• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

使用可穿戴设备对与COVID-19相关的生理体征进行评估。

Assessment of physiological signs associated with COVID-19 measured using wearable devices.

作者信息

Natarajan Aravind, Su Hao-Wei, Heneghan Conor

机构信息

Fitbit Research, 199 Fremont St, Floor 14, San Francisco, CA, 94105, USA.

出版信息

NPJ Digit Med. 2020 Nov 30;3(1):156. doi: 10.1038/s41746-020-00363-7.

DOI:10.1038/s41746-020-00363-7
PMID:33299095
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7705652/
Abstract

Respiration rate, heart rate, and heart rate variability (HRV) are some health metrics that are easily measured by consumer devices, which can potentially provide early signs of illness. Furthermore, mobile applications that accompany wearable devices can be used to collect relevant self-reported symptoms and demographic data. This makes consumer devices a valuable tool in the fight against the COVID-19 pandemic. Data on 2745 subjects diagnosed with COVID-19 (active infection, PCR test) were collected from May 21 to September 11, 2020, consisting of PCR positive tests conducted between February 16 and September 9. Considering male (female) participants, 11.9% (11.2%) of the participants were asymptomatic, 48.3% (47.8%) recovered at home by themselves, 29.7% (33.7%) recovered at home with the help of someone else, 9.3% (6.6%) required hospitalization without ventilation, and 0.5% (0.4%) required ventilation. There were a total of 21 symptoms reported, and the prevalence of symptoms varies by sex. Fever was present in 59.4% of male subjects and in 52% of female subjects. Based on self-reported symptoms alone, we obtained an AUC of 0.82 ± 0.017 for the prediction of the need for hospitalization. Based on physiological signs, we obtained an AUC of 0.77 ± 0.018 for the prediction of illness on a specific day. Respiration rate and heart rate are typically elevated by illness, while HRV is decreased. Measuring these metrics, taken in conjunction with molecular-based diagnostics, may lead to better early detection and monitoring of COVID-19.

摘要

呼吸频率、心率和心率变异性(HRV)是一些可由消费设备轻松测量的健康指标,这些指标可能会提供疾病的早期迹象。此外,可穿戴设备配套的移动应用程序可用于收集相关的自我报告症状和人口统计数据。这使得消费设备成为抗击新冠疫情的宝贵工具。2020年5月21日至9月11日收集了2745名被诊断为新冠病毒感染(活跃感染,PCR检测)患者的数据,这些数据由2月16日至9月9日期间进行的PCR阳性检测组成。考虑男性(女性)参与者,11.9%(11.2%)的参与者无症状,48.3%(47.8%)自行在家康复,29.7%(33.7%)在他人帮助下在家康复,9.3%(6.6%)需要非通气性住院治疗,0.5%(0.4%)需要通气治疗。共报告了21种症状,症状的患病率因性别而异。59.4%的男性受试者和52%的女性受试者出现发热症状。仅基于自我报告的症状,我们预测住院需求的曲线下面积(AUC)为0.82±0.017。基于生理体征,我们预测特定日期患病情况的AUC为0.77±0.018。疾病通常会导致呼吸频率和心率升高,而HRV降低。结合基于分子的诊断方法测量这些指标,可能会实现对新冠病毒更好的早期检测和监测。

相似文献

1
Assessment of physiological signs associated with COVID-19 measured using wearable devices.使用可穿戴设备对与COVID-19相关的生理体征进行评估。
NPJ Digit Med. 2020 Nov 30;3(1):156. doi: 10.1038/s41746-020-00363-7.
2
COVID-19 surveillance based on consumer wearable devices.基于消费者可穿戴设备的新冠病毒监测
Digit Health. 2024 Apr 24;10:20552076241247374. doi: 10.1177/20552076241247374. eCollection 2024 Jan-Dec.
3
Wearable Devices to Diagnose and Monitor the Progression of COVID-19 Through Heart Rate Variability Measurement: Systematic Review and Meta-Analysis.可穿戴设备通过心率变异性测量来诊断和监测 COVID-19 的进展:系统评价和荟萃分析。
J Med Internet Res. 2023 Nov 14;25:e47112. doi: 10.2196/47112.
4
Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study.利用可穿戴设备的生理数据识别 SARS-CoV-2 感染和症状并预测 COVID-19 诊断:观察性研究。
J Med Internet Res. 2021 Feb 22;23(2):e26107. doi: 10.2196/26107.
5
6
Prediction of the efficacy of group cognitive behavioral therapy using heart rate variability based smart wearable devices: a randomized controlled study.基于心率变异性的智能可穿戴设备预测团体认知行为疗法的疗效:一项随机对照研究。
BMC Psychiatry. 2024 Mar 6;24(1):187. doi: 10.1186/s12888-024-05638-x.
7
Safety and Efficacy of Imatinib for Hospitalized Adults with COVID-19: A structured summary of a study protocol for a randomised controlled trial.COVID-19 住院成人患者使用伊马替尼的安全性和疗效:一项随机对照试验研究方案的结构化总结。
Trials. 2020 Oct 28;21(1):897. doi: 10.1186/s13063-020-04819-9.
8
Physiologic Response to the Pfizer-BioNTech COVID-19 Vaccine Measured Using Wearable Devices: Prospective Observational Study.使用可穿戴设备测量的辉瑞-生物科技公司新冠疫苗的生理反应:前瞻性观察研究。
JMIR Form Res. 2021 Aug 4;5(8):e28568. doi: 10.2196/28568.
9
Virtualized clinical studies to assess the natural history and impact of gut microbiome modulation in non-hospitalized patients with mild to moderate COVID-19 a randomized, open-label, prospective study with a parallel group study evaluating the physiologic effects of KB109 on gut microbiota structure and function: a structured summary of a study protocol for a randomized controlled study.用于评估非住院轻中度 COVID-19 患者肠道微生物组调节的自然史和影响的虚拟化临床研究:一项随机、开放标签、前瞻性研究,平行组研究评估 KB109 对肠道微生物组结构和功能的生理影响:一项随机对照研究方案的结构化总结。
Trials. 2021 Apr 2;22(1):245. doi: 10.1186/s13063-021-05157-0.
10
The Impact of Wearable Technologies in Health Research: Scoping Review.可穿戴技术在健康研究中的影响:范围综述。
JMIR Mhealth Uhealth. 2022 Jan 25;10(1):e34384. doi: 10.2196/34384.

引用本文的文献

1
Pulse rate variability is not the same as heart rate variability: findings from a large, diverse clinical population study.脉搏率变异性与心率变异性不同:一项大型多样化临床人群研究的结果
Front Physiol. 2025 Jul 30;16:1630032. doi: 10.3389/fphys.2025.1630032. eCollection 2025.
2
Automatic detection of persistent physiological changes after COVID infection via wearable devices with potential for long COVID management.通过可穿戴设备自动检测新冠病毒感染后持续的生理变化,具有用于管理长期新冠症状的潜力。
Sci Rep. 2025 Aug 11;15(1):29443. doi: 10.1038/s41598-025-15208-0.
3
Classification of Individuals With COVID-19 and Post-COVID-19 Condition and Healthy Controls Using Heart Rate Variability: Machine Learning Study With a Near-Real-Time Monitoring Component.

本文引用的文献

1
Characterizing COVID-19 and Influenza Illnesses in the Real World via Person-Generated Health Data.通过个人生成的健康数据在现实世界中对新冠病毒病和流感疾病进行特征描述。
Patterns (N Y). 2020 Dec 13;2(1):100188. doi: 10.1016/j.patter.2020.100188. eCollection 2021 Jan 8.
2
Heart rate variability with photoplethysmography in 8 million individuals: a cross-sectional study.使用光体积描记法对 800 万人进行心率变异性研究:一项横断面研究。
Lancet Digit Health. 2020 Dec;2(12):e650-e657. doi: 10.1016/S2589-7500(20)30246-6. Epub 2020 Nov 23.
3
Analyzing changes in respiratory rate to predict the risk of COVID-19 infection.
使用心率变异性对新冠肺炎患者、新冠后状况患者和健康对照者进行分类:一项包含近实时监测组件的机器学习研究
J Med Internet Res. 2025 Aug 14;27:e76613. doi: 10.2196/76613.
4
COVID-19 Early Detection in Doctors and Healthcare Workers (CEDiD) study: a cohort study on the feasibility of wearable devices.新冠病毒-19医护人员早期检测(CEDiD)研究:一项关于可穿戴设备可行性的队列研究。
BMJ Open. 2025 Apr 5;15(4):e089598. doi: 10.1136/bmjopen-2024-089598.
5
Neuro-Nutrition and Exercise Synergy: Exploring the Bioengineering of Cognitive Enhancement and Mental Health Optimization.神经营养与运动协同作用:探索认知增强和心理健康优化的生物工程学
Bioengineering (Basel). 2025 Feb 19;12(2):208. doi: 10.3390/bioengineering12020208.
6
Sleep and cardiorespiratory function assessed by a smart bed over 10 weeks post COVID-19 infection.新冠病毒感染后10周内,通过智能床评估睡眠和心肺功能。
Sci Rep. 2025 Jan 21;15(1):2724. doi: 10.1038/s41598-025-87069-6.
7
Smartwatch-based algorithm for early detection of pulmonary infection: Validation and performance evaluation.基于智能手表的肺部感染早期检测算法:验证与性能评估。
Digit Health. 2024 Oct 25;10:20552076241290684. doi: 10.1177/20552076241290684. eCollection 2024 Jan-Dec.
8
Case report: dynamic personalized physiological monitoring in lung cancer using wearable data.病例报告:利用可穿戴数据对肺癌进行动态个性化生理监测
Front Oncol. 2024 Oct 4;14:1420888. doi: 10.3389/fonc.2024.1420888. eCollection 2024.
9
Feasibility of snapshot testing using wearable sensors to detect cardiorespiratory illness (COVID infection in India).使用可穿戴传感器进行快速检测以发现心肺疾病(印度的新冠感染情况)的可行性。
NPJ Digit Med. 2024 Oct 19;7(1):289. doi: 10.1038/s41746-024-01287-2.
10
Evaluation of Machine Learning to Detect Influenza Using Wearable Sensor Data and Patient-Reported Symptoms: Cohort Study.利用可穿戴传感器数据和患者报告症状评估机器学习检测流感:队列研究。
J Med Internet Res. 2024 Oct 4;26:e47879. doi: 10.2196/47879.
分析呼吸频率变化预测 COVID-19 感染风险。
PLoS One. 2020 Dec 10;15(12):e0243693. doi: 10.1371/journal.pone.0243693. eCollection 2020.
4
Pre-symptomatic detection of COVID-19 from smartwatch data.从智能手表数据中进行 COVID-19 的症状前检测。
Nat Biomed Eng. 2020 Dec;4(12):1208-1220. doi: 10.1038/s41551-020-00640-6. Epub 2020 Nov 18.
5
Prevalence of SARS-CoV-2 antibodies in a large nationwide sample of patients on dialysis in the USA: a cross-sectional study.美国大型全国范围内透析患者样本中 SARS-CoV-2 抗体的流行情况:一项横断面研究。
Lancet. 2020 Oct 24;396(10259):1335-1344. doi: 10.1016/S0140-6736(20)32009-2. Epub 2020 Sep 25.
6
It Is Time to Address Airborne Transmission of Coronavirus Disease 2019 (COVID-19).是时候应对2019冠状病毒病(COVID-19)的空气传播问题了。
Clin Infect Dis. 2020 Dec 3;71(9):2311-2313. doi: 10.1093/cid/ciaa939.
7
Prevalence of Asymptomatic SARS-CoV-2 Infection : A Narrative Review.无症状 SARS-CoV-2 感染的流行情况:一项叙述性综述。
Ann Intern Med. 2020 Sep 1;173(5):362-367. doi: 10.7326/M20-3012. Epub 2020 Jun 3.
8
Real-time tracking of self-reported symptoms to predict potential COVID-19.实时跟踪自我报告的症状以预测潜在的 COVID-19。
Nat Med. 2020 Jul;26(7):1037-1040. doi: 10.1038/s41591-020-0916-2. Epub 2020 May 11.
9
Temporal dynamics in viral shedding and transmissibility of COVID-19.新冠病毒脱落和传播的时间动态。
Nat Med. 2020 May;26(5):672-675. doi: 10.1038/s41591-020-0869-5. Epub 2020 Apr 15.
10
The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application.新型冠状病毒肺炎(COVID-19)的潜伏期来自公开报告的确诊病例:估计和应用。
Ann Intern Med. 2020 May 5;172(9):577-582. doi: 10.7326/M20-0504. Epub 2020 Mar 10.