Next Article in Journal
Comparison of Dietary Behaviors and the Prevalence of Metabolic Syndrome in Single- and Multi-Person Households among Korean Adults
Next Article in Special Issue
Recommending Suitable Smart Technology Applications to Support Mobile Healthcare after the COVID-19 Pandemic Using a Fuzzy Approach
Previous Article in Journal
Is Time-Driven Activity-Based Costing Coming out on Top? A Comparison with Activity-Based Costing in the Health Field
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Healthcare
Volume 9
Issue 9
10.3390/healthcare9091115
healthcare-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Evaluation of Preference and Perceived Quality of Health Communication Icons Associated with COVID-19 Prevention Measures

by
Yogi Tri Prasetyo
1,*,
Ratna Sari Dewi
2,
Naiomi M. Balatbat
1,
Michael Lancelot B. Antonio
1,
Thanatorn Chuenyindee
1,3,4,
Anak Agung Ngurah Perwira Redi
5,
Michael Nayat Young
1,
John Francis T. Diaz
6 and
Yoshiki B. Kurata
1,3,7
1
School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
2
Department of Industrial and Systems Engineering, Kampus ITS Sukolilo, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
3
School of Graduate Studies, Mapua University, 658 Muralla St., Intramuros, Manila 1002, Philippines
4
Navaminda Kasatriyadhiraj Royal Thai Air Force Academy, Bangkok 10220, Thailand
5
Industrial Engineering Department, BINUS Graduate Program—Master of Industrial Engineering, Bina Nusantara University, Jakarta 11480, Indonesia
6
Department of Finance and Accounting, Asian Institute of Management, 123 Paseo de Roxas, Legazpi Village, Makati 1229, Philippines
7
Department of Industrial Engineering, Faculty of Engineering, University of Santo Tomas, España Blvd, Manila 1015, Philippines
*
Author to whom correspondence should be addressed.
Healthcare 2021, 9(9), 1115; https://doi.org/10.3390/healthcare9091115
Submission received: 2 July 2021 / Revised: 8 August 2021 / Accepted: 10 August 2021 / Published: 27 August 2021
(This article belongs to the Special Issue Digital Technology to Control the COVID-19 Epidemic Infection: Issues, Successes and Failures)
Browse Figures
Versions Notes

Abstract

:
Icons have been widely utilized to describe and promote COVID-19 prevention measures. The purpose of this study was to analyze the preference and subjective design features of 133 existing icons associated with COVID-19 prevention measures published by the health and medical organizations of different countries. The 133 icons represent nineteen different function names, such as “Wash Hands” and “Wear Face Mask”. A total of 57 participants were recruited to perform two different tests: ranking test and subjective rating test. The ranking test was conducted to elicit the preference ranking of seven icon designs representing each function name. It was followed by a subjective rating test using 13 semantic scales on the two most preferred icons to analyze their perceived quality. Spearmen correlation was applied to derive the possible correlations between users’ rankings and the semantic scales, and Friedman’s test was also performed to determine the true difference between ranking in terms of each semantic scale to provide a fully meaningful interpretation of the data. Generally, findings from the current study showed that the image presented in the icon is the key point that affects the icons’ perceived quality. Interestingly, Spearman’s correlation analysis between preference ranking and semantic scales showed that vague–clear, weak–strong, incompatible–compatible, and ineffective–effective were the four strongest semantic scales that highly correlated with the preference ranking. Considering the significant relationships between the semantic distances and the functions, images depicted in an icon should be realistic and as close as possible to its respected function to cater to users’ preferences. In addition, the results of Spearman’s correlation and Friedman’s test also inferred that compatibility and clarity of icon elements are the main factors determining a particular icon’s preferability. This study is the first comprehensive study to evaluate the icons associated with the COVID-19 prevention measures. The findings of this study can be utilized as the basis for redesigning icons, particularly for icons related to COVID-19 prevention measures. Furthermore, the approach can also be applied and extended for evaluating other medical icons.

1. Introduction

Icons have been widely utilized as a tool to promote COVID-19 prevention measures during the pandemic. They are intended as tools to represent complex information quickly and clearly regarding functions under COVID-19 preventive measures. Upon using them, the goal is to enable visual communication that allows easier transmission of ideas and information compared to written communication [1]. Effective icon designs can overcome language barriers and can successfully convey useful information since they reduce translation requirements and give the information behind them an international look [2].
As for COVID-19 prevention icons, their success and effectivity may have a big impact with regards to virus containment. The best way to prevent illness from the COVID-19 virus is to understand how the virus spreads and to avoid being exposed to it [3]; therefore, accurately understanding and recognizing what functions under COVID-19 preventive measures the visual icons are representing is important. Furthermore, factors that may influence the effectiveness of these icons should be taken into consideration, such as the icon formats [4].
Chi and Dewi [4] classified icons under seven format categories, namely, image-related, concept-related, semi-abstract, word, abbreviation, and combined. Image-related icons are the typical representations of the object or action, concept-related icons represent concepts that are close to but not exactly the concrete image of the action or object, and arbitrary icons have no obvious reference to their intended meaning and can only be meaningful and understood through education [4,5,6,7]. Semi-abstract icons, on the other hand, are combined image-related (concrete representation of an action or object) icons and concept-related or arbitrary icons (an abstract representation of an action or object) [8]. Aside from graphical icons, textual and combined icons are also considered as icon format categories if textual elements are added into the icons [9]. Furthermore, textual icons can be divided into two classifications—word and abbreviation. Because of the obvious mapping of the image-related icon to its referent, it is superior for fast and accurate recognition, while textual icons are better for reaction time [10,11,12,13]. Other graphical icon formats that are concept-related and arbitrary are harder to immediately understand since they have less obvious connection to their intended meaning. Arbitrary icons should be avoided because the need to educate people first to comprehend their meaning requires a considerable amount of funding and time [2].
The majority of the existing COVID-19 prevention icons based on the infographics being released by the World Health Organization and other medical or health organizations of different countries are in image- and concept-related formats. For example, the icons that intend to convey protocols about travel restrictions mostly use a concrete illustration of bags or suitcases or a person pulling baggage as a representation (Figure 1). Semi-abstract icon formats were also applied on some preventive measures’ functions. On semi-abstract travel restriction icons, aside from the concrete pictorial representations of the intended meaning, arbitrary symbols are applied [8]. There are icons with a red-colored circle around it or a punctuation mark in it, representing that it is prohibited or that it should be avoided. However, given the advantages and contribution that the visual icons are providing in raising awareness about COVID-19 prevention, there are instances in which the icons used to represent a COVID-19 preventive measure may cause confusion and incorrect comprehension, especially considering icons with a similar context (e.g., the confusion between the icon for shortness of breath and the one for breathing difficulty). The image element of the icon is the major cause of confusion among its readers. In this case, designers should consider and evaluate the icon characteristics to determine the icon’s recognition performance.
Icon characteristics can be classified into physical (external) and psychophysical (internal). Previous studies about icon physical/external characteristics such as size [14], color [15,16], spacing, and density [15] provided a number of practical design guidelines for icon design. Nonetheless, to address the semantic information conveyed by different icons, other studies focused on the influence of icon internal characteristics [17], widely known as psychophysics in human factors and ergonomics. For example, subjective evaluation methods were utilized by Ng and Chan to explore the effects of sign design characteristics on the comprehensibility of traffic and safety signs [18,19]. On the other hand, McDougall et al. carried out a series of studies regarding icon identification to investigate how these characteristics affect users’ cognitive performance [20,21,22,23,24]. Findings on these suggested that there are actually factors that may modify the performance of the icon, such as its communicativeness, complexity, layout, and semantic distance. These were supported by studies that demonstrated that simple and less complex icons can be more easily recognized [2,25,26,27] and concrete icons can be identified more accurately and quickly by the users [12,28]. Complexity pertains to the details intricated on the icon [19], while semantic distance is the measure of the closeness of what is illustrated in the icon to its true intended meaning [19]. Furthermore, it is also suggested that an icon may perform better if it can express its intended message clearly and if its features were arranged carefully [21].
Despite the wide and frequent application of visual icons as a medium for visual communication for COVID-19, no study has yet existed that is mainly about existing COVID-19 prevention icons. In accordance with the International Standards Organization [29], it is necessary to develop icon design principles to ensure visual clarity and subjective preference for enhancing icon recognition and usability. Moreover, there is a lack of study regarding the evaluation of the perceived quality of existing medical-related icons for the broad population and not limited to medical staff.
The purpose of this study is to analyze the existing icons of COVID-19 prevention measures published by the health and medical organizations of different countries. A ranking test and a subjective rating test were utilized to evaluate the collected icons. This study is the first study to analyze the effectiveness of existing icons for COVID-19 prevention measures. The findings are beneficial for human factors engineers, industrial designers, and the even government, particularly for designing medical-related icons.

2. Material and Methods

2.1. Participants

A total of 57 participants aged between 18 and 40 years old were recruited to participate in this study (Table 1). All of them were the residents of the National Capital Region (Manila), Philippines. Since the data collection was conducted during the COVID-19 pandemic, this study was also conducted in accordance with the Department of Health—Philippines by following COVID-19 safety protocols.
As imposed by the National Ethical Guidelines for Health and Health-Related Research 2017 by the Philippine Health Research Ethics board, all participants were fully informed of the purpose of the research as well as all the procedures within the experiments. The respondents were also asked to complete a consent form before performing the required tasks. Finally, they were also paid 200 PHP after completing the experiment.

2.2. Icon Collection

Seven existing icons representing each of the considered COVID-19 preventive measures functions were collected from the COVID-19 prevention infographics released by the Department of Health (DOH) Philippines, World Health Organization (WHO), European Centre for Disease Prevention and Control, and other medical organizations. A total of 133 icons representing the preventive measures of COVID-19 were evaluated and assessed through a ranking test experiment [30]. This was then followed by a subjective rating test for the top two icons of each respondent from the ranking test. The online experiment was posted and distributed through social media platforms. Table 2 shows the nineteen function names of icons related to COVID-19 preventive measures, while all the icons collected for the current study are displayed in Table 3.

2.3. Ranking Test

In the first phase of the experiment, participants were tasked to rank COVID-19 preventive measure icons within the same function name. Following Chi and Dewi [4], the experiment was administered with a computer program developed using JavaScript and PHP software where respondents ranked the displayed icons from 1 to 7 (See Figure 2). Each participant would rank the most preferred icon under a function name as 1; the next preferred would be ranked as 2, and so on. Thus, the least favored icon was ranked as 7. The icons were laid out in a circular manner to avoid possible sequence effect [31,32] or location bias [33]. The function names were also stated next to the displayed icon to provide appropriate context and description for each function [34]. The experiment was conducted online.

2.4. Subjective Rating Test

According to Liu and Ho [35], subjective rating features are reliable in determining the performance of icons based on recognition accuracy [35]. Additionally, subjective scales are easy to administer since they are more sensitive than objective measurements [36]. Therefore, in this phase of the experiment, participants were asked to rate their top 2 icons from the ranking test (i.e., icons with first and second rank for each function) on the basis of subjective design features such as perceived icon quality, communicativeness [21], layout [21], and complexity and semantic distance [4,19,20], as defined in Table 4. Following Chi et al. [37], semantic scales were then assigned for each of the subjective design features (Table 5).
The respondents’ top two icons were shown one by one and they were instructed to evaluate the appearance of each icon according to the semantic scales (Figure 3). They were made aware that on the 7-point Likert scale, the closer they choose to the left or right semantic scale, the better they think that the icon displayed fits the semantic scale. However, if they choose the middle of the scale, their opinion of the icon fits both semantic scales. Similar to the ranking test, the test on subjective design features was also developed using JavaScript and PHP software and conducted online.

2.5. Statistical Methods

Spearman’s correlation analysis could help readers to find possible correlations between ranking test and the semantic scales. The users’ ranking results were dummy coded 1 for the top ranking and 2 for the second ranking. We hypothesized negative correlations between the ranking test and the semantic scales since more positive semantic scales would lead to better ranking (rank 1 instead of 2). p < 0.05 (two-tailed) was set as the threshold for this statistical analysis.
Further detailed analyses for each of 19 functions were conducted using the Friedman’s test. The Friedman’s test was performed to determine the true difference between ranking in terms of each semantic scale to provide a fully meaningful interpretation of the data. It also helps scholars in designing or choosing pertinent communication icons related to COVID-19 prevention measures. As an example, the true difference between ranking 1 and ranking 2 in terms of semantic number 1 (unlikable–likable) for function number 8 (cover when coughing or sneezing) can be evaluated by the following hypotheses:
Hypothesis 0 (H0).
No true difference between ranking 1 and ranking 2 in terms of “unlikable–likable” for function “cover when coughing or sneezing”.
Hypothesis 1 (H1).
There was true difference between ranking 1 and ranking 2 in terms of “unlikable–likable” for function “cover when coughing or sneezing”.
These two hypotheses were applied for any of the possible conditions (difference between ranking 1 and ranking 2 in terms of each semantic scale to be applied for all the tested functions).

3. Results

Table 3 shows the ranking of the icons per function name based on the responses of the participants. The icons were tabulated with their corresponding mean ranking score and its standard deviation The list of icons in Table 3 were sorted based on the mean ranking values. As presented in Table 3, the image-related and combined icon design formats were preferred by the majority of participants, with the image-related format being ranked first on eight of the nineteen function names, and the combined format also obtaining the first ranks on another eight function names. For the remaining three function names, it was the semi-abstract format that was chosen to be first in the rank.
Table 6 shows the descriptive statistics of the semantic scales for all the tested icons. On average, all the tested icons were rated around the score of 6 (of 1–7 scale) for all the subjective design features. These results indicated that all the tested icons are sufficiently recognizable, compatible, organized, simple, familiar, effective, concrete, likeable, clear, uncluttered, strong, beautiful, and colorful.
Table 7 shows the result of the Spearman’s correlation analysis (two-tailed) between pairs of the semantic scales and the ranks for all functions. The analysis showed that the 13 semantic scales were significantly intercorrelated to each other. Although all semantic scales were also significantly correlated with the ranks, we can highlight that vague–clear, weak–strong, and incompatible–compatible were the three ones with the highest correlation coefficients, i.e., −0.206, −0.205, and −0.200, respectively.
Table 8 represents the Friedman’s test result for determining the true difference between ranking 1 and ranking 2 in terms of “unlikable–likable” for function “cover when coughing or sneezing”. Based on this table, the mean rank for unlikable–likable rank 1 was 1.7 while the mean rank for unlikable–likable rank 2 was 1.3. Chi-square statistic indicated that there was a significant difference between ranking 1 and ranking 2 in terms of “unlikable–likable” (Table 8). In other words, H0 was nullified and H1 was true.
Table 9 represents the summary of all the Friedman’s tests on each semantic scale for all functions. Based on this table, we can see that there were some significant differences between ranking 1 and ranking 2 in different semantic scales. Similar results were gathered for Spearman’s and Friedman’s tests (See Table 9), particularly for weak–strong and incompatible–compatible, where these two semantic scales showed a significant difference between ranking 1 and ranking 2 in 11 out of 19 functions. On the other side, Friedman’s tests showed that for function 1 (Shortness of Breath), Function 5 (Wash Hands), and Function 16 (Wash Clothes Properly), all the semantic scales tested in this study did not have a significant difference between ranking 1 and ranking 2.

4. Discussion

From the ranking test results, it can be concluded that icon users prefer the icons to be in image-related formats. However, for some function names (e.g., shortness of breath and difficulty in breathing), even though the icons are in image-related format, they were still ranked low if the images in the icon were drawn in silhouette-like illustrations, indicating that the users probably like image-related icons only if they are illustrated realistically or in a more concrete way. This may be because the concreteness of the icons improves its ability to convey its meaning, as concrete symbols tend to be more visually obvious since they depict objects, places, and people that are already familiar to us in the real world [41,42]. Therefore, the more concrete the icon is, the better the semantic distance is, and this results in its users being able to react quickly and accurately to it [12,28]. The combined format, which is a combination of icons and textual labels [10], is also at the same level as the image-related format in terms of the number of function names for which it was ranked first. While image-related icons are known to be superior for fast and accurate recognition because of the obvious mapping between the icon and its referent [10,11,12], textual icons, on the other hand, are proven to be better for reaction time [13]. Since the combined format is the combination of the icons and textual labels [10], the reaction and recognition accuracy of the users to these icons could be significantly increased. In the current study, it is noticeable that if image-related icons are incorporated with textual labels, making it a combined format, the icon users favor them. Furthermore, despite the guideline given by the International Standards Organization [29] that the use of abstract symbols should be avoided, results of this study demonstrated that semi-abstract icons, which are combined image-related (concrete representation of an action or object) icons and concept-related or arbitrary icons (abstract representation of an action or object) [8], are still the most preferred on three function names—a result that is similar to that of Chi et al. [5]. On the functions “avoid touching face” and “avoid travelling to places with known cases”, the red circle around the icons and the cross marks might have helped in relating them to their correct function names.
Concept-related and arbitrary icon design formats had consistently low ranking scores. This is understandable considering that concept-related icons outline concepts that are close but are still not the exact concrete image of the action or object, and arbitrary icons have no clear reference to their intended meaning and can only be meaningful and understood through education [4,5,6,7]. As a result, compared to the formats that obtained good ranking scores, the icons designed in these two formats do not have obvious mappings to their referent. Therefore, the connection of the icons designed in these formats to their corresponding function names is harder to distinguish, making them the least favored formats for the users.
The result of Spearman’s correlation and Friedman’s tests infers that compatibility and clarity of icon elements are the main factors determining a particular icon’s preferability. Moreover, the alternative icons with stronger communicativeness in delivering the message should be prioritized to be implemented. Furthermore, the high and significant intercorrelations of Spearman’s test (See Table 7) between weak–strong, ineffective–effective, and vague–clear also suggest the importance of selecting icons with better clarity to effectively and powerfully deliver the messages related to COVID-19 prevention measures. However, this study also reveals that for specific functions, there is no significant relationship between preference and tested semantic scales, inferring the possibility of inclusion of other semantic scales in the future study.
Despite the clear contributions of the study for design guidelines on COVID-19 prevention measures icons, the researchers would like to acknowledge several limitations of this study. First, because the study was facilitated during the middle of a pandemic, the authors resorted to conducting the data gathering online, and the online experiment was answered by a total of 57 participants. To produce more comprehensive results, future researchers may consider increasing the number of respondents. It is also recommended to broaden the scope of the current study to come up with more thorough and specific design references for COVID-19 preventive measures icons. Second, this study only focused on knowing and understanding the preference of the icon users through the ranking test and preference test that were performed. Aside from considering how the icons satisfy the subjective preferences of the users, future research should also incorporate the performance of the icons in terms of the effectiveness to accurately interpret their corresponding function names. Finally, future study should utilize eye-tracker [43] to find the relationship between the results of ranking test and eye movement behavior to provide more meaningful findings.

5. Conclusions

Icon has been widely utilized as a tool to promote COVID-19 prevention measures. The purpose of this study was to analyze 133 existing icons of COVID-19 prevention measures published by the health and medical organizations of different countries.
A rank ordering test was conducted for the seven icons representing each function name, followed by a subjective rating test for the top two chosen icons of the respondent form the ranking test. Generally, findings from the current study showed that the image presented in the icon is the key point on which the perceived quality of the icon depends [44,45], and the preference of users for the icon may rely on this. In this case, designers may consider the cognitive features of an icon such as its familiarity, its concreteness, the complexity of the design intricated on it, its meaningfulness, and its semantic distance or its closeness to its intended meaning [20,46,47].
The current study also further proves that that familiarity and semantic distance should be of primary importance when it comes to selecting icons [22,39]. Interestingly, Spearman’s correlation analysis between ranking and semantic scales showed that incompatible–compatible, vague–clear, weak–strong, and abstract–concrete were the four strongest semantic scales that highly correlated with the preference ranking. In addition, Friedman’s tests inferred that compatibility and clarity of icon elements are the main factors determining a particular icon’s preferability. These suggest that designers should choose images that are realistic and as closely related as possible to the function represented by the icon. It should also be simple and straightforward to reduce complexity. Adding elements on graphical or image-related icons, whether textual or arbitrary symbols, is recommendable since they may increase the cognition of the users into the icons and therefore can make them preferable. Icon design formats having less connection to what they actually depict, such as the concept-related and arbitrary formats, should be avoided since they are more challenging to comprehend and are probably not preferred. Another observable result of the study is that color also affects how well liked the icons are. Black and white and grayscale icons obtained low ranking scores, even though they concretely represent their function name. This gives the conclusion that designers should also consider making the icons colorful so that they may be more visually appealing and likeable.
This study is the first comprehensive study to evaluate the icons associated with the COVID-19 prevention measures. The findings of this study can be utilized as the basis for redesigning icons, particularly for icons related to COVID-19 prevention measures [48]. Furthermore, the approach can also be applied and extended for evaluating other medical icons [49,50], safety icons, disaster-related prevention icons [51], transportation-related icons [52,53], and even entertainment-related icons [54,55,56,57,58].

Author Contributions

Conceptualization, Y.T.P., R.S.D., N.M.B., and M.L.B.A.; methodology, Y.T.P., R.S.D., N.M.B., and M.L.B.A.; software, N.M.B., and M.L.B.A.; validation, Y.T.P., R.S.D., and Y.B.K.; formal analysis, Y.T.P., R.S.D., N.M.B., and M.L.B.A.; investigation, Y.T.P., R.S.D., N.M.B., and M.L.B.A.; resources, Y.T.P., R.S.D., N.M.B., and M.L.B.A.; data curation, Y.T.P.; writing—original draft preparation, Y.T.P., R.S.D., N.M.B., and M.L.B.A.; writing—review and editing, T.C., A.A.N.P.R., M.N.Y., J.F.T.D., and Y.B.K.; visualization, Y.T.P.; supervision, Y.T.P., and R.S.D.; project administration, Y.T.P.; funding acquisition, Y.T.P., T.C., A.A.N.P.R., M.N.Y., and J.F.T.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Mapúa University Directed Research for Innovation and Value Enhancement (DRIVE).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

The authors would like to thank all participants in this study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Vigoroso, L.; Caffaro, F.; Cavallo, E. Occupational safety and visual communication: User-centred design of safety training material for migrant farmworkers in Italy. Saf. Sci. 2020, 121, 562–572. [Google Scholar] [CrossRef]
  2. Horton, W. The Icon Book: Visual Symbols for Computer Systems and Documentation, 1st ed.; Wiley: New York, NY, USA, 1994. [Google Scholar] [CrossRef]
  3. Centers for Disease Control and Prevention. How to Protect Yourself & Others. 2020. Available online: https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/prevention.html (accessed on 12 August 2020).
  4. Chi, C.-F.; Dewi, R.S. Matching performance of vehicle icons in graphical and textual formats. Appl. Ergon. 2014, 45, 904–916. [Google Scholar] [CrossRef]
  5. Chi, C.-F.; Dewi, R.S.; Samali, P.; Hsieh, D.-Y. Preference ranking test for different icon design formats for smart living room and bathroom functions. Appl. Ergon. 2019, 81, 102891. [Google Scholar] [CrossRef] [PubMed]
  6. Lodding, K. Iconic Interfacing. IEEE Comput. Graph. Appl. 1983, 3, 11–20. [Google Scholar] [CrossRef]
  7. Goonetilleke, R.; Shih, H.M.; On, H.K.; Fritsch, J. Effects of training and representational characteristics in icon design. Int. J. Hum. Comput. Stud. 2001, 55, 741–760. [Google Scholar] [CrossRef] [Green Version]
  8. Blattner, M.; Sumikawa, D.; Greenberg, R. Earcons and Icons: Their Structure and Common Design Principles. Hum. Comput. Interact. 1989, 4, 11–44. [Google Scholar] [CrossRef]
  9. Reder, P.J.; Mccallum, C.A. Flexible Mouse-Driven Method of User Interface. U.S. Patent 6,727,919, 27 April 2004. Available online: https://patents.google.com/patent/US6727919B1/en (accessed on 12 August 2020).
  10. Wiedenbeck, S. The use of icons and labels in an end user application program: An empirical study of learning and retention. Behav. Inf. Technol. 1999, 18, 68–82. [Google Scholar] [CrossRef]
  11. Blankenberger, S.; Hahn, K. Effects of icon design on human-computer interaction. Int. J. Man-Mach. Stud. 1991, 35, 363–377. [Google Scholar] [CrossRef]
  12. Stotts, D.B. The Usefulness of Icons on the Computer Interface: Effect of Graphical Abstraction and Functional Representation on Experienced and Novice Users. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 1998, 42, 453–457. [Google Scholar] [CrossRef]
  13. Dewar, R.E.; Ells, J.G.; Mundy, G. Reaction Time as an Index of Traffic Sign Perception. Hum. Factors J. Hum. Factors Ergon. Soc. 1976, 18, 381–391. [Google Scholar] [CrossRef]
  14. Lindberg, T.; Näsänen, R. The effect of icon spacing and size on the speed of icon processing in the human visual system. Displays 2003, 24, 111–120. [Google Scholar] [CrossRef]
  15. Ojanpää, H.; Näsänen, R. Effects of luminance and colour contrast on the search of information on display devices. Displays 2003, 24, 167–178. [Google Scholar] [CrossRef]
  16. Huang, K.-C.; Chang, W.-T.; Wei, W.-L. Effects of visual field, exposure time, and set size on icon search with varied delays using an LCD monitor. J. Soc. Inf. Disp. 2010, 18, 1. [Google Scholar] [CrossRef]
  17. Shen, Z.; Zhang, L.; Li, R.; Liang, R. The effects of icon internal characteristics on complex cognition. Int. J. Ind. Ergon. 2020, 79, 102990. [Google Scholar] [CrossRef]
  18. Ng, A.W.; Chan, A.H. The guessability of traffic signs: Effects of prospective-user factors and sign design features. Accid. Anal. Prev. 2007, 39, 1245–1257. [Google Scholar] [CrossRef]
  19. Ng, A.; Chan, A. Visual and Cognitive Features on Icon Effectiveness. In Proceedings of the International Multiconference of Engineers and Computer Scientists, Hong Kong, China, 19–21 March 2008; Volume 2, pp. 19–21. [Google Scholar]
  20. McDougall, S.J.P.; Curry, M.B.; De Bruijn, O. Measuring symbol and icon characteristics: Norms for concreteness, complexity, meaningfulness, familiarity, and semantic distance for 239 symbols. Behav. Res. Methods Instrum. Comput. 1999, 31, 487–519. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  21. Huang, S.-M.; Shieh, K.-K.; Chi, C.-F. Factors affecting the design of computer icons. Int. J. Ind. Ergon. 2002, 29, 211–218. [Google Scholar] [CrossRef]
  22. Isherwood, S.J.; McDougall, S.; Curry, M.B. Icon Identification in Context: The Changing Role of Icon Characteristics With User Experience. Hum. Factors J. Hum. Factors Ergon. Soc. 2007, 49, 465–476. [Google Scholar] [CrossRef]
  23. Silvennoinen, J.M.; Kujala, T.; Jokinen, J.P. Semantic distance as a critical factor in icon design for in-car infotainment systems. Appl. Ergon. 2017, 65, 369–381. [Google Scholar] [CrossRef] [Green Version]
  24. Tsai, C.-Y. Effect of graphic simplification and graphic metaphor on the memory and identification of travel map. Int. J. Ind. Ergon. 2017, 61, 29–36. [Google Scholar] [CrossRef]
  25. Gittins, D. Icon-based human-computer interaction. Int. J. Man-Mach. Stud. 1986, 24, 519–543. [Google Scholar] [CrossRef]
  26. García, M.; Badre, A.N.; Stasko, J.T. Development and validation of icons varying in their abstractness. Interact. Comput. 1994, 6, 191–211. [Google Scholar] [CrossRef]
  27. McDougall, S.J.P.; de Bruijn, O.; Curry, M.B. Exploring the effects of icon characteristics on user performance: The role of icon concreteness, complexity, and distinctiveness. J. Exp. Psychol. Appl. 2000, 6, 291–306. [Google Scholar] [CrossRef]
  28. Stammers, R.; Hoffman, J. Transfer between Icon Sets and Ratings of Icon Concreteness and Appropriateness. Proc. Hum. Factors Soc. Annu. Meet. 1991, 35, 354–358. [Google Scholar] [CrossRef]
  29. International Standards Organization. Graphical Symbols—Creation and Design of Public Information Symbols—Requirements; ISO Standard 22727; International Standards Organization: Geneva, Switzerland, 2007. [Google Scholar]
  30. Gingold, M.; Shteingart, S.; Green, P. Truck Drivers’ Suggestions and Preferences for Instrument Panel Symbols; Techinical Report UM-HSRI-81-30; The University of Michigan Transportation Research Institute: Ann Arbor, MI, USA, 1981; Available online: https://deepblue.lib.umich.edu/bitstream/handle/2027.42/368/47110.0001.001.pdf;sequence=2 (accessed on 12 August 2020).
  31. Chi, C.-F.; Drury, C. A further note on psychophysical testing of handles. Appl. Ergon. 1988, 19, 315–318. [Google Scholar] [CrossRef]
  32. Chi, C.-F.; Dewi, R.S.; Huang, M.-H. Psychophysical evaluation of auditory signals in passenger vehicles. Appl. Ergon. 2016, 59, 153–164. [Google Scholar] [CrossRef] [PubMed]
  33. Hoekstra, E.; Williams, M.; Green, P. Development and Driver Understanding of Hazard Warning and Location Symbols for IVSAWS; Techinical Report UMTRI-93-16; The University of Michigan Transportation Research Institute: Ann Arbor, MI, USA, 1993; Available online: https://deepblue.lib.umich.edu/bitstream/handle/2027.42/1011/86543.0001.001.pdf?sequence=2 (accessed on 12 August 2020).
  34. Wolff, J.S.; Wogalter, M.S. Comprehension of Pictorial Symbols: Effects of Context and Test Method. Hum. Factors J. Hum. Factors Ergon. Soc. 1998, 40, 173–186. [Google Scholar] [CrossRef] [Green Version]
  35. Liu, Y.-C.; Ho, C.-H. The effects of age on symbol comprehension in central rail hubs in Taiwan. Appl. Ergon. 2012, 43, 1016–1025. [Google Scholar] [CrossRef] [PubMed]
  36. Chi, C.-F.; Lin, F.-T. A Comparison of Seven Visual Fatigue Assessment Techniques In Three Data-Acquisition VDT Tasks. Hum. Factors J. Hum. Factors Ergon. Soc. 1998, 40, 577–590. [Google Scholar] [CrossRef] [PubMed]
  37. Chi, C.-F.; Dewi, R.S.; Surbakti, Y.Y.; Hsieh, D.-Y. The perceived quality of in-vehicle auditory signals: A structural equation modelling approach. Ergonomics 2017, 60, 1471–1484. [Google Scholar] [CrossRef] [PubMed]
  38. Stylidis, K.; Wickman, C.; Söderberg, R. Perceived quality of products: A framework and attributes ranking method. J. Eng. Des. 2020, 31, 37–67. [Google Scholar] [CrossRef] [Green Version]
  39. Bovea, M.D.; Quemades-Beltrán, P.; Pérez-Belis, V.; Juan, P.; Braulio-Gonzalo, M.; Ibáñez-Forés, V. Options for labelling circular products: Icon design and consumer preferences. J. Clean. Prod. 2018, 202, 1253–1263. [Google Scholar] [CrossRef]
  40. Choi, J.H.; Lee, H.-J. Facets of simplicity for the smartphone interface: A structural model. Int. J. Hum. Comput. Stud. 2012, 70, 129–142. [Google Scholar] [CrossRef]
  41. Campbell, J.L.; Hoffmeister, D.H.; Kiefer, R.J.; Selke, D.J.; Green, P.; Richman, J.B. Comprehension Testing of Active Safety Symbols. J. Passeng. Cars Mech. Syst. J. 2004, 113, 197–203. [Google Scholar]
  42. Rogers, Y. Icons at the interface: Their usefulness. Interact. Comput. 1989, 1, 105–117. [Google Scholar] [CrossRef]
  43. Liu, W.; Cao, Y.; Proctor, R.W. How do app icon color and border shape influence visual search efficiency and user experi-ence? Evidence from an eye-tracking study. Int. J. Ind. Ergon. 2021, 84, 103160. [Google Scholar] [CrossRef]
  44. Cahigas, M.M.; Prasetyo, Y.T. Matching-Based Comprehension of Emergency Safety Symbols Among Filipinos: Us-er-Centered Quality Measure. In Proceedings of the 21st Congress of the International Ergonomics Association (IEA 2021), online, 13–18 June 2021; pp. 384–392. [Google Scholar]
  45. Madel, L.; Cahigas, M.; Tri Prasetyo, Y. Kansei Engineering-based Model and Online Content Assessment in Evaluating Ser-vice Design of Lazada Express. In Proceedings of the 2020 The 6th International Conference on Industrial and Business Engineerin, Macau, China, 27–29 September 2020. [Google Scholar]
  46. McDougall, S.; Reppa, I.; Kulik, J.; Taylor, A. What makes icons appealing? The role of processing fluency in predicting icon appeal in different task contexts. Appl. Ergon. 2016, 55, 156–172. [Google Scholar] [CrossRef]
  47. Huang, H.; Yang, M.; Yang, C.; Lv, T. User performance effects with graphical icons and training for elderly novice users: A case study on automatic teller machines. Appl. Ergon. 2019, 78, 62–69. [Google Scholar] [CrossRef]
  48. Prasetyo, Y.T.; Castillo, A.M.; Salonga, L.J.; Sia, J.A.; Seneta, J.A. Factors affecting perceived effectiveness of COVID-19 prevention measures among Filipinos during Enhanced Community Quarantine in Luzon, Philippines: Integrating Protection Motivation Theory and extended Theory of Planned Behavior. Int. J. Infect. Dis. 2020, 99, 312–323. [Google Scholar] [CrossRef] [PubMed]
  49. Salman, Y.B.; Cheng, H.-I.; Patterson, P.E. Icon and user interface design for emergency medical information systems: A case study. Int. J. Med. Inform. 2012, 81, 29–35. [Google Scholar] [CrossRef] [PubMed]
  50. Lamy, J.-B.; Soualmia, L.F.; Kerdelhué, G.; Venot, A.; Duclos, C. Validating the semantics of a medical iconic language using ontological reasoning. J. Biomed. Inform. 2012, 46, 56–67. [Google Scholar] [CrossRef]
  51. Lindell, M.K.; Bostrom, A.; Goltz, J.D.; Prater, C.S. Evaluating hazard awareness brochures: Assessing the textual, graphical, and numerical features of tsunami evacuation products. Int. J. Disaster Risk Reduct. 2021, 61, 102361. [Google Scholar] [CrossRef]
  52. Payre, W.; Diels, C. Designing in-vehicle signs for connected vehicle features: Does appropriateness guarantee comprehension? Appl. Ergon. 2019, 80, 102–110. [Google Scholar] [CrossRef] [PubMed]
  53. Jung, S.; Park, J.; Park, J.; Choe, M.; Kim, T.; Choi, M.; Lee, S. Effect of touch button interface on in-vehicle information systems usability. Int. J. Hum. Comput. Interac. 2021, 1–19. [Google Scholar] [CrossRef]
  54. Jylhä, H.; Hamari, J. Development of measurement instrument for visual qualities of graphical user interface elements (VISQUAL): A test in the context of mobile game icons. User Model. User-Adapted Interact. 2020, 30, 949–982. [Google Scholar] [CrossRef]
  55. Jylhä, H.; Hamari, J. Demographic factors have little effect on aesthetic perceptions of icons: A study of mobile game icons. Internet Res. 2021. ahead-of-print. [Google Scholar] [CrossRef]
  56. Cao, Y.; Zhang, Y.; Ding, Y.; Duffy, V.G.; Zhang, X. Is an anthropomorphic app icon more attractive? Evidence from neu-roergonomomics. Appl. Ergon. 2021, 97, 103545. [Google Scholar] [CrossRef] [PubMed]
  57. Chen, R.; Huang, J.; Zhou, J. Skeuomorphic or flat icons for an efficient visual search by younger and older adults? Appl. Ergon. 2020, 85, 103073. [Google Scholar] [CrossRef]
  58. Chen, M.-S.; Lin, M.-C.; Wang, C.-C.; Chang, C.A. Using HCA and TOPSIS approaches in personal digital assistant menu–icon interface design. Int. J. Ind. Ergon. 2009, 39, 689–702. [Google Scholar] [CrossRef]
Healthcare 09 01115 g001 550
Figure 1. An icon from World Health Organization that intends to convey protocols about travel restriction during the COVID-19 pandemic.
Figure 1. An icon from World Health Organization that intends to convey protocols about travel restriction during the COVID-19 pandemic.
Healthcare 09 01115 g001
Healthcare 09 01115 g002 550
Figure 2. Example of ranking test screen.
Figure 2. Example of ranking test screen.
Healthcare 09 01115 g002
Healthcare 09 01115 g003 550
Figure 3. Example of subjective design feature test screen.
Figure 3. Example of subjective design feature test screen.
Healthcare 09 01115 g003
Table
Table 1. The demographic of the participants (N = 57).
Table 1. The demographic of the participants (N = 57).
CharacteristicsCategoryN%
GenderMale 2543.9
Female3256.1
Age18–254884.2
26–3335.3
34–40610.5
Educational BackgroundHigh School Graduate1831.6
College Graduate3663.2
Master Graduate35.3
Table
Table 2. Function names of icons related to COVID-19 preventive measures.
Table 2. Function names of icons related to COVID-19 preventive measures.
Functions
1. Shortness of Breath
2. Fever
3. Cough or Cold
4. Difficulty in Breathing
5. Wash Hands
6. Consult Doctor/Seek Medical Help
7. Avoid People with Flu-Like Symptoms
8. Cover Face when Coughing/Sneezing
9. Get Information from Trusted Sources
10. Wear Face Mask
11. Avoid Crowded Places/Limit Social Gatherings
12. Dispose Tissue and Face Mask in Waste Can
13. Use Alcohol-Based Hand Sanitizers if Soap and Water are Not Available.
14. Avoid Travelling to Places with Known Cases
15. Avoid Touching your Face
16. Wash Clothes Properly
17. De-Contaminate/Disinfect
18. Home Quarantine/Stay at Home
19. Social Distancing
Table
Table 3. Icons tested in the current study and their summarized results from the preference test.
Table 3. Icons tested in the current study and their summarized results from the preference test.
FunctionRank 1Rank 2Rank 3Rank 4Rank 5Rank 6Rank 7
Shortness of Breath
(1)		 Healthcare 09 01115 i001
(mean: 2.807; SD 1.652)		 Healthcare 09 01115 i002
(mean: 3.175; SD: 1.843)		 Healthcare 09 01115 i003
(mean: 3.351; SD: 1.747)		 Healthcare 09 01115 i004
(mean: 4.351; SD: 1.986)		 Healthcare 09 01115 i005
(mean: 4.386; SD: 2.042)		 Healthcare 09 01115 i006
(mean: 4.93; SD: 1.981)		 Healthcare 09 01115 i007
(mean: 5.000; SD: 1.592)
Source(k)(j)(c)(e)(e)(l)(a)
Fever
(2)		 Healthcare 09 01115 i008
(mean: 2.772; SD: 1.604)		 Healthcare 09 01115 i009
(mean: 2.807; SD: 1.903)		 Healthcare 09 01115 i010
(mean: 3.509; SD: 1.774)		 Healthcare 09 01115 i011
(mean: 3.719; SD: 1.556)		 Healthcare 09 01115 i012
(mean: 3.737; SD: 1.395)		 Healthcare 09 01115 i013
(mean: 5.509; SD: 1.692)		 Healthcare 09 01115 i014
(mean: 5.947; SD:1.54)
Source(m)(a)(e)(e)(c)(e)(e)
Cough or Cold
(3)		 Healthcare 09 01115 i015
(mean: 2.789; SD: 1.75)		 Healthcare 09 01115 i016
(mean: 2.86; SD: 1.575)		 Healthcare 09 01115 i017
(mean: 3.158; SD: 1.601)		 Healthcare 09 01115 i018
(mean: 3.298; SD: 1.669)		 Healthcare 09 01115 i019
(mean: 4.93; SD: 1.438)		 Healthcare 09 01115 i020
(mean: 5.263; SD: 1.685)		 Healthcare 09 01115 i021
(mean: 5.702; SD: 1.792)
Source(a)(n)(e)(a)(a)(d)(a)
Difficulty in Breathing
(4)		 Healthcare 09 01115 i022
(mean: 2.772; SD: 1.991)		 Healthcare 09 01115 i023
(mean: 3.333; SD: 1.786)		 Healthcare 09 01115 i024
(mean: 3.86; SD: 1.652)		 Healthcare 09 01115 i025
(mean: 3.86; SD: 1.726)		 Healthcare 09 01115 i026
(mean: 4.07; SD: 2.017)		 Healthcare 09 01115 i027
(mean: 4.105; SD: 1.961)		 Healthcare 09 01115 i028
(mean: 6.000; SD: 1.268)
Source(e)(a)(n)(a)(b)(m)(a)
Wash Hands
(5)		 Healthcare 09 01115 i029
(mean: 2.526; SD: 1.91)		 Healthcare 09 01115 i030
(mean: 2.895; SD: 1.622)		 Healthcare 09 01115 i031
(mean: 3.544; SD: 1.477)		 Healthcare 09 01115 i032
(mean: 3.544; SD: 1.753)		 Healthcare 09 01115 i033
(mean: 4.579; SD: 1.569)		 Healthcare 09 01115 i034
(mean: 4.614; SD: 1.556)		 Healthcare 09 01115 i035
(mean: 6.298; SD: 1.451)
Source(o)(d)(e)(b)(d)(d)(h)
Consult Doctor/Seek Medical Help
(6)		 Healthcare 09 01115 i036
(mean: 1.789; SD: 1.176)		 Healthcare 09 01115 i037
(mean: 2.965; SD: 1.488)		 Healthcare 09 01115 i038
(mean: 3.439; SD: 1.604)		 Healthcare 09 01115 i039
(mean: 3.807; SD: 1.563)		 Healthcare 09 01115 i040
(mean: 3.965; SD: 1.5)		 Healthcare 09 01115 i041
(mean: 5.386; SD: 0.94)		 Healthcare 09 01115 i042
(mean: 6.649; SD: 1.11)
Source(d)(b)(d)(e)(a)(d)(d)
Avoid People with Flu-Like Symptoms
(7)		 Healthcare 09 01115 i043
(mean: 2.439; SD: 1.239)		 Healthcare 09 01115 i044
(mean: 2.491; SD: 1.571)		 Healthcare 09 01115 i045
(mean: 2.702; SD: 1.792)		 Healthcare 09 01115 i046
(mean: 4.228; SD: 1.268)		 Healthcare 09 01115 i047
(mean: 4.807; SD: 1.608)		 Healthcare 09 01115 i048
(mean: 5.298; SD: 1.488)		 Healthcare 09 01115 i049
(mean: 6.035; SD: 1.309)
Source(d)(d)(d)(a)(a)(d)(g)
Cover when Coughing or Sneezing
(8)		 Healthcare 09 01115 i050
(mean: 2.667; SD: 2.003)		 Healthcare 09 01115 i051
(mean: 3.000; SD: 1.5)		 Healthcare 09 01115 i052
(mean: 3.404; SD: 1.811)		 Healthcare 09 01115 i053
(mean: 4.246; SD: 1.735)		 Healthcare 09 01115 i054
(mean: 4.316; SD: 1.649)		 Healthcare 09 01115 i055
(mean: 4.614; SD: 1.634)		 Healthcare 09 01115 i056
(mean: 5.754; SD: 1.921)
Source(a)(d)(d)(e)(a)(a)(d)
Get Information from Trusted Sources
(9)		 Healthcare 09 01115 i057
(mean: 1.614; SD: 1.013)		 Healthcare 09 01115 i058
(mean: 3.158; SD: 1.544)		 Healthcare 09 01115 i059
(mean: 3.316; SD: 1.844)		 Healthcare 09 01115 i060
(mean: 4.316; SD: 1.919)		 Healthcare 09 01115 i061
(mean: 4.895; SD: 1.41)		 Healthcare 09 01115 i062
(mean: 5.018; SD: 1.458)		 Healthcare 09 01115 i063
(mean: 5.684; SD: 1.429)
Source(d)(h)(e)(e)(a)(a)(a)
Wear Face Mask
(10)		 Healthcare 09 01115 i064
(mean: 2.877; SD: 1.722)		 Healthcare 09 01115 i065
(mean: 2.982; SD: 1.685)		 Healthcare 09 01115 i066
(mean: 3.158; SD: 1.177)		 Healthcare 09 01115 i067
(mean: 3.579; SD: 1.625)		 Healthcare 09 01115 i068
(mean: 3.579; SD: 1.658)		 Healthcare 09 01115 i069
(mean: 5.702; SD: 2.009)		 Healthcare 09 01115 i070
(mean: 6.123; SD: 0.965)
Source(p)(n)(g)(e)(e)(a)(d)
Avoid Crowded Places/Limit Social Gatherings
(11)		 Healthcare 09 01115 i071
(mean: 1.754; SD: 1.023)		 Healthcare 09 01115 i072
(mean: 2.211; SD: 1.333)		 Healthcare 09 01115 i073
(mean: 3.754; SD: 1.418)		 Healthcare 09 01115 i074
(mean: 3.947; SD: 1.231)		 Healthcare 09 01115 i075
(mean: 4.193; SD: 1.381)		 Healthcare 09 01115 i076
(mean: 5.439; SD: 1.35)		 Healthcare 09 01115 i077
(mean: 6.702; SD: 0.626)
Source(d)(d)(h)(e)(d)(d)(d)
Dispose Tissue and Face Mask in Waste Can
(12)		 Healthcare 09 01115 i078
(mean: 1.86; SD: 1.025)		 Healthcare 09 01115 i079
(mean: 2.404; SD: 1.237)		 Healthcare 09 01115 i080
(mean: 2.456; SD: 1.351)		 Healthcare 09 01115 i081
(mean: 4.579; SD: 1.535)		 Healthcare 09 01115 i082
(mean: 5.158; SD: 0.996)		 Healthcare 09 01115 i083
(mean: 5.439; SD: 1.165)		 Healthcare 09 01115 i084
(mean: 6.105; SD: 1.175)
Source(e)(q)(d)(f)(d)(d)(a)
Use Hand Sanitizers
(13)		 Healthcare 09 01115 i085
(mean: 1.386; SD: 0.861)		 Healthcare 09 01115 i086
(mean: 3.228; SD: 1.402)		 Healthcare 09 01115 i087
(mean: 3.649; SD: 1.395)		 Healthcare 09 01115 i088
(mean: 4.351; SD: 1.631)		 Healthcare 09 01115 i089
(mean: 4.772; SD: 1.722		 Healthcare 09 01115 i090
(mean: 5.298; SD: 1.812)		 Healthcare 09 01115 i091
(mean: 5.316; SD: 1.744)
Source(e)(r)(d)(i)(d)(a)(i)
Avoid Traveling to Places with Known Cases
(14)		 Healthcare 09 01115 i092
(mean: 1.807; SD: 1.302)		 Healthcare 09 01115 i093
(mean: 2.702; SD: 1.721)		 Healthcare 09 01115 i094
(mean: 3.702; SD: 1.463)		 Healthcare 09 01115 i095
(mean: 3.86; SD: 1.505		 Healthcare 09 01115 i096
(mean: 4.14; SD: 1.302)		 Healthcare 09 01115 i097
(mean: 5.877; SD: 1.001)		 Healthcare 09 01115 i098
(mean: 5.912; SD: 1.64)
Source(d)(a)(s)(d)(d)(d)(d)
Avoid Touching Face
(15)		 Healthcare 09 01115 i099
(mean: 2.193; SD: 1.517)		 Healthcare 09 01115 i100
(mean: 2.93; SD: 1.689)		 Healthcare 09 01115 i101
(mean: 3.281; SD: 1.77		 Healthcare 09 01115 i102
(mean: 3.404; SD: 1.438)		 Healthcare 09 01115 i103
(mean: 5.105; SD: 1.372)		 Healthcare 09 01115 i104
(mean: 5.14; SD: 1.381)		 Healthcare 09 01115 i105
(mean: 5.947; SD: 1.597)
Source(d)(e)(d)(d)(d)(f)(d)
Wash Clothes Properly
(16)		 Healthcare 09 01115 i106
(mean: 2.667; SD: 1.704)		 Healthcare 09 01115 i107
(mean: 2.579; SD: 1.569)		 Healthcare 09 01115 i108
(mean: 2.825; SD: 1.627)		 Healthcare 09 01115 i109
(mean: 3.842; SD: 1.556		 Healthcare 09 01115 i110
(mean: 4.684; SD: 1.416)		 Healthcare 09 01115 i111
(mean: 4.912; SD: 1.455		 Healthcare 09 01115 i112
(mean: 6.491; SD: 1.002)
Source(i)(d)(d)(i)(i)(i)(i)
De-Contaminate
(17)		 Healthcare 09 01115 i113
(mean: 2.526; SD: 1.477)		 Healthcare 09 01115 i114
(mean: 3.263; SD: 1.904)		 Healthcare 09 01115 i115
(mean: 3.86; SD: 1.807)		 Healthcare 09 01115 i116
(mean: 4.035; SD: 2.026)		 Healthcare 09 01115 i117
(mean: 4.07; SD: 2.17)		 Healthcare 09 01115 i118
(mean: 4.947; SD: 1.807)		 Healthcare 09 01115 i119
(mean: 5.298; SD: 1.388
Source(e)(d)(d)(d)(d)(d)(q)
Home Quarantine/Stay at Home
(18)		 Healthcare 09 01115 i120
(mean: 1.439; SD: 0.982)		 Healthcare 09 01115 i121
(mean: 2.614; SD: 1.221)		 Healthcare 09 01115 i122
(mean: 3.368; SD: 1.588)		 Healthcare 09 01115 i123
(mean: 4.596; SD: 1.545)		 Healthcare 09 01115 i124
(mean: 5.035; SD: 1.752)		 Healthcare 09 01115 i125
(mean: 5.263; SD: 1.232)		 Healthcare 09 01115 i126
(mean: 5.684; SD: 1.242)
Source(d)(d)(d)(e)(e)(q)(f)
Social Distancing
(19)		 Healthcare 09 01115 i127
(mean: 2.158; SD: 1.192)		 Healthcare 09 01115 i128
(mean: 2.281; SD: 1.485		 Healthcare 09 01115 i129
(mean: 3.526 SD: 1.67)		 Healthcare 09 01115 i130
(mean: 4.684; SD: 1.774)		 Healthcare 09 01115 i131
(mean: 4.737; SD: 1.653)		 Healthcare 09 01115 i132
(mean: 5.263; SD: 1.446)		 Healthcare 09 01115 i133
(mean: 5.351; SD: 1.706)
Source(e)(d)(d)(d)(h)(b)(a)
Note: (a) Department of Health, Philippines, (b) Department of National Defense, Philippines, (c) Makati Medical Center, Philippines, (d) World Health Organization, (e) Pan American Health Organization, United State of America, (f) Health Direct, Australia, (g) European Centre for Disease Prevention and Control, Europe, (h) World Heart Federation, (i) Freepik.com, (j) Driblle.com, (k) Margaret Mary Health, (l) San Joaquin Country Public Health Services, (m) Sanford Health Organization, (n) Lompoc Valley Medical Center, (o) Wyckoff Family YMCA, (p) MIT College of Pharmacy Moradabad, (q) Iskandar Malaysia Studios, (r) Sai Sanjeevini Hospitals, and (s) Center for Disease Control and Prevention.
Table
Table 4. Descriptions of subjective design features.
Table 4. Descriptions of subjective design features.
Subjective Design FeaturesDefinition
Perceived Icon QualityOne of the most critical aspects of icon development that defines the successful design [38]
CommunicativenessRefers to how the icon expresses its intended meaning [21].
ComplexityPertains to how complex the details intricated on the icon are [19].
LayoutHow carefully the features of an icon are arranged [21].
Semantic DistanceThe measure of the closeness of what is illustrated in the icon to its true intended meaning [20].
Table
Table 5. Semantic scales and their corresponding subjective design feature.
Table 5. Semantic scales and their corresponding subjective design feature.
Subjective Design FeaturesSemantic Scales
Perceived Icon Quality1. Unlikable—Likable
2. Ugly—Beautiful [39]
3. Ineffective—Effective
Communicativeness4. Vague—Clear
5. Weak—Strong [39]
6. Unfamiliar—Familiar [4,21,39]
Complexity7. Complex—Simple [4,7,40]
Layout8. Disorganized—Organized
9. Cluttered—Uncluttered
10. Dull—Colorful [39]
Semantic Distance11. Unrecognizable—Recognizable [21]
12. Abstract—Concrete [4,39]
13. Incompatible—Compatible
Table
Table 6. Descriptive statistics: semantic scales.
Table 6. Descriptive statistics: semantic scales.
VariableMeanSD
1. unlikeable–likeable6.06511.2062
2. ugly–beautiful5.83471.2821
3. ineffective–effective6.08031.2331
4. vague–clear6.04161.278
5. weak–strong5.98611.2754
6. unfamiliar–familiar6.09741.2317
7. complex–simple6.10531.2513
8. disorganized–organized6.12831.1324
9. cluttered–uncluttered5.99081.27
10. dull–colorful5.76641.4093
11. unrecognizable–recognizable6.17681.1496
12. abstract–concrete6.07941.1405
13. incompatible–compatible 6.14911.1313
Table
Table 7. Spearman’s rank correlation result between ranking and 13 semantic scales.
Table 7. Spearman’s rank correlation result between ranking and 13 semantic scales.
12345678910111213
Unlikable–Likable11
Ugly–Beautiful20.749 **1
Ineffective–Effective30.645 **0.610 **1
Vague–Clear40.584 **0.567 **0.786 **1
Weak–Strong50.609 **0.607 **0.787 **0.796 **1
Unfamiliar–Familiar60.560 **0.553 **0.709 **0.722 **0.749 **1
Complex–Simple70.494 **0.489 **0.661 **0.666 **0.689 **0.727 **1
Disorganized–Organized80.579 **0.569 **0.684 **0.681 **0.685 **0.709 **0.774 **1
Cluttered–Uncluttered90.548 **0.560 **0.637 **0.621 **0.635 **0.655 **0.731 **0.819 **1
Dull–Colorful100.552 **0.619 **0.454 **0.448 **0.471 **0.442 **0.362 **0.460 **0.448 **1
Unrecognizable–Recognizable110.586 **0.526 **0.706 **0.701 **0.682 **0.661 **0.627 **0.639 **0.604 **0.428 **1
Abstract–Concrete120.575 **0.547 **0.703 **0.700 **0.706 **0.653 **0.634 **0.656 **0.632 **0.459 **0.752 **1
Incompatible–Compatible130.587 **0.542 **0.707 **0.681 **0.693 **0.653 **0.613 **0.650 **0.622 **0.453 **0.784 **0.816 **1
Rank−0.195 **−0.134 **−0.199 **−0.206 **−0.205 **−0.169 **−0.162 **−0.176 **−0.152 **−0.145 **−0.194 **−0.195 **−0.200 **
**. Correlation is significant at the 0.01 level (two-tailed).
Table
Table 8. The Friedman’s test result in terms of “unlikable–likable” for function “cover when coughing or sneezing”.
Table 8. The Friedman’s test result in terms of “unlikable–likable” for function “cover when coughing or sneezing”.
Ranks
Mean Rank
Unlikable–Likable Rank 11.7
Unlikable–Likable Rank 21.3
Test Statistics
N57
Chi-Square14.297
df1
Asymptotic significance0.000
Table
Table 9. The summary results of the Friedman’s test in chi-square statistic (χ2).
Table 9. The summary results of the Friedman’s test in chi-square statistic (χ2).
12345678910111213
Function 10.0240.90.9470.10.0211.88402.2730.3641.0893.270.11.256
Function 27.049 **6.149 **1.8826.721 **6.422 **2.54 *4.667 *3.134.787 **6.081 **7.811 **6.4 **
Function 35.818 **1.2565.488 **13.714 **8.100 **4.667 *0.6761.7783.4573.1311.765 **12.6 **9.323 **
Function 44.667 **1.64.568 **7.714 **5.818 **1.19500.8570.12.2733.7891.5244.122 *
Function 521.41.1952.8142.9510.8180.222.1891.2560.0372.51.5240.857
Function 61.9761.9764.122 *1.5240.610.2312.0772.6321.19504.333*1.5245.233 **
Function 72.0771.9762.3140.92.7781.7780.9471.1950.226.721 **0.4212.0772.5
Function 814.297 **5.444 **7.111 **10.314 **9.757 **7.258 **6.818 **3.4576.429 **11.308 **13.333 **14.235 **15.125 **
Function 94.333 **1.1955.488 **2.3816.721 **5.233 **7.049 **2.1893.271.3911.6843.5962.381
Function 106.081 **1.5243.3337.529 **6.125 **2.1336.737 **9 **7.346 **5.444 **5.121 **4.235 **10.125 **
Function 110.151.421.7785.765 **4.235 **25.143 **1.22.8142.4556.5333.457
Function 122.8140.2438.805 **4.333 **5.333 **0.8571.5242.8140.2064.122 **2.1893.7565.769 **
Function 136.429 **1.2560.42.1893.271.4855.121 **1.7783.66.081 **3.1034.235 **1.485
Function 143.1250.10510.526 **10.314 **7.111 **6.737 **5.121 **9.757 **7.111 **0.23111.111 **9 **4.568 **
Function 151.4851.44 **10.756 **6.081 **4.333 **12.6 **9 **4 **2.7787.41 **3.4576.081 **
Function 160.1180.40.1110.0270.1180.7581.1250.1050.220.93.4571.3243.27
Function 1710.800 **7.410 **2.7781.7781.61.1251.3852.2867.529 **6.422 **2.2865.452 **5.765 *
Function 184.235 *2.6358.395 **7.078 **5.488 *4.900*6.095 *8.395 **5.488 *1.976 1.884 3.130 5.000 *
Function 194.333 *2.9517.111 **3.333 2.778 6.818 **5.765 *2.793 4.235 *0.2431.581 4.568 *1.690
**. Chi-square statistic is significant at the 0.01 level. *. Chi-square statistic is significant at the 0.05 level (two-tailed). 1: unlikeable–likeable; 2: ugly–beautiful; 3: ineffective–effective; 4: vague–clear; 5: weak–strong; 6: unfamiliar–familiar; 7: complex–simple; 8: disorganized–organized; 9: cluttered–uncluttered; 10: dull–colorful; 11:unrecognizable–recognizable; 12: abstract–concrete; 13: incompatible–compatible.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Prasetyo, Y.T.; Dewi, R.S.; Balatbat, N.M.; Antonio, M.L.B.; Chuenyindee, T.; Perwira Redi, A.A.N.; Young, M.N.; Diaz, J.F.T.; Kurata, Y.B. The Evaluation of Preference and Perceived Quality of Health Communication Icons Associated with COVID-19 Prevention Measures. Healthcare 2021, 9, 1115. https://doi.org/10.3390/healthcare9091115

AMA Style

Prasetyo YT, Dewi RS, Balatbat NM, Antonio MLB, Chuenyindee T, Perwira Redi AAN, Young MN, Diaz JFT, Kurata YB. The Evaluation of Preference and Perceived Quality of Health Communication Icons Associated with COVID-19 Prevention Measures. Healthcare. 2021; 9(9):1115. https://doi.org/10.3390/healthcare9091115

Chicago/Turabian Style

Prasetyo, Yogi Tri, Ratna Sari Dewi, Naiomi M. Balatbat, Michael Lancelot B. Antonio, Thanatorn Chuenyindee, Anak Agung Ngurah Perwira Redi, Michael Nayat Young, John Francis T. Diaz, and Yoshiki B. Kurata. 2021. "The Evaluation of Preference and Perceived Quality of Health Communication Icons Associated with COVID-19 Prevention Measures" Healthcare 9, no. 9: 1115. https://doi.org/10.3390/healthcare9091115

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop