Background Smoking rates in the United States have been reduced in the past decades to 15% of the general population. However, up to 88% of people with psychiatric symptoms still smoke, leading to high rates of disease and mortality. Therefore, there is a great need to develop smoking cessation interventions that have adequate levels of usability and can reach this population. Objective The objective of this study was to report the rationale, ideation, design, user research, and final specifications of a novel smoking cessation app for people with serious mental illness (SMI) that will be tested in a feasibility trial. Methods We used a variety of user-centered design methods and materials to develop the tailored smoking cessation app. This included expert panel guidance, a set of design principles and theory-based smoking cessation content, development of personas and paper prototyping, usability testing of the app prototype, establishment of app’s core vision and design specification, and collaboration with a software development company. Results We developed Learn to Quit, a smoking cessation app designed and tailored to individuals with SMI that incorporates the following: (1) evidence-based smoking cessation content from Acceptance and Commitment Therapy and US Clinical Practice Guidelines for smoking cessation aimed at providing skills for quitting while addressing mental health symptoms, (2) a set of behavioral principles to increase retention and comprehension of smoking cessation content, (3) a gamification component to encourage and sustain app engagement during a 14-day period, (4) an app structure and layout designed to minimize usability errors in people with SMI, and (5) a set of stories and visuals that communicate smoking cessation concepts and skills in simple terms. Conclusions Despite its increasing importance, the design and development of mHealth technology is typically underreported, hampering scientific innovation. This report describes the systematic development of the first smoking cessation app tailored to people with SMI, a population with very high rates of nicotine addiction, and offers new design strategies to engage this population. mHealth developers in smoking cessation and related fields could benefit from a design strategy that capitalizes on the role visual engagement, storytelling, and the systematic application of behavior analytic principles to deliver evidence-based content.

Background The first randomized trial of a smartphone application (app) for adult smoking cessation (SmartQuit 1.0) revealed key features that predict cessation. These findings guided the revision of this Acceptance & Commitment Therapy (ACT)-based application (SmartQuit 2.0), which was primarily tested to examine participant receptivity, short-term cessation and reduction, and the relationship between program completion, smoking cessation and reduction. Secondarily, outcomes were descriptively compared with the SmartQuit1.0 trial. Method Adult participants (78% female, 25% with high school or less education, 30% unemployed) were recruited into the single-arm pilot trial (N = 99) of SmartQuit 2.0 with a two-month follow-up (85% retention). Results Regarding receptivity, 84% of participants were satisfied with SmartQuit 2.0 (vs. 59% for SmartQuit1.0), 73% would recommend it to a friend (vs. 48% for SmartQuit1.0), 81% found the ACT exercises useful for quitting (vs. 44% for SmartQuit1.0). At the 2-month follow-up, the quit rates were 21% for 7-day point prevalence (vs. 23% for SmartQuit1.0), 11% for 30-day point prevalence (vs. 13% for SmartQuit1.0), and 75% of participants reduced their smoking frequency (vs. 57% for SmartQuit1.0). Among program completers (24% of total sample), the quit rates were 33% for 7-day point prevalence, 28% for 30-day point prevalence, and 88% of participants reduced their smoking frequency. Conclusions The revised app had high user receptivity, modest quit rates, and high smoking reduction rates. Program completion may be key to boosting the app’s effectiveness.

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.

Introduction Although engagement is generally predictive of positive outcomes in technology-based behavioral change interventions, engagement measures remain largely atheoretical and lack treatment-specificity. This study examines the extent to which adherence measures based on the underlying behavioral change theory of an Acceptance and Commitment Therapy (ACT) app for smoking cessation predict smoking outcomes, and user characteristics associated with adherence. Methods Study sample was adult daily smokers in a single arm pilot study (n=84). Using the app’s log file data, we examined measures of adherence to four key components of the ACT behavior change model as predictors of smoking cessation and reduction. We also examined baseline user characteristics associated with adherence measures that predict smoking cessation. Results Fully adherent users (24%) were over four times more likely to quit smoking (OR = 4.45; 95% CI = 1.13, 17.45; p = 0.032). Both an increase in tracking the number of urges passed (OR = 1.02; 95% CI = 1.00, 1.03; p = 0.043) and ACT modules completed (OR = 1.27; 95% CI = 1.01, 1.60; p = 0.042) predicted cessation. Lower baseline acceptance of cravings was associated with over four times higher odds of full adherence (OR = 4.59; 95% CI = 1.35, 15.54; p = 0.014). Conclusions Full adherence and use of specific ACT theory-based components of the app predicted quitting. Consistent with ACT theory, users with low acceptance were most likely to adhere to the app. Further research is needed on ways to promote app engagement.