Differences in Switching Away From Smoking Among Adult Smokers Using JUUL Products in Regions With Different Maximum Nicotine Concentrations: North America and the United Kingdom.

INTRODUCTION: Electronic nicotine delivery systems (ENDS) may improve public health if they facilitate smokers switching away from cigarettes. Conceptually, switching is facilitated when ENDS provide adequate nicotine delivery. Switching rates among smokers who purchased the JUUL System ("JUUL") were compared in the United Kingdom (UK), where regulations limit nicotine concentration to 20 mg/mL versus North America (N.Am.; United States and Canada), where higher concentrations are available. AIMS AND METHODS: Adult established smokers (age ≥21, smoked ≥100 cigarettes, smoking some days or every day at baseline) who newly purchased JUUL were recruited into a longitudinal study (UK: N = 1247; N.Am.: N = 8835). Complete switching (no smoking for ≥30 days) was assessed 1, 3, and 6 months after purchase; propensity score matching (PSM) and logistic regression compared switching after adjusting for baseline characteristics. RESULTS: In both N.Am. and UK, ≥82% of participants reported using the highest JUUL nicotine concentration available (UK: 18 mg/mL; N.Am.: 59 mg/mL). Unadjusted switching rates did not differ at 1 month (17%-18%); unadjusted and adjusted rates were significantly higher in N.Am. (vs. UK) at 3 and 6 months. In the PSM sample, after additional covariate adjustment, rates were significantly higher in N.Am. (vs. UK) at 3 months (31.5% vs. 22.7%; odds ratio [95% confidence interval, CI] = 1.59 [1.25, 2.02]) and 6 months (38.0% vs. 26.0%; odds ratio [95% CI] = 1.79 [1.37, 2.35]). CONCLUSIONS: These results suggest availability of ENDS in nicotine concentrations greater than 20 mg/mL may be associated with increased switching among adult smokers. Differences in smoking and ENDS use characteristics did not explain associations of location and switching; however, between-country differences may be affected by unmeasured factors. IMPLICATIONS: Switching rates were lower among smokers who purchased the JUUL System ("JUUL") in the UK, where regulations limit nicotine concentration to 20 mg/mL versus N.Am. (United States and Canada), where higher concentrations are available-before and after controlling for differences in smoking and ENDS use characteristics. These results suggest availability of ENDS in nicotine concentrations greater than 20 mg/mL may be associated with increased switching among adult smokers. Between-country differences may be affected by unmeasured factors; future research should consider these factors and the extent to which regulatory policy environments may explain differences in switching among adult smokers.

Associations Between Engagement and Outcomes in the SmokefreeTXT Program: A Growth Mixture Modeling Analysis.

INTRODUCTION: Smoking continues to be a leading cause of preventable death. Mobile health (mHealth) can extend the reach of smoking cessation programs; however, user dropout, especially in real-world implementations of these programs, limit their potential effectiveness. Research is needed to understand patterns of engagement in mHealth cessation programs. METHODS: SmokefreeTXT (SFTXT) is the National Cancer Institute's 6-8 week smoking cessation text-messaging intervention. Latent growth mixture modeling was used to identify unique classes of engagement among SFTXT users using real-world program data from 7090 SFTXT users. Survival analysis was conducted to model program dropout over time by class, and multilevel modeling was used to explore differences in abstinence over time. RESULTS: We identified four unique patterns of engagement groups. The largest percentage of users (61.6%) were in the low-engagers declining group; these users started off with low level of engagement and their engagement decreased over time. Users in this group were more likely to drop out from the program and less likely to be abstinent than users in the other groups. Users in the high engagers-maintaining group (ie, the smallest but most engaged group) were less likely to be daily smokers at baseline and were slightly older than those in the other groups. They were most likely to complete the program and report being abstinent. CONCLUSIONS: Our findings show the importance of maintaining active engagement in text-based cessation programs. Future research is needed to elucidate predictors of the various levels of engagement, and to assess whether strategies aimed at increasing engagement result in higher abstinence rates. IMPLICATIONS: The current study enabled us to investigate differing engagement patterns in non-incentivized program participants, which can help inform program modifications in real-world settings. Lack of engagement and dropout continue to impede the potential effectiveness of mHealth interventions, and understanding patterns and predictors of engagement can enhance the impact of these programs.

Level of Alcohol Consumption and Successful Smoking Cessation.

INTRODUCTION: The negative association between heavy alcohol use and likelihood of successful smoking cessation is well established. However, evidence on the effects of moderate alcohol consumption on smoking cessation is sparse. This analysis evaluated the association between alcohol use and smoking and the interaction of alcohol use and use of pharmacotherapy interventions in relation to smoking cessation. METHODS: Data from adults (n = 923) recruited through a smoking cessation website between November 2011 and March 2012 were analyzed. Data on past-year alcohol use, tobacco use, and demographics were collected at baseline. Self-reported smoking abstinence and current alcohol use data were collected at 1 and 7 months posttreatment. Chi-square and multivariate logistic regression analyses were conducted. RESULTS: At 1 month, adjusted odds of continued smoking were 1.54 times greater (95% confidence interval [CI] = 1.05% to 2.23%) for moderate drinkers and 2.59 times greater (95% CI = 1.33% to 4.28%) for heavy drinkers than nondrinkers. At 7 months, adjusted odds of continued smoking were not greater for moderate drinkers than nondrinkers, and were 2.32 times greater (95% CI = 1.35% to 3.96%) among heavy alcohol drinkers than nondrinkers. At 1 month, adjusted odds of smoking cessation were 2.33 times greater (95% CI = 1.04% to 3.09%) for alcohol users assigned to nicotine replacement therapy than for those not assigned to nicotine replacement therapy. This relationship was not observed at 7 months. CONCLUSIONS: Moderate and heavy drinking might impact smoking cessation efforts. Recent moderate drinking may be associated with short-term continued smoking and heavy drinking associated with relapse in the short and long term. IMPLICATIONS: This study suggests that moderate drinking may influence the process to quit smoking. Further study is needed to better understand the implications of moderate drinking for smoking cessation. Providing information alone may not be effective in helping people abstain from drinking during smoking cessation, especially if moderate drinkers do not perceive their behavior as reducing their chance for a successful quit attempt. Tailoring smoking cessation interventions to include strategies to reduce moderate-to-heavy alcohol consumption may improve smoking cessation outcomes among alcohol users attempting to quit smoking.

The Impact of Weight and Weight-Related Perceptions on Smoking Status Among Young Adults in a Text-Messaging Cessation Program.

Introduction: Weight gain and concerns about weight can influence a smoker's ability to successfully quit, and young adults are a subgroup of smokers who are particularly concerned about the impact of quitting on their body weight. This study explored the associations between body mass index, weight perceptions, and smoking status among young adults. Methods: The sample consisted of 4027 young adults between the ages of 18 and 29 who participated in a randomized control trial of the National Cancer Institute's SmokefreeTXT program. Multivariable logistic regression models were used to examine the associations between weight related variables and smoking status. Results: Obese participants had a 0.72 lower odds (95% CI: 0.62, 0.85) of reporting smoking at the end of the program than participants of normal weight, and this difference persisted over time. Weight perceptions were also associated with smoking status. Those who perceived themselves to be slightly underweight/underweight were more likely to report smoking than those who reported being just about the right weight (OR: 1.53, 95% CI: 1.20, 1.95), and those who strongly disagreed that smoking cigarettes helps people keep their weight down were less likely to report smoking at the end of treatment than those who neither agreed nor disagreed with this statement (OR: 0.69, 95% CI: 0.54, 0.87). Conclusions: Weight related factors assessed at baseline predicted smoking status at the end of treatment and through long term follow-up. Smoking cessation programs that tailor content to addresses the specific needs of weight concerned smokers may enhance effectiveness. Implications: This study explores the association between weight related factors and smoking status among young adults, a priority population for smoking cessation efforts. This study demonstrates that both actual weight and weight perceptions (eg, perception of body weight, perception of associations between smoking and weight) are associated with smoking outcomes, and thus need to be a considered in the development of smoking cessation programs.

Analysing user-reported data for enhancement of SmokefreeTXT: a national text message smoking cessation intervention.

OBJECTIVE: This observational study highlights key insights related to participant engagement and cessation among adults who voluntarily subscribed to the nationwide US-based SmokefreeTXT program, a 42-day mobile phone text message smoking cessation program. METHODS: Point prevalence abstinence rates were calculated for subscribers who initiated treatment in the program (n=18 080). The primary outcomes for this study were treatment completion and point prevalence abstinence rate at the end of the 42-day treatment. Secondary outcomes were point prevalence abstinence rates at 7 days postquit, 3 months post-treatment and 6 months post-treatment, as well as response rates to point prevalence abstinence assessments. RESULTS: Over half the sample completed the 42-day treatment (n=9686). The end-of-treatment point prevalence abstinence for subscribers who initiated treatment was 7.2%. Among those who completed the entire 42 days of treatment, the end-of-treatment point prevalence abstinence was 12.9%. For subscribers who completed treatment, point prevalence abstinence results varied: 7 days postquit (23.7%), 3 months post-treatment (7.3%) and 6 months post-treatment (3.7%). Response rates for abstinence assessment messages ranged from 4.36% to 34.48%. CONCLUSIONS: Findings from this study illuminate the need to more deeply understand reasons for subscriber non-response and opt out and, in turn, improve program engagement and our ability to increase the likelihood for participants to stop smoking and measure long-term outcomes. Patterns of opt out for the program mirror the relapse curve generally observed for smoking cessation, thus highlighting time points at which to increase efforts to retain participants and provide additional support or incentives.

Social Network Behavior and Engagement Within a Smoking Cessation Facebook Page.

BACKGROUND: Social media platforms are increasingly being used to support individuals in behavior change attempts, including smoking cessation. Examining the interactions of participants in health-related social media groups can help inform our understanding of how these groups can best be leveraged to facilitate behavior change. OBJECTIVE: The aim of this study was to analyze patterns of participation, self-reported smoking cessation length, and interactions within the National Cancer Institutes' Facebook community for smoking cessation support. METHODS: Our sample consisted of approximately 4243 individuals who interacted (eg, posted, commented) on the public Smokefree Women Facebook page during the time of data collection. In Phase 1, social network visualizations and centrality measures were used to evaluate network structure and engagement. In Phase 2, an inductive, thematic qualitative content analysis was conducted with a subsample of 500 individuals, and correlational analysis was used to determine how participant engagement was associated with self-reported session length. RESULTS: Between February 2013 and March 2014, there were 875 posts and 4088 comments from approximately 4243 participants. Social network visualizations revealed the moderator's role in keeping the community together and distributing the most active participants. Correlation analyses suggest that engagement in the network was significantly inversely associated with cessation status (Spearman correlation coefficient = -0.14, P=.03, N=243). The content analysis of 1698 posts from 500 randomly selected participants identified the most frequent interactions in the community as providing support (43%, n=721) and announcing number of days smoke free (41%, n=689). CONCLUSIONS: These findings highlight the importance of the moderator for network engagement and provide helpful insights into the patterns and types of interactions participants are engaging in. This study adds knowledge of how the social network of a smoking cessation community behaves within the confines of a Facebook group.

Suicide Prevention Referrals in a Mobile Health Smoking Cessation Intervention.

Automated mobile health (mHealth) programs deliver effective smoking cessation interventions through text message platforms. Smoking is an independent risk factor for suicide, so the Department of Veterans Affairs incorporated information about the Veterans Crisis Line into its SmokefreeVET smoking cessation text messaging program. Almost 7% of all SmokefreeVET enrollees have accessed this information. Because of the reach and automated nature of this and similar programs, we recommend including a referral to a suicide prevention hotline for all smoking cessation mHealth interventions.

Tobacco use transitions in the United States: The National Longitudinal Study of Adolescent Health.

OBJECTIVES: The purpose of this study is to evaluate and describe transitions in cigarette and smokeless tobacco (ST) use, including dual use, prospectively from adolescence into young adulthood. METHODS: The current study utilizes four waves of the National Longitudinal Study of Adolescent Health (Add Health) to examine patterns of cigarette and ST use (within 30 days of survey) over time among a cohort in the United States beginning in 7th-12th grade (1995) into young adulthood (2008-2009). Transition probabilities were estimated using Markov modeling. RESULTS: Among the cohort (N=20,774), 48.7% reported using cigarettes, 12.8% reported using ST, and 7.2% reported dual use (cigarettes and ST in the same wave) in at least one wave. In general, the risk for transitioning between cigarettes and ST was higher for males and those who were older. Dual users exhibited a high probability (81%) of continuing dual use over time. CONCLUSIONS: Findings suggest that adolescents who use multiple tobacco products are likely to continue such use as they move into young adulthood. When addressing tobacco use among adolescents and young adults, multiple forms of tobacco use should be considered.

A Descriptive Study of Television News Coverage of Tobacco in the United States: Frequency of Topics, Frames, Exemplars, and Efficacy.

There is a positive correlation between recall of tobacco-related television news and perceived risks of smoking and thoughts about quitting. The authors used Cision US, Inc., to create a sampling frame (N = 61,027) of local and national television news coverage of tobacco from October 1, 2008, to September 30, 2009, and to draw a nationally representative sample (N = 730) for content analysis. The authors conducted a descriptive study to determine the frequency and proportion of stories containing specified tobacco topics, frames, sources, and action messages, and the valence of the coverage. Valence was generally neutral; 68% of stories took a balanced stance, with 26% having a tenor supportive of tobacco control and 6% opposing tobacco control. The most frequently covered topics included smoking bans (n = 195) and cessation (n = 156). The least covered topics included hookah (n = 1) and menthol (n = 0). The majority of coverage lacked quoting any source (n = 345); government officials (n = 144) were the most quoted sources. Coverage lacked action messages or resources; 29 stories (<4%) included a message about cessation or advocacy, and 8 stories (1%) contained a resource such as a quitline. Television news can be leveraged by health communication professionals to increase awareness of underrepresented topics in tobacco control.

Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning.

BACKGROUND: Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public's knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions. OBJECTIVE: Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes. METHODS: Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier. RESULTS: Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40% and 99.34% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59% to 80.62%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; % improvement: 80.62%), Relevance (performance: 0.94; % improvement: 75.26%), Ad or Promotion (performance: 0.89; % improvement: 72.69%), and Marketing (performance: 0.91; % improvement: 72.56%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound. CONCLUSIONS: Social media outlets like Twitter can uncover real-time snapshots of personal sentiment, knowledge, attitudes, and behavior that are not as accessible, at this scale, through any other offline platform. Using the vast data available through social media presents an opportunity for social science and public health methodologies to utilize computational methodologies to enhance and extend research and practice. This study was successful in automating a complex five-category manual content analysis of e-cigarette-related content on Twitter using machine learning techniques. The study details machine learning model specifications that provided the best accuracy for data related to e-cigarettes, as well as a replicable methodology to allow extension of these methods to additional topics.

Social Listening: A Content Analysis of E-Cigarette Discussions on Twitter.

BACKGROUND: Electronic cigarette (e-cigarette) use has increased in the United States, leading to active debate in the public health sphere regarding e-cigarette use and regulation. To better understand trends in e-cigarette attitudes and behaviors, public health and communication professionals can turn to the dialogue taking place on popular social media platforms such as Twitter. OBJECTIVE: The objective of this study was to conduct a content analysis to identify key conversation trends and patterns over time using historical Twitter data. METHODS: A 5-category content analysis was conducted on a random sample of tweets chosen from all publicly available tweets sent between May 1, 2013, and April 30, 2014, that matched strategic keywords related to e-cigarettes. Relevant tweets were isolated from the random sample of approximately 10,000 tweets and classified according to sentiment, user description, genre, and theme. Descriptive analyses including univariate and bivariate associations, as well as correlation analyses were performed on all categories in order to identify patterns and trends. RESULTS: The analysis revealed an increase in e-cigarette-related tweets from May 2013 through April 2014, with tweets generally being positive; 71% of the sample tweets were classified as having a positive sentiment. The top two user categories were everyday people (65%) and individuals who are part of the e-cigarette community movement (16%). These two user groups were responsible for a majority of informational (79%) and news tweets (75%), compared to reputable news sources and foundations or organizations, which combined provided 5% of informational tweets and 12% of news tweets. Personal opinion (28%), marketing (21%), and first person e-cigarette use or intent (20%) were the three most common genres of tweets, which tended to have a positive sentiment. Marketing was the most common theme (26%), and policy and government was the second most common theme (20%), with 86% of these tweets coming from everyday people and the e-cigarette community movement combined, compared to 5% of policy and government tweets coming from government, reputable news sources, and foundations or organizations combined. CONCLUSIONS: Everyday people and the e-cigarette community are dominant forces across several genres and themes, warranting continued monitoring to understand trends and their implications regarding public opinion, e-cigarette use, and smoking cessation. Analyzing social media trends is a meaningful way to inform public health practitioners of current sentiments regarding e-cigarettes, and this study contributes a replicable methodology.

Randomized trial assessing the effect of the JUUL system on switching away from cigarettes and smoking reduction among U.S. adults who smoke cigarettes.

Observational studies show high rates of switching away from cigarettes among adult purchasers of JUUL-brand electronic nicotine delivery system (ENDS); data are needed to evaluate switching with JUUL in randomized general population trials. The association of ENDS flavor availability and switching is pertinent. This study assessed switch rates and smoking reduction among participants randomized to use JUUL in a choice of flavors or tobacco-only, compared to a condition provided smoking-cessation materials. U.S. adults who smoke cigarettes (N = 837; Mage [SD] = 45.99 years [11.48]; 18.76 cigarettes/day [SD = 7.86]; 50.2% female) from an address-based representative panel were randomized to receive JUUL for 6 months (5.0% nicotine; only Virginia Tobacco [N = 285] or choice of flavors [N = 281]), or smoking-cessation materials (quit advice [QA]; N = 271). Self-reported past 30-day smoking and cigarette consumption were assessed at 1, 3, and 6 months. Repeated-measure regressions assessed differences in smoking outcomes between groups. Only 2% of participants were planning to quit smoking within 30 days. Across the 6-month intervention, participants randomized to JUUL (vs. QA) had 6.57-fold greater odds of reporting past 30-day switching (95% CI [3.72-11.63]). Participants in the JUUL (vs. QA) group smoked 27% fewer cigarettes/day versus baseline (Rate Ratio [95% CI] = 0.73 [0.68-0.77]). Over half (51.8%) of the Virginia Tobacco group used other flavors (36.7% nontobacco flavors), contaminating randomized flavor-conditions. JUUL flavor groups did not differ in smoking outcomes (p > .48). Use of JUUL products may support complete switching away from cigarettes, including among those not ready to quit smoking. Results suggest a preference for nontobacco-flavored ENDS among adults who smoke, although smoking outcomes did not differ by flavor. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The relationship among smoking, sleep, and chronic rheumatic conditions commonly associated with pain in the National Health Interview Survey.

Chronic rheumatic conditions are typically characterized by chronic pain and are uniquely associated with increased rates of cigarette smoking and poor sleep quality. However, no study has examined the possible additive or interactive effects of these two health behaviors in individuals diagnosed with a chronic rheumatic condition. The goal of this study is to examine the relationship between cigarette smoking and sleep in a population sample of individuals diagnosed with a chronic rheumatic condition and related functional impairment. Cross sectional survey data was obtained from the 2007 National Health Interview Survey. Individuals diagnosed with a chronic rheumatic condition were more likely to be a former or current smoker compared to non-diagnosed individuals. Individuals with a chronic rheumatic condition were more likely to report <6 h of sleep per night and endorsed significantly more insomnia and daytime sleepiness. There was no interaction between diagnosis of a chronic rheumatic condition and smoking status on any of the sleep outcomes assessed. Finally, an interaction was observed suggesting individuals with a chronic rheumatic condition who currently smoke are more likely to report averaging <6 h of sleep per night and frequent insomnia compared to individuals with a chronic rheumatic condition who never smoked. These results suggest both a unique and additive relationship between smoking and sleep in individuals with a chronic rheumatic condition. Findings can likely be generalized to other conditions commonly associated with chronic pain.

Portrayal of smokeless tobacco in YouTube videos.

OBJECTIVES: Videos of smokeless tobacco (ST) on YouTube are abundant and easily accessible, yet no studies have examined the content of ST videos. This study assesses the overall portrayal, genre, and messages of ST YouTube videos. METHODS: In August 2010, researchers identified the top 20 search results on YouTube by "relevance" and "view count" for the following search terms: "ST," "chewing tobacco," "snus," and "Skoal." After eliminating videos that were not about ST (n = 26), non-English (n = 14), or duplicate (n = 42), a final sample of 78 unique videos was coded for overall portrayal, genre, and various content measures. RESULTS: Among the 78 unique videos, 15.4% were anti-ST, while 74.4% were pro-ST. Researchers were unable to determine the portrayal of ST in the remaining 10.3% of videos because they involved excessive or "sensationalized" use of the ST, which could be interpreted either positively or negatively, depending on the viewer. The most common ST genre was positive video diaries (or "vlogs"), which made up almost one third of the videos (29.5%), followed by promotional advertisements (20.5%) and anti-ST public service announcements (12.8%). While YouTube is intended for user-generated content, 23.1% of the videos were created by professional organizations. CONCLUSIONS: These results demonstrate that ST videos on YouTube are overwhelmingly pro-ST. More research is needed to determine who is viewing these ST YouTube videos and how they may affect people's knowledge, attitudes, and behaviors regarding ST use.

Smokeless tobacco use in the United States military: a systematic review.

INTRODUCTION: Smokeless tobacco (ST) use represents an important target for intervention in the U.S. military population because it impairs "military readiness" and harms the health of the military. This paper aims to provide a systematic review of ST studies conducted in the U.S. military population in order to assess the content of existing ST research in this population, provide estimates of prevalence and clinically relevant use patterns, and discuss how these findings might be used to guide future ST research among this population. METHODS: We reviewed articles published through December 2010 using PubMed and PsycINFO databases, Google Scholar, and any relevant articles' reference lists. Inclusion criteria included focus on a U.S. military sample, English language, measured tobacco use, and ST prevalence was reported or could be calculated. To the extent possible, each article was coded for demographics, socioeconomic status, prevalence, amount, frequency, and length of use, and quit intentions/attempts. RESULTS: Thirty-nine articles met criteria for inclusion. Less than half focused primarily on ST use among military personnel. The remaining studies measured ST use in the context of other behaviors. Findings related to clinically relevant behaviors included a need for more cohort and intervention studies, a better understanding of ST use in combination with cigarettes (i.e., concurrent use), and identifying risk factors for ST initiation and use. DISCUSSION: ST use is prevalent among military personnel, as is concurrent use of cigarettes and ST. We provide a number of recommendations to guide future research in this important, yet understudied, area.

Differences in switching away from cigarettes and JUUL use characteristics among adult menthol and nonmenthol smokers who purchased the JUUL System.

OBJECTIVES: Studies have assessed switching away from cigarettes among adult smokers who use electronic nicotine delivery systems (ENDS), but there is little data assessing differences in likelihood of switching or ENDS use characteristics by menthol smoking. METHODS: Adult (age ≥ 21) established smokers who purchased a JUUL Starter Kit (N = 15,036) completed baseline and 1-, 2-, 3-, 6-, 9- and 12-month assessments. Switching (no past-30-day cigarette smoking) and flavor use were assessed at each follow-up. Repeated-measure logistic regression models evaluated association of menthol smoking and switching across 1 year. RESULTS: Across the 6 follow-ups, more menthol smokers primarily used Menthol/Mint-flavored JUULpods (53.8%) than nonmenthol smokers (22.9%). Only 6.4% of menthol smokers primarily used Tobacco flavors (vs. 25.9% of nonmenthol smokers). Across all follow-ups menthol smokers (41.2% of sample) were more likely to switch than nonmenthol smokers (42.6% vs. 38.8%: OR [95% CI] = 1.17 [1.11, 1.23]); this association remained significant after adjustment for sociodemographic, smoking and JUUL use covariates (aOR [95% CI] = 1.13 [1.05, 1.21]). Nonmenthol smokers, but not menthol smokers, were significantly more likely to switch when primarily using Menthol/Mint-flavored (vs. Tobacco-flavored) JUULpods (aOR [95% CI] = 1.14 [1.04, 1.25]). Differences in baseline smoking characteristics between menthol and nonmenthol smokers were small in magnitude. CONCLUSIONS: Adult menthol (vs. nonmenthol) smokers using JUUL were more likely to switch. More than twice as many menthol (vs. nonmenthol) smokers primarily used Menthol/Mint-flavor JUULpods; few menthol smokers used tobacco flavors. Given these pronounced differences in flavor preferences, availability of ENDS in menthol flavors may be particularly important for menthol smokers, but may also benefit some nonmenthol smokers.

Pharmacokinetic and subjective assessment of prototype JUUL2 electronic nicotine delivery system in two nicotine concentrations, JUUL system, IQOS, and combustible cigarette.

RATIONALE: Electronic nicotine delivery systems and heated tobacco products are noncombustible alternatives for adult smokers. Evidence suggests sufficient nicotine delivery and satisfying effects are necessary to facilitate switching away from smoking; nicotine delivery varies across electronic nicotine delivery systems within limited nicotine concentrations. OBJECTIVES: To assess the nicotine delivery and subjective effects of prototype JUUL2 System in two nicotine concentrations, currently-marketed US JUUL System ("JUUL"), IQOS-brand heated tobacco product, and combustible cigarettes. METHODS: Adult smokers (N = 40) completed a 5-arm cross-over product-use laboratory confinement study. Nicotine pharmacokinetics and subjective effects were assessed following use of: (1) JUUL2 prototype 18 mg/mL nicotine; (2) JUUL2 prototype 40 mg/mL; (3) JUUL 59 mg/mL; (4) IQOS 18 mg/g; and (5) usual brand combustible cigarette, each evaluated during ad libitum (10 min) and controlled (5 min, 10 standardized puffs) use. RESULTS: Nicotine delivery was greatest for combustible cigarettes, followed by JUUL2 prototype 40 mg/mL, IQOS, JUUL2 prototype 18 mg/mL, and JUUL 59 mg/mL. Nicotine delivery from JUUL2 prototype 18 mg/mL was significantly greater than JUUL 59 mg/mL after ad libitum use. JUUL products were significantly more satisfying and effective at reducing craving than IQOS. JUUL2 prototype 40 mg/mL was significantly more aversive than other JUUL products. CONCLUSIONS: Prototype JUUL2 and JUUL 59 mg/mL products were rated higher than IQOS on subjective measures associated with switching away from smoking. The JUUL2 prototype 40 mg/mL produced aversive responses and would require modifications to be a viable product for adult smokers. Nicotine delivery and subjective responses to JUUL2 prototype 18 mg/mL suggest a product based on this prototype may facilitate increased switching among adult smokers.

YouTube as a source of quitting smoking information.

OBJECTIVE: To conduct analyses to determine the extent to which YouTube videos posted specific to smoking cessation were actually about quitting smoking and if so, whether or not they portrayed evidence-based practices (EBPs). METHODS: In August 2008, researchers identified YouTube videos by search strategies, 'relevance' and 'view count' using the following three search terms: 'stop smoking', 'quit smoking' and 'smoking cessation (n=296 for full sample and n=191 for unique videos). RESULTS: Overall, almost 60% of videos contained a message about quitting smoking. Differences were found across search terms for videos about quitting smoking, with 'stop smoking' yielding the highest percentage (80.8%) of videos about quitting smoking. Almost half of the videos (48.9%) contained EBPs for cessation strategies; however, a significant portion contained either non--EBPs (28.4%) or both EBPs and non-EBPs (22.7%). The number of views per an individual video across the six categories ranged from a low of 8 in the 'relevance' strategy and 'smoking cessation' search term to a high of 1,247,540 in the 'view count' strategy and 'stop smoking' search term. Of the top three most viewed videos by strategy and search term, 66.7% included a specific mention of quitting smoking and, of these, the majority included EBPs. CONCLUSION: Results highlight the need to develop and upload videos containing EBPs both to increase the overall proportion of EBP videos in all categories, particularly in 'quit smoking' and 'stop smoking.' Research is needed to study whether YouTube videos influence knowledge, attitudes and behaviours regarding quitting smoking.

Realizing the promise of Web 2.0: engaging community intelligence.

Discussions of Health 2.0, a term first coined in 2005, were guided by three main tenets: (a) health was to involve more participation, because an evolution in the web encouraged more direct consumer engagement in their own health care; (b) data was to become the new "Intel Inside" for systems supporting the vital decisions in health; and (c) a sense of collective intelligence from the network would supplement traditional sources of knowledge in health decision making. Interests in understanding the implications of a new paradigm for patient engagement in health and health care were kindled by findings from surveys such as the National Cancer Institute's Health Information National Trends Survey, showing that patients were quick to look online for information to help them cope with disease. This article considers how these 3 facets of Health 2.0--participation, data, and collective intelligence--can be harnessed to improve the health of the nation according to Healthy People 2020 goals. The authors begin with an examination of evidence from behavioral science to understand how Web 2.0 participative technologies may influence patient processes and outcomes, for better or worse, in an era of changing communication technologies. The article then focuses specifically on the clinical implications of Health 2.0 and offers recommendations to ensure that changes in the communication environment do not detract from national (e.g., Healthy People 2020) health goals. Changes in the clinical environment, as catalyzed by the Health Information Technology for Economic and Clinical Health Act to take advantage of Health 2.0 principles in evidence-based ways, are also considered.

Cancer survivorship in the age of YouTube and social media: a narrative analysis.

BACKGROUND: As evidenced by the increasing popularity of YouTube (www.youtube.com), personal narratives shared through social media are an area of rapid development in communication among cancer survivors. Identifying the thematic and linguistic characteristics of YouTube cancer stories can provide a better understanding of this naturally occurring communication channel and inform social media communication efforts aiming to use personal stories to reach individuals with serious illnesses. OBJECTIVE: The objective of our study was to provide an in-depth description of authentic personal cancer stories. Through a linguistically based narrative analysis of YouTube stories, the analysis explicates the common attributes of these narratives. METHODS: Informed by narrative theories, we conducted an iterative, bottom-up analysis of 35 YouTube videos identified by the search terms "cancer survivor" and "cancer stories". A list of shared thematic and linguistic characteristics was identified and analyzed. RESULTS: A subnarrative on the cancer diagnosis was present in 86% (30/35) of the stories under analysis. These diagnostic narratives were characterized by dramatic tension, emotional engagement, markers of the loss of agency or control, depersonalized reference to the medical personnel, and the unexpectedness of a cancer diagnosis. The analysis highlights the themes of story authenticity and emotional engagement in this online communication medium. CONCLUSIONS: Internet advances have enabled new and efficient exchange of personal stories, including the sharing of personal cancer experience among cancer survivors and their caregivers. The analytic results of this descriptive study point to the common characteristics of authentic cancer survivorship stories online. Furthermore, the results of this descriptive study may inform development of narrative-based communication, particularly in maintaining authenticity and emotional engagement.