The Impact of the COVID-19 Pandemic on Stress, Isolation, Smoking Behaviors, and Motivation to Quit in People with HIV Who Smoke.

People with HIV (PWH) smoke at higher rates compared with the general population and have lower cessation rates. The primary aim of this study was to examine the impact of the COVID-19 pandemic on smoking in PWH. A survey was administered to participants in two smoking cessation trials in the United States. Mean cigarettes per day was 13.9 (SD 8.6), and participants reported they had smoked on average for 30.93 years (SD 10.4). More than half (55.7%) of participants (N = 140) reported not changing their smoking during the pandemic, while 15% reported decreasing, and 25% reported increasing their smoking. In bivariate analyses, worrying about food due to lack of money (χ2 = 9.13, df 2, p = 0.01) and greater Covid-related worry (rs = 0.19, p = 0.02) were significantly associated with increased smoking. Qualitative research may be needed to more clearly elucidate factors related to smoking behaviors among PWH.

The United States National Cancer Institute's Coordinated Research Effort on Tobacco Use as a Major Cause of Morbidity and Mortality among People with HIV.

The use of antiretroviral therapy for people with HIV (PWH) has improved life expectancy. However, PWH now lose more life-years to tobacco use than to HIV infection. Unfortunately, PWH smoke at higher rates and have more difficulty maintaining abstinence than the general population, compounding their risk for chronic disease. In this Commentary, we describe a United States National Cancer Institute-led initiative to address the relative lack of research focused on developing, testing, and implementing smoking cessation interventions for PWH. This initiative supports seven clinical trials designed to systematically test and/or develop and test adaptations of evidence-based smoking cessation interventions for PWH (eg, combination of behavioral and pharmacological). We summarize each project, including setting/recruitment sites, inclusion/exclusion criteria, interventions being tested, and outcomes. This initiative provides critical opportunities for collaboration and data harmonization across projects. The knowledge gained will inform strategies to assist PWH to promote and maintain abstinence, and ensure that these efforts are adaptable and scalable, thereby addressing one of the major threats to the health of PWH. Reducing smoking behavior may be particularly important during the COVID-19 pandemic given that smokers who become infected with SARS-CoV-2 may be at risk for more severe disease. IMPLICATIONS: This Commentary describes a National Cancer Institute-led initiative to advance the science and practice of treating tobacco use among PWH, which is now responsible for more life years lost than HIV. We describe the scope of the problem, the objectives of the initiative, and a summary of the seven funded studies. Harmonization of data across projects will provide information related to treatment mediators and moderators that was not previously possible. Stakeholders interested in tobacco cessation, including researchers, clinicians and public health officials, should be aware of this initiative and the evidence-base it will generate to advance tobacco treatment among this high-risk population.

Pilot Randomized Controlled Trial of a Novel Smoking Cessation App Designed for Individuals With Co-Occurring Tobacco Use Disorder and Serious Mental Illness.

INTRODUCTION: High rates of tobacco use among people with serious mental illness (SMI), along with their unique needs, suggest the importance of developing tailored smoking cessation interventions for this group. Previous early-phase work empirically validated the design and content of Learn to Quit, a theory-based app designed for this population. METHODS: In a pilot randomized controlled trial, we compared the feasibility, acceptability, and preliminary efficacy of Learn to Quit versus QuitGuide, an app designed for the general population. All participants received nicotine replacement therapy and technical assistance. Daily smokers with SMI (N = 62) participated in the trial with outcomes assessed at weeks 4, 8, 12, and 16. RESULTS: Compared to QuitGuide, Learn to Quit participants had similar number of days of app use (34 vs. 32, p = .754), but larger number of app interactions (335 vs. 205; p = .001), longer durations of app use (4.24 hrs. vs. 2.14 hrs; p = .044), and higher usability scores (85 vs. 79, p = .046). At week 16, Learn to Quit led to greater reductions in cigarettes per day (12.3 vs. 5.9 for QuitGuide; p = 0.10). Thirty-day point prevalence abstinence was verified in 12% of Learn to Quit participants versus 3% of QuitGuide participants (odds ratio = 3.86, confidence interval = 0.41 to 36, p = .239). Changes in psychiatric symptoms and adverse events were not clinically significant between conditions. CONCLUSIONS: This pilot trial provides strong evidence of Learn to Quit's usability, feasibility, and safety. Preliminary evidence suggests the app may be efficacious. A randomized controlled efficacy trial is needed to test the app in a larger sample of smokers with SMI. IMPLICATIONS: This study suggests that the Learn to Quit app is a feasible approach to deliver smoking cessation treatment in patients with co-occurring tobacco use disorder and SMI. This means that, if found efficacious, this technology could be used to deploy smoking cessation treatment to larger segments of this population, hence improving public health. Therefore, a randomized controlled trial should be conducted to examine the efficacy of this digital intervention.

What Do Smokers Want in A Smartphone-Based Cessation Application?

Background: Fueled by rapid technological advances over the past decade, there is growing interest in the use of smartphones to aid in smoking cessation. Hundreds of applications have been developed for this purpose, but little is known about how these applications are accessed and used by smokers or what features smokers believe would be most useful. Purpose: The present study sought to understand the prevalence of smartphone ownership and patterns of use among smokers as well as the perceived utility of various smartphone application features for smoking cessation that are currently in development or already available. Methods: Daily cigarette smokers (n = 224) reported on smartphone ownership, their patterns of smartphone usage, and perceived utility of features. Features were ranked according to perceived utility and differences in both perceived utility and general smartphone use patterns were examined as a function of demographic and smoking-related variables. Results: Most smokers (80.4%) own a smartphone, but experience with smoking cessation applications is extremely rare (6.1%). Ownership and patterns of usage differed as a function of demographic and smoking-related variables. Overall, gain-framed features were rated as most useful, while loss-framed and interpersonal features were rated as least useful. Conclusions: Mobile health interventions have the potential to reach a large number of smokers but are currently underutilized. Additional effort is needed to ensure parity in treatment access. Gain-framed messages may be especially useful for engaging smokers, even if other features ultimately drive treatment effects. Implications: This study describes patterns of smartphone usage among smokers and identifies the smartphone application features smokers believe would be most useful during a quit attempt. Findings indicate which subgroups of smokers are most likely to be reached with mobile health interventions and suggests that inclusion of specific features may be helpful for engaging smokers in the smoking cessation process.

Lessons from Practice: Designing Tools to Facilitate Individualized Support for Quitting Smoking.

Many health care providers, with a variety of trainings, counsel clients on quitting smoking on a day-to-day basis. In their clinical practice, they draw from and adapt guidelines and research-based strategies to fit individual client situations and challenges. Designers of technologies to support quitting smoking can learn from these real world practices to create tools that better adapt to individual differences. We present findings from interviews with 28 providers with diverse experiences in smoking cessation counselling. Through analysis of their individualization strategies, challenges, and perceptions of technology, we find that providers: (1) individualize context appropriate coping strategies by involving clients in brainstorming, (2) emphasize the need to support nicotine withdrawal in clients, (3) mitigate social triggers and mediate social support for clients, and (4) need to navigate dependencies with other providers for managing medications and comorbid health conditions of clients. With this empirical understanding, we extend the discussion on the design of technology to support quitting smoking, highlight current barriers to individualization, and suggest future opportunities to address these barriers.

User Experience Evaluation of a Smoking Cessation App in People With Serious Mental Illness.

INTRODUCTION: Smoking rates among people with serious mental illness are 3 to 4 times higher than the general population, yet currently there are no smoking cessation apps specifically designed to address this need. We report the results of a User Experience (UX) evaluation of a National Cancer Institute smoking cessation app, QuitPal, and provide user centered design data that can be used to tailor smoking cessation apps for this population. METHODS: Two hundred forty hours of field experience with QuitPal, 10 hours of recorded interviews and task performances, usage logs and a self-reported usability scale, informed the results of our study. Participants were five individuals recruited from a community mental health clinic with a reported serious mental illness history. Performance, self-reports, usage logs and interview data were triangulated to identify critical usability errors and UX themes emerging from this population. RESULTS: Data suggests QuitPal has below average levels of usability, elevated time on task performances and required considerable amounts of guidance. UX themes provided critical information to tailor smoking cessation apps for this population, such as the importance of breaking down "cessation" into smaller steps and use of a reward system. CONCLUSIONS: This is the first study to examine the UX of a smoking cessation app among people with serious mental illness. Data from this study will inform future research efforts to expand the effectiveness and reach of smoking cessation apps for this highly nicotine dependent yet under-served population. IMPLICATIONS: Data from this study will inform future research efforts to expand the effectiveness and reach of smoking cessation apps for people with serious mental illness, a highly nicotine dependent yet under-served population.

Examining the role of psychological inflexibility, perspective taking, and empathic concern in generalized prejudice.

Research to-date on generalized prejudice has focused primarily on personality factors. Further work is needed identifying manipulable variables that directly inform antiprejudice interventions. This study examined three such variables: empathic concern, perspective taking, and psychological inflexibility/flexibility with prejudiced thoughts, as a test of the flexible connectedness model. A sample of 604 undergraduate students completed online surveys. A model indicated prejudice measures loaded onto a latent variable of generalized prejudice. In a second model, psychological inflexibility, flexibility, empathic concern, and perspective taking were all significant, independent predictors of generalized prejudice. Psychological inflexibility also predicted prejudice above and beyond personality and general inflexibility variables. Results suggest the three components of the flexible connectedness model may be important targets for prejudice interventions.

Determining ethyl glucuronide cutoffs when detecting self-reported alcohol use in addiction treatment patients.

BACKGROUND: Ethyl glucuronide (EtG) is an alcohol biomarker with potential utility as a clinical research and alcohol treatment outcome. Debate exists regarding the appropriate cutoff level for determining alcohol use, particularly with the EtG immunoassay. This study determined the EtG immunoassay cutoff levels that most closely correspond to self-reported drinking in alcohol-dependent outpatients. METHODS: Eighty adults with alcohol dependence and mental illness, taking part in an alcohol treatment study, provided urine samples 3 times per week for up to 16 weeks (1,589 samples). Self-reported drinking during 120 hours prior to each sample collection was assessed. Receiver operating characteristic analyses were conducted to assess the ability of the EtG immunoassay to detect self-reported alcohol use across 24- to 120-hour time periods. Sensitivity and specificity of EtG immunoassay cutoff levels was compared in 100 ng/ml increments (100 to 500 ng/ml) across 24 to 120 hours. RESULTS: Over half (57%) of the 1,589 samples indicated recent alcohol consumption. The EtG immunoassay closely corresponded to self-reported drinking from 24 (area under the curve [AUC] = 0.90, 95% confidence interval [CI]: 0.88, 0.92) to 120 hours (AUC = 0.88, 95% CI: 0.87, 0.90). When cutoff levels were compared across 24 to 120 hours, 100 ng/ml had the highest sensitivity (0.93 to 0.78) and lowest specificity (0.67 to 0.85). Relative to 100 ng/ml, the 200 ng/ml cutoff demonstrated a reduction in sensitivity (0.89 to 0.67), but improved specificity (0.78 to 0.94). The 300, 400, and 500 ng/ml cutoffs demonstrated the lowest sensitivity (0.86 to 0.33) and highest specificity (0.86 to 0.97) over 24 to 120 hours. CONCLUSIONS: For detecting alcohol use for >24 hours, the 200 ng/ml cutoff level is recommended for use as a research and clinical outcome.

Feature-level analysis of a novel smartphone application for smoking cessation.

BACKGROUND: Currently, there are over 400 smoking cessation smartphone apps available, downloaded an estimated 780,000 times per month. No prior studies have examined how individuals engage with specific features of cessation apps and whether use of these features is associated with quitting. OBJECTIVES: Using data from a pilot trial of a novel smoking cessation app, we examined: (i) the 10 most-used app features, and (ii) prospective associations between feature usage and quitting. METHODS: Participants (n = 76) were from the experimental arm of a randomized, controlled pilot trial of an app for smoking cessation called "SmartQuit," which includes elements of both Acceptance and Commitment Therapy (ACT) and traditional cognitive behavioral therapy (CBT). Utilization data were automatically tracked during the 8-week treatment phase. Thirty-day point prevalence smoking abstinence was assessed at 60-day follow-up. RESULTS: The most-used features - quit plan, tracking, progress, and sharing - were mostly CBT. Only two of the 10 most-used features were prospectively associated with quitting: viewing the quit plan (p = 0.03) and tracking practice of letting urges pass (p = 0.03). Tracking ACT skill practice was used by fewer participants (n = 43) but was associated with cessation (p = 0.01). CONCLUSIONS: In this exploratory analysis without control for multiple comparisons, viewing a quit plan (CBT) as well as tracking practice of letting urges pass (ACT) were both appealing to app users and associated with successful quitting. Aside from these features, there was little overlap between a feature's popularity and its prospective association with quitting. Tests of causal associations between feature usage and smoking cessation are now needed.

High levels of agreement between clinic-based ethyl glucuronide (EtG) immunoassays and laboratory-based mass spectrometry.

BACKGROUND: Immunoassay urine drug screening cups that detect use for two or more days are commonly used in addiction treatment settings. Until recently, there has been no comparable immunoassay test for alcohol use in these settings. OBJECTIVES: The aim of this study was to assess the agreement of a commercially available ethyl glucuronide immunoassay (EtG-I) test conducted at an outpatient addiction clinic and lab-based EtG mass spectrometry (EtG-MS) conducted at a drug testing laboratory at three cut-off levels. High agreement between these two measures would support the usefulness of EtG-I as a clinical tool for monitoring alcohol use. METHODS: Forty adults with co-occurring alcohol dependence and serious mental illnesses submitted 1068 urine samples over a 16-week alcohol treatment study. All samples were tested using EtG-I on a benchtop analyzer and 149 were randomly selected for EtG-MS analysis at a local laboratory. Agreement was defined as the number of samples where EtG-I and EtG-MS were both above or below a specific cut-off level. Agreement was calculated at low cut-off levels (100 and 250 ng/ml), as well as at a higher cut-off level (500 ng/ml) recommended by most by commercial drug testing laboratories. RESULTS: Agreement between EtG-I and EtG-MS was high across all cut-off levels (90.6% at 100 ng/ml, and 96.6% at 250 and 500 ng/ml). CONCLUSIONS: EtG immunoassays conducted at low cut-off levels in point-of-care testing settings have high agreement with lab-based EtG-MS. EtG-I can be considered a useful clinical monitoring tool for alcohol use in community-based addiction treatment settings.

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.

Web-based acceptance and commitment therapy smoking cessation treatment for smokers with depressive symptoms.

OBJECTIVE: Smokers with depressive symptoms have more difficulty quitting smoking than the general population of smokers. The present study examines a web-based treatment using acceptance and commitment therapy (ACT) for smokers with depressive symptoms. The study aimed to determine participant receptivity to the intervention and its effects on smoking cessation, acceptance of internal cues, and depressive symptoms. METHODS: Smokers who had positive screening results for depressive symptoms at baseline (n = 94) were selected from a randomized controlled trial (N = 222) comparing web-based ACT for smoking cessation (WebQuit.org) with Smokefree.gov. Forty-five participants (48%) completed the three-month follow-up. RESULTS: Compared to Smokefree.gov, WebQuit participants spent significantly more time on site (p =.001) and had higher acceptance of physical cravings (p =.033). While not significant, WebQuit participants were more engaged and satisfied with their program and were more accepting of internal cues overall. There was preliminary evidence that WebQuit participants had higher quit rates (20% versus 12%) and lower depressive symptoms at follow-up (45% versus 56%) than those in Smokefree.gov. CONCLUSIONS: This was the first study of web-based ACT for smoking cessation among smokers with depressive symptoms, with promising evidence of receptivity, efficacy, impact on a theory-based change process, and possible secondary effects on depression. A fully powered trial of the ACT WebQuit.org intervention specifically for depressed smokers is needed. This was part of a clinical trial registered as NCT#01166334 at www.clinicaltrials.gov .

Preliminary findings: Contingency management targeting psycho-stimulant use results in secondary decreases in smoking for severely mentally ill adults.

BACKGROUND: Treatments for drug addiction and smoking in severely mentally ill (SMI) adults are needed. OBJECTIVES: To investigate the effect of a contingency management (CM) intervention targeting psycho-stimulant on cigarette smoking. METHODS: 126 stimulant dependent SMI smokers were assigned to CM or a non-contingent control condition. Rates of smoking-negative (<3 ppm) carbon monoxide breath-samples were compared. RESULTS: Individuals who received CM targeting psycho-stimulants were 79% more likely to submit a smoking-negative breath-sample relative to controls. CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: This study provides initial evidence that a behavioral treatment for drug use results in reductions in cigarette smoking in SMI adults.

Stagnant daily smoking prevalence between 2008 and 2019 among Black and Hispanic adults with serious psychological distress.

BACKGROUND: Racial/ethnic minority status and mental illness independently drive inequity in cigarette smoking and related morbidity. Racial/ethnic minority groups suffer a disproportionate burden of tobacco-related diseases. People with serious mental illness (SMI) smoke at up to 7 times the rate of the general population. There is a need to quantify smoking prevalence and trends among people at the intersection of both groups. METHODS: This study analyzes 2008-2019 data from the National Survey on Drug Use and Health. Linear time trends of daily smoking prevalence were assessed among people with serious psychological distress (SPD; marker for SMI) and people without SPD reporting White, Black, Hispanic, and Other race/ethnicity using logistic regression, with survey year as the predictor. Models with year-by-smoking status interaction terms and F-tests assessed differential time trends. RESULTS: The prevalence of daily smoking among people without SPD decreased over time among people reporting White (aOR=0.96, p<0.001), Black (aOR=0.96, p<0.001), Hispanic (aOR=0.95, p<0.001), and Other (aOR=0.97, p=0.002) race/ethnicity. Among people with SPD, the smoking prevalence decreased among people with White race/ethnicity (aOR=0.95, p<0.001), with no significant changes among people of Black, Hispanic, and Other race/ethnicity. CONCLUSIONS: Smoking among people with SPD who report Black and Hispanic race/ethnicity has not changed significantly in the past 11 years, despite decreasing among non-SPD and White groups. People who are Black/Hispanic and people with SPD struggle to quit smoking, which is amplified intersectionally. Tailored interventions may be a better mechanism to reduce barriers to smoking cessation in this population.

Review of strategies to investigate low sample return rates in remote tobacco trials: A call to action for more user-centered design research.

Remote collection of biomarkers of tobacco use in clinical trials poses significant challenges. A recent meta-analysis and scoping review of the smoking cessation literature indicated that sample return rates are low and that new methods are needed to investigate the underlying causes of these low rates. In this paper we conducted a narrative review and heuristic analysis of the different human factors approaches reported to evaluate and/or improve sample return rates among 31 smoking cessation studies recently identified in the literature. We created a heuristic metric (with scores from 0 to 4) to evaluate the level of elaboration or complexity of the user-centered design strategy reported by researchers. Our review of the literature identified five types of challenges typically encountered by researchers (in that order): usability and procedural, technical (device related), sample contamination (e.g., polytobacco), psychosocial factors (e.g., digital divide), and motivational factors. Our review of strategies indicated that 35% of the studies employed user-centered design methods with the remaining studies relying on informal methods. Among the studies that employed user-centered design methods, only 6% reached a level of 3 in our user-centered design heuristic metric. None of the studies reached the highest level of complexity (i.e., 4). This review examined these findings in the context of the larger literature, discussed the need to address the role of health equity factors more directly, and concluded with a call to action to increase the application and reporting of user-centered design strategies in biomarkers research.

An examination of neurocognition and theory of mind as predictors of engagement with a tailored digital therapeutic in persons with serious mental illness.

There is an increasing interest in the development and implementation of digital therapeutics (apps) in individuals with serious mental illness (SMI). However, there is limited understanding of the role of neurocognition and social cognition on engagement with apps. The present study is a secondary analysis of a pilot randomized controlled trial (N = 62) comparing a tailored digital intervention to treat tobacco use disorder in individuals with SMI to a standard of care digital intervention for the general population. The purpose of this study was to examine the impact of neurocognition, social cognition, and clinical characteristics on indices of app engagement in users of the tailored app compared to users of the standard of care app. Correlational analyses demonstrated that individuals with low levels of neurocognition and social cognition engaged more often and for longer duration with the tailored app compared to the standard of care app. In a series of multilevel zero-inflated negative binomial models, assignment to the tailored app remained the most robust predictor of app interactions (Risk Ratio [RR] = 1.72; p < .01), duration of app use (RR = 6.47; p < .01), and average length of interaction (RR = 2.70; p < .01), after adjusting for key demographic and clinical characteristics, and two measures of cognition. This is one of the first studies to demonstrate that digital therapeutics can be designed to mitigate the impact of neurocognition and social cognition on device engagement in SMI populations. Recommendations are made to advance the use of new analytic models to uncover patterns of engagement with digital therapeutics.

Engagement with a digital therapeutic for smoking cessation designed for persons with psychiatric illness fully mediates smoking outcomes in a pilot randomized controlled trial.

Understanding the mechanisms of change of digital therapeutics is a critical step to improve digital health outcomes and optimize their development. Access to and engagement with digital content is arguably a core mechanism of change of these interventions. However, the mediational role of app engagement has been largely unexamined. To evaluate the mediational effect of engaging with a digital therapeutic for smoking cessation designed for adults with psychiatric disorders. Secondary analysis of a pilot clinical trial of 62 adults with serious mental illness who were randomized to receive either a tailored digital therapeutic (Learn to Quit) or a digital therapeutic for the general public (NCI QuitGuide). Engagement was captured using background analytics of app utilization, including (a) number of interactions with app content, (b) minutes/day of app use, and (c) number of days used. The main outcome was reductions in cigarettes per day from baseline to the four-month endpoint. Mediational analysis followed the Preacher and Hayes bootstrap method. Number of application interactions fully mediated reductions in cigarettes per day in the Learn to Quit application but not in QuitGuide (Average Causal Mediation Effect = .31, p = .02). Minutes/day of app use played an uncertain role, and number of days used was not a significant mediator. Results suggest that one of the mechanisms of action of the Learn to Quit device, engagement with theory-based content, functioned as intended. Future research of digital therapeutics should emphasize granular approaches to evaluating apps' mechanisms of action.

Usability and Acceptability of Two Smartphone Apps for Smoking Cessation Among Young Adults With Serious Mental Illness: Mixed Methods Study.

BACKGROUND: Young adults with serious mental illness (SMI) have higher smoking rates and lower cessation rates than young adults without SMI. Scalable interventions such as smartphone apps with evidence-based content (eg, the National Cancer Institute's [NCI's] QuitGuide and quitSTART) could increase access to potentially appealing and effective treatment for this group but have yet to be tested in this population. OBJECTIVE: The goal of this user-centered design study is to determine the user experience (including usability and acceptability) of 2 widely available apps developed by the NCI-QuitGuide and quitSTART-among young adult tobacco users with SMI. METHODS: We conducted usability and acceptability testing of QuitGuide and quitSTART among participants with SMI aged between 18 and 35 years who were stable in community mental health treatment between 2019 and 2020. Participants were randomly assigned to use QuitGuide or quitSTART on their smartphones. App usability was evaluated at baseline and following a 2-week field test of independent use via a video-recorded task completion protocol. Using a mixed method approach, we triangulated 4 data sources: nonparticipant observation, open-ended interviews, structured interviews (including the System Usability Scale [SUS]), and backend app use data obtained from the NCI. Quantitative data were analyzed using descriptive statistics, and qualitative data were analyzed using thematic analysis. RESULTS: Participants were 17 smokers who were not interested in quitting, with a mean age of 29 (SD 4) years; 41% (n=7) presented with psychotic disorders. Participants smoked an average of 15 (SD 7) cigarettes per day. The mean SUS scores for QuitGuide were similar at visits one and two (mean 64, SD 18 and mean 66, SD 18, respectively). The mean SUS scores for quitSTART numerically increased from visit one (mean 55, SD 20) to visit two (mean 64, SD 16). Acceptability scores followed the same pattern. Observed task completion rates were at least 75% (7/9 for QuitGuide, 6/8 for quitSTART) for both apps at both visits for all but 2 tasks. During the 13-day trial period, QuitGuide and quitSTART users interacted with their assigned app on an average of 4.6 (SD 2.8) days versus 10.8 (SD 3.5) days, for a mean total of 5.6 (SD 3.8) interactions versus 41 (SD 26) interactions, and responded to a median of 1 notification (range 0-8) versus 18.5 notifications (range 0-37), respectively. Qualitative comments indicated moderate to high satisfaction overall but also included concerns about the accuracy of the apps' feedback. CONCLUSIONS: Both QuitGuide and quitSTART had acceptable levels of usability and mixed levels of acceptability among young adults with SMI. The higher level of engagement with quitSTART suggests that quitSTART may be a favorable tool for young adult smokers with SMI. However, clinical support or coaching may be needed to overcome initial usability issues.