ArticleJournal of medical Internet research2022
Agreement Between Self-reports and Photos to Assess e-Cigarette Device and Liquid Characteristics in Wave 1 of the Vaping and Patterns of e-Cigarette Use Research Study: Web-Based Longitudinal Cohort Study.
Article in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 14 citations in OpenAlex.
- Predictors of vaping relapse based on e-cigarette use measures: A secondary analysis of longitudinal data from the 'Stop Vaping Challenge' cessation application.Tobacco induced diseases · 2026Article
- Use of multiple devices among U.S. adults frequently using ENDS in 2023.Drug and alcohol dependence · 2025Article
- Decisions of the FDA on premarket tobacco product applications: Changes in the number of unique devices and liquids used by US adults who frequently use electronic nicotine delivery systems, 2020-2023.Tobacco induced diseases · 2024Article
- Electronic Cigarette Device and Liquid Characteristics: Comparison of Self-Reports to User-Provided Pictures.Substance use & misuse · 2024Article
- Device and liquid characteristics used with sweet, menthol/mint, and tobacco ENDS liquid flavors: The population-based VAPER study.Addictive behaviors · 2023Article
- Assessing Electronic Nicotine Delivery Systems Device Type Accurately in Surveys.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2023Article
- E-cigarette Use Patterns, Flavors, and Device Characteristics Associated With Quitting Smoking Among a U.S. sample of Adults Using E-cigarettes in a Smoking Cessation Attempt.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2023Article
- The Vaping and Patterns of e-Cigarette Use Research Study: Protocol for a Web-Based Cohort Study.JMIR research protocols · 2023Article
- Evaluation of the Psychometric Properties of Dependence Measures for Exclusive Electronic Cigarette Users.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2023Article
- Transitions in device and liquid characteristic groupings among US adults frequently using electronic nicotine delivery systems (ENDS) over three timepoints, 2020-2021.Tobacco induced diseases · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
backgrounde-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.
objectiveTo 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.
methodsAdult 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).
resultsPercentage 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.
conclusionsPhotos 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.