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Journal Article
Review
Visualizing risks in cancer communication: A systematic review of computer-supported visual aids.
Patient Education and Counseling 2017 August
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: Static graphs show promising but inconsistent results, while research on dynamic visualizations is scarce and must be interpreted cautiously due to methodical limitations.
PRACTICE IMPLICATIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: Static graphs show promising but inconsistent results, while research on dynamic visualizations is scarce and must be interpreted cautiously due to methodical limitations.
PRACTICE IMPLICATIONS: 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.
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