Evidence map›Paper›PMID 39715631›Full record

ArticleBMJ evidence-based medicine2025

Identifying and counteracting fraudulent responses in online recruitment for health research: a scoping review.

Josielli Comachio, Adam Poulsen, Adeola Bamgboje-Ayodele, Aidan Tan, Julie Ayre, Rebecca Raeside, Rajshri Roy, Edel O'Hagan

Abstract readScoping Review
In one paragraph

Article in BMJ evidence-based medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed.

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  20. I have been scammed in my qualitative research.Research integrity and peer review · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Josielli ComachioSydney Musculoskeletal Health, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia josielli.comachio@sydney.edu.au.ORCID 0000-0002-3286-6998
Adam PoulsenBrain and Mind Centre, The University of Sydney, Sydney, New South Wales, Australia.ORCID 0000-0002-0001-3894
Adeola Bamgboje-AyodeleBiomedical Informatics and Digital Health, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.ORCID 0000-0002-5629-1236
Aidan TanNHMRC Clinical Trials Centre, Camperdown, New South Wales, Australia.ORCID 0000-0003-0354-4006
Julie AyreSydney Health Literacy Lab, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.ORCID 0000-0002-5279-5189
Rebecca RaesideSusan Wakil School of Nursing and Midwifery, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.ORCID 0000-0003-2016-6393
Rajshri RoyDiscipline of Nutrition and Dietetics, Susan Wakil School of Nursing and Midwifery, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.ORCID 0000-0002-7939-1790
Edel O'HaganWestmead Applied Research Centre, School of Medicine, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.ORCID 0000-0002-1914-5918

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study aimed to describe how health researchers identify and counteract fraudulent responses when recruiting participants online.

designScoping review. ELIGIBILITY CRITERIA: Peer-reviewed studies published in English; studies that report on the online recruitment of participants for health research; and studies that specifically describe methodologies or strategies to detect and address fraudulent responses during the online recruitment of research participants. SOURCES OF EVIDENCE: Nine databases, including Medline, Informit, AMED, CINAHL, Embase, Cochrane CENTRAL, IEEE Xplore, Scopus and Web of Science, were searched from inception to April 2024. CHARTING

methodsTwo 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.

results23 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.

conclusionThere is insufficient evaluation of strategies to mitigate fraud in online health research, which hinders the ability to offer evidence-based guidance to researchers on their effectiveness. Researchers should employ a combination of strategies to counteract fraudulent responses when recruiting online to optimise data integrity.

Indexed as

Biomedical ResearchFraudInternetPatient SelectionScientific MisconductHumansEvidence-Based PracticeMethodsPUBLIC HEALTH

Identifiers

PMID39715631
PMCPMC12171401

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.