Evidence map›Paper›PMID 37352392›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2024

Visualization of health information within immersive virtual reality environments.

Allyson Ferguson, Denise M Goldsmith, Patricia Flatley Brennan

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Advancing the science of visualization of health data for lay audiences.Journal of the American Medical Informatics Association : JAMIA · 2024
    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

3 authors.

Allyson FergusonNational Institute of Nursing Research, Advanced Visualization Branch, Bethesda, Maryland, USA.
Denise M GoldsmithNational Institute of Nursing Research, Advanced Visualization Branch, Bethesda, Maryland, USA.
Patricia Flatley BrennanNational Institute of Nursing Research, Advanced Visualization Branch, Bethesda, Maryland, USA.

Funding

Advanced Visualization BranchZIANR000037 · NINR · NATIONAL INSTITUTE OF NURSING RESEARCH · PI ZENK, SHANNON · 2021 to 2024
$2.7M
Intramural NIH HHS ZIA NR000037NIH HHSNINR NIH HHS
6 · The paper itself

Abstract

The Advanced Visualization Branch of the National Institute of Nursing Research uses computer technologies to study information visualization in support of self-care management. Advanced technologies, such as immersive virtual reality (IVR), afford researchers the opportunity to study health information visualization where user-initiated information search in visually dense settings precedes acquisition, interpretation, and use. While IVR has broad applicability in healthcare, we chose to target lay people managing chronic disease because of the growing unmet need to translate clinical recommendations into everyday behaviors. To explore how lay people seek, acquire, and interpret health information in everyday settings, we developed an IVR grocery store. In this environment, a person can locate food products, read and compare nutrition labels, and use information to make food selections. The goal of this perspective is to introduce the opportunities afforded by IVR to both present and study health information visualization and to highlight critical design considerations.

Indexed as

Virtual RealityComputersHealth FacilitiesHumanshealth information visualizationimmersive virtual realityself-care management

Identifiers

PMID37352392
PMCPMC10797262

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.