背景:可穿戴活动跟踪器,包括健身带和智能手表,通过监测生理参数提供疾病检测的潜力。然而,它们作为特定疾病诊断工具的准确性仍然不确定。
目的:本系统综述和荟萃分析旨在评估可穿戴活动跟踪器是否可用于检测疾病和医疗事件。
方法:搜索了从开始到2023年4月1日发表的十个电子数据库。如果研究人员使用可穿戴活动跟踪器来诊断或检测医疗状况或事件(例如,跌倒)在成年人的自由生活条件下。进行荟萃分析以评估曲线下的总面积(%),准确度(%),灵敏度(%),特异性(%),和阳性预测值(%)。进行亚组分析以评估设备类型(Fitbit,Oura戒指,和混合)。使用JoannaBriggs研究所诊断测试准确性研究关键评估清单评估偏倚风险。
结果:共纳入28项研究,共涉及1,226,801名参与者(年龄范围28.6-78.3)。总的来说,16项(57%)研究使用可穿戴设备诊断COVID-19,5项(18%)研究用于房颤,3(11%)心律失常或异常脉搏的研究,3(11%)的跌倒研究,和1(4%)的病毒症状研究。使用的设备是Fitbit(n=6),苹果手表(n=6),Oura环(n=3),设备的组合(n=7),EmpaticaE4(n=1),DynaportMoveMonitor(n=2),三星Galaxy手表(n=1),和其他或未指定(n=2)。对于COVID-19检测,荟萃分析显示,曲线下的合并面积为80.2%(95%CI71.0%-89.3%),准确率为87.5%(95%CI81.6%-93.5%),灵敏度为79.5%(95%CI67.7%-91.3%),特异性为76.8%(95%CI69.4%-84.1%)。对于心房颤动检测,合并阳性预测值为87.4%(95%CI75.7%-99.1%),灵敏度为94.2%(95%CI88.7%-99.7%),特异性为95.3%(95%CI91.8%-98.8%)。对于跌倒检测,合并敏感性为81.9%(95%CI75.1%-88.1%),特异性为62.5%(95%CI14.4%-100%).
结论:可穿戴活动跟踪器在疾病检测中显示出希望,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,需要进一步的研究和改进,以提高其诊断精度和适用性。
背景:ProsperoCRD42023407867;https://www.crd.约克。AC.uk/prospro/display_record.php?RecordID=407867。
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.
BACKGROUND: Prospero CRD42023407867; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=407867.