The REDCap consortium: Building an international community of software platform partners.

Re: A Comprehensive 6-mo Prostate Cancer Patient Empowerment Program Decreases Psychological Distress Among Men Undergoing Curative Prostate Cancer Treatment: A Randomized Clinical Trial

Return on time investment: Research resources

Background Nowadays, technological advances enable instant data collection in a secure and confidential manner. Integrating electronic data capture technologies into the online clinical trial design, has been rarely investigated. The aim of this study was to test the feasibility and logistics of the online clinical trial model in osteoarthritis. Methods We used Research Electronic Data Capture (REDCap), a versatile and secure database, as the data management platform. Without face-to-face interactions, the majority of the communications among investigators and between investigators and participants were through the REDCap platform. The embedded randomisation procedure was also completed on the same platform. Results We developed the online trial model in a 12-week, phase II, placebo-controlled, randomised clinical trial (RCT) investigating the efficacy and safety of a supplement combination in people with hand osteoarthritis (RADIANT study). We replicated the same platform and questionnaire model to a larger hybrid RCT, a phase III, two-year placebo-controlled trial investigating the use of stem cells in the treatment of knee osteoarthritis (SCUlpTOR study). In the RADIANT study, we screened 301 participants within five months’ timeframe between October 2019 and March 2020 during the pandemic of COVID. 106 eligible participants were randomised, and the adherence rate was 100%. By leveraging advanced REDCap features, including custom record status dashboards, automated alerts and notifications, text messaging integration via Twilio, and comprehensive project dashboards, we significantly enhanced the efficiency of the recruitment process and participant management. These improvements also effectively reduced the survey burden on participants, contributing to the overall success and robustness of our data collection methodology. In the SCUlpTOR study, we successfully completed the recruitment between May 2021 to Nov 2023 in two sites with screening of 10,110 participants and eventually 321 participants were randomised. The visualisation of the process provides an efficient system to adjust the recruitment strategy in a cost-effective and efficient manner (Figure 1). Conclusion This online trial implementation and data management system successfully work for RCT recruitment and conduction. The two well-performed trials prove the feasibility of online clinic trial model. This is particularly important in the post-COVID era when face-to-face interactions may be limited. However, the incorporation of technology and secure data management system in the implementation of RCT is still underexplored. Further validation is needed to generalize this method to a broader trial design in other medical disorders.

There is a growing trend for studies run by academic and nonprofit organizations to have regulatory submission requirements. As a result, there is greater reliance on REDCap, an electronic data capture (EDC) widely used by researchers in these organizations. This paper discusses the development and implementation of the Rapid Validation Process (RVP) developed by the REDCap Consortium, aimed at enhancing regulatory compliance and operational efficiency in response to the dynamic demands of modern clinical research. The RVP introduces a structured validation approach that categorizes REDCap functionalities, develops targeted validation tests, and applies structured and standardized testing syntax. This approach ensures that REDCap can meet regulatory standards while maintaining flexibility to adapt to new challenges. Results from the application of the RVP on recent successive REDCap software version releases illustrate significant improvements in testing efficiency and process optimization, demonstrating the project's success in setting new benchmarks for EDC system validation. The project's community-driven responsibility model fosters collaboration and knowledge sharing and enhances the overall resilience and adaptability of REDCap. As REDCap continues to evolve based on feedback from clinical trialists, the RVP ensures that REDCap remains a reliable and compliant tool, ready to meet regulatory and future operational challenges.

ObjectiveThe objective of this study is to evaluate the use of a supplementary data management application to meet surveillance demands for foodborne disease in Tennessee and to highlight successes, challenges, and opportunities identified through this process.IntroductionThe Tennessee Department of Health (TDH) Foodborne Disease Program conducts routine surveillance for foodborne illnesses and enteric disease outbreaks and participates in statewide enhanced surveillance as part of the Foodborne Disease Center for Outbreak Response Enhancement (FoodCORE) and the Foodborne Diseases Active Surveillance Network (FoodNet) supported by the Centers for Disease Control and Prevention (CDC). TDH uses the CDC NEDSS Base System (NBS) application for routine disease surveillance. However, NBS serves multiple disease programs within TDH and modifications to the system for the rapidly changing data demands, grant requirements, and outbreak needs of the foodborne program, may not be a priority for the system as a whole. In 2014, the TDH Foodborne Disease Program began using the Research Electronic Data Capture (REDCap) application as a solution to changing surveillance needs. FoodCORE, FoodNet, and routine surveillance data elements are entered into REDCap to supplement NBS, depending on program specific needs and system capability.MethodsREDCap was queried for FoodCORE, FoodNet, and routine surveillance projects. Projects were categorized by surveillance activity type. Epidemiologists provided qualitative feedback on successes and challenges in using REDCap to supplement NBS, which were then categorized into attributes according to existing frameworks for evaluating public health surveillance systems.1, 2ResultsAs of August 2018, the TDH Foodborne program housed 45 individual REDCap databases dedicated to surveillance. Four primary database categories were identified: routine case-based surveillance (8), enhanced/active surveillance (6), aggregate outbreak/cluster surveillance tracking (6), and outbreak-specific databases (25). The REDCap application programming interface (API) and an open database connection to NBS within SAS 9.4 (Cary, NC) were used to create unilateral data flow from NBS to REDCap, where possible. Successes and challenges in using REDCap fell into six main surveillance system attributes: Flexibility, Ease of Data Management, Stability, Simplicity, Efficiency, and Acceptability. Successes included the high level of control over data and databases offered by REDCap, the flexibility to rapidly implement program-specific changes, and the accessibility and reliability of REDCap as a de facto back-up of NBS data. Challenges included lack of interoperability between REDCap databases and with NBS, leading to dual data entry, overuse of REDCap resulting in unnecessarily complex and decentralized data storage (Figure 1), and increased personnel time on data management and extraction for metrics and reports.ConclusionsUsing REDCap in Tennessee to supplement an existing disease surveillance application increased flexibility and functionality of the foodborne disease surveillance system, but also added complexity and time involved in data management. The Nationally Notifiable Diseases Surveillance System Modernization Initiative (NMI) is developing a standardized message mapping guide (MMG) in collaboration with states and CDC, which incorporates FoodNet data elements and would transition data collection tools in NBS for foodborne diseases to a more portable and flexible format. Implementation of this MMG could minimize case-based data entry into REDCap. Tools that offer increased interoperability between NBS and REDCap and between REDCap databases could also improve the efficiency of using complementary applications for rapidly changing foodborne disease surveillance needs.

ObjectivesHigh-quality clinical research is dependent on adequate design, methodology, and data collection. The utilization of electronic data capture (EDC) systems is recommended to optimize research data through proper management. This paper’s objective is to present the procedures of REDCap (Research Electronic Data Capture), which supports research development, and to promote the utilization of this software among the scientific community.MethodsREDCap’s web application version 10.4.1 released on 2021 (Vanderbilt University) is an EDC system suitable for clinical research development. This paper describes how to join the REDCap consortium and presents how to develop survey instruments and use them to collect and analyze data.ResultsSince REDCap is a web application that stimulates knowledge-sharing among the scientific community, its development is not finished and it is constantly receiving updates to improve the system. REDCap’s tools provide access control, audit trails, and data security to the research team.ConclusionsREDCap is a web application that can facilitate clinical research development, mainly in health fields, and reduce the costs of conducting research. Its tools allow researchers to make the best use of EDC components, such as data storage.

This paper describes the use of Research Electronic Data Capture (REDCap) to conduct one of the follow-up waves of the 2004 Pelotas birth cohort. The aim is to point out the advantages and limitations of using this electronic data capture environment to collect data and control every step of a longitudinal epidemiological research, specially in terms of time savings and data quality. We used REDCap as the main tool to support the conduction of a birth cohort follow-up. By exploiting several REDCap features, we managed to schedule assessments, collect data, and control the study workflow. To enhance data quality, we developed specific reports and field validations to depict inconsistencies in real time. Using REDCap it was possible to investigate more variables without significant increases on the data collection time, when comparing to a previous birth cohort follow-up. In addition, better data quality was achieved since negligible out of range errors and no validation or missing inconsistencies were identified after applying over 7,000 interviews. Adopting electronic data capture solutions, such as REDCap, in epidemiological research can bring several advantages over traditional paper-based data collection methods. In favor of improving their features, more research groups should migrate from paper to electronic-based epidemiological research.

Self-administered web-based questionnaires are widely used to collect health data from patients and clinical research participants. REDCap (Research Electronic Data Capture; Vanderbilt University) is a global, secure web application for building and managing electronic data capture. Unfortunately, stakeholder needs and preferences of electronic data collection via REDCap have rarely been studied. This study aims to survey REDCap researchers and administrators to assess their experience with REDCap, especially their perspectives on the advantages, challenges, and suggestions for the enhancement of REDCap as a data collection tool. We conducted a web-based survey with representatives of REDCap member organizations in the United States. The survey captured information on respondent demographics, quality of patient-reported data collected via REDCap, patient experience of data collection with REDCap, and open-ended questions focusing on the advantages, challenges, and suggestions to enhance REDCap's data collection experience. Descriptive and inferential analysis measures were used to analyze quantitative data. Thematic analysis was used to analyze open-ended responses focusing on the advantages, disadvantages, and enhancements in data collection experience. A total of 207 respondents completed the survey. Respondents strongly agreed or agreed that the data collected via REDCap are accurate (188/207, 90.8%), reliable (182/207, 87.9%), and complete (166/207, 80.2%). More than half of respondents strongly agreed or agreed that patients find REDCap easy to use (165/207, 79.7%), could successfully complete tasks without help (151/207, 72.9%), and could do so in a timely manner (163/207, 78.7%). Thematic analysis of open-ended responses yielded 8 major themes: survey development, user experience, survey distribution, survey results, training and support, technology, security, and platform features. The user experience category included more than half of the advantage codes (307/594, 51.7% of codes); meanwhile, respondents reported higher challenges in survey development (169/516, 32.8% of codes), also suggesting the highest enhancement suggestions for the category (162/439, 36.9% of codes). Respondents indicated that REDCap is a valued, low-cost, secure resource for clinical research data collection. REDCap's data collection experience was generally positive among clinical research and care staff members and patients. However, with the advancements in data collection technologies and the availability of modern, intuitive, and mobile-friendly data collection interfaces, there is a critical opportunity to enhance the REDCap experience to meet the needs of researchers and patients.

BackgroundWeb-based health interventions are increasingly common and are promising for patients with voice disorders because web-based participation does not require voice use. To address needs such as Health Insurance Portability and Accountability Act compliance, unique user access, the ability to send automated reminders, and a limited development budget, we used the Research Electronic Data Capture (REDCap) data management platform to deliver a patient-facing psychological intervention designed for patients with voice disorders. This was a novel use of REDCap.ObjectiveWe aimed to evaluate the usability of the intervention, with this intervention serving as a use case for REDCap-based patient-facing interventions.MethodsWe used REDCap survey instruments to develop the web-based voice intervention modules, then conducted usability evaluations using (1) heuristic evaluations by 2 evaluators, and (2) formal usability testing with 7 participants, consisting of predetermined tasks, a think-aloud protocol, ease-of-use measurements, a product reaction card, and a debriefing interview.ResultsHeuristic evaluations found strengths in visibility of system status and real-world match, and weaknesses in user control and help documentation. Based on this feedback, changes to the intervention were made before usability testing. Overall, usability testing participants found the intervention useful and easy to use, although testing revealed some concerns with design, content, and terminology. Some concerns were readily addressed, and others required adaptations within REDCap.ConclusionsThe REDCap version of a complex web-based patient-facing intervention performed well in heuristic evaluation and formal usability testing. REDCap can effectively be used for patient-facing intervention delivery, particularly if the limitations of the platform are anticipated and mitigated.

BackgroundProviding user-friendly electronic data collection tools for large multicenter studies is key for obtaining high-quality research data. Research Electronic Data Capture (REDCap) is a software solution developed for setting up research databases with integrated graphical user interfaces for electronic data entry. The Swiss Mother and Child HIV Cohort Study (MoCHiV) is a longitudinal cohort study with around 2 million data entries dating back to the early 1980s. Until 2022, data collection in MoCHiV was paper-based.ObjectiveThe objective of this study was to provide a user-friendly graphical interface for electronic data entry for physicians and study nurses reporting MoCHiV data.MethodsMoCHiV collects information on obstetric events among women living with HIV and children born to mothers living with HIV. Until 2022, MoCHiV data were stored in an Oracle SQL relational database. In this project, R and REDCap were used to develop an electronic data entry platform for MoCHiV with migration of already collected data.ResultsThe key steps for providing an electronic data entry option for MoCHiV were (1) design, (2) data cleaning and formatting, (3) migration and compliance, and (4) add-on features. In the first step, the database structure was defined in REDCap, including the specification of primary and foreign keys, definition of study variables, and the hierarchy of questions (termed “branching logic”). In the second step, data stored in Oracle were cleaned and formatted to adhere to the defined database structure. Systematic data checks ensured compliance to all branching logic and levels of categorical variables. REDCap-specific variables and numbering of repeated events for enabling a relational data structure in REDCap were generated using R. In the third step, data were imported to REDCap and then systematically compared to the original data. In the last step, add-on features, such as data access groups, redirections, and summary reports, were integrated to facilitate data entry in the multicenter MoCHiV study.ConclusionsBy combining different software tools—Oracle SQL, R, and REDCap—and building a systematic pipeline for data cleaning, formatting, and comparing, we were able to migrate a multicenter longitudinal cohort study from Oracle SQL to REDCap. REDCap offers a flexible way for developing customized study designs, even in the case of longitudinal studies with different study arms (ie, obstetric events, women, and mother-child pairs). However, REDCap does not offer built-in tools for preprocessing large data sets before data import. Additional software is needed (eg, R) for data formatting and cleaning to achieve the predefined REDCap data structure.

Background REDCap (Research Electronic Data Capture) is a software application and workflow methodology designed to collect and manage data for research studies. REDCap study databases are secure, web-based applications and easy to create, launch and manage on a project-by-project basis. REDCap uses a study-specific data dictionary to eliminate all programming requirements for the creation of electronic case report forms and participant survey instruments for individual studies – making it extremely fast to develop and launch for any size study. Vanderbilt developed and launched REDCap in 2004 and began sharing the software with other academic and non-profit institutions in 2005 at no cost under a unique consortium dissemination model. The consortium now consists of 322 academic and non-profit partner institutions across six continents serving 38,600 end-users (http://www.projectredcap.org). This presentation will provide a description of the REDCap software platform, global consortium and low-cost institutional models for supporting data management across the entire clinical and translational research enterprise.

OBJECTIVES/SPECIFIC AIMS:.Outline the development and purpose of the partnership brokering database in REDCap. Provide an overview of the tool and how it works. Discuss how this tool facilitates partnership-brokering activities and discuss plans for future use METHODS/STUDY POPULATION: Research Electronic Data Capture (REDCap) is a secure, web-based application developed at Vanderbilt University to assist with systematic data management of small and medium sized projects. CCH utilized REDCap to build a custom data management warehouse entitled the Partnership Brokering Tool. Information compiled in various formats (handwritten notes, spreadsheets, etc.) over the past 10 years by CCH staff, was then systematically organized and entered into the Partnership Brokering Tool. The tool captures information such as individual contact information, organizational affiliation (academic, community, faith, government etc.), research interests (35 categories - asthma, diabetes, heart disease, etc.), communities of foci (children, elderly, LGBTQ, ethnicity, etc.), and target geographic community served (Chicago north, south, suburban, Illinois, etc.). RESULTS/ANTICIPATED RESULTS: Data was compiled on 451 community groups and organizations and 77 partners in academia thus far. Community organizations represent a range of community sectors including advocacy and policy groups, community-based, faith-based organizations, foundations, media, schools, etc. throughout the Chicagoland area. Data analysis activities are underway, however, results will also be shared regarding characteristics of the communities these organizations serve including:. Age range. Special populations (as defined by the CSTI grant). Underrepresented racial and ethnic communities. DISCUSSION/SIGNIFICANCE OF IMPACT: The Partnership Brokering Tool has provided a format for CCH to systematically gather information about the relationships staff have cultivated with community groups and organizations. Unlike an email management system, this REDCap project is highly useful in capturing the parameters of our partner pool, identifying partnership gaps, and matching individuals interested in collaborating with researchers or community organizations that have a particular skill set or research interest. The Partnership Brokering Tool has also facilitated stakeholder engagement dedicated to guiding the centers’ overall goals, objectives, and programming. Finally, utilizing REDCap has streamlined efforts in reporting quantitative and qualitative data about these organizations. In the next phase of this project, CCH will utilize the database to assess the nature of the relationship between CCH and community groups and organizations.

The aim of this study was to provide evidence on the application of Research Electronic Data Capture for collecting repeated data during a 7-day period among older adults. Fifty-seven adults (≥50 years) with type 2 diabetes were recruited. Participants completed one sleep diary upon awaking and one self-care diary before going to bed each day for 7 days. The diaries were administered via the Research Electronic Data Capture Web-based system and were completed via participants' own electronic devices. Objective compliance rate, time used to complete each diary, and participant experience were described. Approximately 80% (n = 45) of the participants used Research Electronic Data Capture. Among these participants, the noncompliance rate ranged between 0% and 8.9% for the sleep diary and 0% and 13.1% for the self-care diary. Participants spent 4.2 to 8.7 minutes on the sleep diary and 3.5 to 7.1 minutes on the self-care diary. It took the participants a longer time to complete the diaries during the first day than during the following 6 days. Few participants reported technical issues or felt inconvenient or stressful with completing the Research Electronic Data Capture diaries. Overall, the compliance rates were high. Completing the diaries was not time-consuming and participants were largely satisfied with the Research Electronic Data Capture data collection. Research Electronic Data Capture has aided the longitudinal data collection. With adequate training, Research Electronic Data Capture is an efficient tool to collect repeated data among older adults and thus is recommended for future research.

The Opioid Analgesic Reduction Study is a double-blind, prospective, clinical trial investigating analgesic effectiveness in the management of acute post-surgical pain after impacted third molar extraction across five clinical sites. Specifically, Opioid Analgesic Reduction Study examines a commonly prescribed opioid combination (hydrocodone/acetaminophen) against a non-opioid combination (ibuprofen/acetaminophen). The Opioid Analgesic Reduction Study employs a novel, electronic infrastructure, leveraging the functionality of its data management system, Research Electronic Data Capture, to not only serve as its data reservoir but also provide the framework for its quality management program. Within the Opioid Analgesic Reduction Study, Research Electronic Data Capture is expanded into a multi-function management tool, serving as the hub for its clinical data management, project management and credentialing, materials management, and quality management. Research Electronic Data Capture effectively captures data, displays/tracks study progress, triggers follow-up, and supports quality management processes. At 72% study completion, over 12,000 subject data forms have been executed in Research Electronic Data Capture with minimal missing (0.15%) or incomplete or erroneous forms (0.06%). Five hundred, twenty-three queries were initiated to request clarifications and/or address missing data and data discrepancies. Research Electronic Data Capture is an effective digital health technology that can be maximized to contribute to the success of a clinical trial. The Research Electronic Data Capture infrastructure and enhanced functionality used in Opioid Analgesic Reduction Study provides the framework and the logic that ensures complete, accurate, data while guiding an effective, efficient workflow that can be followed by team members across sites. This enhanced data reliability and comprehensive quality management processes allow for better preparedness and readiness for clinical monitoring and regulatory reporting.

Stress echo (SE) 2030 study is an international, prospective, multicenter cohort study that will include >10,000 patients from ≥20 centers from ≥10 countries. It represents the logical and chronological continuation of the SE 2020 study, which developed, validated, and disseminated the “ABCDE protocol” of SE, more suitable than conventional SE to describe the complex vulnerabilities of the contemporary patient within and beyond coronary artery disease. SE2030 was started with a recruitment plan from 2021 to 2025 (and follow-up to 2030) with 12 subprojects (ranging from coronary artery disease to valvular and post-COVID-19 patients). With these features, the study poses particular challenges on quality control assurance, methodological harmonization, and data management. One of the significant upgrades of SE2030 compared to SE2020 was developing and implementing a Research Electronic Data Capture (REDCap)-based infrastructure for interactive and entirely web-based data management to integrate and optimize reproducible clinical research data. The purposes of our paper were: first, to describe the methodology used for quality control of imaging data, and second, to present the informatic infrastructure developed on RedCap platform for data entry, storage, and management in a large-scale multicenter study.