期刊文章%@ 2291-5222 %I JMIR Publicatio卡塔尔世界杯8强波胆分析ns %V 10% N 10% P e35722 %T基于数字生物标志物的研究:%A Motahari-Nezhad,Hossein %A Fgaier,Meriem %A Mahdi Abid,Mohamed %A Péntek,Márta %A Gulácsi,László %A Zrubka,Zsombor %+布达佩斯科维努斯大学商业与管理博士学院，Fővám tér 8，布达佩斯，1093，匈牙利，36 702097967，h.motahari.lib@gmail.com %K范围审查%K数字生物标志物%K健康%K行为数据%K生理数据%K数字健康%K远程监测%K可穿戴%K可植入%K可消化%K便携式%K传感器%K数字健康%K手机%D 2022 %7 24.10.2022 %9审查%J JMIR Mhealth Uhealth %G英文%X背景:传感器和数字设备彻底改变了行为和生理数据的测量、收集和存储，催生了数字生物标志物这个新术语。目的:本研究旨在探讨涉及数字生物标志物的随机对照试验的系统综述(SRs)所涵盖的临床证据的范围。方法:该范围评审使用PRISMA-ScR(用于系统评审的首选报告项目和范围评审的元分析扩展)指南组织。由于搜索仅限于英文出版物，数字生物标志物的全文sr包括涉及人类的随机对照试验，并报告了参与者健康状况的变化。PubMed和Cochrane图书馆的搜索时间限制在2019年和2020年。分别使用世界卫生组织的疾病分类系统(《国际疾病分类》第11版)、卫生干预措施(《国际卫生干预措施分类》)和身体功能(《国际功能、残疾和健康分类》[ICF])对人口、干预措施和结果进行分类。结果:共有31个sr符合纳入标准。 The majority of SRs studied patients with circulatory system diseases (19/31, 61%) and respiratory system diseases (9/31, 29%). Most of the prevalent interventions focused on physical activity behavior (16/31, 52%) and conversion of cardiac rhythm (4/31, 13%). Looking after one’s health (physical activity; 15/31, 48%), walking (12/31, 39%), heart rhythm functions (8/31, 26%), and mortality (7/31, 23%) were the most commonly reported outcomes. In total, 16 physiological and behavioral data groups were identified using the ICF tool, such as looking after one’s health (physical activity; 14/31, 45%), walking (11/31, 36%), heart rhythm (7/31, 23%), and weight maintenance functions (7/31, 23%). Various digital devices were also studied to collect these data in the included reviews, such as smart glasses, smartwatches, smart bracelets, smart shoes, and smart socks for measuring heart functions, gait pattern functions, and temperature. A substantial number (24/31, 77%) of digital biomarkers were used as interventions. Moreover, wearables (22/31, 71%) were the most common types of digital devices. Position sensors (21/31, 68%) and heart rate sensors and pulse rate sensors (12/31, 39%) were the most prevalent types of sensors used to acquire behavioral and physiological data in the SRs. Conclusions: In recent years, the clinical evidence concerning digital biomarkers has been systematically reviewed in a wide range of study populations, interventions, digital devices, and sensor technologies, with the dominance of physical activity and cardiac monitors. We used the World Health Organization’s ICF tool for classifying behavioral and physiological data, which seemed to be an applicable tool to categorize the broad scope of digital biomarkers identified in this review. To understand the clinical value of digital biomarkers, the strength and quality of the evidence on their health consequences need to be systematically evaluated. %M 36279171 %R 10.2196/35722 %U https://mhealth.www.mybigtv.com/2022/10/e35722 %U https://doi.org/10.2196/35722 %U http://www.ncbi.nlm.nih.gov/pubmed/36279171