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Mitigating fraud in a fully decentralized clinical trial of a digital health intervention

Bibliographic Data

ID7146829
AuthorsJessie B Moore (0000-0003-4657-0722, Stanford University), Amy Chieng (0000-0003-2739-7338, Stanford University), Maddison C Pirner (0000-0002-3993-0606, Woebot Health , San Francisco, CA), Sarah Pajarito (0000-0003-3201-0353, Woebot Health , San Francisco, CA), Erin A Vogel (0000-0002-7193-8720, TSET Health Promotion Research Center, University of Oklahoma Health Sciences Center , Oklahoma City, OK), Molly A Bowdring (0000-0001-5115-274X, Stanford University), Lauren Bullard (0000-0002-7368-2618, Woebot Health , San Francisco, CA), Athena Robinson (0000-0002-1393-1563, Woebot Health , San Francisco, CA), Judith J Prochaska (0000-0001-7925-326X, Stanford University)
Year2025
Volume59
Issue1
Publication date2025-01-04
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueAnnals of Behavioral Medicine (JOURNAL)
Journal identifiersISSN: 0883-6612 • E-ISSN: 1532-4796
PublisherOxford University Press (PUBLISHER • GB)
DOI10.1093/abm/kaaf047
PMID40548528
OpenAlexW4411623023
LanguageEN
References cited26

BACKGROUND: Decentralized clinical trials (DCT), digital survey methodologies, and health monitoring technologies create the potential to reduce study participant burden as well as enhance sample diversity and enrollment pace. However, fraudulent participant activity poses a significant threat to study validity and data integrity. PURPOSE: This study quantifies fraudulent attempts at participation in a DCT of a mobile mental health intervention for problematic substance use and discusses methods to prevent and detect fraudulent activity. METHODS: Adults residing in the US reporting problematic substance use were recruited for a fully remote DCT via 2 primary channels: social media advertisements and survey panels. The DCT offered incentives totaling up to $100 for completing assessments over the 12-week study. To prevent and detect fraudulent activity, the research team utilized VPN and proxy detection, checked for duplicate identifiers (e.g., emails, phone numbers, IP Addresses), and compared age and date of birth (DOB) responses across timepoints. Descriptive statistics and group comparisons across the 2 sources of recruitment methodology were utilized to quantify and characterize fraudulent activity. RESULTS: Of the 2,781 eligible screeners completed, 1,725 (62%) were determined to be fraudulent prior to randomization, detected most commonly by duplicate identifiers (65%) and/or VPN and proxy detection (47%). Of the 258 randomized participants, 51 (20%) were later determined to be fraudulent based upon age and/or DOB mismatch. Notable patterns in fraudulent activity (e.g., 42% of fraudulent screening respondents reported the exact age of 30 years; stylistic formatting of email address accounts) were identified. The fraudulent recruitment rate was higher for social media advertising (85%) than survey panels (26%). CONCLUSIONS: Both social media and survey panel recruitment resulted in high levels of fraudulent activity in a DCT of a mobile mental health intervention. Researchers conducting DCTs and/or online surveys are urged to take several precautions and preventative measures to insulate against fraudulent activity including embedding identity verification procedures in consent processes. Researchers should consider making personal contact with a participant to verify identity as well as remain vigilant for fraudulent activity and its real-time dynamic potential. TRIAL REGISTRATION: NCT04925570.

Alternative medicine · Incentive · Internet privacy · mHealth · Pathology · Protocol (science) · Proxy (statistics) · Psychiatry · Psychological intervention · Computer Science · Electronic Health Records Systems · Ethics in Clinical Research · Medicine · Psychology · Social Media in Health Education

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Citation velocityhistorical
Highly citedNo

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