@文章{信息:doi/10.2196/14909，作者="张慧张洁李宏宝陈毅鑫杨斌郭斌陈玉涛陈云岱"，标题="单中心前置移动房颤应用程序在真实世界环境下持续监测房颤的验证:试验队列研究"，期刊="J医学互联网研究"，年="2019"，月="12"，日="3"，卷="21"，数="12"，页数="e14909"，关键词="房颤;photoplethysmography;连续检测;准确;智能手机;聪明的乐队;背景:心房颤动是临床最常见的复发性心律失常，大多数临床事件发生在院外。低检出率和不遵守指导方针是心房颤动管理的主要障碍。光容量描记术是一种用于房颤筛查的新技术。然而，基于光容积描记术的智能设备用于心房颤动检测及其影响检测的潜在临床因素的验证有限。 Objective: This study aimed to explore the feasibility of photoplethysmography-based smart devices for the detection of atrial fibrillation in real-world settings. Methods: Subjects aged ≥18 years (n=361) were recruited from September 14 to October 16, 2018, for screening of atrial fibrillation with active measurement, initiated by the users, using photoplethysmography-based smart wearable devices (ie, a smart band or smart watches). Of these, 200 subjects were also automatically and periodically monitored for 14 days with a smart band. The baseline diagnosis of ``suspected'' atrial fibrillation was confirmed by electrocardiogram and physical examination. The sensitivity and accuracy of photoplethysmography-based smart devices for monitoring atrial fibrillation were evaluated. Results: A total of 2353 active measurement signals and 23,864 periodic measurement signals were recorded. Eleven subjects were confirmed to have persistent atrial fibrillation, and 20 were confirmed to have paroxysmal atrial fibrillation. Smart devices demonstrated >91{\%} predictive ability for atrial fibrillation. The sensitivity and specificity of devices in detecting atrial fibrillation among active recording of the 361 subjects were 100{\%} and about 99{\%}, respectively. For subjects with persistent atrial fibrillation, 127 (97.0{\%}) active measurements and 2240 (99.2{\%}) periodic measurements were identified as atrial fibrillation by the algorithm. For subjects with paroxysmal atrial fibrillation, 36 (17{\%}) active measurements and 717 (19.8{\%}) periodic measurements were identified as atrial fibrillation by the algorithm. All persistent atrial fibrillation cases could be detected as ``atrial fibrillation episodes'' by the photoplethysmography algorithm on the first monitoring day, while 14 (70{\%}) patients with paroxysmal atrial fibrillation demonstrated ``atrial fibrillation episodes'' within the first 6 days. The average time to detect paroxysmal atrial fibrillation was 2 days (interquartile range: 1.25-5.75) by active measurement and 1 day (interquartile range: 1.00-2.00) by periodic measurement (P=.10). The first detection time of atrial fibrillation burden of <50{\%} per 24 hours was 4 days by active measurement and 2 days by periodic measurementThe first detection time of atrial fibrillation burden of >50{\%} per 24 hours was 1 day for both active and periodic measurements (active measurement: P=.02, periodic measurement: P=.03). Conclusions: Photoplethysmography-based smart devices demonstrated good atrial fibrillation predictive ability in both active and periodic measurements. However, atrial fibrillation type could impact detection, resulting in increased monitoring time. Trial Registration: Chinese Clinical Trial Registry of the International Clinical Trials Registry Platform of the World Health Organization ChiCTR-OOC-17014138; http://www.chictr.org.cn/showprojen.aspx?proj=24191. ", issn="1438-8871", doi="10.2196/14909", url="//www.mybigtv.com/2019/12/e14909", url="https://doi.org/10.2196/14909", url="http://www.ncbi.nlm.nih.gov/pubmed/31793887" }