scholarly journals Use of research electronic data capture (REDCap) in a COVID-19 randomized controlled trial: a practical example

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sina Kianersi ◽  
Maya Luetke ◽  
Christina Ludema ◽  
Alexander Valenzuela ◽  
Molly Rosenberg
Keyword(s):  
Data Collection ◽  
Web Application ◽  
Controlled Trial ◽  
Antibody Test ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Protective Behavior ◽  
Electronic Data ◽  
Randomized Controlled ◽  
Longitudinal Data Collection

Abstract Background Randomized controlled trials (RCT) are considered the ideal design for evaluating the efficacy of interventions. However, conducting a successful RCT has technological and logistical challenges. Defects in randomization processes (e.g., allocation sequence concealment) and flawed masking could bias an RCT’s findings. Moreover, investigators need to address other logistics common to all study designs, such as study invitations, eligibility screening, consenting procedure, and data confidentiality protocols. Research Electronic Data Capture (REDCap) is a secure, browser-based web application widely used by researchers for survey data collection. REDCap offers unique features that can be used to conduct rigorous RCTs. Methods In September and November 2020, we conducted a parallel group RCT among Indiana University Bloomington (IUB) undergraduate students to understand if receiving the results of a SARS-CoV-2 antibody test changed the students’ self-reported protective behavior against coronavirus disease 2019 (COVID-19). In the current report, we discuss how we used REDCap to conduct the different components of this RCT. We further share our REDCap project XML file and instructional videos that investigators can use when designing and conducting their RCTs. Results We reported on the different features that REDCap offers to complete various parts of a large RCT, including sending study invitations and recruitment, eligibility screening, consenting procedures, lab visit appointment and reminders, data collection and confidentiality, randomization, blinding of treatment arm assignment, returning test results, and follow-up surveys. Conclusions REDCap offers powerful tools for longitudinal data collection and conduct of rigorous and successful RCTs. Investigators can make use of this electronic data capturing system to successfully complete their RCTs. Trial registration The RCT was prospectively (before completing data collection) registered at ClinicalTrials.gov; registration number: NCT04620798, date of registration: November 9, 2020.

scholarly journals Use of Research Electronic Data Capture (REDCap) in different phases of a COVID-19 randomized controlled trial: A practical example

2021 ◽  
Author(s):  
Sina Kianersi ◽  
Maya Luetke ◽  
Christina Ludema ◽  
Alexander Valenzuela ◽  
Molly Rosenberg
Keyword(s):  
Data Collection ◽  
Undergraduate Students ◽  
Web Application ◽  
Controlled Trial ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Instructional Videos ◽  
Randomized Controlled ◽  
Longitudinal Data Collection

Abstract Background: Randomized controlled trials (RCT) are considered the ideal design for evaluating the efficacy of interventions. However, conducting a successful RCT has technological and logistical challenges. Defects in randomization processes (e.g., allocation sequence concealment) and flawed masking could bias an RCT’s findings. Moreover, investigators need to address other logistics common to all study designs, such as study invitations, eligibility screening, consenting procedure, and data confidentiality protocols. Research Electronic Data Capture (REDCap) is a secure, browser-based web application widely used by researchers for survey data collection. REDCap offers unique features that can be used to conduct rigorous RCTs.Methods: In September and November 2020, we conducted a parallel group RCT among Indiana University Bloomington (IUB) undergraduate students regarding their seropositivity for Coronavirus Disease 2019 (COVID-19) antibodies. In the current report, we discuss how we used REDCap to conduct the different components of this RCT. We further share XML REDCap files and instructional videos that investigators can use when designing and conducting their RCTs.Results and Conclusions: We report on the different features that REDCap offers to complete various parts of a large RCT, including sending study invitations and recruitment, eligibility screening, consenting procedures, lab visit appointment and reminders, data collection and confidentiality, randomization, blinding of treatment arm assignment, returning test results, and follow-up surveys. REDCap offers powerful tools for longitudinal data collection and conduct of rigorous and successful RCTs.

scholarly journals Electronic data collection in epidemiological research

2016 ◽  
Vol 07 (03) ◽  
pp. 672-681 ◽  
Author(s):  
Aluísio Barros ◽  
Cauane Blumenberg
Keyword(s):  
Data Collection ◽  
Data Quality ◽  
Birth Cohort ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Epidemiological Research ◽  
Electronic Data Collection ◽  
And Control

SummaryThis 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.Citation: Blumenberg C, Barros AJD. Electronic data collection in epidemiological research: The use of REDCap in the Pelotas birth cohorts

scholarly journals The experience of the clinical trial center at Alessandria Hospital regarding the activation of COVID-19 clinical trials during the pandemic

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Annalisa Roveta ◽  
Fabio Giacchero ◽  
Carolina Pelazza ◽  
Serena Penpa ◽  
Costanza Massarino ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Data Collection ◽  
Clinical Research ◽  
Fast Track ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Web Based ◽  
Taking Charge

Objective: The aim is to evaluate the speed in the activation of Covid-19 clinical trials at SS. Antonio e Biagio e Cesare Arrigo Hospital of Alessandria during the pandemic. Methods: Data collection related to the activation and the conduction of clinical trials was managed using a database created through a web-based platform REDCap (Research Electronic Data Capture). Results: 32 studies were activated in the period between March 23 and July 31, 2020. An average time of 14 days elapsed between taking charge of the request and the issuance of the authorization act. Conclusions: During the emergency it was possible to activate the trials quickly thanks to fast-track procedures, optimizing COVID-19 clinical research.

scholarly journals Electronic Data Capture Versus Conventional Data Collection Methods in Clinical Pain Studies: Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Lindsay A Jibb ◽  
James S Khan ◽  
Puneet Seth ◽  
Chitra Lalloo ◽  
Lauren Mulrooney ◽  
...  
Keyword(s):  
Systematic Review ◽  
Data Collection ◽  
Pain Score ◽  
Meta Analysis ◽  
Ease Of Use ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Data Completeness ◽  
Related Data

BACKGROUND The most commonly used means to assess pain is by patient self-reported questionnaires. These questionnaires have traditionally been completed using paper-and-pencil, telephone, or in-person methods, which may limit the validity of the collected data. Electronic data capture methods represent a potential way to validly, reliably, and feasibly collect pain-related data from patients in both clinical and research settings. OBJECTIVE The aim of this study was to conduct a systematic review and meta-analysis to compare electronic and conventional pain-related data collection methods with respect to pain score equivalence, data completeness, ease of use, efficiency, and acceptability between methods. METHODS We searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica Database (EMBASE), and Cochrane Central Register of Controlled Trials (CENTRAL) from database inception until November 2019. We included all peer-reviewed studies that compared electronic (any modality) and conventional (paper-, telephone-, or in-person–based) data capture methods for patient-reported pain data on one of the following outcomes: pain score equivalence, data completeness, ease of use, efficiency, and acceptability. We used random effects models to combine score equivalence data across studies that reported correlations or measures of agreement between electronic and conventional pain assessment methods. RESULTS A total of 53 unique studies were included in this systematic review, of which 21 were included in the meta-analysis. Overall, the pain scores reported electronically were congruent with those reported using conventional modalities, with the majority of studies (36/44, 82%) that reported on pain scores demonstrating this relationship. The weighted summary correlation coefficient of pain score equivalence from our meta-analysis was 0.92 (95% CI 0.88-0.95). Studies on data completeness, patient- or provider-reported ease of use, and efficiency generally indicated that electronic data capture methods were equivalent or superior to conventional methods. Most (19/23, 83%) studies that directly surveyed patients reported that the electronic format was the preferred data collection method. CONCLUSIONS Electronic pain-related data capture methods are comparable with conventional methods in terms of score equivalence, data completeness, ease, efficiency, and acceptability and, if the appropriate psychometric evaluations are in place, are a feasible means to collect pain data in clinical and research settings.

scholarly journals Capturing a Stata Dataset and Releasing it into REDcap

2017 ◽  
Vol 17 (1) ◽  
pp. 130-138
Author(s):  
Seth T. Lirette ◽  
Samantha R. Seals ◽  
Chad Blackshear ◽  
Warren May
Keyword(s):  
Data Collection ◽  
Data Entry ◽  
Data Capture ◽  
Data Dictionary ◽  
Electronic Data Capture ◽  
Application Research ◽  
Electronic Data ◽  
Web Based ◽  
Internet Connection ◽  
Technology Advances

With technology advances, researchers can now capture data using web-based applications. One such application, Research Electronic Data Capture (REDCap), allows for data entry from any computer with an Internet connection. As the use of REDCap has increased in popularity, we have observed the need to easily create data dictionaries and data collection instruments for REDCap. The command presented in this article, redcapture, demonstrates one method to create a REDCap-ready data dictionary using a loaded Stata dataset, illustrated by examples of starting from an existing dataset or completely starting from scratch.

scholarly journals Research Development Using REDCap Software

2021 ◽  
Vol 27 (4) ◽  
pp. 341-349
Author(s):  
Klauss Kleydmann Sabino Garcia ◽  
Amanda Amaral Abrahão
Keyword(s):  
Clinical Research ◽  
Data Storage ◽  
Web Application ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Research Development ◽  
Electronic Data ◽  
Survey Instruments ◽  
Conducting Research ◽  
Audit Trails

Objectives: High-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.Methods: REDCap’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.Results: Since 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.Conclusions: REDCap 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.

scholarly journals State of use of Electronic Data Capture (EDC) tools in randomized controlled trials in India: results from a survey

2021 ◽  
Author(s):  
Santam Chakraborty ◽  
Indranil Mallick ◽  
Tapesh Bhattacharyya ◽  
Moses Arunsingh S ◽  
Rimpa Basu Achari ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Randomized Controlled Trials ◽  
Online Survey ◽  
Multivariable Analysis ◽  
Data Capture ◽  
Controlled Trials ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Software Cost ◽  
Randomized Controlled

Abstract Introduction Electronic data capture (EDC) tools can improve data quality gathered in clinical trials and may be more cost-effective than paper forms. However, limited data is available on the adoption of EDC tools in randomized controlled trials (RCT) conducted in India. Methods We invited investigators of registered randomized controlled trials in India to an online survey. The questionnaire included questions on the use of EDC (or alternative data capture methods) and features available. An EDC sophistication level (ranging from 1–6) was computed from the responses obtained. Respondents were also asked about barriers to the implementation of EDC in their setting. The EDC adoption rate (EAR) was defined as the proportion of clinical trials where EDC with a sophistication level of 2 or more was used. Multivariable logistic regression was used to identify factors that predicted EDC adoption. Results Responses were received for 400 trials, with an EAR of 27.5% (95% confidence intervals : 23.4–32.1%, n = 110). The number of sites influenced EDC adoption (odds ratio : 1.26, 95% CI : 1.12–1.47, p = 0.001) on multivariable analysis. EAR did not increase over time. The key barriers identified for not using an EDC were lack of technical support (170, 63.0%) and software cost (132, 48.9%). Conclusion The survey shows a low EAR in randomized trials registered in India. The barriers identified in the survey would need systematic solutions to improve the EAR in the future.

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