BLOCK IT OUT! PRESENTING TEST RESULTS WITH CLEARLY DEFINED CATEGORIES INCREASES UNDERSTANDING OF THE RESULTS
Methods: Participants (N=4,833) completed an online survey experiment in which they were presented with a hypothetical scenario where they received hemoglobin A1c test results from a blood draw done between clinical visits for management of Type 2 diabetes. Test results were randomly presented in one of three line graph formats: (1) solid, single-color that only showed the standard range, (2) color gradient utilizing a stoplight color theme (green to red) to indicate risk categories or (3) solid-color blocks that indicated discrete risk categories utilizing the stoplight color theme. The A1c test value was also randomized, being within the standard range (5.4%) or one of three higher levels (6.4%, 7.1%, or 8.4%). Our primary outcome measures were 1) how good or bad the participant thought their test result was for their health, with “don’t know” as a response option and 2) graph preferences. Individual difference measures included subjective and objective numeracy, and graphical literacy.
Results: Controlling for numeracy and graphical literacy, both A1c test value and graph format had significant effects on risk perceptions (p’s<.05). More importantly, we observed a significant A1c test value by format interaction (p<.001): Participants were more sensitive to changes in A1c values when they viewed the stoplight-colored block design with discrete risk categories than when they saw either of the other line graph formats. Respondents also significantly preferred the stoplight-colored block design (p’s<.03). The A1c test value was not a significant predictor of “don’t know” responses (p=.88), but the stoplight-colored gradient design did result in higher rates of “don’t know” responses than either the solid single-color and stoplight-colored block designs (p=.01).
Conclusion: Presenting laboratory test results using a line graph that utilizes stoplight-colored blocks that clearly demarcate discrete risk categories decreases confusion, increases sensitivity to differences in test values, and is more preferred to line graphs that utilize a solid, single-color or a stoplight-colored gradient.