Safeguarding Decentralized Research from Insincere Participants: Case Series and Scoping Review

INTRODUCTION: Decentralized clinical trials increase access to research but also heighten risks of insincere participation, including misrepresentation and bots. This review examines case studies and literature to assess impacts on data integrity and identify mitigation strategies.

METHODS: We first present four case studies from the University of North Carolina at Chapel Hill illustrating insincere participation in decentralized clinical research. We reviewed literature to characterize prevalence, risks, and mitigation strategies. Databases searched included PubMed, Google Scholar, Academic Search Premier, ProQuest Central, and PsycInfo using Boolean operators. Search terms included combinations of imposter, insincere, deception, fraud, bots, and scammers with participant-related terms. Peer-reviewed articles addressing insincere participation or ethical considerations in participant verification were included.

RESULTS: Across four UNC-Chapel Hill case studies, insincere participation manifested as duplicate enrollment attempts, identity and eligibility misrepresentation, scripted or vague responses, automation fraud, and rapid response clustering, often linked to online recruitment and disclosed compensation. Consequences included falsified data, increased staff burden, disrupted study activities, and ethical challenges related to compensation. Review of 20 published studies identified four primary categories of insincere participation:fraudulent or inattentive individuals, duplicate takers, click-farm respondents, and automated bots. The literature emphasized layered, proportionate mitigations strategies.

DISCUSSION: Investigators must remain vigilant of the potential for insincere participation in decentralized research and actively consider recruitment, screening, and compensation strategies to mitigate this risk.A flexible, risk-based framework enables teams to protect study integrity and preserve efficiency.