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Usefulness of Modern Activity Trackers for Monitoring Exercise Behavior in Chronic Cardiac Patients: Validation Study.

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Abstract:

BACKGROUND:Improving physical activity (PA) is a core component of secondary prevention and cardiac (tele)rehabilitation. Commercially available activity trackers are frequently used to monitor and promote PA in cardiac patients. However, studies on the validity of these devices in cardiac patients are scarce. As cardiac patients are being advised and treated based on PA parameters measured by these devices, it is highly important to evaluate the accuracy of these parameters in this specific population. OBJECTIVE:The aim of this study was to determine the accuracy and responsiveness of 2 wrist-worn activity trackers, Fitbit Charge 2 (FC2) and Mio Slice (MS), for the assessment of energy expenditure (EE) in cardiac patients. METHODS:EE assessed by the activity trackers was compared with indirect calorimetry (Oxycon Mobile [OM]) during a laboratory activity protocol. Two groups were assessed: patients with stable coronary artery disease (CAD) with preserved left ventricular ejection fraction (LVEF) and patients with heart failure with reduced ejection fraction (HFrEF). RESULTS:A total of 38 patients were included: 19 with CAD and 19 with HFrEF (LVEF 31.8%, SD 7.6%). The CAD group showed no significant difference in total EE between FC2 and OM (47.5 kcal, SD 112 kcal; P=.09), in contrast to a significant difference between MS and OM (88 kcal, SD 108 kcal; P=.003). The HFrEF group showed significant differences in EE between FC2 and OM (38 kcal, SD 57 kcal; P=.01), as well as between MS and OM (106 kcal, SD 167 kcal; P=.02). Agreement of the activity trackers was low in both groups (CAD: intraclass correlation coefficient [ICC] FC2=0.10, ICC MS=0.12; HFrEF: ICC FC2=0.42, ICC MS=0.11). The responsiveness of FC2 was poor, whereas MS was able to detect changes in cycling loads only. CONCLUSIONS:Both activity trackers demonstrated low accuracy in estimating EE in cardiac patients and poor performance to detect within-patient changes in the low-to-moderate exercise intensity domain. Although the use of activity trackers in cardiac patients is promising and could enhance daily exercise behavior, these findings highlight the need for population-specific devices and algorithms.

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JMIR mHealth and uHealth

JMIR MHEALTH UHEALTH
最新影响因子:4.947 期刊ISSN:2291-5222 CiteScore:
出版周期: 是否OA:YES 出版年份:0

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期刊投稿地址:

自引率:13.10% 研究方向:-
出版地区:

SCI期刊coverage:Science Citation Index Expanded(科学引文索引扩展)

NCBI查询:PubMed Central (PMC)链接全文检索(pubmed central)

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JMIR mHealth and uHealth 期刊简介

英文简介:

Focus and Scope JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a newer spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2015: 4.532). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and we are expecting the first impact factor for JMU in 2017 which will be at least 2.84. The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics. JMIR mHealth and uHealth publishes even faster and has a broader scope with including papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research. In addition to peer-reviewing paper submissions by researchers, JMIR mHealth and uHealth offers peer-review of medical apps itself. JMIR mHealth and uHealth journal features a rapid and thorough peer-review process, professional copyediting, professional production of PDF, XHTML, and XML proofs. JMIR mHealth and uHealth adheres to the same quality standards as JMIR and all articles published here are also cross-listed in the Table of Contents of JMIR, the worlds'' leading medical journal in health sciences / health services research and health informatics . We are looking for papers covering for example the following themes: Ubiquitous Health (uHealth) mHealth for Data Collection and Research Usability and user perceptions of mHealth mHealth in the Developing World and for Global Health mHealth in a Clinical Setting mHealth for Symptom and Disease Monitoring mHealth for Wellness, Behavior Change and Prevention mHealth for Screening Text-messaging (SMS)-Based Interventions Design and Formative Evaluation of Mobile Apps Security and Privacy of mHealth and uHealth Quality Evaluation and Descriptive Analysis of Multiple Existing Mobile Apps mHealth for Treatment Adherence Use and User Demographics of mHealth mHealth for Telemedicine and Homecare mHealth for Patient Education mHealth in Medical Education and Training Evaluation and Research Methodology for mHealth Wearable Devices and Sensors Fitness Trackers and Smart Pedometers/Accelerometers Google Glass and Augmented Reality Applications Product Reviews and Tutorials in mHealth

中文简介:(来自Google、百度翻译)

重点和范围 JMIR mHealth and uHealth (JMU, ISSN 2291-5222)是领先的电子健康期刊JMIR的一个较新的分支期刊(Impact Factor 2015: 4.532)。JMIR mHealth和uHealth在PubMed、PubMed Central和Science Citation Index Expanded (SCIE)中都有索引,我们预计2017年JMU的第一个影响因子至少为2.84。该杂志专注于移动和平板电脑计算、普及和泛在计算、可穿戴计算和domotics中的健康和生物医学应用。JMIR mHealth和uHealth的发表速度更快,范围更广,其中包含的论文比《医学互联网研究杂志》(Journal of Medical Internet Research)上发表的论文更具技术性或更具构造性。除了研究人员提交的同行评审论文外,JMIR mHealth和uHealth还提供对医疗应用程序本身的同行评审。JMIR mHealth和uHealth journal具有快速和全面的同行评审过程,专业的复制,专业的PDF、XHTML和XML证明的生产。JMIR mHealth和uHealth遵循与JMIR相同的质量标准,这里发表的所有文章也交叉列在JMIR的目录中,JMIR是世界领先的健康科学/健康服务研究和健康信息学医学杂志。 我们正在寻找涵盖以下主题的论文,例如: 无处不在的健康(uHealth) 用于数据收集和研究的移动健康 可用性和用户对移动健康的看法 发展中国家的移动卫生和全球卫生 移动健康的临床设置 用于症状和疾病监测的移动健康 移动健康促进健康,行为改变和预防 移动健康筛查 短信(SMS)的干预措施 移动应用程序的设计与形成性评价 mHealth和uHealth的安全和隐私 多个现有移动应用的质量评估和描述性分析 mHealth用于治疗依从性 移动健康的使用和用户统计 远程医疗和家庭医疗的移动健康 移动健康为患者提供教育 医学教育和培训中的移动健康 移动健康评估与研究方法 可穿戴设备和传感器 健身追踪器和智能计步器/加速计 谷歌玻璃和增强现实应用 mHealth中的产品评论和教程

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JMIR mHealth and uHealth 期刊中科院评价数据

最新中科院分区

大类(学科) 小类(学科) 学科排名
医学 HEALTH CARE SCIENCES & SERVICES(卫生保健科学和服务) 2区
MEDICAL INFORMATICS(医学信息学) 2区
7/94
2/25

最新公布的期刊年发文量

年度总发文量 年度论文发表量 年度综述发表量
147 131 16
总被引频次:2576
特征因子:0.007920

影响因子趋势图

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JMIR mHealth and uHealth 期刊CiteScore评价数据

最新CiteScore值

=

引文计数(2018)

文献(2015-2017)

=

次引用

篇文献

文献总数(2014-2016):
被引比率:%
SJR:
SCLmago期刊等级衡量经过加权后的期刊受引用次数,引用次数的加权值由施引期刊的学科领域和声望(SJR)决定。
SNIP:
每篇文章中来源出版物的标准化影响将实际受引用情况对照期刊所属学科领域中预期的受引用情况进行衡量。

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JMIR mHealth and uHealth 投稿经验

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    想投的要考虑清楚,这本收审稿费,官网的投稿系统问题不少。
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