• 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

相似文献

1
Charting a Course for Smartphones and Wearables to Transform Population Health Research.为智能手机和可穿戴设备绘制变革人口健康研究的路线图。
J Med Internet Res. 2023 Feb 7;25:e42449. doi: 10.2196/42449.
2
Charting a course for smartphones and wearables to transform population health research.为智能手机和可穿戴设备规划一条变革人群健康研究的道路。
J Med Internet Res. 2023 Feb 7;25:e42449. doi: 10.2196/preprints.42449.
3
Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.在流行地区,服用抗叶酸抗疟药物的人群中,叶酸补充剂与疟疾易感性和严重程度的关系。
Cochrane Database Syst Rev. 2022 Feb 1;2(2022):CD014217. doi: 10.1002/14651858.CD014217.
4
Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis.使用智能手机和可穿戴设备的移动医疗数据预测抑郁症状严重程度的挑战:回顾性分析。
J Med Internet Res. 2023 Aug 14;25:e45233. doi: 10.2196/45233.
5
The future of Cochrane Neonatal.考克兰新生儿协作网的未来。
Early Hum Dev. 2020 Nov;150:105191. doi: 10.1016/j.earlhumdev.2020.105191. Epub 2020 Sep 12.
6
[Evaluation of physical activity using smartphones and wearable devices in healthcare: Current situation and future perspectives].[利用智能手机和可穿戴设备评估医疗保健中的身体活动:现状与未来展望]
Nihon Koshu Eisei Zasshi. 2021 Sep 7;68(9):585-596. doi: 10.11236/jph.20-143. Epub 2021 Jun 11.
7
Use of Smartphones and Wearables for Arrhythmia Monitoring.智能手机和可穿戴设备在心律失常监测中的应用。
Card Electrophysiol Clin. 2021 Sep;13(3):509-522. doi: 10.1016/j.ccep.2021.04.004. Epub 2021 Jul 8.
8
Smartphones for musculoskeletal research - hype or hope? Lessons from a decennium of mHealth studies.智能手机在肌肉骨骼研究中的应用——炒作还是希望?移动医疗研究十年来的经验教训。
BMC Musculoskelet Disord. 2022 May 23;23(1):487. doi: 10.1186/s12891-022-05420-8.
9
The Use of Patient-Generated Health Data From Consumer-Grade Mobile Devices in Clinical Workflows: Protocol for a Systematic Review.消费级移动设备生成的患者健康数据在临床工作流程中的应用:系统评价方案
JMIR Res Protoc. 2023 Feb 27;12:e39389. doi: 10.2196/39389.
10
Use of Tablets and Smartphones to Support Medical Decision Making in US Adults: Cross-Sectional Study.平板电脑和智能手机在美国成年人中用于支持医疗决策的使用情况:横断面研究。
JMIR Mhealth Uhealth. 2020 Aug 12;8(8):e19531. doi: 10.2196/19531.

引用本文的文献

1
Consumer Wearable Usage to Collect Health Data Among Adults Living in Germany: Nationwide Observational Survey Study.德国成年人使用消费级可穿戴设备收集健康数据:全国性观察性调查研究。
JMIR Mhealth Uhealth. 2025 Jun 11;13:e59199. doi: 10.2196/59199.
2
Passive sensing at scale to transform understanding of poor mental health.大规模被动传感以转变对心理健康不佳的理解。
Lancet Digit Health. 2025 Mar;7(3):e172-e174. doi: 10.1016/j.landig.2025.01.002.
3
A systematic review of passive data for remote monitoring in psychosis and schizophrenia.对用于精神病和精神分裂症远程监测的被动数据的系统评价。
NPJ Digit Med. 2025 Jan 27;8(1):62. doi: 10.1038/s41746-025-01451-2.
4
Association Between Wearable Device Use and Quality of Life in Patients With Idiopathic Inflammatory Myopathies and Primary Systemic Vasculitis.特发性炎性肌病和原发性系统性血管炎患者使用可穿戴设备与生活质量之间的关联
Cureus. 2024 Apr 24;16(4):e58948. doi: 10.7759/cureus.58948. eCollection 2024 Apr.

本文引用的文献

1
A systematic review of engagement reporting in remote measurement studies for health symptom tracking.健康症状追踪远程测量研究中参与度报告的系统评价
NPJ Digit Med. 2022 Jun 29;5(1):82. doi: 10.1038/s41746-022-00624-7.
2
Accuracy and Acceptability of Wrist-Wearable Activity-Tracking Devices: Systematic Review of the Literature.腕戴式活动追踪设备的准确性和可接受性:文献系统综述。
J Med Internet Res. 2022 Jan 21;24(1):e30791. doi: 10.2196/30791.
3
Research priorities for exacerbations of COPD.慢性阻塞性肺疾病急性加重的研究重点
Lancet Respir Med. 2021 Aug;9(8):824-826. doi: 10.1016/S2213-2600(21)00227-7. Epub 2021 May 14.
4
What happens after James Lind Alliance Priority Setting Partnerships? A qualitative study of contexts, processes and impacts.詹姆斯·林德联盟优先事项设定合作之后会发生什么?一项关于背景、过程和影响的定性研究。
Res Involv Engagem. 2020 Jul 11;6:41. doi: 10.1186/s40900-020-00210-9. eCollection 2020.
5
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.
6
Human-Centered Design Strategies for Device Selection in mHealth Programs: Development of a Novel Framework and Case Study.以人为中心的设计策略在移动医疗项目中的设备选择:新型框架的开发与案例研究。
JMIR Mhealth Uhealth. 2020 May 7;8(5):e16043. doi: 10.2196/16043.
7
Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation.大规模评估智能手表以识别心房颤动。
N Engl J Med. 2019 Nov 14;381(20):1909-1917. doi: 10.1056/NEJMoa1901183.
8
RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices.RADAR-Base:开源移动健康平台,用于使用传感器、可穿戴设备和移动设备收集、监测和分析数据。
JMIR Mhealth Uhealth. 2019 Aug 1;7(8):e11734. doi: 10.2196/11734.
9
Physical Activity Surveillance Through Smartphone Apps and Wearable Trackers: Examining the UK Potential for Nationally Representative Sampling.通过智能手机应用程序和可穿戴追踪器进行身体活动监测:研究英国全国代表性抽样的潜力。
JMIR Mhealth Uhealth. 2019 Jan 29;7(1):e11898. doi: 10.2196/11898.
10
Investigating the Extent to Which Patients Should Control Access to Patient Records for Research: A Deliberative Process Using Citizens' Juries.调查患者在多大程度上应控制用于研究的患者记录的访问权限:使用公民陪审团的审议过程
J Med Internet Res. 2018 Mar 28;20(3):e112. doi: 10.2196/jmir.7763.

为智能手机和可穿戴设备绘制变革人口健康研究的路线图。

Charting a Course for Smartphones and Wearables to Transform Population Health Research.

机构信息

Centre for Epidemiology Versus Arthritis, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom.

Centre for Health Informatics, Manchester Academic Health Science Centre, Manchester, United Kingdom.

出版信息

J Med Internet Res. 2023 Feb 7;25:e42449. doi: 10.2196/42449.

DOI:10.2196/42449
PMID:36749628
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11334374/
Abstract

The use of data from smartphones and wearable devices has huge potential for population health research, given the high level of device ownership; the range of novel health-relevant data types available from consumer devices; and the frequency and duration with which data are, or could be, collected. Yet, the uptake and success of large-scale mobile health research in the last decade have not met this intensely promoted opportunity. We make the argument that digital person-generated health data are required and necessary to answer many top priority research questions, using illustrative examples taken from the James Lind Alliance Priority Setting Partnerships. We then summarize the findings from 2 UK initiatives that considered the challenges and possible solutions for what needs to be done and how such solutions can be implemented to realize the future opportunities of digital person-generated health data for clinically important population health research. Examples of important areas that must be addressed to advance the field include digital inequality and possible selection bias; easy access for researchers to the appropriate data collection tools, including how best to harmonize data items; analysis methodologies for time series data; patient and public involvement and engagement methods for optimizing recruitment, retention, and public trust; and methods for providing research participants with greater control over their data. There is also a major opportunity, provided through the linkage of digital person-generated health data to routinely collected data, to support novel population health research, bringing together clinician-reported and patient-reported measures. We recognize that well-conducted studies need a wide range of diverse challenges to be skillfully addressed in unison (eg, challenges regarding epidemiology, data science and biostatistics, psychometrics, behavioral and social science, software engineering, user interface design, information governance, data management, and patient and public involvement and engagement). Consequently, progress would be accelerated by the establishment of a new interdisciplinary community where all relevant and necessary skills are brought together to allow for excellence throughout the life cycle of a research study. This will require a partnership of diverse people, methods, and technologies. If done right, the synergy of such a partnership has the potential to transform many millions of people's lives for the better.

摘要

使用智能手机和可穿戴设备的数据在人群健康研究方面具有巨大潜力,因为设备拥有率很高;消费者设备可以提供各种新颖的健康相关数据类型;并且可以以或高或低的频率和时长收集数据。然而,在过去十年中,大规模移动健康研究的采用和成功并未达到这一强烈推动的机会水平。我们认为,数字个人生成的健康数据是回答许多首要研究问题所必需的,并用詹姆斯·林德联盟优先设置伙伴关系中的说明性示例对此进行了论证。然后,我们总结了英国的两个举措的调查结果,这些举措考虑了为实现数字个人生成的健康数据在重要的人群健康研究中的未来机会而需要做什么以及如何实施这些解决方案所面临的挑战和可能的解决方案。为了推进该领域,必须解决的重要领域包括数字不平等和可能的选择偏差;研究人员方便地获得适当的数据收集工具,包括如何最好地协调数据项;时间序列数据的分析方法;患者和公众参与和参与方法,以优化招募、保留和公众信任;以及为研究参与者提供更多控制其数据的方法。通过将数字个人生成的健康数据与常规收集的数据联系起来,还有一个主要机会可以支持新颖的人群健康研究,将临床医生报告的和患者报告的措施结合起来。我们认识到,精心进行的研究需要协同解决广泛的各种挑战(例如,流行病学、数据科学和生物统计学、心理测量学、行为和社会科学、软件工程、用户界面设计、信息治理、数据管理以及患者和公众参与和参与方面的挑战)。因此,通过建立一个新的跨学科社区,汇集所有相关和必要的技能,可以在研究的整个生命周期中实现卓越,从而加速进展。这将需要多样化的人员、方法和技术的合作。如果做得好,这种合作的协同作用有可能改善数百万人的生活。