Ben Singh, Sebastien Chastin, Aaron Miatke, Rachel Curtis, Dorothea Dumuid, Jacinta Brinsley, Ty Ferguson, Kimberley Szeto, Catherine Simpson, Emily Eglitis, Iris Willems, Carol Maher
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引用次数: 0
摘要
背景:可穿戴活动追踪器(包括健身手环和智能手表)可通过监测生理参数来检测疾病。然而,它们作为特定疾病诊断工具的准确性仍不确定:本系统综述和荟萃分析旨在评估可穿戴活动追踪器是否可用于检测疾病和医疗事件:方法:检索了十个电子数据库中从开始到 2023 年 4 月 1 日发表的研究。如果研究使用可穿戴活动追踪器诊断或检测成人在自由生活条件下的医疗状况或事件(如跌倒),则符合条件。元分析用于评估总体曲线下面积(%)、准确性(%)、灵敏度(%)、特异性(%)和阳性预测值(%)。对设备类型(Fitbit、Oura 环和混合型)进行了分组分析。采用乔安娜-布里格斯研究所的诊断测试准确性研究关键评估清单对偏倚风险进行了评估:共纳入 28 项研究,涉及 1,226,801 名参与者(年龄范围为 28.6-78.3 岁)。共有 16 项(57%)研究将可穿戴设备用于诊断 COVID-19,5 项(18%)研究用于诊断心房颤动,3 项(11%)研究用于诊断心律失常或脉搏异常,3 项(11%)研究用于诊断跌倒,1 项(4%)研究用于诊断病毒性症状。使用的设备有:Fitbit(n=6)、苹果手表(n=6)、Oura 戒指(n=3)、多种设备组合(n=7)、Empatica E4(n=1)、Dynaport MoveMonitor(n=2)、三星 Galaxy 手表(n=1)以及其他或未指定设备(n=2)。荟萃分析显示,COVID-19 检测的曲线下面积为 80.2%(95% CI 71.0%-89.3%),准确率为 87.5%(95% CI 81.6%-93.5%),灵敏度为 79.5%(95% CI 67.7%-91.3%),特异性为 76.8%(95% CI 69.4%-84.1%)。在检测心房颤动方面,汇总的阳性预测值为 87.4%(95% CI 75.7%-99.1%),灵敏度为 94.2%(95% CI 88.7%-99.7%),特异性为 95.3%(95% CI 91.8%-98.8%)。在跌倒检测方面,汇总灵敏度为 81.9%(95% CI 75.1%-88.1%),特异度为 62.5%(95% CI 14.4%-100%):结论:可穿戴活动追踪器在疾病检测方面大有可为,在识别心房颤动和COVID-19方面具有显著的准确性。虽然这些发现令人鼓舞,但仍需进一步研究和改进,以提高其诊断精度和适用性:Prospero CRD42023407867; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=407867。
Real-World Accuracy of Wearable Activity Trackers for Detecting Medical Conditions: Systematic Review and Meta-Analysis.
Background: Wearable activity trackers, including fitness bands and smartwatches, offer the potential for disease detection by monitoring physiological parameters. However, their accuracy as specific disease diagnostic tools remains uncertain.
Objective: This systematic review and meta-analysis aims to evaluate whether wearable activity trackers can be used to detect disease and medical events.
Methods: Ten electronic databases were searched for studies published from inception to April 1, 2023. Studies were eligible if they used a wearable activity tracker to diagnose or detect a medical condition or event (eg, falls) in free-living conditions in adults. Meta-analyses were performed to assess the overall area under the curve (%), accuracy (%), sensitivity (%), specificity (%), and positive predictive value (%). Subgroup analyses were performed to assess device type (Fitbit, Oura ring, and mixed). The risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Diagnostic Test Accuracy Studies.
Results: A total of 28 studies were included, involving a total of 1,226,801 participants (age range 28.6-78.3). In total, 16 (57%) studies used wearables for diagnosis of COVID-19, 5 (18%) studies for atrial fibrillation, 3 (11%) studies for arrhythmia or abnormal pulse, 3 (11%) studies for falls, and 1 (4%) study for viral symptoms. The devices used were Fitbit (n=6), Apple watch (n=6), Oura ring (n=3), a combination of devices (n=7), Empatica E4 (n=1), Dynaport MoveMonitor (n=2), Samsung Galaxy Watch (n=1), and other or not specified (n=2). For COVID-19 detection, meta-analyses showed a pooled area under the curve of 80.2% (95% CI 71.0%-89.3%), an accuracy of 87.5% (95% CI 81.6%-93.5%), a sensitivity of 79.5% (95% CI 67.7%-91.3%), and specificity of 76.8% (95% CI 69.4%-84.1%). For atrial fibrillation detection, pooled positive predictive value was 87.4% (95% CI 75.7%-99.1%), sensitivity was 94.2% (95% CI 88.7%-99.7%), and specificity was 95.3% (95% CI 91.8%-98.8%). For fall detection, pooled sensitivity was 81.9% (95% CI 75.1%-88.1%) and specificity was 62.5% (95% CI 14.4%-100%).
Conclusions: Wearable activity trackers show promise in disease detection, with notable accuracy in identifying atrial fibrillation and COVID-19. While these findings are encouraging, further research and improvements are required to enhance their diagnostic precision and applicability.
期刊介绍:
JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636.
The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics.
JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.