Identifying and counteracting fraudulent responses in online recruitment for health research

What is it about?

This study aimed to describe how health researchers identify and counteract fraudulent responses when recruiting participants online. This study aimed to describe how health researchers identify and counteract fraudulent responses when recruiting participants online. Nine databases, including Medline, Informit, AMED, CINAHL, Embase, Cochrane CENTRAL, IEEE Xplore, Scopus and Web of Science, were searched from inception to April 2024. Two authors independently screened and selected each study and performed data extraction, following the Joanna Briggs Institute’s methodological guidance for scoping reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. A predefined framework guided the evaluation of fraud identification and mitigation strategies within the studies included. This framework, adapted from a participatory mapping study that identified indicators of fraudulent survey responses, allowed for systematic assessment and comparison of the effectiveness of various antifraud strategies across studies.

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Photo by Jefferson Santos on Unsplash

Photo by Jefferson Santos on Unsplash

Why is it important?

23 studies were included. 18 studies (78%) reported encountering fraudulent responses. Among the studies reviewed, the proportion of participants excluded for fraudulent or suspicious responses ranged from as low as 3% to as high as 94%. Survey completion time was used in six studies (26%) to identify fraud, with completion times under 5 min flagged as suspicious. 12 studies (52%) focused on non-confirming responses, identifying implausible text patterns through specific questions, consistency checks and open-ended questions. Four studies examined temporal events, such as unusual survey completion times. Seven studies (30%) reported on geographical incongruity, using IP address verification and location screening. Incentives were reported in 17 studies (73%), with higher incentives often increasing fraudulent responses. Mitigation strategies included using in-built survey features like Completely Automated Public Turing test to tell Computers and Humans Apart (34%), manual verification (21%) and video checks (8%). Most studies recommended multiple detection methods to maintain data integrity.

Perspectives