Diverse User Research Articles

Exploring people's perceptions of LLM-generated advice

When searching and browsing the web, more and more of the information we encounter is generated or mediated through large language models (LLMs). This can be looking for a recipe, getting help on an essay, or looking for relationship advice. Yet, there is limited understanding of how individuals perceive advice provided by these LLMs. In this paper, we explore people's perception of LLM-generated advice, and what role diverse user characteristics (i.e., personality and technology readiness) play in shaping their perception. Further, as LLM-generated advice can be difficult to distinguish from human advice, we assess the perceived creepiness of such advice. To investigate this, we run an exploratory study (N = 91), where participants rate advice in different styles (generated by GPT-3.5 Turbo). Notably, our findings suggest that individuals who identify as more agreeable tend to like the advice more and find it more useful. Further, individuals with higher technological insecurity are more likely to follow and find the advice more useful, and deem it more likely that a friend could have given the advice. Lastly, we see that advice given in a ‘skeptical’ style was rated most unpredictable, and advice given in a ‘whimsical’ style was rated least malicious—indicating that LLM advice styles influence user perceptions. Our results also provide an overview of people's considerations on likelihood, receptiveness, and what advice they are likely to seek from these digital assistants. Based on our results, we provide design takeaways for LLM-generated advice and outline future research directions to further inform the design of LLM-generated advice for support applications targeting people with diverse expectations and needs.

APPOINTMENT MAKER USING COMPUTERIZED VOICE

The Appointment Maker Using Computerized Voice is a novel system designed to streamline the process of scheduling appointments in healthcare settings through the integration of voice recognition and natural language processing technologies. By leveraging speech-to-text and text-to-speech capabilities, the system enables users to interact with the appointment booking interface using spoken commands, eliminating the need for manual input and enhancing accessibility for individuals with diverse technological proficiencies. This intuitive interface simplifies the appointment scheduling process, allowing users to book, reschedule, or cancel appointments with ease. Furthermore, the Appointment Maker employs machine learning models to understand and interpret user queries, providing accurate responses and assistance in real-time. By leveraging pre-trained language models and fine-tuning them on domain-specific data, the system demonstrates robust performance in understanding diverse user inputs and generating contextually relevant responses. This capability enhances user experience by providing personalized and timely assistance, akin to interacting with a human receptionist.The Appointment Maker features a backend database for storing and managing appointment and user information, ensuring data integrity, security, and efficient resource utilization. The relational database schema facilitates seamless coordination between users, healthcare providers, and administrative staff, enabling efficient scheduling and management of appointments. Overall, the Appointment Maker Using Computerized Voice represents a significant advancement in healthcare administration, offering a user-friendly and efficient solution for appointment scheduling while improving accessibility and patient experience. Key Words: Computerized Voice ,Natural Language Understanding (NLU), Speech to Text, Text to Speech.

TRANSFORMING THE WAY TRAVELERS EXPLORE: A PHP-DRIVEN TOURS AND TRAVELS PLATFORM

Abstract: The Ambai Tours and Travels web application is a revolutionary travel planning tool that offers a user-centric platform for creating, booking, and managing itineraries. Its user-centric design allows travelers to create personalized itineraries based on their interests and preferences. The platform streamlines the entire travel planning process, from destination exploration to accommodation booking and activity reservations. It uses advanced technologies like machine learning and data analytics to provide personalized recommendations, ensuring authenticity and fulfillment. The intuitive interface and user experience foster engagement and loyalty among its diverse user base. The application’s role in the travel ecosystem is significant, driving digital transformation, enhancing industry competitiveness, and reshaping consumer expectations. In conclusion, the Ambai Tours and Travels web application represents the convergence of technology and travel, paving the way for a new era of convenience, customization, and discovery for travelers worldwide. The main purpose of Tour and travels management system is to provide a best facility and travelling services for a owner to book hotels, flight and bus ticket for trip purpose. We have developed tour and travels management system to provide a search platform find their tour places according to choices. Project is based on a web application that helps to store and manage information and procedures that travels have to deal with every day and keep track of the Customer. The pages are made by HTML, CSS, Javascript, Bootstrap, the database is handled by MySQL, the application logic is controlled by PHP. Tourism is important in many ways- it can be for leisure, business, education, culture and for fun. Tourism has become a popular global leisure activity. The Tour and Travel Management System (TTMS) is a crucial tool in the travel and tourism industry, enhancing efficiency, accuracy, and customer satisfaction. It streamlines and automates key processes in travel planning, booking, and management, including itinerary planning, reservation management, customer relationship management, and financial transactions. IndexTerms – HTML, CSS, Javascript. Bootsrap, MySQL, PHP.

Exploration of a method for insight into accessibility design flaws based on touch dynamics

The widespread use of smartphones implies that intelligent systems must address the negative emotions experienced by a diverse user group when encountering usability difficulties. Therefore, exploring the relationship between users’ emotions and operational behaviors is crucial for further enhancing the accessibility of intelligent systems. Touch dynamics, as a method of dynamic touch data monitoring, has already provided many models for emotion recognition, which are beneficial in gaining insight into users’ accessibility needs for smartphones and exploring intelligent solutions to design issues. This paper validates this aspect through interviews and experimental studies. The study first conducted an open-ended interview, and then used a touch behavior experiment with emotional labels and a questionnaire to validate the association between accessibility design flaws in intelligent systems and user emotions. The interview results show that middle-aged and older smartphone users have difficulty activating accessibility tools independently. Consequently, this paper establishes a dataset for emotion classification based on touch gesture data, which is used to gain insight into implicit difficulties in the interaction process through negative emotions. From a touch experiment and questionnaire survey of 57 sample users, there is approximately a 78.57% match between the accessibility needs of intelligent systems and users’ negative emotions, with interaction fluency and interface cognition being the main reasons for this phenomenon. Therefore, touch dynamics, as a non-invasive method of emotion recognition, can use negative emotional feedback to gain insights into user needs that are difficult to detect by other methods. This has application value for enhancing users’ emotional experience and promoting a transformation in interaction design thinking.

Enhancing active aging through IRAGE: Mitigating social isolation with intergenerational gaming

This paper presents the outcomes of a pioneering study that explores the potential of remote intergenerational communication to combat social isolation among children and older adults, especially under constraints posed by pandemics such as COVID-19. Acknowledging the limited mobility of many older adults, this research aims to provide insights into how digital platforms can facilitate meaningful exchanges between generations. Utilizing a mixed methodology approach, the study first conducted a user interaction analysis to outline guidelines for participant engagement with the Information and Communication Technology (ICT)-based tool called IRAGE (Intergenerational Remote Access to Gaming Experiences) designed specifically for this purpose. Following the development of the ICT tool, three sessions of the remote intergenerational experience were held, during which participants' interactions were recorded and subsequently analyzed quantitatively and qualitatively. Key findings from the study reveal that remote intergenerational communication can significantly mitigate feelings of isolation among older adults, contributing to their mental health and emotional well-being. Moreover, the study highlights the effectiveness of the web-based platform in facilitating these interactions, with older adults and children finding the user interface intuitive and the overall experience engaging. These outcomes underscore the importance of leveraging technology to maintain social connections during challenging times and offer valuable guidelines for developing ICT tools that cater to the needs of diverse user groups. By demonstrating the feasibility and benefits of remote intergenerational communication, this research contributes to the broader discourse on active aging and the role of digital technologies in promoting social inclusion and emotional health.

Design overview and usability of the codesigned My Surgical Pathway E-health tool for supporting patient self-managed surgical preparation and recovery

IntroductionSurgery plays a critical role in global health, often facing significant quality challenges such as high rates of postoperative complications, variability in patient outcomes, and inconsistencies in care pathways, due to the complex nature of the perioperative journey. The My Surgical Pathway (MSP) app is an e-health intervention designed to address these challenges by supporting self-management and integrated care throughout the entire perioperative journey. This study aims to describe the features and functions of MSP and report on the results of usability testing from both patients and healthcare workers. MethodsThis mixed-methods study evaluated the usability of the My Surgical Pathway (MSP) app using quantitative surveys, qualitative feedback, and observation data from patients and healthcare workers. Structured 2-hour user testing sessions included interaction with the MSP app, completion of the User Version of the Mobile Application Rating Scale (uMARS), and think-aloud activities. Data analysis combined quantitative and qualitative insights to identify usability issues and inform potential improvements. ResultsThe mixed-method user-testing evaluation of the co-designed e-health intervention revealed high overall app quality scores, with an average of 4.50 out of 5. All user-type interfaces received mean scores above the minimum acceptability threshold of 3.0, demonstrating the intervention's efficacy in supporting patient surgery journeys, enhancing healthcare worker coordination, and meeting diverse user needs. Notably, the highest individual item scores were for credibility of source (4.92) and interactivity (4.85), highlighting the application's reliability and engaging nature. ConclusionsThe study evaluated the 'My Surgical Pathway' (MSP) e-health intervention, co-designed with consumers and healthcare workers to support self-management of surgical care. Results indicate high user satisfaction, with an overall app quality score of 4.50 out of 5 on the uMARS tool. Users praised MSP's engagement, education, and responsiveness, highlighting its potential to improve patient experiences and clinician workflows. Recommendations for future research include expanding participant diversity and hospital settings, and employing longitudinal designs for a comprehensive evaluation.

Investigating library users’ perceived indoor environmental quality: SEM-Logit analysis study in a university library

This study aims to identify the key factors that influence library users’ satisfaction with the indoor environmental quality (IEQ) in a contemporary university library, focusing on both perceived IEQ and individual user characteristics. Through a thorough literature review, it pinpoints indoor environment quality (IEQ) and individual users characteristics as critical influences of satisfaction. Data were collected from 404 users within four specific study areas of a selected university library. The study employs structural equation modeling (SEM) and generalized ordered logistic regression (GOLRM), to examine the relationships between perceived IEQ factors and user satisfaction. The SEM showed a good fit and highlighted significant impacts of acoustic quality (coefficient = 0.24, p < 0.01) and interior design (coefficient = 0.65, p < 0.001) on satisfaction. GOLRM analysis revealed that factors such as “age” (odds ratio = 0.810, p = 0.039), “feeling” (odds ratio = 1.420, p = 0.017), and “location (6th floor group-based study area)” (odds ratio = 2.844, p = 0.004) significantly enhance satisfaction, though advancing from medium to higher satisfaction levels poses challenges. These insights suggest that customizing library spaces to meet diverse user needs can markedly improve satisfaction. The study underscores the importance of integrating environmental and personal factors in library design and showcases the effectiveness of SEM and GOLRM in exploring complex interactions within built environments. This approach offers a valuable framework for designers and facility managers to align library spaces with user expectations, making a substantial contribution to the field of built environmental design research.

Tone Tracker

Abstract: Tone Tracker's integration of BERT and LSTM technologies allows it to predict and flag offensive language in both Tamil and English social media comments. BERT, a transformer-based model, enables the tool to understand the semantic context of text in multiple languages, ensuring accurate detection of inappropriate content regardless of language. The incorporation of LSTM further enhances this capability by capturing nuanced contextual information, refining the tool's proficiency in content moderation for both Tamil and English content. With its user-friendly features, Tone Tracker becomes accessible to a diverse user base, empowering them to swiftly remove offensive content in both languages and contribute to fostering a secure digital environment. This groundbreaking innovation not only boosts content moderation efficiency but also ensures scalability across various digital platforms, making it adaptable to the linguistic diversity of online communities. Ultimately, Tone Tracker, powered by LSTM and BERT, plays a pivotal role in cultivating positive online spaces where users can engage confidently and respectfully in both Tamil and English.

Multimodal LLM Driven Computer Interface

Abstract: This paper introduces the Multimodal-driven Computer Interface, a framework that enables multimodal models to interact with and control a computer. The framework receives user input through various modalities (e.g., speech, text, gestures), processes it through a multimodal fusion algorithm, and generates appropriate actions using a decision-making module. These actions are then executed on the computer through platform-specific APIs. Currently, it is integrated with the multimodal LLM and operates on Windows, Mac, and Linux systems. While the framework demonstrates promising results, challenges remain in improving the accuracy of mouse click location prediction and adapting to diverse user needs and preferences. The Multimodaldriven Computer Interface has the potential to revolutionize human-computer interaction, opening up new possibilities for accessibility, productivity, and entertainment.

Is pay-or-consent for privacy justifiable? Evidence from different users' privacy attitudes toward behavioral data collection in South Korea

As regulators began prohibiting online platforms from collecting personal data based on the “take-it-or-leave-it” basis, platform firms must adopt more refined user consent rules such as the pay-or-consent approach. Ensuring sufficient user options could increase the welfare of privacy-sensitive users but reduce the efficiency of data-driven business models. To balance the benefits and costs of enhanced privacy protection, regulators should understand the diversity in users' attitudes toward behavioral data collection in free online platforms. Tradeoffs among privacy, conveniences, and free services based on users' heterogeneous preferences are considered to investigate the user's different privacy attitudes in free online platforms. Three distinct user groups were found: the first one reluctantly accepts the “take-it-or-leave-it” condition because of the lack of alternatives, the second one accepts it for free services, and the third one accepts it because it does not matter. These three user segments constituted 32.9%, 47.0%, and 20.1% of all the respondents, respectively. The pay-or-consent approach can be justifiable in terms of balancing the benefits and costs of the privacy regulations if it properly reflects privacy-sensitive users' willingness to pay for privacy.

InstaSynth: Opportunities and Challenges in Generating Synthetic Instagram Data with ChatGPT for Sponsored Content Detection

Large Language Models (LLMs) raise concerns about lowering the cost of generating texts that could be used for unethical or illegal purposes, especially on social media. This paper investigates the promise of such models to help enforce legal requirements related to the disclosure of sponsored content online. We investigate the use of LLMs for generating synthetic Instagram captions with two objectives: The first objective (fidelity) is to produce realistic synthetic datasets. For this, we implement content-level and network-level metrics to assess whether synthetic captions are realistic. The second objective (utility) is to create synthetic data useful for sponsored content detection. For this, we evaluate the effectiveness of the generated synthetic data for training classifiers to identify undisclosed advertisements on Instagram. Our investigations show that the objectives of fidelity and utility may conflict and that prompt engineering is a useful but insufficient strategy. Additionally, we find that while individual synthetic posts may appear realistic, collectively they lack diversity, topic connectivity, and realistic user interaction patterns.

AI-Enhanced Accessibility Solutions using Chatbot

The "AI-Enhanced Accessibility Solutions" project addresses the real-world challenge of enhancing accessibility for individuals through advanced technologies. Utilizing React.js, Next.js, Tailwind, and AI APIs, this study focuses on generating inclusive content, including text, images, videos, code, and chatbot interactions. The primary objectives are to improve user interfaces, employ AI-driven content generation techniques, and ensure compliance with accessibility standards. The platform allows users to input self-generated prompts to produce customized data, such as text, images, videos, and code, tailored to their needs. An interactive AI-powered chatbot assists users in daily activities, adapting responses based on the quality of prompts provided, ensuring personalized and effective results. Key features include AI-generated content designed for various abilities. This includes alternative text for images, simplified text for cognitive accessibility, and adaptive chatbot responses. The project also focuses on generating accessible code, adhering to best web development practices. Tailwind CSS is used to optimize content presentation, addressing factors like color contrast and readability. The study includes a comparative analysis of accessibility features in popular apps versus those generated by our system. User testing and feedback mechanisms are employed to validate the project's effectiveness in meeting diverse user needs. This project not only advances AI-driven content creation but also represents a significant step towards creating inclusive digital environments. It emphasizes the importance of accessibility in technology, ensuring that digital content is accessible to all individuals, regardless of their abilities. Keywords: AI-Enhanced Accessibility, Inclusive Content, React.js, Next.js, AI APIs, User Interfaces, Accessibility Standards, AI-Powered Chatbot, Cognitive Accessibility

DEVELOPMENT OF AN OPEN EDUCATION RESOURCES (OER) SYSTEM: A COMPARATIVE ANALYSIS AND IMPLEMENTATION APPROACH

Several institutions are collaborating on the development of a new web-based Open Education Resources (OER) system designed exclusively for non-commercial educational purposes. This initiative is underpinned by meticulous research aimed at constructing an OER system that optimizes user experiences across diverse user profiles. A significant emphasis is placed on utilizing open-source tools, frameworks, and technologies. The project includes a comparative analysis of the top five open-source Learning Management Systems (LMS), providing critical insights to inform the development process. The primary objective is to create a web-based system that facilitates the sharing of educational resources for non-commercial users, leveraging information and communication technologies. The project is structured around two key teams: a research team and a development team. This comprehensive approach is intended to establish a robust, user-centric OER system, informed by insights from existing platforms and the latest advancements in open education resource development. KEYWORDS: Open Education Resources (OER), Web-based System (WBS), Learning Management Systems (LMS)

Exploring Digital Love: A Conversational Analysis of Online Courtship among Adults

This study explores the shift from traditional to digital courtship, focusing on individuals aged 18-50, active on dating apps and social media. It identifies types of Conversation Analysis such as Adjacency Pairs and Topic Management. Interestingly, the study finds that respondents commonly reciprocate gestures and respond cautiously to unknown messages. It also highlights the prevalent use of emoticons and humor in online courtship. Consequently, the research concludes that the suitor typically initiates the conversation in online dating, and respondents have varied approaches to building relationships. In light of these findings, the study recommends awareness of the diverse user base and potential risks in online dating.

DNA Cryptography for Enhanced Data Storage Security in Cloud Environments

Despite the persistent security challenges inherent in cloud systems, a distributed cloud environment necessitates an access control model that is contextually aware to effectively manage these challenges. This model should incorporate a role activation process based on the user's contextual information. Within this role activation process, the rationale behind data collection and usage is disclosed, enabling administrators to establish context-based policies. Consequently, role permissions are dynamically activated based on the association of roles with context. To mitigate complications in the role-based access control model, users are categorized into classes or groups, each with its own access control standards. Access to specific resources is determined by the user's identity upon request. Traditional access control models often fall short in cloud environments due to their inability to address all aspects of the diverse entities, resources, and users present. In the proposed access control system with perception reasoning, entities are expanded using Extensible Access Control Markup Language (XACML), while a trust module monitors user behavior dynamically, detecting and restricting malicious users attempting illegal data access. This includes assigning an identity tag to malicious users, which involves task and data classification along with database tagging.

Development and Validation of Cloud Applications Usability Development Scale

Cloud computing has revolutionized technology and education by providing scalable virtual resources accessible via the internet. This study emphasizes the importance of usability in cloud services, discussing various scales and metrics while highlighting their strengths and weaknesses. It underscores the necessity for a comprehensive usability scale that integrates both subjective and objective measures to suit diverse user scenarios and populations. The article introduces the Cloud Applications/Services Usability Development Scale, comprising five usability dimensions: Capable, Personal, Reliable, Valuable, and Secure. To validate the instrument, factor analysis was performed on 28 items for each construct, utilizing Principal Component Analysis and Varimax Rotation. The Kaiser-Meyer-Olkin (KMO) and Bartlett’s Test were applied to assess sampling adequacy. Construct validity and reliability were scrutinized using AMOS 21, a statistical analysis software for structural equation modeling (SEM). A confirmation factor analysis was employed to assess both converging and discriminating validity. A survey conducted among actual cloud application users, specifically Google Drive users in Lahore’s public libraries, demonstrated the strong construct validity and reliability of the developed instrument. The Cloud Applications/Services Usability Development Scale proved effective in evaluating the usability of cloud-based applications. This study not only contributes valuable insights to the development and validation of usability measurement tools for cloud applications but also addresses a significant gap in local and international literature. Furthermore, it serves as a model for creating and validating measurement tools for cloud services, offering practical implications for usability testing and methodological advancements in the field of cloud computing.

CLUE: A CLUSTERING-BASED TEST REDUCTION APPROACH FOR SOFTWARE PRODUCT LINES

Nowadays, organizations have increasingly turned to software product lines (SPLs)/configurable systems to meet diverse user needs and market demands. SPLs offer configurability and scalability but pose a formidable challenge in testing. Assuring the quality of variants across numerous configurations demands innovative testing strategies that balance effectiveness and efficiency. To improve testing productivity, test reduction techniques have been widely employed in non-configurable code. However, test reduction for SPL systems remains mostly unexplored. This paper introduces CLUE, a novel test reduction approach to enhance testing productivity in SPLs. Our idea is that to effectively and efficiently reveal failures, tests should be distinctive and cover diverse behaviors of the system. Meanwhile, similar tests covering the same/similar behaviors of an SPL system do not need to be redundantly executed multiple times. CLUE clusters the system’s tests into distinctive groups containing similar tests. Within each cluster, tests are prioritized based on the number of feature interactions, a common root cause of defects in SPLs. CLUE continually selects and executes the top-prioritized test in each cluster until a bug is revealed or the allocated effort budget is exhausted. To evaluate CLUE, we conducted several experiments on a dataset of six widely-used SPL systems. The results show that CLUE enables developers to detect defects earlier, requiring up to 88% less effort than existing approaches. Additionally, using only 50% of the original test suites, CLUE can identify most of the bugs in the buggy SPL systems while maintaining fault localization performance.

The Psychosocial Impacts of Generative AI Harms

The rapid emergence of generative Language Models (LMs) has led to growing concern about the impacts that their unexamined adoption may have on the social well-being of diverse user groups. Meanwhile, LMs are increasingly being adopted in K-20 schools and one-on-one student settings with minimal investigation of potential harms associated with their deployment. Motivated in part by real-world/everyday use cases (e.g., an AI writing assistant) this paper explores the potential psychosocial harms of stories generated by five leading LMs in response to open-ended prompting. We extend findings of stereotyping harms analyzing a total of 150K 100-word stories related to student classroom interactions. Examining patterns in LM-generated character demographics and representational harms (i.e., erasure, subordination, and stereotyping) we highlight particularly egregious vignettes, illustrating the ways LM-generated outputs may influence the experiences of users with marginalized and minoritized identities, and emphasizing the need for a critical understanding of the psychosocial impacts of generative AI tools when deployed and utilized in diverse social contexts.

Specification of Self-Adaptive Privacy-Related Requirements within Cloud Computing Environments (CCE).

This paper presents a novel approach to address the challenges of self-adaptive privacy in cloud computing environments (CCE). Under the Cloud-InSPiRe project, the aim is to provide an interdisciplinary framework and a beta-version tool for self-adaptive privacy design, effectively focusing on the integration of technical measures with social needs. To address that, a pilot taxonomy that aligns technical, infrastructural, and social requirements is proposed after two supplementary surveys that have been conducted, focusing on users' privacy needs and developers' perspectives on self-adaptive privacy. Through the integration of users' social identity-based practices and developers' insights, the taxonomy aims to provide clear guidance for developers, ensuring compliance with regulatory standards and fostering a user-centric approach to self-adaptive privacy design tailored to diverse user groups, ultimately enhancing satisfaction and confidence in cloud services.

National Public Health Dashboards: Protocol for a Scoping Review.

The COVID-19 pandemic highlighted the importance of robust public health data systems and the potential utility of data dashboards for ensuring access to critical public health data for diverse groups of stakeholders and decision makers. As dashboards are becoming ubiquitous, it is imperative to consider how they may be best integrated with public health data systems and the decision-making routines of diverse audiences. However, additional progress on the continued development, improvement, and sustainability of these tools requires the integration and synthesis of a largely fragmented scholarship regarding the purpose, design principles and features, successful implementation, and decision-making supports provided by effective public health data dashboards across diverse users and applications. This scoping review aims to provide a descriptive and thematic overview of national public health data dashboards including their purpose, intended audiences, health topics, design elements, impact, and underlying mechanisms of use and usefulness of these tools in decision-making processes. It seeks to identify gaps in the current literature on the topic and provide the first-of-its-kind systematic treatment of actionability as a critical design element of public health data dashboards. The scoping review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. The review considers English-language, peer-reviewed journal papers, conference proceedings, book chapters, and reports that describe the design, implementation, and evaluation of a public health dashboard published between 2000 and 2023. The search strategy covers scholarly databases (CINAHL, PubMed, Medline, and Web of Science) and gray literature sources and uses snowballing techniques. An iterative process of testing for and improving intercoder reliability was implemented to ensure that coders are properly trained to screen documents according to the inclusion criteria prior to beginning the full review of relevant papers. The search process initially identified 2544 documents, including papers located via databases, gray literature searching, and snowballing. Following the removal of duplicate documents (n=1416), nonrelevant items (n=839), and items classified as literature reviews and background information (n=73), 216 documents met the inclusion criteria: US case studies (n=90) and non-US case studies (n=126). Data extraction will focus on key variables, including public health data characteristics; dashboard design elements and functionalities; intended users, usability, logistics, and operation; and indicators of usefulness and impact reported. The scoping review will analyze the goals, design, use, usefulness, and impact of public health data dashboards. The review will also inform the continued development and improvement of these tools by analyzing and synthesizing current practices and lessons emerging from the literature on the topic and proposing a theory-grounded and evidence-informed framework for designing, implementing, and evaluating public health data dashboards. DERR1-10.2196/52843.