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 An Electronic Data Capture Framework (ConnEDCt) for Global and Public Health Research: Design and Implementation

10.2196/18580 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e18580 ◽  
Author(s):  
Caleb J Ruth ◽  
Samantha Lee Huey ◽  
Jesse T Krisher ◽  
Amy Fothergill ◽  
Bryan M Gannon ◽  
...  
Keyword(s):  
Clinical Research ◽  
Data Storage ◽  
Data Entry ◽  
Public Health Research ◽  
Ease Of Use ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Cross Sectional ◽  
Clinical Surveillance

Background When we were unable to identify an electronic data capture (EDC) package that supported our requirements for clinical research in resource-limited regions, we set out to build our own reusable EDC framework. We needed to capture data when offline, synchronize data on demand, and enforce strict eligibility requirements and complex longitudinal protocols. Based on previous experience, the geographical areas in which we conduct our research often have unreliable, slow internet access that would make web-based EDC platforms impractical. We were unwilling to fall back on paper-based data capture as we wanted other benefits of EDC. Therefore, we decided to build our own reusable software platform. In this paper, we describe our customizable EDC framework and highlight how we have used it in our ongoing surveillance programs, clinic-based cross-sectional studies, and randomized controlled trials (RCTs) in various settings in India and Ecuador. Objective This paper describes the creation of a mobile framework to support complex clinical research protocols in a variety of settings including clinical, surveillance, and RCTs. Methods We developed ConnEDCt, a mobile EDC framework for iOS devices and personal computers, using Claris FileMaker software for electronic data capture and data storage. Results ConnEDCt was tested in the field in our clinical, surveillance, and clinical trial research contexts in India and Ecuador and continuously refined for ease of use and optimization, including specific user roles; simultaneous synchronization across multiple locations; complex randomization schemes and informed consent processes; and collecting diverse types of data (laboratory, growth measurements, sociodemographic, health history, dietary recall and feeding practices, environmental exposures, and biological specimen collection). Conclusions ConnEDCt is customizable, with regulatory-compliant security, data synchronization, and other useful features for data collection in a variety of settings and study designs. Furthermore, ConnEDCt is user friendly and lowers the risks for errors in data entry because of real time error checking and protocol enforcement.

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 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 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 An Electronic Data Capture Framework (ConnEDCt) for Global and Public Health Research: Design and Implementation (Preprint)

2020 ◽  
Author(s):  
Caleb J Ruth ◽  
Samantha Lee Huey ◽  
Jesse T Krisher ◽  
Amy Fothergill ◽  
Bryan M Gannon ◽  
...  
Keyword(s):  
Clinical Research ◽  
Data Storage ◽  
Data Entry ◽  
Public Health Research ◽  
Ease Of Use ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Cross Sectional ◽  
Clinical Surveillance

BACKGROUND When we were unable to identify an electronic data capture (EDC) package that supported our requirements for clinical research in resource-limited regions, we set out to build our own reusable EDC framework. We needed to capture data when offline, synchronize data on demand, and enforce strict eligibility requirements and complex longitudinal protocols. Based on previous experience, the geographical areas in which we conduct our research often have unreliable, slow internet access that would make web-based EDC platforms impractical. We were unwilling to fall back on paper-based data capture as we wanted other benefits of EDC. Therefore, we decided to build our own reusable software platform. In this paper, we describe our customizable EDC framework and highlight how we have used it in our ongoing surveillance programs, clinic-based cross-sectional studies, and randomized controlled trials (RCTs) in various settings in India and Ecuador. OBJECTIVE This paper describes the creation of a mobile framework to support complex clinical research protocols in a variety of settings including clinical, surveillance, and RCTs. METHODS We developed ConnEDCt, a mobile EDC framework for iOS devices and personal computers, using Claris FileMaker software for electronic data capture and data storage. RESULTS ConnEDCt was tested in the field in our clinical, surveillance, and clinical trial research contexts in India and Ecuador and continuously refined for ease of use and optimization, including specific user roles; simultaneous synchronization across multiple locations; complex randomization schemes and informed consent processes; and collecting diverse types of data (laboratory, growth measurements, sociodemographic, health history, dietary recall and feeding practices, environmental exposures, and biological specimen collection). CONCLUSIONS ConnEDCt is customizable, with regulatory-compliant security, data synchronization, and other useful features for data collection in a variety of settings and study designs. Furthermore, ConnEDCt is user friendly and lowers the risks for errors in data entry because of real time error checking and protocol enforcement.

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