How lay people understand and make sense of personalized disease risk information.

BACKGROUND: Disease risk calculators are increasingly web-based, but previous studies have shown that risk information often poses problems for lay users. OBJECTIVE: To examine how lay people understand the result derived from an online cardiometabolic risk calculator. DESIGN: A qualitative study was performed, using the risk calculator in the Dutch National Prevention Program for cardiometabolic diseases. The study consisted of three parts: (i) attention: completion of the risk calculator while an eye tracker registered eye movements; (ii) recall: completion of a recall task; and (iii) interpretation: participation in a semi-structured interview. SETTING AND PARTICIPANTS: We recruited people from the target population through an advertisement in a local newspaper; 16 people participated in the study, which took place in our university laboratory. RESULTS: Eye-tracking data showed that participants looked most extensively at numerical risk information. Percentages were recalled well, whereas natural frequencies and verbal labels were remembered less well. Five qualitative themes were derived from the interview data: (i) numerical information does not really sink in; (ii) the verbal categorical label made no real impact on people; (iii) people relied heavily on existing knowledge and beliefs; (iv) people zoomed in on risk factors, especially family history of diseases; and (v) people often compared their situation to that of their peers. DISCUSSION AND CONCLUSION: Although people paid attention to and recalled the risk information to a certain extent, they seemed to have difficulty in properly using this information for interpreting their risk.

The effects of infographics and several quantitative versus qualitative formats for cardiovascular disease risk, including heart age, on people's risk understanding.

OBJECTIVE: To study how comprehension of cardiovascular disease (CVD) risk is influenced by: (1) infographics about qualitative risk information, with/without risk numbers; (2) which qualitative risk dimension is emphasized; (3) heart age vs. traditional risk format. METHODS: For aim 1, a 2 (infographics versus text) x 2 (risk number versus no risk number) between-subjects design was used. For aim 2, three pieces of information were tested within-subjects. Aim 3 used a simple comparison group. Participants (45-65 yrs old) were recruited through an online access panel; low educated people were oversampled. They received hypothetical risk information (20%/61yrs). Primary outcomes: recall, risk appraisals, subjective/objective risk comprehension. SECONDARY OUTCOMES: behavioral intentions, information evaluations. RESULTS: Infographics of qualitative risk dimensions negatively affected recall, subjective risk comprehension and information evaluations. No effect of type of risk dimension was found on risk perception. Heart age influenced recall, comprehension, evaluations and affective risk appraisals. CONCLUSION: Infographics of hypothetical CVD risk information had detrimental effects on measures related to risk perception/comprehension, but effects were mainly seen in undereducated participants. Heart age influenced perceptions/comprehension of hypothetical risk in a way that seemed to support understanding. PRACTICE IMPLICATIONS: Heart age seems a fruitful risk communication approach in disease risk calculators.