A systematic review of visual image theory, assessment, and use in skin cancer and tanning research.

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A Systematic Review of Visual Image Theory, Assessment, and Use in Skin Cancer and Tanning Research a

Jennifer E. McWhirter & Laurie Hoffman-Goetz

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School of Public Health and Health Systems, University of Waterloo , Waterloo , Ontario , Canada Published online: 10 Feb 2014.

To cite this article: Jennifer E. McWhirter & Laurie Hoffman-Goetz (2014) A Systematic Review of Visual Image Theory, Assessment, and Use in Skin Cancer and Tanning Research, Journal of Health Communication: International Perspectives, 19:6, 738-757, DOI: 10.1080/10810730.2013.837562 To link to this article: http://dx.doi.org/10.1080/10810730.2013.837562

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A Systematic Review of Visual Image Theory, Assessment, and Use in Skin Cancer and Tanning Research JENNIFER E. MCWHIRTER AND LAURIE HOFFMAN-GOETZ School of Public Health and Health Systems, University of Waterloo, Waterloo, Ontario, Canada Visual images increase attention, comprehension, and recall of health information and influence health behaviors. Health communication campaigns on skin cancer and tanning often use visual images, but little is known about how such images are selected or evaluated. A systematic review of peer-reviewed, published literature on skin cancer and tanning was conducted to determine (a) what visual communication theories were used, (b) how visual images were evaluated, and (c) how visual images were used in the research studies. Seven databases were searched (PubMed= MEDLINE, EMBASE, PsycINFO, Sociological Abstracts, Social Sciences Full Text, ERIC, and ABI=INFORM) resulting in 5,330 citations. Of those, 47 met the inclusion criteria. Only one study specifically identified a visual communication theory guiding the research. No standard instruments for assessing visual images were reported. Most studies lacked, to varying degrees, comprehensive image description, image pretesting, full reporting of image source details, adequate explanation of image selection or development, and example images. The results highlight the need for greater theoretical and methodological attention to visual images in health communication research in the future. To this end, the authors propose a working definition of visual health communication.

Visual communication is defined as any optically stimulating message (Lester, 2006). Health communication research has shown that visual images influence attention, recall, and comprehension of health information, as well as health behaviors (Houts, Doak, Doak, & Loscalzo, 2006). Visual images for health communication have been studied in terms of the visual portrayal of health issues in the mass media (McClure, Puhl, & Heurer, 2011; McWhirter, Hoffman-Goetz, & Clarke, 2012), patient education in medical situations (Morrow, Hier, Menard, & Von Leirer, 1998), discharge instructions for older adults (Choi, 2011), pictorial warnings on tobacco products (Hammond, 2011), maps to communicate health risks (Bayram & Ibrahim, 2005; Brewer, 2006; Parrott, Hopfer, Ghetian, & Lengerick, 2007), and graphs to display numeric health and risk information (Lipkus & Holland, 1999). This present study focuses on visual images (i.e., photographs) related to skin cancer communication. Images hold particular relevance to skin cancer since the signs and symptoms are largely visual, as is the diagnosis (Abbasi et al., 2004). Furthermore, ultraviolet light Address correspondence to Laurie Hoffman-Goetz, School of Public Health and Health Systems, Faculty of Applied Health Sciences, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, N2L 3G1 Canada. E-mail: [email protected]

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Visual Images and Skin Cancer

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exposure (from the sun or artificial tanning lamps) results in observable changes to the skin (e.g., tan, burn, freckles). Images are important in self-examination of the skin for abnormal lesions by patients (Girardi et al., 2006), can influence sun-protection behaviors (Mahler, Kulik, Gerrard, & Gibbons, 2006) and, as key predictors of indoor tanning bed use—a known carcinogen—are reasons connected to physical appearance (Hillhouse, Turrisi, Holwiski, & McVeigh, 1999; Lazovich et al., 2004). Worldwide, two to three million people are diagnosed annually with nonmelanoma skin cancer and 132,000 with melanoma skin cancer (World Health Organization, 2012) making skin cancer a significant public health issue. Theory is important for developing and refining health communication efforts, helping to increase campaign effectiveness (Legler et al., 2002). Theoretical approaches are mentioned in slightly more than one third of health education studies (Painter, Brova, Hynes, Mays, & Glanz, 2008). The extent to which visual communication theories guide health communication research is unknown. Researchers and practitioners have called for greater attention to visual images in health, the need for more research on visuals and health, and have pointed to a lack of theory to guide their use in health contexts (Entwistle & Williams, 2008; Houts et al., 2006; Williams & Cameron, 2009). Equally important is the apparent lack of a definition of visual health communication. Given the relevance of visual images in health communication and calls for greater attention to the imagery in health research and practice, we conducted a systematic review to determine how visual images were used in skin cancer and tanning communication research. Our specific questions of interest were (a) what visual communication theoretical approaches were used; (b) how visual images were assessed in the research (i.e., what tools were used); and (c) how visual images were used and reported on in the studies (e.g., selected, piloted, described). We focused on skin cancer and tanning research because these are significant public health issues, the causes are often preventable (thus, there is a role for health communication initiatives), and both have important visual aspects to them. Before reporting on the results of this systematic review, we offer a brief overview of major visual communication theories and tools.

Visual Communication Theory Several theories are prominent in visual communication (for in-depth coverage, see Lester, 2006; Smith, Moriarty, Barbatsis, & Kenney, 2005; Williams & Newton, 2007), and many are relevant to health communication. Because these theories may not be immediately familiar to health communication researchers, the following section provides a brief overview. Visual Cognitive Theory Visual cognitive theory considers how the mind processes visual information and how behavior is based on that information. Cognition refers to the ‘‘mental process of knowing or understanding’’ with two main cognitive systems: rational, which uses logic and reason to form knowledge; and intuitive, which obtains knowledge directly or experientially. Important here is the idea of hemispheric specialization with the left side of the brain as verbal, analytical, and logical; the right brain is described as visual, global, and perceptual (Bogen, 1975; Sperry, 1973). Knowledge derived from the visual can be intuitive as well as rational (Williams, 2005). According to visual cognitive theory, a person viewing an image does not simply witness an image or object before them, but actively reaches a conclusion about what is being

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perceived through mental activities (Lester, 2006). Mental activities, which affect visual perception, include the following (Bloomer, 1990): . . . . .


. . . .

Memory (pictures are important memory aids) Projection (projecting an image of one thing to an image of something else) Expectation (preconception of how something should appear can lead viewer to poor visual perception) Selectivity (which images are attended to) Habituation (ignoring images to which viewer is constantly exposed) Salience (visual stimuli are more likely to be noticed if they have meaning or significance to viewer) Dissonance (different images compete for viewer attention) Culture (affects how signs are interpreted and importance assigned to them by viewer) Words (conscious thought is framed by words and therefore affect understanding and recall of images)

Attribute Activation On the basis of cognitive theory, attribute activation is related to how humans process, select, critique, or create visual portrayals. It is concerned with how image components represent information. Viewers match a visual representation or attribute (i.e., ‘‘the elements of the image that communicate meaning’’) with the dimensions of the information (i.e., ‘‘the knowledge structures that are activated by the image in the mind of the viewer’’) to either create or comprehend information (Steed, 2006, p. 4). For example, a red circle (attribute of the image) indicates a person’s position on a map which, in turn, indicates their location in space (dimensions of information). Pictorial Superiority Effect and Dual Coding Theory Another important visual information theory is that of the pictorial superiority effect, which states that recall and recognition of visual information is greater than that of verbal or text information (Paivio & Csapo, 1973). McBride and Dosher (2002) suggested three reasons why this is the case. First, pictures hold an advantage over words because they are dual-coded into verbal and visual memory, thus increasing recall performance (Paivio, 1991). This is known as dual coding theory (Paivio, 1996). Second, each picture is encoded in the mind more uniquely and thus can be more readily retrieved (Nelson, 1979). Third, pictures are more likely than words are to access meaning in the brain during encoding; the interaction between encoding and retrieval increases performance on recall and recognition tasks involving pictures (Weldon & Roediger, 1987). Cognitive Load Theory Cognitive load theory suggests that learning is most fruitful under conditions which are optimized for how the human brain processes information (Sweller, 1988). Information is processed first in short-term working memory (which is rather limited in capacity) before long-term memory. Cognitive load theory, together with working memory (Baddeley, 1998), implies that only a limited amount of cognitive processing of either verbal or visual information can take place at any point in time and if this is exceeded learning is inhibited (Chandler & Sweller, 1991; Sweller, 1999). How specific types of visual communication can be informed by cognitive load theory in order to maximize the effectiveness of communicating and teaching with visual information has been explored (e.g., Bunch & Lloyd, 2006).


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Visual Persuasion Beyond the initial cognitive processing of images, there are theories related to persuasion, argument, and the deeper meaning of what we see. Visual persuasion (also called visual rhetoric) focuses on how an image is communicated to and persuades the viewer (Foss, 2005). Lester (2006) referred to persuasion as the use of ‘‘factual information and emotional appeals to change a person’s mind and promote a desired behavior’’ (p. 73). Visual persuasion plays a significant role in marketing and advertising (Messaris, 1997) and health-related attitudes and behaviors. Visual Argument Because theories of argumentation tend to avoid the visual components of argument and persuasion in favor of the verbal, a theory of visual argument was developed. Birdsell and Groarke (1996) suggested visual images can be arguments (i.e., provide reasons for a conclusion). The case for visual arguments is based on three prerequisites: (a) visuals have meaning that is not necessarily arbitrary, (b) images must be considered in context (immediate visual context, immediate verbal context, and visual culture), and (c) there must be recognition of the link between representation (stands for) and resemblance (looks like). Visual Semiology Semiology considers signs (units of meaning) within an image and how images create meaning for the viewer. Semiology allows the viewer to disassemble an image and trace ‘‘how it works in relation to broader systems of meaning’’ (Rose, 2001, p. 69). Semiology has been described both as a methodology (Rose, 2001) and a theory (Moriarty, 2005). Signs—the core unit of semiology—are anything that stand for something else (Rose, 2001). A sign consists of the signified (concept=object the sign represents) and the signifier (the image the sign takes) (Williams & Newton, 2007). Images of people can be signs, too: In advertising communication research, photographs ‘‘depend on signs of humans which symbolize particular qualities to their audience’’ (Rose, 2001, p. 80). The qualities of the human signifiers become associated with the product or behavior being advertised.

Visual Communication Tools and Methods There are several methods of analyzing visual images. Content analysis is based on details about the image’s objective (e.g., image size) and subjective (e.g., themes) content. Eye-tracking methodology involves the objective measure of where a subject looks within an image, for how long, and in what order. Semiotic, rhetorical, and discourse analyses are a cluster of methods that involve analyzing the image for deeper levels of meaning, including how these connect to broader sociological constructs and power structures in society. Projected interviewing is an approach which uses images to elicit responses or help with recall of memories in interview situations (Rose, 2001). To our knowledge, there are no standard instruments or approaches for assessing visual images (i.e., photographs) in health contexts. While there are instruments for evaluation of text—such as readability levels, literacy levels, and numeracy levels—similar tools have not been described for visual images (Friedman & Hoffman-Goetz, 2006). However, the Suitability Assessment of Materials ‘‘offers a systematic method to objectively assess the suitability of health information materials for a particular audience in a short period of time’’ (Doak, Doak, & Root, 1996, p. 1). It is a recognized tool used for evaluating illustrations (line drawings) and

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graphics (tables and charts) accompanying health-related text but has not been used routinely to evaluate photographs in health communication. With these visual communication theories, tools, and methods in mind, we aimed to systematically investigate the use of visual images in skin cancer and tanning research.

Method


Search Strategy We conducted a systematic review in September 2011 using the guidelines of Petticrew and Roberts (2005), Pope, Mays, and Popay (2007), and the Cochrane Collaboration (2011) to obtain peer-reviewed articles about visual images and skin health (specifically skin cancer or tanning). The databases searched, and the corresponding number of articles screened from each, included: PubMeb=MEDLINE (n ¼ 1,616), EMBASE (n ¼ 709), PsycINFO (n ¼ 2,483), Sociological Abstracts (n ¼ 39), Social Sciences Full Text (n ¼ 164), ERIC (n ¼ 104), and ABI=INFORM (n ¼ 215). Search terms were generated from the following: the topic of the systematic review; terms and keywords from known articles of relevance; thesauri in relevant databases (e.g., PsycINFO thesaurus); and MeSH terms. A full list of search terms has been published elsewhere (McWhirter & Hoffman-Goetz, 2012). Full-text articles were retrieved based on the relevance of the studies conveyed by titles and abstracts of citations. Where titles and abstracts were ambiguous, but the use of images seemed possible based on the content of the abstract, full text of these articles were examined. If visual images were used in the methodology, those studies were included. Hand-searching of references lists was also conducted. Figure 1 illustrates the flow of the literature search process. Selection Criteria The inclusion criteria for studies in this review were as follows: published in English, peer-reviewed, dealt predominately with skin cancer or ultraviolet exposure (e.g., skin self-examination, indoor or outdoor tanning, sun-protection behaviors), used visual images (e.g., photographs, illustrations), was quantitative in research design or methodology, and were geared toward the general population or patients. Studies were excluded if they used patient- or subject-created images (e.g., drawings by patients), images created in the mind (i.e., ‘‘visualization’’), strictly film or video images, images for diagnosis or treatment by a physician, visuals conveying numerical or geographical information (e.g., graphs, charts, maps), review articles or meta-analyses, or if they focused on special populations (e.g., organ transplant recipients, medical students) as participants. Only quantitative studies were included in this review for the following reasons. First, compared to qualitative research, image effects would be more likely to be tested empirically. Second, we expected that quantitative studies would focus on image selection using theories, piloting imagery before use, and clearly describing images so that images could be used in public health practice. Third, limiting our review to quantitative studies helped to avoid issues related to research design noise from mixed methods (Harden & Thomas, 2005) and is an acceptable way of partitioning studies (Pope et al., 2007). Data Collection The information extracted for each study included the following: author, title, date of publication, whether a validated assessment tool or method from visual



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Figure 1. Flow chart for search and selection of studies.

communication was used (and if so, what tool was used), whether a specific visual communication theory was identified, whether other theory was mentioned in connection to visual images, whether research about visual images was cited, how images were selected or developed, image source, whether images were pretested, whether images were described adequately, and whether example images were included. Our primary objective was to gather information to report on how the studies dealt with images rather than to empirically assess the images. An overall score out of 8 for study quality specific to visuals was calculated on the basis of the following criteria: validated assessment tool used, visual theory cited, other theory cited, selection of images described, image sources reported, whether images were piloted, images described, and example images included. Scoring and descriptions of these criteria are outlined in Table 1. This score provides an assessment of the quality of how visual images were used rather than study quality in other domains (e.g., external validity, randomization, adequate sample size). Higher scores indicate greater quality of the study with respect to visual image use. All criteria were weighted as 1 with a score of 0 assigned for ‘‘no’’ and a score of 1 assigned for ‘‘yes.’’

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Table 1. Visual-related appraisal criteria for the studies Item


Images assessed with validated tool Visual communication theory cited Other theory cited Selection of images described Image sources reported Images sufficiently described

Images pretested or piloted Example images included Total score

Explanation

Score assignment

Score

Were the images assessed or evaluated using a validated assessment tool for images from visual communication? Such a tool would apply to many aspects of the images and not just one (e.g., skin tone). Yes or no? Was theory from the realm of visual communication explicitly cited? Yes or no?

No ¼ 0 Yes ¼ 1

=1

No ¼ 0 Yes ¼ 1

=1

Was other theory (e.g., psychology, health behavior) explicitly cited? Yes or no? Was the selection (e.g., reasons, methods, how, why) of images described within the study? Yes, no, or partially (some reasons for selection of all images or full reasons for only some images)? Were the sources of the images stated in the study? Yes, no, or partially?

No ¼ 0 Yes ¼ 1 No ¼ 0 Partially ¼ 0.5 Yes ¼ 1

=1

No ¼ 0 Partially ¼ 0.5 Yes ¼ 1 No ¼ 0 Partially ¼ 0.5 Yes ¼ 1

=1

No ¼ 0 Yes ¼ 1

=1

No ¼ 0 Yes ¼ 1

=1

Were the images used described sufficiently in the study (not an external source)? This would refer to describing the images in such a way that the experiment could be replicated accurately to at least some reasonable extent. Yes, no, or partially (all images partly described or some images fully described)? Were the images pretested or piloted with subjects before use in the research? Must be stated in methodology rather than relying on pretesting from another study. Yes or no? Was there at least one example image included in the study? Yes or no?

=1

=1

=8

The notable exceptions to this scoring were for three criteria: image sources, image selection, and image description which were each scored as either no (0), partially (0.5), or yes (1). This weighting was deemed appropriate because many studies were found to report partly, but not fully, on how images were selected, where they came from, and what they looked like. Had we only scored 0 (not reported) or 1 (reported) we would have lost this important information. Initially, all articles were coded by one reader using the coding categories and criteria in Table 1. To ensure reliability of the data extraction process, a second reader coded a 25% random sample of the articles. The overall Cohen’s kappa was calculated to be 0.98, indicative of very high interrater reliability for coding.

Results In this review, we included 47 studies. An overview of the results is provided in Table 2. The mean score out of 8 on the use of visual images in the studies was low at 2.7 (SD ¼ 1.0).

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Validated assessment tool used No No No No No No No No No No No No No No No No No No No No No No No No No No No No

Study

Banerjee et al. (2008) Boer et al. (2006) Borland et al. (1995) Borland et al. (1997) Branstrom et al. (2002) Broadstock et al. (1992) Chiu et al. (2006) Cho & Salmon (2006) Chung et al. (2010) Cox et al. (2009) Dalianis et al. (2011) Dixon et al. (2011) Emmons et al. (2010) Gaudy-Marqueste et al. (2011) Gibbons et al. (2005) Girardi et al. (2006) Hanrahan et al. (1997) Hay et al. (2006) Isaacowitz (2005) Jackson & Aiken (2006) Jung et al. (2010) Kundu et al. (2010) Lee et al. (2008) Luo & Isaacowitz (2007) Mahler et al. (1997) Mahler et al. (2003) Mahler et al. (2005) Mahler et al. (2006)

Table 2. Use of visual images in the studies

No No No No No No No No No No No No No No No Yes No No No No No No No No No No No No

Visual theory cited No No No No No No No Yes No Yes No Yes Yes No Yes No No Yes No Yes No No No No No Yes Yes Yes

Other theory cited Partial Partial Partial Yes Partial Yes Partial No Partial Partial Yes Partial Yes Partial Yes Yes Yes Partial Partial Partial Partial Partial No Yes Partial Partial Partial Partial

Selection of images described Yes No No Partial No Yes Yes No Yes No Yes Yes Yes Partial Yes No Yes Yes Partial No Yes Partial Partial Yes No Partial Partial Partial

Image sources reported Partial Partial Yes Yes Yes Yes Yes No Partial No Yes Partial Partial Partial Yes Partial Yes No Partial No Yes No No Partial No Partial Partial Partial

Images sufficiently described Yes Yes Yes No No No No Yes No Yes Yes No No No No No No No Yes No No No No Yes Yes No No No

Images piloted


No Yes No No Yes No Yes No No No No No No Yes No Yes Yes No Yes No Yes No No Yes No No No No

3 3 2.5 2.5 2.5 3 3.5 2 2 2.5 4 3 3.5 2.5 4 3.5 4 2.5 3.5 1.5 3.5 1 0.5 4.5 1.5 2.5 2.5 2.5

Overall score (=8)

(Continued )

Example images included

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Validated assessment tool used No No No No No No No No No No No No No No No No No No No

Study

Mahler et al. (2007) Mahler et al. (2008) Mahler, Beckerley, & Vogel (2010) Mahler, Kulik, et al. (2010) Miles & Meehan (1995) Novick (1997) Oliveria, Chau, et al. (2004) Oliveria, Dusza, et al. (2004) Pagoto et al. (2010) Phelan et al. (2003) Putnam & Yanagisako (1985) Robinson & Turrisi (2006) Robinson et al. (2009) Robinson et al. (2010) Speelman et al. (2010) Stephenson & Witte (1998) Stock et al. (2009) Titus-Ernstoff et al. (1996) Weinstock et al. (2004)

Table 2. Continued

No No No No No No No No No No No No No No No No No No No

Visual theory cited Yes Yes Yes Yes No No No Yes No No No No No No Yes Yes Yes No No

Other theory cited Partial Partial Yes Partial Yes Partial Partial Partial Yes Partial No Partial No Partial Yes Partial Partial Partial Partial

Selection of images described Partial Partial Yes Partial Yes Yes Yes Partial Yes Yes No Partial No No Yes Partial Partial No Yes

Image sources reported Partial Partial Partial Partial Yes Partial Partial No No Partial No Partial No No Partial Partial Partial Partial Partial

Images sufficiently described No No Yes Yes No No No No No No No No No No No Yes No No No

Images piloted


No No No No No Yes Yes No No Yes Yes No No Yes Yes No No Yes No

Example images included

2.5 2.5 4.5 3.5 3 3 3 2 2 3 1 1.5 0 1.5 4.5 3.5 2.5 2 2

Overall score (=8)


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Visual Communication Theory Only one study directly identified theory from visual communication as guiding the work. Giardi and colleagues (2006) cited research on visual learning of patterns stating that ‘‘we unconsciously build our own recognition pattern from the images of each object class, which thereafter permits a spontaneous holistic process of identification by associating the object with its pattern’’ (p. 2276). Thus, the Gestalt rules of grouping and how this plays into image recognition were specifically referenced. Gaudy-Marqueste, Dubois, Richard, Bonnelye, and Grob (2011) stated that ‘‘most individuals have an innate ability to learn from images,’’ but this was not linked to a specific reference. Borland, Mee, and Meehan (1997) mentioned that complex pattern recognition is developed on the basis of seeing examples, but they cited dermatologic research as support. Other areas of visual communication were implied but not explicitly mapped to theory; these were persuasion and vividness (Stephenson & Witte, 1998) and mechanisms of visual salience and attention (Isaacowitz, 2005). A small number of studies (6.4%) mentioned visual communication research in the discussion, but not in the methodology. The reference to visual communication research included visual models and memory (Robinson & Ortiz, 2009), color recognition and lighting conditions (Robinson & Turrisi, 2006), and visual memory, patterns, learning and experience (Girardi et al., 2006). While most studies did not specifically identify a visual communication theory that informed the work, many studies included visual communication theories in an implicit way (i.e., not named or cited, but the study design, goals, or assumptions could be traced to a theory from the realm of visual communication). For example, Emmons and colleagues (2010) may have used the pictorial superiority effect because they tested the effectiveness of an educational program to influence sun protective attitudes and intentions with and without photographs. Gaudy-Marqueste and colleagues (2011) appeared to use semiology when they paired an image of a benign mole with an image of a dolphin and an image of melanoma with an image of a shark. Other Theory Eighteen studies (38.3%) studies specifically cited other (nonvisual communication) theories guiding the research and connected them to the use of imagery. These ‘‘other’’ theories came from health education, health behavior, and health psychology domains and are presented in Table 3. Visual Communication Tools and Methods None of the studies used an assessment tool from the field of visual communication to appraise images used in the interventions. None of the studies reported using the Suitability Assessment of Materials to assess images prior to use, which was expected because the studies primarily used photographs and the Suitability Assessment of Materials is designed to assess illustrations. However, two skin-specific visual assessment criteria were used: Fitzpatrick Skin Type (Fitzpatrick, 1988) and the American Cancer Society’s ABCDE criteria (asymmetry, border irregularity, color variegation, diameter greater than 6 mm, and evolution; Abassi et al., 2004). Fitzpatrick Skin Type was used to analyze skin type in visual images in one study (Chung, Gordon, Veledar, & Chen, 2010) and to assess participants’ skin tone directly in six studies, all conducted by Mahler and colleagues (Mahler, Kulik, Butler, Gerrard, & Gibbons, 2008; Mahler et al., 2006; Mahler, Kulik, Gerrard, & Gibbons, 2007; Mahler, Kulik,

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Table 3. Other theories cited by the studies Theory (nonvisual communication) Cultivation theory Extended Parallel Process Model (fear appeals)


Health belief model

Image norms Persuasion theory Pluralistic ignorance Prototype model of health behavior Protection motivation theory Prototype–Willingness model Psychological sun protection model Self-efficacy beliefs Self-regulation theory Skill acquisition theory Social cognitive theory Social comparison theory Social norms Stages of change Terror management theory Theory of planned behavior

Theory of alternative behaviors Transtheoretical model

Studies that cited the theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Dixon et al. (2011) Mahler, Beckerley, & Vogel (2010) Cho & Salmon (2006) Emmons et al. (2011) Stephenson & Witte (1998) Jackson & Aiken (2006) Mahler et al. (2003) Mahler et al. (2006) Mahler et al. (2007) Oliveria, Dusza, et al. (2004) Stock et al. (2009) Jackson & Aiken (2006) Dixon et al. (2011) Mahler et al. (2008) Stock et al. (2009) Mahler et al. (2003) Mahler et al. (2006) Mahler et al. (2007) Gibbons et al. (2005) Jackson & Aiken (2006) Mahler et al. (2003) Hay et al. (2006) Speelman et al. (2010) Dixon et al. (2011) Hay et al. (2006) Mahler, Beckerley, & Vogel (2010) Mahler, Kulik, et al. (2010) Mahler et al. (2008) Cho & Salmon (2006) Cox et al. (2009) Mahler et al. (2003) Mahler et al. (2006) Mahler et al. (2007) Mahler et al. (2008) Mahler et al. (2005) Cho & Salmon (2006)

Gerrard, & Gibbons, 2010; Mahler, Kulik, Gibbons, Gerrard, & Harrell, 2003; Mahler et al., 2005). Eight studies used the ABCDE criteria explicitly (Borland, Marks, Gibbs, & Hill, 1995; Borland et al., 1997; Branstrom, Hedblad, Krakau, & Ullen, 2002; Girardi et al., 2006; Kundu et al., 2010; Robinson & Ortiz, 2009; Robinson & Turrisi, 2006; Robinson, Turrisi, Mallett, & Stapleton, 2010). Two studies (4.3%) used eye-tracking methodology to evaluate attention to skin cancer images (Isaacowitz, 2005; Luo & Isaacowitz, 2007). None of the studies used semiotic=rhetorical=discourse analysis or projected interviewing. Use of Visual Images Ten (21.3%) studies provided fully detailed image descriptions. For example, in one case this included describing the subject in the image in terms of age, ethnicity, attire, gender, facial expression, as well quantity of images and exposure time (Broadstock, Borland, & Gason, 1992). Twenty-five studies (53.2%) gave partial descriptions of



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the images. For example, ultraviolet photographs of subjects’ faces were described in detail, but visual images of photoaging (wrinkles and age spots) were not (Mahler et al., 2006). Twelve studies (25.5%) provided no sufficient description of the images. Twenty studies (42.6%) fully reported the image source. This included naming where the images came from (e.g., Dalianis, Critchfield, Howard, Jordan, & Derenne, 2011) or stating that they were developed as part of the study (e.g., Novick, 1997). Fifteen studies (31.9%) partially reported their sources. This included stating a nonspecific source, such as a slide library of images of skin cancer without stating to what hospital the slide library belonged (e.g., Miles & Meehan, 1995). Twelve studies (25.5%) did not report any image source. Twelve studies (25.5%) provided reasons for image selection. For example, Cox and colleagues (2009) selected images of attractive celebrities with pale skin and tanned skin to convey that both skin tones are attractive. Thirty-one studies (66.0%) gave partial reasons for selection of images, such as the study by Boer, Ter Huurne, and Taal (2006): Pictures of sunburns were chosen to depict the outcome of unprotected sun exposure, but it was unclear why an illustration of a smiling sun was included in the stimulus materials shown to participants. Four studies (8.5%) did not report any reason for image selection. Visual images were piloted with participants before the interventions in 12 studies (25.5%; e.g., Banerjee, Campo, & Greene, 2008). Sixteen studies (34.0%) included one or more example image (e.g., Oliveria, Chau, et al., 2004).

Discussion Summary This systematic review identified 47 peer-reviewed published studies about skin cancer and tanning that used visual images. The number of studies on skin cancer and tanning that included visual images is encouraging: Attention to images begins to build the necessary research upon which practice can be based. However, it was apparent that most studies were atheoretical in image selection and evaluation. Only one study specifically identified a visual communication theory. Slightly more than one third of the studies clearly referenced other noncommunication theories to varying extents and related those theories directly to the use of images. Given the importance of theory for informing health communication and behavior programs (Noar & Zimmerman, 2005), the absence of visual communication theory is concerning. Several factors may contribute to the omission of visual communication theory. First, it could be an oversight by the researchers or reflect disciplinary solitudes (i.e., the field of visual communication is separate from the health communication area). For example, visual communication texts do not usually give attention to health as a field (e.g., Smith et al., 2005) and health communication texts do not typically have large sections devoted to visual communication (e.g., Schiavo, 2007). Second, visual communication techniques may not be viewed as readily transferrable to health communication. Analytic techniques in visual communication are not always focused on the measurement of impact of the images on viewers (Messaris, 2003); however, in health communication it is precisely this viewer response to imagery that is of concern. Last, visual communication is a large and diverse field that lacks a single conceptual base and that borrows theories from many other disciplines such as psychology, aesthetics, and mass communication (Moriarty, 1997). This may make it seem inaccessible to researchers from health disciplines. None of the research described in this review used a standard assessment tool for evaluating the images, which probably reflects the absence of instruments being


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available for health researchers. This absence potentially hinders the quality of the studies by introducing bias in image selection. For example, several studies (e.g., Banerjee et al., 2008; Cox et al., 2009) selected images of women with different levels of a tan (e.g., light, medium, dark tan) to show participants. Tan levels seemed to be determined subjectively by the researchers, without an assessment tool, and images of tan levels were not pretested with participants. This subjective bias in image selection or creation may affect validity of the study results: If the difference between the tan levels of two different women in two different images is not significant enough for viewers to distinguish between, the outcome of interest may not be influenced in a statistically significant way. It also limits comparisons between different studies using similar methods: A dark tan may not objectively be the same in one study as in another. The conceptual and methodological issues introduced by an atheoretical approach and lack of image assessment could be addressed by providing a clear rationale for image selection, clearly and fully describing image sources, pretesting the images, and including example images. However, a significant number of studies did not adequately address these issues, which raises concerns. The lack of reporting related to images makes the interpretation of study outcomes difficult. For example, if a study showed the images were influential (e.g., motivated sunscreen use), it would be important to determine how or why they worked (i.e., What aspects of the images were influential?). Replication of the visual aspects of these studies for future research also becomes challenging. It is unclear why there was limited reporting about the use of visual images. Barriers to including example images may relate to editorial rules of the journal, copyright issues, costs incurred by the authors for printing the images, or other reasons. Of greater concern regarding image use in the studies is that only a quarter of studies pretested visual images with participants. Research shows that both a theoretical approach and an empirical approach of pretesting leads to improved health campaign materials (Whittingham, Ruiter, Castermans, Huiberts, & Kok, 2008). It is also recognized that cancer-related health communication materials need to be pretested (National Cancer Institute, 2012). Without theory guiding the selection of images or the pretesting of images to determine effectiveness, human and financial resources may be squandered and conclusions about health outcomes biased or inconclusive. The study by Stephenson and Witte (1998) illustrated the importance of piloting visuals. These researchers included high-threat images (e.g., woman with face eaten away, Bridgette Bardot with wrinkles) and low-threat images (e.g., brown spots on arm, moles on chest) as visual examples of skin cancer and ultraviolet damage to the skin. Pretesting revealed that the researchers were wrong in their assumption that an image of Bridgette Bardot with wrinkles was a highly threatening image and it was removed from the study materials as a result. Had pretesting of the images not taken place, the results would be affected because the stimulus materials would not have been operating in the assumed way. Implications for Future Research The importance of theory to inform health research is well-known (Fishbein & Yzer, 2003) as is the importance of visuals for health communication (Houts et al., 2006). Theory— especially visual communication theory—should be considered during the selection and use of images in research about skin cancer and tanning. Future research could benefit from (a) the use of visual communication theory and (b) the continued and increased use of other theories with special effort given to linking those theories specifically and directly to image selection and the visual aspects of study design.



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The explicit use of visual communication theories could strengthen health communication studies that use images in a number of ways. For example, the pictorial superiority effect might help researchers to identify the influence of images and text on attention and recall of sun safety information and behaviours related to sun protection. Our novel results add to the health communication field by highlighting the absence of a standard tool to analyze visual images. Such a tool is very much needed because, without it, how visual imagery is selected for content, ability to be understood by the viewer, or effect on health behavior remains unclear. Future research should be focused on the development and validation of such a tool for health communication. Application of visual imagery in the health communication field requires that researchers agree on what this entails. As a first step in understanding how and why visual imagery enhances health communication, a clear definition is needed. In the same way it became important to distinguish health communication and visual communication as subdomains of communication, we suggest that visual health communication can also be viewed as a subfield of study. Although there are widely accepted definitions of visual communication (Lester, 2006) and health communication (Ratzan et al., 1994; Schiavo, 2007), we propose the term visual health communication to indicate the intersection between these two areas. The term has previously emerged in the literature in an organic way (Maes, Foesenek, & Hoogwegt, 2008; Parrott et al., 2005), and here, we offer an initial working definition: Visual health communication is an area of theory, research, and practice that involves the use of visual imagery (e.g., photographs, illustrations, maps, graphs, diagrams) to convey information about health and disease in order to improve health-related knowledge, attitudes, and behaviors of individuals and populations.

Limitations Only English-language studies were used, which may mean other important research was over-looked. We restricted the review to quantitative studies. The search strategy involved using terms related to visual imagery; studies that did not state these in the title, abstract, or keywords may have been unintentionally excluded. Only published, peer-reviewed studies were used, and hence, source selection and=or publication bias may be present. A standard validated appraisal tool to assess study quality was not used in this review because it was not applicable to the assessment of the quality of use of visual images in health communication studies. The review was limited to studies dealing with skin cancer and tanning; however, other disease areas and health issues are also deserving of attention. Last, we did not assess skin health behavior outcomes arising from use of visual imagery. Conclusions To our knowledge, this is the first systematic review to examine theoretical and methodological approaches to visual images in health communication research. The approach toward the images was often ad hoc and unrefined. The results reflect low reporting of image sources, rationale for image selection, and detailed description of images. Moreover, example images were not often provided and there was low use of piloting of images. These factors limit the replication of the work in future skin cancer and tanning research. All of the studies lacked validated image

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assessment tools, reflecting the need for instrument development. There was an absence of visual communication theory in these studies, although other health education–type theories were applied. We acknowledge that the studies included in this review make important contributions to the literature; however, greater attention to and rigor toward visual imagery elements might have strengthened them. Improvements are needed regarding the comprehensive reporting of, and methodological and theoretical rigor toward, the use of visual images in skin cancer and tanning research.


Acknowledgments The authors thank Drs. J. Garcia and J. F. Arocha for feedback on an earlier version of this work. The authors also thank J. Stapleton, health librarian, for help with development of the search strategy.

Funding This research was supported by the Canadian Institutes of Health Research.

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Research Priorities, Measures, and Recommendations for Assessment of Tobacco Use in Clinical Cancer Research.

A qualitative systematic review of studies using the normalization process theory to research implementation processes.

Incidence and prevalence of non-melanoma skin cancer in Australia: A systematic review.

Health technology assessment of image-guided radiotherapy (IGRT): A systematic review of current evidence.

A protocol for a systematic review of the use of process evaluations in knowledge translation research.

Cervical cancer prevention and treatment research in Africa: a systematic review from a public health perspective.

Assessment of cannabis use disorders: a systematic review of screening and diagnostic instruments.

A Comparison of Visual Assessment and Automated Digital Image Analysis of Ki67 Labeling Index in Breast Cancer.

Single-nucleotide polymorphisms in pigment genes and nonmelanoma skin cancer predisposition: a systematic review.

Patient engagement in research: a systematic review.

Retractions in orthopaedic research: A systematic review.

Quality of life and symptom assessment in randomized clinical trials of bladder cancer: A systematic review.