Evidence map›Paper›PMID 32917513›Full record

ArticleResearch in social & administrative pharmacy : RSAP2021

Evaluation of pharmaceutical pictograms by older "turkers": A cross-sectional crowdsourced study.

Shih-Yin Lin, Hilaire J Thompson, Laura A Hart, Musetta C C Fu, George Demiris

Abstract read
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Article in Research in social & administrative pharmacy : RSAP, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Shih-Yin LinDepartment of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Seattle, WA, USA. Electronic address: sl199@nyu.edu.
Hilaire J ThompsonDepartment of Biobehavioral Nursing and Health Informatics, School of Nursing, University of Washington, Seattle, WA, USA. Electronic address: hilairet@uw.edu.
Laura A HartDepartment of Pharmacy, School of Pharmacy, University of Washington, Seattle, WA, USA.
Musetta C C FuDepartment of Pulmonary, Critical Care & Sleep Medicine, University of Washington, Seattle, WA, USA. Electronic address: mcf22@uw.edu.
George DemirisDepartment of Biobehavioral Health Sciences, School of Nursing, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: gdemiris@nursing.upenn.edu.

Funding

University of Washington Biomedical Informatics and Data Science Research Training ProgramT15LM007442 · NLM · UNIVERSITY OF WASHINGTON · PI JOHN H. GENNARI, Andrea L. Hartzler · 2002 to 2026
$19.6M
NLM NIH HHS T15 LM007442
6 · The paper itself

Abstract

backgroundWell-designed pharmaceutical pictograms may improve patients' understanding of medication instructions. However, the iterative participatory design process required to produce effective pictograms can be costly in terms of money, time, and effort. Crowdsourcing has been applied to bring down the costs of the participatory design process, but the feasibility of using this approach with older adults remains largely unknown.

objectivesTo evaluate the feasibility of using Amazon Mechanical Turk (MTurk), a leading crowdsourcing platform, for participatory pictogram evaluation with older adults (55+) and to evaluate the comprehensibility of USP pictogram, identify common misinterpretations, and explore the relationship between selected participant characteristics and their pictogram comprehension performance.

methods108 older adults (56.5% female; 57-80 years of age) were recruited via MTurk to complete a cross-sectional online survey that asked them to interpret 15 USP pictograms and answer questions about their health and health literacy.

resultsIt was feasible to perform pictogram evaluation with older adults on MTurk, as shown by ease of recruitment and high data quality. Of the 15 pictograms tested, seven (46.7%) resulted in a comprehensibility score below the threshold established by the American National Standards Institute (ANSI), eight (53.3%) elicited common misinterpretations, and two (13.3%) resulted in ANSI-defined "critical confusion." Age (P = 0.04) was associated with pictogram comprehension performance. Certain issues with the pictogram subtitles emerged during the evaluation.

conclusionsMTurk is a feasible platform for participatory pictogram evaluation, even for a sole target of older adults. The USP should develop a pictogram user manual, redesign pictograms confusing to older adults, and establish policies and procedures to ensure that pictogram subtitles conform to evidence-based best practices and standards for patient-centered written drug information.

Indexed as

CrowdsourcingHealth LiteracyPharmaceutical PreparationsAgedCross-Sectional StudiesFemaleHumansMalePharmaceutical PreparationsComprehensibilityCrowdsourcingMechanical turkPharmaceutical pictogram

Identifiers

PMID32917513
PMCPMC7897753

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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.