Research Electronic Data Capture (REDCap) for Population-Based Data Collection in Low- and Middle-Income Countries: Opportunities, Challenges, and Solutions
Health research requires high-quality data, and population-based health research comes with specific opportunities and challenges for data collection. Electronic data capture can mitigate some of the challenges of working with large populations in multiple, sometimes difficult-to-reach, l...
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2025-06-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2025/1/e65377 |
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| Summary: | Health research requires high-quality data, and population-based health research comes with specific opportunities and challenges for data collection. Electronic data capture can mitigate some of the challenges of working with large populations in multiple, sometimes difficult-to-reach, locations. This viewpoint paper aims to describe experiences during the implementation of two mixed methods studies in Vietnam, Nepal, and Indonesia, focusing on understanding lived experiences of the COVID-19 pandemic across 3 countries and understanding knowledge and behaviors related to antibiotic use in Vietnam. We present the opportunities, challenges, and solutions arising through using Research Electronic Data Capture (REDCap) for designing, collecting, and managing data. Electronic data capture using REDCap made it possible to collect data from large populations in different settings. Challenges related to working in multiple languages, unstable internet connections, and complex questionnaires with nested forms. Some data collectors lacked the digital skills to comfortably use REDCap. To overcome these challenges, we included regular team meetings, training, supervision, and automated error-checking procedures. The main types of errors that remained were incomplete and duplicate records due to disruption during data collection. However, with immediate access to data, we were able to identify and troubleshoot these problems quickly, while data collection was still in progress. By detailing our lessons learned—such as the importance of iterative testing, regular intersite meetings, and customized modifications—we provide a roadmap for future projects to boost productivity, enhance data quality, and effectively conduct large-scale population-based research. Our suggestions will be beneficial for research teams working with electronic data capture for population-based data. |
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| ISSN: | 1438-8871 |