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<\/p>\nScientific studies into conditions from cancer to HIV are being plagued by imposter participants, undermining the reliability of health research and potentially harming patients, Oxford academics have warned.<\/p>\n
Researchers who use online systems to recruit participants for surveys and trials are finding that up to 94 per cent of applicants have to be disqualified as \u201cfraudulent or suspicious\u201d before their studies can go ahead.<\/p>\n
This can be because they are people who are lying about having the disease being investigated, or because they are suspected to be automated bots that flood trials with fake applications.<\/p>\n
But some of these imposters are likely to be slipping through the cracks and taking part in the studies. This would skew the results of health research, leading health policymakers to make incorrect decisions and ultimately causing harm to patients, according to a warning published in the BMJ.<\/p>\n
\u201cThe undetected presence of imposter participants in quantitative datasets threatens the integrity of health research and, by extension, the policies and clinical decisions built on it,\u201d wrote the academics, led by Eileen Morrow, a doctoral research fellow at the Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences at the University of Oxford. \u201cResults clouded by imposter participants may affect clinical treatment.\u201d<\/p>\n
Academics remain mystified as to the motivation behind the imposters. The most likely driver for many is financial, when there is a study that pays participants for their time. <\/p>\n
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But analysis suggests that the same problem is seen with studies that offer no cash incentives, with academics suggesting: \u201cProposed alternatives include boredom, curiosity or even an ideological intent to disrupt research.\u201d<\/p>\n
A review published in the journal BMJ Evidence-Based Medicine looked at 23 studies and found that 18 had reported \u201cencountering fraudulent responses\u201d.<\/p>\n
Most were studies that investigated specific behaviours or groups of people, including users of alcohol, tobacco, opioids and other drugs, cancer survivors, Covid-19 patients and people living with HIV, the review found.<\/p>\n
An online survey investigating how ovarian cancer patients rated the communication from their doctors had 576 applications to take part. Most came in between midnight and 4am.<\/p>\n
\u201cThe authors judged 94 per cent of responses to be fraudulent and the remaining 6 per cent suspicious, with no participant deemed unquestionably legitimate,\u201d the academics said. \u201cAs a result, they closed and relaunched the survey with stricter protocols.\u201d<\/p>\n
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Most drug trials are likely to be more secure as patients are recruited via clinics and verified by doctors<\/p>\n
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The problem was not unique to surveys. A randomised controlled trial, the gold standard for making objective assessments in science, into the effectiveness of an app to help people reduce their alcohol intake found that 76 per cent of online applicants were bots. A further 4 per cent were \u201cdeceptive human respondents\u201d.<\/p>\n
Morrow said that she had once interviewed a person for a study who had claimed to be a UK healthcare professional. When asked to prove their membership of a professional body they \u201cabruptly ended the call\u201d. They then enrolled again under the same name and same email address, \u201cthis time claiming to be a child who had undergone surgery\u201d.<\/p>\n
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Eileen Morrow said that imposter participants were putting the research behind clinical decisions at risk<\/p>\n
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She said that most drug trials were \u201crelatively secure\u201d as participants tended to be recruited via clinics and had their diagnoses verified by doctors, but survey-style studies that collect data about patients\u2019 experiences or test health apps were more at risk.<\/p>\n
Morrow said she was not aware of study papers having to be retracted after publication due to imposter participants but warned: \u201cUnless researchers notice anomalies in their datasets they may never think to look for imposters, and most detection methods are unverified.\u201d<\/p>\n