纵向语音监测在一个分散的带你自己的设备试验呼吸系统疾病检测

IF 12.4 1区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Mar Santamaria, Yiorgos Christakis, Charmaine Demanuele, Yao Zhang, Pirinka Georgiev Tuttle, Fahimeh Mamashli, Jiawei Bai, Rogier Landman, Kara Chappie, Stefan Kell, John G. Samuelsson, Kisha Talbert, Leonardo Seoane, W. Mark Roberts, Edmond Kato Kabagambe, Joseph Capelouto, Paul Wacnik, Jessica Selig, Lukas Adamowicz, Sheraz Khan, Robert J. Mather
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引用次数: 0

摘要

急性呼吸系统疾病监测(AcRIS)研究是一项低介入试验,旨在检查呼吸系统疾病患者的声音变化。这种纵向试验是此类试验中的第一次,通过“自带设备”移动应用程序以完全分散的方式进行。该应用程序支持基于社交媒体的招聘、远程同意、在家采集样本,以及在现实环境中进行日常远程语音和症状捕捉。从2021年4月到2022年4月,该试验招募了9151名参与者,随访时间长达8周。尽管逆转录聚合酶链反应(RT-PCR)阳性参与者出现轻微症状,但用于筛选呼吸系统疾病的两种机器学习算法达到了预先指定的成功标准。对独立队列的算法测试表明,该算法的敏感性随着症状的增加而增加,而特异性保持一致。研究结果表明,语音特征可以识别患有病毒性呼吸道疾病的个体,并为完全分散的临床试验设计、操作和采用提供有价值的见解(研究于2021年2月5日在ClinicalTrials.gov (NCT04748445)注册)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Longitudinal voice monitoring in a decentralized Bring Your Own Device trial for respiratory illness detection

Longitudinal voice monitoring in a decentralized Bring Your Own Device trial for respiratory illness detection

The Acute Respiratory Illness Surveillance (AcRIS) Study was a low-interventional trial that examined voice changes with respiratory illnesses. This longitudinal trial was the first of its kind, conducted in a fully decentralized manner via a Bring Your Own Device mobile application. The app enabled social-media-based recruitment, remote consent, at-home sample collection, and daily remote voice and symptom capture in real-world settings. From April 2021 to April 2022, the trial enrolled 9151 participants, followed for up to eight weeks. Despite mild symptoms experienced by reverse transcription polymerase chain reaction (RT-PCR) positive participants, two machine learning algorithms developed to screen respiratory illnesses reached the pre-specified success criteria. Algorithm testing on independent cohorts demonstrated that the algorithm’s sensitivity increased as symptoms increased, while specificity remained consistent. Study findings suggest voice features can identify individuals with viral respiratory illnesses and provide valuable insights into fully decentralized clinical trials design, operation, and adoption (study registered at ClinicalTrials.gov (NCT04748445) on 5 February 2021).

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来源期刊
CiteScore
25.10
自引率
3.30%
发文量
170
审稿时长
15 weeks
期刊介绍: npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.
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