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This paper is in the following e-collection/theme issue:

Viewpoints and Perspectives (1451) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (1769) Crowdsourcing and Mechanical Turks (114) Gestational Diabetes (63) Security in Digital Health (86)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58450, first published .
Digital interface with user profiles, data security icons, and search functions

Identifying Fraudulent Responses in a Study Exploring Delivery Options for Pregnancies Impacted by Gestational Diabetes: Lessons Learned From a Web-Based Survey

Identifying Fraudulent Responses in a Study Exploring Delivery Options for Pregnancies Impacted by Gestational Diabetes: Lessons Learned From a Web-Based Survey

Authors of this article:

Emma Ruby1, 2 Author Orcid Image ;   Serine Ramlawi2 Author Orcid Image ;   Alexa Clare Bowie2 Author Orcid Image ;   Stephanie Boyd2 Author Orcid Image ;   Alysha Dingwall-Harvey2 Author Orcid Image ;   Ruth Rennicks White2, 3 Author Orcid Image ;   Darine El-Chaâr2, 3, 4, 5 Author Orcid Image ;   Mark Walker2, 3, 4, 5, 6 Author Orcid Image
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Journals

  1. Willard-Grace R, Klima T, Dedhia M, Lo E, Nisnevich A, Gray A, Henry H. Selecting fraud detection methods: Using epidemiological test diagnostics to evaluate responses in a cross-sectional questionnaire study (Preprint). Journal of Medical Internet Research 2025 View

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  2. Ryan I, Roedig U, Stol K. Proceedings of the 2026 IEEE/ACM 48th International Conference on Software Engineering. Weak Programmers Need Not Apply, LLMs Welcome! Survey Screening in the AI Era View