Evidence map›Paper›PMID 33106087›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2021

Risk Ladder, Table, or Bulleted List? Identifying Formats That Effectively Communicate Personalized Risk and Risk Reduction Information for Multiple Diseases.

Erika A Waters, Julia Maki, Ying Liu, Nicole Ackermann, Chelsey R Carter, Hank Dart, Deborah J Bowen, Linda D Cameron, Graham A Colditz

Open access · greenAbstract read
In one paragraph

Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.6field-weighted citation impact, top 15% 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

7 citing papers in PubMed, 20 citations in OpenAlex.

  1. Trial
  2. Article
  3. Observational
  4. Risk-Adapted Breast Screening for Women at Low Predicted Risk of Breast Cancer: An Online Discrete Choice Experiment.Medical decision making : an international journal of the Society for Medical Decision Making · 2024
    Article
  5. Testing Explanations for Skepticism of Personalized Risk Information.Medical decision making : an international journal of the Society for Medical Decision Making · 2023
    Article
  6. Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants.Medical decision making : an international journal of the Society for Medical Decision Making · 2022
    Article
  7. Article
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

9 authors at 2 institutions in 1 country.

Erika A WatersDepartment of Surgery, Division of Public Health Sciences, Washington University in St. Louis, Saint Louis, MO, USA.ORCID 0000-0001-7402-0133
Julia MakiDepartment of Surgery, Division of Public Health Sciences, Washington University in St. Louis, Saint Louis, MO, USA.
Ying LiuDepartment of Surgery, Division of Public Health Sciences, Washington University in St. Louis, Saint Louis, MO, USA.
Nicole AckermannDepartment of Surgery, Division of Public Health Sciences, Washington University in St. Louis, Saint Louis, MO, USA.
Chelsey R CarterDepartment of Surgery, Division of Public Health Sciences, Washington University in St. Louis, Saint Louis, MO, USA.
Hank DartDepartment of Surgery, Division of Public Health Sciences, Washington University in St. Louis, Saint Louis, MO, USA.
Deborah J BowenUniversity of Washington, Merced, CA, USA.
Linda D CameronUniversity of California, Merced, CA, USA.
Graham A ColditzDepartment of Surgery, Division of Public Health Sciences, Washington University in St. Louis, Saint Louis, MO, USA.
Washington University in St. Louis · USUniversity of California, Merced · US

Funding

WU INSTITUTE OF CLINICAL AND TRANSLATIONAL SCIENCESUL1TR002345 · NCATS · WASHINGTON UNIVERSITY · PI William G. Powderly · 2017 to 2026
$97.8M
COMMUNICATING MULTIPLE DISEASE RISKS: A TRANSLATION OF RISK PREDICTION SCIENCER01CA190391 · NCI · WASHINGTON UNIVERSITY · PI WATERS, ERIKA A · 2015 to 2018
$1.4M
NCATS NIH HHS UL1 TR002345NCI NIH HHS R01 CA190391
6 · The paper itself

Abstract

backgroundPersonalized medicine may increase the amount of probabilistic information patients encounter. Little guidance exists about communicating risk for multiple diseases simultaneously or about communicating how changes in risk factors affect risk (hereafter "risk reduction"). PURPOSE: To determine how to communicate personalized risk and risk reduction information for up to 5 diseases associated with insufficient physical activity in a way laypeople can understand and that increases intentions.

methodsWe recruited 500 participants with <150 min weekly of physical activity from community settings. Participants completed risk assessments for diabetes, heart disease, stroke, colon cancer, and breast cancer (women only) on a smartphone. Then, they were randomly assigned to view personalized risk and risk reduction information organized as a bulleted list, a simplified table, or a specialized vertical bar graph ("risk ladder"). Last, they completed a questionnaire assessing outcomes. Personalized risk and risk reduction information was presented as categories (e.g., "very low"). Our analytic sample (

resultsAnalyses of covariance with post hoc comparisons showed that the risk ladder elicited higher gist comprehension than the bulleted list (

conclusionRisk ladders can communicate the gist meaning of multiple pieces of risk information to individuals from many sociodemographic backgrounds and with varying levels of facility with numbers and graphs.

Indexed as

Cost of IllnessProfessional-Patient RelationsAdultAgedFemaleHealth LiteracyHumansMaleMiddle AgedRisk AssessmentRisk FactorsRisk Reduction BehaviorSurveys and Questionnairespersonalized medicinephysical activityrisk communicationvisual display

Identifiers

PMID33106087
PMCPMC7772263
OpenAlexW3094097136

What OpenQuestion holds

Textmetadata
LicenceTDM
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.