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Abstract
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The problem: common challenges regarding data collection, management, storage, and confidentiality
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A solution: REDCap
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Solutions to the challenges
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Example of the use of REDCap in the international research project APPROACH-IS II
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Conclusion
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Note from the authors
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Funding
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Data availability
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References
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, Liesbet Van Bulck KU Leuven Department of Public Health and Primary Care, KU Leuven – University of Leuven, Kapucijnenvoer 35, Box 7001, 3000 Leuven, Belgium Research Foundation Flanders (FWO) , Egmontstraat 5, 1000 Brussels, Belgium Search for other works by this author on: Oxford Academic Martien Wampers University Psychiatric Center, University Hospitals Leuven, Leuvensesteenweg 517, 3070 Kortenberg, Belgium Search for other works by this author on: Oxford Academic Philip Moons KU Leuven Department of Public Health and Primary Care, KU Leuven – University of Leuven, Kapucijnenvoer 35, Box 7001, 3000 Leuven, Belgium Institute of Health and Care Sciences, University of Gothenburg, Arvid Wallgrens backe 1, 413 46 Gothenburg, Sweden Department of Paediatrics and Child Health, University of Cape Town, Klipfontein Rd, Rondebosch, 7700 Cape Town, South Africa Corresponding author. Tel: +32 16 37 33 15, Fax: +32 16 33 69 70, Email: philip.moons@kuleuven.be Search for other works by this author on: Oxford Academic
European Journal of Cardiovascular Nursing, Volume 21, Issue 1, January 2022, Pages 85–91, https://doi.org/10.1093/eurjcn/zvab104
Published:
06 November 2021
Article history
Received:
16 October 2021
Editorial decision:
19 October 2021
Accepted:
20 October 2021
Published:
06 November 2021
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Liesbet Van Bulck, Martien Wampers, Philip Moons, Research Electronic Data Capture (REDCap): tackling data collection, management, storage, and privacy challenges, European Journal of Cardiovascular Nursing, Volume 21, Issue 1, January 2022, Pages 85–91, https://doi.org/10.1093/eurjcn/zvab104
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Abstract
Data are the basis of research; without data, there is no research. However, growing internationalization of research, increased complexity of study designs, and stricter legislation make high-quality data collection, management, and storage more important, but also more challenging than ever. This article provides an overview of common challenges clinical researchers face when collecting, managing, and storing data and how REDCap, Research Electronic Data Capture, can be a possible solution to address these challenges.
Confidentiality, Data collection, Data management, Information storage and retrieval, Privacy, REDCap
Learning objectives
To critique the possible challenges and pitfalls in clinical research projects when collecting, managing, and storing data.
To outline that Research Electronic Data Capture (REDCap) is a possible tool to be used in clinical research projects.
To understand the specific REDCap features that can facilitate data collection, management, and storage in clinical research projects.
The problem: common challenges regarding data collection, management, storage, and confidentiality
Data collection challenges
A key challenge when collecting and managing research data is avoiding, or at least limiting, the number of errors in the dataset. These errors can occur at different times during data collection and can have diverse causes. For instance, when asked to complete surveys, participants may select more than one response option in a multiple-choice item while only one should be selected. This can cause errors while computing scale scores. Respondents may also accidently skip items. Also, to minimize missing data, it is important to be able to monitor in an efficient and user-friendly way who has already completed the survey. This can be very complicated, as participants usually do not all participate in the study at the same time. When general-purpose applications (e.g. EXCEL) are used for digital data entry, errors related to unclear documentation of the variables or use of different measurement units (e.g. centimetres/meters or inches/centimetres, in international research) can occur.
Besides the potential errors, there are several other data collection challenges. Setting up an international, multi-site study with surveys in different languages can be challenging and access to an internet or WiFi connection is not always guaranteed in clinical settings. Such challenges make electronic data collection in clinics troublesome.
Data management challenges
In many studies, a mixture of multiple questionnaires, direct data entry, and externally collected data (e.g. data from an experimental task) is used. This usually leads to the creation of different databases. Similarly, in longitudinal studies, where data are collected at different time points, new databases and/or sets of variables are often created for the different measurement waves. This data fragmentation, or having different datasets within one project containing data of the same participants, holds a potential risk of systematic missingness when merging the different databases and makes it difficult to keep a good overview of the data available in the study. The latter may, eventually, cause problems in terms of data documentation, and thereby also the extent to which reliable data analysis and data sharing are possible.
Over the past years, funders and research centres are increasingly requesting researchers to develop a data management plan.1 In addition to information about data documentation and data storage, the plan should also explain how manipulations to the data collection instruments or data itself will be tracked. Such logging of metadata, including by whom and when the manipulation was made, gives an idea about the quality of the data and helps users to monitor what happened to the data.2,3 Traditional tools such as spreadsheets or text files do not allow such logging.
Data storage challenges
Long-term retention and storage after publication of research data is mandatory.4 During that period, other researchers can ask to share the data and research documents. It is increasingly recommended by journals and research institutions to share data as widely and freely as possible, as sharing data strengthens open scientific transparency and permits the examination of topics that were not envisioned by the original investigators.1,5 However, data often still ends up in spreadsheets or text files that are stored on local computers without detailed information about the meaning of the data.6,7 Such data files are very difficult to safely share with other researchers across diverse institutions or departments. Documents are often transferred by email, which should certainly be avoided. Moreover, one of the biggest challenges is that spreadsheets and text files only have limited options in terms of documentation of the variables. A codebook is sometimes missing, and the protocol or other study documents are stored at a different location. When data are stored locally, there is the risk that the data are lost when a researcher leaves the research unit.
Confidentiality and financial challenges
An increasing number of incidents related to data breaches and cyber-attacks have fuelled the awareness of the importance of data security and privacy in healthcare research.8 Current data protection regulations, such as the General Data Protection Regulation in Europe, set out the requirements regarding data management and storage for research. Data needs to be processed in a manner that ensures appropriate confidentiality of personal and identifying data, including protection against accidental loss, destruction, or damage, using appropriate technical or organizational measures.9 Only when it is absolutely necessary to be kept in a data file, personal data should be encrypted and/or pseudonymized.9 Again, the spreadsheets and text files which are often used for data collection, management, and storage, will not make it possible to comply with these data protection regulations.
The most important barriers to improving data management for biomedical researchers are financial issues and unmet expectations of institutional support.7 Indeed, certainly for small research projects, the budget, human resources, and IT infrastructures are often limited.6,7 Usually, in such projects, there is only a small budget for professional IT support and commercial electronic data capture systems.7 Furthermore, healthcare researchers often have too limited computer skills to build these systems themselves.6 Hence, biomedical and clinical researchers need a secure data management tool that is intuitive and requires minimal programming skills and that can be used at a low cost.
A solution: REDCap
Research Electronic Data Capture (REDCap) is a web-based application developed by Vanderbilt University that was designed to provide research teams with an intuitive and reusable tool for collecting, managing, and storing research data collected at one more time points in a secure environment.3,10 The tool is widely used in the academic community, with currently over 1.3 million ongoing research projects.
Access to REDCap
Individual licences for REDCap are not available. To create a new REDCap project, one needs an affiliation with an institution that is part of the REDCap consortium.11 This REDCap consortium is an international community of partners from all over the world, which currently consists of over 5460 institutional partners from 143 countries.12 Only non-profit and government organizations are eligible to join the REDCap consortium. All institutions of the consortium are represented by a REDCap administrator, who maintains the REDCap system at their institution, runs installations to push updates and supports users.
To access the online REDCap platform, users need a device that is connected to the internet and a REDCap account. There is no need to install software, which avoids compatibility issues. REDCap can run on multiple operating systems such as Windows, Linux, and MacOS.3,10 Each REDCap system is maintained at each institution. Data entry can also be performed offline using the REDCap mobile app, which can be downloaded on an Apple or Android phone or tablet (see Table1).12
Table 1
Open in new tab
REDCap features that can facilitate collection, management, storage, and confidentiality of the data
| Feature | Explanation |
|---|---|
| REDCap features that can facilitate data collection | |
| Data validation | REDCap allows users to specify the format in which certain information needs to be filled out. Hence, one can indicate different date/time formats and the minimum and maximum values when a number needs to be entered. |
| Required fields | Items within a form/survey can be marked as ‘required fields’ which implies that the form cannot be saved when the item has not been completed. When a required field is skipped, respondents receive a warning when they try to save the form informing them about the item(s) that have been skipped. |
| Data quality tool | This tool not only includes several built-in data quality check rules (e.g. detecting invalid values, unallowed missingness), but also allows users to build their own data quality check rules to detect project-specific errors. |
| Personalized invitations/public surveys | When creating a survey in REDCap, one can choose to make the survey public and easy to distribute to many participants or one can make it a private survey that can only be assessed with a personalized invitation. |
| Overview of responders and non-responders | REDCap provides a clear overview of the included participants and tracks who has and has not yet responded. REDCap can send out reminders. |
| REDCap mobile app | Users can also collect their data using the REDCap mobile app on a smartphone or tablet. This relatively new tool allows users to perform offline data collection. |
| Data Import Tool | In the case that data have been collected partially already or data from an additional source should be added, the Data Import Tool can be used to import the data from a CSV file. A CSV template can be downloaded showing the structure of the database. While doing the import, REDCap performs quality checks. |
| Multilingual hook | The multilingual hook is an external module that can be installed in the REDCap project that enables one to set up surveys in different languages without the need to create additional variables or surveys. |
| REDCap features that can facilitate data management | |
| Surveys and direct data entry combined for one record | For each record, a set of surveys and/or data entry forms can be filled out. This allows researchers to manage all the data of one participant together. |
| Several measurement waves (longitudinal project) | For longitudinal projects, REDCap allows users to define the number and the name of events and to link instruments to these events. That way, data entry and data collection can be performed longitudinally. |
| Data logging | REDCap tracks every action performed in the project in the logging tool, including data creation, deletion, views, and edits. For every action, the tool reports the time, the user, and the action(s) performed. |
| REDCap features that can facilitate data storage | |
| Codebook | Documentation describing all variables in the project is automatically generated. This codebook can be exported as PDF to share it as project documentation. |
| Data dictionary | REDCap automatically generates a Data Dictionary for every project in CSV format. This document can be shared to describe the variables and structure of the project and can be imported to other REDCap projects to copy its data structure. |
| File repository | The file repository allows users to add other documents, such as the study protocol, to the project to keep all relevant information together. |
| Collaborative access to data across academic departments and institutions | REDCap allows people from different institutions to login to the database and to enter, collect, and manage data simultaneously. There is no need for all collaborators to be affiliated with an institution that is part of the REDCap consortium. One centre can set up the project and can give collaborative access to people from all over the world. |
| Data export function | Data can be exported to common statistical packages, such as CSV, Excel, SPSS, SAS, R, or XML for statistical analysis. |
| REDCap features that can facilitate confidentiality of the data | |
| User authentication | Access to the REDCap database requires user authentication with a unique username and password. In some institutions, multifactor authentication is required, in which the identity of the user should also be confirmed by logging into a device. |
| Role-based security | The main administrator of the project can assign a role to each individual working on the project and thereby give this person specific access privileges and decide on the extent to which the data is accessible and adjustable by this individual. |
| DAGs | DAGs restrict viewing of data within a database. They are typically used in multi-site projects or double-blinded projects, where certain users should only be able to view data from their participants. |
| Identifier fields | Fields such as name, email address, and date of birth can be marked as an identifier field, which means that the information contained in the field could lead to the identification of the respondent. During data export there are several options to remove identifier fields from the exported dataset. |
| Integrity check by the REDCap coordinator | When the project is in production mode and real data have been collected, it is still possible to make changes to the design and instruments of the project in draft mode (see Figure1). However, individual REDCap systems may require that all changes be reviewed by a REDCap administrator who performs accuracy and integrity checks. This feature does not apply in all centres that use REDCap. |
| Feature | Explanation |
|---|---|
| REDCap features that can facilitate data collection | |
| Data validation | REDCap allows users to specify the format in which certain information needs to be filled out. Hence, one can indicate different date/time formats and the minimum and maximum values when a number needs to be entered. |
| Required fields | Items within a form/survey can be marked as ‘required fields’ which implies that the form cannot be saved when the item has not been completed. When a required field is skipped, respondents receive a warning when they try to save the form informing them about the item(s) that have been skipped. |
| Data quality tool | This tool not only includes several built-in data quality check rules (e.g. detecting invalid values, unallowed missingness), but also allows users to build their own data quality check rules to detect project-specific errors. |
| Personalized invitations/public surveys | When creating a survey in REDCap, one can choose to make the survey public and easy to distribute to many participants or one can make it a private survey that can only be assessed with a personalized invitation. |
| Overview of responders and non-responders | REDCap provides a clear overview of the included participants and tracks who has and has not yet responded. REDCap can send out reminders. |
| REDCap mobile app | Users can also collect their data using the REDCap mobile app on a smartphone or tablet. This relatively new tool allows users to perform offline data collection. |
| Data Import Tool | In the case that data have been collected partially already or data from an additional source should be added, the Data Import Tool can be used to import the data from a CSV file. A CSV template can be downloaded showing the structure of the database. While doing the import, REDCap performs quality checks. |
| Multilingual hook | The multilingual hook is an external module that can be installed in the REDCap project that enables one to set up surveys in different languages without the need to create additional variables or surveys. |
| REDCap features that can facilitate data management | |
| Surveys and direct data entry combined for one record | For each record, a set of surveys and/or data entry forms can be filled out. This allows researchers to manage all the data of one participant together. |
| Several measurement waves (longitudinal project) | For longitudinal projects, REDCap allows users to define the number and the name of events and to link instruments to these events. That way, data entry and data collection can be performed longitudinally. |
| Data logging | REDCap tracks every action performed in the project in the logging tool, including data creation, deletion, views, and edits. For every action, the tool reports the time, the user, and the action(s) performed. |
| REDCap features that can facilitate data storage | |
| Codebook | Documentation describing all variables in the project is automatically generated. This codebook can be exported as PDF to share it as project documentation. |
| Data dictionary | REDCap automatically generates a Data Dictionary for every project in CSV format. This document can be shared to describe the variables and structure of the project and can be imported to other REDCap projects to copy its data structure. |
| File repository | The file repository allows users to add other documents, such as the study protocol, to the project to keep all relevant information together. |
| Collaborative access to data across academic departments and institutions | REDCap allows people from different institutions to login to the database and to enter, collect, and manage data simultaneously. There is no need for all collaborators to be affiliated with an institution that is part of the REDCap consortium. One centre can set up the project and can give collaborative access to people from all over the world. |
| Data export function | Data can be exported to common statistical packages, such as CSV, Excel, SPSS, SAS, R, or XML for statistical analysis. |
| REDCap features that can facilitate confidentiality of the data | |
| User authentication | Access to the REDCap database requires user authentication with a unique username and password. In some institutions, multifactor authentication is required, in which the identity of the user should also be confirmed by logging into a device. |
| Role-based security | The main administrator of the project can assign a role to each individual working on the project and thereby give this person specific access privileges and decide on the extent to which the data is accessible and adjustable by this individual. |
| DAGs | DAGs restrict viewing of data within a database. They are typically used in multi-site projects or double-blinded projects, where certain users should only be able to view data from their participants. |
| Identifier fields | Fields such as name, email address, and date of birth can be marked as an identifier field, which means that the information contained in the field could lead to the identification of the respondent. During data export there are several options to remove identifier fields from the exported dataset. |
| Integrity check by the REDCap coordinator | When the project is in production mode and real data have been collected, it is still possible to make changes to the design and instruments of the project in draft mode (see Figure1). However, individual REDCap systems may require that all changes be reviewed by a REDCap administrator who performs accuracy and integrity checks. This feature does not apply in all centres that use REDCap. |
DAGs, data access groups; REDCap, Research Electronic Data Capture.
Table 1
Open in new tab
REDCap features that can facilitate collection, management, storage, and confidentiality of the data
| Feature | Explanation |
|---|---|
| REDCap features that can facilitate data collection | |
| Data validation | REDCap allows users to specify the format in which certain information needs to be filled out. Hence, one can indicate different date/time formats and the minimum and maximum values when a number needs to be entered. |
| Required fields | Items within a form/survey can be marked as ‘required fields’ which implies that the form cannot be saved when the item has not been completed. When a required field is skipped, respondents receive a warning when they try to save the form informing them about the item(s) that have been skipped. |
| Data quality tool | This tool not only includes several built-in data quality check rules (e.g. detecting invalid values, unallowed missingness), but also allows users to build their own data quality check rules to detect project-specific errors. |
| Personalized invitations/public surveys | When creating a survey in REDCap, one can choose to make the survey public and easy to distribute to many participants or one can make it a private survey that can only be assessed with a personalized invitation. |
| Overview of responders and non-responders | REDCap provides a clear overview of the included participants and tracks who has and has not yet responded. REDCap can send out reminders. |
| REDCap mobile app | Users can also collect their data using the REDCap mobile app on a smartphone or tablet. This relatively new tool allows users to perform offline data collection. |
| Data Import Tool | In the case that data have been collected partially already or data from an additional source should be added, the Data Import Tool can be used to import the data from a CSV file. A CSV template can be downloaded showing the structure of the database. While doing the import, REDCap performs quality checks. |
| Multilingual hook | The multilingual hook is an external module that can be installed in the REDCap project that enables one to set up surveys in different languages without the need to create additional variables or surveys. |
| REDCap features that can facilitate data management | |
| Surveys and direct data entry combined for one record | For each record, a set of surveys and/or data entry forms can be filled out. This allows researchers to manage all the data of one participant together. |
| Several measurement waves (longitudinal project) | For longitudinal projects, REDCap allows users to define the number and the name of events and to link instruments to these events. That way, data entry and data collection can be performed longitudinally. |
| Data logging | REDCap tracks every action performed in the project in the logging tool, including data creation, deletion, views, and edits. For every action, the tool reports the time, the user, and the action(s) performed. |
| REDCap features that can facilitate data storage | |
| Codebook | Documentation describing all variables in the project is automatically generated. This codebook can be exported as PDF to share it as project documentation. |
| Data dictionary | REDCap automatically generates a Data Dictionary for every project in CSV format. This document can be shared to describe the variables and structure of the project and can be imported to other REDCap projects to copy its data structure. |
| File repository | The file repository allows users to add other documents, such as the study protocol, to the project to keep all relevant information together. |
| Collaborative access to data across academic departments and institutions | REDCap allows people from different institutions to login to the database and to enter, collect, and manage data simultaneously. There is no need for all collaborators to be affiliated with an institution that is part of the REDCap consortium. One centre can set up the project and can give collaborative access to people from all over the world. |
| Data export function | Data can be exported to common statistical packages, such as CSV, Excel, SPSS, SAS, R, or XML for statistical analysis. |
| REDCap features that can facilitate confidentiality of the data | |
| User authentication | Access to the REDCap database requires user authentication with a unique username and password. In some institutions, multifactor authentication is required, in which the identity of the user should also be confirmed by logging into a device. |
| Role-based security | The main administrator of the project can assign a role to each individual working on the project and thereby give this person specific access privileges and decide on the extent to which the data is accessible and adjustable by this individual. |
| DAGs | DAGs restrict viewing of data within a database. They are typically used in multi-site projects or double-blinded projects, where certain users should only be able to view data from their participants. |
| Identifier fields | Fields such as name, email address, and date of birth can be marked as an identifier field, which means that the information contained in the field could lead to the identification of the respondent. During data export there are several options to remove identifier fields from the exported dataset. |
| Integrity check by the REDCap coordinator | When the project is in production mode and real data have been collected, it is still possible to make changes to the design and instruments of the project in draft mode (see Figure1). However, individual REDCap systems may require that all changes be reviewed by a REDCap administrator who performs accuracy and integrity checks. This feature does not apply in all centres that use REDCap. |
| Feature | Explanation |
|---|---|
| REDCap features that can facilitate data collection | |
| Data validation | REDCap allows users to specify the format in which certain information needs to be filled out. Hence, one can indicate different date/time formats and the minimum and maximum values when a number needs to be entered. |
| Required fields | Items within a form/survey can be marked as ‘required fields’ which implies that the form cannot be saved when the item has not been completed. When a required field is skipped, respondents receive a warning when they try to save the form informing them about the item(s) that have been skipped. |
| Data quality tool | This tool not only includes several built-in data quality check rules (e.g. detecting invalid values, unallowed missingness), but also allows users to build their own data quality check rules to detect project-specific errors. |
| Personalized invitations/public surveys | When creating a survey in REDCap, one can choose to make the survey public and easy to distribute to many participants or one can make it a private survey that can only be assessed with a personalized invitation. |
| Overview of responders and non-responders | REDCap provides a clear overview of the included participants and tracks who has and has not yet responded. REDCap can send out reminders. |
| REDCap mobile app | Users can also collect their data using the REDCap mobile app on a smartphone or tablet. This relatively new tool allows users to perform offline data collection. |
| Data Import Tool | In the case that data have been collected partially already or data from an additional source should be added, the Data Import Tool can be used to import the data from a CSV file. A CSV template can be downloaded showing the structure of the database. While doing the import, REDCap performs quality checks. |
| Multilingual hook | The multilingual hook is an external module that can be installed in the REDCap project that enables one to set up surveys in different languages without the need to create additional variables or surveys. |
| REDCap features that can facilitate data management | |
| Surveys and direct data entry combined for one record | For each record, a set of surveys and/or data entry forms can be filled out. This allows researchers to manage all the data of one participant together. |
| Several measurement waves (longitudinal project) | For longitudinal projects, REDCap allows users to define the number and the name of events and to link instruments to these events. That way, data entry and data collection can be performed longitudinally. |
| Data logging | REDCap tracks every action performed in the project in the logging tool, including data creation, deletion, views, and edits. For every action, the tool reports the time, the user, and the action(s) performed. |
| REDCap features that can facilitate data storage | |
| Codebook | Documentation describing all variables in the project is automatically generated. This codebook can be exported as PDF to share it as project documentation. |
| Data dictionary | REDCap automatically generates a Data Dictionary for every project in CSV format. This document can be shared to describe the variables and structure of the project and can be imported to other REDCap projects to copy its data structure. |
| File repository | The file repository allows users to add other documents, such as the study protocol, to the project to keep all relevant information together. |
| Collaborative access to data across academic departments and institutions | REDCap allows people from different institutions to login to the database and to enter, collect, and manage data simultaneously. There is no need for all collaborators to be affiliated with an institution that is part of the REDCap consortium. One centre can set up the project and can give collaborative access to people from all over the world. |
| Data export function | Data can be exported to common statistical packages, such as CSV, Excel, SPSS, SAS, R, or XML for statistical analysis. |
| REDCap features that can facilitate confidentiality of the data | |
| User authentication | Access to the REDCap database requires user authentication with a unique username and password. In some institutions, multifactor authentication is required, in which the identity of the user should also be confirmed by logging into a device. |
| Role-based security | The main administrator of the project can assign a role to each individual working on the project and thereby give this person specific access privileges and decide on the extent to which the data is accessible and adjustable by this individual. |
| DAGs | DAGs restrict viewing of data within a database. They are typically used in multi-site projects or double-blinded projects, where certain users should only be able to view data from their participants. |
| Identifier fields | Fields such as name, email address, and date of birth can be marked as an identifier field, which means that the information contained in the field could lead to the identification of the respondent. During data export there are several options to remove identifier fields from the exported dataset. |
| Integrity check by the REDCap coordinator | When the project is in production mode and real data have been collected, it is still possible to make changes to the design and instruments of the project in draft mode (see Figure1). However, individual REDCap systems may require that all changes be reviewed by a REDCap administrator who performs accuracy and integrity checks. This feature does not apply in all centres that use REDCap. |
DAGs, data access groups; REDCap, Research Electronic Data Capture.
From development mode to archived mode: workflow of the REDCap project
An overview of the workflow of a REDCap project is illustrated in Figure1.13,14 When affiliated to an institution which is part of the consortium, the process of setting up a new REDCap project starts with a request of the research team to the local REDCap administrator. This REDCap administrator will create a new project, which is set in development mode. The designer tool allows the research team to create the database, by setting up data entry forms (see Figure2) and surveys. The forms and surveys can be thoroughly tested and revised. When ready, the project is set in production mode by the REDCap administrator. As of then, the system can be used to collect, enter, and extract real data. If it is necessary to make changes to a project while it is already in production mode, the project can be switched to draft mode, during which changes can be made to the instruments or settings. The data activities can continue while the project is in draft mode. When returning to production mode, the changes are reviewed by the REDCap administrator. Eventually, when the study is finished, a project can be put in inactive mode, in which the data can still be accessed and extracted. However, no data entry is possible anymore in this mode. Finally, the project can be put in archived mode or can be deleted. When put in archived mode, the project can be un-archived at any time (Figure1).
Figure 1
Workflow of a REDCap project.13,14 Icons from Flaticon are used. REDCap, Research Electronic Data Capture.
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Figure 2
Example of a typical data entry instrument in REDCap. REDCap, Research Electronic Data Capture.
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Solutions to the challenges
REDCap features that facilitate data collection, management, and/or storage are presented in Table1.
Data collection challenges
REDCap has several features that can help reducing the errors in dataset while collecting and entering data, such as data validation, required fields, and data quality tool. Personalized invitations can be sent out, thereby prohibiting duplicate submissions and allowing keeping track of who filled out the survey. The REDCap mobile app allows offline data entry, and the data import tool allows adding data from external sources. Also, the external module ‘multilingual hook’ can be installed, enabling participants to fill out the survey in different languages, without having to create additional variables or surveys.
Data management challenges
For each record in REDCap, surveys can be sent out and direct data entry can be performed in one or several measurement waves. That way, all data of one participant can be stored together and researchers can maintain a good overview of which data are available and which data are missing. There is no need to create different variables for data that are measured at different measurement moments.
REDCap has a very comprehensive logging tool in which all the manipulations are reported in detail. Also, REDCap keeps track of who accessed the data and when.
Data storage challenges
REDCap automatically generates a codebook and data dictionary, which provides good documentation of the meaning of the variables. The file repository allows to store different documents, such as the study protocol, within the REDCap project. These tools highly facilitate the reuse and interpretability of the data and the research project in general. Access can be granted to people from all over the world, which creates the opportunity to share data in a safe and secure way. The features to perform analyses in REDCap are very limited, but data can be exported from REDCap to common statistical packages, such as SPSS, Stata, SAS, or R.
Confidentiality and financial challenges
REDCap can be configured in ways to be compliant with different privacy regulations worldwide.3,10 Some features are built-in to simplify the process of setting up a secure data collection, such as user authentication, role-based security, data access groups, identifier fields, and an integrity check by the REDCap administrator.
Importantly, currently, there is no cost for an organization to obtain the REDCap software, though institutions may pass on a fee for IT infrastructure and support to their users. Other comparable data management tools, such as Viedoc and IBM Clinical Development, are not available for free and thus, they are only good options for projects that have sufficient and dedicated funding for data management tools.10 Also, REDCap is user-friendly. No specific programming skills are needed to work with it. As the involved REDCap administrator is usually someone of the IT department, professional IT support can be provided at low threshold.
Example of the use of REDCap in the international research project APPROACH-IS II
APPROACH-IS II (Assessment of Patterns of Patient-Reported Outcomes in Adults with Congenital Heart disease—International Study II) is a global collaborative investigation of patient-reported outcomes and experiences, frailty, and cognitive functioning among adults with congenital heart defects (ClinicalTrials.gov: NCT04902768).15,16 APPROACH-IS II is the sequel to APPROACH-IS,17,18 in which over 4000 patients were enrolled from 15 countries around the world. APPROACH-IS II is a cross-sectional study of an even bigger magnitude, as the sample is estimated to be more than 10 000 patients. Data are collected in over 60 centres in 34 countries around the world and surveys are distributed in over 20 languages. The Coordinating Centre is the University of Leuven in Belgium. Patient-reported outcomes and experiences of patients are measured using self-reported surveys, which are completed by patients on paper or online. REDCap has been chosen for the data management of this project. The use of certain key features in this project, which were especially handy for this international large-scale project, are detailed in Table2.
Table 2
Open in new tab
Examples of the application of a couple of features of REDCap in the international research project APPROACH-IS II
| Feature | Use of this feature in APPROACH-IS II |
|---|---|
| Collaborative access to data across academic departments and institutions | The REDCap database for APPROACH-IS II can be accessed and used from all over the world. By using REDCap for this project, it can be avoided that data have to be sent over by email, as all data can be entered securely into REDCap by all collaborators. As the project for this study is set up in the Coordinating Centre KU Leuven in Belgium, which is part of the REDCap Consortium, all collaborators can join this REDCap project located in Belgium and there is no need for their institution to be part of the REDCap consortium as well. A disadvantage is, however, that all collaborators must create a KU Leuven account. |
| User authentication | Thanks to the user authentication, and other features related to privacy and security, REDCap can be configured to be highly secure and compliant with international privacy regulations (e.g. GDPR, HIPAA), which is very important for APPROACH-IS II. Moreover, the tool is well known by ethical committees and widely used all over the world. Storing data on REDCap and gathering informed consent using this tool is, therefore, more acceptable for the involved ethics committees worldwide. |
| DAGs | For APPROACH-IS II, over 60 DAGs are set up to organize the data per participating centre. That way, all researchers can work in one project, which facilitates the data management for the Coordinating Center, but the participating centres have only access to the data of the participants from their centre. |
| Multilingual hook | APPROACH-IS II makes use of the multilingual hook. Doing so, for every variable, a translation of the question-and-answer possibilities is implemented in REDCap for different languages, without the need to create new variables or surveys. The disadvantage is, however, is that titles of matrices and new sections cannot be translated. Therefore, in APPROACH-IS II, the multilingual hook is only used for regional differences in languages. For example, when English participants open the survey, they can indicate whether they want to complete the survey in English from Canada, English from the USA, English from Cameroon, etc. However, because no new sections and titles of matrices could be translated, the multilingual hook could not cope with over 20 languages involved in this project and additional REDCap projects had to be created to cope with this difficulty. Future REDCap development may address this issue. |
| Feature | Use of this feature in APPROACH-IS II |
|---|---|
| Collaborative access to data across academic departments and institutions | The REDCap database for APPROACH-IS II can be accessed and used from all over the world. By using REDCap for this project, it can be avoided that data have to be sent over by email, as all data can be entered securely into REDCap by all collaborators. As the project for this study is set up in the Coordinating Centre KU Leuven in Belgium, which is part of the REDCap Consortium, all collaborators can join this REDCap project located in Belgium and there is no need for their institution to be part of the REDCap consortium as well. A disadvantage is, however, that all collaborators must create a KU Leuven account. |
| User authentication | Thanks to the user authentication, and other features related to privacy and security, REDCap can be configured to be highly secure and compliant with international privacy regulations (e.g. GDPR, HIPAA), which is very important for APPROACH-IS II. Moreover, the tool is well known by ethical committees and widely used all over the world. Storing data on REDCap and gathering informed consent using this tool is, therefore, more acceptable for the involved ethics committees worldwide. |
| DAGs | For APPROACH-IS II, over 60 DAGs are set up to organize the data per participating centre. That way, all researchers can work in one project, which facilitates the data management for the Coordinating Center, but the participating centres have only access to the data of the participants from their centre. |
| Multilingual hook | APPROACH-IS II makes use of the multilingual hook. Doing so, for every variable, a translation of the question-and-answer possibilities is implemented in REDCap for different languages, without the need to create new variables or surveys. The disadvantage is, however, is that titles of matrices and new sections cannot be translated. Therefore, in APPROACH-IS II, the multilingual hook is only used for regional differences in languages. For example, when English participants open the survey, they can indicate whether they want to complete the survey in English from Canada, English from the USA, English from Cameroon, etc. However, because no new sections and titles of matrices could be translated, the multilingual hook could not cope with over 20 languages involved in this project and additional REDCap projects had to be created to cope with this difficulty. Future REDCap development may address this issue. |
DAGs, data access groups; REDCap, Research Electronic Data Capture.
Table 2
Open in new tab
Examples of the application of a couple of features of REDCap in the international research project APPROACH-IS II
| Feature | Use of this feature in APPROACH-IS II |
|---|---|
| Collaborative access to data across academic departments and institutions | The REDCap database for APPROACH-IS II can be accessed and used from all over the world. By using REDCap for this project, it can be avoided that data have to be sent over by email, as all data can be entered securely into REDCap by all collaborators. As the project for this study is set up in the Coordinating Centre KU Leuven in Belgium, which is part of the REDCap Consortium, all collaborators can join this REDCap project located in Belgium and there is no need for their institution to be part of the REDCap consortium as well. A disadvantage is, however, that all collaborators must create a KU Leuven account. |
| User authentication | Thanks to the user authentication, and other features related to privacy and security, REDCap can be configured to be highly secure and compliant with international privacy regulations (e.g. GDPR, HIPAA), which is very important for APPROACH-IS II. Moreover, the tool is well known by ethical committees and widely used all over the world. Storing data on REDCap and gathering informed consent using this tool is, therefore, more acceptable for the involved ethics committees worldwide. |
| DAGs | For APPROACH-IS II, over 60 DAGs are set up to organize the data per participating centre. That way, all researchers can work in one project, which facilitates the data management for the Coordinating Center, but the participating centres have only access to the data of the participants from their centre. |
| Multilingual hook | APPROACH-IS II makes use of the multilingual hook. Doing so, for every variable, a translation of the question-and-answer possibilities is implemented in REDCap for different languages, without the need to create new variables or surveys. The disadvantage is, however, is that titles of matrices and new sections cannot be translated. Therefore, in APPROACH-IS II, the multilingual hook is only used for regional differences in languages. For example, when English participants open the survey, they can indicate whether they want to complete the survey in English from Canada, English from the USA, English from Cameroon, etc. However, because no new sections and titles of matrices could be translated, the multilingual hook could not cope with over 20 languages involved in this project and additional REDCap projects had to be created to cope with this difficulty. Future REDCap development may address this issue. |
| Feature | Use of this feature in APPROACH-IS II |
|---|---|
| Collaborative access to data across academic departments and institutions | The REDCap database for APPROACH-IS II can be accessed and used from all over the world. By using REDCap for this project, it can be avoided that data have to be sent over by email, as all data can be entered securely into REDCap by all collaborators. As the project for this study is set up in the Coordinating Centre KU Leuven in Belgium, which is part of the REDCap Consortium, all collaborators can join this REDCap project located in Belgium and there is no need for their institution to be part of the REDCap consortium as well. A disadvantage is, however, that all collaborators must create a KU Leuven account. |
| User authentication | Thanks to the user authentication, and other features related to privacy and security, REDCap can be configured to be highly secure and compliant with international privacy regulations (e.g. GDPR, HIPAA), which is very important for APPROACH-IS II. Moreover, the tool is well known by ethical committees and widely used all over the world. Storing data on REDCap and gathering informed consent using this tool is, therefore, more acceptable for the involved ethics committees worldwide. |
| DAGs | For APPROACH-IS II, over 60 DAGs are set up to organize the data per participating centre. That way, all researchers can work in one project, which facilitates the data management for the Coordinating Center, but the participating centres have only access to the data of the participants from their centre. |
| Multilingual hook | APPROACH-IS II makes use of the multilingual hook. Doing so, for every variable, a translation of the question-and-answer possibilities is implemented in REDCap for different languages, without the need to create new variables or surveys. The disadvantage is, however, is that titles of matrices and new sections cannot be translated. Therefore, in APPROACH-IS II, the multilingual hook is only used for regional differences in languages. For example, when English participants open the survey, they can indicate whether they want to complete the survey in English from Canada, English from the USA, English from Cameroon, etc. However, because no new sections and titles of matrices could be translated, the multilingual hook could not cope with over 20 languages involved in this project and additional REDCap projects had to be created to cope with this difficulty. Future REDCap development may address this issue. |
DAGs, data access groups; REDCap, Research Electronic Data Capture.
Conclusion
Using REDCap can help clinical researchers overcome challenges regarding data collection, management, storage, and confidentiality. The tool can currently be used at a very low cost, does not require extensive programming skills and is relatively user-friendly. Individual licences are not available. A new REDCap project can only be set up at an institution that is part of the REDCap consortium. The tool can be of interest for cardiovascular nursing researchers, as demonstrated in the APPROACH-IS II research project. We recommend nurse researchers to familiarize themselves with REDCap and to find out to what extent this tool can facilitate the organization of their research projects.
Note from the authors
The list of challenges and REDCap features provided in this article is not exhaustive. The selected challenges and features are based on the experiences of the authors of this article. There are many more REDCap features available. More information about the other features can be found here: https://projectredcap.org/.
Funding
This work was supported by the Research Foundation Flanders (1154719N to L.V.B.).
Conflict of interest: none declared.
Data availability
No new data were generated or analysed for this article.
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© The Author(s) 2021. Published by Oxford University Press on behalf of the European Society of Cardiology.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
Topic:
- confidentiality
- information storage and retrieval
- privacy
- statutes and laws
- data management
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