Effect of online infographics for enhancing health literacy among patients with type 2 diabetes in primary care unit during the COVID-19 pandemic: a randomized controlled trial.

BACKGROUND: Health literacy (HL) in patients with type 2 diabetes mellitus (DM) can help control disease and prevent complications. However, most patients with type 2 DM have inadequate HL; therefore, their HL must be further improved. This study aimed to determine the effects of online infographics on improving HL among patients with type 2 DM. METHODS: This randomized controlled trial was conducted from July 2022 to September 2022, at the primary care unit of Songklanagarind Hospital, Thailand; 30 patients with type 2 DM were randomly assigned to the experimental (n = 15; three types of infographics) and control (n = 15; three types of pamphlets) groups. Infographics and pamphlets were distributed weekly via social media platforms. The S-TOFHLA Thai version and Thai-FCCHL were used to evaluate HL. Chi-square, Fisher's exact, Wilcoxon rank-sum, t-test, paired t-test, and McNemar's chi-square tests were used. RESULTS: The median age of 30 participants was 56 years. The mean duration of DM was 9.6 years, with a median HbA1c level of 7.5 mg%. Most participants (80%) had adequate HL in S-TOFHLA, whereas 63.3% had adequate HL in FCCHL. All participants in the infographic group who had inadequate HL in the S-TOFHLA pre-test achieved adequate HL. Meanwhile, only 50% of patients in the pamphlet group achieved adequate HL. Regarding FCCHL, 50% of patients in the infographic group and 60% in the pamphlet group who had inadequate HL in the pretest achieved adequate HL. However, no statistical significance in achieving adequate HL was found in either group. The mean differences (SD) in S-TOFHLA between before and after intervention were 12.53 (8.77; p = 0.0007) and 10.13 (9.88; p = 0.001) in the infographic and pamphlet groups, respectively. Regarding FCCHL, the mean differences (SD) were 3.47 (4.29) and 3.20 (2.91) in the infographic group (p = 0.003) and pamphlet (p = 0.002) groups, respectively. No statistical significance in the mean difference was found between both groups. CONCLUSIONS: Novel online infographics and pamphlets did not significantly differ in achieving adequate HL among patients with type 2 DM who should receive health education about disease control and complication prevention. However, both interventions can increase and maintain HL levels. Online educational media can be appropriate during the COVID-19 pandemic. Nevertheless, further larger-scale studies should be performed to examine the impact of other DM educational media on HL promotion. TRIAL REGISTRATION: The Thai Clinical Trials Registry (TCTR) with registry ID TCTR20230425001 (date of registration 25/04/2023).

Color Quest: An interactive tool for exploring color palettes and enhancing accessibility in data visualization.

Data visualization plays a vital role in modern scientific communication across diverse domains, shaping the understanding of complex information through color choices. However, the significance of color palette selection goes beyond aesthetics and scientific communication, encompassing accessibility for all, especially individuals with color vision deficiencies. To address this challenge, we introduce "Color Quest," an intuitive Shiny app that empowers users to explore color palettes for data visualization while considering inclusivity. The app allows users to visualize palettes across various types of plots and maps envisioning how they appear to individuals with color blindness. In addition, it enables users to visualize palettes on their own custom-uploaded images. This short communication presents the app's design, interactive interface, and transformative potential in enhancing data visualization practices. Developed using open-source standards, Color Quest aligns with accessibility discussions, offering a practical tool and platform for raising awareness about inclusive design. Its open-source nature fosters transparency, community collaboration, and long-term sustainability. Color Quest's practicality renders it indispensable for scientific domains, simplifying palette selection and promoting accessibility. Its impact extends beyond academia to diverse communication settings, harmonizing information dissemination, aesthetics and accessibility for more impactful scientific communication.

MHCpLogics: an interactive machine learning-based tool for unsupervised data visualization and cluster analysis of immunopeptidomes.

The major histocompatibility complex (MHC) encodes a range of immune response genes, including the human leukocyte antigens (HLAs) in humans. These molecules bind peptide antigens and present them on the cell surface for T cell recognition. The repertoires of peptides presented by HLA molecules are termed immunopeptidomes. The highly polymorphic nature of the genres that encode the HLA molecules confers allotype-specific differences in the sequences of bound ligands. Allotype-specific ligand preferences are often defined by peptide-binding motifs. Individuals express up to six classical class I HLA allotypes, which likely present peptides displaying different binding motifs. Such complex datasets make the deconvolution of immunopeptidomic data into allotype-specific contributions and further dissection of binding-specificities challenging. Herein, we developed MHCpLogics as an interactive machine learning-based tool for mining peptide-binding sequence motifs and visualization of immunopeptidome data across complex datasets. We showcase the functionalities of MHCpLogics by analyzing both in-house and published mono- and multi-allelic immunopeptidomics data. The visualization modalities of MHCpLogics allow users to inspect clustered sequences down to individual peptide components and to examine broader sequence patterns within multiple immunopeptidome datasets. MHCpLogics can deconvolute large immunopeptidome datasets enabling the interrogation of clusters for the segregation of allotype-specific peptide sequence motifs, identification of sub-peptidome motifs, and the exportation of clustered peptide sequence lists. The tool facilitates rapid inspection of immunopeptidomes as a resource for the immunology and vaccine communities. MHCpLogics is a standalone application available via an executable installation at: https://github.com/PurcellLab/MHCpLogics.

Picture analysis of billboards and infographic graphics advertising COVID-19 on promoting preventive behaviors and taking vaccination against the Coronavirus disease pandemic.

Today, advertising science is a tool that helps advertisers to design their advertising to meet the needs of the audience. In this regard, knowing and understanding the audience is one of the most important points that advertisers should pay attention to before advertising in order to better attract the audience. This study has been done with the aim of billboards and infographics analysis related to promoting preventive behaviors and vaccination against the Coronavirus disease pandemic and investigating the opinion of the general adult population of Iran. The method used in this research is the qualitative method. In this research, according to the type of data and research goals, Kress and Van Leeuwen's discourse theory method has been used. The sample size includes 36 advertising billboards and infographics. Data collection has been done through searching the sites and websites of health networks and medical education centers in Iran, taking pictures of infographics and billboards in public places, and also receiving archive files of pictures from the public relations of health networks and medical services. The data was collected from February 19, 2020 to December 30, 2022 (the time frame of the pandemic and public vaccination program in Iran). Then, an online survey about promoting preventive behaviors and taking vaccination against the Coronavirus disease pandemic was designed in SurveyMonkey and its link was provided to the audience through virtual networks and other platforms. The assessment of validity involved experts in infection control and linguistics. The reliability of the measurement, determined through the Cronbach's alpha internal consistency coefficient, yielded a coefficient of 0.968. In this study, data analysis was conducted using IBM SPSS Statistics software, version 15.0 (IBM Corp., Armonk, NY, USA). Finally, users' opinions about of billboards and infographics were analyzed using descriptive statistics. The results of component analysis and surveys show that visual components such as «The staring look at the spectator (Demand)¼, «Head-on Shot (inclusion)¼, «Down Shot (Creating a sense of participation for the represented person)¼, «Close-up (intimate/individual relationship)¼, «Level Shot (equality)¼ and «High-Angle Shot (Presenting power)¼ in medical advertising has had a great impact in arousing public opinion to create a positive attitude towards preventive measures and vaccination during the Coronavirus disease epidemic. The results of this research show that in visual communication, visual components play a significant role in creating and maintaining target ideologies. Also, advertising in the field of preventive measures in medical sciences requires certain rules that determine people's culture and the main foundation of their attitude and thinking. Therefore, it is necessary to know such knowledge and learn it by the medical staff to deal with critical situations.

Text analysis of billboards and infographic graphics advertising COVID-19 on promoting preventive behaviors and taking vaccination against the coronavirus pandemic and investigating the opinions of the Iranian adult population.

BACKGROUND: Advertising is one of the most important solutions that health centers and medical services around the world use to try to encourage public opinion to create a positive attitude towards preventive measures and vaccination. This study has been done with the aim of text analysis of billboards and infographics related to promoting preventive behaviors and vaccination against the coronavirus pandemic and providing solutions and models for preventive information and advertising in the field of health. METHODS: The study method in this research is a combination of qualitative and content analysis. Data collection was done in a targeted manner. The sample size includes 33 advertising billboards and infographics. Data collection has been done through searching the sites and websites of health networks and medical education centers in Iran, taking pictures of infographics and billboards in public places, and also receiving archive files of pictures from the public relations of health networks and medical services. The data was collected from February 19, 2020 to December 30, 2022 (the time frame of the pandemic and public vaccination program in Iran). The data was analyzed based on the three-dimensional discourse analysis theory of Fairclough. Then, an online survey about promoting preventive behaviors and vaccination against the coronavirus pandemic in the format of billboards and infographics was designed in SurveyMonkey and its link was provided to the audience through virtual networks and other platforms. The age group of people was selected from 18 to 70 years. Considering that the number of participants should be representative of the entire community under investigation, therefore, based on Cochran's formula, the sample size was equal to 350 people. Finally, users' opinions were analyzed using descriptive statistics. The assessment of validity involved experts in infection control and linguistics. The reliability of the measurement, determined through the Cronbach's alpha internal consistency coefficient, yielded a coefficient of 0.968. RESULTS: The results show that among the four linguistic components of words, syntax, coherence and text structure; "live metaphors", "pronoun "we", "collocation and reference", and "attitude markers" have the most impact on the audience. The frequency percentage of the data shows that these language elements have tremendous power in attracting the audience to perform preventive behaviors. The results show that the language reflects the culture, opinions and needs of people in the society. Also, the results show that encouraging people to perform preventive behaviors should be through the integration of medical information with motivational linguistic factors in order to attract the audience more. CONCLUSIONS: It can be concluded that the use of the appropriate pattern of medical advertising discourse and correct communication strategies, will help public participation in the field of epidemic control. The language of effective health education and health communication during an epidemic must be related to the ways of thinking and speaking of ordinary people. Also, words with metaphorical and ironic meanings have a high potential to influence the health performance of people in society and increase public awareness of health communication. Therefore, using them to create a new value system with the aim of controlling and overcoming the consequences of the epidemic is very effective.

A quantitative study examining infographic assessment guidelines for undergraduate nursing students with specific learning difficulties (SpLDs).

This research investigates the perceived clarity and usefulness of infographic versus traditional text-based assessment guidelines among undergraduate nursing students with and without specific learning difficulties (SpLDs). Through quantitative analysis, the study reveals that undergraduate nursing students with SpLDs significantly prefer infographics over text-based guidelines, both in terms of clarity and usefulness (p < .001). Interestingly, there were no statistically significant differences in the perceptions of students without SpLDs. These findings suggest that the use of infographics as a tool for presenting assessment guidelines could contribute to more inclusive educational practices. The research further highlights the potential of infographics to not only make complex information more accessible but also to cater to diverse learning needs. As higher education institutions strive to be more inclusive, adapting assessment guidelines to suit the varied learning styles and cognitive needs of all students, particularly those with SpLDs, becomes increasingly important. This paper provides initial evidence to support the adoption of infographic-based assessment guidelines as a step towards achieving this goal.

Exploring patient perspectives on EQ-5D-5L data visualization within an individualized decision aid for total knee arthroplasty (TKA) in Alberta, Canada.

BACKGROUND: Decision aids can help patients set realistic expectations. In this study, we explored alternative presentations to visualise patient-reported outcomes (EQ-5D-5L) data within an online, individualized patient decision aid for total knee arthroplasty (TKA) that, in part, generates individualized comparisons based on age, sex and body mass index, to enhance usability prior to implementation into routine clinical practice. METHODS: We used data visualization techniques to modify the presentation of EQ-5D-5L outcomes data within the decision aid. The EQ-5D-5L data was divided into two parts allowing patients to compare themselves to similar individuals (1) pre-surgery and (2) 1-year post-surgery. We created 2 versions for each part and sought patient feedback on comprehension, usefulness, and visual appeal. Patients from an urban orthopedic clinic were recruited and their ratings and comments were recorded using a researcher-administered checklist. Data were managed using Microsoft Excel, R version 3.6.1 and ATLAS.ti V8 and analyzed using descriptive statistics and directed content analysis. RESULTS: A total of 24 and 25 patients participated in Parts 1 and 2, respectively. Overall, there was a slight preference for Version 1 in Part 1 (58.3%) and Version 2 in Part 2 (64%). Most participants demonstrated adequate comprehension for all versions (range 50-72%) and commented that the instructions were clear. While 50-60% of participants rated the content as useful, including knowing the possible outcomes of surgery, some participants found the information interesting only, were unsure how to use the information, or did not find it useful because they had already decided on a treatment. Participants rated visual appeal for all versions favorably but suggested improvements for readability, mainly larger font and image sizes and enhanced contrast between elements. CONCLUSIONS: Based on the results, we will produce an enhanced presentation of EQ-5D-5L data within the decision aid. These improvements, along with further usability testing of the entire decision aid, will be made before implementation of the decision aid in routine clinical practice. Our results on patients' perspectives on the presentation of EQ-5D-5L data to support decision making for TKA treatments contributes to the knowledge on EQ-5D-5L applications within healthcare systems for clinical care.

Infographics on risks associated with COVID-19 and the willingness to get the AstraZeneca vaccine: two randomized online experiments.

BACKGROUND: Germans hesitated to get vaccinated with AstraZeneca in the COVID-19 pandemic after reports of blood clots. METHODS: In two preregistered online experiments with stratified randomization (Study 1 N = 824, Study 2: N = 1,056), we tested whether providing evidence-based benefit-risk information reduces the perceived risk of the AstraZeneca vaccine and the perceived probability of blood clots due to the AstraZeneca vaccine and increases the vaccination intention. In Study 1, participants saw no infographic (control) or one of two infographics (low vs. high exposure risk varied by the underlying incidence rates). Study 2 additionally varied the infographic design displaying the risk information (presented as table, circle icons, or manikin-like icons). RESULTS: The infographic decreased the risk perception of the vaccine compared to no infographic (Study 1: Cohens d = 0.31, 95% CI [0.14, 0.48]; Study 2: Cohens d = 0.34, 95% CI [0.06, 0.62]), but it did not influence the perceived probability of blood clots due to the AstraZeneca vaccine (Study 2: Cohens d = 0.05, 95% CI [-0.23, 0.33]). Also, the infographic design did not affect the perceived probability of blood clots (Study 2). The vaccination intention was not affected by viewing the infographic (Study 1: Cohens d = 0.04, 95% CI [-0.13, 0.21]; Study 2: Cohens d = 0.04, 95% CI [-0.24, 0.32]) nor the presented infection rate (Study 1: Cohens d = 0.07, 95% CI [-0.09, 0.24], Study 2: Cohens d = 0.01, 95% CI [-0.12, 0.15]) but by risk perceptions, sociodemographic characteristics, confidence in the AstraZeneca vaccine, and preference for alternative vaccines. CONCLUSIONS: The evidence-based benefit-risk information helped putting the risk of vaccinations into perspective. Nevertheless, objective risk information alone did not affect vaccination intention that was low due to the preexisting lacking vaccine confidence.

Towards a Regulatory Framework for Electronic Medical Record Data Visualization.

Physicians struggle to retrieve data from electronic medical records. We evaluated a digital tool that enhances physician efficiency in retrieving and analyzing patient information for treatment decision-making. Our use case is the care of diabetic patients. Evaluation results showed that healthcare providers who used the i4C (Insights for Care) dashboard experienced greater time efficiency than those who used traditional EMR information retrieval methods. A comprehensive evaluation of the i4C Dashboard confirms its effectiveness in facilitating diabetic care data management, as well as its potential application to a wide range of healthcare scenarios. In order to further maximize its effectiveness on clinical efficiency and patient care, future research should focus on improving its usability and scalability.

Status and trends of RGS16 based on data visualization analysis: A review.

G-protein signaling regulator 16 (RGS16) has been confirmed that RGS16 is associated with cancer, neurodegenerative diseases, and cardiovascular diseases. Moreover, many studies have shown that RGS16 can be used as a biomarker for cancer diagnosis and prognosis. We used CiteSpace and VOS viewer software to perform a bibliometric analysis of 290 publications in the core collection of Web of Science. All the articles come from 399 institutions, including 618 authors, 179 journals, 40 countries, 115 keywords, 1 language, two types of papers, and reviews. The United States has the largest number of publications. The Research Center of Allergy and Infectious Diseases (NIAID) publishes the most papers, Emory University is the most recent of all institutions with the most recent results in the RGS16 study. Cell biology is the most studied discipline, and the most studied topic is migration. Drury published RGS16-related articles with the most citations (n = 15), and Berman published articles with the most citations (n = 106). The biological applications of RGS16 are currently a hot area of RGS16 research, including inflammation, cancer, ulcerative colitis, metabolic acidosis, platelet activation, and thrombosis. The current scientometrics study provides an overview of RGS16 research from 1995 to 2022. This study provides an overview of current and potential future research hotspots in the field of RGS16 and can be used as a resource for interested researchers.

Protocol for visualizing complex volatile metabolomics data in clinical setups using EDaViS software.

Here, we present a protocol for using Early Data Visualization Script, a user-friendly software tool to visualize complex volatile metabolomics data in clinical setups. We describe steps for tabulating data and adjusting visual output to visualize complex time-resolved volatile omics data using simple charts and graphs. We then demonstrate possible modifications by detailing procedures for the adaptation of four basic functions. For complete details on the use and execution of this protocol, please refer to Sukul et al. (2022)1 and Remy et al. (2022).2.

Graphical Presentations in Systemic Anticancer Treatment Network Meta-Analyses: A Systematic Review.

Network meta-analysis (NMA) draws conclusions about indirect comparisons of randomized clinical trials and is considered high-level evidence. Most NMA publications make use of network plots to portray results. Network plots are complex graphics that can have many visual attributes to portray useful information, such as node size, color, and graph layout. We analyzed the network plots from 162 NMAs of systemic anticancer therapy efficacy using a set of 16 attributes. Our review showed that the current state of network plot data visualizations within the NMA space lacks diversity and does not make use of many of the visual attributes available to convey information. More thoughtful design choices should be placed behind these important visualizations, which can carry clinical significance and help derive treatment plans for patients.

User-Centered Data Display for Clinicians to Diagnose and Manage Hypertension.

The accurate diagnosis and effective treatment of hypertension are hindered by challenges stemming from limited access to comprehensive blood pressure (BP) data and the need for clinical context surrounding BP readings. Using handwritten tables for home-monitored BP exacerbates issues related to integration of electronic health records (EHRs) and trend analysis. This study employs user-centered design principles to develop prototypes for BP data visualization, with the primary goal of harmonizing disparate BP data sources to empower clinicians in precise hypertension diagnosis and management.

A Data Visualisation Approach in Understanding the Obstetrics Patients Conversion.

As the leading maternal and child hospital in Singapore, it is important to understand the current hospital standing and maintain our competitiveness by monitoring the population movement. Through the use of data visualization techniques, the team processed historical data from 2012 to 2020 and presented new data insights for the hospital management to identify potential areas for improvement to increase the delivery rate in the hospital.

Data Visualization of CRISPR-Cas9 Guide RNA Design Tools.

With the advancement of genomic engineering and genetic modification techniques, the uptake of computational tools to design guide RNA increased drastically. Searching for genomic targets to design guides with maximum on-target activity (efficiency) and minimum off-target activity (specificity) is now an essential part of genome editing experiments. Today, a variety of tools exist that allow the search of genomic targets and let users customize their search parameters to better suit their experiments. Here we present an overview of different ways to visualize these searched CRISPR target sites along with specific downstream information like primer design, restriction enzyme activity and mutational outcome prediction after a double-stranded break. We discuss the importance of a good visualization summary to interpret information along with different ways to represent similar information effectively.

Piloting a Big Data Epidemiology Approach to Support Frail, Homebound, and Bedridden People.

Frail, homebound, and bedridden people (FHBP) are people living at home whose daily life is physically limited to the boundary of their houses because of their ongoing health, energy, and psychosocial or socio-functional impairments. This definition needs a scientific, systematic, and data-driven view of the distribution (frequency, pattern) and determinants (causes, risk factors) of health-related states and adverse events experienced by FHBP. Thus, we piloted a big data epidemiology approach (Multiple Correspondence Analysis and data visualization) from 300 survey responses about FHBP experiences and identified a positive correlation between perceived health status and reported impairments.

Cellograph: a semi-supervised approach to analyzing multi-condition single-cell RNA-sequencing data using graph neural networks.

With the growing number of single-cell datasets collected under more complex experimental conditions, there is an opportunity to leverage single-cell variability to reveal deeper insights into how cells respond to perturbations. Many existing approaches rely on discretizing the data into clusters for differential gene expression (DGE), effectively ironing out any information unveiled by the single-cell variability across cell-types. In addition, DGE often assumes a statistical distribution that, if erroneous, can lead to false positive differentially expressed genes. Here, we present Cellograph: a semi-supervised framework that uses graph neural networks to quantify the effects of perturbations at single-cell granularity. Cellograph not only measures how prototypical cells are of each condition but also learns a latent space that is amenable to interpretable data visualization and clustering. The learned gene weight matrix from training reveals pertinent genes driving the differences between conditions. We demonstrate the utility of our approach on publicly-available datasets including cancer drug therapy, stem cell reprogramming, and organoid differentiation. Cellograph outperforms existing methods for quantifying the effects of experimental perturbations and offers a novel framework to analyze single-cell data using deep learning.

Community engagement in the development of health-related data visualizations: a scoping review.

OBJECTIVE: This scoping review aims to address a gap in the literature on community engagement in developing data visualizations intended to improve population health. The review objectives are to: (1) synthesize literature on the types of community engagement activities conducted by researchers working with community partners and (2) characterize instances of "creative data literacy" within data visualizations developed in community-researcher partnerships. METHODS: Using the 2018 PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, the review focuses on peer-reviewed journal articles from 2010 to 2022 in PubMed, Web of Science, and Google Scholar. A community engagement tool was applied to the studies by independent reviewers to classify levels of community engagement, social determinants, and vulnerable populations. RESULTS: Twenty-seven articles were included in the scoping review. Twelve articles worked with vulnerable populations. Four articles attempted to alleviate barriers to representation in their respective studies, with addressing language barriers being the most prevalent approach. Thirteen articles considered social determinants of health. Sixteen studies engaged in iterative approaches with intended users when developing the visualization or tool. DISCUSSION: Only a few significant examples of creative data literacy are incorporated in the studies. We recommend a specific focus on engaging intended users at every step of the development process, addressing language and cultural differences, and empowering intended users as data storytellers. CONCLUSIONS: There is room for deeper and more meaningful community involvement in the development of health-related data visualizations geared towards them.

Participant-guided development of bilingual genomic educational infographics for Electronic Medical Records and Genomics Phase IV study.

OBJECTIVE: Developing targeted, culturally competent educational materials is critical for participant understanding of engagement in a large genomic study that uses computational pipelines to produce genome-informed risk assessments. MATERIALS AND METHODS: Guided by the Smerecnik framework that theorizes understanding of multifactorial genetic disease through 3 knowledge types, we developed English and Spanish infographics for individuals enrolled in the Electronic Medical Records and Genomics Network. Infographics were developed to explain concepts in lay language and visualizations. We conducted iterative sessions using a modified "think-aloud" process with 10 participants (6 English, 4 Spanish-speaking) to explore comprehension of and attitudes towards the infographics. RESULTS: We found that all but one participant had "awareness knowledge" of genetic disease risk factors upon viewing the infographics. Many participants had difficulty with "how-to" knowledge of applying genetic risk factors to specific monogenic and polygenic risks. Participant attitudes towards the iteratively-refined infographics indicated that design saturation was reached. DISCUSSION: There were several elements that contributed to the participants' comprehension (or misunderstanding) of the infographics. Visualization and iconography techniques best resonated with those who could draw on prior experiences or knowledge and were absent in those without. Limited graphicacy interfered with the understanding of absolute and relative risks when presented in graph format. Notably, narrative and storytelling theory that informed the creation of a vignette infographic was most accessible to all participants. CONCLUSION: Engagement with the intended audience who can identify strengths and points for improvement of the intervention is necessary to the development of effective infographics.