Intuitive Data Visualization for Enhanced Patient Comprehension.

The pilot study explores how data visualization influences patient comprehension and engagement in understanding hyperlipidemia test results across diverse patient groups. Employing Gestalt theory and the Relational Information Display (RID) framework, intuitive visual tools were developed using Google Sheets, QlikView®, and Microsoft® Excel®. The survey conducted with patients used a Likert scale to evaluate six different line and bar graphs, each presenting the same LDL cholesterol data. The study emphasized the creation of graphs that were easily interpretable. The survey aimed to assess preferences for various data visualization formats. The survey results indicated that patients preferred stacked area charts, while healthcare providers favored line charts. The results highlight the importance of user-centric design and the effective application of theoretical frameworks in creating visualizations that enhance patient engagement and comprehension. The study highlights the role of tailored data visualizations in healthcare, emphasizing the need for such tools in user-centered health technology.

A User-Centered Data Visualization of Atherosclerotic Heart Disease Risk Factors.

High cholesterol levels significantly contribute to the risk of atherosclerotic cardiovascular disease (ACVD), with a notable portion of ischemic heart disease cases linked to elevated cholesterol levels. Effective graphical displays of lipid panel tests and other cardiac risk factors are crucial for quick and accurate data interpretation, enabling early intervention for individuals with hyperlipidemia. Applying design theories such as Gestalt and distributed cognitive theories is essential for creating user-centered graphical data displays in the context of cardiovascular (CV) risk factors. The proposed dashboard informed by these theories is expected to help healthcare providers better address cardiovascular disease (CVD), enhancing diagnosis, treatment, and prevention. Moreover, this approach may help alleviate clinical provider burnout, improve patient outcomes, and reduce provider stress, thus contributing to safer and more effective healthcare systems.

Durga: an R package for effect size estimation and visualization.

Statistical analysis and data visualization are integral parts of science communication. One of the major issues in current data analysis practice is an overdependency on-and misuse of-p-values. Researchers have been advocating for the estimation and reporting of effect sizes for quantitative research to enhance the clarity and effectiveness of data analysis. Reporting effect sizes in scientific publications has until now been mainly limited to numeric tables, even though effect size plotting is a more effective means of communicating results. We have developed the Durga R package for estimating and plotting effect sizes for paired and unpaired group comparisons. Durga allows users to estimate unstandardized and standardized effect sizes and bootstrapped confidence intervals of the effect sizes. The central functionality of Durga is to combine effect size visualizations with traditional plotting methods. Durga is a powerful statistical and data visualization package that is easy to use, providing the flexibility to estimate effect sizes of paired and unpaired data using different statistical methods. Durga provides a plethora of options for plotting effect size, which allows users to plot data in the most informative and aesthetic way. Here, we introduce the package and its various functions. We further describe a workflow for estimating and plotting effect sizes using example data sets.

Protocol for enhancing visualization clarity for categorical spatial datasets using Spaco.

In categorical data visualization, appropriate color arrangements can avoid perceptual ambiguity and help perceive underlying data patterns. We introduce a protocol to assign contrastive colors to neighboring categories using both Python and R packages. We describe steps for calculating the interlacement between clusters and generating a proper color palette and calculating color contrast. We then detail procedures for aligning cluster interlacement and color contrast to get an optimized cluster-color assignment, achieving clear categorical visualization. For complete details on the use and execution of this protocol, please refer to Jing et al.1.

Visualization strategies to aid interpretation of high-dimensional genotoxicity data.

This article describes a range of high-dimensional data visualization strategies that we have explored for their ability to complement machine learning algorithm predictions derived from MultiFlow® assay results. For this exercise, we focused on seven biomarker responses resulting from the exposure of TK6 cells to each of 126 diverse chemicals over a range of concentrations. Obviously, challenges associated with visualizing seven biomarker responses were further complicated whenever there was a desire to represent the entire 126 chemical data set as opposed to results from a single chemical. Scatter plots, spider plots, parallel coordinate plots, hierarchical clustering, principal component analysis, toxicological prioritization index, multidimensional scaling, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection are each considered in turn. Our report provides a comparative analysis of these techniques. In an era where multiplexed assays and machine learning algorithms are becoming the norm, stakeholders should find some of these visualization strategies useful for efficiently and effectively interpreting their high-dimensional data.

LipidSig 2.0: integrating lipid characteristic insights into advanced lipidomics data analysis.

In the field of lipidomics, where the complexity of lipid structures and functions presents significant analytical challenges, LipidSig stands out as the first web-based platform providing integrated, comprehensive analysis for efficient data mining of lipidomic datasets. The upgraded LipidSig 2.0 (https://lipidsig.bioinfomics.org/) simplifies the process and empowers researchers to decipher the complex nature of lipids and link lipidomic data to specific characteristics and biological contexts. This tool markedly enhances the efficiency and depth of lipidomic research by autonomously identifying lipid species and assigning 29 comprehensive characteristics upon data entry. LipidSig 2.0 accommodates 24 data processing methods, streamlining diverse lipidomic datasets. The tool's expertise in automating intricate analytical processes, including data preprocessing, lipid ID annotation, differential expression, enrichment analysis, and network analysis, allows researchers to profoundly investigate lipid properties and their biological implications. Additional innovative features, such as the 'Network' function, offer a system biology perspective on lipid interactions, and the 'Multiple Group' analysis aids in examining complex experimental designs. With its comprehensive suite of features for analyzing and visualizing lipid properties, LipidSig 2.0 positions itself as an indispensable tool for advanced lipidomics research, paving the way for new insights into the role of lipids in cellular processes and disease development.

Evaluation of different landing pages on behavioural engagement with the CARA dashboard: A user research protocol.

BACKGROUND: CARA set out to develop a data-visualisation platform to facilitate general practitioners to develop a deeper understanding of their patient population, disease management and prescribing through dashboards. To support the continued use and sustainability of the CARA dashboards, dashboard performance and user engagement have to be optimised. User research places people at the centre of the design process and aims to evaluate the needs, behaviours and attitudes of users to inform the design, development and impact of a product. OBJECTIVE: To explore how different initial key messages impact the level of behavioural engagement with a CARA dashboard. METHODS: Participating general practices can upload their practice data for analysis and visualisation in CARA dashboards. Practices will be randomised to one of three different initial landing pages: the full dashboard or one of two key messages: a between comparison (their practice prescribing with the average of all other practices) or within comparison (with practice data of the same month the previous year) with subsequent continuation to the full dashboard. Analysis will determine which of the three landing pages encourages user interaction, as measured by the number of 'clicks', 'viewings' and 'sessions'. Dashboard usage data will be collected through Google analytics. DISCUSSION: This study will provide evidence of behavioural engagement and its metrics during the implementation of the CARA dashboards to optimise and sustain interaction. TRIAL REGISTRATION: ISRCTN32783644 (Registration date: 02/01/2024).

Novel data visualization and risk analytic algorithm in a low-middle income country.

To discuss the use of T3™, a data aggregation, visualization, and risk analytic platform in a single centre and its framework for implementation of such a tool in clinical care. We share experience of a tool implemented in a tertiary care Intensive Care Unit (ICU) with limited resources. Superusers were identified and trained. Implementation involved monitoring, evaluation, and user engagement data for continuous emphasis on the use of this tool. Persistent display of T3 data enhanced nursing operational efficiency. Its use was expanded to use in nurses rounds and handover, mortality and morbidity meetings, clinical team teaching through selected teaching cases and analysis of stored data with different research questions. However, lack of infrastructure and technological comprehension, paucity of multidisciplinary teams makes it a challenge in its implementation. Clear framework of implantation and pre-designed studies to determine the clinical usage and effectiveness are important for wide-spread use of such tools.

Graph-Driven Simultaneous and Proportional Estimation of Wrist Angle and Grasp Force via High-Density EMG.

Myoelectric prostheses are generally unable to accurately control the position and force simultaneously, prohibiting natural and intuitive human-machine interaction. This issue is attributed to the limitations of myoelectric interfaces in effectively decoding multi-degree-of-freedom (multi-DoF) kinematic and kinetic information. We thus propose a novel multi-task, spatial-temporal model driven by graphical high-density electromyography (HD-EMG) for simultaneous and proportional control of wrist angle and grasp force. Twelve subjects were recruited to perform three multi-DoF movements, including wrist pronation/supination, wrist flexion/extension, and wrist abduction/adduction while varying grasp force. Experimental results demonstrated that the proposed model outperformed five baseline models, with the normalized root mean square error of 13.2% and 9.7% and the correlation coefficient of 89.6% and 91.9% for wrist angle and grasp force estimation, respectively. In addition, the proposed model still maintained comparable accuracy even with a significant reduction in the number of HD-EMG electrodes. To the best of our knowledge, this is the first study to achieve simultaneous and proportional wrist angle and grasp force control via HD-EMG and has the potential to empower prostheses users to perform a broader range of tasks with greater precision and control, ultimately enhancing their independence and quality of life.

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.

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.

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).

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.