Switching to Progressively Reduced Nicotine Content Cigarettes in Smokers With Low Socioeconomic Status: A Double-Blind Randomized Clinical Trial.

INTRODUCTION: The Food and Drug Administration issued an advanced notice of proposed rulemaking for setting a product standard for nicotine levels in cigarettes, with an emphasis on minimally or non-addicting very low nicotine content (VLNC). METHODS: A 33 week, two-arm, double-blind randomized trial conducted in Hershey, Pennsylvania, USA and Washington, DC, USA included adult daily cigarette smokers (≥5 cigarettes per day) with less than a college degree, and who had no plans to quit within the next six months. Participants were randomized to either reduced nicotine content (RNC) study cigarettes tapered every three weeks to a final VLNC (0.2 mg/cigarette) for six weeks or to usual nicotine content (UNC) study cigarettes (11.6 mg/cigarette). Outcomes included acceptability of study cigarettes measured by attrition (primary outcome), compliance, reduction in cigarette dependence and tobacco biomarkers, and post-intervention cessation. RESULTS: The RNC (n = 122) versus UNC (n = 123) group had higher attrition (adjusted Hazard Ratio 3.4; 95% confidence interval [CI] 1.99 to 5.81). At the end of the intervention, cotinine levels were 50% lower in the RNC group (mean group difference -137 ng/mL; 95% CI -172, -102). The RNC group smoked fewer CPD (-4.1; 95% CI -6.44, -1.75) and had lower carbon monoxide levels (-4.0 ppm; 95% CI -7.7, -0.4). Forty seven percent (29/62) of the RNC group were biochemically-confirmed compliant with smoking VLNC cigarettes (mean cotinine = 8.9 ng/ml). At three month follow-up, only compliant VLNC smokers quit with an assisted quit attempt (N = 6/22, 27%). CONCLUSIONS: This study supports a VLNC standard in cigarettes. IMPLICATIONS: Differential dropout and noncompliance indicate some smokers had difficulty transitioning to cigarettes with reduced nicotine. These smokers will benefit from supplemental nicotine in medicinal or noncombustible tobacco products if a nicotine reduction standard is established. Other smokers successfully transitioned to very low nicotine content cigarettes exclusively and substantially reduced their exposure to nicotine.

Measurement of Electronic Cigarette Frequency of Use Among Smokers Participating in a Randomized Controlled Trial.

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.

Characteristics of Adult Cigarette Smokers Who "Relight" and the Effects of Exposure to Tobacco Smoke Constituents.

INTRODUCTION: About half of smokers relight their cigarette, a habit that is a risk factor for chronic bronchitis and possibly lung cancer. Little is known about the characteristics of smokers who relight and their dependence on nicotine. It is unknown whether relighting affects exposure to tobacco smoke constituents. This study examined the characteristics of relighters of usual brand cigarettes and whether relighting affects exposure to selected tobacco smoke constituents. METHODS: We explored relighting status and frequency, using baseline data from 248 adult smokers participating in studies of reduced nicotine cigarettes in relation to demographic and cigarette characteristics, smoking behaviors, nicotine dependence, biomarkers of exposure (exhaled carbon monoxide, blood cotinine), and biomarkers of oxidative stress (ratio of oxidized/reduced glutathione). RESULTS: 69.4% (n = 172) of subjects reported relighting, and they relit an average of five cigarettes out of 20. Both relighters and non-relighters smoked a mean of 20 cigarettes per day (p = .6). Relighting was significantly associated with higher nicotine dependence, use of longer rod cigarettes, older age, lower income, and unemployment. There were no significant associations between relighting and blood cotinine, exhaled carbon monoxide or measures of oxidized/reduced blood glutathione. CONCLUSIONS: The majority of subjects were relighters, who had higher levels of nicotine dependence than non-relighters. Relighters had similar levels of plasma cotinine and exhaled carbon monoxide to non-relighters. IMPLICATIONS: No study has compared the cigarette characteristics and biomarkers of exposure of adult cigarette smokers who relight with those who do not. Relighting behavior was common in our sample and was associated with low income, not currently working, higher nicotine dependence, cigarette rod length, daily cigarette use years, and a lifetime history of depressed mood.

A Method for Classifying User-Reported Electronic Cigarette Liquid Flavors.

BACKGROUND: Along with the growth in popularity of electronic cigarette devices (e-cigs), the variety of e-cig liquids (e-liquid) available to users has also grown. Although some studies have published data about the use of flavored e-liquid, there is no standardized way to group flavors, making it difficult to interpret the data and replicate results across studies. The current study describes a method to classify user-reported e-liquid flavors and presents the resulting proportion of users in each flavor group in a large online survey of e-cig users. METHODS: Three thousand seven hundred sixteen participants completed an online survey about their e-cig use and responded to the following open-ended question regarding their use of e-liquid, "What is your favorite flavor and what brand of flavored liquid do you prefer?" Researchers used a 3 step method to determine the flavor attributes present in the e-liquids reported using an online search engine. Once all flavor attributes were identified, researchers used the constant comparative method to group the flavor attributes and delineate how to classify flavors with mixed components (eg, cinnamon Red Hots as a candy not a spice). RESULTS: The resulting classification scheme and proportions of e-liquids in each category were as follows: Tobacco (23.7%), Menthol/mint (14.8%), Fruit (20.3%), Dessert/sweets (20.7%), Alcohol (2.8%), Nuts/spices (2.0%), Candy (2.1%), Coffee/tea (4.3%), Beverage (3.1%), Unflavored (0.4%), and Don't Know/Other (5.8%). CONCLUSION: To better understand the use of flavored e-liquids, standardized methods to classify the flavors could facilitate data interpretation and comparison across studies. This study proposes a method for classifying the characterizing flavors in e-liquids used most commonly by experienced e-cig users. IMPLICATIONS: Current studies on the use of flavored e-liquid have used unclear methods to collect and report information on the use of flavors. This study adds a proposed method for classifying the flavors in the e-liquids used most commonly by experienced e-cig users. With a clear and explicit method for classifying self-reported flavors, future study results may be more easily compared.