Literature DB >> 28973820

Designing Graphs that Promote Both Risk Understanding and Behavior Change.

Yasmina Okan1, Eric R Stone2, Wändi Bruine de Bruin1,3.   

Abstract

Graphs show promise for improving communications about different types of risks, including health risks, financial risks, and climate risks. However, graph designs that are effective at meeting one important risk communication goal (promoting risk-avoidant behaviors) can at the same time compromise another key goal (improving risk understanding). We developed and tested simple bar graphs aimed at accomplishing these two goals simultaneously. We manipulated two design features in graphs, namely, whether graphs depicted the number of people affected by a risk and those at risk of harm ("foreground+background") versus only those affected ("foreground-only"), and the presence versus absence of simple numerical labels above bars. Foreground-only displays were associated with larger risk perceptions and risk-avoidant behavior (i.e., willingness to take a drug for heart attack prevention) than foreground+background displays, regardless of the presence of labels. Foreground-only graphs also hindered risk understanding when labels were not present. However, the presence of labels significantly improved understanding, eliminating the detrimental effect of foreground-only displays. Labels also led to more positive user evaluations of the graphs, but did not affect risk-avoidant behavior. Using process modeling we identified mediators (risk perceptions, understanding, user evaluations) that explained the effect of display type on risk-avoidant behavior. Our findings contribute new evidence to the graph design literature: unlike what was previously feared, we demonstrate that it is possible to design foreground-only graphs that promote intentions for behavior change without a detrimental effect on risk understanding. Implications for the design of graphical risk communications and decision support are discussed.
© 2017 Society for Risk Analysis.

Entities:  

Keywords:  Graph design; medical decision making; risk communication

Year:  2017        PMID: 28973820     DOI: 10.1111/risa.12895

Source DB:  PubMed          Journal:  Risk Anal        ISSN: 0272-4332            Impact factor:   4.000


  3 in total

1.  Presenting self-monitoring test results for consumers: the effects of graphical formats and age.

Authors:  Da Tao; Juan Yuan; Xingda Qu
Journal:  J Am Med Inform Assoc       Date:  2018-08-01       Impact factor: 4.497

2.  Policies to influence perceptions about COVID-19 risk: The case of maps.

Authors:  Claudia Engel; Jonathan Rodden; Marco Tabellini
Journal:  Sci Adv       Date:  2022-03-18       Impact factor: 14.136

3.  Cocreation with Dutch patients of decision-relevant information to support shared decision-making about adjuvant treatment in breast cancer care.

Authors:  Inge S van Strien-Knippenberg; Marieke C S Boshuizen; Domino Determann; Jasmijn H de Boer; Olga C Damman
Journal:  Health Expect       Date:  2022-05-17       Impact factor: 3.318

  3 in total

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