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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Sep 23, 2024
Open Peer Review Period: Sep 11, 2024 - Oct 9, 2024
Date Accepted: Dec 21, 2024
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Detecting Deception and Ensuring Data Integrity in a Nationwide mHealth Randomized Controlled Trial: Factorial Design Survey Study

Kezbers KM, Robertson MC, Hébert ET, Montgomery A, Businelle MS

Detecting Deception and Ensuring Data Integrity in a Nationwide mHealth Randomized Controlled Trial: Factorial Design Survey Study

J Med Internet Res 2025;27:e66384

DOI: 10.2196/66384

PMID: 39874573

PMCID: 11815295

Detecting deception and implementing procedures to preserve data integrity in nationwide mobile health randomized controlled trials

  • Krista M Kezbers; 
  • Michael C Robertson; 
  • Emily T Hébert; 
  • Audrey Montgomery; 
  • Michael S Businelle

ABSTRACT

Background:

Social behavioral research studies have increasingly shifted to remote recruitment and enrollment procedures. This shifting landscape necessitates evolving best practices to help mitigate the negative impacts of deceptive attempts at enrolling into behavioral research.

Objective:

The objective of the current research was to develop and implement robust deception detection procedures during the enrollment period of a remotely conducted randomized controlled trial.

Methods:

A factorial design study was conducted from November 2021 to September 2022 to identify mHealth survey design features associated with the highest completion rates of smartphone-based ecological momentary assessments (N=485). Recruitment was conducted remotely via Facebook advertisements, a 5-minute REDCap pre-screener, and a screening and enrollment phone call. The research team created and implemented a 12-step checklist (eg, address verification, texting a copy of picture identification) to identify and prevent potentially deceptive attempts to enroll in the study. Descriptive statistics were calculated to understand the prevalence of various types of deceptive attempts at study enrollment.

Results:

Facebook advertisements resulted in 5236 initiations of the REDCap pre-screener. A digital deception detection procedure was implemented for those that were deemed pre-eligible (n=1928). This procedure resulted in 26% (501/1928) of pre-screeners being flagged as potentially deceptive. Completing multiple pre-screeners and providing invalid addresses were the most common reasons pre-screeners were flagged. An additional 1% (18/1928) of pre-screeners were flagged as potentially deceptive during the subsequent study screening and enrollment phone call. Post-enrollment social security number checks revealed that 3 fully enrolled participants out of 485 provided erroneous social security numbers during the screening process.

Conclusions:

Implementation of a deception detection procedure in a remotely conducted randomized controlled trial resulted in a substantial proportion of cases being flagged as potentially engaging in deceptive attempts at study enrollment. Implementing systematic deception detection procedures may support study administration, data quality, and participant safety in remotely conducted behavioral research. Clinical Trial: Clinicaltrials.gov number: NCT05194228; https://clinicaltrials.gov/study/NCT05194228


 Citation

Please cite as:

Kezbers KM, Robertson MC, Hébert ET, Montgomery A, Businelle MS

Detecting Deception and Ensuring Data Integrity in a Nationwide mHealth Randomized Controlled Trial: Factorial Design Survey Study

J Med Internet Res 2025;27:e66384

DOI: 10.2196/66384

PMID: 39874573

PMCID: 11815295

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