Evidence map›Paper›PMID 24642037›Full record

ArticleJournal of medical Internet research2014

Animated randomness, avatars, movement, and personalization in risk graphics.

Holly O Witteman, Andrea Fuhrel-Forbis, Harindra C Wijeysundera, Nicole Exe, Mark Dickson, Lisa Holtzman, Valerie C Kahn, Brian J Zikmund-Fisher

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 2 pooled it
40.4field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 37 citations in OpenAlex.

  1. Insufficient evidence for interactive or animated graphics for communicating probability.Journal of the American Medical Informatics Association : JAMIA · 2024
    Pooled it
  2. Pooled it
  3. Does Animation Improve Comprehension of Risk Information in Patients with Low Health Literacy? A Randomized Trial.Medical decision making : an international journal of the Society for Medical Decision Making · 2020
    Trial
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  7. Visualizing machine learning-based predictions of postpartum depression risk for lay audiences.Journal of the American Medical Informatics Association : JAMIA · 2024
    Article
  8. Article
  9. Biological age calculators to motivate lifestyle change: Environmental scan of online tools and evaluation of behaviour change techniques.Health promotion journal of Australia : official journal of Australian Association of Health Promotion Professionals · 2023
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  10. Review
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  14. Precision public health-the Emperor's new clothes.International journal of epidemiology · 2019
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 2 countries.

Holly O WittemanDepartment of Family and Emergency Medicine, Faculty of Medicine, Laval University, Quebec City, QC, Canada. holly@witteman.ca.
Andrea Fuhrel-Forbis
Harindra C Wijeysundera
Nicole Exe
Mark Dickson
Lisa Holtzman
Valerie C Kahn
Brian J Zikmund-Fisher
University of Michigan–Ann Arbor · USHealth Sciences Centre · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRisk 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.

objectiveThe 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.

methodsA 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.

resultsAnimated 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).

conclusionsAnimated 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.

Indexed as

Cardiovascular DiseasesComputer GraphicsRisk AssessmentUser-Computer InterfaceAdultAgedCommunicationComprehensionFemaleHealth BehaviorHumansLife StyleMaleMiddle AgedMovementRisk Factorsanimationavatarcardiovascular diseasehealth communicationicon arraypictographrisk graphics

Identifiers

PMID24642037
PMCPMC3978557
OpenAlexW2066734537

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.