Background: The United States Food and Drug Administration has prioritized understanding the dependence potential of electronic cigarettes (e-cigs). Dependence is often estimated in part by examining frequency of use; however measures of e-cig use are not well developed because of varying product types. This study used an e-cig automatic puff counter to evaluate the value of self-reported e-cig use measures in predicting actual use (puffs). Methods: Data were collected from a two-site randomized placebo-controlled trial evaluating the effects of e-cigs on toxicant exposure in smokers attempting to reduce their cigarette consumption. Participants randomized to an e-cig condition self-reported their e-cig frequency of use (times per day - one "time"consists of around 15 puffs or lasts around 10 minutes) on the Penn State Electronic Cigarette Dependence Index (PSECDI) and kept daily diary records of the number of puffs per day from the e-cig automatic puff counter. A linear mixed-effects model was used to determine the predictive value of the times per day measure. Correlations were used to further investigate the relationship. Results: A total of 259 participants with 1165 observations of e-cig use were analyzed. Self-reported e-cig use in times per day was a significant predictor of e-cig puffs per day (p <. 01). The Spearman correlation between measures was r equal to. 58. Examination of individual participant responses revealed some potential difficulties reporting and interpreting times per day because of the difference in use patterns between cigarettes and e-cigs. Conclusion: This study provides evidence that the self-reported PSECDI measure of times per day is a significant predictor of actual frequency of e-cig puffs taken. Implications: Self-reported measures of e-cig frequency of use are predictive of actual use, but quantifying e-cig use in patterns similar to cigarettes is problematic.
All Science Journal Classification (ASJC) codes
- Public Health, Environmental and Occupational Health