Patrick Wedgeworth

Background: Fully remote online health research is vulnerable to fraudulent participation that can threaten data validity and divert study resources. Although fraud mitigation is increasingly necessary in online research, mitigation efforts are rarely described systematically or evaluated transparently across studies. Objective: This study demonstrates the application of the configure, assess, triage, corroborate, and hone (CATCH) framework to an ongoing remote mental health study and evaluates how structured mitigation procedures have affected participant progression and operational costs. Methods: This study was conducted within an ongoing, fully remote mental health study evaluating hallucinations through longitudinal digital data collection. Following detection of fraudulent activity, the CATCH framework was applied to organize mitigation procedures into stages and refine detection and verification workflows. New screening safeguards were configured, classification thresholds were recalibrated, enrolled participants were reassessed and triaged accordingly, and secondary corroboration through synchronous video verification was introduced when warranted. Procedures were honed through ongoing monitoring in response to emerging fraud patterns. We compared 2 temporally defined cohorts before (March-August 2025) and after (September 2025-January 2026) the implementation of the refined procedures. Primary outcomes included the stage at which fraudulent participants were identified and cost per eligible participant, decomposed into costs attributable to fraudulent participants, nonfraudulent participants, and fraud-mitigation technology. Participant-level costs were estimated using a stage-based model incorporating participant compensation and research staff effort. Sensitivity analyses examined alternative compensation and staffing scenarios to assess the generalizability of the proposed cost-accounting mechanism. Results: Following CATCH implementation, fraudulent participants were identified earlier in the study workflow, reducing progression to later study stages. Overall, 41.5% (161/388) of reassessed participants were discharged for suspected fraud, while 58.5% (227/388) were retained following adjudication. Among those flagged as high risk and invited to video verification, 1.8% (3/164) successfully confirmed their identity, providing a lower-bound estimate of the false-positive classification rate. The mean sunk cost per fraudulent participant decreased from US $12.75 (SD US $40.76) in the pre-CATCH cohort to US $3.29 (SD US $10.91) in the post-CATCH cohort. Total cost per eligible participant decreased from US $170.48 to US $134.90, driven by a substantial reduction in the fraud-related portion from US $80.46 to US $19.73. Sensitivity analyses showed reductions in fraud-related cost per eligible participant across all alternative costing scenarios. To our knowledge, this is the first empirical demonstration of a structured fraud-mitigation framework implemented and evaluated within an active health study. Conclusions: Structuring fraud mitigation using an explicit framework enabled systematic documentation of procedural adaptations and their operational consequences. Beyond study-specific findings, this work illustrates how organizing fraud mitigation into defined, reportable stages can promote transparency, reproducibility, and cumulative learning across remote health studies. Treating fraud mitigation as an integral methodological component is essential for safeguarding scientific integrity and legitimate resource expenditure in online research.

BACKGROUND Caring Contacts is a low-cost and low-resource suicide prevention intervention that involves sending a series of non-demanding messages that convey care and support. While Caring Contacts is well studied among veteran and adult populations, intervention implementation preferences remain underexplored for adolescents and young adults, especially regarding its delivery in primary care settings. As primary care providers are often constrained in their capacity to respond to the time-sensitive needs of these patients while they wait to be connected to specialty care, identifying if current technologies that can help alleviate these time barriers are acceptable among adolescents and young adult patients is an important next step. OBJECTIVE We evaluated the preferences of adolescents and young adults regarding the delivery of Caring Contacts. We also examined their perspectives on if and how artificial intelligence should be incorporated into the Caring Contacts intervention. METHODS Semi-structured interviews were conducted with 10 adolescents and young adults who reported a history of suicidal thoughts and behaviors. Interview transcripts were analyzed using qualitative coding techniques to evaluate their preferences and identify overarching themes. RESULTS Participants described a preference for receiving Caring Contacts via SMS text messaging, noting its perceived usability as the main contributor to this preference. While patient portals and physical letters were noted as other acceptable delivery methods, challenges to accessing them easily and in a timely manner made them less desirable compared to SMS text messaging. Regarding the incorporation of artificial intelligence into Caring Contacts, participants generally viewed these technologies negatively and with hesitancy describing it as potentially impersonal given the context of suicidality and wanting transparency if it were to be used. CONCLUSIONS This study suggests that adolescents and young adults have distinct preferences for how they want Caring Contacts to be delivered compared to previously studied veteran and adult patient populations. Artificial intelligence may help address primary care provider time and resource constraints, but its implementation must carefully address the concerns raised by adolescents and young adults, particularly around transparency in its use and its perceived impersonal nature.

BACKGROUND Fully remote online health research is vulnerable to fraudulent participation that can threaten data validity and divert study resources. Although fraud mitigation is increasingly necessary in online research, mitigation efforts are rarely described systematically or evaluated transparently across studies. OBJECTIVE This study demonstrates the application of the configure, assess, triage, corroborate, and hone (CATCH) framework to an ongoing remote mental health study and evaluates how structured mitigation procedures have affected participant progression and operational costs. METHODS This study was conducted within an ongoing, fully remote mental health study evaluating hallucinations through longitudinal digital data collection. Following detection of fraudulent activity, the CATCH framework was applied to organize mitigation procedures into stages and refine detection and verification workflows. New screening safeguards were configured, classification thresholds were recalibrated, enrolled participants were reassessed and triaged accordingly, and secondary corroboration through synchronous video verification was introduced when warranted. Procedures were honed through ongoing monitoring in response to emerging fraud patterns. We compared 2 temporally defined cohorts before (March-August 2025) and after (September 2025-January 2026) the implementation of the refined procedures. Primary outcomes included the stage at which fraudulent participants were identified and cost per eligible participant, decomposed into costs attributable to fraudulent participants, nonfraudulent participants, and fraud-mitigation technology. Participant-level costs were estimated using a stage-based model incorporating participant compensation and research staff effort. Sensitivity analyses examined alternative compensation and staffing scenarios to assess the generalizability of the proposed cost-accounting mechanism. RESULTS Following CATCH implementation, fraudulent participants were identified earlier in the study workflow, reducing progression to later study stages. Overall, 41.5% (161/388) of reassessed participants were discharged for suspected fraud, while 58.5% (227/388) were retained following adjudication. Among those flagged as high risk and invited to video verification, 1.8% (3/164) successfully confirmed their identity, providing a lower-bound estimate of the false-positive classification rate. The mean sunk cost per fraudulent participant decreased from US $12.75 (SD US $40.76) in the pre-CATCH cohort to US $3.29 (SD US $10.91) in the post-CATCH cohort. Total cost per eligible participant decreased from US $170.48 to US $134.90, driven by a substantial reduction in the fraud-related portion from US $80.46 to US $19.73. Sensitivity analyses showed reductions in fraud-related cost per eligible participant across all alternative costing scenarios. To our knowledge, this is the first empirical demonstration of a structured fraud-mitigation framework implemented and evaluated within an active health study. CONCLUSIONS Structuring fraud mitigation using an explicit framework enabled systematic documentation of procedural adaptations and their operational consequences. Beyond study-specific findings, this work illustrates how organizing fraud mitigation into defined, reportable stages can promote transparency, reproducibility, and cumulative learning across remote health studies. Treating fraud mitigation as an integral methodological component is essential for safeguarding scientific integrity and legitimate resource expenditure in online research.

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.