Interactive graphics for expressing health risks: development and qualitative evaluation.
ABSTRACT Recent findings suggest that interactive game-like graphics might be useful in communicating probabilities. We developed a prototype for a risk communication module, focusing on eliciting users' preferences for different interactive graphics and assessing usability and user interpretations. Feedback from five focus groups was used to design the graphics. The final version displayed a matrix of square buttons; clicking on any button allowed the user to see whether the stick figure underneath was affected by the health outcome. When participants used this interaction to learn about a risk, they expressed more emotional responses, both positive and negative, than when viewing any static graphic or numerical description of a risk. Their responses included relief about small risks and concern about large risks. The groups also commented on static graphics: arranging the figures affected by disease randomly throughout a group of figures made it more difficult to judge the proportion affected but often was described as more realistic. Interactive graphics appear to have potential for expressing risk magnitude as well as the feeling of risk. This affective impact could be useful in increasing perceived threat of high risks, calming fears about low risks, or comparing risks. Quantitative studies are planned to assess the effect on perceived risks and estimated risk magnitudes.
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ABSTRACT: Making evidence-based decisions often requires comparison of two or more options. Research-based evidence may exist which quantifies how likely the outcomes are for each option. Understanding these numeric estimates improves patients' risk perception and leads to better informed decision making. This paper summarises current "best practices" in communication of evidence-based numeric outcomes for developers of patient decision aids (PtDAs) and other health communication tools. An expert consensus group of fourteen researchers from North America, Europe, and Australasia identified eleven main issues in risk communication. Two experts for each issue wrote a "state of the art" summary of best evidence, drawing on the PtDA, health, psychological, and broader scientific literature. In addition, commonly used terms were defined and a set of guiding principles and key messages derived from the results. The eleven key components of risk communication were: 1) Presenting the chance an event will occur; 2) Presenting changes in numeric outcomes; 3) Outcome estimates for test and screening decisions; 4) Numeric estimates in context and with evaluative labels; 5) Conveying uncertainty; 6) Visual formats; 7) Tailoring estimates; 8) Formats for understanding outcomes over time; 9) Narrative methods for conveying the chance of an event; 10) Important skills for understanding numerical estimates; and 11) Interactive web-based formats. Guiding principles from the evidence summaries advise that risk communication formats should reflect the task required of the user, should always define a relevant reference class (i.e., denominator) over time, should aim to use a consistent format throughout documents, should avoid "1 in x" formats and variable denominators, consider the magnitude of numbers used and the possibility of format bias, and should take into account the numeracy and graph literacy of the audience. A substantial and rapidly expanding evidence base exists for risk communication. Developers of tools to facilitate evidence-based decision making should apply these principles to improve the quality of risk communication in practice.BMC Medical Informatics and Decision Making 01/2013; 13 Suppl 2:S7. DOI:10.1186/1472-6947-13-S2-S7 · 1.50 Impact Factor
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ABSTRACT: Risk communication involves conveying two inherently difficult concepts about the nature of risk: the underlying random distribution of outcomes and how a population-based proportion applies to an individual. The objective of this study was to test whether 4 design factors in icon arrays-animated random dispersal of risk events, avatars to represent an individual, personalization (operationalized as choosing the avatar's color), and a moving avatar-might help convey randomness and how a given risk applies to an individual, thereby better aligning risk perceptions with risk estimates. A diverse sample of 3630 adults with no previous heart disease or stroke completed an online nested factorial experiment in which they entered personal health data into a risk calculator that estimated 10-year risk of cardiovascular disease based on a robust and validated model. We randomly assigned them to view their results in 1 of 10 risk graphics that used different combinations of the 4 design factors. We measured participants' risk perceptions as our primary outcome, as well as behavioral intentions and recall of the risk estimate. We also assessed subjective numeracy, whether or not participants knew anyone who had died of cardiovascular causes, and whether or not they knew their blood pressure and cholesterol as potential moderators. Animated randomness was associated with better alignment between risk estimates and risk perceptions (F1,3576=6.12, P=.01); however, it also led to lower scores on healthy lifestyle intentions (F1,3572=11.1, P<.001). Using an avatar increased risk perceptions overall (F1,3576=4.61, P=.03) and most significantly increased risk perceptions among those who did not know a particular person who had experienced the grave outcomes of cardiovascular disease (F1,3576=5.88, P=.02). Using an avatar also better aligned actual risk estimates with intentions to see a doctor (F1,3556=6.38, P=.01). No design factors had main effects on recall, but animated randomness was associated with better recall for those at lower risk and worse recall for those at higher risk (F1,3544=7.06, P=.01). Animated randomness may help people better understand the random nature of risk. However, in the context of cardiovascular risk, such understanding may result in lower healthy lifestyle intentions. Therefore, whether or not to display randomness may depend on whether one's goal is to persuade or to inform. Avatars show promise for helping people grasp how population-based statistics map to an individual case.Journal of Medical Internet Research 01/2014; 16(3):e80. DOI:10.2196/jmir.2895 · 4.67 Impact Factor
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ABSTRACT: Men receiving androgen deprivation therapy for prostate cancer have low knowledge of osteoporosis (OP) and engage in few healthy bone behaviors (HBBs). A multicomponent intervention was piloted in this population. Changes in OP knowledge, self-efficacy, health beliefs, and engagement in HBBs were evaluated. A pre-post pilot study was performed in a convenience sample of men recruited from the Princess Margaret Cancer Centre. Men were sent personalized letters explaining their dual x-ray absorptiometry (DXA) results and fracture risk assessment with an OP-related education booklet. Participants completed questionnaires assessing OP knowledge, self-efficacy, health beliefs, and current engagement in HBBs at baseline (T1) and 3 months post-intervention (T2). Paired t tests and McNemar's test were used to assess changes in outcomes. A total of 148 men completed the study. There was an increase in OP knowledge (9.7 ± 4.3 to 11.4 ± 3.3, p < 0.0001) and feelings of susceptibility (16.5 ± 4.3 to 17.4 ± 4.7, p = 0.015), but a decrease in total self-efficacy (86.3 ± 22.9 to 81.0 ± 27.6, p = 0.007) from baseline to post-intervention. Men made appropriate changes in their overall daily calcium intake (p ≤ 0.001), and there was uptake of vitamin D supplementation from 44 % (n = 65) to 68 % (n = 99) (p < 0.0001). Men with bone loss (osteopenia or OP) had a greater change in susceptibility (1.9 ± 4.3 vs. -0.22 ± 4.2, p = 0.005) compared to men with normal bone density. Our results provide preliminary evidence that a multicomponent intervention such as the one described can lead to increased knowledge and feelings of susceptibility regarding OP and can enhance uptake of some HBBs.Supportive Care in Cancer 04/2014; 22(9). DOI:10.1007/s00520-014-2183-6 · 2.09 Impact Factor