The use of electronic nicotine delivery systems (ENDS), including disposable e-cigarettes, has been prevalent. Existing chemical analyses of ENDS focused on e-liquids rather than aerosols and failed to consider particle sizes and aerosol respiratory deposition fractions, which are key factors for inhalation doses. This study investigated the organic chemical and metal constituents in size-segregated ENDS aerosol and assessed the deposited doses and health risks of these substances. Aerosol chemical analyses were conducted on two popular disposable ENDS products: Puff Bar (Grape) and Air Bar (Watermelon Ice). An ENDS aerosol was generated and delivered into a Micro-Orifice Uniform Deposit Impactor to collect size-segregated aerosol samples, in which organic chemicals and metals were analyzed. Daily and lifetime doses for each chemical were estimated. Cancer and non-cancer risk assessments were conducted based on the deposited doses. We found that e-cigarette aerosol contains certain harmful organic chemicals and metals documented to result in respiratory problems. Estimated respiratory cancer risks corresponding to chromium from both ENDS products and nickel from Air Bar (Watermelon Ice) were substantially above the conventionally acceptable risk. The method, findings, and implications can contribute to the extant literature of ENDS toxicity studies as well as inform tobacco regulation and future large-scale studies.

Background: Wearable devices have been increasingly adopted to collect physiological data such as heart rate that may infer momentary risk of substance use. Yet, innovative methods capable for handling these complex time series data as presented in the statistics or data science literature may not be accessible to substance use researchers.Objectives: This study introduces a series of statistical methods to analyze heart rate data and identify features that are associated with nicotine vaping.Methods: Nontechnical description of the methods coupled with the information about open-source software packages that implemented these methods was provided. The analytical procedure included 5 steps: (1) de-noising by the singular spectrum analysis (SSA); (2) sleep region identification by the Sum of Single Effects (SuSiE) model; (3) repeated heart rate pattern identification by the matrix profile; (4) dimension reduction by the linear regression; and (5) comparing repeated heart rate patterns across non-vaping and vaping regions by the linear mixed model. Secondary analysis was conducted on heart rate and ecological momentary assessment (EMA) data collected from 35 young adult e-cigarette users (66% female) for 7 days.Results: Effectiveness of the methods was demonstrated by graphical presentations showing that the extracted features characterize sleep patterns and heart rate changes before and after vaping events quite well. Secondary analysis found that heart rate was higher and changed faster before vaping.Conclusion: Statistical methods can effectively extract useful features from heart rate data that may inform momentary vaping risk and optimal timings for delivering messages in mobile-phone based interventions.

Existing e-cigarette dependence scales are mainly validated based on retrospective overall consumption or perception. Further, given that the majority of adult e-cigarette users also use combustible cigarettes, it is important to determine whether e-cigarette dependence scales capture the product-specific dependence. This study fills in the current knowledge gaps by validating e-cigarette dependence scales using novel indices of dynamic patterns of e-cigarette use behaviors and examining the association between dynamic patterns of smoking and e-cigarette dependence among dual users. Secondary analysis was conducted on the 2-week ecological momentary assessment data from 116 dual users. The Smoothly Clipped Absolute Deviation penalty (SCAD) was adopted to select important indices for dynamic patterns of consumption or craving and estimate their associations with e-cigarette dependence scales. The fitted linear regression models support the hypothesis that higher e-cigarette dependence is associated with higher levels of e-cigarette consumption and craving as well as lower instability of e-cigarette consumption. Controlling for dynamic patterns of vaping, dual users with lower e-cigarette dependence tend to report higher day-to-day dramatic changes in combustible cigarette consumption but not higher average levels of smoking. We found that more stable use patterns are related to higher levels of dependence, which has been demonstrated in combustible cigarettes and we have now illustrated in e-cigarettes. Furthermore, the e-cigarette dependence scales may capture the product-specific average consumption but not product-specific instability of consumption. This study provides empirical support for three e-cigarette dependence measures: PS-ECDI, e-FTCD, and e-WISDM, based on dynamic patterns of e-cigarette consumption and craving revealed by EMA data that have great ecological validity. This is the first study that introduces novel indices of dynamic patterns and demonstrates their potential applications in vaping research.