Hardesty Jeffrey J, Crespi Elizabeth, Nian Qinghua, Sinamo Joshua K, Breland Alison B, Eissenberg Thomas, Welding Kevin, Kennedy Ryan David, Cohen Joanna E
Institute for Global Tobacco Control, Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
Center for the Study of Tobacco Products, Department of Psychology, Virginia Commonwealth University, Richmond, VA, United States.
JMIR Res Protoc. 2023 Mar 2;12:e38732. doi: 10.2196/38732.
In total, 3.2% of American adults report using e-cigarettes every day or some days. The Vaping and Patterns of E-cigarette Use Research (VAPER) Study is a web-based longitudinal survey designed to observe patterns in device and liquid use that suggest the benefits and unintended consequences of potential e-cigarette regulations. The heterogeneity of the e-cigarette devices and liquids on the market, the customizability of the devices and liquids, and the lack of standardized reporting requirements result in unique measurement challenges. Furthermore, bots and survey takers who submit falsified responses are threats to data integrity that require mitigation strategies.
This paper aims to describe the protocols for 3 waves of the VAPER Study and discuss recruitment and data processing experiences and lessons learned, including the benefits and limitations of bot- and fraudulent survey taker-related strategies.
American adults (aged ≥21 years) who use e-cigarettes ≥5 days per week are recruited from up to 404 Craigslist catchment areas covering all 50 states. The questionnaire measures and skip logic are designed to accommodate marketplace heterogeneity and user customization (eg, different skip logic pathways for different device types and customizations). To reduce reliance on self-report data, we also require participants to submit a photo of their device. All data are collected using REDCap (Research Electronic Data Capture; Vanderbilt University). Incentives are US $10 Amazon gift codes delivered by mail to new participants and electronically to returning participants. Those lost to follow-up are replaced. Several strategies are applied to maximize the odds that participants who receive incentives are not bots and are likely to possess an e-cigarette (eg, required identity check and photo of a device).
In total, 3 waves of data were collected between 2020 and 2021 (wave 1: n=1209; wave 2: n=1218; wave 3: n=1254). Retention from waves 1 to 2 was 51.94% (628/1209), and 37.55% (454/1209) of the wave 1 sample completed all 3 waves. These data were mostly generalizable to daily e-cigarette users in the United States, and poststratification weights were generated for future analyses. Our data offer a detailed examination of users' device features and specifications, liquid characteristics, and key behaviors, which can provide more insights into the benefits and unintended consequences of potential regulations.
Relative to existing e-cigarette cohort studies, this study methodology has some advantages, including efficient recruitment of a lower-prevalence population and collection of detailed data relevant to tobacco regulatory science (eg, device wattage). The web-based nature of the study requires several bot- and fraudulent survey taker-related risk-mitigation strategies, which can be time-intensive. When these risks are addressed, web-based cohort studies can be successful. We will continue to explore methods for maximizing recruitment efficiency, data quality, and participant retention in subsequent waves.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/38732.
总计3.2%的美国成年人表示每天或有时会使用电子烟。电子烟使用及模式研究（VAPER）是一项基于网络的纵向调查，旨在观察设备和烟液使用模式，以提示潜在电子烟法规的益处和意外后果。市场上电子烟设备和烟液的异质性、设备和烟液的可定制性以及缺乏标准化报告要求导致了独特的测量挑战。此外，机器人程序和提交虚假回复的调查对象对数据完整性构成威胁，需要采取缓解策略。
本文旨在描述VAPER研究3个阶段的方案，并讨论招募和数据处理的经验及教训，包括与机器人程序和欺诈性调查对象相关策略的益处和局限性。
从覆盖美国所有50个州的多达404个克雷格列表集水区招募每周使用电子烟≥5天的美国成年人（年龄≥21岁）。问卷测量和跳转逻辑旨在适应市场异质性和用户定制（例如，针对不同设备类型和定制的不同跳转逻辑路径）。为减少对自我报告数据的依赖，我们还要求参与者提交其设备的照片。所有数据均使用REDCap（研究电子数据采集；范德比尔特大学）收集。激励措施是向新参与者邮寄10美元亚马逊礼品卡，向回访参与者以电子方式发放。对失访者进行替换。应用了几种策略来最大化获得激励的参与者不是机器人程序且可能拥有电子烟的几率（例如，要求身份验证和设备照片）。
2020年至2021年期间共收集了3个阶段的数据（第1阶段：n = 1209；第2阶段：n = （此处原文有误，应为1218）；第3阶段：n = 1254）。从第1阶段到第2阶段的保留率为51.94%（628/1209），第1阶段样本中的37.55%（454/1209）完成了所有3个阶段。这些数据大多可推广到美国的日常电子烟使用者，并为未来分析生成了事后分层权重。我们的数据详细考察了用户的设备特征和规格、烟液特性以及关键行为，这可为潜在法规的益处和意外后果提供更多见解。
相对于现有的电子烟队列研究，本研究方法具有一些优势，包括有效招募低患病率人群以及收集与烟草监管科学相关的详细数据（例如，设备功率）。该研究基于网络的性质需要几种与机器人程序和欺诈性调查对象相关的风险缓解策略，这可能会耗费大量时间。当这些风险得到解决时，基于网络的队列研究可以取得成功。我们将继续探索在后续阶段最大化招募效率、数据质量和参与者保留率的方法。
国际注册报告识别码（IRRID）：DERR1-10.2196/38732 。
