Literature DB >> 23146416

Effects of prospective-user factors and sign design features on guessability of pharmaceutical pictograms.

Alan H S Chan1, Ken W L Chan.   

Abstract

OBJECTIVE: To examine the associations between the guessing performance of 25 pharmaceutical pictograms and five sign features for naïve participants. The effect of prospective-user factors on guessing performance was also investigated.
METHODS: A total of 160 Hong Kong Chinese people, drawn largely from a young student population, guessed the meanings of 25 pharmaceutical pictograms that were generally not familiar to them. Participants then completed a questionnaire about their drug buying and drug label reading habits, and their demographics and medication history. Finally they rated five features (familiarity, concreteness, complexity, meaningfulness, and semantic distance) of the pharmaceutical pictograms using 0-100 scales.
RESULTS: For all pharmaceutical pictograms, mean and standard deviation of guessability score were 64.8 and 17.1, respectively. Prospective-user factors of 'occupation', 'age' and 'education level' significantly affected guessing performance. For sign features, semantic closeness was the best predictor of guessability score, followed by simplicity, concreteness, meaningfulness and familiarity.
CONCLUSION: User characteristics and sign features are critical for pharmaceutical pictograms. To be effective, pharmaceutical pictograms should have obvious and direct connections with familiar things and it is recommended that pharmaceutical pictograms should be designed with consideration of the five sign features investigated here. PRACTICE IMPLICATIONS: This study provides useful information and recommendations to assist interface designers to create and evaluate icons for pharmaceutical products and to design more user-friendly pharmaceutical pictograms. However, further work is needed to see how older people respond to such pharmaceutical pictograms.
Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

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Mesh:

Year:  2012        PMID: 23146416     DOI: 10.1016/j.pec.2012.10.009

Source DB:  PubMed          Journal:  Patient Educ Couns        ISSN: 0738-3991


  8 in total

1.  Health Literacy Level and Comprehension of Prescription and Nonprescription Drug Information.

Authors:  Meehoh Kim; David Suh; Joseph A Barone; Sun-Young Jung; Wenchen Wu; Dong-Churl Suh
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2.  Recall of Pharmaceutical Pictograms by Older Adults.

Authors:  Régis Vaillancourt; Cindy N Giby; Bradley P Murphy; Annie Pouliot; Anne Trinneer
Journal:  Can J Hosp Pharm       Date:  2019-12-01

3.  Investigation of the Contributory Factors to the Guessability of Traffic Signs.

Authors:  Jing Liu; Huiying Wen; Dianchen Zhu; Wesley Kumfer
Journal:  Int J Environ Res Public Health       Date:  2019-01-08       Impact factor: 3.390

4.  Guessability of U.S. pharmaceutical pictograms in Iranian prospective users.

Authors:  Mahnaz Saremi; Zeinab S Shekaripour; Soheila Khodakarim
Journal:  Pharm Pract (Granada)       Date:  2020-03-14

Review 5.  Provision and Need for Medicine Information in Asia and Africa: A Scoping Review of the Literature.

Authors:  Pitchaya Nualdaisri; Sarah A Corlett; Janet Krska
Journal:  Drug Saf       Date:  2021-03-05       Impact factor: 5.606

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

Authors:  Shih-Yin Lin; Hilaire J Thompson; Laura A Hart; Musetta C C Fu; George Demiris
Journal:  Res Social Adm Pharm       Date:  2020-08-22

7.  Crowdsourcing participatory evaluation of medical pictograms using Amazon Mechanical Turk.

Authors:  Bei Yu; Matt Willis; Peiyuan Sun; Jun Wang
Journal:  J Med Internet Res       Date:  2013-06-03       Impact factor: 5.428

8.  Testing of Candidate Icons to Identify Acetaminophen-Containing Medicines.

Authors:  Saul Shiffman; Helene Cotton; Christina Jessurun; Mark A Sembower; Steve Pype; Jerry Phillips
Journal:  Pharmacy (Basel)       Date:  2016-01-27
  8 in total

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