Evidence map›Paper›PMID 30912759›Full record

ArticleJournal of medical Internet research2019

Patient Judgments About Hypertension Control: The Role of Variability, Trends, and Outliers in Visualized Blood Pressure Data.

Victoria Anne Shaffer, Pete Wegier, K D Valentine, Jeffery L Belden, Shannon M Canfield, Sonal J Patil, Mihail Popescu, Linsey M Steege, Akshay Jain, Richelle J Koopman

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Patient judgments about hypertension control: the role of patient numeracy and graph literacy.Journal of the American Medical Informatics Association : JAMIA · 2022
    Article
  6. Article
  7. Article
  8. Article
  9. Use of Enhanced Data Visualization to Improve Patient Judgments about Hypertension Control.Medical decision making : an international journal of the Society for Medical Decision Making · 2020
    Article
  10. 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

10 authors.

Victoria Anne ShafferUniversity of Missouri, Department of Psychological Sciences, Columbia, MO, United States.ORCID 0000-0002-2533-7115
Pete WegierSinai Health System, Temmy Latner Center for Palliative Care, Toronto, ON, Canada.ORCID 0000-0003-0191-136X
K D ValentineUniversity of Missouri, Department of Psychological Sciences, Columbia, MO, United States.ORCID 0000-0001-6349-5395
Jeffery L BeldenUniversity of Missouri, Department of Family & Community Medicine, Columbia, MO, United States.ORCID 0000-0002-4514-8602
Shannon M CanfieldUniversity of Missouri, Department of Family & Community Medicine, Columbia, MO, United States.ORCID 0000-0002-6577-8484
Sonal J PatilUniversity of Missouri, Department of Family & Community Medicine, Columbia, MO, United States.ORCID 0000-0002-5725-3928
Mihail PopescuUniversity of Missouri, Department of Health Management & Informatics, Columbia, MO, United States.ORCID 0000-0002-6145-8096
Linsey M SteegeUniversity of Wisconsin, School of Nursing, Madison, WI, United States.ORCID 0000-0002-8508-7787
Akshay JainUniversity of Missouri, Department of Electrical and Computer Engineering, Columbia, MO, United States.ORCID 0000-0003-4217-9320
Richelle J KoopmanUniversity of Missouri, Department of Family & Community Medicine, Columbia, MO, United States.ORCID 0000-0002-0662-2391

Funding

Optimizing Display of Blood Pressure Data to Support Clinical Decision MakingR01HS023328 · AHRQ · UNIVERSITY OF MISSOURI-COLUMBIA · PI KOOPMAN, RICHELLE J · 2015 to 2019
$2.2M
AHRQ HHS R01 HS023328
6 · The paper itself

Abstract

backgroundUncontrolled hypertension is a significant health problem in the United States, even though multiple drugs exist to effectively treat this chronic disease.

objectiveAs part of a larger project developing data visualizations to support shared decision making about hypertension treatment, we conducted a series of studies to understand how perceptions of hypertension control were impacted by data variations inherent in the visualization of blood pressure (BP) data.

methodsIn 3 Web studies, participants (internet sample of patients with hypertension) reviewed a series of vignettes depicting patients with hypertension; each vignette included a graph of a patient's BP. We examined how data visualizations that varied by BP mean and SD (Study 1), the pattern of change over time (Study 2), and the presence of extreme values (Study 3) affected patients' judgments about hypertension control and the need for a medication change.

resultsParticipants' judgments about hypertension control were significantly influenced by BP mean and SD (Study 1), data trends (whether BP was increasing or decreasing over time-Study 2), and extreme values (ie, outliers-Study 3).

conclusionsPatients' judgment about hypertension control is influenced both by factors that are important predictors of hypertension related-health outcomes (eg, BP mean) and factors that are not (eg, variability and outliers). This study highlights the importance of developing data visualizations that direct attention toward clinically meaningful information.

Indexed as

AdultAgedAntihypertensive AgentsBlood PressureBlood Pressure DeterminationFemaleHumansHypertensionMaleMiddle AgedUnited StatesYoung AdultAntihypertensive Agentsdata visualizationhypertensionhypertension controlpatients’ judgmentprimary care

Identifiers

PMID30912759
PMCPMC6454346

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Registered trials

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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.