Deutsche Krebsgesellschaft (German Cancer Society), Kuno-Fischer-Straße 8, 14057 Berlin, Germany; Department Health Sciences, Hamburg University of Applied Sciences (HAW Hamburg), Ulmenliet 20, 21033 Hamburg, Germany; School of Engineering and Computing, University of the West of Scotland (UWS), Paisley, PA1 2BE Scotland, UK.
Deutsche Krebsgesellschaft (German Cancer Society), Kuno-Fischer-Straße 8, 14057 Berlin, Germany.
Patient Educ Couns. 2017 Aug;100(8):1421-1431. doi: 10.1016/j.pec.2017.02.003. Epub 2017 Feb 6.
Health websites are becoming important sources for cancer information. Lay users, patients and carers seek support for critical decisions, but they are prone to common biases when quantitative information is presented. Graphical representations of risk data can facilitate comprehension, and interactive visualizations are popular. This review summarizes the evidence on computer-supported graphs that present risk data and their effects on various measures.
The systematic literature search was conducted in several databases, including MEDLINE, EMBASE and CINAHL. Only studies with a controlled design were included. Relevant publications were carefully selected and critically appraised by two reviewers.
Thirteen studies were included. Ten studies evaluated static graphs and three dynamic formats. Most decision scenarios were hypothetical. Static graphs could improve accuracy, comprehension, and behavioural intention. But the results were heterogeneous and inconsistent among the studies. Dynamic formats were not superior or even impaired performance compared to static formats.
Static graphs show promising but inconsistent results, while research on dynamic visualizations is scarce and must be interpreted cautiously due to methodical limitations.
Well-designed and context-specific static graphs can support web-based cancer risk communication in particular populations. The application of dynamic formats cannot be recommended and needs further research.
健康网站正成为癌症信息的重要来源。非专业人士、患者和护理人员在做出关键决策时会寻求支持,但当呈现定量信息时,他们容易受到常见的偏差影响。风险数据的图形表示可以促进理解,交互式可视化也很受欢迎。本综述总结了关于呈现风险数据的计算机支持图形的证据,以及它们对各种测量指标的影响。
系统文献检索在多个数据库中进行,包括 MEDLINE、EMBASE 和 CINAHL。仅纳入具有对照设计的研究。由两名评审员仔细选择和批判性评价相关出版物。
共纳入 13 项研究。其中 10 项研究评估了静态图形,3 项研究评估了动态格式。大多数决策情景都是假设的。静态图形可以提高准确性、理解和行为意向。但研究结果存在异质性且不一致。与静态格式相比,动态格式并没有表现出优势,甚至可能会降低性能。
静态图形显示出有希望但不一致的结果,而关于动态可视化的研究则很少,由于方法学上的限制,必须谨慎解释。
精心设计和特定于情境的静态图形可以特别支持基于网络的癌症风险沟通。不能推荐动态格式的应用,需要进一步研究。