Relative Efficacy of an Acceptance and Commitment Therapy-Based Smartphone App with a Standard US Clinical Practice Guidelines-Based App for Smoking Cessation in Dual Users of Combustible and Electronic Cigarettes: Secondary Findings from a Randomized Trial.

BACKGROUND: While smartphone apps for smoking cessation have shown promise for combustible cigarette smoking cessation, their efficacy in helping dual users of combustible and electronic cigarettes (e-cigarettes) to quit cigarettes remains unknown. This study utilized data from a randomized trial to determine if an Acceptance and Commitment Therapy (ACT)-based app (iCanQuit) was more efficacious than a US Clinical Practice Guidelines-based app (QuitGuide) for combustible cigarette smoking cessation among 575 dual users. METHODS: The primary cessation outcome was self-reported, complete-case 30-day abstinence from combustible cigarettes at 12 months. Logistic regression assessed the interaction between dual use and treatment arm on the primary outcome in the full trial sample (N = 2,415). We then compared the primary outcome between arms among dual users (iCanQuit: n = 297; QuitGuide: n = 178). Mediation analyses were conducted to explore mechanisms of action of the intervention: acceptance of cues to smoke and app engagement. Results: There was an interaction between dual use of combustible and e-cigarettes and treatment arm on the primary outcome (p = 0.001). Among dual users, 12-month abstinence from cigarettes did not differ between arms (23% for iCanQuit vs. 27% for QuitGuide, p = 0.40). Mediation analysis revealed a significant positive indirect effect of the iCanQuit app on 12-month abstinence from cigarettes through acceptance of emotions that cue smoking (p = 0.004). CONCLUSIONS: Findings from this study of dual users of combustible and e-cigarettes showed no evidence of a difference in quit rates between arms. Acceptance of emotions that cue smoking is a potential mechanism contributing to cigarette smoking abstinence among dual users.

Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials.

BACKGROUND: A single generalizable metric that accurately predicts early dropout from digital health interventions has the potential to readily inform intervention targets and treatment augmentations that could boost retention and intervention outcomes. We recently identified a type of early dropout from digital health interventions for smoking cessation, specifically, users who logged in during the first week of the intervention and had little to no activity thereafter. These users also had a substantially lower smoking cessation rate with our iCanQuit smoking cessation app compared with users who used the app for longer periods. OBJECTIVE: This study aimed to explore whether log-in count data, using standard statistical methods, can precisely predict whether an individual will become an iCanQuit early dropout while validating the approach using other statistical methods and randomized trial data from 3 other digital interventions for smoking cessation (combined randomized N=4529). METHODS: Standard logistic regression models were used to predict early dropouts for individuals receiving the iCanQuit smoking cessation intervention app, the National Cancer Institute QuitGuide smoking cessation intervention app, the WebQuit.org smoking cessation intervention website, and the Smokefree.gov smoking cessation intervention website. The main predictors were the number of times a participant logged in per day during the first 7 days following randomization. The area under the curve (AUC) assessed the performance of the logistic regression models, which were compared with decision trees, support vector machine, and neural network models. We also examined whether 13 baseline variables that included a variety of demographics (eg, race and ethnicity, gender, and age) and smoking characteristics (eg, use of e-cigarettes and confidence in being smoke free) might improve this prediction. RESULTS: The AUC for each logistic regression model using only the first 7 days of log-in count variables was 0.94 (95% CI 0.90-0.97) for iCanQuit, 0.88 (95% CI 0.83-0.93) for QuitGuide, 0.85 (95% CI 0.80-0.88) for WebQuit.org, and 0.60 (95% CI 0.54-0.66) for Smokefree.gov. Replacing logistic regression models with more complex decision trees, support vector machines, or neural network models did not significantly increase the AUC, nor did including additional baseline variables as predictors. The sensitivity and specificity were generally good, and they were excellent for iCanQuit (ie, 0.91 and 0.85, respectively, at the 0.5 classification threshold). CONCLUSIONS: Logistic regression models using only the first 7 days of log-in count data were generally good at predicting early dropouts. These models performed well when using simple, automated, and readily available log-in count data, whereas including self-reported baseline variables did not improve the prediction. The results will inform the early identification of people at risk of early dropout from digital health interventions with the goal of intervening further by providing them with augmented treatments to increase their retention and, ultimately, their intervention outcomes.

Acceptance and Commitment Therapy-Based Smartphone Applications for Cessation of Tobacco Use among Adults with High Nicotine Dependence: Results from the iCanQuit Randomized Trial.

Background: With 1 in 2 adult tobacco users being highly dependent on nicotine, population-based interventions specifically designed for this group are urgently needed. This study used data from a randomized trial to evaluate whether (1) Acceptance and Commitment Therapy (ACT) delivered via a smartphone application (iCanQuit) would be more efficacious for cessation of nicotine-containing tobacco products than the US Clinical Practice Guidelines (USCPG)-based application (QuitGuide) among highly nicotine-dependent adults, (2) the effect of treatment on cessation was mediated by increases in acceptance of cravings to smoke, and (3) treatment utilization and satisfaction differed by arm. Methods: A total of 1452 highly nicotine-dependent adults received the iCanQuit or QuitGuide application for 12-months. Cessation outcomes were self-reported complete-case 30-day abstinence of nicotine-containing tobacco products (e.g., combustible cigarettes, e-cigarettes, chewing tobacco, snus, hookahs, cigars, cigarillos, tobacco pipes, and kreteks) at 3, 6, and 12-month post-randomization timepoints, missing-as-smoking, and multiple imputation analyses. Acceptance of cues to smoke and satisfaction with the applications was also reported. Results: Participants who received iCanQuit were significantly more likely to report 30-day abstinence of nicotine-containing tobacco products than those who received QuitGuide at 12-months (24% vs. 17%; OR = 1.47 95% CI: 1.11, 1.95). iCanQuit participants utilized their application more frequently and reported greater satisfaction than those who received QuitGuide. Increases in participants' acceptance of cues to smoke mediated the intervention effect on cessation of nicotine-containing tobacco products. Conclusions: Among nicotine-dependent adults, an application-delivered ACT-based intervention was more engaging and efficacious than a USCPG-based intervention for cessation of nicotine-containing tobacco products.

Older adults who smoke: Do they engage with and benefit from web-based smoking cessation interventions?

Quitting smoking at any age increases life expectancy, but older adults face barriers to receiving cessation services. Despite the promise of web-based smoking cessation interventions to help address access barriers, whether older adults who participate in smoking cessation programs engage with and benefit from these tools at the same rate as younger adults remains unknown. In this secondary analysis, we compared engagement and satisfaction with two web-based smoking cessation interventions and quit rates between older, middle-aged, and young adults in the United States enrolled in the WebQuit trial between March 2014 and August 2015. Participants were divided into age groups: older (60 years and older, n = 439/2637), middle-aged (40-59 years, n = 1308/2637), and young adults (18-39 years, n = 890/2637). Treatment engagement and satisfaction, and 12-month quit rates (self-reported complete-case 30-day PPA and missing-as-smoking) were compared between groups. Older adults engaged more with the websites than young adults through multiple indicators of intervention engagement (i.e., number of sessions, unique days of use, and time spent on the site), and older adults spent more time on the site per session than their counterparts. Satisfaction with websites was high (81%) and non-differential between groups. Older and middle-aged adults quit smoking at a similar rate as younger adults (24%, 24%, 27%, respectively, p = 0.905). Older and middle-aged adults who participated in a web-delivered smoking cessation intervention engaged more with the intervention than their younger counterparts and they quit smoking at a similar rate, thereby demonstrating high acceptability and potential of digital interventions to help older adults quit smoking. Trial registration:ClinicalTrials.gov Identifier: NCT1166334.

Efficacy of an acceptance and commitment therapy-based smartphone application for helping rural populations quit smoking: Results from the iCanQuit randomized trial.

Limited access to evidence-based smoking cessation interventions among rural populations contributes to high rates of cigarette smoking and poor cessation outcomes. Yet, accessible digital interventions for cessation focusing on rural populations are lacking. In a secondary analysis, we determined the acceptability and efficacy of an Acceptance and Commitment Therapy (ACT)-based smartphone application (iCanQuit) relative to a U.S. Clinical Practice Guidelines (USCPG)-based smartphone application (QuitGuide) for smoking cessation among rural participants enrolled in the two-arm randomized iCanQuit trial. Participants were enrolled between May 2017 and September 2018 and randomized to either receive iCanQuit or QuitGuide for 12-months. Rural residence was determined by sub-county level Rural-Urban Commuting Area codes. A total of 550 rural participants were recruited from 43 U.S. states. Self-reported complete-case 30-day point-prevalence abstinence was 15% (33/226) for iCanQuit vs. 9% (22/253) for QuitGuide at 3-months (OR = 1.83; 95% CI: 1.03, 3.25) and 29% (66/231) for iCanQuit vs. 25% (64/288) for QuitGuide at 12-months (OR = 1.19 95% CI: 0.80, 1.79). Retention rate was 89% at 12-months and did not differ by arm. iCanQuit vs. QuitGuide participants were significantly more engaged and satisfied with the iCanQuit application. Increased acceptance of internal cues to smoke mediated the effect of treatment on cessation. Findings suggest that iCanQuit had significantly higher short-term quit rates, descriptively higher long-term quit rates, and operated through its hypothesized mechanisms of action relative to QuitGuide. Future larger studies are needed to further evaluate the efficacy of and methods for disseminating the iCanQuit application for smoking cessation among U.S. rural adults nationwide. Trial registrationClinicalTrials.gov Identifier: NCT02724462.

Full-scale Randomized Trial Comparing Acceptance and Commitment Therapy Telephone-Delivered Coaching With Standard Telephone-Delivered Coaching Among Medicare/Uninsured Quitline Callers.

INTRODUCTION: The aim of this study was to compare Acceptance and Commitment Therapy (ACT) telephone-delivered coaching with standard quitline (QL) telephone-delivered coaching. METHODS: Medicare/uninsured adults (analyzable sample N = 1170) who smoked at least 10 cigarettes per day were recruited from Optum, a major US provider of QL services, in a two-arm stratified double-blind randomized trial with main outcome of self-reported missing = smoking 30-day point prevalence abstinence (PPA) at the 12-month follow-up. Participants were mean (SD) age 47.4 (12.7), 61% female, and 72% white race. Five sessions of telephone-delivered ACT or QL interventions were offered. Both arms included combined nicotine patch (4 weeks) and gum or lozenge (2 weeks). RESULTS: The 12-month follow-up data retention rate was 67.8%. ACT participants reported their treatment was more useful for quitting smoking (92.0% for ACT vs. 82.3% for QL; odds ratio [OR] = 2.48; 95% confidence interval [CI]: 1.53 to 4.00). Both arms had similar 12-month cessation outcomes (missing = smoking 30-day PPA: 24.6% for ACT vs. 28.8% for QL; OR =.81; 95% CI: 0.62 to 1.05) and the ACT arm trended toward greater reductions in number cigarettes smoked per day (-5.6 for ACT vs. -1.7 QL, among smokers; p = .075). CONCLUSIONS: ACT telephone-delivered coaching was more satisfying, engaging, and was as effective as standard QL telephone-delivered coaching. ACT may help those who fail to quit after standard coaching or who choose not to use nicotine replacement therapy. IMPLICATIONS: In a sample of Medicare and uninsured QL callers, a large randomized trial with long-term follow-up showed that ACT) telephone-delivered coaching was more satisfying, engaging, and was as effective as standard QL telephone-delivered coaching-which has followed the same behavior change approach since the 1990s. This newer model of coaching might be a welcome addition to QL services.

Efficacy and Utilization of Smartphone Applications for Smoking Cessation Among American Indians and Alaska Natives: Results From the iCanQuit Trial.

INTRODUCTION: There is tremendous need for efficacious and accessible interventions for smoking cessation among American Indians and Alaska Natives. We tested the efficacy of an Acceptance and Commitment Therapy (ACT)-based smartphone application (iCanQuit) versus US Clinical Practice Guidelines-based smartphone application (QuitGuide) for smoking cessation among American Indians and Alaska Natives. AIMS AND METHODS: We compared cessation, changes in ACT-based processes, engagement and satisfaction between American Indian and Alaska Native iCanQuit (n = 89) and QuitGuide (n = 80) participants enrolled in the iCanQuit trial. The primary outcome was self-reported, complete-case, 30-day point-prevalence abstinence. Follow-up timepoints were 12, 6, and 3 months. RESULTS: Randomized American Indians and Alaska Natives from 31 US states (70% urban, 30% rural, with 25% of participants residing on tribal land). The outcome data retention rates were 93%, 92%, and 90% at the 12-, 6-, and 3-month follow-ups, respectively, with no differential retention between arms. The 30-day point-prevalence abstinence for iCanQuit versus QuitGuide was 30% versus 18% at 12 months (odds ratio [OR] = 1.96; 95% confidence interval [CI]: 0.90 to 4.26) 25% versus 11% at 6 months (OR = 2.62; 95% CI: 1.06 to 6.45), and 15% versus 6% at 3 months (OR = 2.93; 95% CI: 0.90 to 9.59). Increases in acceptance of internal cues to smoke mediated the effect of treatment on smoking cessation at 12 months. iCanQuit arm participants were also significantly more engaged and satisfied with their assigned application. CONCLUSIONS: In a nationwide sample with high data retention and participant engagement, this is the first study to show that a digital intervention may be efficacious for helping American Indians and Alaska Natives quit smoking. IMPLICATIONS: This is the first study to provide evidence of an efficacious, accessible, and engaging treatment for helping American Indians and Alaska Natives quit smoking. Compared to a US Clinical Practice Guidelines-based smartphone application (QuitGuide), an ACT-based smartphone application (iCanQuit) was more efficacious, engaging, and satisfactory among American Indians and Alaska Natives nationwide. Our results will inform the tailoring of the iCanQuit smartphone application for American Indian and Alaska Native tribal communities and organizations with potential for broad dissemination and high impact.

Smoking Cessation Smartphone App Use Over Time: Predicting 12-Month Cessation Outcomes in a 2-Arm Randomized Trial.

BACKGROUND: Little is known about how individuals engage over time with smartphone app interventions and whether this engagement predicts health outcomes. OBJECTIVE: In the context of a randomized trial comparing 2 smartphone apps for smoking cessation, this study aimed to determine distinct groups of smartphone app log-in trajectories over a 6-month period, their association with smoking cessation outcomes at 12 months, and baseline user characteristics that predict data-driven trajectory group membership. METHODS: Functional clustering of 182 consecutive days of smoothed log-in data from both arms of a large (N=2415) randomized trial of 2 smartphone apps for smoking cessation (iCanQuit and QuitGuide) was used to identify distinct trajectory groups. Logistic regression was used to determine the association of group membership with the primary outcome of 30-day point prevalence of smoking abstinence at 12 months. Finally, the baseline characteristics associated with group membership were examined using logistic and multinomial logistic regression. The analyses were conducted separately for each app. RESULTS: For iCanQuit, participants were clustered into 3 groups: "1-week users" (610/1069, 57.06%), "4-week users" (303/1069, 28.34%), and "26-week users" (156/1069, 14.59%). For smoking cessation rates at the 12-month follow-up, compared with 1-week users, 4-week users had 50% higher odds of cessation (30% vs 23%; odds ratio [OR] 1.50, 95% CI 1.05-2.14; P=.03), whereas 26-week users had 397% higher odds (56% vs 23%; OR 4.97, 95% CI 3.31-7.52; P<.001). For QuitGuide, participants were clustered into 2 groups: "1-week users" (695/1064, 65.32%) and "3-week users" (369/1064, 34.68%). The difference in the odds of being abstinent at 12 months for 3-week users versus 1-week users was minimal (23% vs 21%; OR 1.16, 95% CI 0.84-1.62; P=.37). Different baseline characteristics predicted the trajectory group membership for each app. CONCLUSIONS: Patterns of 1-, 3-, and 4-week smartphone app use for smoking cessation may be common in how people engage in digital health interventions. There were significantly higher odds of quitting smoking among 4-week users and especially among 26-week users of the iCanQuit app. To improve study outcomes, strategies for detecting users who disengage early from these interventions (1-week users) and proactively offering them a more intensive intervention could be fruitful.

Identifying Content-Based Engagement Patterns in a Smoking Cessation Website and Associations With User Characteristics and Cessation Outcomes: A Sequence and Cluster Analysis.

INTRODUCTION: Using WebQuit as a case study, a smoking cessation website grounded in Acceptance and Commitment Therapy, we aimed to identify sequence clusters of content usage and examine their associations with baseline characteristics, change to a key mechanism of action, and smoking cessation. METHODS: Participants were adult smokers allocated to the WebQuit arm in a randomized controlled trial (n = 1,313). WebQuit contains theory-informed content including goal setting, self-monitoring and feedback, and values- and acceptance-based exercises. Sequence analysis was used to temporally order 30-s website usage segments for each participant. Similarities between sequences were assessed with the optimal matching distance algorithm and used as input in an agglomerative hierarchical clustering analysis. Associations between sequence clusters and baseline characteristics, acceptance of cravings at 3 months and self-reported 30-day point prevalence abstinence at 12 months were examined with linear and logistic regression. RESULTS: Three qualitatively different sequence clusters were identified. "Disengagers" (576/1,313) almost exclusively used the goal-setting feature. "Tryers" (375/1,313) used goal setting and two of the values- and acceptance-based components ("Be Aware," "Be Willing"). "Committers" (362/1,313) primarily used two of the values- and acceptance-based components ("Be Willing," "Be Inspired"), goal setting, and self-monitoring and feedback. Compared with Disengagers, Committers demonstrated greater increases in acceptance of cravings (p = .01) and 64% greater odds of quit success (ORadj = 1.64, 95% CI = 1.18, 2.29, p = .003). DISCUSSION: WebQuit users were categorized into Disengagers, Tryers, and Committers based on their qualitatively different content usage patterns. Committers saw increases in a key mechanism of action and greater odds of quit success. IMPLICATIONS: This case study demonstrates how employing sequence and cluster analysis of usage data can help researchers and practitioners gain a better understanding of how users engage with a given eHealth intervention over time and use findings to test theory and/or to improve future iterations to the intervention. Future WebQuit users may benefit from being directed to the values- and acceptance-based and the self-monitoring and feedback components via reminders over the course of the program.

Comparative Effectiveness of Group-Delivered Acceptance and Commitment Therapy versus Cognitive Behavioral Therapy for Smoking Cessation: A Randomized Controlled Trial.

INTRODUCTION: Preliminary trial data suggest group-delivered acceptance and commitment therapy (ACT) might be effective for smoking cessation. If so, this could offer a viable alternative to mainstream behavioral therapies, such as those grounded in cognitive behavioral therapy (CBT). The goal of the current study was to compare the effectiveness of group-delivered ACT versus group-delivered CBT in a rigorous randomized trial design with long-term follow-up. METHODS: Participants (n = 450) were recruited from the Kaiser Permanente Washington health care system and randomized to either ACT-based group counseling or an attention-matched CBT-based group program. All were prescribed an 8-week course of nicotine patches. The primary outcome was self-reported 30-day point prevalence abstinence at 12 months post-randomization assessed with missing values imputed as smoking. Sensitivity analyses using multiple imputation and complete cases were examined, as were biochemically confirmed and 6-month outcomes. RESULTS: Thirty-day point prevalence abstinence rates at the 12-month follow-up did not differ between study arms in the primary analysis (13.8% ACT vs. 18.1% CBT, adjusted odds ratio = 0.68 [95% CI = 0.35 to 1.27], p = .23) or the sensitivity analyses. CONCLUSIONS: Group-based ACT and CBT had similar long-term quit rates in this methodologically rigorous randomized trial. Group-based ACT is a reasonable alternative to group-based CBT for smoking cessation. IMPLICATIONS: This study compared the effectiveness of group-based ACT with group-based CBT for smoking cessation using a rigorous, large-scale, attention-matched, randomized trial with 1-year follow-up. One-year cessation rates did not differ between group-based ACT and CBT, suggesting ACT-based intervention is a reasonable alternative to CBT-based counseling for smoking cessation. The results add to the nascent but growing literature assessing ACT and other mindfulness-based treatments for smoking cessation.

Which Method of Assessing Depression and Anxiety Best Predicts Smoking Cessation: Screening Instruments or Self-Reported Conditions?

INTRODUCTION: Affective disorders and symptoms (ADS) are predictive of lower odds of quitting smoking. However, it is unknown which approach to assessing ADS best predicts cessation. This study compared a battery of ADS screening instruments with a single, self-report question on predicting cessation. Among those who self-reported ADS, we also examined if an additional question regarding whether participants believed the condition(s) might interfere with their ability to quit added predictive utility to the single-item question. METHODS: Participants (N = 2637) enrolled in a randomized controlled trial of web-based smoking treatments completed a battery of five ADS screening instruments and answered a single-item question about having ADS. Those with a positive self-report on the single-item question were also asked about their interference beliefs. The primary outcome was complete-case, self-reported 30-day point prevalence abstinence at 12 months. RESULTS: Both assessment approaches significantly predicted cessation. Screening positive for ≥ one ADS in the battery was associated with 23% lower odds of quitting than not screening positive for any (p = .023); those with a positive self-report on the single-item had 39% lower odds of quitting than self-reporting no mental health conditions (p < .001). Area under the receiver operating characteristic curve values for the two assessment approaches were similar (p = .136). Adding the interference belief question to the single-item assessment significantly increased the area under the receiver operating characteristic curve value (p = .042). CONCLUSIONS: The single-item question assessing ADS had as much predictive validity, and possibly more, than the battery of screening instruments for identifying participants at risk for failing to quit smoking. Adding a question about interference beliefs significantly increased the predictive utility of the single-item question. IMPLICATIONS: This is the first study to demonstrate that a single-item question assessing ADS has at least as much predictive validity, and possibly more, than a battery of validated screening instruments for identifying smokers at highest risk for cessation failure. This study also demonstrates adding a question about interference beliefs significantly adds to the predictive utility of a single, self-report question about mental health conditions. Findings from this study can be used to inform decisions regarding how to assess ADS in the context of tobacco treatment settings.

Long-Term Smoking Cessation Outcomes for Sexual Minority Versus Nonminority Smokers in a Large Randomized Controlled Trial of Two Web-Based Interventions.

INTRODUCTION: Despite greater smoking prevalence among sexual minority (SM) individuals relative to non-SM individuals, minimal research has examined whether SM smokers have differential success at quitting, and no prior treatment studies have examined differences within SM subgroups. There is also limited knowledge of the psychosocial characteristics of treatment-seeking SM smokers, which could inform targeted treatments. To address these gaps, we compared treatment outcomes and baseline characteristics for SM and non-SM smokers and for bisexual versus lesbian or gay smokers in a large randomized controlled trial of two web-based cessation treatments. METHODS: Trial participants completed a survey to assess baseline characteristics, including self-identification as either SM (n = 253; lesbian or gay, n = 122; bisexual, n = 131) or non-SM (n = 2384). The primary cessation outcome was complete-case, self-reported 30-day abstinence at 12 months after randomization. RESULTS: Cessation outcomes did not differ significantly for SM versus non-SM smokers (24% vs. 25%, adjusted OR = 0.91, 95% CI = 0.65 to 1.28) or across SM subgroups (24% for bisexual vs. 23% for lesbian or gay, adjusted OR = 1.01, 95% CI = 0.51 to 2.00), and there were no interactions with treatment group assignment. At baseline, SM smokers differed from non-SM smokers on most demographics, were more likely to screen positive for all mental health conditions assessed, and had greater exposure to other smokers in the home. CONCLUSIONS: Substantial differences in baseline characteristics of SM versus non-SM smokers and bisexual versus lesbian or gay smokers did not translate into differential treatment outcomes. Nonetheless, SM smokers' willingness or ability to quit smoking could be enhanced by taking their unique psychosocial profile into account when designing targeted interventions. IMPLICATIONS: The findings of this study, which included the largest sample of SM smokers in a prospective intervention trial to date, support those of a small extant body of literature showing no differences in treatment-assisted cessation outcomes between SM and non-SM smokers. Regardless of their quit rates relative to non-SM smokers, SM smokers' willingness or ability to quit smoking could potentially be enhanced by taking their unique psychosocial profile into account in intervention design, including their younger age, lower socioeconomic status, greater likelihood of being racial or ethnic minorities, and greater prevalence of mental health symptoms.

Pilot Randomized Controlled Trial of Web-Delivered Acceptance and Commitment Therapy Versus Smokefree.gov for Smokers With Bipolar Disorder.

INTRODUCTION: Smokers with bipolar disorder (BD) are less successful at quitting than the general population. In this study, we evaluated in a pilot randomized controlled trial a novel, targeted, web-based intervention for smokers with BD based on acceptance and commitment therapy (ACT) and designed for reach and disseminability. AIMS AND METHODS: Daily smokers (n = 51) with bipolar I or II disorder were recruited from four US sites and randomly assigned to one of two web-based smoking cessation interventions-ACT-based WebQuit Plus (n = 25) or Smokefree.gov (n = 26) over a 10-week treatment period. All participants received nicotine patch for 8 weeks. Key outcomes were trial design feasibility, intervention acceptability, and cessation at end of treatment and 1-month follow-up. RESULTS: We screened 119 to enroll 51 participants (target sample size = 60) over 24 months. The most common reason for ineligibility was the inability to attend study appointments. Retention was 73% at end of treatment and 80% at follow-up, with no differences by arm. The mean number of logins was twice as high for WebQuit Plus (10.3 vs. 5.3). The usefulness of program skills was rated higher for WebQuit Plus (75% vs. 29%). Biochemically confirmed, 7-day abstinence at end of treatment was 12% in WebQuit Plus versus 8% in Smokefree.gov (odds ratio = 1.46, 95% confidence interval = 0.21 to 9.97). At follow-up, abstinence rates were 8% in both arms. CONCLUSIONS: Trial design produced favorable retention rates, although alternative recruitment methods will be needed for a larger trial. At end of treatment, acceptability and estimated effect size of WebQuit Plus relative to Smokefree.gov were promising and support continued program refinement and evaluation. IMPLICATIONS: In this first randomized controlled trial of a targeted intervention for smokers with BD, we found that the ACT-based WebQuit Plus intervention, delivered in combination with the nicotine patch, had promising acceptability and cessation outcomes relative to Smokefree.gov. The observed signals for acceptability and cessation suggest that the WebQuit Plus program should be refined based on participant feedback and evaluated in a larger trial. Feasibility findings from this study also provide direction for refining trial procedures to enhance the recruitment of smokers with BD.

Comparing Treatment Acceptability and 12-Month Cessation Rates in Response to Web-Based Smoking Interventions Among Smokers Who Do and Do Not Screen Positive for Affective Disorders: Secondary Analysis.

BACKGROUND: Web-based cessation programs are now common for intervening with smokers. However, it remains unclear how acceptable or effective these interventions are among people with affective disorders and symptoms (ADS; eg, depression and anxiety). Research examining this is extremely limited, with mixed results on cessation rates. Additional large studies are needed to more fully understand whether Web-based interventions are similarly used and equally effective among people with and without affective disorder symptomology. If not, more targeted Web-based interventions may be required. OBJECTIVE: The goal of the research was to compare Web-based treatment acceptability (defined by satisfaction and use) and 12-month cessation outcomes between smokers with and without ADS. METHODS: Participants (N=2512) were adult smokers enrolled in a randomized, comparative effectiveness trial of two Web-based smoking interventions designed for the general population of smokers. At baseline, participants reported demographic and smoking characteristics and completed measures assessing ADS. Participants were then classified into subgroups based on their self-reported ADS-either into a no ADS group or into six nonmutually exclusive subgroups: depression, posttraumatic stress disorder (PTSD), panic disorder (PD), generalized anxiety disorder (GAD), social anxiety disorder (SAD), and more than one ADS. Surveys at 12 months postrandomization included subjective ratings of treatment acceptability and self-reported smoking cessation. Treatment use (ie, number of log-ins and total duration of exposure) was assessed via automated records. RESULTS: Relative to the no ADS group, all six ADS subgroups reported significantly greater satisfaction with their assigned Web treatment program, but they spent less time logged in than those with no ADS. For number of log-ins, a treatment arm by ADS group interaction was observed across all ADS subgroups except GAD, suggesting that relative to the no ADS group, they logged in less to one website but not the other. At the 12-month follow-up, abstinence rates in the no ADS group (153/520, 29.42%) were significantly higher than for participants who screened positive for depression (306/1267, 24.15%; P=.03), PTSD (294/1215, 24.19%; P=.03), PD (229/1003, 23.83%; P=.009), and two or more ADS (323/1332, 24.25%; P=.03). Post hoc analyses suggest the lower quit rates may be associated with differences in baseline nicotine dependence and levels of commitment to resist smoking in difficult situations. Website use did not explain the differential abstinence rates. CONCLUSIONS: Despite reporting higher levels of treatment satisfaction, most smokers with ADS used their assigned intervention less often and had lower quit rates than smokers with no ADS at treatment onset. The results support the need for developing more targeted interventions for smokers with ADS. TRIAL REGISTRATION: Clinical Trials.gov NCT01812278; https://clinicaltrials.gov/ct2/show/NCT01812278 (Archived by WebCite at http://www.webcitation.org/78L9cNdG4).

Participant Recruitment and Retention in Remote eHealth Intervention Trials: Methods and Lessons Learned From a Large Randomized Controlled Trial of Two Web-Based Smoking Interventions.

BACKGROUND: Despite having many advantages, online eHealth trials are not without challenges-notably, participant recruitment, and outcome data retention. Moreover, publications from these trials rarely provide detailed information on the methods used for recruitment and retention or discuss implications of the methods for future studies. OBJECTIVE: To address this need for empirical guidance regarding recruitment and outcome data retention planning, we aim to describe the methods and lessons learned from the recruitment and retention procedures used in a large randomized trial of 2 Web-based smoking cessation interventions. METHODS: To ensure a demographically and geographically diverse participant sample, we used the recruitment strategies (1) traditional, (2) Web-based, and (3) online survey panel methods and adaptively modified each in response to recruitment success. At baseline, participants indicated how they heard about the study and answered demographic questions. To maximize trial retention at each of the 3-, 6-, and 12-month assessment points, 4 survey modalities (first Web, followed by phone, mail, and postcard) were sequentially timed over a 30-day period. Participants received US $25 for submitting their responses, regardless of modality, and received an additional US $10 bonus for completing the Web survey within 24h of electronic notification. RESULTS: We randomized 2637 smokers in 16 months and achieved 88% retention at 12-months. Participants (79.26% female, 72.60% Caucasian) were recruited from all 50 states. The majority of participants were recruited through Facebook (49.43%), followed by the survey panel (20.85%), free internet sources (14.54%), traditional media (11.34%), and Google ads (3.84%). Descriptively, participant demographics varied by recruitment source. Of the completed follow-up surveys, most were completed by Web (92%). Retention rates did not vary by recruitment source. CONCLUSIONS: Continuous monitoring and refinement of multiple recruitment methods, particularly of online advertising campaigns, allowed us to maximize the effectiveness of recruitment strategies in recruiting a large, diverse sample of smokers. Likewise, offering multiple follow-up survey modalities in sequential order along with time-dependent bonus incentives enabled us to obtain outcome data from a very high level of enrolled participants for the duration of the trial protocol. These strategies may be similarly useful in other trials. TRIAL REGISTRATION: ClinicalTrials.gov NCT01812278; https://clinicaltrials.gov/ct2/show/NCT01812278 (Archived by WebCite at http://www.webcitation.org/71gy5GLvO).

Trajectories of 12-Month Usage Patterns for Two Smoking Cessation Websites: Exploring How Users Engage Over Time.

BACKGROUND: Little is known about how individuals engage with electronic health (eHealth) interventions over time and whether this engagement predicts health outcomes. OBJECTIVE: The objectives of this study, by using the example of a specific type of eHealth intervention (ie, websites for smoking cessation), were to determine (1) distinct groups of log-in trajectories over a 12-month period, (2) their association with smoking cessation, and (3) baseline user characteristics that predict trajectory group membership. METHODS: We conducted a functional clustering analysis of 365 consecutive days of log-in data from both arms of a large (N=2637) randomized trial of 2 website interventions for smoking cessation (WebQuit and Smokefree), with a primary outcome of 30-day point prevalence smoking abstinence at 12 months. We conducted analyses for each website separately. RESULTS: A total of 3 distinct trajectory groups emerged for each website. For WebQuit, participants were clustered into 3 groups: 1-week users (682/1240, 55.00% of the sample), 5-week users (399/1240, 32.18%), and 52-week users (159/1240, 12.82%). Compared with the 1-week users, the 5- and 52-week users had 57% higher odds (odds ratio [OR] 1.57, 95% CI 1.13-2.17; P=.007) and 124% higher odds (OR 2.24, 95% CI 1.45-3.43; P<.001), respectively, of being abstinent at 12 months. Smokefree users were clustered into 3 groups: 1-week users (645/1309, 49.27% of the sample), 4-week users (395/1309, 30.18%), and 5-week users (269/1309, 20.55%). Compared with the 1-week users, 5-week users (but not 4-week users; P=.99) had 48% higher odds (OR 1.48, 95% CI 1.05-2.07; P=.02) of being abstinent at 12 months. In general, the WebQuit intervention had a greater number of weekly log-ins within each of the 3 trajectory groups as compared with those of the Smokefree intervention. Baseline characteristics associated with trajectory group membership varied between websites. CONCLUSIONS: Patterns of 1-, 4-, and 5-week usage of websites may be common for how people engage in eHealth interventions. The 5-week usage of either website, and 52-week usage only of WebQuit, predicted a higher odds of quitting smoking. Strategies to increase eHealth intervention engagement for 4 more weeks (ie, from 1 week to 5 weeks) could be highly cost effective. TRIAL REGISTRATION: ClinicalTrials.gov NCT01812278; https://www.clinicaltrials.gov/ct2/show/NCT01812278 (Archived by WebCite at http://www.webcitation.org/6yPO2OIKR).

Positive Affect as a Predictor of Smoking Cessation and Relapse: Does It Offer Unique Predictive Value among Depressive Symptom Domains?

BACKGROUND: Prior studies have suggested that, among the domains of depressive symptoms, low positive affect (PA) may have a distinct relationship with smoking cessation and relapse. However, the empirical basis for PA-focused interventions cessation is limited, with some mixed findings. OBJECTIVES: Using a large, diverse sample of treatment-seeking smokers, this study tested the hypothesis that PA adds unique predictive value beyond the effects of the other symptom domains in models of cessation and relapse. METHODS: Adult smokers participating in a smoking cessation trial (n = 450) were included in this post hoc analysis. Cessation outcomes included smoking abstinence at end of treatment and at 6-month follow-up. Relapse was defined as recurrence of smoking at 6-month follow-up among the end-of-treatment abstainers. Depressive symptoms were assessed at baseline using the Center for Epidemiologic Studies-Depression (CES-D) scale. RESULTS: With the exception of PA, all of the CES-D domains predicted reduced likelihood of smoking abstinence at end of treatment and cotinine-confirmed (but not self-reported) abstinence at 6 months, as did total CES-D score (all p-values < .05). None of the symptom domains predicted relapse. Conclusions/Importance: Our results provide further evidence that current depressive symptoms predict worse cessation outcomes, but they fail to support recent work suggesting that low PA has incremental predictive value for cessation or relapse beyond the other depressive symptom domains. To improve quit rates for smokers with depressive symptoms, evidence-based mood management interventions should be included in treatment planning.

Smartphone Ownership Among US Adult Cigarette Smokers: 2014 Health Information National Trends Survey (HINTS) Data.

BACKGROUND: Despite increasing interest in smartphone apps as a platform for delivery of tobacco cessation interventions, no previous studies have evaluated the prevalence and characteristics of smokers who can access smartphone-delivered interventions. OBJECTIVE: To guide treatment development in this new platform and to evaluate disparities in access to smartphone-delivered interventions, we examined associations of smartphone ownership with demographics, tobacco use and thoughts about quitting, other health behaviors, physical and mental health, health care access, and Internet and technology utilization using a nationally representative sample of US adult smokers. METHODS: Data were from the National Cancer Institute's 2014 Health Information National Trends Survey 4 (HINTS 4), Cycle 4. This mailed survey targeted noninstitutionalized individuals aged 18 years or older using two-stage stratified random sampling. For this analysis, we restricted the sample to current smokers with complete data on smartphone ownership (n=479). RESULTS: Nearly two-thirds (weighted percent=63.8%, 248/479) of smokers reported owning a smartphone. Those who were younger (P<.001), employed (P=.002), never married (P=.002), and had higher education (P=.002) and income (P<.001) had the highest rates of ownership. Smartphone owners did not differ from nonowners on frequency of smoking, recent quit attempts, or future plans to quit smoking, although they reported greater belief in the benefits of quitting (P=.04). Despite being equally likely to be overweight or obese, smartphone owners reported greater fruit and vegetable consumption (P=.03) and were more likely to report past-year efforts to increase exercise (P=.001) and to lose weight (P=.02). No differences in health care access and utilization were found. Smartphone owners reported better physical and mental health in several domains and higher access to and utilization of technology and the Internet, including for health reasons. CONCLUSIONS: Smartphone ownership among smokers mirrors many trends in the general population, including the overall rate of ownership and the association with younger age and higher socioeconomic status. Apps for smoking cessation could potentially capitalize on smartphone owners' efforts at multiple health behavior changes and interest in communicating with health care providers via technology. These data also highlight the importance of accessible treatment options for smokers without smartphones in order to reach smokers with the highest physical and mental health burden and prevent worsening of tobacco-related health disparities as interventions move to digital platforms.

Acceptance and Commitment Therapy and nicotine patch for smokers with bipolar disorder: preliminary evaluation of in-person and telephone-delivered treatment.

OBJECTIVES: People with bipolar disorder are two to three times more likely to smoke and 50% less likely to quit than the general population. New treatments are needed to improve smoking cessation outcomes in this group. The study aim was to develop and pilot test a novel cessation intervention for smokers with bipolar disorder using Acceptance and Commitment Therapy (ACT) combined with nicotine patches. METHODS: The ten-session ACT intervention was initially evaluated as in-person, individual counseling (n = 10), then as telephone-delivered counseling (n = 6). Participants were adult smokers with no more than mild current symptoms of bipolar disorder. RESULTS: For the in-person protocol, end-of-treatment outcomes were: 80% retention, 40% of participants with carbon monoxide (CO)-verified seven-day point prevalence abstinence (PPA), 90% satisfied with treatment, 8.3 of ten sessions attended, and 54% increase in acceptance of cravings to smoke (i.e., ACT's theory-based change process) from baseline. The seven-day PPA at one-month follow-up was 30%. For the telephone protocol, end-of-treatment outcomes were: 67% retention, 33% reporting seven-day PPA, 100% satisfied with treatment, 6.7 of ten treatment calls completed, and 55% increase in acceptance from baseline. At one-month follow-up, seven-day PPA was 17%. The proportion of treatment completers who used at least 80% of the nicotine patches was 62.5% for the in-person protocol and 0% for the telephone protocol. CONCLUSIONS: Both in-person and telephone-delivered ACT were feasible. Despite low adherence to nicotine patches, the intervention showed preliminary evidence of facilitating quitting and impacting ACT's change mechanism. A randomized, controlled trial of this targeted ACT intervention is now needed.

Predictors of Utilization of a Novel Smoking Cessation Smartphone App.

BACKGROUND: Understanding the characteristics of high and low utilizers of smartphone applications (apps) for smoking cessation would inform development of more engaging and effective apps, yet no studies to date have addressed this critical question. Informed by prior research on predictors of cessation Web site utilization, this study examines the degree to which baseline demographic factors (gender, age, and education), smoking-related factors (smoking level and friends' smoking), and psychological factors (depression and anxiety) are predictive of utilization of a smartphone app for smoking cessation called SmartQuit. MATERIALS AND METHODS: Data came from 98 participants randomized to SmartQuit as part of a pilot trial from March to May 2013. We used negative binomial count regressions to examine the relationship between user characteristics and utilization of the app over an 8-week treatment period. RESULTS: Lower education (risk ratio [RR]=0.492; p=0.021), heavier smoking (RR=0.613; p=0.033), and depression (RR=0.958; p=0.017) prospectively predicted lower app utilization. Women (RR=0.320; p=0.022), those with lower education (RR=0.491; p=0.013), and heavier smokers (RR=0.418; p=0.039) had lower utilization of app features known to predict smoking cessation. CONCLUSIONS: Many of the predictors of utilization of smoking cessation apps are the same as those of cessation Web sites. App-delivered smoking cessation treatment effectiveness could be enhanced by focusing on increasing engagement of women, those with lower education, heavy smokers, and those with current depressive symptoms.