Factors Associated With Successful E-Cigarette Cessation Among a Convenience Sample of Adult Users.

Background: A growing body of literature suggests that many people who use e-cigarettes become dependent and have difficulty quitting. Most people who use e-cigarettes have interest in quitting, yet there is currently a lack of evidence to inform interventions for e-cigarette cessation. Objective: The purpose of this study was to identify factors associated with successful e-cigarette quit attempts among a large sample of people who use e-cigarettes. Methods: Participants (n=586) were people who use e-cigarettes who reported at least one lifetime attempt to quit their e-cigarette use. Adjusted logistic regression models were performed to examine differences in e-cigarette use characteristics and quit methods between people who currently use e-cigarettes and who quit e-cigarettes. Results: Most participants were people who currently use e-cigarettes and only 27.5% reported successfully quitting. Most participants (90.6%) used e-cigarettes that contained nicotine, and over half (54.0%) used closed-system e-cigarette devices with replaceable pre-filled pods or cartridges. The quit method most commonly used overall (63.1%) and for people who quit e-cigarettes (70.8%) was cold turkey. Past 30-day cigarette use and past 30-day other tobacco use was significantly associated with reduced odds of quitting, and there were no e-cigarette characteristics significantly associated with successful cessation. Nicotine replacement therapy was the only e-cigarette cessation method that was significantly associated with increased odds of quitting after adjusting for past 30-day cigarette and other tobacco use. Conclusions: These results suggests that cigarette use, other tobacco use, and quit method used may significantly influence the likelihood of e-cigarette cessation. Future research is needed to determine the effectiveness of interventions for e-cigarette cessation using nicotine replacement therapy.

Mobile Health Interventions for Substance Use Disorders.

Substance use disorders (SUDs) have an enormous negative impact on individuals, families, and society as a whole. Most individuals with SUDs do not receive treatment because of the limited availability of treatment providers, costs, inflexible work schedules, required treatment-related time commitments, and other hurdles. A paradigm shift in the provision of SUD treatments is currently underway. Indeed, with rapid technological advances, novel mobile health (mHealth) interventions can now be downloaded and accessed by those that need them anytime and anywhere. Nevertheless, the development and evaluation process for mHealth interventions for SUDs is still in its infancy. This review provides a critical appraisal of the significant literature in the field of mHealth interventions for SUDs with a particular emphasis on interventions for understudied and underserved populations. We also discuss the mHealth intervention development process, intervention optimization, and important remaining questions. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 20 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Impact of Financial Disclosures and Health Warnings on Youth and Young Adult Perceptions of Pro-E-cigarette Instagram Posts.

INTRODUCTION: We examined the impact of financial disclosures and warning labels on pro-e-cigarette Instagram posts and their association with attitudes toward the ad and product among youth and young adults. METHODS: During March to May 2021, we conducted a factorial experiment using an online convenience sample of youth and young adults (N = 1687, Mage = 21.54). Participants were randomized to one of four conditions: pro-e-cigarette Instagram posts with only a financial disclosure, only a warning label, both a financial disclosure + warning label, or no financial disclosure or warning label. After viewing the posts, participants answered questions regarding their attitude toward the ad and the product. We used one-way ANOVA to estimate the association of condition on outcomes controlling for demographics. RESULTS: Young adults who viewed Instagram posts with only a financial disclosure reported more positive attitudes toward the ad than those who viewed posts with both a financial disclosure + warning label (p < .05). Young adults who viewed posts with only a financial disclosure reported more positive attitudes toward the product than those who viewed posts with only a warning label, both a financial disclosure + warning label, and without either (ps < .05). Differences were not statistically significant for youth. E-cigarette use status was associated with increased positive attitudes toward the ad (p < .001) and product (p < .001) for all participants. CONCLUSIONS: Our results can inform policy interventions to mitigate the effects of e-cigarette social media marketing among youth and young adults. Including financial disclosures may not decrease appeal of e-cigarettes compared to posts without either. IMPLICATIONS: Findings from the study suggest that a warning label may be more effective in reducing the effects of pro-e-cigarette social media posts than a financial disclosure among young adults. Public health officials should examine additional strategies beyond financial disclosures and warning labels (eg, social media peer mentoring program) to offset the persuasive effects of pro-e-cigarette social media marketing posts on young people. Additional policy interventional efforts are needed to limit the impact of e-cigarette social media marketing.

Nicotine dependence among young adults: Comparing exclusive ENDS users to exclusive cigarette smokers.

PURPOSE: No studies of young adults have compared symptoms of nicotine dependence among exclusive past 30-day (P30D) ENDS users versus exclusive P30D cigarette smokers. METHODS: Participants at Wave 14 (Fall 2021) of The Texas Adolescent and Tobacco Marketing Surveillance System (TATAMS) (n = 2,341; mean age = 20.95 years old) who reported P30D exclusive ENDS use (n = 212) and P30D exclusive cigarette smoking (n = 46). Symptoms of nicotine dependence were measured with the Hooked On Nicotine Checklist (the 10-item HONC scale). An independent samples t-test compared average HONC scores between groups, and a Pearson (or Fisher's Exact) Chi-Square (X2) test compared the prevalence of symptom(s) between groups. RESULTS: The average HONC score was significantly higher for exclusive P30D ENDS users than exclusive P30D cigarette smokers (3.51 vs. 1.91, p ≤ 0.001). Compared to exclusive P30D cigarette smokers, a significantly higher proportion of exclusive P30D ENDS users reported having felt addicted (45.28% vs. 15.22%, p ≤ 0.001), having strong cravings (50.00% vs. 28.26%, p = 0.007), difficulty not using in prohibited places (i.e., school or work) (24.06% vs. 4.35%, p = 0.002), difficulty concentrating (21.23% vs. 6.52%, p = 0.021), and feeling nervous, restless, or anxious (28.77% vs. 10.87%, p = 0.012). CONCLUSIONS: Symptoms of nicotine dependence were elevated among young adults who were exclusive P30D users of ENDS relative to exclusive P30D cigarette smokers.

Associations between morning affect and later-day smoking urges and behavior.

OBJECTIVE: Affective experiences are associated with smoking urges and behavior. Few studies have examined the temporal nature of these associations within a day, such as whether positive and negative affect in the morning are associated with smoking urges and behavior later in the day. METHOD: Participants (N = 63; MAge = 50 years, 48% female; 60% White) were randomized into one of three smoking cessation interventions and answered up to five daily ecological momentary assessments for 28 days during a quit attempt (M = 21.0 days, SD = 7.1). Before analysis, scores for morning positive and negative affect and later-day smoking urges and behavior were calculated. RESULTS: On days when individuals' morning positive affect was higher than usual, later-day smoking urges tended to be lower than usual. In contrast, on days when individuals' morning negative affect was higher than usual, later-day smoking urges tended to be higher than usual, and smoking was more likely. Further, individuals who had higher characteristic morning positive affect tended to have less intense later-day smoking urges, whereas those who tended to have higher characteristic morning negative affect tended to have more intense later-day smoking urges. CONCLUSIONS: Morning positive and negative affect were associated with later-day smoking urges, and morning negative affect was related to later-day smoking behavior. Future research should examine whether interventions that boost positive affect on mornings when it is lower than usual and attenuate negative affect on mornings when it is higher than usual, may reduce the intensity of smoking urges and the likelihood of smoking later in the day. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Varenicline Combined With Oral Nicotine Replacement Therapy and Smartphone-Based Medication Reminders for Smoking Cessation: Feasibility Randomized Controlled Trial.

BACKGROUND: Varenicline and oral nicotine replacement therapy (NRT) have each been shown to increase the likelihood of smoking cessation, but their combination has not been studied. In addition, smoking cessation medication adherence is often poor, thus, challenging the ability to evaluate medication efficacy. OBJECTIVE: This study examined the effects of combined varenicline and oral NRT and smartphone medication reminders on pharmacotherapy adherence and smoking abstinence among adults enrolled in smoking cessation treatment. METHODS: A 2×2 factorial design was used. Participants (N=34) were randomized to (1) varenicline + oral NRT (VAR+NRT) or varenicline alone (VAR) and (2) smartphone medication reminder messages (REM) or no reminder messages (NREM) over 13 weeks. Participants assigned to VAR+REM received varenicline reminder prompts, and those assigned to VAR+NRT+REM also received reminders to use oral NRT. The other 2 groups (VAR+NREM and VAR+NRT+NREM) did not receive medication reminders. Participants were not blinded to intervention groups. All participants received tobacco cessation counseling. Smartphone assessments of smoking as well as varenicline and NRT use (if applicable) were prompted daily through the first 12 weeks after a scheduled quit date. Descriptive statistics were generated to characterize the relations between medication and reminder group assignments with daily smoking, daily varenicline adherence, and daily quantity of oral NRT used. Participants completed follow-up assessments for 26 weeks after the quit date. RESULTS: Participants were predominantly White (71%), and half were female (50%). On average, participants were 54.2 (SD 9.4) years of age, they smoked an average of 19.0 (SD 9.0) cigarettes per day and had smoked for 34.6 (SD 12.7) years. Descriptively, participants assigned to VAR+NRT reported more days of smoking abstinence compared to VAR (29.3 vs 26.3 days). Participants assigned to REM reported more days of smoking abstinence than those assigned to NREM (40.5 vs 21.8 days). Participants assigned to REM were adherent to varenicline on more days compared to those assigned to NREM (58.6 vs 40.5 days), and participants assigned to VAR were adherent to varenicline on more days than those assigned to VAR + NRT (50.7 vs 43.3 days). In the subsample of participants assigned to VAR+NRT, participants assigned to REM reported more days where ≥5 pieces of NRT were used than NREM (14.0 vs 7.4 days). Average overall medication adherence (assessed via the Medication Adherence Questionnaire) showed the same pattern as the daily smartphone-based adherence assessments. CONCLUSIONS: Preliminary findings indicated that smoking cessation interventions may benefit from incorporating medication reminders and combining varenicline with oral NRT, though combining medications may be associated with poorer adherence. Further study is warranted. TRIAL REGISTRATION: ClinicalTrials.gov NCT03722966; https://classic.clinicaltrials.gov/ct2/show/NCT03722966.

Unsheltered homeless and unstably housed adults have higher levels of stress and more health risk factors than sheltered homeless adults.

In the United States, approximately 580,000 individuals were homeless on a single night in 2020. Homelessness can be categorized into three subgroups: sheltered homeless, unsheltered homeless, and unstably housed. Few studies have examined the relations between homelessness subtypes, shelter service utilization, levels of stress experienced, and health risk factors. This study aimed to empirically examine whether shelter status the previous night was related to current stress, recent utilization of shelter-based mental health services, and current health risk factors. Data were collected at multiple homeless shelters in 2016 in the Oklahoma City area (N=575). All participants completed assessments of demographic characteristics, including age, sex, race, marital status, years of education, and incarceration history and victimization. Multiple linear and logistic regression analyses were conducted to examine relations between homelessness subgroups and outcomes (shelter-based service utilization, health risk factors, and stressors). Results indicated that the sheltered group was younger and more likely to be White than the unsheltered group, had higher levels of education, and reported more lifetime months in jail than the unstably housed group. In addition, unsheltered homeless and unstably housed adults used fewer shelter-based health services, exhibited more health risk factors, experienced greater levels of stress, and had higher levels of food insecurity than sheltered homeless adults. Homeless adults who reside at shelters benefit most from available shelter services. The development of policies and programs targeted toward increasing sheltering options for unsheltered and unstably housed adults is needed.

Longitudinal Transition Patterns of Tobacco Use Among Youth and Young Adults Never Tobacco Product Users: Findings From the Population Assessment of Tobacco and Health Study, 2014-2019.

Aims: To identify, visualize, and describe the prevalence of within-product patterns of tobacco use behaviors for e-cigarettes, cigarettes, and hookah (TP) by 3 age groups (ie, 12-14-year-old, 15-17-year-old, and 18-20-year-old) with U.S. nationally representative data. Methods: In 2014-2015, never users of each (TP) and age group were followed-up longitudinally between 2015-2019 using five transition states: non-susceptible to (TP) use, susceptible to (TP) use, ever (TP) use, past 30-day (TP) use, and discontinued past 30-day (TP) use. Sankey diagrams were used to graphically visualize patterns in tobacco use behaviors across time. Results: Among 12-14-year-old who were never users and susceptible to each TP from 2014-2017, 7% initiated ever e-cigarette use and 9.4% first reported past 30-day use by 2018-2019; 5.8% initiated ever cigarette use and 3% first reported past 30-day cigarette use by 2018-2019; and, 4.5% initiated ever hookah use and 1.0% first reported past 30-day hookah use by 2018-2019. Among 15-17-year-old who were never users and susceptible to each TP from 2014-2017, 4.2% initiated ever e-cigarette use and 9.0% first reported past 30-day use by 2018-2019; 4.5% initiated ever cigarette use and 3% first reported past 30-day cigarette use by 2018-2019; and, 4.5% initiated ever hookah use and 2.4% first reported past 30-day hookah use by 2018-2019. Among 18-20-year-old who were never users and susceptible to each TP from 2014-2017, 3.2% initiated ever e-cigarette use and 3.6% first reported past 30-day e-cigarette use by 2018-2019; 3.0% initiated ever cigarette use and 2.3% first reported past 30-day cigarette use; and, 2.8% initiated ever hookah use and 1.0% first reported past 30-day hookah use by 2018-2019. Conclusions: From 2014 to 2019, onset and progression of e-cigarette, cigarette, and hookah use occurred more frequently in 12-14 and 15-17-year-old than in young adults 18-20-year-old.

E-Cigarette Quit Attempts and Experiences in a Convenience Sample of Adult Users.

Most e-cigarette users report planning to quit, but there is a paucity of evidence-based interventions for e-cigarette cessation. In the absence of interventions for e-cigarette cessation, we sought to understand how and why e-cigarette users attempt to quit on their own. Participants were recruited from Amazon Mechanical Turk, an online crowdsourcing platform. Those who reported they had ever used e-cigarettes regularly and had attempted to quit e-cigarette use were eligible for participation. Measures included demographic characteristics, other tobacco product use, e-cigarette device characteristics, barriers to quitting e-cigarettes, and facilitators to quitting e-cigarettes. A content analysis was conducted on twotwo open-ended questions that asked about advice respondents had for others trying to quit vaping and resources they wished they had during their quit attempt. Descriptive analyses were performed (means/standard errors; frequencies/proportions). A total of 89.0% reported using an e-cigarette with nicotine, 20.2% reported a nicotine concentration of 4-6 mg/mL%, 32.8% reported using multiple flavors, and 77.7% reported using their e-cigarette every day or some days. The primary reason reported for wanting to quit e-cigarettes was health concerns (42.2%), and 56.7% reported trying to quit "cold turkey". During quit attempts, 41.0% reported intense cravings and 53.1% reported stress as a trigger. From the content analysis, the most commonly cited suggestion for those wanting to quit e-cigarettes was distractions/hobbies (19.9%), followed by reducing/tapering down nicotine (16.9%). Descriptive information on demographics, e-cigarette use, device characteristics, barriers, facilitators, and quit methods provides a first step in identifying factors that contribute to successful interventions designed for e-cigarette cessation.

Tobacco advertising exposure and product use among young adults: An ecological momentary assessment approach.

INTRODUCTION: Tobacco-related content is prevalent on social media, yet many methods of measuring exposure are inadequate due to the personalized nature of online marketing. The purpose of this paper is to examine the association between exposure to pro-tobacco messages (both industry-sponsored and user-generated) and the use of tobacco products, as reported via ecological momentary assessment (EMA). METHODS: Young adults (n = 175) were instructed to record all sightings of marketing (both in-person and online) related to tobacco for 28 days. Tobacco product use and recall of message encounters were assessed daily using app-initiated EMA. RESULTS: Participants who reported exposure to tobacco messages were significantly more likely to report using tobacco, adjusting for gender, age, race/ethnicity, baseline use of any tobacco product, and having friends who use tobacco and e-cigarettes (p <.001). For each industry-sponsored message viewed, the odds of using tobacco or e-cigarettes in a given day increased by a factor of 1.77 (95 % CI = 1.41, 2.23). For each user-generated message viewed, the odds of using tobacco or e-cigarettes in a given day increased by a factor of 1.52 (95 % CI = 1.27, 1.83). DISCUSSION: To our knowledge, this is the first study to specifically examine the association between exposure to user-generated messages and daily tobacco use. The findings suggests that there is a unique element to user-generated messages that distinguishes them from both traditional marketing and from simple peer influence.

A Scoping Review and Meta-analysis of the Use of Remote Biochemical Verification Methods of Smoking Status in Tobacco Research.

INTRODUCTION: Increasing digital delivery of smoking cessation interventions has resulted in the need to employ novel strategies for remote biochemical verification. AIMS AND METHODS: This scoping review and meta-analysis aimed to investigate best practices for remote biochemical verification of smoking status. The scientific literature was searched for studies that reported remotely obtained (not in-person) biochemical confirmation of smoking status (ie, combustible tobacco). A meta-analysis of proportions was conducted to investigate key outcomes, which included rates of returned biological samples and the ratio of biochemically verified to self-reported abstinence rates. RESULTS: A total of 82 studies were included. The most common samples were expired air (46%) and saliva (40% of studies), the most common biomarkers were carbon monoxide (48%) and cotinine (44%), and the most common verification methods were video confirmation (37%) and mail-in samples for lab analysis (26%). Mean sample return rates determined by random-effects meta-analysis were 70% for smoking cessation intervention studies without contingency management (CM), 77% for CM studies, and 65% for other studies (eg, feasibility and secondary analyses). Among smoking cessation intervention studies without CM, self-reported abstinence rates were 21%, biochemically verified abstinence rates were 10%, and 47% of individuals who self-reported abstinence were also biochemically confirmed as abstinent. CONCLUSIONS: This scoping review suggests that improvements in sample return rates in remote biochemical verification studies of smoking status are needed. Recommendations for reporting standards are provided that may enhance confidence in the validity of reported abstinence rates in remote studies. IMPLICATIONS: This scoping review and meta-analysis included studies using remote biochemical verification to determine smoking status. Challenges exist regarding implementation and ensuring high sample return rates. Higher self-reported compared to biochemically verified abstinence rates suggest the possibility that participants in remote studies may be misreporting abstinence or not returning samples for other reasons (eg, participant burden, inconvenience). Remote biochemical confirmation of self-reported smoking abstinence should be included in smoking cessation studies whenever feasible. However, findings should be considered in the context of challenges to sample return rates. Better reporting guidelines for future studies in this area are needed.

Bayesian continuous-time hidden Markov models with covariate selection for intensive longitudinal data with measurement error.

Intensive longitudinal data collected with ecological momentary assessment methods capture information on participants' behaviors, feelings, and environment in near real-time. While these methods can reduce recall biases typically present in survey data, they may still suffer from other biases commonly found in self-reported data (e.g., measurement error and social desirability bias). To accommodate potential biases, we develop a Bayesian hidden Markov model to simultaneously identify risk factors for subjects transitioning between discrete latent states as well as risk factors potentially associated with them misreporting their true behaviors. We use simulated data to demonstrate how ignoring potential measurement error can negatively affect variable selection performance and estimation accuracy. We apply our proposed model to smartphone-based ecological momentary assessment data collected within a randomized controlled trial that evaluated the impact of incentivizing abstinence from cigarette smoking among socioeconomically disadvantaged adults. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Associations Between Perceptions of e-Cigarette Harmfulness and Addictiveness and the Age of E-Cigarette Initiation Among the Population Assessment of Tobacco and Health (PATH) Youth.

Introduction: Youth perceptions of harmfulness and addictiveness of e-cigarettes may impact the age that they initiate e-cigarette use, but this has not been investigated previously. Methods: Youth (12-17 years old) never e-cigarette users at their first wave of PATH participation (waves 1-3, 2013-2016) were included. PATH questions on absolute perceptions of e-cigarette harmfulness and addictiveness were used as exposures. Interval-censored Cox proportional hazards models were used to estimate the impact of perceptions of harmfulness, and perceptions of addictiveness on (i) the age of initiation of e-cigarette use and (ii) age of first reporting past 30-day e-cigarette use, while controlling for covariates. Results: Youth who perceive e-cigarettes as having no/little harm had increased risk of initiating both ever e-cigarette use (AHR = 2.04; 95%CI = 1.74-2.40) and past 30-day e-cigarette use (AHR = 2.64; 95%CI = 2.07-3.37) at earlier ages compared to youth who perceive e-cigarettes as having a lot of harm. Youth who perceive the likelihood of becoming addicted to e-cigarettes to be very/somewhat unlikely had increased risk of an earlier age of both ever (AHR = 1.28; 95%CI = 1.07-1.52) and past 30-day (AHR = 1.36; 95%CI = 1.04-1.79) e-cigarette initiation compared to youth who perceived the likelihood of becoming addicted to e-cigarettes to be somewhat/very likely. Conclusion: These results highlight the importance of communicating to youth the potential for health harms and addiction from e-cigarette use in prevention and intervention campaigns, as those with the lowest perceptions of harmfulness and addictiveness had the earliest ages of e-cigarette initiation.

Non-adherence to psychiatric medication in adults experiencing homelessness is associated with incurred concussions.

This study investigated the relationship between concussions and medication adherence among 247 adults experiencing homelessness in Oklahoma City, Oklahoma, who were prescribed medication for a psychiatric disorder. Participants were asked whether they had "ever experienced a blow to the head that caused a concussion," and medication adherence was measured by asking participants whether they had taken their psychiatric medication yesterday. The data were analyzed using univariate and multivariable logistic regressions. Results showed that more than half of the sample had a concussion history (61.9%), and homeless adults with a concussion history had higher odds of non-adherence to psychiatric medications compared with those who reported no concussion history [OR = 2.13 (95% CI = 1.08, 4.18)]. Findings suggest that medication non-adherence is associated with incurred concussions. Raising awareness among service providers of the relationship between traumatic brain injury and medication adherence may increase efforts to improve adherence in this underserved population.

Redesigning culturally tailored intervention in the precision health era: Self-management science context.

BACKGROUND: Nurse scientists have significantly contributed to health equity and ensuring cultural tailoring of interventions to meet unique needs of individuals. Methodologies for cultural tailoring of self-mangament interventions among marginalized populations have limitedly accommodated intersectionality and group heterogeneity when addressing health needs. PURPOSE: Identify methodological limitations in cultural tailoring of interventions among priority populations and issue recommendations on cultural elements that researchers can target to ensure valid cultural tailoring approaches. METHODS: Synthesis of literature on health equity, self-management, and implementation and dissemination research. FINDINGS: Among priority populations, intersectionality and group heterogeneity has made group-based cultural tailoring approaches less effective in eliciting desirable health outcomes. Precision health methodology could be useful for cultural tailoring of interventions due to the methodology's focus on individual-level tailoring approaches. DISCUSSION: We offer ways to advance health equity research using precision health approaches in cultural tailoring through targeting unique elements of culture and relevant psychosocial phenotypes.

Development and preliminary effectiveness of a smartphone-based, just-in-time adaptive intervention for adults with alcohol misuse who are experiencing homelessness.

BACKGROUND: Adults experiencing homelessness have much higher rates of alcohol misuse than housed individuals. This study describes the development and preliminary effectiveness of a smartphone-based, just-in-time adaptive intervention (JITAI) to reduce alcohol use among adults experiencing homelessness. METHODS: We conducted a pilot trial (N = 41; mean age [SD] = 45.2 [11.5]; 19.5% women) of the Smart-T Alcohol JITAI where participants completed brief ecological momentary assessments (EMAs) each day, received personalized treatment messages following each EMA, and accessed on-demand intervention content for 4 weeks. The prediction algorithm and treatment messages were developed based on an independent but similar sample as part of the trial. We examined three drinking outcomes: daily drinking (yes/no), drinks per day, and heavy episodic drinking, controlling for scores on the Alcohol Use Disorders Identification Test (AUDIT) at baseline, age, and sex using quadratic growth curve models. RESULTS: Over the 4-week period, participants showed a decline in all alcohol use outcomes. Participants also reported high levels of satisfaction with the JITAI. CONCLUSIONS: Use of the Smart-T Alcohol JITAI was well received and provided encouraging evidence that it may reduce any drinking, drinks per day, and heavy episodic drinking among adults experiencing homelessness.

The Longitudinal Impact of Seeing and Posting Tobacco-related Social Media on Tobacco Use Behaviors Among Youth (Aged 12-17): Findings From the 2014-2016 Population Assessment of Tobacco and Health (PATH) Study.

Introduction: This study examined the impact of seeing and posting tobacco-related content on social media on tobacco use outcomes in youth. Methods: Longitudinal secondary analyses of youth in the nationally representative 2014-2015 Population Assessment of Tobacco and Health (PATH) study were conducted to examine the association between the interaction of (i) seeing and (ii) posting tobacco-related social media content with previous ever use of each tobacco product, and 3 outcomes in 2015-2016: past 30-day e-cigarette use, past 30-day combustible product use, and past 30-day dual use of e-cigarettes and at least one combustible product. Six weighted multiple logistic regression models (2 interaction exposures*3 outcomes) were used to assess these associations, while adjusting for covariates. Results: Among youth never users in 2014-2015, seeing tobacco-related social media content was significantly associated with past 30-day e-cigarette use (AOR 1.92; 95% CI= 1.36-2.71), and past 30-day dual use of e-cigarettes and at least one combustible product (AOR= 2.11; 95% CI= 1.08- 4.13) in 2015-2016. Among youth ever users in 2014-2015, posting tobacco-related content on social media was significantly associated with all 3 outcomes: past 30-day day e-cigarette use (AOR= 2.09;95%CI=1.23-3.55), past 30-day combustible product use (AOR=2.86; 95%CI=1.67-4.88), and past 30-day dual use of these products (AOR=3.02;95%CI=1.45-6.31), after adjusting for covariates. Conclusions: Seeing and posting tobacco-related content on social media predicts tobacco use among youth, nationwide. Results suggest that interventions and policies prohibiting tobacco-related content on social media are needed to curb the impact of social media on youth tobacco-use.

An Integrated mHealth App for Smoking Cessation in Black Smokers With Anxiety: Protocol for a Randomized Controlled Trial.

BACKGROUND: Black smokers have greater difficulty in quitting and higher rates of smoking-related diseases and disabilities than the general population. The smoking disparities experienced by this group are, in part, a consequence of multiple chronic life stressors (eg, racial discrimination) that engender increased exposure to interoceptive stress symptoms (eg, anxiety), which can ultimately lead to smoking as a means of immediate emotion regulation. OBJECTIVE: This study aimed to culturally adapt and initially test a novel mobile intervention (ie, Mobile Anxiety Sensitivity Program for Smoking [MASP]) that targets anxiety sensitivity (AS; a proxy for difficulty and responsivity to interoceptive stress) among Black smokers. The MASP intervention is culturally informed to address interoceptive stress management difficulties among Black smokers and is thus hypothesized to facilitate smoking cessation. METHODS: In phase 1, a total of 25 Black smokers with elevated AS will be administered MASP for 6 weeks. Following the completion of phase 1, we will further refine the MASP based on qualitative and quantitative data from participants to produce the final MASP iteration. In phase 2, a total of 200 Black smokers with elevated AS will be enrolled and randomly assigned to receive nicotine replacement therapy and either the smartphone-based National Cancer Institute QuitGuide app for standard mobile smoking cessation treatment or the MASP intervention. All participants in phases 1 and 2 will be enrolled remotely and will complete a web-based study screener; smartphone-based baseline assessment; daily smartphone-based ecological momentary assessments for 6 weeks; phone-based end-of-treatment qualitative interviews; and smartphone-based follow-up assessments at postbaseline weeks 1, 2 (quit date), 3, 4, 5, 6, 28, and 54 (weeks 28 and 54 follow-ups will be completed by phase 2 participants only). The MASP intervention is intended to offset barriers to treatment and encourage treatment engagement via smartphones. RESULTS: This project was funded in September 2020. Phase 1 data collection began in January 2022. Phase 2 data collection is scheduled to begin in July 2022. CONCLUSIONS: If successful, data from this study will support culturally informed treatment approaches for Black smokers and, pending findings of efficacy, provide an evidence-based mobile intervention for smoking cessation that is ready for dissemination and implementation. TRIAL REGISTRATION: ClinicalTrials.gov NCT04838236; https://clinicaltrials.gov/ct2/show/NCT04838236. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/38905.

Quit Stage and Intervention Type Differences in the Momentary Within-Person Association Between Negative Affect and Smoking Urges.

Background: Smoking urges and negative affect play important roles in daily cigarette smoking and smoking lapse during a cessation attempt. Traditionally, laboratory research has considered negative affect as a potential cause of smoking urges. A deeper understanding of momentary associations between negative affect and smoking urges during a smoking cessation attempt can inform treatment development efforts. This study examined whether the within-person association between negative affect and smoking urges differed before and after a quit attempt, and by intervention type. Methods: Data are from a pilot randomized controlled trial comparing 3 smoking cessation interventions. Participants were randomly assigned to: (1) a novel, smartphone-based just-in-time adaptive intervention that tailored treatment content in real-time (Smart-T2; n = 24), (2) the National Cancer Institute QuitGuide app (n = 25), or (3) a clinic-based tobacco cessation program (TTRP; n = 23) that followed Clinical Practice Guidelines. All participants received up to 12 weeks of nicotine replacement therapy and completed up to 5 assessments per day (M PreQuit = 25.8 assessments, SD = 6.0; M PostQuit = 107.7 assessments, SD = 37.1) of their negative affect and smoking urges during the 7 days (M = 6.6 days, SD = 1.0) prior to their quit-date and the 29 days (M = 25.8 days, SD = 6.4) after their quit-date. Prior to analysis, repeated measures of smoking urges were decomposed into between-person and within-person components. Results: After accounting for baseline nicotine dependence, Bayesian multilevel models indicated that the extent of within-person association between negative affect and smoking urges was stronger in the post-quit stage of the intervention than the pre-quit stage. Results also indicated that in the post-quit stage of the intervention, the within-person association between negative affect and smoking urges was weaker for those in the Smart-T2 and TTRP groups compared with those in the QuitGuide group. The extent of this within-person association did not differ between those in the Smart-T2 and TTRP groups. Conclusions: These findings offer preliminary evidence that the momentary within-person association between negative affect and smoking urges increases following a quit attempt, and that the TTRP and Smart-T2 interventions may weaken this association. Research is needed to replicate and expand upon current findings in a fully powered randomized controlled trial. Clinical Trial Registration: ClinicalTrials.gov NCT02930200; https://clinicaltrials.gov/show/NCT02930200.

Technology-mediated just-in-time adaptive interventions (JITAIs) to reduce harmful substance use: a systematic review.

BACKGROUND AND AIMS: Lapse risk when trying to stop or reduce harmful substance use is idiosyncratic, dynamic and multi-factorial. Just-in-time adaptive interventions (JITAIs) aim to deliver tailored support at moments of need or opportunity. We aimed to synthesize evidence on decision points, tailoring variables, intervention options, decision rules, study designs, user engagement and effectiveness of technology-mediated JITAIs for reducing harmful substance use. METHODS: Systematic review of empirical studies of any design with a narrative synthesis. We searched Ovid MEDLINE, Embase, PsycINFO, Web of Science, the ACM Digital Library, the IEEE Digital Library, ClinicalTrials.gov, the ISRCTN register and dblp using terms related to substance use/mHealth/JITAIs. Outcomes were user engagement and intervention effectiveness. Study quality was assessed with the mHealth Evidence Reporting and Assessment checklist. FINDINGS: We included 17 reports of 14 unique studies, including two randomized controlled trials. JITAIs targeted alcohol (S = 7, n = 120 520), tobacco (S = 4, n = 187), cannabis (S = 2, n = 97) and a combination of alcohol and illicit substance use (S = 1, n = 63), and primarily relied on active measurement and static (i.e. time-invariant) decision rules to deliver support tailored to micro-scale changes in mood or urges. Two studies used data from prior participants and four drew upon theory to devise decision rules. Engagement with available JITAIs was moderate-to-high and evidence of effectiveness was mixed. Due to substantial heterogeneity in study designs and outcome variables assessed, no meta-analysis was performed. Many studies reported insufficient detail on JITAI infrastructure, content, development costs and data security. CONCLUSIONS: Current implementations of just-in-time adaptive interventions (JITAIs) for reducing harmful substance use rely on active measurement and static decision rules to deliver support tailored to micro-scale changes in mood or urges. Studies on JITAI effectiveness are lacking.