Background Disparities in exposure to and density of tobacco advertising are well established; however, it is still unclear how e-cigarette and heated tobacco product (HTP) advertising vary by age, education, sex, gender identity, race/ethnicity, sexual orientation, socioeconomic status (SES), and/or urban/rural area. Through a scoping review, we sought to identify potential disparities in exposure to e-cigarette and HTP advertising and promotion across populations. Methods In January 2020, a systematic literature search was conducted in five databases: PubMed, Scopus, Embase, Web of Science, and the Cochrane Library. The search was updated in October 2020. Articles reporting on exposure to e-cigarette and/or HTP advertising and promotion across age, education, sex, gender identity, race/ethnicity, sexual orientation, SES, and/or urban/rural areas were included for full-text review (n = 25). Of those, 15 were deemed relevant for data extraction. Results The majority of the studies were from the U.S. (n = 12) and cross-sectional (n = 14). Studies were published between 2014 and 2020 and focused on determining causal relationships that underlie disparities; only one study assessed HTP advertising and promotion. Exposure to e-cigarette and HTP advertising was assessed at the individual-level (e.g., recall seeing ads on television) and at the neighborhood-level (e.g., ad density at the point-of-sale). Studies addressed differences across age (n = 6), education (n = 2), sex (n = 6), gender identity and sexual orientation (n = 3), race/ethnicity (n = 11), SES (n = 5), and urban/rural (n = 2). The following populations were more likely to be exposed to e-cigarette advertising: youth, those with more than a high school diploma, males, sexual and gender minorities, Whites, and urban residents. At the neighborhood-level, e-cigarette advertisements were more prevalent in non-White neighborhoods. Conclusions Exposure to e-cigarette/HTP advertising varies based on sociodemographic characteristics, although the literature is limited especially regarding HTPs. Higher exposure among youth might increase tobacco-related disparities since it can lead to nicotine/tobacco use. Research should incorporate and apply a health equity lens from its inception to obtain data to inform the elimination of those disparities.

Background 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. Objective 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. Methods 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). Results 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. Conclusions 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

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Background e-Cigarette device and liquid characteristics are highly customizable; these characteristics impact nicotine delivery and exposure to toxic constituents. It is critical to understand optimal methods for measuring these characteristics to accurately assess their impacts on user behavior and health. Objective To inform future survey development, we assessed the agreement between responses from survey participants (self-reports) and photos uploaded by participants and the quantity of usable data derived from each approach. Methods Adult regular e-cigarette users (≥5 days per week) aged ≥21 years (N=1209) were asked questions about and submitted photos of their most used e-cigarette device (1209/1209, 100%) and liquid (1132/1209, 93.63%). Device variables assessed included brand, model, reusability, refillability, display, and adjustable power. Liquid variables included brand, flavor, nicotine concentration, nicotine formulation, and bottle size. For each variable, percentage agreement was calculated where self-report and photo data were available. Krippendorff α and intraclass correlation coefficient (ICC) were calculated for categorical and continuous variables, respectively. Results were stratified by device (disposable, reusable with disposable pods or cartridges, and reusable with refillable pods, cartridges, or tanks) and liquid (customized and noncustomized) type. The sample size for each calculation ranged from 3.89% (47/1209; model of disposable devices) to 95.12% (1150/1209; device reusability). Results Percentage agreement between photos and self-reports was substantial to very high across device and liquid types for all variables except nicotine concentration. These results are consistent with Krippendorff α calculations, except where prevalence bias was suspected. ICC results for nicotine concentration and bottle size were lower than percentage agreement, likely because ICC accounts for the level of disagreement between values. Agreement varied by device and liquid type. For example, percentage agreement for device brand was higher among users of reusable devices (94%) than among users of disposable devices (75%). Low percentage agreement may result from poor participant knowledge of characteristics, user modifications of devices inconsistent with manufacturer-intended use, inaccurate or incomplete information on websites, or photo submissions that are not a participant’s most used device or liquid. The number of excluded values (eg, self-report was “don’t know” or no photo submitted) differed between self-reports and photos; for questions asked to participants, self-reports had more usable data than photos for all variables except device model and nicotine formulation. Conclusions Photos and self-reports yield data of similar accuracy for most variables assessed in this study: device brand, device model, reusability, adjustable power, display, refillability, liquid brand, flavor, and bottle size. Self-reports provided more data for all variables except device model and nicotine formulation. Using these approaches simultaneously may optimize data quantity and quality. Future research should examine how to assess nicotine concentration and variables not included in this study (eg, wattage and resistance) and the resource requirements of these approaches.