The Relationship Between Trauma Exposure and Adult Tobacco Use: Analysis of the National Epidemiologic Survey on Alcohol and Related Conditions (III).

INTRODUCTION: Previous research has examined cigarette smoking in trauma exposed populations. However, the relationships between trauma exposure and use of other tobacco products (eg, cigars, e-cigarettes) and specific trauma exposure characteristics that may be associated with tobacco use are understudied. AIMS AND METHODS: Using the 2012-2013 National Epidemiologic Survey on Alcohol and Related Conditions-III (N = 36 151 adults), we conducted weighted bivariate analyses of tobacco use among participants with no trauma exposure, trauma exposure, and trauma exposure with post-traumatic stress disorder (trauma + PTSD), stratified by tobacco product use. We also performed weighted logistic regressions testing relationships between trauma exposure and tobacco use, controlling for behavioral health (BH) conditions (mood, anxiety, substance use, personality disorders) and sociodemographics. RESULTS: Approximately 44% of participants had experienced trauma; 6% experienced trauma + PTSD. Trauma exposed participants had a higher prevalence of tobacco use (30%--46% vs. 22%) and poly-tobacco use (34%--35% vs. 28%) than unexposed participants. Cigarettes were the most used tobacco product; trauma + PTSD (19%), and trauma (15%) participants had a higher prevalence of e-cigarette use than unexposed participants (11%). Trauma exposure was associated with current tobacco use (AOR = 1.36 trauma + PTSD; 1.23 trauma) (but not former use), particularly among participants exposed to violence/abuse (AOR = 1.23). Personality and substance use disorders were strongly associated with current and former tobacco use. CONCLUSIONS: Trauma exposure, PTSD, and experiences of violence/abuse are associated with current tobacco use. BH conditions may also play a role in current and former tobacco use. Recognizing and addressing trauma exposure and BH conditions among tobacco users may improve cessation rates in these populations. IMPLICATIONS: This study contributes to research on tobacco use disparities in behavioral health populations by providing a comprehensive examination of tobacco use in trauma exposed individuals. Prior research has examined cigarette smoking, but not other tobacco product use in these populations. This study presents findings on multiple tobacco use behaviors (tobacco product, poly-tobacco use, cessation attempts) in trauma exposed populations and characteristics of trauma exposure (severity, type of traumatic event) associated with tobacco use. These findings underscore the importance of further examining the implications of trauma exposure for tobacco use and of screening and addressing trauma in cessation treatment.

Engagement and Short-term Abstinence Outcomes Among Blacks and Whites in the National Cancer Institute's SmokefreeTXT Program.

INTRODUCTION: Text-messaging programs for smoking cessation improve abstinence outcomes in the general population. However, little is known about engagement and abstinence outcomes among African Americans in text-messaging smoking cessation programs. The current study compares engagement and abstinence between Blacks and Whites in the National Cancer Institute's SmokefreeTXT program. METHOD: Data were from Blacks (n = 1333) and Whites (n = 7154) who enrolled in the 6-week SmokefreeTXT program between August 2017 and June 2018. We assessed the association between race and program initiation and completion; responses to weekly smoking cessation, mood, and craving assessments; and self-reported abstinence using multivariable logistic regression. RESULTS: Blacks and Whites initiated the program at a similar frequency, yet Blacks were more likely to complete the program (adjusted odds ratio [AOR] = 1.71, 95% confidence interval [CI] = 1.43 to 2.06). Blacks were less likely to respond to all seven abstinence, mood, and craving assessments (eg, AOR of quit day responses = 0.63, 95% CI = 0.51 to 0.77; 6-week AOR = 0.50, 95% CI = 0.34 to 0.72). Self-reported abstinence was lower among Blacks for all seven smoking assessments (eg, quit day abstinence AOR = 0.52, 95% CI = 0.41 to 0.68; 6-week abstinence AOR = 0.58, 95% CI = 0.38 to 0.89). CONCLUSION: Although Blacks were more likely than Whites to complete the SmokefreeTXT program, they were less likely to engage with the program by responding to assessment questions and had lower abstinence rates. Qualitative research may reveal unique barriers to engagement among Blacks. IMPLICATIONS: Black smokers enrolled in a nationwide mobile smoking cessation program at a rate comparable to White smokers. However, they were significantly less likely to engage with the program or quit smoking. This study highlights the need to examine barriers to cessation for Black smokers.

Characteristics and Correlates of Recent Successful Cessation Among Adult Cigarette Smokers, United States, 2018.

We assessed characteristics and correlates of recent successful cessation (quitting smoking for 6 months or longer within the past year) among US adult cigarette smokers aged 18 years or older. Estimates came from the July 2018 fielding of the 2018-2019 Tobacco Use Supplement to the Current Population Survey (N = 26,759). In 2018, 7.1% of adult smokers reported recent successful cessation. Recent successful cessation varied by certain demographic characteristics, noncigarette tobacco product use, smoke-free home rules, and receipt of advice to quit from a medical doctor. To help more smokers quit, public health practitioners can ensure that evidence-based tobacco control interventions, including barrier-free access to evidence-based cessation treatments, are reaching all tobacco users, especially those who face greater barriers to quitting.

The 2016 Tips From Former Smokers® Campaign: Associations With Quit Intentions and Quit Attempts Among Smokers With and Without Mental Health Conditions.

INTRODUCTION: People living with mental health conditions (MH+) are more likely to smoke cigarettes than people without mental health conditions (MH-) and to experience tobacco-related disparities. The Tips From Former Smokers® (Tips®) campaign is a proven population-level strategy for motivating smokers to quit. In 2016, Tips included ads featuring Rebecca, a former smoker with depression. We evaluated self-reported frequency of exposure to the Rebecca and other Tips ads in association with quit intentions and quit attempts among MH+ and MH- smokers. METHODS: Intentions to quit and past 6-month quit attempts lasting at least 24 hours were reported from a two-wave longitudinal online survey conducted before and after the 2016 Tips campaign with a nationally representative sample of US adult cigarette smokers with (MH+, N = 777) and without (MH-, N = 1806) lifetime mental health conditions. RESULTS: In 2016, among MH+ respondents, greater exposure to the Rebecca ads was significantly associated with increased odds of intending to quit in the next 30 days (adjusted odds ratio [AOR] = 1.40, p < .05) and with reporting a quit attempt in the past 6 months (AOR = 1.25, p < .05). Among MH- respondents, greater exposure to the other Tips ads was associated with increased odds of making a quit attempt (AOR = 1.19, p < .05). CONCLUSIONS: Exposure to the Rebecca ads was associated with a greater likelihood of intentions to quit and quit attempts among MH+ smokers; whereas, exposure to the other (non-mental-health-related) Tips ads was associated with a greater likelihood of quit attempts among MH- smokers. IMPLICATIONS: National media campaigns are an important population-level strategy for reaching specific population groups who are experiencing tobacco-related disparities. The findings support the inclusion of ads featuring people living with mental health conditions in national tobacco education media campaigns, such as Tips.

State-Specific Cessation Behaviors Among Adult Cigarette Smokers - United States, 2014-2015.

This study assessed state-specific smoking cessation behaviors among US adult cigarette smokers aged 18 years or older. Estimates came from the 2014-2015 Tobacco Use Supplement to the Current Population Survey (N = 163,920). Prevalence of interest in quitting ranged from 68.9% (Kentucky) to 85.7% (Connecticut); prevalence of making a quit attempt in the past year ranged from 42.7% (Delaware) to 62.1% (Alaska); prevalence of recently quitting smoking ranged from 3.9% (West Virginia) to 11.1% (District of Columbia); and prevalence of receiving quit advice from a medical doctor in the past year ranged from 59.4% (Nevada) to 81.7% (Wisconsin). These findings suggest that opportunities exist to encourage and help more smokers to quit.

Characterizing perceived usability and its correlation with smoking cessation: An analysis of user assessments of the smoking cessation app quitSTART.

Background: As smartphone ownership has become common in all demographic groups in the United States, smartphone applications (apps) for smoking cessation have grown in popularity due to their potential for supporting the diverse populations in the United States who are attempting to quit smoking. Usability is commonly assessed in mobile health (mHealth) technology as an important aspect of the user experience that could influence users' adherence to a health app and health outcomes. However, the variation of perceived usability across demographic groups, and the implications of that variation for app success, have not been well studied. Objective: The aims of this study were to characterize variation in the perceived usability of the National Cancer Institute Smokefree.gov Initiative smoking cessation app quitSTART across demographic groups, and to assess the correlation between perceived usability and short-term smoking cessation. Methods: We conducted a secondary analysis of data from a randomized controlled trial conducted from 2020 to 2021, which used a 16-item modified version of the mHealth App Usability Questionnaire (MAUQ) to quantify perceived usability four weeks after app download among 131 smokers attempting to quit. Responses were coded on a 5-point Likert-type scale ranging from strongly disagree (1) to strongly agree (5) and total perceived usability was calculated as the sum of all 16 items (range: 16-80). Associations between participant demographic characteristics (gender, race, education level, age, etc.) and total usability were determined using an ANCOVA model. A multivariable logistic regression model was used to assess the association between usability and smoking cessation, also assessed 4 weeks after app download. Results: The ANCOVA model demonstrated that race was associated with perceived usability, with participants from a racial minority group reporting higher total usability than White participants (p < 0.001). White participants had an adjusted mean total usability of 55.8 (95 % CI: 52.8, 58.8) while racial minority group participants had an adjusted mean total usability of 66.5 (95 % CI: 61.2, 71.6). Other participant demographic characteristics, such as gender and sexual minority status, were not associated with mean total usability. Total usability was positively associated with smoking cessation (OR: 1.04, 95 % CI: 1.00, 1.08, p = 0.031). Conclusions: Total perceived usability of quitSTART was higher among adults from a racial minority group compared to White adults, and perceived usability was positively associated with cessation success. These findings emphasize the importance of ensuring high usability of mHealth smoking cessation apps for diverse populations.

Identifying Patterns of Smoking Cessation App Feature Use That Predict Successful Quitting: Secondary Analysis of Experimental Data Leveraging Machine Learning.

BACKGROUND: Leveraging free smartphone apps can help expand the availability and use of evidence-based smoking cessation interventions. However, there is a need for additional research investigating how the use of different features within such apps impacts their effectiveness. OBJECTIVE: We used observational data collected from an experiment of a publicly available smoking cessation app to develop supervised machine learning (SML) algorithms intended to distinguish the app features that promote successful smoking cessation. We then assessed the extent to which patterns of app feature use accounted for variance in cessation that could not be explained by other known predictors of cessation (eg, tobacco use behaviors). METHODS: Data came from an experiment (ClinicalTrials.gov NCT04623736) testing the impacts of incentivizing ecological momentary assessments within the National Cancer Institute's quitSTART app. Participants' (N=133) app activity, including every action they took within the app and its corresponding time stamp, was recorded. Demographic and baseline tobacco use characteristics were measured at the start of the experiment, and short-term smoking cessation (7-day point prevalence abstinence) was measured at 4 weeks after baseline. Logistic regression SML modeling was used to estimate participants' probability of cessation from 28 variables reflecting participants' use of different app features, assigned experimental conditions, and phone type (iPhone [Apple Inc] or Android [Google]). The SML model was first fit in a training set (n=100) and then its accuracy was assessed in a held-aside test set (n=33). Within the test set, a likelihood ratio test (n=30) assessed whether adding individuals' SML-predicted probabilities of cessation to a logistic regression model that included demographic and tobacco use (eg, polyuse) variables explained additional variance in 4-week cessation. RESULTS: The SML model's sensitivity (0.67) and specificity (0.67) in the held-aside test set indicated that individuals' patterns of using different app features predicted cessation with reasonable accuracy. The likelihood ratio test showed that the logistic regression, which included the SML model-predicted probabilities, was statistically equivalent to the model that only included the demographic and tobacco use variables (P=.16). CONCLUSIONS: Harnessing user data through SML could help determine the features of smoking cessation apps that are most useful. This methodological approach could be applied in future research focusing on smoking cessation app features to inform the development and improvement of smoking cessation apps. TRIAL REGISTRATION: ClinicalTrials.gov NCT04623736; https://clinicaltrials.gov/study/NCT04623736.

User Engagement With Mood-Related Content on the National Cancer Institute Smokefree.Gov Initiative Cessation Resources.

AIMS: This study aimed to examine engagement with mood-related content on the Smokefree.gov Initiative's smoking cessation resources. METHODS: Smokefree.gov website analytics (July 2018-July 2019) were analyzed for user interactions with mood content on informational webpages and interactive self-assessment tools (mood quizzes, smoking quit plans). We also examined mood feature engagement (texting or pressing a command button for mood support) among text program and app users (July 2018-July 2019). RESULTS: Mood webpage views comprised 1.5% of all Smokefree.gov webpage views. Depression quizzes represented 82.9% of quiz completions, and stress quizzes represented 0.4% of quiz completions. Stress, anxiety, and low mood were the most reported smoking triggers on quit plans. Approximately 7,000 text program users and 3,000 app users sought real-time mood support. CONCLUSIONS: Given the importance of mood management in the smoking cessation process these findings demonstrate promising opportunities to increase the reach of mood support resources.

Using Digital Technologies to Reach Tobacco Users Who Want to Quit: Evidence From the National Cancer Institute's Smokefree.gov Initiative.

The rapid growth of smartphone ownership and broadband access has created new opportunities to reach smokers with cessation information and support using digital technologies. These technologies can both complement and be integrated with traditional support modalities such as telephone quitlines and 1-on-1 clinical cessation counseling. The National Cancer Institute's Smokefree.gov Initiative provides free, evidence-based cessation support to the public through a multimodal suite of digital interventions, including several mobile-optimized websites, text messaging programs, and 2 mobile applications. In addition to digital resources directed at the general population, the Smokefree.gov Initiative includes population-specific resources targeted to adolescents, women, military veterans, Spanish speakers, older adults, and other populations. This paper describes the reach and use of the Smokefree.gov Initiative's resources over a 5-year period between 2014 and 2018, including how users interact with the program's digital content in ways that facilitate engagement with live counseling support. Use of Smokefree.gov Initiative resources has grown steadily over time; in 2018 alone, approximately 7-8 million people accessed Smokefree.gov Initiative web- and mobile-based resources. Smokefree.gov Initiative utilization data show that people take advantage of the full range of technology tools and options offered as part of the Smokefree.gov Initiative's multiplatform intervention. The Smokefree.gov Initiative experience suggests that offering different, complementary technology options to meet the needs and preferences of smokers has the potential to meaningfully expand the reach of cessation treatment.

A content analysis of smartphone apps for adolescent smoking cessation.

Adolescent cigarette smokers have an increased risk of sustained smoking into adulthood. Smartphone applications (apps) for smoking cessation are a promising treatment resource. However, research on apps for adolescent smoking cessation is limited. This study compared smoking cessation mobile apps targeting an adolescent audience with popular cessation apps for a general audience. Adolescent and general-audience apps were identified by searching the Google Play and Apple App Stores (November 2016). Two coders assessed each app for adherence to clinical practice guidelines for tobacco and adolescent-specific content (2016-2017) and developed a summary score that summed all adherence and adolescent content criteria. Eight adolescent apps were identified and compared with the top 38 general apps (as ranked by Apple and Google). Both general and adolescent apps commonly had adherence content related to developing a quit plan (general: 73.68 per cent; adolescent: 87.50 per cent) and enhancing motivation by describing the rewards of not smoking (general: 76.32 per cent; adolescent: 62.50 per cent). Adolescent-specific content such as peer influence on smoking was common in adolescent apps but not in general apps (general: 5.26 per cent; adolescent: 62.50 per cent). Adolescent apps had a higher general adherence content summary score [t (44) = 2.55, p = .01] and a higher adolescent content summary score [t (7.81) = 2.47, p = .04] than the general apps. On average, adolescent cessation apps included more adherence content and adolescent-specific content than general apps. Future research is needed to determine the extent to which adolescents engage with the adherence content available in these apps.

Predictors of Retention in an Adult Text Messaging Smoking Cessation Intervention Program: Cohort Study.

BACKGROUND: Mobile health tools such as text messaging programs can support smoking cessation. However, high rates of disengagement from these tools decrease their effectiveness. OBJECTIVE: The purpose of this study was to identify user characteristics associated with retention in an adult text messaging smoking cessation intervention. METHODS: Adults initiating a quit attempt using the publicly available program SmokefreeTXT between March 6 and June 21, 2016 (n=6215), were included. Data were collected to assess nicotine dependence, frequency of being around other smokers, time of the day for cigarette cravings, extrinsic and intrinsic motivation to quit smoking, confidence in quitting, and long-term intention to be smoke free. Multivariable survival analysis modeling for time to opt out was conducted to identify characteristics associated with opting out over the course of the intervention, adjusting for age, sex, and smoking frequency, reset of the quit date by the user, and the number of days enrolled before initiating the quit attempt. Among those who opted out, multivariable multinomial logistic regression analysis was used to identify predictors of opting out early (within 3 days and between 4 and 7 days into the quit attempt) compared to opting out late (more than 7 days into the quit attempt), adjusting for the same confounders. RESULTS: Survival analyses indicated that younger age, female sex, higher levels of nicotine dependence, lower intention to be smoke free, and enrolling in SmokefreeTXT ≤1 week before initiating the quit attempt were associated with an increased risk of opting out. For example, users who smoked within 5 minutes of waking up were 1.17 times more likely to opt out than those who smoked more than 5 minutes after waking up (95% CI 1.01-1.35). Among users who opted out from SmokefreeTXT, logistic regression modeling indicated that compared to users who were never or rarely around other smokers, those who were sometimes around other smokers had 1.96 times more likely to opt out within the first 3 days of the quit attempt (95% CI 1.18-3.25). In addition, compared to users with high levels of long-term quit intention, users with lower levels of intention had 1.80 times the odds of opting out between 4 and 7 days into the quit attempt (95% CI 1.02-3.18). Users who reset their quit date after initiating a quit attempt were less likely to opt out at either time point compared with those who did not reset their quit date. CONCLUSIONS: Several user characteristics are associated with retention in an adult text messaging smoking cessation program. These results provide guidance on potential characteristics that should be addressed in future text messaging smoking cessation programs. Providing additional support to users with these characteristics may increase retention in text messaging programs and ultimately lead to smoking cessation.