Bots and Fake Participants: Ensuring Valid and Reliable Data Collection Using Online Participant Recruitment Methods

Area of Science:

• Health Sciences
• Clinical Research
• Digital Health

Background:

• Online recruitment offers broad reach for health sciences research but introduces risks like bot interference and fraudulent participation.
• Maintaining data integrity is crucial for the validity and reliability of research findings when using online recruitment methods.

Purpose of the Study:

• To address challenges in online participant recruitment for health sciences research.
• To propose a novel framework, The Swiss Cheese Model of Study Participant Fraud Prevention, to mitigate fraud in online recruitment.

Main Methods:

• A case-control study utilizing online recruitment methods across the United States.
• Adaptation of Reason's Swiss Cheese Model to develop a fraud prevention strategy.
• Illustration of ten specific prevention and verification measures for online recruitment.

Main Results:

• Online recruitment expands participant access but necessitates robust fraud prevention strategies.
• The proposed Swiss Cheese Model offers a structured, layered approach to minimize participant fraud.
• Implementation of designed recruitment media, compensation, vetting, and data verification is key.

Conclusions:

• A multi-layered approach is essential for ensuring data integrity in online health research recruitment.
• The Swiss Cheese Model of Study Participant Fraud Prevention provides a practical framework for researchers.
• Adopting these measures enhances the validity and reliability of research findings in the digital era.