Adrijana Svenšek, Lucija Gosak, Mateja Lorber, Gregor Štiglic, Nino Fijačko
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However, the usability evaluation and inclusion of visualization of these apps are often unclear and variable.</p><p><strong>Objective: </strong>The primary objective of this study is to review and compare the usability of existing apps designed to estimate CVD risk using the mHealth App Usability Questionnaire (MAUQ). This is not a traditional usability study involving user interaction design, but rather an assessment of how effectively these applications meet usability standards as defined by the MAUQ.</p><p><strong>Methods: </strong>First, we used predefined criteria to review 16 out of 2238 apps to estimate CVD risk in the Google Play Store and the Apple App Store. Based on the apps' characteristics (ie, developed for health care professionals or patient use) and their functions (single or multiple CVD risk calculators), we conducted a descriptive analysis. Then we also compared the usability of existing apps using the MAUQ and calculated the agreement among 3 expert raters.</p><p><strong>Results: </strong>Most apps used the Framingham Risk Score (8/16, 50%) and Atherosclerotic Cardiovascular Disease Risk (7/16, 44%) prognostic models to estimate CVD risk. The app with the highest overall MAUQ score was the MDCalc Medical Calculator (mean 6.76, SD 0.25), and the lowest overall MAUQ score was obtained for the CardioRisk Calculator (mean 3.96, SD 0.21). The app with the highest overall MAUQ score in the \"ease-of-use\" domain was the MDCalc Medical Calculator (mean 7, SD 0); in the domain \"interface and satisfaction,\" it was the MDCalc Medical Calculator (mean 6.67, SD 0.33); and in the domain \"usefulness,\" it was the ASCVD Risk Estimator Plus (mean 6.80, SD 0.32).</p><p><strong>Conclusions: </strong>We found that the Framingham Risk Score is the most widely used prognostic model in apps for estimating CVD risk. 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引用次数: 0
摘要
背景:心血管疾病(CVD)是世界范围内死亡和残疾的主要原因,其预防是一个主要的公共卫生重点。及早发现健康问题并评估风险水平可以大大提高降低死亡率的机会。通过向用户提供个性化的反馈、教育和激励,移动应用程序可以帮助评估和管理心血管疾病风险。将可视化分析融入应用程序是教育社会的有效方法。然而,这些应用程序的可用性评估和可视化通常是不明确和可变的。目的:本研究的主要目的是回顾和比较现有应用程序的可用性,这些应用程序旨在使用移动健康应用程序可用性问卷(MAUQ)来估计心血管疾病风险。这不是涉及用户交互设计的传统可用性研究,而是评估这些应用程序如何有效地满足由MAUQ定义的可用性标准。方法:首先,我们使用预定义的标准审查了2238个应用程序中的16个,以估计bb0 Play Store和Apple App Store中的心血管疾病风险。基于应用程序的特点(即为卫生保健专业人员或患者使用而开发)及其功能(单个或多个心血管疾病风险计算器),我们进行了描述性分析。然后,我们还使用MAUQ比较了现有应用程序的可用性,并计算了3位专家评分者之间的一致性。结果:大多数应用程序使用Framingham风险评分(8/16,50%)和动脉粥样硬化性心血管疾病风险(7/16,44%)预后模型来估计心血管疾病风险。总体MAUQ得分最高的应用程序是MDCalc医疗计算器(平均6.76,SD 0.25),最低的是CardioRisk计算器(平均3.96,SD 0.21)。在“易用性”领域中,MAUQ总分最高的应用程序是MDCalc医疗计算器(平均值7,标准差0);在“界面和满意度”领域,是MDCalc医疗计算器(平均值6.67,标准差0.33);在“有用性”领域,它是ASCVD风险估计加(平均值6.80,标准差0.32)。结论:我们发现Framingham风险评分是应用程序中最广泛用于估计心血管疾病风险的预后模型。“易用性”领域获得了最高的评级。虽然超过一半的应用程序既适合医疗保健专业人员也适合患者,但只有少数应用程序提供了评估心血管疾病风险的复杂可视化效果。只有不到四分之一的应用程序包含可视化,而那些包含可视化的应用程序是单个计算器。我们对应用程序的分析表明,它们是评估心血管疾病风险的合适工具。
Review and Comparative Evaluation of Mobile Apps for Cardiovascular Risk Estimation: Usability Evaluation Using mHealth App Usability Questionnaire.
Background: Cardiovascular diseases (CVD) are the leading cause of death and disability worldwide, and their prevention is a major public health priority. Detecting health issues early and assessing risk levels can significantly improve the chances of reducing mortality. Mobile apps can help estimate and manage CVD risks by providing users with personalized feedback, education, and motivation. Incorporating visual analysis into apps is an effective method for educating society. However, the usability evaluation and inclusion of visualization of these apps are often unclear and variable.
Objective: The primary objective of this study is to review and compare the usability of existing apps designed to estimate CVD risk using the mHealth App Usability Questionnaire (MAUQ). This is not a traditional usability study involving user interaction design, but rather an assessment of how effectively these applications meet usability standards as defined by the MAUQ.
Methods: First, we used predefined criteria to review 16 out of 2238 apps to estimate CVD risk in the Google Play Store and the Apple App Store. Based on the apps' characteristics (ie, developed for health care professionals or patient use) and their functions (single or multiple CVD risk calculators), we conducted a descriptive analysis. Then we also compared the usability of existing apps using the MAUQ and calculated the agreement among 3 expert raters.
Results: Most apps used the Framingham Risk Score (8/16, 50%) and Atherosclerotic Cardiovascular Disease Risk (7/16, 44%) prognostic models to estimate CVD risk. The app with the highest overall MAUQ score was the MDCalc Medical Calculator (mean 6.76, SD 0.25), and the lowest overall MAUQ score was obtained for the CardioRisk Calculator (mean 3.96, SD 0.21). The app with the highest overall MAUQ score in the "ease-of-use" domain was the MDCalc Medical Calculator (mean 7, SD 0); in the domain "interface and satisfaction," it was the MDCalc Medical Calculator (mean 6.67, SD 0.33); and in the domain "usefulness," it was the ASCVD Risk Estimator Plus (mean 6.80, SD 0.32).
Conclusions: We found that the Framingham Risk Score is the most widely used prognostic model in apps for estimating CVD risk. The "ease-of-use" domain received the highest ratings. While more than half of the apps were suitable for both health care professionals and patients, only a few offered sophisticated visualizations for assessing CVD risk. Less than a quarter of the apps included visualizations, and those that did were single calculators. Our analysis of apps showed that they are an appropriate tool for estimating CVD risk.
期刊介绍:
JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636.
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 since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.