Effect of Messaging Content on Engagement With a Short Messaging Service for Perioperative Smoking Cessation.

INTRODUCTION: We determined whether a tobacco cessation intervention targeting brief perioperative abstinence ("quit for a bit") increased engagement of surgical patients with treatment compared with an intervention targeting long-term postoperative abstinence ("quit for good"). AIMS AND METHODS: Surgical patients who smoke were stratified according to intended duration of postoperative abstinence, and then randomized within strata to receive either a "quit for a bit" or a "quit for good" intervention. Both employed initial brief counseling and short message service (SMS) to deliver treatment up to 30 days after surgery. The primary outcome of treatment engagement was defined as the rate at which subjects actively responded to system requests delivered via SMS. RESULTS: The engagement index did not differ between intervention groups (median [25th, 75th] of 23.7% [8.8, 46.0] for "quit for a bit" group (n = 48) and 22.2% [4.8, 46.0] for "quit for good" group (n = 50), p = .74), nor did the proportion of patients continuing SMS use after study completion (33% and 28%, respectively). Exploratory abstinence outcomes on the morning of surgery and 7 and 30 days after surgery did not differ between groups. Program satisfaction was high in both groups and did not differ. There was no significant interaction between intended abstinence duration and any outcome, ie, alignment of intent with intervention did not affect engagement. CONCLUSIONS: Tobacco cessation treatment delivered via SMS was well accepted by surgical patients. Tailoring an SMS intervention to focus on the benefits of short-term abstinence for surgical patients did not increase engagement in treatment or perioperative abstinence rates. IMPLICATIONS: Treatment of surgical patients for tobacco use is efficacious and reduces postoperative complications. However, implementation in clinical practice has proved challenging, and new methods of engaging these patients in cessation treatment are needed. We found that tobacco use treatment delivered via SMS was feasible and well utilized by surgical patients. Tailoring an SMS intervention to focus on the benefits of short-term abstinence for surgical patients did not increase engagement in treatment or perioperative abstinence rates. CLINICAL TRIAL NUMBER: NCT03839043. Registry URL: https://clinicaltrials.gov/ct2/show/NCT03839043.

E-cigarette and combusted tobacco abstinence among young adults: Secondary analyses from a U.S.-based randomized controlled trial of vaping cessation.

OBJECTIVE: To examine patterns of abstinence from e-cigarettes, combusted tobacco products (CTPs), both, or neither among young adults enrolled in a U.S.-based randomized trial of a text message vaping cessation intervention. METHODS: At baseline, 1829 young adult e-cigarette users were categorized as Exclusive E-cigarette Users (no past 30-day CTP use; n = 1036, 56.6%) or Dual Users (past 30-day CTP use; n = 793, 43.4%). Four groups were defined at 7-months: 1) Dual Abstinent, 2) Exclusive Vaping, 3) Exclusive CTP Use, and 4) Dual Users. The proportion of participants who were Dual Abstinent was the outcome of interest. RESULTS: At follow-up, 22.1% (95% CI: 20.3, 24.1) of participants were Dual Abstinent, 44.8% (95% CI: 42.5, 47.1) reported Exclusive Vaping, 6.3% (95% CI: 5.2, 7.5) reported Exclusive CTP Use, and 26.8% (95% CI: 24.8, 28.9) were Dual Users. A higher proportion of participants randomized to Intervention were Dual Abstinent (25.9%, 95% CI 23.1, 28.9) compared to Control (18.5%, 95% CI 16.0, 21.1; p = .0002). Analyses of treatment effects on dual abstinence by baseline tobacco product use favored Intervention over Control among both Exclusive E-cigarette Users (p = .019) and Dual Users (p = .0014). CONCLUSION: A text message vaping cessation intervention was effective in promoting dual abstinence from e-cigarettes and CTPs among young adults. The advantage of treatment over control was equivalent for Exclusive E-cigarette Users and Dual Users. Rates of dual abstinence were higher among exclusive vapers than dual users, signaling the need for more research to optimize cessation programs for poly-tobacco users.

Tailored text message and web intervention for smoking cessation in U.S. socioeconomically-disadvantaged young adults: A randomized controlled trial.

The prevalence of cigarette smoking in young adults is higher among those with socioeconomic disadvantage than those without. Low treatment-seeking among young adult smokers is compounded by few efficacious smoking cessation interventions for this group, particularly socioeconomically-disadvantaged young adults (SDYA) who smoke cigarettes. The goal of this study was to test a tailored smoking-cessation intervention for SDYA. 343 SDYA aged 18-30 living in the U.S. (85% female) who smoke cigarettes with access to a smartphone and interest in quitting smoking in the next six months were recruited online in Spring 2020 and randomized to referral to online quit resources (usual care control; n = 171) or a 12-week tailored text message smoking-cessation program with a companion web-based intervention (n = 172). Intent to treat analyses examined associations between study condition, self-reported 30-day point prevalence abstinence (PPA), and confidence to quit smoking at 12 weeks, controlling for potential confounders. Intervention group participants had greater self-reported 30-day PPA at 12-weeks than controls (adjusted relative risk 3.93, 95% CI 2.14-7.24). Among those who continued smoking, the intervention increased confidence to quit (0.81 points, 95% confidence interval 0.08-1.53). Weekly engagement in the intervention predicted greater cessation. A tailored text message intervention for SDYA increased smoking abstinence and confidence to quit at the end-of-treatment. Findings may have been influenced by recruitment at the start of the COVID pandemic but suggest that text messaging is an acceptable and efficacious cessation strategy for SDYA smokers. Future studies should examine the impact on longer-term smoking-cessation and importance of intervention tailoring for SDYA.

Correction: Optimizing Text Messages to Promote Engagement With Internet Smoking Cessation Treatment: Results From a Factorial Screening Experiment.

[This corrects the article DOI: 10.2196/17734.].

Optimizing Text Messages to Promote Engagement With Internet Smoking Cessation Treatment: Results From a Factorial Screening Experiment.

BACKGROUND: Smoking remains a leading cause of preventable death and illness. Internet interventions for smoking cessation have the potential to significantly impact public health, given their broad reach and proven effectiveness. Given the dose-response association between engagement and behavior change, identifying strategies to promote engagement is a priority across digital health interventions. Text messaging is a proven smoking cessation treatment modality and a powerful strategy to increase intervention engagement in other areas of health, but it has not been tested as an engagement strategy for a digital cessation intervention. OBJECTIVE: This study examined the impact of 4 experimental text message design factors on adult smokers' engagement with an internet smoking cessation program. METHODS: We conducted a 2×2×2×2 full factorial screening experiment wherein 864 participants were randomized to 1 of 16 experimental conditions after registering with a free internet smoking cessation program and enrolling in its automated text message program. Experimental factors were personalization (on/off), integration between the web and text message platforms (on/off), dynamic tailoring of intervention content based on user engagement (on/off), and message intensity (tapered vs abrupt drop-off). Primary outcomes were 3-month measures of engagement (ie, page views, time on site, and return visits to the website) as well as use of 6 interactive features of the internet program. All metrics were automatically tracked; there were no missing data. RESULTS: Main effects were detected for integration and dynamic tailoring. Integration significantly increased interactive feature use by participants, whereas dynamic tailoring increased the number of features used and page views. No main effects were found for message intensity or personalization alone, although several synergistic interactions with other experimental features were observed. Synergistic effects, when all experimental factors were active, resulted in the highest rates of interactive feature use and the greatest proportion of participants at high levels of engagement. Measured in terms of standardized mean differences (SMDs), effects on interactive feature use were highest for Build Support System (SMD 0.56; 95% CI 0.27 to 0.81), Choose Quit Smoking Aid (SMD 0.38; 95% CI 0.10 to 0.66), and Track Smoking Triggers (SMD 0.33; 95% CI 0.05 to 0.61). Among the engagement metrics, the largest effects were on overall feature utilization (SMD 0.33; 95% CI 0.06 to 0.59) and time on site (SMD 0.29; 95% CI 0.01 to 0.57). As no SMD >0.30 was observed for main effects on any outcome, results suggest that for some outcomes, the combined intervention was stronger than individual factors alone. CONCLUSIONS: This factorial experiment demonstrates the effectiveness of text messaging as a strategy to increase engagement with an internet smoking cessation intervention, resulting in greater overall intervention dose and greater exposure to the core components of tobacco dependence treatment that can promote abstinence. TRIAL REGISTRATION: ClinicalTrials.gov NCT02585206; https://clinicaltrials.gov/ct2/show/NCT02585206. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2015-010687.

Discussions of Alcohol Use in an Online Social Network for Smoking Cessation: Analysis of Topics, Sentiment, and Social Network Centrality.

BACKGROUND: Few Internet smoking cessation programs specifically address the impact of alcohol use during a quit attempt, despite its common role in relapse. This study used topic modeling to describe the most prevalent topics about alcohol in an online smoking cessation community, the prevalence of negative sentiment expressed about alcohol use in the context of a quit attempt (i.e., alcohol should be limited or avoided during a quit attempt) within topics, and the degree to which topics differed by user social connectivity within the network. METHODS: Data were analyzed from posts from the online community of a larger Internet cessation program, spanning January 1, 2012 to May 31, 2015 and included records of 814,258 online posts. Posts containing alcohol-related content (n = 7,199) were coded via supervised machine learning text classification to determine whether the post expressed negative sentiment about drinking in the context of a quit attempt. Correlated topic modeling (CTM) was used to identify a set of 10 topics of at least 1% prevalence based on the frequency of word occurrences among alcohol-related posts; the distribution of negative sentiment and user social network connectivity was examined across the most salient topics. RESULTS: Three salient topics (with prevalence ≥10%) emerged from the CTM, with distinct themes of (i) cravings and temptations; (ii) parallel between nicotine addiction and alcoholism; and (iii) celebratory discussions of quit milestones including "virtual" alcohol use and toasts. Most topics skewed toward nonnegative sentiment about alcohol. The prevalence of each topic differed by users' social connectivity in the network. CONCLUSIONS: Future work should examine whether outcomes in Internet interventions are improved by tailoring social network content to match user characteristics, topics, and network behavior.

Inferring Smoking Status from User Generated Content in an Online Cessation Community.

Introduction: User generated content (UGC) is a valuable but underutilized source of information about individuals who participate in online cessation interventions. This study represents a first effort to passively detect smoking status among members of an online cessation program using UGC. Methods: Secondary data analysis was performed on data from 826 participants in a web-based smoking cessation randomized trial that included an online community. Domain experts from the online community reviewed each post and comment written by participants and attempted to infer the author's smoking status at the time it was written. Inferences from UGC were validated by comparison with self-reported 30-day point prevalence abstinence (PPA). Following validation, the impact of this method was evaluated across all individuals and time points in the study period. Results: Of the 826 participants in the analytic sample, 719 had written at least one post from which content inference was possible. Among participants for whom unambiguous smoking status was inferred during the 30 days preceding their 3-month follow-up survey, concordance with self-report was almost perfect (kappa = 0.94). Posts indicating abstinence tended to be written shortly after enrollment (median = 14 days). Conclusions: Passive inference of smoking status from UGC in online cessation communities is possible and highly reliable for smokers who actively produce content. These results lay the groundwork for further development of observational research tools and intervention innovations. Implications: A proof-of-concept methodology for inferring smoking status from user generated content in online cessation communities is presented and validated. Content inference of smoking status makes a key cessation variable available for use in observational designs. This method provides a powerful tool for researchers interested in online cessation interventions and establishes a foundation for larger scale application via machine learning.

Mining User-Generated Content in an Online Smoking Cessation Community to Identify Smoking Status: A Machine Learning Approach.

Online smoking cessation communities help hundreds of thousands of smokers quit smoking and stay abstinent each year. Content shared by users of such communities may contain important information that could enable more effective and personally tailored cessation treatment recommendations. This study demonstrates a novel approach to determine individuals' smoking status by applying machine learning techniques to classify user-generated content in an online cessation community. Study data were from BecomeAnEX.org, a large, online smoking cessation community. We extracted three types of novel features from a post: domain-specific features, author-based features, and thread-based features. These features helped to improve the smoking status identification (quit vs. not) performance by 9.7% compared to using only text features of a post's content. In other words, knowledge from domain experts, data regarding the post author's patterns of online engagement, and other community member reactions to the post can help to determine the focal post author's smoking status, over and above the actual content of a focal post. We demonstrated that machine learning methods can be applied to user-generated data from online cessation communities to validly and reliably discern important user characteristics, which could aid decision support on intervention tailoring.

Improving Adherence to Smoking Cessation Treatment: Smoking Outcomes in a Web-based Randomized Trial.

Background: Partial adherence in Internet smoking cessation interventions presents treatment and evaluation challenges. Increasing adherence may improve outcomes. Purpose: To present smoking outcomes from an Internet randomized trial of two strategies to encourage adherence to tobacco dependence treatment components: (i) a social network (SN) strategy to integrate smokers into an online community and (ii) free nicotine replacement therapy (NRT). In addition to intent-to-treat analyses, we used novel statistical methods to distinguish the impact of treatment assignment from treatment utilization. Methods: A total of 5,290 current smokers on a cessation website (WEB) were randomized to WEB, WEB + SN, WEB + NRT, or WEB + SN + NRT. The main outcome was 30-day point prevalence abstinence at 3 and 9 months post-randomization. Adherence measures included self-reported medication use (meds), and website metrics of skills training (sk) and community use (comm). Inverse Probability of Retention Weighting and Inverse Probability of Treatment Weighting jointly addressed dropout and treatment selection. Propensity weights were used to calculate Average Treatment effects on the Treated. Results: Treatment assignment analyses showed no effects on abstinence for either adherence strategy. Abstinence rates were 25.7%-32.2% among participants that used all three treatment components (sk+comm +meds).Treatment utilization analyses revealed that among such participants, sk+comm+meds yielded large percentage point increases in 3-month abstinence rates over sk alone across arms: WEB = 20.6 (95% CI = 10.8, 30.4), WEB + SN = 19.2 (95% CI = 11.1, 27.3), WEB + NRT = 13.1 (95% CI = 4.1, 22.0), and WEB + SN + NRT = 20.0 (95% CI = 12.2, 27.7). Conclusions: Novel propensity weighting approaches can serve as a model for establishing efficacy of Internet interventions and yield important insights about mechanisms. Clinical Trials.gov: NCT01544153.

How US Smokers Refer to E-cigarettes: An Examination of User-Generated Posts From a Web-Based Smoking Cessation Intervention, 2008-2015.

INTRODUCTION: A challenge in Electronic Nicotine Delivery System (ENDS) research is how to refer to these devices in ways that are meaningful to current or potential users. The objectives of this study were to: (1) describe the frequency of ENDS terms in a web-based smoking cessation intervention; and (2) determine whether terms vary by US geographic region and date. METHODS: Data were drawn from public posts between 2008-2015 on http://BecomeAnEX.org and limited to US users. We conducted "exact" and "fuzzy" searches to find posts containing ENDS keywords using custom Python scripts, and extracted geocoding data and date for each post. We examined counts and frequencies of ENDS terms by unique user, by unique user and region, and by unique user and date. RESULTS: We identified 1023 unique US website users who had written a post containing one or more ENDS keywords. Posters were majority female (79%), educated (78% attended at least some college), and had a median age of 47 years. Overall, 92% of ENDS posters employed the term "e-cigarette" or a derivation. Derivations of "vape" became increasingly popular in 2013, whereas "NJoy" and "blu" were employed by fewer than 2% of posters. We found no variation in frequency of ENDS terms by US region. CONCLUSIONS: Researchers may have confidence that "e-cigarette" and "vape" are recognizable terms among US treatment-seeking smokers. Conversely, terms such as "ENDS," commonly employed by researchers and public health advocates, are not used by smokers and may be an impediment to tobacco control research. IMPLICATIONS: Researchers may have confidence that "e-cigarette," and, to a lesser extent, "vape" are recognizable terms among US adult smokers referring to ENDS (including accessories, brand names, and actions). Conversely, terms such as "electronic nicotine delivery systems," commonly employed by researchers and public health advocates, are not used by US smokers and may be an impediment to tobacco control research and practice.

Improving Adherence to Smoking Cessation Treatment: Intervention Effects in a Web-Based Randomized Trial.

BACKGROUND: Web-based smoking cessation interventions can deliver evidence-based treatments to a wide swath of the population, but effectiveness is often limited by insufficient adherence to proven treatment components. This study evaluated the impact of a social network (SN) intervention and free nicotine replacement therapy (NRT) on adherence to evidence-based components of smoking cessation treatment in the context of a Web-based intervention. METHODS: A sample of adult U.S. smokers (N = 5290) was recruited via BecomeAnEX.org, a free smoking cessation Web site. Smokers were randomized to one of four arms: (1) an interactive, evidence-based smoking cessation Web site (WEB) alone; (2) WEB in conjunction with an SN intervention designed to integrate participants into the online community (WEB+SN); (3) WEB plus free NRT (WEB+NRT); and (4) the combination of all treatments (WEB+SN+NRT). Adherence outcomes assessed at 3-month follow-up were as follows: Web site utilization metrics, use of skills training components, intratreatment social support, and pharmacotherapy use. RESULTS: WEB+SN+NRT outperformed all others on Web site utilization metrics, use of practical counseling tools, intratreatment social support, and NRT use. It was the only intervention to promote the sending of private messages and the viewing of community pages over WEB alone. Both social network arms outperformed WEB on most metrics of online community engagement. Both NRT arms showed higher medication use compared to WEB alone. CONCLUSIONS: This study demonstrated the effectiveness of two approaches for improving adherence to evidence-based components of smoking cessation treatment. Integrated approaches to medication provision and social network engagement can enhance adherence to components known to improve cessation. IMPLICATIONS: This study demonstrated that an integrated approach to medication provision and social network integration, when delivered through an online program, can enhance adherence across all three recommended components of an evidence-based smoking cessation program (skills training, social support, and pharmacotherapy use). Nicotine replacement therapy-when provided as part of an integrated program-increases adherence to other program elements, which in turn augment its own therapeutic effects. An explicit focus on approaches to improve treatment adherence is an important first step to identifying leverage points for optimizing intervention effectiveness.

Baseline Characteristics and Generalizability of Participants in an Internet Smoking Cessation Randomized Trial.

BACKGROUND: The potential for sampling bias in Internet smoking cessation studies is widely recognized. However, few studies have explicitly addressed the issue of sample representativeness in the context of an Internet smoking cessation treatment trial. PURPOSE: The purpose of the present study is to examine the generalizability of participants enrolled in a randomized controlled trial of an Internet smoking cessation intervention using weighted data from the National Health Interview Survey (NHIS). METHODS: A total of 5290 new users on a smoking cessation website enrolled in the trial between March 2012 and January 2015. Descriptive statistics summarized baseline characteristics of screened and enrolled participants, and multivariate analysis examined predictors of enrollment. Generalizability analyses compared demographic and smoking characteristics of trial participants to current smokers in the 2012-2014 waves of NHIS (n = 19,043) and to an NHIS subgroup based on Internet use and cessation behavior (n = 3664). Effect sizes were obtained to evaluate the magnitude of differences across variables. RESULTS: Predictors of study enrollment were age, gender, race, education, and motivation to quit. Compared to NHIS smokers, trial participants were more likely to be female, college educated, and daily smokers and to have made a quit attempt in the past year (all effect sizes 0.25-0.60). In comparisons with the NHIS subgroup, differences in gender and education were attenuated, while differences in daily smoking and smoking rate were amplified. CONCLUSIONS: Few differences emerged between Internet trial participants and nationally representative samples of smokers, and all were in expected directions. This study highlights the importance of assessing generalizability in a focused and specific manner. CLINICALTRIALS.GOV: #NCT01544153.

A Multirelational Social Network Analysis of an Online Health Community for Smoking Cessation.

BACKGROUND: Online health communities (OHCs) provide a convenient and commonly used way for people to connect around shared health experiences, exchange information, and receive social support. Users often interact with peers via multiple communication methods, forming a multirelational social network. Use of OHCs is common among smokers, but to date, there have been no studies on users' online interactions via different means of online communications and how such interactions are related to smoking cessation. Such information can be retrieved in multirelational social networks and could be useful in the design and management of OHCs. OBJECTIVE: To examine the social network structure of an OHC for smoking cessation using a multirelational approach, and to explore links between subnetwork position (ie, centrality) and smoking abstinence. METHODS: We used NetworkX to construct 4 subnetworks based on users' interactions via blogs, group discussions, message boards, and private messages. We illustrated topological properties of each subnetwork, including its degree distribution, density, and connectedness, and compared similarities among these subnetworks by correlating node centrality and measuring edge overlap. We also investigated coevolution dynamics of this multirelational network by analyzing tie formation sequences across subnetworks. In a subset of users who participated in a randomized, smoking cessation treatment trial, we conducted user profiling based on users' centralities in the 4 subnetworks and identified user groups using clustering techniques. We further examined 30-day smoking abstinence at 3 months postenrollment in relation to users' centralities in the 4 subnetworks. RESULTS: The 4 subnetworks have different topological characteristics, with message board having the most nodes (36,536) and group discussion having the highest network density (4.35×10(-3)). Blog and message board subnetworks had the most similar structures with an in-degree correlation of .45, out-degree correlation of .55, and Jaccard coefficient of .23 for edge overlap. A new tie in the group discussion subnetwork had the lowest probability of triggering subsequent ties among the same two users in other subnetworks: 6.33% (54,142/855,893) for 2-tie sequences and 2.13% (18,207/855,893) for 3-tie sequences. Users' centralities varied across the 4 subnetworks. Among a subset of users enrolled in a randomized trial, those with higher centralities across subnetworks generally had higher abstinence rates, although high centrality in the group discussion subnetwork was not associated with higher abstinence rates. CONCLUSIONS: A multirelational approach revealed insights that could not be obtained by analyzing the aggregated network alone, such as the ineffectiveness of group discussions in triggering social ties of other types, the advantage of blogs, message boards, and private messages in leading to subsequent social ties of other types, and the weak connection between one's centrality in the group discussion subnetwork and smoking abstinence. These insights have implications for the design and management of online social networks for smoking cessation.

E-Cigarettes and Smoking Cessation: Insights and Cautions From a Secondary Analysis of Data From a Study of Online Treatment-Seeking Smokers.

BACKGROUND: Evidence from observational studies regarding the association between electronic cigarette (e-cigarette) use and cessation is mixed and difficult to interpret. Utilizing 2 analytic methods, this study illustrates challenges common in analyses of observational data, highlights measurement challenges, and reports associations between e-cigarette use and smoking cessation. METHODS: Data were drawn from an ongoing web-based smoking cessation trial. The sample was comprised of 2,123 participants with complete 3-month follow-up data. Logistic regression models with and without entropy balancing to control for confounds were conducted to evaluate the association between e-cigarette use and 30-day cigarette smoking abstinence. RESULTS: At follow-up, 31.7% of participants reported using e-cigarettes to quit in the past 3 months. E-cigarette users differed from nonusers on baseline characteristics including cigarettes per day, Fagerström score, quit attempt in the past year, and previous use of e-cigarettes to quit. At follow-up, e-cigarette users made more quit attempts and employed more cessation aids than smokers who did not use e-cigarettes to quit. E-cigarette use was negatively associated with abstinence after adjustment for baseline characteristics; however, the association was not significant after additional adjustment for use of other cessation aids at 3 months. CONCLUSIONS: The magnitude and significance of the estimated association between e-cigarette use and cessation in this study were dependent upon the analytical approach. Observational studies should employ multiple analytic approaches to address threats to validity. Future research should employ better measures of patterns of and reasons for e-cigarette use, frequency of e-cigarette use, and concurrent use of cessation aids.

Changes over time in reasons for quitting vaping among treatment-seeking young people from 2019 to 2022.

Objectives: The landscape of availability, policies, and norms around e-cigarette use and cessation has changed rapidly in the last few years. There is also high interest in quitting vaping among teens and young adults. Understanding the motivation of those who want to quit vaping is important for effective intervention development. This analysis compares user-submitted reasons for quitting (RFQ) vaping from 2022 to a previous analysis from 2019 to determine whether motivations have shifted among young people. Methods: We reviewed 2000 RFQ submissions from users who enrolled in a vaping cessation text message program in 2022. Each response was coded by ≥ 2 researchers and categorized into one of 16 themes. Findings were compared to the 2019 analysis using item-wise comparisons. Results: The most frequent RFQ in 2022 were health (56.1%), social influence (15.8%), and other (11.7%). In comparison to 2019, health remained the top reason, but the rank order of all other reasons shifted. Theme prevalence changed significantly, with cost decreasing and health increasing. Among health-related sub-categories, current and mental health increased compared to 2019 submissions. Discussion: RFQ among young people shifted between 2019 and 2022. We observed greater concern about current and mental health, possibly from experiencing negative health impacts from vaping or from increased awareness of these impacts. The lower prevalence of cost may reflect the widespread availability of cheaper e-cigarettes. RFQ likely change rapidly with the fluctuating e-cigarette landscape and should be considered in cessation interventions, promotional campaigns, and policy.

Impact of seasonality on recruitment, retention, adherence, and outcomes in a web-based smoking cessation intervention: randomized controlled trial.

BACKGROUND: Seasonal variations in smoking and quitting behaviors have been documented, with many smokers seeking cessation assistance around the start of the New Year. What remains unknown is whether smokers who are recruited to cessation treatment trials during the New Year are as motivated to quit, or as likely to enroll in a research trial, adhere to a research protocol, and benefit from a cessation intervention compared to those who are recruited during other times of the year. OBJECTIVE: The objective of this study was to determine whether smokers recruited during the New Year period differ on measures of motivation and desire to quit, recruitment and retention rates, website utilization rates, and short-term cessation outcomes compared to smokers recruited at other times. METHODS: Participants were current smokers who had registered on a free Web-based cessation program (BecomeAnEX.org) and were invited to participate in a clinical trial. The New Year period was defined according to a clear peak and drop in the proportion of visitors who registered on the site, spanning a 15-day period from December 26, 2012 to January 9, 2013. Two other 15-day recruitment periods during summer (July 18, 2012 to August 1, 2012) and fall (November 7, 2012 to November 21, 2012) were selected for comparison. Data were examined from 3 sources: (1) a Web-based clinical trials management system that automated the recruitment and enrollment process, (2) self-report assessments at baseline and 3 months postrandomization, and (3) online tracking software that recorded website utilization during the first 3 months of the trial. RESULTS: Visitors to BecomeAnEX during the New Year period were more likely to register on the site than smokers who visited during summer or fall (conversion rates: 7.4%, 4.6%, 4.9%, respectively; P<.001), but there were no differences in rates of study acceptance, consent, randomization, 3-month follow-up survey completion, or cessation between the 3 periods. New Year participants were older, more educated, more likely to be employed full time, and more likely to have a relationship partner compared with participants recruited at other times during the year, but did not differ on measures of motivation and desire to quit. CONCLUSIONS: Smokers visiting a Web-based cessation program during the New Year period were more likely to register for treatment and differ on several demographic variables, but showed similar patterns of treatment engagement, retention, follow-up, and short-term cessation outcomes compared with participants who visited the site during other periods of the year. These results allay scientific concerns about recruiting participants during this time frame and are reassuring for researchers conducting Web-based cessation trials.

Treatment-seeking young people enrolled in a United States vaping cessation intervention trial report high frequency of use and nicotine dependence.

Objective: This study examines characteristics of participants enrolled in a vaping cessation intervention trial and study generalizability of the treatment-seeking sample to the broader population of young people interested in quitting vaping to evaluate dissemination of an evidence-based treatment program. Methods: Data was obtained from 697 treatment-seeking participants (aged 13-24 years) from a vaping cessation intervention trial conducted between February and March 2023 with This is Quitting and 665 participants who expressed intent to quit vaping in the Truth Longitudinal Cohort, a nationally representative longitudinal study of United States youth and young adults (aged 15-24 years), collected October 2022 to February 2023. Comparisons were made using two-sample Pearson Chi-square tests and t-tests. Results: Treatment-seeking young people were younger, less racially diverse, more diverse in sexual orientation, and reported higher psychological distress, frequency of e-cigarette use, and nicotine dependence, relative to the nationally representative sample of young people expressing intentions to quit vaping. Conclusions: Participants enrolled in treatment reported higher levels of dependence and use frequency than the broader population, suggesting that many young e-cigarette users with lower dependence who want to quit might not be reached by current treatment dissemination efforts. The implication of these findings is that communications for vaping cessation programs may need to be adjusted to have broader appeal among young people.

Delivery of smoking cessation treatment via live chat: An analysis of client-centered coaching skills and behavior change techniques.

OBJECTIVE: This qualitative study explored whether evidence-based tobacco cessation treatment components can be deployed via web-based live chat coaching. METHODS: N = 100 randomly selected chats were coded. Researchers used a structured coding guide to note the presence of 3 Motivational Interviewing (MI) skills and 61 behavior change techniques (BCTs). RESULTS: MI skills were observed in 86% of chats: 31 chats incorporated one skill, 31 incorporated two, and 24 incorporated all three. Open-ended questions were most common (76%), followed by affirmations (47%) and reflective listening statements (38%). BCTs were observed in 100% of chats: 21% involved one-five BCTs, 69% involved six-10 BCTs, and 10% involved 11 or more BCTs. Mean number of BCTs per chat was 7.25 (SD=2.5; range 2-17). The most common BCTs were Social Support (99%), Reward/Threat (95%), Natural Consequences (82%), Regulation (82%), Goals/Planning (64%), and Self Belief (42%). CONCLUSIONS: Tobacco cessation coaching using MI skills and evidence-based BCTs can be delivered via live chat. This synchronous modality allows the delivery of an intervention tailored to the user's motivations and goals. PRACTICE IMPLICATIONS: Web-based live chat can broaden the reach of tobacco treatment specialists to deploy evidence-based counseling skills and behavior change techniques in personalized, accessible coaching.

Effectiveness of an optimized text message and Internet intervention for smoking cessation: A randomized controlled trial.

AIMS: To evaluate the effectiveness of a combined internet and text message intervention for smoking cessation compared with an internet intervention alone. The text message intervention was optimized for engagement in an earlier multiphase optimization (MOST) screening phase. DESIGN: A parallel, two-group, individually randomized clinical trial (RCT) was conducted in a MOST confirming phase. Recruitment spanned December 2018 to March 2019. Follow-up was conducted at 3 and 9 months, beginning March 2019 and ending January 2020. SETTING: United States: a digital study conducted among new registrants on a free tobacco cessation website. PARTICIPANTS: Eligible individuals were 618 adult current smokers in the United States, age 18 years or older who signed up for text messages during website registration (67.2% female, 70.4% white). INTERVENTIONS: The treatment arm (WEB+TXT; n = 311) received access to the website and text messaging. The control arm (WEB; n = 307) received access to the website alone. MEASUREMENTS: The primary outcome was self-reported 30-day point prevalence abstinence (ppa) at 9 months post-randomization analyzed under intent to treat (ITT), counting non-responders as smoking. Secondary outcomes included 3-month measures of 30-day ppa, intervention engagement and intervention satisfaction. FINDINGS: Abstinence rates at 9 months were 23.1% among WEB+TXT and 23.2% among WEB (OR = 1.00, 95% CI = 0.69-1.45; P = 0.99). WEB+TXT increased engagement with 5 of 6 interactive features (standardized mean difference (SMD) = 0.26-0.47, all P < 0.001) and repeat website visits (48.7% vs 38.9%, SMD = 0.14, P = 0.02). Satisfaction metrics favored WEB+TXT (satisfied: 96.3% vs 90.5%, SMD = 0.17, P = 0.008; recommend to friend: 95.9% vs 90.1%, SMD = 0.16, P = 0.028). CONCLUSIONS: A randomized controlled trial found no evidence that a combined internet and text message intervention for smoking cessation compared with an internet intervention alone increased 9-month abstinence rates among adult current smokers in the United States, despite evidence of higher levels of intervention engagement and satisfaction at 3 months.

"It's really addictive and I'm trapped:" A qualitative analysis of the reasons for quitting vaping among treatment-seeking young people.

BACKGROUND: Debate continues over how e-cigarettes have impacted the health of young people, and what regulatory policies should be enacted. The debate has appropriately been informed by quantitative studies, often focused on initiation, prevalence, and product transition among the general population and demographic segments. Factors driving cessation and subjective experiences that motivate young users to quit have been largely absent from the debate. This qualitative study highlights the range of motivating experiences among a population of treatment-seeking young e-cigarette users. METHODS: Three researchers coded reasons for quitting provided by a sample of n = 1000 youth (13-17) and n = 1000 young adults (18-24) enrolled in a text message cessation program. Data spanned January 18 - February 22, 2019. Codes were adapted from previous literature. RESULTS: The most common reasons were health (50.9%; "I want my lungs back"), financial cost (21.7%; "I don't have enough money to feed my addiction"), freedom from addiction (16.0%; "i hate juuling. it's taking over my life"), and social influence (10.1%; "it's affecting my friendships"). Selected quotes highlight a broad range of additional ways in which e-cigarette use negatively impacted young people, including decreased academic performance and mental health. CONCLUSIONS: Young people trying to quit e-cigarettes are motivated by a diversity of reasons including health, financial, social, and academic. The range of impacts should be considered in discussions of policies intended to protect young people, and incorporated into cessation programs designed to serve them.