animated visualization

  • 文章类型: Journal Article
    目的:我们试图分析健康概率数据(如疾病或副作用的机会)的交互式可视化和动画,这些数据在静态图形或数字通信的头对头比较中进行了研究。
    方法:关于健康中的数字交流方法的大型系统综述的二次分析。
    结果:我们对研究进行了分组,以表明多种研究人员已经研究了4种类型的动画或交互式可视化:模拟概率事件的经验的;那些证明这些事件的随机性的;那些通过将注意力依次引导到不同的信息项来减少信息过载的;以及那些促进精细思维的。总的来说,这4种可视化并没有显示出提高理解力的有力证据,风险感知,或静态图形上的健康行为。
    结论:目前还没有强有力的证据表明,交互性或动画在传达健康概率方面比静态图形更有效。我们讨论了两种可能性:最有效的可视化尚未被研究,并且可视化效果无效。
    结论:未来的研究应严格比较参与者的表现与新颖的交互式或动画可视化与静态可视化的表现。这些证据将有助于确定健康传播者是否应该强调新颖的交互式可视化或依赖较旧的视觉交流形式,更广泛的受众可以接触到,包括那些有限的数字访问。
    OBJECTIVE: We sought to analyze interactive visualizations and animations of health probability data (such as chances of disease or side effects) that have been studied in head-to-head comparisons with either static graphics or numerical communications.
    METHODS: Secondary analysis of a large systematic review on ways to communicate numbers in health.
    RESULTS: We group the research to show that 4 types of animated or interactive visualizations have been studied by multiple researchers: those that simulate experience of probabilistic events; those that demonstrate the randomness of those events; those that reduce information overload by directing attention sequentially to different items of information; and those that promote elaborative thinking. Overall, these 4 types of visualizations do not show strong evidence of improving comprehension, risk perception, or health behaviors over static graphics.
    CONCLUSIONS: Evidence is not yet strong that interactivity or animation is more effective than static graphics for communicating probabilities in health. We discuss 2 possibilities: that the most effective visualizations haven\'t been studied, and that the visualizations aren\'t effective.
    CONCLUSIONS: Future studies should rigorously compare participant performance with novel interactive or animated visualizations against their performance with static visualizations. Such evidence would help determine whether health communicators should emphasize novel interactive visualizations or rely on older forms of visual communication, which may be accessible to broader audiences, including those with limited digital access.
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  • 文章类型: Journal Article
    背景:Ixekizumab,一种选择性靶向白细胞介素17A的高亲和力单克隆抗体,是一种经批准的治疗斑块状银屑病的方法。这项研究旨在使用动画可视化作为工具来简化ixekizumab临床试验的复杂数据。
    方法:开发了动画可视化以显示ixekizumab临床试验的结果和11个已批准的生物制剂的贝叶斯网络荟萃分析。可视化同时突出了治疗过程中患者的总体评分和个体进展。
    结果:动画以有效和科学的方式提供了来自复杂数据的关键信息和信息,这些信息在视觉上也令人愉悦且易于理解。动画突出显示:(1)疾病严重程度从基线快速降低;(2)ixekizumab在皮肤和指甲牛皮癣治疗中的持续疗效;(3)对试验中的治疗效果和临床改善进行并排比较;(4)随时间的变化,同时可视化显示个人结果,并总结反应;(5)基于贝叶斯网络荟萃分析,间接比较相对治疗效果与其他生物制剂。
    结论:使用具有不同临床终点的多种动态可视化方法,证明了ixekizumab治疗银屑病的快速和持续疗效。与传统的静态图形相比,动画可视化提供了对复杂数据的更简单,更全面的理解。
    BACKGROUND: Ixekizumab, a high-affinity monoclonal antibody that selectively targets interleukin-17A, is an approved treatment for plaque psoriasis. This study aimed to use animated visualizations as a tool to simplify complex data from ixekizumab clinical trials.
    METHODS: Animated visualizations were developed to show outcomes from ixekizumab clinical trials and a Bayesian network meta-analysis of 11 approved biologics. The visualizations simultaneously highlighted both aggregate scores and the individual progression of patients over the course of treatment.
    RESULTS: The animations provided key messages and information from the complex data in efficient and scientific ways that were also visually pleasing and simple to understand. The animations highlighted (1) rapid reduction in disease severity from baseline; (2) sustained efficacy of ixekizumab in the treatment of skin and nail psoriasis; (3) side-by-side comparisons of treatment efficacy and clinical improvement across trials; (4) simultaneous visual presentation of individual results with summary response over time; and (5) indirect comparison of relative treatment effects with other biologics based on Bayesian network meta-analysis.
    CONCLUSIONS: The rapid and sustained efficacy of ixekizumab in the treatment of psoriasis was demonstrated using multiple dynamic visualizations with different clinical endpoints. Animated visualizations provided a simpler and more comprehensive understanding of complex data than conventional static figures.
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