An Electronic Data Capture Framework (ConnEDCt) for Global and Public Health Research : Design and Implementation
©Caleb J Ruth, Samantha Lee Huey, Jesse T Krisher, Amy Fothergill, Bryan M Gannon, Camille Elyse Jones, Elizabeth Centeno-Tablante, Laura S Hackl, Susannah Colt, Julia Leigh Finkelstein, Saurabh Mehta. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 13.08.2020..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:22 |
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| Enthalten in: |
Journal of medical Internet research - 22(2020), 8 vom: 13. Aug., Seite e18580 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ruth, Caleb J [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 15.01.2021 Date Revised 15.01.2021 published: Electronic Citation Status MEDLINE |
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| doi: |
10.2196/18580 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM313628823 |
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| 100 | 1 | |a Ruth, Caleb J |e verfasserin |4 aut | |
| 245 | 1 | 3 | |a An Electronic Data Capture Framework (ConnEDCt) for Global and Public Health Research |b Design and Implementation |
| 264 | 1 | |c 2020 | |
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| 500 | |a Date Completed 15.01.2021 | ||
| 500 | |a Date Revised 15.01.2021 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a ©Caleb J Ruth, Samantha Lee Huey, Jesse T Krisher, Amy Fothergill, Bryan M Gannon, Camille Elyse Jones, Elizabeth Centeno-Tablante, Laura S Hackl, Susannah Colt, Julia Leigh Finkelstein, Saurabh Mehta. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 13.08.2020. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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) | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Electronic Data Capture (EDC) | |
| 650 | 4 | |a data collection | |
| 650 | 4 | |a data management | |
| 650 | 4 | |a data science | |
| 650 | 4 | |a database management systems | |
| 650 | 4 | |a global health | |
| 650 | 4 | |a health information management | |
| 650 | 4 | |a longitudinal studies | |
| 650 | 4 | |a population surveillance | |
| 650 | 4 | |a public health | |
| 650 | 4 | |a randomized controlled trial | |
| 700 | 1 | |a Huey, Samantha Lee |e verfasserin |4 aut | |
| 700 | 1 | |a Krisher, Jesse T |e verfasserin |4 aut | |
| 700 | 1 | |a Fothergill, Amy |e verfasserin |4 aut | |
| 700 | 1 | |a Gannon, Bryan M |e verfasserin |4 aut | |
| 700 | 1 | |a Jones, Camille Elyse |e verfasserin |4 aut | |
| 700 | 1 | |a Centeno-Tablante, Elizabeth |e verfasserin |4 aut | |
| 700 | 1 | |a Hackl, Laura S |e verfasserin |4 aut | |
| 700 | 1 | |a Colt, Susannah |e verfasserin |4 aut | |
| 700 | 1 | |a Finkelstein, Julia Leigh |e verfasserin |4 aut | |
| 700 | 1 | |a Mehta, Saurabh |e verfasserin |4 aut | |
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| 773 | 1 | 8 | |g volume:22 |g year:2020 |g number:8 |g day:13 |g month:08 |g pages:e18580 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.2196/18580 |3 Volltext |
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