Evidence map›Paper›PMID 31919723›Full record

ArticleJournal of general internal medicine2020

Acceptability of an Interactive Computer-Animated Agent to Promote Patient-Provider Communication About Breast Density: a Mixed Method Pilot Study.

Christine Gunn, Ariel Maschke, Timothy Bickmore, Mark Kennedy, Margaret F Hopkins, Michael D C Fishman, Michael K Paasche-Orlow, Erica T Warner

Abstract read
In one paragraph

Article in Journal of general internal medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 4 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

11 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Christine GunnDepartment of Medicine, Section of General Internal Medicine, Women's Health Unit, Boston University School of Medicine, 801 Massachusetts Avenue, First Floor, Boston, MA, 02118, USA. Christine.Gunn@bmc.org.ORCID 0000-0003-3340-2834
Ariel MaschkeDepartment of Medicine, Section of General Internal Medicine, Women's Health Unit, Boston University School of Medicine, 801 Massachusetts Avenue, First Floor, Boston, MA, 02118, USA.
Timothy BickmoreKhoury College of Computer Sciences, Northeastern University, Boston, USA.
Mark KennedyBoston Public Health Commission, Boston, USA.
Margaret F HopkinsDepartment of Medicine, Massachusetts General Hospital, Boston, USA.
Michael D C FishmanDepartment of Radiology, Boston Medical Center, Section of Breast Imaging, Boston University School of Medicine, Boston, USA.
Michael K Paasche-OrlowDepartment of Medicine, Section of General Internal Medicine, Boston Medical Center, Boston University School of Medicine, Boston, USA.
Erica T WarnerDepartment of Medicine, Massachusetts General Hospital, Boston, USA.

Funding

Using Community-Engaged Methods to Design a Tool to Promote Informed Breast Cancer Screening Decisions for Low Literacy WomenK07CA221899 · NCI · DARTMOUTH COLLEGE · PI GUNN, CHRISTINE MARIE · 2018 to 2022
$642k
Race/Ethnicity and Mammographic Density: Genetic and Lifestyle FactorsK01CA188075 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI WARNER, ERICA T · 2014 to 2017
$518k
NCI NIH HHS 1K07CA221899NCI NIH HHS K01 CA188075NCI NIH HHS K01CA188075NCI NIH HHS K07 CA221899
6 · The paper itself

Abstract

backgroundHalf of women undergoing mammography have dense breasts. Mandatory dense breast notification and educational materials have been shown to confuse women, rather than empower them.

objectiveThis study used a mixed method, multi-stakeholder approach to assess acceptability of an interactive, computer-animated agent that provided breast density information to women and changes in knowledge, satisfaction, and informational needs.

designA pre-post survey and qualitative focus groups assessed the acceptability of the computer-animated agent among women. An anonymous, online survey measuring acceptability was delivered to a multi-stakeholder group.

participantsEnglish-speaking, mammography-eligible women ages 40-74 were invited and 44 women participated in one of nine focus groups. In addition, 14 stakeholders representing primary care, radiology, patient advocates, public health practitioners, and researchers completed the online survey.

interventionsA prototype of a computer-animated agent was delivered to women in a group setting; stakeholders viewed the prototype independently. MAIN MEASURES: Data collected included open-ended qualitative questions that guided discussion about the content and form of the computer-animated agent. Structured surveys included domains related to knowledge, acceptability, and satisfaction. Stakeholder acceptability was measured with a series of statements about aspects of the intervention and delivery approach and are reported as the proportion of respondents who endorsed each statement. KEY

resultsSix of 12 knowledge items demonstrated improvement post-intervention, satisfaction with the agent was high (81%), but the number of unanswered questions did not improve (67% vs. 54%, p = 0.37). Understanding of the distinction between connective and fatty tissue in the breast did not increase (30% vs. 26%, p = 0.48). Results of the multi-stakeholder survey suggest broad acceptability of the approach and agent.

conclusionsFindings highlight the benefits of a brief interactive educational exposure as well as misperceptions that persisted. Results demonstrate the need for an evidence-based, accessible intervention that is easy to understand for patients.

Indexed as

Breast DensityBreast NeoplasmsAdultAgedCommunicationComputersFemaleHumansMammographyMiddle AgedPilot Projectsacceptabilitybreast densityhealth information technologymammographypatient-provider communication

Identifiers

PMID31919723
PMCPMC7174461

What OpenQuestion holds

Textmetadata
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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.