Reed Nicole D, Bull Sheana, Shrestha Umit, Sarche Michelle, Kaufman Carol E
Centers for American Indian and Alaska Native Health, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.
Community and Behavioral Health, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.
JMIR Res Protoc. 2024 Jun 13;13:e52281. doi: 10.2196/52281.
While the advantages of using the internet and social media for research recruitment are well documented, the evolving online environment also enhances motivations for misrepresentation to receive incentives or to "troll" research studies. Such fraudulent assaults can compromise data integrity, with substantial losses in project time; money; and especially for vulnerable populations, research trust. With the rapid advent of new technology and ever-evolving social media platforms, it has become easier for misrepresentation to occur within online data collection. This perpetuation can occur by bots or individuals with malintent, but careful planning can help aid in filtering out fraudulent data.
Using an example with urban American Indian and Alaska Native young women, this paper aims to describe PRIOR (Protocol for Increasing Data Integrity in Online Research), which is a 2-step integration protocol for combating fraudulent participation in online survey research.
From February 2019 to August 2020, we recruited participants for formative research preparatory to an online randomized control trial of a preconceptual health program. First, we described our initial protocol for preventing fraudulent participation, which proved to be unsuccessful. Then, we described modifications we made in May 2020 to improve the protocol performance and the creation of PRIOR. Changes included transferring data collection platforms, collecting embedded geospatial variables, enabling timing features within the screening survey, creating URL links for each method or platform of data collection, and manually confirming potentially eligible participants' identifying information.
Before the implementation of PRIOR, the project experienced substantial fraudulent attempts at study enrollment, with less than 1% (n=6) of 1300 screened participants being identified as truly eligible. With the modified protocol, of the 461 individuals who completed a screening survey, 381 did not meet the eligibility criteria assessed on the survey. Of the 80 that did, 25 (31%) were identified as ineligible via PRIOR. A total of 55 (69%) were identified as eligible and verified in the protocol and were enrolled in the formative study.
Fraudulent surveys compromise study integrity, validity of the data, and trust among participant populations. They also deplete scarce research resources including respondent compensation and personnel time. Our approach of PRIOR to prevent online misrepresentation in data was successful. This paper reviews key elements regarding fraudulent data participation in online research and demonstrates why enhanced protocols to prevent fraudulent data collection are crucial for building trust with vulnerable populations.
ClinicalTrials.gov NCT04376346; https://www.clinicaltrials.gov/study/NCT04376346.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52281.
虽然利用互联网和社交媒体进行研究招募的优势已有充分记录,但不断演变的在线环境也增加了为获取奖励或“恶搞”研究而进行虚假陈述的动机。这种欺诈行为可能会损害数据完整性,导致项目时间、资金大量损失,尤其是对于弱势群体而言,会破坏研究信任。随着新技术的迅速出现和社交媒体平台的不断演变,在线数据收集过程中更容易出现虚假陈述。这种情况可能由机器人或恶意个人造成,但精心规划有助于过滤掉欺诈性数据。
以美国城市印第安人和阿拉斯加原住民年轻女性为例,本文旨在描述PRIOR(在线研究数据完整性增强协议),这是一种用于打击在线调查研究中欺诈性参与的两步整合协议。
从2019年2月至2020年8月,我们为一项孕前健康计划的在线随机对照试验的形成性研究招募参与者。首先,我们描述了最初防止欺诈性参与的协议,但事实证明该协议并不成功。然后,我们描述了2020年5月为改进协议性能和创建PRIOR所做的修改。这些修改包括转移数据收集平台、收集嵌入式地理空间变量、在筛查调查中启用计时功能、为每种数据收集方法或平台创建URL链接以及手动确认潜在合格参与者的识别信息。
在实施PRIOR之前,该项目在研究招募过程中遭遇了大量欺诈性尝试,在1300名接受筛查的参与者中,只有不到1%(n = 6)被确定为真正合格。采用修改后的协议后,在461名完成筛查调查的个体中,有381人不符合调查评估的资格标准。在符合标准的80人中,有25人(31%)通过PRIOR被确定为不合格。共有55人(69%)被确定为合格并在协议中得到验证,随后被纳入形成性研究。
欺诈性调查会损害研究的完整性、数据的有效性以及参与者群体之间的信任。它们还会消耗稀缺的研究资源,包括受访者补偿和人员时间。我们采用的PRIOR方法成功地防止了在线数据中的虚假陈述。本文回顾了在线研究中欺诈性数据参与的关键要素,并说明了为何加强防止欺诈性数据收集的协议对于与弱势群体建立信任至关重要。
ClinicalTrials.gov NCT04376346;https://www.clinicaltrials.gov/study/NCT04376346。
国际注册报告识别码(IRRID):DERR1-10.2196/52281。