BMC Medical Informatics and Decision Making (Oct 2023)

Quest markup for developing FAIR questionnaire modules for epidemiologic studies

  • Daniel E. Russ,
  • Nicole M. Gerlanc,
  • Brian Shen,
  • Bhaumik Patel,
  • Amy Berrington de González,
  • Neal D. Freedman,
  • Julie M. Cusack,
  • Mia M. Gaudet,
  • Montserrat García-Closas,
  • Jonas S. Almeida

DOI
https://doi.org/10.1186/s12911-023-02338-6
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 7

Abstract

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Abstract Background Online questionnaires are commonly used to collect information from participants in epidemiological studies. This requires building questionnaires using machine-readable formats that can be delivered to study participants using web-based technologies such as progressive web applications. However, the paucity of open-source markup standards with support for complex logic make collaborative development of web-based questionnaire modules difficult. This often prevents interoperability and reusability of questionnaire modules across epidemiological studies. Results We developed an open-source markup language for presentation of questionnaire content and logic, Quest, within a real-time renderer that enables the user to test logic (e.g., skip patterns) and view the structure of data collection. We provide the Quest markup language, an in-browser markup rendering tool, questionnaire development tool and an example web application that embeds the renderer, developed for The Connect for Cancer Prevention Study. Conclusion A markup language can specify both the content and logic of a questionnaire as plain text. Questionnaire markup, such as Quest, can become a standard format for storing questionnaires or sharing questionnaires across the web. Quest is a step towards generation of FAIR data in epidemiological studies by facilitating reusability of questionnaires and data interoperability using open-source tools.

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