智能可穿戴设备在心血管领域应用的专家建议

智能可穿戴设备的分类

健康领域的智能可穿戴设备存在不同分类方式。根据国际电子技术委员会2021年的分类方法[1],智能可穿戴设备分为4大类;近身电子产品(佩戴在身体附近但不直接接触身体)、贴身电子产品(佩戴在身体表面且直接接触身体)、体内电子产品(植入体内)、电子纺织品(与至少1个电子元件或设备结合的衣物)。我国2018年发布的《可穿戴产品分类与标识》根据与身体接触程度、穿戴位置、产品形态、应用领域对智能可穿戴设备进行了详细分类[2]。技术咨询公司高德纳将智能可穿戴设备按照已有的产品形态分为:智能手表、腕带、耳戴式设备、头戴式显示器、智能服装、智能贴片[5]。

为便于阐述,本建议将涉及的智能可穿戴设备分为以下类别:智能手表/腕带/戒指、智能贴片和智能服装(表2)。由于篇幅限制,尽管体内电子产品(如心脏起搏器)在有些分类中也隶属智能可穿戴设备,但在此不涉及,其使用建议可参照中华医学会相应的指南及专家共识。

表2 智能可穿戴设备的分类及代表产品举例

|类别|代表产品| |---|---| |智能手表/腕带/戒指|苹果手表、华为手表/腕带、荣耀手表/腕带、佳明手表、Fitbit腕带、小米手表/腕带、华米手表、Oura戒指| |智能贴片|AliveCor手持心电片、Zio贴片、ePatch贴片、BodyGuardian贴片、Vital Patch贴片、联影智融心电贴片| |智能服装|ReDS背心、LifeVest背心|

智能可穿戴设备的心血管相关生理参数

1. 脉率:智能可穿戴设备通过光电容积描记(photoplethysmography, PPG)获取脉率[6],即分析脉搏周期内动脉血流的周期性改变以检测心脏搏动。智能可穿戴设备测量脉率的平均绝对百分比误差(mean absolute percentage error, MAPE)在各研究中平均约3%,静息时MAPE大多<10%[7],但在肤色深、心律失常人群及运动时测量,准确性受影响[8-14]。

2. 心电图:智能手表/腕带获取的心电图与传统心电图的肢体导联Ⅰ类似,智能贴片、智能服装则可以获取多导联心电图[15]。近年也有研究显示,将智能手表/腕带放置在不同位置可以获取Ⅰ导联外的其他导联心电图,与普通心电图的形态相似度较高[16-21]。

3. 血压:智能可穿戴设备测量血压的方法主要包括气囊法与无气囊法。

大多数电子血压计采用的是气囊法,测量时需将气囊固定在动脉表面并加压超过动脉收缩压,在逐渐减压过程中分析气囊压力,通过设备内置算法得出收缩压及舒张压[22]。部分气囊法智能手表在规范使用下所测得的血压准确度高,在研究中符合美国医疗器械促进协会/欧洲高血压学会/国际标准化组织规定的血压误差(±5±8) mmHg标准(1 mmHg=0.133 kPa)[23-27]。

无气囊法无需气囊加压、测量过程无感,可以单独利用PPG波形或者结合心电图、心冲击图等信号计算血压[28-29]。无气囊法主要测量血压较基线测量值的变化,因此需要定期使用血压计校准。无气囊法血压设备能否利用血流动力学追踪到个体的血压变化目前存在争议[30],且准确性无法达到血压计标准[31],暂无法作为医疗级血压测量工具。

4. 脉搏血氧饱和度(saturation of peripheral oxygen, $SpO_2$): $SpO_2$是评估肺部疾病进展状况的重要参数,同时与心血管疾病也在密切联系,如可用于评估左心衰竭患者病情。智能可穿戴设备利用红光与红外光的PPG计算出动脉内氧合血红蛋白与总血红蛋白的比值,即$SpO_2$。关于智能可穿戴设备测量$SpO_2$的准确性的研究较少,且大多数并未与金标准血气分析进行比较,已有研究中误差大多在7%以内[32-33]。

5. 呼吸频率:智能可穿戴设备主要通过PPG信号检测呼吸频率[34]。设备通过PPG获取使用者的脉搏波形后,根据呼吸导致的脉搏波形改变间接推断呼吸频率。如果使用者处于睡眠或静息状态时,还可以通过加速度传感器直接检测胸腹部呼吸运

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动[35]。在多项研究中智能可穿戴设备检测静息状态呼吸频率的误差平均为0.68次/min,大部分研究显示误差<3次/min[36],准确性较高。

6. 运动与能量消耗:智能可穿戴设备通过内置的加速度传感器及陀螺仪传感器检测使用者的运动类型、运动量[37]。关于智能可穿戴设备测量步数的研究中,大部分MAPE<10%[77],但对卒中患者、使用轮椅人群,及在上肢剧烈活动过程中进行测量时,准确性受影响[38-40]。设备对运动类型的检测准确率较高[41-42]。

智能可穿戴设备通过预先输入的年龄、性别、身高、体重以及实时检测的运动、脉率计算能量消耗。总能量消耗分为静息能量消耗与活动能量消耗两部分。静息能量消耗将年龄、性别、身高、体重代入公式计算,表示维持清醒状态下基础器官功能所需的最低能量[43]。活动能量消耗根据运动类型及脉率计算,各厂家的计算方法尚未公布,目前计算准确性尚不理想[44]。在多项研究中智能可穿戴设备平均低估能量消耗 3 kcal/min (1 kcal=4.184 kJ)[7],大部分MAPE>20%[45],准确性不高。

7. 容量负荷:智能可穿戴设备进行容量监测的技术手段较多,电阻抗和介电性是目前较为成熟的方法。电阻抗在物理学中表示对交流电的阻碍作用[46]。当使用者(肺部或全身)液体负荷增加时,对电流的阻碍作用减小,电阻抗降低,可以在临床心力衰竭发作前进行预警[47]。目前研究显示经皮设备的电阻抗对心力衰竭发作的预警比体重变化值更准确,与植入式设备的体内电阻抗预测效果相当[48-54]。介电系数是描述物体对电场屏蔽效果的物理量,水的介电系数高,空气的介电系数低,因此测量经胸的介电系数可以评估肺水含量[55]。

此外,肺部液体容量增加导致的射频特性改变、心力衰竭加重导致的心脏声学标志物异常也可用于心力衰竭加重的早期预警。

智能可穿戴设备在心血管领域的应用

一、心律失常

心律失常通常呈阵发性,但目前的检测手段多局限于医疗场所,且只能短时间监测,导致大量漏诊。智能可穿戴设备通过心电图与PPG功能实时记录节律与心率,有助于心律失常的早期发现与长期管理。

(一) 心房颤动

心房颤动是智能可穿戴设备研究最广泛的领域, 相关证据也最为充分。目前认为智能可穿戴设备可

以参与心房颤动的早期发现、长期监测等各环节。

1. 心房颤动的筛查:约1/3的心房颤动患者无任何临床表现[56]。由于心房颤动患病率与年龄呈正相关,2020年欧洲心脏病学会的心房颤动指南推荐对≥65岁的老年人或高卒中风险者进行心房颤动筛查[57]。智能可穿戴设备较传统筛查手段(脉搏触诊、12导联心电图或24h动态心电图等)监测时间更长、更舒适便捷,对于较难捕捉的阵发性心房颤动更有价值。目前智能可穿戴设备可以通过PPG(脉率监测)或心电图(心电监测)两种方式筛查心房颤动。

PPG是智能手表/腕带常用的心房颤动筛查方法。Apple Heart研究纳入约42万例无心房颤动病史的智能手表/腕带使用者,中位监测时间117d,其中0.52%的使用者在监测期间收到脉率不规则的通知,心房颤动警报的阳性预测值为84%[58]。MAFA-Ⅱ研究纳入约19万例无心房颤动病史的智能手表/腕带使用者,最终0.2%的使用者被判定为疑似心房颤动,其中87%最终确诊心房颤动[59]。Fitbit Heart研究纳入约46万例无心房颤动病史的智能手表/腕带使用者,其中1.0%出现可疑心房颤动,心房颤动警报的阳性预测值为98%[60]。

心电图是智能贴片筛查心房颤动的常用方法,近年来部分智能手表/腕带也配备了心电图功能。用于心房颤动筛查的智能贴片根据使用方式可以分为贴附式与手持式。mSToPS研究利用贴附式贴片对年龄>75岁或卒中高危人群进行心房颤动筛查,相比不使用贴片者心房颤动的诊断率更高(3.9%比0.9%)[61]。REHEARSE-AF研究在卒中风险增加的≥65岁门诊患者中利用手持式贴片检测心房颤动,12个月内心房颤动检出率为3.8%[62]。一项荟萃分析表明智能手表/腕带的单导联心电图检测心房颤动的灵敏度与特异度均为95%[63]。

在智能可穿戴设备种类选择上,有荟萃分析显示基于PPG的智能手表/腕带与基于心电图的智能贴片筛查心房颤动的灵敏度与特异度相当,均在95%以上[64]。二者的区别主要在于:(1)智能贴片对于其他心律失常也有筛查作用,因此可能合并其他心律失常者建议使用智能贴片;(2)不同产品的其他功能存在差异,可以根据需要监测的生理指标进行选择。

心房颤动相关的脑栓塞占缺血性卒中的20%~30%,占隐源性卒中的10%[57]。有研究显示,在隐源性卒中患者中使用智能可穿戴设备相比24h动态心电图可以诊断出更多的心房颤动[65-66]。

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2. 心房颤动的长期监测:心房颤动可能由日常生活中接触到的多种因素诱发,很难确定个性化的易感因素。有研究使用手持式心电贴片结合患者自我报告以探究心房颤动的诱发因素,结果显示酒精和低质量睡眠是心房颤动发作的重要因,且患者可以通过减少暴露以降低心房颤动的发作频率[67-68]。

一些心房颤动治疗药物会延长QT间期,监测服用此类药物患者的QT间期至关重要[69]。一项利用手持式心电贴片测量健康志愿者和接受索他洛尔或多非利特的住院患者QT间期准确性的研究表明,心电贴片与12导联心电图测量的QTc间期类似[70]。另一项研究使用手持式心电贴片监测口服多非利特患者的QT间期,结果显示其与普通12导联心电图上的QTc间期无显著差异(均在±20 ms以内)[71]。

心房颤动消融术后仍有部分患者会复发,而智能可穿戴设备可以提供长期、有效的心房颤动复发监测[72]。一项研究显示,使用手持式心电贴片对心房颤动消融术后患者进行心电监测,在检测复发方面灵敏度达100%,特异度为97%[73]。也有研究认为间断使用手持式心电贴片相比佩戴24h动态心电图可以发现更多的心房颤动复发[74]。

建议

对于尚未确诊心房颤动的隐源性卒中患者、年龄≥75岁者或卒中高危人群($CHA_2DS_2-VASc$评分≥2分的男性或≥3分的女性),强烈推荐使用有循证医学证据的智能可穿戴设备通过PPG或心电图筛查心房颤动。

对于尚未确诊心房颤动的年龄≥65岁者,推荐使用有循证医学证据的智能可穿戴设备通过PPG或心电图筛查心房颤动。

对于服用抗心律失常药物或消融术后的心房颤动患者,可以考虑使用智能切房监测心房颤动。

对于阵发性心房颤动患者,可以考虑使用智能可穿戴设备寻找发作诱因以减少暴露。

对于服用影响QT间期药物的心房颤动患者,可以考虑使用智能可穿戴设备的心电图测量QT间期。

用于心房颤动筛查或监测的智能可穿戴设备选择上,可以考虑根据所需监测时长、设备可获得性等选择智能手表/腕带或贴附式/手持式心电贴片。

(二)室性心律失常

室性心律失常轻者可表现为无症状或心悸,重者可导致血流动力学异常甚至猝死。在室性心律失常领域,智能可穿戴设备除用于检测,还可用于治疗。

1. 室性心律失常的筛查:PPG较难识别室性心律失常,因此目前仅能通过心电图筛查。另外,由于室性心律失常发作时间通常短暂,需要使用持续监测心电图的贴附式心电贴片。目前研究显示佩戴时间1-2周的贴附式心电贴片相比佩戴时间仅24~48 h的动态心电图可以发现更多室性心律失常,主要得益于佩戴时间的延长[75-78]。此外,贴附式心电贴片还可以辅助评估室性心律失常负荷[79]。尽管智能手表/腕带也有成功检测室性心律失常的报道[80-81],但通常是偶然发现,不应将其作为常规筛查手段。

2. 室性心律失常的治疗:可穿戴心律转复除颤器(wearable cardioverter defibrillator, WCD)也可以归类为智能可穿戴设备,但与一般智能可穿戴设备仅能用于监测不同的是,WCD兼具监测及治疗功能[82-83]。WCD由4个部分构成:棉质背心、监测电极、除颤电极、挂在腰间的处理器及电池。当监测电极识别到恶性室性心律失常后会发出噪声及振动提醒患者,如患者意识清醒可以取消电击,否则将通过除颤电极实施电击。

目前指南中 WCD 主要推荐用于恶性心律失常风险高但不适合植入植入式心脏复律除颤器(implantable cardioverter defibrillator, ICD)的患者[84-86]。研究认为 WCD 转复成功率与 ICD 类似,但仍有极少部分患者因未随时佩戴、未正确佩戴、发生不可电击性恶性心律失常等猝死,其误放电风险很低[87-92]。但也有研究认为 WCD 并未降低急性心肌梗死后患者的死亡风险[93]。

建议

如果智能可穿戴设备的心电图功能检测到室性心律失常,强烈推荐行动态心电图检查进行确认。

对于室性心律失常风险较高者或怀疑存在室性心律失常者,可以考虑使用贴附式心电贴片筛查室性心律失常,但最终结果需要心电学专家进行确认。

对于存在ICD指征但暂不适合植入ICD的患者,可以考虑使用WCD。

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(三) 其他心律失常

1. 可疑心律失常的筛查:对于可疑心律失常者,使用便捷、监测时间长的智能可穿戴设备可以辅助判断是否存在心律失常。多项研究认为手持式或贴附式心电贴片可以协助急诊就诊的心悸及晕厥患者筛查是否存在心律失常[76, 94-96]。有研究对比了传统动态心电图与14d贴附式心电贴片监测对可疑心律失常患者的诊断率,结果显示心电贴片发现的心律失常事件数量是动态心电图的1.5倍,这种差异主要由于监测时间的延长[75]。还有研究认为从佩戴贴附式心电贴片到首次发现心律失常往往超过48h,因此24h动态心电图存在严重局限性,而智能可穿戴设备则可以突破这一时间限制[78]。运动员由于职业特殊性,很难在运动过程中使用传统动态心电图监测心律失常,智能可穿戴设备则提供了一种便捷的检测方法,目前已有成功诊断运动期间心律失常的报道[97]。

准确性方面,由于智能可穿戴设备的心电图与传统12导联心电图某些导联相似度较高[17, 19-21, 98-101],因此在心律失常检测中较为准确[102]。有研究显示手持式心电贴片区分室上性心动过速与窦性心动过速的灵敏度为89%,特异度为91%[103]。在一项对比智能手表与12导联心电图的研究中,心动过缓患者全部被准确识别,且智能手表可以区分81%的窦性心动过缓与房室传导阻滞[104]。另一项研究显示动态心电图与贴附式心电贴片同时佩戴期间,心电贴片检测到的心律失常事件占动态心电图的85%[75]。

智能可穿戴设备还可通过改变接触部位增加诊断准确性。在一项智能手表研究中,将手表分别放置在传统12导联心电图的体表位置以获得I、II导联和V₁~V₆导联心电图,结果显示智能手表获得不同导联心电图可增加束支传导阻滞和心动过缓的诊断准确性[16]。

2. 高危人群心律失常的筛查:急性心血管事件或心脏术后出现心律失常的风险较高,智能可穿戴设备可以帮助发现心律失常,可能影响治疗策略。对于心肌梗死后患者,有研究显示贴附式心电贴片可以在大部分患者中发现心律失常[105]。

经导管主动脉瓣置换(transcatheter aortic valve replacement, TAVR)术后传导阻滞是常见并发症,智能可穿戴设备可用于筛查。一项研究使用贴附式心电贴片对TAVR术后患者进行了持续30 d的监测,识别出了大量出院后才出现起搏器

指征的患者[106]。另一项研究使用贴附式心电贴片识别出了大量于TAVR术后2~3个月发生缓慢性心律失常的患者[107]。有研究使用智能手表间检查心电图,同样可以筛查出TAVR术后传导阻滞[108]。

建议

如果智能可穿戴设备的心电图功能检测到心 律失常,强烈推荐行动态心电图检查进行确认。

对于仅能记录单导联心电图的智能可穿戴设备,可以考虑在出现不适症状时通过调整智能可穿戴设备放置位置以采集多导联心电图。

对于疑似心律失常者,可以考虑使用贴附式心电贴片筛查心律失常,最终结果需要心电学专家进行确认。

对于急性心血管事件或心脏术后的患者,可以考虑使用智能可穿戴设备筛查心律失常(优选贴附式心电贴片),最终结果需要心电学专家进行确认。

二、冠心病

智能可穿戴设备可以通过实时心电图发现缺血改变,从而改善心肌缺血的诊断与管理。另外,冠心病的长期管理对患者的运动、心率有要求,智能可穿戴设备可以提供持续的运动与心率监测,有助于调整治疗方案。

1. 筛查与辅助诊断: 急性或慢性心肌缺血可以在心电图上产生多种异常改变, 其中ST段及T波异常最为常见[109-110], 为智能可穿戴设备通过心电图检测心肌缺血提供了方法。传统心电图需要12/18导联检测并定位心肌缺血[111]。智能贴片、智能服装通常受限于体积、舒适度、耗电量等因素, 虽可持续监测心电图但很难采集完整12/18导联; 而智能手表/腕带需要双手同时接触设备以采集心电图, 因此仅能采集类似I导联的单导联心电图且无法持续监测。有研究使用人工智能加持的单导联心电图识别急性ST段抬高型心肌梗死(ST-segment elevation myocardial infarction, STEMI), 效果较好, 但对于导联非对应部位的心肌缺血识别效果仍不佳[112-113]。

智能贴片以单导联心电图为主,目前也出现了多导联心电图贴片。一项研究使用仅2个电极的智能贴片合成12导联心电图用于STEMI 诊

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断,灵敏度、特异度分别达89%与84%[114]。另一项研究在急救车上使用12导联心电图智能贴片与传统12导联心电图机进行比较,结果显示智能贴片获取心电图的时间更短[115]。智能服装与使用者接触面积大,方便采集多导联心电图,目前有多项研究报道智能服装可以持续监测12导联心电图,采集质量较高[116-118],未来可能用于缺血诊断。

智能手表/腕带日常佩戴仅能间断监测单导联心电图,对心肌缺血的灵敏度不高[104]。目前研究显示可以通过将设备放置于躯干不同位置获得类似于标准12导联的心电图,形态相似度高[101],诊断STEMI的灵敏度、特异度分别为93%与95%,诊断非ST段抬高型心肌梗死的灵敏度、特异度分别为94%与92%[119-120]。有案例报道,智能手表/腕带记录下缺血性心电图改变从而加快诊断[121-123],提示在无法进行传统心电图时可以尝试使用智能手表/腕带记录心电图以辅助心肌缺血诊断。

2.长期管理:既往研究显示冠心病患者静息心率升高与心肌缺血、心血管事件及死亡相关[124,125],目前认为冠心病患者的心率是重要干预目标。智能可穿戴设备通过PPG或心电图功能持续监测心率,测量准确度较高,但在肤色深、心律失常人群及运动等情况下,PPG测量心率的准确性受影响。

我国专家共识提出冠心病患者的静息心率应控制在55~60次/min[126]。智能可穿戴设备测量冠心病患者的心率准确性较高[127]。一项研究使用智能手表/腕带监测急性心肌梗死出院后患者的心率变化,结果显示患者心率的下降速度与合并症相关[128]。另一项研究对比了智能贴片与智能手表/腕带评估心脏康复期间的心率准确性,结果显示运动期间智能贴片的准确性仍较高,而智能手表/腕带受运动影响[129]。可以考虑使用智能可穿戴设备监测冠心病患者的静息心率以调整二级预防治疗。

运动量是观察心血管病患者病情变化、指导患者康复的重要指标[130-131],智能可穿戴设备可以测量运动量以指导冠心病长期管理。一项荟萃分析纳入了14项心脏康复的随机对照研究,结果显示智能可穿戴设备辅助的心脏康复可以明显改善心肺健康状况[132]。另一项荟萃分析显示,在智能可穿戴设备的辅助下心脏康复患者的每日运动量明

显增加[133]。有荟萃分析认为使用智能可穿戴设备可以改善患者血压、血脂、腰围等重要心血管危险因素[134]。还有研究成果成功使用智能可穿戴设备监测开胸心脏术后患者的活动情况[135-136]。能量消耗往往计算不准确(见前文“运动与能量消耗”部分),因此参考价值较低。

建议

对于冠心病患者,可以考虑使用智能可穿戴设备监测静息心率以调整治疗方案。

对于需要长期心脏康复的冠心病患者,可以考虑使用有运动监测功能的智能可穿戴设备监测患者的运动情况。

在怀疑出现心肌缺血事件时,如果无法获得常规心电图检查,可以考虑使用能采集心电图的智能可穿戴设备记录心电图,且最好通过移动智能可穿戴设备的位置采集多导联心电图。

不推荐将智能可穿戴设备监测的冠心病患者运动状态下心率作为调整治疗的依据。

不推荐将智能可穿戴设备计算的能量消耗作为心脏康复的参考。

三、高血压

高血压传统的诊断方法主要基于间断的单次血压测量,但单次血压测量存在较大偏倚风险[137],且无法评估短期及长期的血压变异性[138]。动态血压监测可以发现白大衣高血压、隐匿性高血压并可评估血压变异性,但存在设备数量有限、测量舒适度不高、无法长期使用等问题[139]。通过使用智能可穿戴设备进行血压监测有望改善单次血压测量与现有动态血压监测的局限性,近年的国内相关共识对此进行了推荐[140]。

1. 筛查与辅助诊断:中国高血压患者逾2亿,但知晓率仍较低[4]。目前部分智能可穿戴设备血压测量的准确性已得到认证[26, 141-142],将大幅提高隐匿性高血压和白大衣高血压的诊断率。一项研究显示,使用气囊法血压压腕表测量坐位及仰卧位时血压与传统血压计平均差异均在5mmHg以内,提示该设备适合测量夜间睡眠期间的血压[143]。气囊法血压腕表亦可获得血压变异率[144]。有研究在直立倾斜试验过程中使用智能手表采用气囊法测量患者血压,结果显示在患者晕厥前可以检测到血压下降,且大部分患者在血压最低时仍可以测出血

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压[145],提示智能可穿戴设备有助于发现体位性低血压。

一项研究使用气囊法血压腕表持续测量血压并与传统动态血压监测结果进行比较,结果显示诊室内与诊室外两种设备的血压测量平均差异均较小(0.8、3.2 mmHg),诊室内与诊室外两种设备测量差异<10 mmHg的比例分别为58.7%与47.2%,诊室外两种设备的差异略大主要归因于测量姿势不标准[146],提示使用智能可穿戴设备测量血压需注意测量方法。

尽管无气囊法测量血压准确性不足,尚无法用于高血压管理,但可能有助于高血压的检出。目前尚无使用无气囊法血压测量进行大规模高血压筛查的研究,但有研究显示无气囊法血压与标准袖带式血压相关性较高[147-155],因此,对于无气囊法智能可穿戴设备提示血压升高者,应建议前往医疗机构进一步确认有无高血压。

另外为了测量更准确,使用智能手表/腕带测量血压有以下注意事项:(1)双臂血压不对称者需测量血压较高一侧。(2)需根据手腕周长选择合适尺寸的腕带。(3)高血脂、糖尿病等疾病多伴有动脉硬化,可能影响测量结果。(4)如曾接受桡动脉穿刺术,血压测量应选择对侧非穿刺的桡动脉。(5)测量时需尽量让测量位置与心脏平齐。

2.长期管理:多项临床试验表明,在实现目标血压方面,血压远程监测比常规诊室管理更有效[156-158]。基于智能可穿戴设备的血压监测可能是未来血压管理的常规策略。

动脉硬化是多种心血管疾病的重要危险因素,颈-脉搏波传播速度(carotid-femoral pulse wave velocity, cfPWV)是评估动脉硬化的金标准之一,可用于心血管疾病的预警。有研究采用智能手表采集心电图及PPG,进而估算cfPWV,与动脉硬化检测仪所测得的cfPWV一致性良好[159]。

脉搏波传导时间是指脉搏沿动脉血管传播所需时间,可代替脉搏波传播速度间接反映各种危险因素对动脉血管的损伤,评估动脉血管结构和功能的早期异常。有研究利用智能手表采集腕部脉搏波和心电图信号,结果显示脉搏波传导时间具有血管硬化风险预警价值[160-161]。

另外,我国及欧洲的高血压患者心率管理共识提出应控制心率<80次/min[162-163],可以考虑使用智能可穿戴设备观察高血压患者静息心率以调整影响心率的降压药物。

建议

对于尚未确诊高血压者,如果智能可穿戴设备提示血压升高,推荐在正规医疗机构进行规范血压测量,必要时完善动态血压监测检查。

对于已经确诊高血压的患者,可以考虑使用经国际标准校验或国家医疗器械注册认证的气囊法智能手表/腕带监测血压,但需要关注测量注意事项。

对于怀疑隐匿性高血压、白大衣高血压、夜间高血压及体位性低压患者,可以考虑使用经国际标准校验或国家医疗器械注册认证的气囊法智能手表/腕带监测血压,但需要注意测量注意事项。

对于高血压患者,可以考虑通过已认证的智能可穿戴设备获取血压变异性并初步评估血管功能。

对于高血压患者,可以考虑使用智能可穿戴设备的PPG或心电图功能监测静息心率以调整治疗方案。

四、心力衰竭

容量监测是心力衰竭恶化预警的重要手段,优于传统的体重监测[164-165],当前部分智能可穿戴设备能够进行容量监测,为心力衰竭的早期识别和长期管理提供了有效的手段。智能可穿戴设备还可以辅助评估心功能,为疑似与确诊心力衰竭者提供客观的心功能评价手段。此外,智能可穿戴设备可帮助监测心力衰竭患者的静息心率以调整治疗。

1. 容量监测:心力衰竭容量监测领域新技术、新设备层出不穷,此处仅介绍已证实可以改善心力衰竭管理、获得美国食品药品监督管理局批准的技术及设备,但需要注意截至目前均未获得我国国家药品监督管理局批准,因此使用尚受限。

经皮电阻抗方面,LINK-HF研究使用一款能同时监测经皮电阻抗、心电图及多项生理信号的胸部智能贴片计算患者每日生理指标综合参数,对心力衰竭加重住院进行预警,结果显示在心力衰竭加重前10d内进行预警的灵敏度介于76%~88%、特异度达85%,从开始报警到入院的中位时间为6.5d[48]。

介电性方面,SMILE-HF研究使用一款智能背心监测心力衰竭患者的经胸介电性,结果显示使用智能背心的患者相比常规护理心力衰竭再入院率

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低约48%,且对于再入院患者,智能背心还可以降低住院时间并延长住院间隔[166]。

射频特性方面,BMAD-HF研究使用一种能贴附在胸前的贴片实时检测患者肺部的射频特性,当患者的肺水指数连续3d超过阈值即进行主动干预,最终在出院90d内减少了38%的心力衰竭再入院[167]。

综上,目前心力衰竭容量监测领域的多种技术手段在小规模随机对照研究中显示出降低心力衰竭恶化风险、改善生活质量的能力,但仍需要在多中心、大规模的研究中进一步证明其价值。

2. 心功能评估:6 min步行试验是一种检测心肺功能的简单试验,传统的6 min步行试验无法连续监测患者的生理指标,通过引入智能可穿戴设备便可持续监测SpO₂、心率、血压。目前,有研究认为智能可穿戴设备可以协助观察患者心肺功能恶化分级[168-169],预测6 min步行试验期间发生不良事件的风险[170],帮助患者在院外完成6 min步行试验评估[171-172]。

新型冠状病毒感染期间,有多项研究认为智能可穿戴设备可以通过测量SpO₂及其他生理参数实时观察并预测患者病情进展[173-177]。心力衰竭急性发作与新型冠状病毒感染恶化具有相似性,均可表现为呼吸频率增加、SpO₂下降,尽管目前尚无智能可穿戴设备通过监测呼吸频率与SpO₂成功预测心力衰竭急性加重的研究,但鉴于此前新型冠状病毒感染相关研究以及智能可穿戴设备测量呼吸频率及SpO₂的准确性,功能简单的智能可穿戴设备可能具有预测心力衰竭急性加重的潜力。在纳入心力衰竭加重住院后出院患者的研究提示,呼吸频率增快是心力衰竭再入院的一个重要预测因素[178-179]。但需要注意的是,在患者呼吸困难期间因患者的手腕难以静止,可能频繁出现SpO₂及呼吸率测量失败的情况[180-181]。

此外,可能通过智能可穿戴设备测量的心脏声学标志物评估心功能。有研究使用间断测量的10s心脏声学标志物指导心力衰竭治疗,声学标志物包括电-机械激动时间与第三心音,最终声学指导组相比常规护理组1年心力衰竭再住院或死亡减少了39%[182]。

3. 心率管理:对于射血分数降低的心力衰竭患者,目前国内外指南推荐将心率控制在50~70次/min[183-185]。智能可穿戴设备测量心力衰竭患者的静息心率准确性较高,但在运动时受影响[186],

可以考虑使用智能可穿戴设备监测心力衰竭患者静息心率以指导B受体阻滞剂及伊伐布雷定等药物的使用。

建议

对于心力衰竭患者,无论射血分数如何均可以考虑在常规心力衰竭治疗策略的基础上参考智能可穿戴设备的容量监测结果调整治疗方案,可以考虑综合患者喜好、舒适度、设备可获得性选择设备。

对于慢性心力衰竭患者,可以考虑通过智能可穿戴设备获取的SpO₂和呼吸频率进行疾病监测。

对于行6 min步行试验的患者,可以考虑使用智能可穿戴设备监测试验过程中的SpO₂、呼吸频率、心率、血压变化。

对于心力衰竭患者,可以考虑使用智能可穿戴设备的PPG或心电图功能监测患者静息心率以调整治疗方案。

五、无明确心血管病的健康成人

目前国内外的心血管病一级预防指南均认为对于无明确心血管病的健康成人,应该通过增加体力活动、戒烟、控制体重、优化饮食、控制血压/血脂/血糖等手段降低未来心血管病风险[187-189]。智能可穿戴设备可以帮助增加体力活动以促进心血管健康。

对于健康成人,有研究显示使用智能可穿戴设备可以促进使用者对自身运动、能量消耗的关注,有助于增加使用者的运动量[190-191]。一项荟萃分析中智能可穿戴设备可以增加每日步数1312步、增加每周中重度体力活动57 min并减少每日久坐时间27 min[192]。另一项荟萃分析中智能可穿戴设备可以增加每日步数1800步、额外步行40 min并降低体重1kg[193]。根据各国的心血管病一级预防指南,每周应至少进行150 min中等强度体力活动或75 min高强度体力活动,同时尽量减少久坐时间[187-189]。可以考虑使用智能可穿戴设备促进运动以改善心血管病一级预防现状。

建议

对于无心血管病的健康成人,可以考虑使用 有运动监测功能的智能可穿戴设备以促进运动、 减少久坐。

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目前存在的问题:(1)各公司生产的智能可穿戴设备生理参数测量准确性存在较大差异,因此对诊疗的指导作用不能一概而论,亟需出台相关标准来进行规范。(2)智能可穿戴设备的数据存在隐私性,导致采集的数据无法直接用于硬件及算法改进。需要在符合隐私标准、主动自愿的情况下吸引使用者上传数据以协助改进。(3)目前智能可穿戴设备的研究多为观察性研究,亟待通过高质量的多中心随机对照研究证实智能可穿戴设备在疾病筛查和长期监测中的作用。(4)现阶段智能可穿戴设备的部分技术仍有缺陷,需要在运动体征测量、无气囊血压测量、无创血糖监测、能量消耗计算等领域继续改进。(5)老年人是心血管病高危人群,但智能可穿戴设备在老年人中使用率不高,需要提高老年人的主动健康管理与度。(6)对于少部分使用者,智能可穿戴设备可能增加健康焦虑进而影响正常生活。

尽管已开展不少相关研究,但智能可穿戴设备在心血管领域的应用目前仍较局限。伴随着智能可穿戴设备渗透率增加与人群健康意识提升,智能可穿戴设备无论在心血管病的一级预防或二级预防都会大有作为,补足院外长期管理这一环。通过医疗工作者、智能可穿戴设备使用者、智能可穿戴设备开发者的共同努力,期待能改善全民心血管健康状况。

执笔专家:张萍(清华大学附属北京清华长庚医院),唐熠达(北京大学第三医院),孙艺红(首都医科大学北京安贞医院),刘婧(北京大学人民医院),李晶洁(哈尔滨医科大学附属第一医院)、郭丽珠(首都医科大学北京安贞医院)、费金韬(清华大学)

核心专家:韩雅玲(解放军北部战区总医院)、马长生(首都医科大学安贞医院)、李玉明(天津康汇医院)、张萍(清华大学附属北京清华长庚医院)

专家组成员(以姓氏笔画为序):王召军(哈尔滨医科大学附属第一医院),王祖禄(解放军北部战区总医院),王焱(厦门心血管病医院),牛楠(大连医科大学附属第二医院),石蓓(遵义医科大学附属第一医院),申晓彧(山西医科大学附属第一医院),曲鹏(大连理工大学医学部),刘兵(北京医院),刘启明(中南大学湘雅二院),刘海伟(解放军北部战区总医院),刘斌(吉林大学第二医院),刘静(首都医科大学附属北京安贞医院),孙英贤(中国医科大学附属第一医院),寿锡凌(陕西省人民医院),严晓伟(中国医学科学院北京协和医院),李卫(中国医学科学院阜外心血管病医院),吴文静(中日友好医院),吴永健(中国医学科学院阜外心血管病医院),陈茂(四川大学华西医院),邵春丽(北京大学第三医院)

院),何榕(清华大学附属北京清华长庚医院),周胜华(中南大学湘雅二院),庞文跃(中国医科大学附属盛京医院),洪葵(南昌大学附属第二医院),袁祖贻(西安交通大学附属第一医院,袁璟(华中科技大学同济医学院附属协和医院),徐凯(解放军北部校区总医院),郭晓刚(浙江大学附属第一医院),曹丰(解放军总医院),喜杨(北京大学人民医院),董建增(首都医科大学附属北京安贞医院),蒋捷(北京大学第一医院),程翔(华中科技大学附属协和医院),曾春雨(陆军特色医学中心大坪医院),薛亚军(清华大学附属北京清华长庚医院)

利益冲突 所有作者声明无利益冲突

参考文献

[1] International Electrotechnical Commission. Wearable electronic devices and technologies [S/OL]. 2021[2024-11-01]. https://webstore.iec.ch/publication/62635.

[2] 国家市场监督管理总局,中国国家标准化管理委员会GB/T 37035-2018 可穿戴产品分类与标识[S/OL].2018[2024-11-01]. http://openstd.samr.gov.cn/bzgk/gb/newGbInfo? hcno=44537EF741C35C3E4027FDEC2C58842E.

[3] GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019[J]. Lancet, 2020, 396(10258): 1204-1222. DOI: 10.1016/S0140-6736(20)30925-9.

[4] 国家心血管病中心.中国心血管健康与疾病报告2023[M].北京:中国协和医科大学出版社,2024.

[5] Gartner forecasts global spending on wearable devices to total $81.5 billion in 2021 [EB/OL]. 2021[2024-11-01]. https://www.gartner.com/en/newsroom/press-releases/2021-01-11-gartner-forecasts-global-spending-on-weara-ble-devices-to-total-81-5-billion-in-2021.

[6] Allen J. Photoplethysmography and its application in clinical physiological measurement[J]. Physiol Meas, 2007, 28(3): R1-R39. DOI: 10.1088/0967-3334/28/3/R01.

[7] Doherty C, Baldwin M, Keogh A, et al. Keeping pace with wearables: a living umbrella review of systematic reviews evaluating the accuracy of consumer wearable technologies in health measurement[J]. Sports Med, 2024, 54(11):2907-2926. DOI: 10.1007/s40279-024-02077-2.

[8] Sequeira N. D'Souza D, Angaran P, et al. Common wearable devices demonstrate variable accuracy in measuring heart rate during supraventricular tachycardia [J]. Heart Rhythm, 2020, 17(5 Pt B): 854-859. DOI: 10.1016/j.hrthm.2020.02.018.

[9] Al-Kaisey AM, Koshy AN, Ha FJ, et al. Accuracy of wrist-worn heart rate monitors for rate control assessment in atrial fibrillation[J]. Int J Cardiol, 2020, 300: 161-164. DOI: 10.1016/j.ijcard.2019.11.120.

[10] Thomson EA, Nuss K, Comstock A, et al. Heart rate measures from the Apple Watch, Fitbit Charge HR 2, and electrocardiogram across different exercise intensities[J].

中华心血管病杂志2025年12月第53卷第12期 Chin J Cardiol, December 2025, Vol. 53, No. 12

1361

1361

J Sports Sci, 2019, 37(12): 1411-1419. DOI: 10.1080/02640414.2018.1560644. [11] Koshy AN, Sajeev JK, Nerlekar N, et al. Smart watches for heart rate assessment in atrial arrhythmias[1]. Int J Cardiol, 2018, 266: 124-127. DOI: 10.1016/j.ijcard.2018.02.073. [12] Gillinov S, Etiwy M, Wang R, et al. Variable accuracy of wearable heart rate monitors during aerobic exercise[J]. Med Sci Sports Exerc, 2017, 49(8): 1697-1703. DOI: 10.1249/MSS.000000000001284. [13] Navalta JW, Montes J, Bodell NG, et al. Concurrent heart rate validity of wearable technology devices during trail running[J]. PLoS One, 2020, 15(8): e0238569. DOI: 10.1371/journal.pone.0238569. [14] Paradiso C, Colino F, Liu S. The validity and reliability of the mi band wearable device for measuring and heart rate[J]. Int J Exerc Sci, 2020, 13(4): 689-701. DOI: 10.70252/NJHQ9420. [15] 国家市场监督管理总局,中国国家标准化管理委员会.医用电气设备第2-25部分:心电图机的基本安全和基本性能专用要求[SOL/20].2021[2024-11-01].http://openads.samr.gov.cn/bzgk/gb/newGbInfo? hcno=2A466CD480F7CC 7829AF88F1D221F10E. [16] Ploux S, Strik M, Caillol T, et al. Beyond the wrist: using a smartwatch electrocardiogram to detect electrocardiographic abnormalities[J]. Arch Cardiovasc Dis, 2022, 115(1):29-36. DOI: 10.1016/j.acv.d2021.11.003. [17] Striepe S, Michaelis A, Markel F, et al. Use of the Apple Watch iECG in adult congenital heart disease patients[J]. Indian Pacing Electrophysiol J, 2022, 22(3): 131-136. DOI: 10.1016/j.ipej.2022.01.007. [18] Nasarre M, Strik M, Daniel Ramirez F, et al. Using a smartwatch electrocardiogram to detect abnormalities associated with sudden cardiac arrest in young adults[J]. Europe, 2022, 24(3): 406-412. DOI: 10.1093/eupace/euab192. [19] Spaccarotella C, Migliarino S, Mongiardo A, et al. Measurement of the QT interval using the Apple Watch[J]. Sci Rep, 2021, 11(1): 10817. DOI: 10.1038/s41598-021-89199-z. [20] Cobos GMÁ. Standard and precordial leads obtained with an Apple Watch[J]. Ann Intern Med, 2020, 172(6): 436-437. DOI: 10.7326/M19-2018. [21] Behzadi A, Sepehri Shamloo A, Mouratis K, et al. Feasibility and reliability of SmartWatch to obtain 3-lead electrocardiogram recordings[J]. Sensors (Basel), 2020, 20(18):5074. DOI: 10.3390/s20185074. [22] Chandrasekhar A, Yavarimanesh M, Hahn JO, et al. Formulas to explain popular oscilometric blood pressure estimation algorithms[J]. Front Physiol, 2019, 10: 1415. DOI: 10.3389/fphys.2019.01415. [23] Sayer G, Piper G, Vorovich E, et al. Continuous monitoring of blood pressure using a wrist-worn cuffless device[J]. Am J Hypertens, 2022, 35(5): 407-413. DOI: 10.1093/ajh/hpac020. [24] Zeng WW, Chan SW, Tomlinson B. Patient preferences for ambulatory blood pressure monitoring devices: Wrist-type or arm-type? [J]. PLoS One, 2021, 16(8): e0255871. DOI: 10.1371/journal.pone.0255871. [25] Hofstede A, Lomme M, Gossink S, et al. The Cloud DX connected HealthKit Pulsewave in home blood pressure monitoring during pregnancy: a clinical evaluation and

user experience study[J]. Pregnancy Hypertens, 2022, 28: 1-8. DOI: 10.1016/j.preghy.2022.01.004. [26] Wang L, Xian H, Guo J, et al. A novel blood pressure monitoring technique by smart HUAWEI WATCH: a validation study according to the ANSI/AAMI/ISO 81060-2: 2018 guidelines[J]. Front Cardiovasc Med, 2022, 9-923655. DOI: 10.3389/fcvm.2022.923655. [27] Yi L, Lv ZH, Hu SY, et al. Validating the accuracy of multifunctional smartwatch sphygmomanometer to monitor blood pressure[J]. J Geriatr Cardiol, 2022, 19(11): 843-852. DOI: 10.11909/j.issn.1671-5411.2022.11.004. [28] Elgendi M, Fletcher R, Liang Y, et al. The use of photoplethysmography for assessing hypertension[J]. NPJ Digit Med, 2019, 2:60. DOI: 10.1038/s41746-019-0136-7. [29] Welykholowa K, Hosanee M, Chan G, et al. Multimodal photoplethysmography-based approaches for improved detection of hypertension[J]. J Clin Med, 2020, 9(4):1203. DOI: 10.3390/jcm9041203. [30] Mukkamala R, Yavarimanesh M, Natarajan K, et al. Evaluation of the accuracy of cuffless blood pressure measurement devices: challenges and proposals[J]. Hypertension, 2021, 78(5): 1161-1167. DOI: 10.1161/HYPERTENSIONAHA.121.17747. [31] Islam S, Chow CK, Daryabeygikhotbehsara R, et al. Wearable cuffless blood pressure monitoring devices: a systematic review and meta-analysis[J]. Eur Heart J Digit Health, 2022, 3(2):323-337. DOI: 10.1093/ehjdh/ztac021. [32] Zhang Z, Khatami R. Can we trust the oxygen saturation measured by consumer smartwatches? [J]. Lancet Respir Med, 2022, 10(5):e47-e48. DOI: 10.1016/S2213-2600(22)00103-5. [33] Windisch P, Schröder C, Förster R, et al. Accuracy of the apple watch oxygen saturation measurement in adults: a systematic review [J]. Cureus, 2023, 15(2): e35355. DOI: 10.7759/cureus.35355. [34] Natarajan A, Su HW, Heneghan C, et al. Measurement of respiratory rate using wearable devices and applications to COVID-19 detection[J]. NPJ Digit Med, 2021, 4(1):136. DOI: 10.1038/s41746-021-00493-6. [35] Leube J, Zschocke J, Kluge M, et al. Reconstruction of the respiratory signal through ECG and wrist accelerometer data[J]. Sci Rep, 2020, 10(1): 14530. DOI: 10.1038/s41598-021-71539-0. [36] Chan PY, Ryan NP, Chen D, et al. Novel wearable and contactless heart rate, respiratory rate, and oxygen saturation monitoring devices: a systematic review and meta-analysis[J]. Anaesthesia, 2022, 77(11): 1268-1280. DOI: 10.1111/anae.15834. [37] Norris M, Anderson R, Kenny IC. Method analysis of accelerometers and gyroscopes in running gait: a systematic review [J]. Proc Inst Mech Eng P J Sport Eng Technol, 2014, 228(1):3-15. DOI: org/10.1177/1754337113502472. [38] Rozanski GM, Aqui A, Sivakumaran S, et al. Consumer wearable devices for activity monitoring among individuals after a stroke: a prospective comparison[J]. JMIR Cardio, 2018, 2(1):e1. DOI: 10.2196/cardio.8199. [39] Karinharju KS, Boughey AM, Tweedy SM, et al. Validity of the Apple Watch(®) for monitoring push counts in people using manual wheelchairs[J]. J Spinal Cord Med, 2021, 44(2): 212-220. DOI: 10.1080/10790268.2019.1576444. [40] Holleck JL. Apple Watch Steps[J]. J Gen Intern Med, 2019,

· 1362 ·

中华心血管病杂志2025年12月第53卷卷第12期 Chin J Cardiol, December 2025, Vol. 53, No. 12

34(1):12-13. DOI:

[41] Fuller D, Anarak JR, Simango B, et al. Predicting lying, sitting, walking and running using Apple Watch and Fitbit data[J]. BMJ Open Sport Exerc Med, 2021, 7(1):e001004. DOI: 10.1136/bmjsem-2020-001004.

[41] Fuller D, Anarak J, IR, Simango B, et al. Predicting lying, sitting, walking and running using Apple Watch and Fitbit data[J]. BMJ Open Sport Exerc Med, 2021, 7(1):e001004. DOI: 10.1136/bmjsem-2020-001004.

[42] Chow HW, Yang CC. Accuracy of optical heart rate sensing technology in wearable fitness trackers for young and older adults: validation and comparison study[J]. JMIR Mhealth Uhealth, 2020, 8(4):e14707. DOI: 10.2196/14707.

[43] Heymsfield SB, Smith B, Dahle J, et al. Resting energy expenditure: from cellular to whole-body level, a mechanistic historical perspective[J]. Obesity (Silver Spring), 2021, 29(3):500-511. DOI: 10.1002/oby.23090.

[44] Germini F, Noronha N, Borg Debono V, et al. Accuracy and acceptability of wrist-wearable activity-tracking devices: systematic review of the literature[J]. J Med Internet Res, 2022, 24(1):e30791. DOI: 10.2196/30791.

[45] O'Driscil R, Turcij I, Beaulieu K, et al. How well do activity monitors estimate energy expenditure? A systematic review and meta-analysis of the validity of current technologies[J]. Br J Sports Med, 2020, 54(6): 332-340. DOI: 10.1136/bjsports-2018-099643.

[46] Editors of Encyclopaedia Britannica. Electrical impedance [EB/OL]. 2022[2024-11-01]. https://www.britannica.com/science/electrical-impedance.

[47] Hankinson SJ, Williams CH, Ton VK, et al. Should we overcome the resistance to bioelectrical impedance in heart failure? [J]. Expert Rev Med Devices, 2020, 17(8): 785-794. DOI: 10.1080/17434440.2020.1791701.

[48] Stehlik J, Schmalfuss C, Bozkurt B, et al. Continuous wearable monitoring analytics predict heart failure hospitalization: the LINK-HF multicenter study[J]. Circ Heart Fail, 2020, 13(3): e006513. DOI: 10.1161/CIRCHEARTFAILURE.119.006513.

[49] Cuba-Gyllensten I, Gastelurrutia P, Riistama J, et al. A novel wearable vest for tracking pulmonary congestion in acutely decompensated heart failure[J]. Int J Cardiol, 2014, 177(1): 199-201. DOI: 10.1016/j.ijcard.2014.09.041.

[50] Lee S, Squillace G, Smeets C, et al. Congestive heart failure patient monitoring using wearable Bio-impedance sensor technology[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2015, 2015:438-441. DOI: 10.1109/EMBC.2015.7318393.

[51] Cuba Gyllensten I, Bonomi AG, Goode KM, et al. Early indication of decompensated heart failure in patients on home-teemonitoring: a comparison of prediction algorithms based on daily weight and noninvasive transthoracic bio-impedance[J]. JMIR Med Inform, 2016, 4(1):e3. DOI: 10.2196/medinform.4842.

[52] Darling CE, Dovancescu S, Saczynski JS, et al. Bioimpedance-based heart failure deterioration prediction using a prototype fluid accumulation vest-mobile mobile dyad: an observational study[J]. JMIR Cardio, 2017, 1(1):e: DOI: 10.2196/cardio.6057.

[53] Smeets C, Lee S, Groenendaal W, et al. The added value of in-hospital tracking of the efficacy of decongestion therapy and prognostic value of a wearable thoracic impedance sensor in acutely decompensated heart failure with volume overload: prospective cohort study[J]. JIMR Cardio, 2020, 4(1):e12141. DOI: 10.2196/12141.

[54] Boehmer JP, Hariharan R, Devecchi FG, et al. A multisensor algorithm predicts heart failure events in

patients with implanted devices: results from the MultiSENSE Study[J]. JACC Heart Fail, 2017, 5(3):216-225. DOI: 10.1016/j.jchf.2016.12.011.

[55] Amir O, Rappaport D, Zafrir B, et al. A novel approach to monitoring pulmonary congestion in heart failure: initial animal and clinical experiences using remote dielectric sensing technology[J]. Congest Heart Fail, 2013, 19(3): 149-155. DOI: 10.1111/chf.12021.

[56] Du X, Guo L, Xia S, et al. Atrial fibrillation prevalence, awareness and management in a nationwide survey of adults in China[J]. Heart, 2021, 107(7): 535-541. DOI: 10.1136/heartj1-2020-317915.

[57] Hindricks G, Potpara T, Dagres N, et al. 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): the task force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) Developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC[J]. Eur Heart J, 2021, 42(5): 373-498. DOI: 10.1093/eurheartj/ehaa612.

[58] Perez MV, Mahaffey KW, Hedlin H, et al. Large-scale assessment of a smartwatch to identify atrial fibrillation [J]. N Engl J Med, 2019, 381(20): 1909-1917. DOI: 10.1056/NEJMoa1901183.

[59] Guo Y, Wang H, Zhang H, et al. Mobile photoplethysmographic technology to detect atrial fibrillation[J]. J Am Coll Cardiol, 2019, 74(19):2365-2375. DOI: 10.1016/j.iacc.2019.08.019.

[60] Lubitz SA, Faranesh AZ, Selvaggi C, et al. Detection of atrial fibrillation in a large population using wearable devices: the fitbit heart study[J]. Circulation, 2022, 146(19):1415-1424. DOI: 10.161/CIRCULATIONAHA.122.060291.

[61] SteinhihL SR, Waalen J, Edwards AM, et al. Effect of a home-based wearable continuous ECG monitoring patch on detection of undiagnosed atrial fibrillation: the mSToPS randomized clinical trial[J]. JAMA, 2018, 320(2): 146-155. DOI: 10.1001/jama.2018.8102.

[62] Halcox J, Wareham K, Cardew A, et al. Assessment of remote heart rhythm sampling using the AliveCor heart monitor to screen for atrial fibrillation: the REHEARSE-AF Study[J]. Circulation, 2017, 136(19): 1784-1794. DOI: 10.1161/CIRCULATIONAHA.117.030583.

[63] Shahid S, Iqbal M, Saeed H, et al. Diagnostic accuracy of apple watch electrocardiogram for atrial fibrillation: a systematic review and meta-analysis[J]. JACC Adv, 2025, 4(2):101538. DOI: 10.1016/j.jacadv.2024.101538.

[64] Sibomana O, Hakayuwa CM, Obianke A, et al. Diagnostic accuracy of EGG smart chest patches versus PPG smartwatches for atrial fibrillation detection: a systematic review and meta-analysis[J]. BMC Cardiovasc Disord, 2025, 135(1): 132.1: DOI.1186/s12872-025-04582-2.

[65] Koh KT, Law WC, Zaw WM, et al. Smartphone electrocardiogram for detecting atrial fibrillation after a cerebral ischaemic event: a multicentre randomized controlled trial[J]. Europace, 2021, 23(7): 1016-1023. DOI: 10.1093/europace/euab036.

[66] Kaura A, Striha L, Chan FK, et al. Early prolonged ambulatory cardiac monitoring in stroke (EPACS): an open-label randomised controlled trial[J]. Eur J Med Res,

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1363

2019, 24(1):25. DOI: 10.1186/s40001-019-0383-8. [67] Marcus GM, Modrow MF, Schmid CH, et al. Individualized studies of triggers of paroxysmal atrial fibrillation: the I-STOP-Afib randomized clinical trial[J]. JAMA Cardiol, 2022, 7(2):167-174. DOI: 10.1001/jamacardio.2021.5010. [68] Wong CX, Modrow MF, Sigona K, et al. Preceding night sleep quality and atrial fibrillation episodes in the I-STOP-AIFB randomized trial[J]. JACC Clin Electrophysiol, 2024, 10(1):56-64. DOI: 10.1016/j.jacep.2023.09.009. [69] Zareba W, Moss AJ, Schwartz PJ, et al. Influence of the genotype on the clinical course of the long-QT syndrome. International Long-QT Syndrome Registry Research Group[J]. N Engl J Med, 1998, 339(14): 960-965. DOI: 10.1056/NEJM199810013391404. [70] Garabelli P, Stavrakis S, Albert M, et al. Comparison of QT interval readings in normal sinus rhythm between a smartphone heart monitor and a 12-lead ECG for healthy volunteers and inpatients receiving sotalol or difoflietil [J]. J Cardiovasc Electrophysiol, 2016, 27(7): 827-832. DOI: 10.1111/jice.12976. [71] Chung EH, Guise KD. QTC intervals can be assessed with the AliveCor heart monitor in patients on difoflietil for atrial fibrillation[J]. J Electrocardiol, 2015, 48(1):8-9. DOI: 10.1016/j.jelectrocard.2014.10.005. [72] Calkins H, Hindricks G, Cappato R, et al. 2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation[J]. Heart Rhythm, 2017, 14(10):e275-e444. DOI: 10.1016/j.hrthm.2017.05.012. [73] Taraki KG, Wazni OM, Callahan T, et al. Using a novel wireless system for monitoring patients after the atrial fibrillation ablation procedure: the ITransmit study[J]. Heart Rhythm, 2015, 12(3): 554-559. DOI: 10.1016/j.hrthm.2014.11.015. [74] Hermans A, Gawalko M, Pluymaekers N, et al. Long-term intermittent versus short continuous heart rhythm monitoring for the detection of atrial fibrillation recurrences after catheter ablation[J]. Int J Cardiol, 2021, 329:105-112. DOI: 10.1016/j.ijcard.2020.12.077. [75] Barrett PM, Komatireddy R, Haaser S, et al. Comparison of 24-hour Holter monitoring with 14-day novel adhesive patch electrocardiographic monitoring[J]. Am J Med, 2014, 12(1): 95. e11-17. DOI: 10.1016/j.ajmed.2013.10.003. [76] Schreiber D, Sattar A, Drigalla D, et al. Ambulatory cardiac monitoring for discharged emergency department patients with possible cardiac arrhythmias[J]. West J Emerg Med, 2014, 15(2): 194-198. DOI: 10.5811/westjem.2013.11.18973. [77] Pradhan S, Robinson JA, Shivapour JK, et al. Ambulatory arrhythmia detection with ZIO® XT patch in pediatric patients: a comparison of devices[J]. Pediatr Cardiol, 2019, 40(5):921-924. DOI: 10.1007/s00246-019-02089-0. [78] Solomon MD, Yang J, Sung SH, et al. Incidence and timing of potentially high-risk arrhythmias detected through long term continuous ambulatory electrocardiographic monitoring[J]. BMC Cardiovasc Disord, 2016, 16:35. DOI: 10.1186/s12872-016-0210-x. [79] Krumeman A, Di Biase L, Gerstenfeld E, et al. Premature ventricular complexes: assessing burden density in a large national cohort to better define optimal ECG

monitoring duration[J]. Heart Rhythm, 2024, 21(8): 129-1295. DOI: 10.1016/j.hrth.2024.04.066. [80] Gu K, Marshall K, Robertson-Stovel Q, et al. Polymorphic ventricular tachycardia detected with a smartwatch[J]. CJC Open, 2022, 4(4): 424-427. DOI: 10.1016/j. jcco.2021.12.003. [81] Burke J, Haigney M, Borne R, et al. Smartwatch detection of ventricular tachycardia: Case series[J]. HeartRhythm Case Rep, 2020, 6(10): 800-804. DOI: 10.1016/j. hrcr2020.08.003. [82] Sharma PS, Bordachar P, Ellenbogen KA. Indications and use of the wearable cardiac defibrillator[J]. Eur Heart J, 2017, 38(4):258-267. DOI: 10.1093/eurheartj/ehw353. [83] Sr PJP Allen LA, Kudenchuk PJ, et al. Wearable cardioverter-defibrillator therapy for the prevention of sudden cardiac death: a science advisory from the American Heart Association[J]. Circulation, 2016, 133(17):1715-1727. DOI: 10.1161/CIR.00000000000000394. [84] Al-Khatib SM, Stevenson WG, Ackerman MJ, et al. 2017 AHA/ACC/HRS Guideline for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: a report of the American College of Cardiology(American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society [J]. J Am Coll Cardiol, 2018, 72(14): e91-e220. DOI: 10.1016/j. jacc.2017.10.054. [85] 中华医学会心电生理和起搏分会,中国医师协会心律学专业委员会.2020室性心律失常中国专家共识(2016共识升级版)[J].中华心律失常学杂志,2020,24(3):188-258. DOI: 10.3760/cma.jcn.113859-20200509-00116. [86] Zeppenfeld K, Tfel-Hansen J, de Riva M, et al. 2022 ESC Guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death[J]. Eur Heart J, 2022, 43(40): 3997-4126. DOI: 10.1093/eurheartj/ehac262. [87] Reek S, Geller JC, Meltendorf U, et al. Clinical efficacy of a wearable defibrillator in acutely terminating episodes of ventricular fibrillation using biphasic shocks[J]. Pacing Clin Electrophysiol, 2003, 26(10): 2016-2022. DOI: 10.1046/j.1460-9592.2003.00311.x. [88] Feldman AM, Klein H, Tchou P, et al. Use of a wearable defibrillator in terminating tachyarrhythmias in patients at high risk for sudden death: results of the WEARIT/BIROAD[J]. Pacing Clin Electrophysiol, 2004, 27(1):4-9. DOI: 10.1111/j.1540-8159.2004.00378.x. [89] Epstein AE, Abraham WT, Bianco NR, et al. Wearable cardioverter-defibrillator use in patients perceived to be at high risk early post-myocardial infarction[J]. J Am Coll Cardiol, 2013, 62(21): 2000-2007. DOI: 10.1016/j. jacc.2013.05.086. [90] Kutyifa V, Moss J, Klein H, et al. Use of the wearable cardioverter defibrillator in high-risk cardiac patients: data from the Prospective Registry of Patients Using the Wearable Cardioverter Defibrillator (WEARIT-II Registry) [J]. Circulation, 2015, 132(17):1613-1619. DOI: 10.1161/CIRCULATIONAHA.115.015677. [91] Wäßnig NK, Günther M, Quick S, et al. Experience with the wearable cardioverter-defibrillator in patients at high risk for sudden cardiac death[J]. Circulation, 2016, 134(9): 635-643. DOI: 10.1161/CIRCULATIONAHA.115.019124. [92] Kandzari DE, Perumal R, Bhatt DL. Frequency and

· 1364 ·

中华心血管病杂志2025年12月第53卷第12期 Chin J Cardiol, December 2025, Vol. 53, No. 12

implications of ischemia prior to ventricular tachyarrhythmia in patients treated with a wearable cardioverter defibrillator following myocardial infarction [J]. Clin Cardiol, 2016, 39(7): 399-405. DOI: 10.1002/clc.22544. [93] Olgin JE, Fletcher MJ, Vittinghoff E, et al. Wearable cardioverter-defibrillator after myocardial infarction[J]. N Engl J Med, 2018, 379(13): 1205-1215. DOI: 10.1056/NEJMoa1800781. [94] Reed MJ, Muir A, Cullen J, et al. Establishing a smartphone ambulatory ECG service for patients presenting to the emergency department with pre-syncope and palpitations [J]. Medicina (Kaunas), 2021, 57(2): 147. DOI: 10.3390/medicina57020147. [95] Cullen J, Reed MJ, Muir A, et al. Experience of a smartphone ambulatory ECG clinic for emergency department patients with palpitation: a single-centre cohort study[J]. Eur Emerg Med, 2021, 28(6): 463-468. DOI: 10.1097/MEJ.000000000000859. [96] Reed MJ, Grubb NR, Lang CC, et al. Diagnostic yield of an ambulatory patch monitor in patients with unexplained syncope after initial evaluation in the emergency department: the PATCH-ED study[J]. Emerg Med, 2018, 35(8):477-485. DOI: 10.1136/emermed-2018-207570. [97] Jewson JL, Orchard JW, Seismarian C, et al. Use of a smartphone electrocardiogram to diagnose arrhythmias during exercise in athletes: a case series[J]. Eur Heart J Case Rep, 2022, 6(4): ytac126. DOI: 10.1093/ehjcr/ytac126. [98] Paech C, Kobel M, Michaelis A, et al. Accuracy of the Apple Watch single-lead ECG recordings in pre-term neonates [J]. Cardiol Young, 2022, 32(10): 1633-1637. DOI: 10.1017/S1047951121004765. [99] Kobel M, Kalden P, Michaelis A, et al. Accuracy of the Apple Watch iECG in children with and without congenital heart disease[J]. Pediatr Cardiol, 2022, 43(1): 191-196. DOI: 10.1007/s00246-021-02715-w. [100] Strik M, Caillol T, Ramirez FD, et al. Validating QT-interval measurement using the Apple Watch ECG to enable remote monitoring during the COVID-19 pandemic[J]. Circulation, 2020, 142(4): 416-418. DOI: 10.1161/CIRCULATIONAHA.120.048253. [101] van der Zande J, Strik M, Dubois R, et al. Using a Smartwatch to record precordial electrocardiograms: a validation study[J]. Sensors (Basel), 2023, 23(5): 2555. DOI: 10.3390/s23052555. [102] Pay L, Yumurta AÇ, Satti DI, et al. Arrhythmias beyond atrial fibrillation detection using smartwatches: a systematic review[J]. Anatol J Cardiol, 2023, 27(3): 126-131. DOI: 10.1474/AnatolJCardiol.2023.2799. [103] Wegner FK, Kochhäuser S, Frommeyer G, et al. Prospective blinded evaluation of smartphone-based ECG for differentiation of supraventricular tachycardia from inappropriate sinus tachycardia[J]. Clin Res Cardiol, 2021, 110(6):905-912. DOI: 10.1007/s00392-021-01856-5. [104] Caillol T, Strik M, Ramirez FD, et al. Accuracy of a smartwatch-derived ECG for diagnosing bradyarrhythmias, tachyarrhythmias, and cardiac ischemia[J]. Circ Arrhythm Electrophysiol, 2021, 14(1): 0009260. DOI: 10.1161/CIRCEP.120.009260. [105] Kwun JS, Yoon CH, Kim SH, et al. Surveillance of

arrhythmia in patients after myocardial infarction using wearable electrocardiogram patch devices: prospective cohort study[J]. JMIR Cardio, 2022, 6(1): e35615. DOI: 10.2196/35615. [106] Tian Y, Padmanabhan D, McLeod CJ, et al. Utility of 30-day continuous ambulatory monitoring to identify patients with delayed occurrence of atrioventricular block after transcatheter aortic valve replacement[J]. Circ Cardiovasc Interv, 2019, 12(12): e007635. DOI: 10.1161/CIRCINTERVENTIONS.118.007635. [107] Taraki KG, Patel D, Krishnaswamy A, et al. Bradyarrhythmias detected by extended rhythm recording in patients undergoing transcatheter aortic valve replacement (Brady-TAVR Study) [J]. Heart Rhythm, 2022, 19(3):381-388. DOI: 10.1016/j.hrthm.2021.11.020. [108] Liu X, Fan J, Guo Y, et al. Wearable smartwatch facilitated remote health management for patients undergoing transcatheter aortic valve replacement[J]. J Am Heart Assoc, 2022, 11(7):e023219. DOI: 10.1161/JAHA.121.023219. [109] Ross J Jr. Electorcardiographic ST-segment analysis in the characterization of myocardial ischemia and infarction[J]. Circulation, 1976, 53(3 Suppl):I73-81. [110] Gottlieb S.O. Diagnostic procedures for myocardial ischaemia[J]. Eur Heart J, 1996, 17 Suppl G:53-58. DOI: 10.1093/eurheartj/17.suppl g.53. [111] Gibson CM, Giaglo LN, Southard MC, et al. Diagnostic and diagnostic value of ambulatory ECG (Holter) monitoring in patients with coronary heart disease: a review[J]. J Thromb Thrombolysis, 2007, 23(2): 135-145. DOI: 10.1077/31239-006-9015-6. [112] Gibson CM, Mehta S, Ceschim M, et al. Evolution of single-lead EGG for STEMI detection using a deep learning approach[J]. Int J Cardiol, 2022, 346:47-52. DOI: 10.1016/j.ijcard.2021.11.039. [113] Martin H, Morar U, Izquierdo W, et al. Real-time frequency-independent single-Lead and single-beat myocardial infarction detection[J]. Artif Intell Med, 2021, 121:102179. DOI: 10.1016/j.artmed.2021.102179. [114] Muhlestein JB, Anderson JL, Bethea CF, et al. Feasibility of combining serial smartphone single-lead electrodiograms for the diagnosis of ST-elevation myocardial infarction[J]. Am Heart J, 2020, 221:125-135. DOI: 10.1016/j.abj.2019.12.016. [115] Yoon S, Kim T, Roh T, et al. Twelve-lead electrocardiogram acquisition with a patchy-type wireless device in ambulance transport: simulation-based randomized controlled trial[J]. JMIR Mhealth Uhealth, 2021, 9(4): e24142. DOI: 10.2196/24142. [116] Zhang H, Tian L, Lu H, et al. Wearable 12-lead ECG acquisition system with fabric electrodes[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2017, 2017:4439-4442. DOI: 10.1077/8037841. [117] Fouassier D, Roy X, Blanchard A, et al. Assessment of signal quality measured with a smart 12-lead ECG acquisition T-shirt[J]. Ann Noninvasive Electrocardiol, 2020, 25(1):e12682. DOI: 10.1111/anec.12682. [118] Nigusse AB, Mengistie DA, Malengier B, et al. Wearable smart textiles for long-term electrocardiography monitoring-a review[J]. Sensors (Basel), 2021, 21(12): 4174. DOI: 10.3390/s21124174. [119] Samol A, Bischof K, Luani B, et al. Single-lead ECG

中华心血管病杂志2025年12月第53卷第12期 Chin J Cardiol, December 2025, Vol. 53, No. 12

1365

recordings including einthoven and wilson leads by a smartwatch: a new era of patient directed early ECG differential diagnosis of cardiac diseases? [J]. Sensors (Basel), 2019, 19(20):4377. DOI: 10.3390/s19204377. [120] Spaccarotella C, Polimeni A, Migliarino S, et al. Multichannel electrocardiograms obtained by a smartwatch for the diagnosis of st-segment changes[J]. JAMA Cardiol, 2020, 5(10): 1176-1180. DOI: 10.1001/jamacardio.2020.3994. [121] Drexler M, Elsner C, Gabelmann V, et al. Apple Watch detecting coronary ischaemia during chest pain episodes or an apple a day may keep myocardial infarction away[J]. Eur Heart J, 2020, 41(23): 2224. DOI: 10.1093/eurheartj/ehaa290. [122] Stark K, Czermak T, Massberg S, et al. Watch out for ST-elevation myocardial infarction: a case report of ST-elevation in single-lead electrocardiogram tracing of smartwatch[J]. Eur Heart J Case Rep, 2020, 4(6):1-4. DOI: 10.1093/ehjcr/ytaa353. [123] Koster RW, de Winter RJ, Verberne HJ, et al. Severe silent ischaemia detected with an Apple Watch in the home setting: a case report[J]. Eur Heart J Case Rep, 2024, 8(2): ytae043. DOI: 10.1093/ehjcr/ytaa043. [124] Jensen MT, Marott JL, Allin KH, et al. Resting heart rate is associated with cardiovascular and all-cause mortality after adjusting for inflammatory markers: the Copenhagen City Heart Study[J]. Eur J Prev Cardiol, 2012, 19(1):102-108. DOI: 10.1177/1741826710394274. [125] Jensen MT, Suadicani P, Hein HO, et al. Elevated resting heart rate, physical fitness and all-cause mortality: a 16-year follow-up in the Copenhagen Male Study[J]. Heart, 2013, 99(12): 882-887. DOI: 10.1136/heartjnl-2012-303375. [126] 应用β肾上腺素能受体阻滞剂规范治疗冠心病中国专家共识组.应用β肾上腺素能受体阻滞剂规范治疗冠心病的中国专家共识[J].中国循环杂志,2020,35(2):108-123. DOI: 10.3969/j.issn.1000-3614.2020.02.002. [127] Sartor F, Gelissen J, van Dinther R, et al. Wrist-worn optical and chest strap heart rate comparison in a heterogeneous sample of healthy individuals and in coronary artery disease patients[J]. BMC Sports Sci Med Rehabil, 2018, 10:10. DOI: 10.1186/s13102-018-0098-0. [128] Weng D, Ding J, Sharma A, et al. Heart rate trajectories in patients recovering from acute myocardial infarction: A longitudinal analysis of Apple Watch heart rate recordings [J]. Cardiovasc Digit Health J, 2021, 2(5): 270-281. DOI: 10.1016/j.cvdhj.2021.05.003. [129] Etiwy M, Akhrass Z, Gillinov L, et al. Accuracy of wearable heart rate monitors in cardiac rehabilitation[J]. Cardiovasc Diagn Ther, 2019, 9(3): 262-271. DOI: 10.21037/cdt.2019.04.08. [130] Bozkurt B, Fonarov GC, Goldberg LR, et al. Cardiac rehabilitation for patients with heart failure: JACC expert panel[J]. J Am Coll Cardiol, 2021, 77(11):1454-1469. DOI: 10.1016/j.jacc.2021.01.030. [131] Thomas RJ, Beatty AL, Beckie TM, et al. Home-based cardiac rehabilitation: a scientific statement from the American Association of Cardiovascular and Pulmonary Rehabilitation, the American Heart Association, and the American College of Cardiology[J]. J Am Coll Cardiol, 2019, 74(1):133-153. DOI: 10.1016/j.jacc.2019.03.008.

[132] Antoniou V, Davos CH, Kapreli E, et al. Effectiveness of home-based cardiac rehabilitation, using wearable sensors, as a multicomponent, cutting-edge intervention: a systematic review and meta-analysis[J]. J Clin Med, 2022, 11(13):3772. DOI: 10.3390/jcm11133772. [133] Ashur C, Cascino TM, Lewis C, et al. Do Wearable activity trackers increase physical activity among cardiac rehabilitation participants? a systematic review and meta-analysis[J]. J Cardiopulm Rehabil Prev, 2021, 41(4): 249-256. DOI: 10.1097/HCR.000000000000592. [134] Franssen W, Franssen G, Spaas J, et al. Can consumer wearable activity tracker-based interventions improve physical activity and cardiometabolic health in patients with chronic diseases? A systematic review and meta-analysis of randomised controlled trials[J]. Int J Behav Nutr Phys Act, 2020, 17(1): 57. DOI: 10.1186/s12966-020-00955-2. [135] This L, Fresiello L, Oosterlinck W, et al. Assessment of physical activity by wearable technology during rehabilitation after cardiac surgery: explorative prospective monocentric observational cohort study[J]. JMIR Mhealth Uhealth, 2019, 7(1):e9865. DOI: 10.2196/mhealth.9865. [136] Hauguel-Moreau M, Naudin C, N'Guyen L, et al. Smart bracelet to assess physical activity after cardiac surgery: A prospective study[J]. PLoS One, 2020, 15(12): e0241368. DOI: 10.1371/journal.pone.0241368. [137] Kallioinen N, Hill A, Horswill MS, et al. Sources of inaccuracy in the measurement of adult patients' resting blood pressure in clinical settings: a systematic review[J]. J Hypertens, 2017, 35(3): 421-441. DOI: 10.1097/HJH.0000000000001197. [138] Schutte AE, Kollias A, Stergiou GS. Blood pressure and its variability: classic and novel measurement techniques[J]. Nat Rev Cardiol, 2022, 19(10): 643-654. DOI: 10.1038/s41569-022-00690-0. [139] Shimbo D, Abdalla M, Falzon L, et al. Role of ambulatory and home blood pressure monitoring in clinical practice: a narrative review[J]. Ann Intern Med, 2015, 163(9): 691-700. DOI: 10.7326/M15-1270. [140] 中国高血压联盟,北京高血压防治协会心血管代谢医学专业委员会.智能可穿戴设备在中青年血压管理中应用中国专家共识[J].中华高血压杂志,2022,30(8):720-724. DOI: 10.16439/j.issn.1673-7245.2022.08.005. [141] Kuwabara M, Harada K, Hishiki Y, et al. Validation of two watch-type wearable blood pressure monitors according to the ANSI/AAMI/ISO81060-2 : 2013 guidelines: Omron HEM-6410T-ZM and HEM-6410T-ZL[J]. J Clin Hypertens (Greenwich), 2019, 21(6): 853-858. DOI: 10.1111/jch.13499. [142] Zhang W, Zhou YN, Zhou Y, et al. Validation of the watch-type HUAWEI Watch D oscillometric wrist blood pressure monitor in adult Chinese[J]. Blood Press Monit, 2022, 27(5): 353-356. DOI: 10.1097/MBP.000000000000000608. [143] Kuwabara M, Harada K, Hishiki Y, et al. Validation of a wrist-type home nocturnal blood pressure monitor in the sitting and supine position according to the ANSI/AAMI/ISO81060-2 : 2013 guidelines: Omron HEM-9601T[J]. J Clin Hypertens (Greenwich), 2020, 22(6): 970-978. DOI: 10.1111/j.ch.13864. [144] Tomitani N, Kanegae H, Suzuki Y, et al. Stress-induced

· 1366 ·

中华心血管病杂志2025年12月第12期 Chin J Cardiol, December 2025, Vol. 53, No. 12

blood pressure elevation self-measured by a wearable watch-type device[J]. Am J Hypertens, 2021, 34(4): 377-382. DOI: 10.1093/ajh/paa139. [145] Groppelli A, Rafanelli M, Testa GD, et al. Feasibility of blood pressure measurement with a wearable (watch-type) monitor during impending syncopal episodes[J]. J Am Heart Assoc, 2022, 11(16): e026420. DOI: 10.1161/JAHA.122.026420. [146] Kario K, Shimbo D, Tomitani N, et al. The first study comparing a wearable watch-type blood pressure monitor with a conventional ambulatory blood pressure monitor on in-office and out-of-office settings[J]. J Clin Hypertens (Greenwich), 2020, 22(2): 135-141. DOI: 10.1111/jch.13799. [147] Haque CA, Kwon TH, Kim KD. Cuffless blood pressure estimation based on monte carlo simulation using photoplethysmography signals[J]. Sensors (Basel), 2022, 22(3): 115-382. DOI: 10.1339/s202301175. [148] Mahmud S, Ibtehaz N, Khandakar A, et al. A shallow U-Net architecture for reliably predicting blood pressure (BP) from photoplethysmogram (PPG) and electrocardiogram (ECG) signals[J]. Sensors (Basel), 2022, 22(3): 919. DOI: 10.3390/s22030919. [149] Farki A, Baradaran Kazemzadeh R, Akhondzadeh Noughabi E. A novel clustering-based algorithm for continuous and noninvasive cuff-less blood pressure estimation[J]. J Health Eng, 2022, 2022: 3549238. DOI: 10.1155/2022/3549238. [150] Kido K, Chen Z, Huang M, et al. Discussion of cuffless blood pressure prediction using plethysmograph based on a longitudinal experiment: is the individual model necessary[J]. Life (Basel), 2021, 12(1): 11. DOI: 10.3390/s12010011. [151] Dagamseh A, Qanwah Q, Al Quran H, et al. Towards a portable-noninvasive blood pressure monitoring system utilizing the photoplethysmogram signal[J]. Biomed Opt Express, 2021, 12(12): 7732-7751. DOI: 10.1364/BOE.444535. [152] El Haj C, Kyriacou PA. Recurrent neural network models for blood pressure monitoring using PPG morphological features [J]. Annu Int Conf IEEE Eng Med Biol Soc, 2021, 2021: 1865-1868. DOI: 10.1109/EMBC46164.2021.9630319. [153] Haddad S, Boukhayma A, Di Pietrantoon G, et al. Photoplethysmography based blood pressure monitoring using using the senbiosys ring[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2021, 2021: 1609-1612. DOI: 10.1109/EMBC46164.2021.9630161. [154] Hosanee M, Chan G, Welykholova K, et al. Cuffless single-site photoplethysmography for blood pressure monitoring[J]. J Clin Med, 2020, 9(3): 723: DOI: 10.3390/jcm9030723. [155] Lee HY, Burkard T. The advent of cuffless mobile device blood pressure measurement: remaining challenges and pitfalls[J]. Korean Circ J, 2022, 52(3): 198-204. DOI: 10.4070/kcj.2021.0405. [156] Kim YN, Shin DG, Park S, et al. Randomized clinical trial to assess the effectiveness of remote patient monitoring and physician care in reducing office blood pressure[J]. Hypertens Res, 2015, 38(7): 491-497. DOI: 10.1038/hr.2015.32. [157] McManus RJ, Mant J, Bray EP, et al. Telemonitoring and self-management in the control of hypertension in the control of hypertension (TEN-HMS) and Int Conf IEEE Eng Med Model: 10.1016/37(36): 163-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36): 164-172. DOI: 10.1016/37(36

self-management in the control of hypertension (TASMINH2): a randomised controlled trial[J]. Lancet, 2010, 376(9736): 163-172. DOI: 10.1016/S0140-6736(10)60964-6. [158] Kario K, Nomura A, Harada N, et al. Efficacy of a digital therapeutics system in the management of essential hypertension: the HERB-DH1 pivotal trial[J]. Eur Heart J, 2021, 42(40): 4111-4122. DOI: 10.1093/eurheart/ehab559. [159] Sun N, Wang L, Xi Y, et al. Accuracy evaluation of carotid-femoral pulse wave velocity estimated by smart terminal watch[J]. Front Cardiovasc Med, 2022, 9:893557. DOI: 10.3389/fcvm.2022.893557. [160] Lubin M, Gerbelot R, Prada R, et al. Evaluation of a dual-PPG system for pulse transit time monitoring[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2021, 2021: 1349-1352. DOI: 10.1109/EMBC46164.2021.9630814. [161] 王利,何史林,李蓓,等.智能手表检测正常成人和高血压患者左腕脉搏波传导时间的探讨[J].中国医疗设备,2017,32(6): 87-89,98. DOI: 10.3969/j.issn.1674-1633.2017.06.022. [162] 高血压心率管理多学科共识组.中国高血压患者心率管理多学科专家共识(2021年版)[J].中国全科医学,2021, 24(20): 2501-2507, 2519. DOI: 10.12114/j.issn.1007-9572.2021.00.595. [163] Palatini P, Rosei A, Casiglia E, et al. Management of the hypertensive patient with elevated heart rate: Statement of the Second Consensus Conference endorsed by the European Society of Hypertension[J]. J Hypertens, 2016, 34(5):813-821. DOI: 10.1097/HJH.0000000000000865. [164] Zhang J, Goide KM, Cuddhy PF, et al. Predicting hospitalization due to worsening heart failure using daily weight measurement: analysis of the Trans-European Network-Home-Care Management System (TEN-HMS) study[J]. Eur J Heart Fail, 2009, 11(4): 420-427. DOI: 10.1093/eurjhf/hfp033. [165] Lyngà P, Persson H, Hägg-Martinell A, et al. Weight monitoring in patients with severe heart failure (WISH). A randomized controlled trial[J]. Eur J Heart Fail, 2012, 14(4):438-444. DOI: 10.1093/eurjhf/hfs023. [166] Abraham WT, Anker S, Burkhoff D, et al. Primary results of the sensible medical innovations lung fluid status monitor allows reducing readmission rate of heart failure patients (SMILE) trial[J]. Journal of Cardiac Failure, 2019, 25(11): 938. [167] Boehmer J, Cremer S, Abo-Auda W, et al. Impact of heart failure management using thoracic fluid monitoring from a novel wearable sensor: results of the benefits of microcor (μCor") in ambulatory decompensated heart failure (BMAD) trial [C/OL]. Proceedings of the American College of Cardiology Annual Scientific Session, New Orleans, LA, USA, 2023 [2024-11-01]. https://www.ac.org/~media/Clinical/PDF-Files/Approved-PDFs/2023/03/04/ACC23/06Mar/930 am-BMAD-acc-2023.pdf. [168] Li J, Li X, Deng M, et al. Features and predictive value of 6-min walk test outcomes in interstitial lung disease: an observation study using wearable monitors[J]. BMJ Open, 2022, 12(6): e055077. DOI: 10.1136/bmjopen-2021-055077. [169] Hasan Shandhi MM, Aras M, Wynn S, et al. Cardiac function monitoring for patients undergoing cancer

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treatments using wearable seismocardiography: a proof-of-concept study[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2020, 2020: 4075-4078. DOI: 10.1109/EMBC44109.2020.9176074. [170] Wang J, Zang Y, Wu Q, et al. Predicting adverse events during six-minute walk test using continuous physiological signals[J]. Front Physiol, 2022, 13:887954. DOI: 10.3389/fphys.2022.887954. [171] Sokas D, Paliakaité B, Rapalis A, et al. Detection of walk tests in free-living activities using a wrist-worn device[J]. Front Physiol, 2021, 12: 706545. DOI: 10.3389/fphys.2021.706545. [172] Rens N, Gandhi N, Mak J, et al. Activity data from wearables as an indicator of functional capacity in patients with cardiovascular disease[J]. PLoS One, 2021, 16(3):e0247834. DOI: 10.1371/journal.pone.0247834. [173] Yamagami K, Nomura A, Kometani M, et al. Early detection of symptom exacerbation in patients with SARS-CoV-2 infection using the fitbit charge 3 (DEXTERITY): pilot evaluation[J]. JMIR Form Res, 2021, 5(9):e30819. DOI: 10.2196/30819. [174] Un KC, Wong CK, Lau YM, et al. Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients[J]. Sci Rep, 2021, 11(1):4388. DOI: 10.1038/s41598-021-82771-7. [175] Chan M, Ganti VG, Heller JA, et al. Enabling continuous wearable reflectance pulse oximetry at the sternum[J]. Biosensors (Basel), 2021, 11(12): 521. DOI: 10.3390/bios11120521. [176] Wurzer D, Spielhagen P, Siegmann A, et al. Remote monitoring of COVID-19 positive high-risk patients in domestic isolation: A feasibility study[J]. PLoS One, 2021, 16(9):e0257095. DOI: 10.1371/journal.pone.0257095. [177] Mishra T, Wang M, Metwally AA, et al. Pre-symptomatic detection of COVID-19 from smartwatch data[J]. Nat Biomed Eng, 2020, 4(12): 1208-1220. DOI: 10.1038/s41551-020-00640-6. [178] Allataifeh A, Al Ahmad M. Simultaneous piezoelectric noninvasive detection of multiple vital signs[J]. Sci Rep, 2020, 10(1):416. DOI: 10.1038/s41598-019-57326-6. [179] Bennett MK, Shao M, Gorodeski EZ. Home monitoring of heart failure patients at risk for hospital readmission using a novel under-the-mattress piezoelectric sensor: A preliminary single centre experience[J]. J Telemed Telecare, 2017, 23(1): 60-67. DOI: 10.1177/1357633X 15618810. [180] Liu Y, Arnaert A, da Costa D, et al. Experiences of patients with chronic obstructive pulmonary disease using the apple watch series 6 versus the traditional finger pulse oximeter for home SpO₂ self-monitoring: qualitative study part 2[J]. JMIR Aging, 2023, 6: e41539. DOI: 10.2196/41539. [181] Arslan B, Sener K, Guven R, et al. Accuracy of the Apple Watch in measuring oxygen saturation: comparison with pulse oximetry and ABG[J]. Ir J Med Sci, 2024, 193(1):477-483. DOI: 10.1007/s11845-023-03456-w. [182] Sung SH, Huang CJ, Cheng HM, et al. Effect of acoustic

cardiography-guided management on 1-year outcomes in patients with acute heart failure[J]. J Card Fail, 2020, 26(2):142-150. DOI: 10.1016/j.cardfail.2019.09.012. [183] McDonagh TA, Metra M, Adamo M, et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure[J]. Eur Heart J, 2021, 42(36): 3599-3726. DOI: 10.1093/eurheartj/ehab368. [184] Heidenreich PA, Bozkurt B, Aguilar D, et al. 2022 AHA/ACC/HFSA Guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines [J]. Circulation, 2022, 145(18):e895-e1032. DOI: 10.1161/CIR.000000000000063.063. [185] 中华医学会心血管病学分会,中国医师协会心血管内科医师分会,中国医师协会心力衰竭专业委员会,等.中国心力衰竭诊断和治疗指南2024[J].中华心血管病杂志,2024,52(3):235-275. DOI: 10.3760/cma.j.cn112148-20231101-00405. [186] Moayedi Y, Abdulmajeed R, Duero Posada J, et al. Assessing the use of wrist-worn devices in patients with heart failure: feasibility study[J]. JMIR Cardio, 2017, 1(2): e8. DOI: 10.2196/cardio.8301. [187] Visseren FLJ, Mach F, Smulders YM, et al. 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice[J]. Eur Heart J, 2021, 42(34):3227-3337. DOI: 10.1093/eurheartj/ehab484. [188] Arnett DK, Blumenthal RS, Albert MA, et al. 2019 ACC/AHA Guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines[J]. Circulation, 2019, 140(11): e596-e646. DOI: 10.1161/CIR.0000000000000678. [189] 中华医学会心血管病学分会,中国康复医学会心脏预防与康复专业委员会,中国老年学和老年医学会心脏专业委员会,等.中国心血管病一级预防指南基层版[J].中华心血管病杂志,2023,51(4): 343-363. DOI: 10.3760/cma.j.cn112148-20230106-00014. [190] Javed A, Kim DS, Hershman SG, et al. Personalized digital behaviour interventions increase short-term physical activity: a randomized control crossover trial substudy of the MyHeart Counts Cardiovascular Health Study[J]. Eur Heart J Digit Health, 2023, 4(5):411-419. DOI: 10.1093/ehjdh/ztad047. [191] Shcherbina A, Hershman SG, Lazzeroni L, et al. The effect of digital physical activity interventions on daily step count: a randomized controlled crossover substudy of the MyHeart Counts Cardiovascular Health Study[J]. Lancet Digit Health, 2019, 1(7): e344-e352. DOI: 10.1016/S2589-7500(19)30129-3. [192] Longhini J, Marzaro C, Bargeri S, et al. Wearable devices to improve physical activity and reduce sedentary behaviour: an umbrella review[J]. Sports Med Open, 2024, 10(1):9. DOI: 10.1186/s40798-024-00678-9. [193] Ferguson T, Olds T, Curtis R, et al. Effectiveness of wearable activity trackers to increase physical activity and improve health: a systematic review of systematic reviews and meta-analyses[J]. Lancet Digit Health, 2022, 4(8):e615-e626. DOI: 10.1016/S2589-7500(22)00111-X.

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