Accuracy and Usability of a Novel Algorithm for Detection of Irregular Pulse Using a Smartwatch Among Older Adults: Observational Study.
Accuracy and Usability of a Novel Algorithm for Detection of Irregular Pulse Using a Smartwatch Among Older Adults: Observational Study.
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DOI：
10.2196/13850
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发表时间：
2019-05-15
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影响因子：
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通讯作者：
McManus, David D
中科院分区：
文献类型：
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作者：
Ding, Eric Y;Han, Dong;McManus, David D
背景：心房颤动（AF）通常是阵发性的，症状轻微，阻碍了其诊断。Smartwatches可以提高AF护理，促进长期，非侵入性monitoring.Objective：本研究旨在研究的准确性和可用性的心律失常歧视使用smartwatch.METHODS：共40名成年人提出了一个心脏病诊所戴着智能手表和霍尔特监测和执行脚本运动，以模拟日常生活活动（ADL）。参与者的临床和社会人口学特征从医疗记录中提取。参与者完成了一份调查问卷，评估设备可用性的不同领域。脉搏记录进行了盲分析，使用实时可实现的算法，并与黄金标准的霍尔特monitoring.RESULTS：参与者的平均年龄为71（SD 8）岁;大多数参与者有房颤的危险因素，23%（9/39）是在AF。约一半的参与者拥有智能手机，但没有拥有智能手表。参与者佩戴智能手表42分钟（SD 14），同时产生运动噪声以模拟ADL。该算法确定了314个30秒无噪声脉搏段中的53个与AF一致。与金标准相比，该算法对识别不规则脉搏表现出良好的灵敏度（98.2%），特异性（98.1%）和准确性（98.1%）。三分之二的参与者认为智能手表非常有用。年轻的年龄和先前的心脏复律与更大的整体舒适度和舒适度与数据隐私与使用智能手表的节奏monitorings.CONCLUSIONS：实时可实现的算法分析智能手表脉搏记录表现出较高的准确性识别老年参与者之间的脉搏不规则。尽管年龄大，缺乏对智能手表的熟悉，以及合并症的高负担，但参与者发现智能手表是高度可接受的。
BACKGROUND: Atrial fibrillation (AF) is often paroxysmal and minimally symptomatic, hindering its diagnosis. Smartwatches may enhance AF care by facilitating long-term, noninvasive monitoring.OBJECTIVE: This study aimed to examine the accuracy and usability of arrhythmia discrimination using a smartwatch.METHODS: A total of 40 adults presenting to a cardiology clinic wore a smartwatch and Holter monitor and performed scripted movements to simulate activities of daily living (ADLs). Participants' clinical and sociodemographic characteristics were abstracted from medical records. Participants completed a questionnaire assessing different domains of the device's usability. Pulse recordings were analyzed blindly using a real-time realizable algorithm and compared with gold-standard Holter monitoring.RESULTS: The average age of participants was 71 (SD 8) years; most participants had AF risk factors and 23% (9/39) were in AF. About half of the participants owned smartphones, but none owned smartwatches. Participants wore the smartwatch for 42 (SD 14) min while generating motion noise to simulate ADLs. The algorithm determined 53 of the 314 30-second noise-free pulse segments as consistent with AF. Compared with the gold standard, the algorithm demonstrated excellent sensitivity (98.2%), specificity (98.1%), and accuracy (98.1%) for identifying irregular pulse. Two-thirds of participants considered the smartwatch highly usable. Younger age and prior cardioversion were associated with greater overall comfort and comfort with data privacy with using a smartwatch for rhythm monitoring, respectively.CONCLUSIONS: A real-time realizable algorithm analyzing smartwatch pulse recordings demonstrated high accuracy for identifying pulse irregularities among older participants. Despite advanced age, lack of smartwatch familiarity, and high burden of comorbidities, participants found the smartwatch to be highly acceptable.