Background: Why imposter participants matter in health research
Imposter participants—individuals who deliberately provide false or misleading data, or automated bots that mimic human responses—pose a growing challenge to health research. As The BMJ highlights, this issue undermines not only the data used to derive clinical insights but also the policies and medical decisions built on those findings. In an era where online recruitment underpins a wide range of studies from surveys to randomized controlled trials, safeguarding the integrity of participant data is more critical than ever.
What researchers mean by imposter participation
The term covers a spectrum of behaviors. Some participants lie about eligibility, demographics, or health status; others complete tasks with deceptive intent; and some are bots designed to imitate human responses. The motivations behind such behavior remain uncertain. While monetary incentives may drive some cases, many studies do not offer direct payment, suggesting boredom, curiosity, ideological disruption, or other motives could be at play. The lack of clarity around motive makes detection and prevention even more challenging.
Evidence of impact
Recent analyses underscore the scope of the problem. A 2025 review found that 18 of 23 studies seeking imposter signals in their data detected them, with prevalence ranging from 3% to as high as 94%. Even modest rates can distort estimates, bias results, and mislead clinical interpretations, especially in studies with small sample sizes or where online recruitment dominates the data stream. The consequences extend beyond academia, potentially shaping guidelines, patient safety considerations, and resource allocation.
Recommended safeguards and how they help
The authors argue for routine integration of imposter detection and prevention into the design and execution of online research. Practical safeguards include:
- Identity verification procedures (e.g., multi-factor checks) to confirm participant eligibility.
- CAPTCHA-type tests and task-based challenges that are resistant to automation.
- Monitoring data patterns for anomalies (rapid completion times, improbable response patterns, duplicate IPs).
- Transparent reporting of safeguards used, their limitations, and any detected instances.
Importantly, transparent reporting allows journals, funders, and readers to assess a study’s robustness and reproducibility. The authors call for consistent standards that describe what was done to deter and detect imposter participation and what could not be ruled out.
Responsibilities of researchers, journals, and funders
Researchers should embed detection and prevention measures from the study design stage, balancing data quality with respect for participant privacy and representativeness. Journals should encourage comprehensive reporting of safeguards and an explicit statement about any detected imposter activity. Funders and institutions must invest in infrastructure, training, and ongoing updates to keep pace with evolving tactics used by imposter participants and bots.
Implications for clinical interpretation and policy
Clinicians and policymakers rely on online research to inform practice and policy. When imposter participation is not addressed, the integrity of the evidence base weakens and the downstream decisions built on that evidence become less trustworthy. The authors’ warning—imposter participants are not merely a nuisance but a systemic threat—emphasizes the need for a proactive, coordinated response across the research ecosystem.
What comes next
In an online recruitment landscape that remains central to health studies, proactive measures, standardized reporting, and ongoing training are essential. The path forward includes developing better detection algorithms, refining verification processes to minimize impact on diverse populations, and fostering a culture of transparency in reporting. If researchers fail to act, the very legitimacy of health research—and the clinical care it informs—may be at stake.
