Evidence map›Paper›PMID 31670816›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2020

Patient preferences for visualization of longitudinal patient-reported outcomes data.

Samantha Stonbraker, Tiffany Porras, Rebecca Schnall

Abstract readEvaluation Study
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 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Trial
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  11. Seeing things the same way: perspectives and lessons learned from research-design collaborations.Journal of the American Medical Informatics Association : JAMIA · 2024
    Article
  12. Community engagement in the development of health-related data visualizations: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024
    Article
  13. Article
  14. Article
  15. Review
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  18. Meeting the information and communication needs of health disparate populations.Journal of the American Medical Informatics Association : JAMIA · 2022
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  19. Article
  20. Review
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

3 authors.

Samantha StonbrakerColumbia University School of Nursing, New York, New York, USA.
Tiffany PorrasColumbia University School of Nursing, New York, New York, USA.
Rebecca SchnallColumbia University School of Nursing, New York, New York, USA.

Funding

Reducing Health Disparities Through Informatics - Genomics SupplementT32NR007969 · NINR · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI SUZANNE BAKKEN, Rebecca Schnall · 2002 to 2026
$7.9M
Video Information Provider for HIV-Associated Non-AIDS (VIP-HANA) SymptomsR01NR015737 · NINR · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI SCHNALL, REBECCA · 2015 to 2019
$2.8M
Mentoring and Research in Self-Management for Health Promotion and Disease PreventionK24NR018621 · NINR · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI SCHNALL, REBECCA · 2019 to 2022
$384k
Information visualizations to facilitate clinician-patient communication in HIV care (Info Viz: HIV)K99NR017829 · NINR · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI STONBRAKER, SAMANTHA · 2018 to 2019
$181k
NINR NIH HHS K24 NR018621NINR NIH HHS K99 NR017829NINR NIH HHS R01 NR015737NINR NIH HHS T32 NR007969
6 · The paper itself

Abstract

objectiveThe study sought to design symptom reports of longitudinal patient-reported outcomes data that are understandable and meaningful to end users. MATERIALS AND

methodsWe completed a 2-phase iterative design and evaluation process. In phase I, we developed symptom reports and refined them according to expert input. End users then completed a survey containing demographics, a measure of health literacy, and items to assess visualization preferences and comprehension of reports. We then collected participants' perspectives on reports through semistructured interviews and modified them accordingly. In phase II, refined reports were evaluated in a survey that included demographics, validated measures of health and graph literacy, and items to assess preferences and comprehension of reports. Surveys were administered using a think-aloud protocol.

resultsFifty-five English- and Spanish-speaking end users, 89.1% of whom had limited health literacy, participated. In phase I, experts recommended improvements and 20 end users evaluated reports. From the feedback received, we added emojis, changed date and font formats, and simplified the y-axis scale of reports. In phase II, 35 end users evaluated refined designs, of whom 94.3% preferred reports with emojis, the favorite being a bar graph combined with emojis, which also promoted comprehension. In both phases, participants literally interpreted reports and provided suggestions for future visualizations.

conclusionsA bar graph combined with emojis was participants' preferred format and the one that promoted comprehension. Target end users must be included in visualization design to identify literal interpretations of images and ensure final products are meaningful.

Indexed as

Computer GraphicsConsumer Health InformaticsPatient PreferencePatient Reported Outcome MeasuresFemaleHealth LiteracyHIV InfectionsHumansInterviews as TopicMaleMiddle AgedSelf CareSelf-Managementgraph literacyhealth literacyInformation visualizationmobile healthnursing informatics

Identifiers

PMID31670816
PMCPMC7025335

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.