Journal article
Let’s Talk aBOT Scam Online Survey Completions in Health Behavior Research: Tutorial With Case Studies, Practical Guidance, and a Checklist for Researchers
JMIR Public Health and Surveillance, Vol.11, pp.1-10
2025
PMID: 41439662
Abstract
Background:
Online data collection can reach large populations efficiently and cost-effectively. However, the increase in bots and scammers (ie, person- or software-based fraudulent completions) completing online surveys raises data integrity issues and wastes scarce research resources.
Objective:
This paper aims to describe case studies and experiences in which bot or scam completions of online surveys occurred within the health behavior field (specifically physical activity and nutrition). Lessons learned and a checklist of strategies to assist researchers before, during, and after data collection to reduce the incidence of and identify bot or scam completions are provided.
Methods:
Four diverse case studies are presented from studies that used online recruitment and data collection methods for cross-sectional surveys by parents about children’s screen time, cross-sectional surveys by adults about transport-related physical activity, qualitative interviews for a proposed trauma-informed physical activity program for female victim-survivors of intimate partner violence, and the Australian component of a large multicountry prospective study targeting university students. The strategies used to identify and prevent bot or scam online survey completions are explored.
Results:
High levels (7%-80%) of suspected bot or scam completions were identified in a number of these studies. Participant characteristics and outcome variables were significantly different between included and excluded participants (eg, excluded responses had a higher percentage of male parents and children, higher social media use, and lower physical activity guideline adherence). The learnings from these case studies and the wider literature are combined to create a checklist of strategies that researchers can use to prevent and identify bot or scam completions. These include strategies before data collection (when creating study collateral), during survey design and development (including the use of inbuilt platform functions and the design of the survey questions and structure), following data collection (indicators of potential bot or scam completions), and recommendations for reporting of bots or scams.
Conclusions:
The checklist, based on the included case studies and wider literature, can be used to help researchers who use online recruitment and data collection methods at each stage, from planning and conducting through to analyzing and reporting their findings. Researchers should include several steps to prevent and identify fraudulent survey responses when creating surveys and completing data cleaning. This checklist should also be considered in grant applications and ethics applications. This will provide greater confidence in the research findings and reduce unnecessary waste of research time and resources.
Details
- Title
- Let’s Talk aBOT Scam Online Survey Completions in Health Behavior Research: Tutorial With Case Studies, Practical Guidance, and a Checklist for Researchers
- Authors
- Lauren Arundell (Corresponding Author) - Deakin UniversityJo Salmon - Deakin UniversityAnthony Walsh - Deakin UniversityKathleen Dullaghan - Deakin UniversityHeilok Cheng - Deakin UniversityThea Baker - Deakin UniversityMegan Teychenne - Deakin UniversityFelipe Schuch - Universidade Federal do Rio de JaneiroDebora Tornquist - Universidade Federal de Santa MariaAnna Timperio - Deakin University
- Publication details
- JMIR Public Health and Surveillance, Vol.11, pp.1-10
- Publisher
- JMIR Publications, Inc.
- Date published
- 2025
- DOI
- 10.2196/76622
- ISSN
- 2369-2960
- PMID
- 41439662
- Copyright note
- ©Lauren Arundell, Jo Salmon, Anthony Walsh, Kathleen Dullaghan, Heilok Cheng, Thea Baker, Megan Teychenne, Felipe Schuch, Debora Tornquist, Anna Timperio. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 24.Dec.2025. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.
- Data Availability
- The datasets generated or analyzed during this study are not publicly available due to ethical approval not permitting this, but are available from the corresponding author on reasonable request.
- Grants
- Grant note
- FS is supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq Grant 314105/2023-9) and Centro de Aperfeiçoamento de Pessoal de Nível Superior (CAPES code 0001). DT is supported by the Foundation for Research Support of Rio Grande do Sul State (Brazil; 23/2551-0000140-3).
- Organisation Unit
- School of Health - Public Health
- Language
- English
- Record Identifier
- 991212678002621
- Output Type
- Journal article
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