Quantifying e-cigarette use is challenging because of the wide variety of products and the lack of a clear, objective demarcation of a use event. This study aimed to characterize difference between retrospective and real-time measures of the quantity of e-cigarette use and identify the covariates that may account for discrepancies between the two types of measures.
This study analyzed data from 401 college student e-cigarette users in Indiana and Texas who responded to a web survey (retrospective) and 7-day EMA (real-time) on their e-cigarette use behavior, dependence symptomatology, e-cigarette product characteristics and use contexts from Fall 2019 to Fall 2021. Generalized linear mixed models were used to model the real-time measures of quantity offset by the retrospective average quantity.
Although the number of times using e-cigarettes per day seems to be applicable to both retrospective and real-time measures, the number reported via EMA was 8.5 times the retrospective report. E-cigarette users with higher e-cigarette primary dependence motives tended to report more daily nicotine consumption via EMA than their retrospective reports (i.e., perceived average consumption). Other covariates that were associated with discrepancies between real-time and retrospective reports included gender, nicotine concentration, using a menthol- or fruit-flavored product, co-use with alcohol, and being with others when vaping
The study found extreme under-reporting of e-cigarette consumption on retrospective surveys. Important covariates identified to be associated with higher than average consumption may be considered as potential targets for future vaping interventions.
This is the first study that characterizes the direction and magnitude of the difference between retrospective and real-time measures of the quantity of e-cigarette use among young adults – the population most likely to use e-cigarettes. An average retrospective account of vaping events per day may significantly underestimate e-cigarette use frequency among young adults. The lack of insight into the degree of consumption among users with heavy primary dependence motives illustrates the importance of incorporating self-monitoring into cessation interventions.