Evidence map›Paper›PMID 31999316›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2020

Visual analogies, not graphs, increase patients' comprehension of changes in their health status.

Meghan Reading Turchioe, Lisa V Grossman, Annie C Myers, Dawon Baik, Parag Goyal, Ruth M Masterson Creber

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 1 pooled it
–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

39 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  17. Visualizing machine learning-based predictions of postpartum depression risk for lay audiences.Journal of the American Medical Informatics Association : JAMIA · 2024
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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

6 authors.

Meghan Reading TurchioeDivision of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA.
Lisa V GrossmanDepartment of Biomedical Informatics, College of Physicians and Surgeons, Columbia University, New York, New York, USA.
Annie C MyersDivision of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA.
Dawon BaikCollege of Nursing, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.
Parag GoyalDepartment of Medicine, Weill Cornell Medicine, New York, New York, USA.
Ruth M Masterson CreberDivision of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA.

Funding

Disparities in COVID Disease Severity and Outcomes in New York CityUL1TR002384 · NCATS · WEILL MEDICAL COLL OF CORNELL UNIV · PI JULIANNE L IMPERATO-MCGINLEY · 2017 to 2026
$86.2M
Should we return patient reported outcomes to human subjects participants?-exploring ethical obligationsR00NR016275 · NINR · WEILL MEDICAL COLL OF CORNELL UNIV · PI MASTERSON CREBER, RUTH MARIE · 2018 to 2020
$896k
Data-driven shared decision-making to reduce symptom burden in atrial fibrillationK99NR019124 · NINR · WEILL MEDICAL COLL OF CORNELL UNIV · PI TURCHIOE, MEGHAN READING · 2020 to 2021
$175k
NCATS NIH HHS UL1 TR002384NINR NIH HHS K99 NR019124NINR NIH HHS R00 NR016275
6 · The paper itself

Abstract

objectivesPatients increasingly use patient-reported outcomes (PROs) to self-monitor their health status. Visualizing PROs longitudinally (over time) could help patients interpret and contextualize their PROs. The study sought to assess hospitalized patients' objective comprehension (primary outcome) of text-only, non-graph, and graph visualizations that display longitudinal PROs. MATERIALS AND

methodsWe conducted a clinical research study in 40 hospitalized patients comparing 4 visualization conditions: (1) text-only, (2) text plus visual analogy, (3) text plus number line, and (4) text plus line graph. Each participant viewed every condition, and we used counterbalancing (systematic randomization) to control for potential order effects. We assessed objective comprehension using the International Organization for Standardization protocol. Secondary outcomes included response times, preferences, risk perceptions, and behavioral intentions.

resultsOverall, 63% correctly comprehended the text-only condition and 60% comprehended the line graph condition, compared with 83% for the visual analogy and 70% for the number line (P = .05) conditions. Participants comprehended the visual analogy significantly better than the text-only (P = .02) and line graph (P = .02) conditions. Of participants who comprehended at least 1 condition, 14% preferred a condition that they did not comprehend. Low comprehension was associated with worse cognition (P < .001), lower education level (P = .02), and fewer financial resources (P = .03).

conclusionsThe results support using visual analogies rather than text to display longitudinal PROs but caution against relying on graphs, which is consistent with the known high prevalence of inadequate graph literacy. The discrepancies between comprehension and preferences suggest factors other than comprehension influence preferences, and that future researchers should assess comprehension rather than preferences to guide presentation decisions.

Indexed as

Computer GraphicsHealth StatusPatient Reported Outcome MeasuresAdultAgedAged, 80 and overComprehensionConsumer Health InformaticsFemaleHeart FailureHospitalizationHumansMaleMiddle AgedUser-Computer Interfaceconsumer health informaticsdata visualizationhealth communicationhuman-computer interactionpatient-reported outcome measures

Identifiers

PMID31999316
PMCPMC7309237

What OpenQuestion holds

Textmetadata
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.