍的理解，可能为未来的个性化干预提供信息。该项目的结果为改进各种疾病的评估铺平了道路，例如老年人和神经系统疾病患者的跌倒风险评估。该项目还将在佛蒙特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师和行业专业人士聚集在一起。该小组将参加由UVM生物医学创新中心主办的年度数字心理健康黑客马拉松，重点关注农村心理健康。这个NSF CAREER项目推动了儿童心理健康、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等领域的发展。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)开发和验证上下文感知的多模态数字生物标志物，并使用它们来识别跨诊断的潜在类别；2)开发、验证和优化儿童内化障碍的多模态、多域数字表型；3)开发一个开源平台，集成了从可穿戴和移动技术中提取生物标志物、潜在类别和表型的算法。为本项目提供信息的数据将从内化障碍风险较高的儿童中收集。每个孩子将完成一系列简短的情绪诱导任务，在此过程中，可穿戴设备和手机将收集运动、语言、肌电、皮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、幼儿跨诊断潜在特征以及这些疾病的生理标志物与其行为症状之间关系的科学认识。该平台将推动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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

UVM KID Study: Identifying Multimodal Features and Optimizing Wearable Instrumentation to Detect Child Anxiety

UVM KID 研究：识别多模式特征并优化可穿戴仪器以检测儿童焦虑

• DOI: 10.1109/embc48229.2022.9871090
• 发表时间: 2022
• 期刊: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC
• 作者: Loftness, Bryn C.;Halvorson-Phelan, Julia;O'Leary, Aisling;Cheney, Nick;McGinnis, Ellen W.;McGinnis, Ryan S.
• 通讯作者: McGinnis, Ryan S.

Toward Digital Phenotypes of Early Childhood Mental Health via Unsupervised and Supervised Machine Learning

通过无监督和监督机器学习实现幼儿心理健康的数字表型

• DOI: 10.1109/embc40787.2023.10340806
• 发表时间: 2023
• 期刊: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC
• 作者: Loftness, Bryn C.;Rizzo, Donna M.;Halvorson-Phelan, Julia;O’Leary, Aisling;Prytherch, Shania;Bradshaw, Carter;Brown, Anna Jane;Cheney, Nick;McGinnis, Ellen W.;McGinnis, Ryan S.
• 通讯作者: McGinnis, Ryan S.

Preterm Preeclampsia Risk Modelling: Examining Hemodynamic, Biochemical, and Biophysical Markers Prior to Pregnancy

早产先兆子痫风险模型：怀孕前检查血流动力学、生化和生物物理标志物

• DOI: 10.1109/embc40787.2023.10340404
• 发表时间: 2023
• 期刊: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC
• 作者: Loftness, Bryn C.;Bernstein, Ira;McBride, Carole A.;Cheney, Nick;McGinnis, Ellen W.;McGinnis, Ryan S.
• 通讯作者: McGinnis, Ryan S.

The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health

• DOI: 10.1109/jbhi.2023.3337649
• 发表时间: 2024-04-01
• 期刊: IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
• 影响因子: 7.7
• 作者: Loftness，Bryn C.;Halvorson-Phelan，Julia;McGinnis，Ellen W.
• 通讯作者: McGinnis，Ellen W.