Practical guidance for mitigating fraud in online research: The Configure, Assess, Triage, Corroborate, and Hone (CATCH) framework

Abstract

Fraudulent participation is a growing challenge in digital health research, particularly in online studies where duplicate identities, automated responses, and coordinated sign-ups can distort recruitment, compromise validity, and divert resources. Safeguards intended to prevent fraud might also risk excluding legitimate participants, raising concerns about sample representativeness and study generalizability. Although a wide range of technical and behavioral strategies exists, guidance is lacking on how to organize these methods and report outcomes consistently across studies. To address this gap, we introduce the Configure, Assess, Triage, Corroborate, and Hone (CATCH) framework, a hybrid fraud detection–mitigation model with actionable recommendations for investigators. CATCH begins with pre-study configuration to prepare for fraud mitigation and proceeds through systematic assessment of fraud risk, triage of candidates into risk categories, and corroboration of inconclusive cases, while honing strategies through ongoing monitoring. The framework emphasizes transparent documentation and reporting of actions and outcomes to facilitate comparability across studies and continuous methodological refinement. As fraudulent participation grows and emerging technologies act as both risks and solutions, CATCH can help guide investigators’ efforts to maximize data integrity in digital health research. By synthesizing existing fraud-mitigation strategies into a unified, staged framework, CATCH offers practical guidance for structuring decisions, documenting actions, and balancing data integrity with inclusivity.

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