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. 2023 Sep 5;33(12):1049–1058. doi: 10.1177/10497323231198196

Using Photographs to Understand the Context of Health: A Novel Two-Step Systematic Process for Coding Visual Data

Jane Jih 1,2,3,✉, Antony Nguyen 1,3,4, Jasmin Woo 1,3,5, Alison Ly 1,3,6, Janet K Shim 7
PMCID: PMC10552334  PMID: 37669693

Abstract

In qualitative research, photographs and other visual data have been used with oral narratives in ethnography, interviews, and focus groups to convey and understand the perceptions, attitudes, and lived experiences of participants. Visual methodologies that incorporate photographic data include photo elicitation, which has varied approaches with the inclusion of photographs generated by researchers or participants, and Photovoice, which is a form of photo elicitation focused on participatory action research. Current literature provides insufficient guidance on a systematic coding process of visual data elements that could maximize capturing of visual data for qualitative analysis. We describe our rationale and process for developing a two-step systematic process for coding visual data, specifically photographs. The two-step systematic process for coding photographs involves coding the foreground (focal point) and then the background of the photograph, using separate codebooks. Application of this two-step coding approach resulted in surfacing additional rich data on the health-related contexts and environments in which participants lived. Incorporation of this methodology could enhance understanding of the context of health and generate ideas and new directions of inquiry.

Keywords: photo elicitation, visual methodology, photo coding, health-related contextual factors

Introduction

Photographs can be a powerful, effective way to communicate complex emotions, thoughts, and experiences. In qualitative research, photographs can be used in conjunction with oral narratives in ethnography, interviews, and focus groups to convey and understand the perceptions, lived experiences, and narratives of participants in a range of visual methodologies including photo elicitation (Glaw et al., 2017; Harper, 2002; Mitchell, 2008; Romera Iruela, 2023). Studies incorporating photo elicitation may have varied approaches informed by the theoretical orientation that guides the research, including whether photographs are provided or taken by researchers or produced by participants in research studies (Romera Iruela, 2023). One approach of photo elicitation is Photovoice, a participatory action research method that is theoretically rooted in documentary photography, empowerment education, and feminist theory, in which participants take photos to depict experiences that otherwise may be difficult to share with spoken words alone (Wang & Burris, 1997; Wang, 1999). This methodology seeks to equilibrate the researcher–participant power balance, resulting in eliciting participant narratives along with visual images that represent participants’ personal lived experiences more fully than non-participatory methods. This approach has been used with various vulnerable and racial/ethnic minority communities across the life course (Wang, 1999).

The frameworks and interpretive approaches used to analyze participant-generated photographic (and other visual) images critically shape our understanding of the intention and meaning behind each individual image and a participant’s images together as a collective. Scholars, particularly in cultural and visual studies, such as Stuart Hall and Gillian Rose have published foundational work exploring the practices of making meaning of visual images, including photographs, and the use of visual images as communication tools and representation of shared culture including concepts and ideas (Griffin, 2012; Hall, 1997; Rose, 2014). Hall (1997) argued that the meanings of images (as with any system of language) are fluid and multiple and should be analyzed taking into account the intent and purpose of image creation by its producer, the context of power and social structures where the image exists and is examined, and reflection on what may not be visible in the frame (see also Griffin, 2012). To find meaning behind visual images, we must take steps in considering individuals involved in the entirety of the process: Who created the content? Who is interpreting the content? Who is the intended audience? Consideration of the entire process from conception of the image to interpretation is also vital (Clark & Morriss, 2017; Drew & Guillemin, 2014).

In addition, participant-generated photographs are often saturated with supplemental information about participants’ daily lives, living conditions, the settings and contexts that are meaningful to them, and other things that matter to them. Thus, complementing the rich discourse around intention and making meaning of visual images, in this paper we explore how to identify, extract, and analyze these supplemental data that are often embedded in participants’ photographs.

However, even general descriptions of analytic procedures by researchers with or without participant involvement to code photographic data generated by participants have not been well documented (Drew & Guillemin, 2014). Beyond describing general approaches, most studies including participant-generated photographs as a form of data do not provide step-by-step examples of how visual elements were coded. Thus, a gap in the methodological literature is the lack of a well-described and detailed photo coding analytical process that can be applied to research studies. As one contribution to filling this gap, we describe below our development of a novel two-step systematic coding approach for participant-generated photographs that can enhance understanding of the contexts and environments relevant for health.

Literature Review

We performed a literature review to explore the range of reported methods used to analyze photos in health-related research, including those that incorporate Photovoice, other studies with photo elicitation beyond Photovoice, and studies focused on photographic content from social media.

Studies Incorporating Photovoice

In Photovoice, participants can use disposable, digital, or smartphone cameras to take photos in response to a photo-taking or question prompt. After a photo-taking period, researchers engage participants in photo elicitation interviews or focus groups, contextualizing what they chose to photograph and why (Wang & Burris, 1997; Wang, 1999; Glenis & Boulton, 2017; Lee et al., 2019; Baig et al., 2019; Cheezum et al., 2019; Lofton et al., 2020; Aparicio et al., 2021; Craft-Blacksheare et al., 2021; Feingold et al., 2021; Morrow et al., 2022; Bood et al., 2022; Jackson et al., 2022). Researchers may use sets of guided discussion questions with participants to learn more about their photos. In studies that have used Photovoice, transcripts of oral narratives from sharing and reflecting on photographs taken for the study as well as other forms of participant-generated text such as journal entries (Glenis & Boulton, 2017; Morrow et al., 2022) or photo captions (Bood et al., 2022; Jackson et al., 2022; Lee et al., 2019) are coded in tandem with photography; of note, some studies, aligned with the participant empowerment underpinnings of Photovoice, engage participants in the analytic or photo-sorting process (Glenis & Boulton, 2017; Jackson et al., 2022; Lofton et al., 2020; Morrow et al., 2022). Bood et al. reported how the “first author described what could be seen on the photo and then applied codes to the photo and caption” (p. 3355). Sometimes, the qualitative analytic approach (such as thematic analysis) to photo coding is noted, but the actual steps taken to surface and code visual elements are absent. Collectively, these studies tend not to include comprehensive descriptions of replicable procedures and examples of how a systematic method was used to code visual content in photographs.

Other Photo Elicitation Studies Beyond Photovoice

Other photo elicitation studies beyond Photovoice also engaged with participants to take photographs to tell their story or in response to provided photo-taking prompts followed by interviews exploring the photographs (Bailey et al., 2021; Fleury et al., 2009; Kolb, 2008; Michalek et al., 2020; Oliffe & Bottorff, 2007; Padgett et al., 2013; Rice et al., 2015). In these studies, if mentioned, photographs were sorted into researcher-determined categories and/or visual data was coded using a grounded theory, thematic or content analysis approach. Rice et al. reported initially sorting “photos into groups that appeared to be similar,” and based on the coding of participant narratives into categories, then “photos were regrouped to place similar meaning photos within the same category” (p. 5). However, the studies did not provide details on the specific processes by which researchers examined different parts of a photo and how researchers decided which code to use.

We were able to identify one photo elicitation interview study about traditional Islamic bath house neighborhoods in Mediterranean cities where the researcher described in depth how each photograph was analyzed (Kolb, 2008). First, Kolb created an objective description of the content in each photo’s front, middle, and back sectors, referred to as a “scientific reading” without deeper analytical interpretation (p. 20). Second, Kolb used “cultural and historical background” to analyze and interpret the photos, revealing “the deeper story told by the photo” (p. 21). In this article, a participant’s photo of the Sidi M’Cid Bridge in Constantine, Algeria, was used as an example of this analysis process. Kolb described the appearance of natural scenery around the bridge, for example, noting that “a bridge with two arches crosses the valley in the background … on the left side the street becomes visible with a white car and the street lamp” (p. 19). Next, as part of the analytical interpretation, Kolb noted that this bridge was built by a French designer and references the history of French colonialism in Algeria. Kolb then likened the bridge to “outsider knowledge, introduced by the occupying force,” but noted that it also “guarantees mobility” to those living in the older sector of the city (pp. 20–21). As in most photo elicitation studies, photos were then sorted into a few major categories and analyzed alongside interview data.

Coding Photographic Images From Social Networking Sites

Photographs posted by users on social networking sites have been considered a source of visual data for research that could provide insights to user behaviors, perspectives, and impressions on health-related topics (Hum et al., 2011; Mercier et al., 2020; Tiggemann & Zaccardo, 2018). For example, Hum et al. (2011) used predetermined code categories to code Facebook profile photographs to determine if the photo content was gendered. While the researchers underwent 3 hours of coding training with 20 profile pictures not included in the study, the processes in which the researchers identified and then applied codes to different visual components of the profile photographs were not described. Similarly, Tiggemann and Zaccardo (2018) coded images from Instagram with predetermined content categories and subcategories but did not describe how images were systemically analyzed and assigned to categories and subcategories by each coder. Finally, Mercier et al. (2020) analyzed Instagram posts including the posts’ text, photos, hashtags, and emojis using a more inductive approach. In this study, researchers conducted open coding through an “iterative process” to identify “common themes that emerged from the text and images” (pp. 167–168). The procedures by which images were analyzed and coded were however not detailed.

Our literature review underscores the growing interest to include and code visual data, often in the form of photographs, in conjunction with oral and written qualitative data, to explore and understand health-related narratives of individuals, particularly those from diverse backgrounds and lived experiences. However, our literature review also shows that there is a paucity of detailed guidance on a systematic coding process of visual data elements independent of and in conjunction with participant explanations. Our review also highlights the range of participant involvement in the analytic process of visual data, which can supplement researcher-driven interpretations in making meaning of visual images. As one contribution to developing and disseminating methods to maximize the empirical utility of photographic data, we describe our rationale and iterative process for developing a two-step systematic approach for coding photographs. We also explore what we learned during the development of this methodology that is relevant to health-related research and the implications of incorporating this approach in research and beyond.

Development of Systematic Coding Process for Photographs

Rationale to Developing a Two-Step Systematic Process for Coding Visual Data

In 2014, we conducted a community-based pilot study in San Francisco, California, using Photovoice-guided focus groups of older Filipino adults with a history of cardiovascular disease and/or related risk factors with the goal to understand how and why eating habits develop (Jih et al., 2018). An orientation session was held at a local community center, in which participants received instructions on Photovoice photo-taking activity in English and Tagalog and how to use a disposable camera provided by the study. As part of the study, participants were asked to take photos that depict their “food experience” which we defined as “daily dietary choices and activities.” Participants took 2–3 photos daily over a 10-day study period. Disposable cameras were returned 1 week prior to the focus group session, and photos were both printed and converted into digital files. Audio-recorded focus groups sessions were facilitated by a bilingual Tagalog/English research team member using a focus group guide incorporating elements of the SHOWeD method, with each participant invited to share a subset of self-selected photos and their narrative to the group (Wang, 1999). We used a community-engaged grounded theory approach to analyze transcripts in which community partners participated in the creation and refinement of the codebook and initial identification of themes, and preliminary findings were presented in an open forum at the local community center with all participants invited to attend. Once themes were identified from focus group transcripts, the research team including community partners identified representative participant-generated photographs by consensus to illustrate key themes. Approval of this study protocol including primary data collection, review of ethical considerations for human research participants, and secondary data analysis was obtained from our institutional review board (#13-12074). All participants provided written informed consent prior to enrollment.

At the time of the primary analysis of this study’s data, we did not engage in a formal analytic process of photographs taken by participants. However, upon later viewing of the participant photographs, we noted that while most photographs depicted meals and food items, some photos also portrayed elements that were “outside” of the visual focus such as home environment, other individuals, and personal items. Recognizing the potential value of these additional visual data in the photographs, we were motivated to explore if we could systematically code photographic data to surface as much contextual information as possible.

The final nudge towards the development of this two-step photographic coding process was from an additional study led by the first author. This additional study included patient participant–generated photographic data that the patient participant shared with their primary care clinician in a clinic visit (Jih et al., 2023). This additional study highlighted how visual elements beyond the primary focus of participant-generated photographs provided valuable contextual information relevant to health research and clinical care.

Together, these formative research observations, paired with the lack of systematic qualitative guidance regarding coding of photographs for health-focused research, motivated our team to develop analytic procedures to code participant-generated photographs. Our objective was to capture both the informative elements in the foreground (focal point) of photographs as well as contextual visual data in the background elements of photographs that were captured incidentally but ultimately proved to be empirically illuminating. We acknowledge the tension of this researcher-initiated analytic photo coding procedures with the core principles of the Photovoice methodology. These analytic procedures to code photographic data can be complementary and supplemental (and not displacing) to participants’ voice and agency that is central to the Photovoice approach.

Two-Step Photo Coding Process: Foreground and Background

Here, we describe how we iteratively developed a two-step method that involves the systematic identification and separate coding of the foreground and background of the photographic content to surface and collect all visual elements that both relate to participants’ intended response to the photo-taking prompt as well as additional aspects of socio-contextual and lived experiences. This method was, in part, informed and inspired by traditional approaches to analyzing textual data, in which analysis focuses on all aspects of the data presented to prioritize and understand both the implicit and explicit thoughts and understandings of participants on topics and ideas relevant to them. Thus, similar to inductive approaches to analyzing textual data, critical examination of photographic data beyond the intended focus can more fully capture participants’ experiences.

Defining Foreground and Background

Two coders (who had not reviewed any focus group transcripts collected with the photographs) reviewed all photographs available from the study of Photovoice-guided focus groups of older Filipino adults in PDF documents on a tablet device to familiarize themselves with the range of photographic elements in the photos. Then, the coders met to define the criteria of what is to be considered the foreground and background. Based on the initial scan of all available photos and knowledge of the photo-taking prompt, the coders agreed to define the foreground as the section of the photo that is the photographer’s focal point depicting their “food experience” and marked this area with red borders (Figure 1). As for the background, it was defined as all other areas of the photo not included within the red borders.

Figure 1.

Figure 1.

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Examples of foreground demarcation of photographs.

Open Coding and Development of Codebooks

We engaged in an open coding process to inform the development of codebooks. The two coders separately used an inductive approach to open-code the same subset of photographs by annotating the photographs in a PDF document on a tablet device using Notability, a note-taking application. The coders first drew a box to demarcate the foreground (Figure 1) and annotated the photograph by writing notes within the PDF document on the visual data related to the participant’s food experience (the prompt given to participants to guide their picture-taking). After reviewing the foreground of a photo, coders then reviewed the background and annotated what they saw that depicted the contextual elements beyond the focal point of the photograph.

The coders met periodically to discuss their demarcation of foreground and background for each photograph to ensure that there was consensus on the criteria differentiating the two. They also discussed their annotations and together grouped them into common overarching categories. To ensure the systematic and deliberate analysis of all photographic elements of photos and to avoid overlooking any visual data, two separate codebooks were developed and refined, one for the foreground and the other for the background. The two separate codebooks enabled coders to examine relationships between codes. By having two separate codebooks, we were able to see which codes that were exclusive to the foreground or background overlapped within each separate codebook (i.e., intrarelationships) and which overlapped between the codebooks (i.e., interrelationships). This process supported capturing maximally all of the visual data available in photographs; this method differed from more holistic coding of photographs in which an entire photo is assigned a single code.

Photo Coding Process Using Software

All digital files of photos were exported from a secure online cloud storage system and uploaded to Dedoose, an online qualitative analysis software. Both foreground and background codebooks were inputted into Dedoose. For the photographic dataset used, we developed the foreground codebook, which contained 11 codes while the background codebook contained 18 codes. Both coders, who had developed the codebooks, then separately coded photos. Coders continued the process, for each photo, of first defining the foreground and applying codes from the foreground codebook and then defining the background and applying codes from the background codebook. Photos were coded again after codebook development to ensure that all visual content was captured with codes from the codebook and that code notes adequately explained the content of codes.

Using an in-software function within Dedoose, coders were able to draw boxes to set the boundaries of the foreground and background and apply codes to each box (see Figure 2). Using their on-screen cursor, coders clicked to initiate the drawing of boxes and dragged their cursor to adjust the size and boundaries of the boxes. For each photo, coders first drew a box to set the boundaries of the foreground and applied codes. Coders then drew a box that covered the entirety of the image to capture the background and applied codes. After each box is drawn, they both remain on each image and are linked with their respective applied codes that can be displayed by hovering the cursor over the box. For instance, if a user were to hover their cursor over the box that captures the background, the codes that were applied related to the background would appear, and the codes applied to the foreground would not appear for this box.

Figure 2.

Figure 2.

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All images are of the same participant photo: (a) Coder 1’s Dedoose interface highlighting foreground boundaries and applied codes from the foreground codebook, (b) Coder 1’s Dedoose interface highlighting background boundaries and applied codes from the background codebook, (c) Coder 2’s Dedoose interface highlighting foreground boundaries and applied codes from the foreground codebook, (d) Coder 2’s Dedoose interface highlighting background boundaries and applied codes from the background codebook, and (e) both coders’ foreground and background boundaries in Dedoose.

Given the original study’s aim to explore the dietary behaviors of Filipino adults with history of cardiovascular disease and/or related risk factors and the photo-taking prompt of depicting “food experience,” we created a code labeled “[F = foreground] Diet-Related Information” within the foreground codebook, in which the code was defined as “the focus of the photo depicts the diet of the participant.” Another code was also created for the background codebook, labeled “[B = background] Diet-Related Information,” but with an alternative definition to capture a broader array of contextual elements, for example, “the photo depicts certain foods and diet management tools.” During this process, both coders met to discuss their coding choices and foreground and background boundaries. Through this iterative process of coding and meeting, both foreground and background codebooks continued to undergo refinement until no new codes were identified.

What Was Learned With the Two-Step Photo Coding Approach

Using this two-step systematic photographic coding approach, we were able to maximally code the visual data available in both the foreground and background of the photographs. From a set of 166 photographs analyzed as part of this work, codes from the background codebook were applied 938 times to photos while codes from the foreground codebook were used 630 times. We found that both the foreground and background of each photograph provided health-related information that sometimes supplemented each other, and other times were distinct. Using the Dedoose function of co-occurrence frequencies, we were also able to identify and quantify interrelationships between foreground and background codes. Notably, we discovered that background codes surfaced different elements and components about the physical home environment and social networks than what was seen in the foreground. These data proved to be pertinent for understanding participants’ social determinants of health and health habits: for instance, we noted food logs, constrained living spaces that limited mobility, and the presence of social ties. Had the background of the photographs not been coded, this additional visual data may not have been surfaced and may have been missed, precluding further inquiry or exploration. We speculate that had participants been involved in the photo coding process (in defining the foreground/background or codebook development), new dimensions and interpretations of the visual data may also have been surfaced.

Implications on Health-Related Research and Interactions Incorporating Visual Data

Our novel sequential systematic process for coding visual data of first coding the foreground (focal point) of photographs, followed by coding of the background of the photographs, permitted the inclusive and comprehensive coding of many individual elements of photographs that may not otherwise be explicitly or specifically coded or analyzed. Our process suggests that by analyzing first the foreground and then the background of each photo, one can surface many details related to the context of health and could expand understanding of the participant’s lived experiences and generate ideas and new directions of inquiry. While this methodology was developed with the use of photographic content, it could potentially be applied to any form of visual data that could be saved and viewed by research team members.

There could be potential for our two-step systematic process for coding or surfacing visual data to have applications outside of academic and research settings such as healthcare interactions and patient–clinician communication, particularly in the ambulatory setting. In part accelerated by the COVID-19 pandemic, the use of telehealth including video conferencing and transmission of images of health-related concerns through patient portals is more common. Potentially, patient-generated images shared with their clinician or the observed background of patients’ home environment during a video telehealth visit could be rich sources of information about contextual factors important to health that could impact clinical recommendations and plans. Patient-generated photographs taken in response to photo-taking prompts about relevant health and social factors (e.g., dietary habits, medication use, and social networks) could be a powerful communication tool centered on patients’ agency and voice and diminish the power differential between patients and clinicians—drawing from the theoretical basis and strengths of participatory visual methodologies. Potential risks to consider as this area of work progresses include the patient consent process to include photographs into clinical care and guarding against visual content perpetuating stereotypes held by clinicians. As noted by Switzer (2018), photographs created as part of the research process could have unintended consequences such as perceived surveillance of participant activities, the belief that photographs are “truth” (even if false or altered) and if photographs taken out of the research context, could reinforce biases and further stigmatize marginalized communities or populations.

Opportunities and Limitations

Photovoice studies center on participant empowerment and agency, give greater voice to participants through photos, and aim to catalyze social or community change. Thus, our photo coding methodology represents a researcher-initiated secondary analysis that diverges from the theoretical commitments of the Photovoice approach. Unexpected visual data (from both the participant and researcher perspective) may be surfaced through the application of this two-step photo coding methodology. Thus, to honor commitments to co-production with participants, and to ensure that the analytic process continues to serve the goals of health and social justice, researchers should consider incorporating study participants as part of this proposed photo coding analytic process at any step. In studies that incorporate visual data that is publicly available (e.g., images from social media sites), inclusion of participants in this process is likely less feasible and imperative.

The two-step systematic photo coding methodology proposed here aims to be a complementary approach to help make meaning of visual images produced and shared by research participants. Our visual analytic methodology could also help support researchers to more consciously incorporate reflexivity and consider how to balance their own interpretive processes with research participants’ intents and purpose of the photographs or other visual images they created and shared (Mitchell, 2008). Through this two-step systematic photo coding approach, researchers and participants could have opportunities to more intentionally engage in collaborative interpretive processes to make and find meaning from visual images, which could enhance insight on the topics or phenomena of interest. Thus, we see our methodology as aligned with Rose (2014), who argues that at the intersection of visual culture and visual methodologies is the shared use of images as communication tools, more so than a representational system. Image-based elicitation interviews can help uncover aspects of participants’ daily life experiences that may be “taken-for-granted” and “articulate thoughts and feelings that usually remain implicit” (p. 28) (Rose, 2014).

Lastly, we undertook this development of a two-step photo coding methodology after having conducted the primary analysis of the focus group data from the original pilot Photovoice study. In practice, initiation of coding of visual data can start while researchers are continuing to collect data, just as with oral and written narratives from interviews, focus groups, or field observations. Initial impressions identified from coding of visual data, paired with memoing, can help identify additional concepts or topics to explore in subsequent data collection. These initial findings might modify photo-taking prompts for participants and/or lead to the incorporation of additional probes or questions to the interview or focus group guide. These added insights can add new dimensions to learning about the contextual factors important to the health research topic at hand.

In summary, we describe our rationale and process for developing a two-step systematic process for coding visual data using participant-generated photographs from a pilot community-based Photovoice study focused on dietary behaviors. We argue that our systematic process for coding the foreground (focal point) and then the background of photographic data using separate codebooks maximally captures and surfaces visual data that may be highly relevant for health-related research. In our example, application of this two-step coding approach resulted in rich contextual health-related data including unique, distinct codes from the foreground and background. In studies that collect visual data, incorporation of this novel photo coding methodology could enhance understanding of the contextual factors of health and generate new directions of inquiry.

Footnotes

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The photographs used to develop the two-step systematic coding process for visual data were collected in a study that was funded by the National Institute on Aging of the National Institutes of Health (award number: P30 AG15272 with additional support from award number: R03 AG050880). This work was supported by funding from the University of California San Francisco Hellman Fellows Fund, the National Center for Advancing Translational Sciences of the National Institutes of Health (award number: KL2 TR001870), and the National Institute on Minority Health and Health Disparities of the National Institutes of Health (award number: K23 MD015089).

ORCID iD

Jane Jih https://orcid.org/0000-0001-9367-7376

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