Smoking Cessation Smartphone App Research Articles

A smoking cessation smartphone app that delivers real-time 'context aware' behavioural support: the Quit Sense feasibility RCT.

During a quit attempt, cues from a smoker's environment are a major cause of brief smoking lapses, which increase the risk of relapse. Quit Sense is a theory-guided Just-In-Time Adaptive Intervention smartphone app, providing smokers with the means to learn about their environmental smoking cues and provides 'in the moment' support to help them manage these during a quit attempt. To undertake a feasibility randomised controlled trial to estimate key parameters to inform a definitive randomised controlled trial of Quit Sense. A parallel, two-arm randomised controlled trial with a qualitative process evaluation and a 'Study Within A Trial' evaluating incentives on attrition. The research team were blind to allocation except for the study statistician, database developers and lead researcher. Participants were not blind to allocation. Online with recruitment, enrolment, randomisation and data collection (excluding manual telephone follow-up) automated through the study website. Smokers (323 screened, 297 eligible, 209 enrolled) recruited via online adverts on Google search, Facebook and Instagram. Participants were allocated to 'usual care' arm (n = 105; text message referral to the National Health Service SmokeFree website) or 'usual care' plus Quit Sense (n = 104), via a text message invitation to install the Quit Sense app. Follow-up at 6 weeks and 6 months post enrolment was undertaken by automated text messages with an online questionnaire link and, for non-responders, by telephone. Definitive trial progression criteria were met if a priori thresholds were included in or lower than the 95% confidence interval of the estimate. Measures included health economic and outcome data completion rates (progression criterion #1 threshold: ≥ 70%), including biochemical validation rates (progression criterion #2 threshold: ≥ 70%), recruitment costs, app installation (progression criterion #3 threshold: ≥ 70%) and engagement rates (progression criterion #4 threshold: ≥ 60%), biochemically verified 6-month abstinence and hypothesised mechanisms of action and participant views of the app (qualitative). Self-reported smoking outcome completion rates were 77% (95% confidence interval 71% to 82%) and health economic data (resource use and quality of life) 70% (95% CI 64% to 77%) at 6 months. Return rate of viable saliva samples for abstinence verification was 39% (95% CI 24% to 54%). The per-participant recruitment cost was £19.20, which included advert (£5.82) and running costs (£13.38). In the Quit Sense arm, 75% (95% CI 67% to 83%; 78/104) installed the app and, of these, 100% set a quit date within the app and 51% engaged with it for more than 1 week. The rate of 6-month biochemically verified sustained abstinence, which we anticipated would be used as a primary outcome in a future study, was 11.5% (12/104) in the Quit Sense arm and 2.9% (3/105) in the usual care arm (estimated effect size: adjusted odds ratio = 4.57, 95% CIs 1.23 to 16.94). There was no evidence of between-arm differences in hypothesised mechanisms of action. Three out of four progression criteria were met. The Study Within A Trial analysis found a £20 versus £10 incentive did not significantly increase follow-up rates though reduced the need for manual follow-up and increased response speed. The process evaluation identified several potential pathways to abstinence for Quit Sense, factors which led to disengagement with the app, and app improvement suggestions. Biochemical validation rates were lower than anticipated and imbalanced between arms. COVID-19-related restrictions likely limited opportunities for Quit Sense to provide location tailored support. The trial design and procedures demonstrated feasibility and evidence was generated supporting the efficacy potential of Quit Sense. Progression to a definitive trial is warranted providing improved biochemical validation rates. This trial is registered as ISRCTN12326962. This award was funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme (NIHR award ref: 17/92/31) and is published in full in Public Health Research; Vol. 12, No. 4. See the NIHR Funding and Awards website for further award information.

Preliminary Efficacy of a Cognitive Behavioral Therapy-Based Smartphone App for Smoking Cessation in China: Randomized Controlled Pilot Trial.

The overall prevalence of cigarette smokers in China is very high, and China's total cigarette consumption makes up more than 40% of the world's consumption. In view of the lack of smoking cessation services and social support in China and the effectiveness of mobile phone apps for quitting smoking in other countries, we carried out a smartphone app-based smoking cessation trial in China. This study aimed to evaluate the efficacy of a cognitive behavioral therapy (CBT)-based smoking cessation smartphone app among smokers seeking treatment in China. We conducted a randomized controlled, web-based pilot clinical trial in China between February 23 and June 27, 2021. Eligible participants were randomly assigned to the smoking cessation app intervention group or the control group in a ratio of 1:1. The intervention group received the CBT smoking cessation intervention using a smartphone app, and the control group received a "thank you" message. The intervention was 4 weeks long, and the patients were followed up for 4 weeks. The primary outcome was self-reported continuous smoking abstinence at week 4 after the quit date. The secondary outcomes included self-reported 7-day point prevalence of smoking abstinence; reduction of the number of cigarettes smoked per day at weeks 1, 2, 3, and 4; and program acceptability. A total of 973 people were recruited to quit smoking, of whom 262 completed basic information, 56 were excluded, and 206 were randomized and included in the final analysis. There were 189 (91.7%) men and 17 (8.3%) women, with an average age of 34.46 (SD 7.53) years and an average daily smoking rate of 15.93 (SD 7.10) cigarettes/day. We found 30 (29.7%) of the 101 participants in the intervention group and 7 (6.7%) of the 105 participants in the control group reported continuous smoking cessation after the quit date at week 4 (odds ratio 5.92, 95% CI 3.78-9.26; P<.001). The 7-day point prevalence abstinence rate of the intervention group varied from 42.6% (43/101) to 46.5% (47/101) after 1, 2, 3, and 4 weeks, while the control group varied from 18.1% (19/105) to 26.7% (28/105). Compared to the control group, continued smokers consumed 1.5-3.0 fewer cigarettes per day in the intervention group. The overall program got positive user feedback with a high satisfaction rate (66/87, 76%) and an average Mobile Application Rating Scale user version score of 3.46. Our pilot study provided preliminary evidence that the CBT-based smoking cessation smartphone app led to improved smoking quit rates versus control in Chinese smokers. The study demonstrated the CBT-based smartphone app may be an effective and feasible digital treatment model to help smokers quit, which may improve smoking cessation service quality and accessibility in China. ClinicalTrials.gov NCT04421170; https://clinicaltrials.gov/study/NCT04421170. RR2-10.1136/bmjopen-2020-041985.

Effectiveness of the QuitSure Smartphone App for Smoking Cessation: Findings of a Prospective Single Arm Trial.

Digital therapies, especially smartphone apps for active and continuous smoking cessation support, are strongly emerging as an alternative smoking cessation therapy. In the Indian context, there is a growing interest in the use of app-based smoking cessation programs; however, there is limited evidence regarding their effectiveness in achieving long-term continuous abstinence. This study aimed to evaluate the long-term abstinence effect (up to 30-d abstinence postprogram completion) of a smartphone app, QuitSure, for smoking cessation in active smokers from India. In this prospective single-arm study, participants who signed up for the QuitSure app were enrolled in this study. The primary end point was the prolonged abstinence (PA) rate from weeks 1 to 4 (day 7 to day 30). Furthermore, data for withdrawal symptoms, relapse reasons, and reasons for not continuing the program were also assessed. The quit rate was calculated considering only the participants who followed up and completed the survey sent to them (per protocol) at day 7 and at day 30, respectively. The PA rate at day 7 was found to be 64.5% (111/172; 95% CI 56% to 72%), and the PA rate at day 30 was found to be 55.8% (72/129; 95% CI 45% to 65%). Within the 7-day abstinence period, 60.4% (67/111) of the participants did not have any withdrawal symptoms. The most common mild withdrawal symptoms were mild sleep disturbance (21/111, 18.9%), mild digestive changes (19/111, 17.1%), and coughing (17/111, 15.3%). Severe withdrawal symptoms were rare, with only 5.4% (6/111) experiencing them. For those achieving 30-day postprogram abstinence, 85% (61/72) had no mild withdrawal symptoms, and 99% (71/72) had no severe withdrawal symptoms. Among successful quitters at day 7, a total of 72.1% (80/111) reported minimal to no cravings, which increased to 88% (63/72) at day 30. Furthermore, 78% (56/72) of those with PA at day 30 reported no change in weight or reduced weight. Among participants experiencing relapse, 48% (28/58) cited intense cravings, 28% (16/58) mentioned facing a tragedy, and 26% (15/58) reported relapsing due to alcohol consumption. The PA rates as a result of the QuitSure program were found to be better than those reported in the results of other smoking-cessation app programs' studies. The QuitSure app yields high PA rates and ameliorates symptoms associated with smoking cessation. In order to obtain conclusive evidence regarding the effectiveness and efficacy of the QuitSure program, future research should include appropriate control measures. Nevertheless, the QuitSure program can serve as a valuable adjunct to a conventional smoking cessation treatment program to aid sustained abstinence.

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.

Do medications increase the efficacy of digital interventions for smoking cessation? Secondary results from the iCanQuit randomized trial.

iCanQuit is a smartphone application (app) proven efficacious for smoking cessation in a Phase III randomized controlled trial (RCT). This study aimed to measure whether medications approved by the US Food and Drug Administration (FDA) for smoking cessation would further enhance the efficacy of iCanQuit, relative to its parent trial comparator-the National Cancer Institute's (NCI's) QuitGuide app. Secondary analysis of the entire parent trial sample of a two-group (iCanQuit and QuitGuide), stratified, doubled-blind RCT. United States. Participants who reported using an FDA-approved cessation medication on their own (n= 619) and those who reported no use of cessation medications (n= 1469). Participants were randomized to receive iCanQuit app or NCI's QuitGuide app. Use of FDA-approved medications was measured at 3 months post-randomization. Smoking cessation outcomes were measured at 3, 6 and 12 months. The primary outcome was 12-month self-reported 30-day point prevalence abstinence (PPA). The data retention rate at the 12-month follow-up was 94.0%. Participants were aged 38.5 years, 71.0% female, 36.6% minority race/ethnicity, 40.6% high school or less education, residing in all 50 US States and smoking 19.2 cigarettes/day. The 29.6% of all participants who used medications were more likely to choose nicotine replacement therapy (NRT; 78.8%) than other cessation medications (i.e. varenicline or bupropion; 18.3 and 10.5%, respectively) and use did not differ by app treatment assignment (all P> 0.05). There was a significant (P= 0.049) interaction between medication use and app treatment assignment on PPA. Specifically, 12-month quit rates were 34% for iCanQuit versus 20% for QuitGuide [odds ratio (OR) = 2.36, 95% confidence interval (CI) = 1.59, 3.49] among participants reporting any medication use, whereas among participants reporting no medication use, quit rates were 28% for iCanQuit versus 22% for QuitGuide (OR = 1.41, 95% CI = 1.09, 1.82). Results were stronger for those using only NRT: 40% quit rates for iCanQuit versus 18% quit rates for QuitGuide (OR = 3.57, 95% CI = 2.20, 5.79). The iCanQuit smartphone app for smoking cessation was more efficacious than the QuitGuide smartphone app, regardless of whether participants used medications to aid cessation. Smoking cessation medications, especially nicotine replacement therapy, might enhance the efficacy of the iCanQuit app.

Developing a Game (Inner Dragon) Within a Leading Smartphone App for Smoking Cessation: Design and Feasibility Evaluation Study.

Several stand-alone smartphone apps have used serious games to provide an engaging approach to quitting smoking. So far, the uptake of these games has been modest, and the evidence base for their efficacy in promoting smoking cessation is still evolving. The feasibility of integrating a game into a popular smoking cessation app is unclear. The aim of this paper was to describe the design and iterative development of the Inner Dragon game within Smoke Free, a smartphone app with proven efficacy, and the results of a single-arm feasibility trial as part of a broad program that seeks to assess the effectiveness of the gamified app for smoking cessation. In phase 1, the study team undertook a multistep process to design and develop the game, including web-based focus group discussions with end users (n=15). In phase 2, a single-arm study of Smoke Free users who were trying to quit (n=30) was conducted to assess the feasibility and acceptability of the integrated game and to establish the feasibility of the planned procedures for a randomized pilot trial. Phase 1 led to the final design of Inner Dragon, informed by principles from psychology and behavioral economics and incorporating several game mechanics designed to increase user engagement and retention. Inner Dragon users maintain an evolving pet dragon that serves as a virtual avatar for the users' progress in quitting. The phase-2 study established the feasibility of the study methods. The mean number of app sessions completed per user was 13.8 (SD 13.1; median 8; range 1-46), with a mean duration per session of 5.8 (median 1.1; range 0-81.1) minutes. Overall, three-fourths (18/24, 75%) of the participants entered the Inner Dragon game at least once and had a mean of 2.4 (SD 2.4) sessions of game use. The use of Inner Dragon was positively associated with the total number of app sessions (correlation 0.57). The mean satisfaction score of participants who provided ratings (11/24, 46%) was 4.2 (SD 0.6) on a 5-point scale; however, satisfaction ratings for Inner Dragon were only completed by 13% (3/24) of the participants. Findings supported further development and evaluation of Inner Dragon as a beneficial feature of Smoke Free. The next step of this study is to conduct a randomized pilot trial to determine whether the gamified version of the app increases user engagement over a standard version of the app.

American Indian Perspectives on Culturally Aligning a Digital Smoking Cessation Resource.

Innovative smoking cessation approaches that overcome barriers such as traveling to program site or that require the staff and infrastructure for sustaining are likely needed to improve smoking quit rates among American Indian (AI) peoples in the United States. In this study, qualitative methods identified recommendations from AI peoples to guide alignment of an evidence-based smoking cessation smartphone app (i.e., QuitGuide) to the culture and needs of AI persons. Semi-structured interviews were conducted with AI adults who smoke (n = 40) and with public health professionals (n = 6). Questions included: "The app asks if something triggered you to slip and lists several options. What options were you expecting to see on this list?" as well as how to make the app more engaging such as "What would make the app more helpful for AI peoples, like you, who want to quit smoking?." Constant comparative techniques were used to develop codes and themes. Loss, grief, and not accessing traditional tobacco were put forward as smoking triggers to be addressed in the app. Features that help users connect with and learn about AI cultures and promote healing, such as encouraging traditional tobacco use, being in community, embracing Native spirituality, and participating in cultural crafting were recommended. Some noted the need to motivate AI peoples to think about legacy and ability to care for younger generations and Indigenizing the app with Native imagery. Themes pointed towards promotion of strengths-based factors, such as healing, cultural connectedness and traditional tobacco use, in the app. Results will be used to culturally align a smartphone app for smoking cessation among AI peoples and may be insightful for other tribal, federal, and state public health efforts aimed at advancing health equity for AI peoples.

Smartphone Apps for Smoking Cessation: Systematic Framework for App Review and Analysis.

Cigarette smoking is a leading cause of preventable death, and identifying novel treatment approaches to promote smoking cessation is critical for improving public health. With the rise of digital health and mobile apps, these tools offer potential opportunities to address smoking cessation, yet the functionality of these apps and whether they offer scientifically based support for smoking cessation are unknown. The goal of this research was to use the American Psychiatric Association app evaluation model to evaluate the top-returned apps from Android and Apple app store platforms related to smoking cessation and investigate the common app features available for end users. We conducted a search of both Android and iOS app stores in July 2021 for apps related to the keywords "smoking," "tobacco," "smoke," and "cigarette" to evaluate apps for smoking cessation. Apps were screened for relevance, and trained raters identified and analyzed features, including accessibility (ie, cost), privacy, clinical foundation, and features of the apps, using a systematic framework of 105 objective questions from the American Psychiatric Association app evaluation model. All app rating data were deposited in mindapps, a publicly accessible database that is continuously updated every 6 months given the dynamic nature of apps available in the marketplace. We characterized apps available in July 2021 and November 2022. We initially identified 389 apps, excluded 161 due to irrelevance and nonfunctioning, and rated 228, including 152 available for Android platforms and 120 available for iOS platforms. Some of the top-returned apps (71/228, 31%) in 2021 were no longer functioning in 2022. Our analysis of rated apps revealed limitations in accessibility and features. While most apps (179/228, 78%) were free to download, over half had costs associated with in-app purchases or full use. Less than 65% (149/228) had a privacy policy addressing the data collected in the app. In terms of intervention features, more than 56% (128/228) of apps allowed the user to set and check in on goals, and more than 46% (106/228) of them provided psychoeducation, although few apps provided evidence-based support for smoking cessation, such as peer support or skill training, including mindfulness and deep breathing, and even fewer provided evidence-based interventions, such as acceptance and commitment therapy or cognitive behavioral therapy. Only 12 apps in 2021 and 11 in 2022 had published studies supporting the feasibility or efficacy for smoking cessation. Numerous smoking cessation apps were identified, but analysis revealed limitations, including high rates of irrelevant and nonfunctioning apps, high rates of turnover, and few apps providing evidence-based support for smoking cessation. Thus, it may be challenging for consumers to identify relevant, evidence-based apps to support smoking cessation in the app store, and a comprehensive evaluation system of mental health apps is critically important.

Predicting the Users’ Level of Engagement with a Smartphone Application for Smoking Cessation: Randomized Trial and Machine Learning Analysis

Introduction: Studies of the users’ engagement with smoking cessation application (apps) can help understand how these apps are used by smokers, in order to improve their reach and efficacy. Objective: The present study aimed at identifying the best predictors of the users’ level of engagement with a smartphone app for smoking cessation and at examining the relationships between predictors and outcomes related to the users’ level of engagement with the app. Methods: A secondary analysis of data from a randomized trial testing the efficacy of the Stop-Tabac smartphone app was used. The experimental group used the “full” app and the control group used a “dressed down” app. The study included a baseline and 1-month and 6-month follow-up questionnaires. A total of 5,293 participants answered at least the baseline questionnaires; however, in the current study, only the 1,861 participants who answered at least the baseline and the 1-month follow-up questionnaire were included. Predictors were measured at baseline and after 1 month and outcomes after 6 months. Data were analyzed using machine learning algorithms. Results: The best predictors of the outcomes were, in decreasing order of importance, intention to stop smoking, dependence level, perceived helpfulness of the app, having quit smoking after 1 month, self-reported usage of the app after 1 month, belonging to the experimental group (vs. control group), age, and years of smoking. Most of these predictors were also significantly associated with the participants’ level of engagement with the app. Conclusions: This information can be used to further target the app to specific groups of users, to develop strategies to enroll more smokers, and to better adapt the app’s content to the users’ needs.

Classification of Lapses in Smokers Attempting to Stop: A Supervised Machine Learning Approach Using Data From a Popular Smoking Cessation Smartphone App.

Smoking lapses after the quit date often lead to full relapse. To inform the development of real time, tailored lapse prevention support, we used observational data from a popular smoking cessation app to develop supervised machine learning algorithms to distinguish lapse from non-lapse reports. We used data from app users with ≥20 unprompted data entries, which included information about craving severity, mood, activity, social context, and lapse incidence. A series of group-level supervised machine learning algorithms (eg, Random Forest, XGBoost) were trained and tested. Their ability to classify lapses for out-of-sample (1) observations and (2) individuals were evaluated. Next, a series of individual-level and hybrid algorithms were trained and tested. Participants (N = 791) provided 37 002 data entries (7.6% lapses). The best-performing group-level algorithm had an area under the receiver operating characteristic curve (AUC) of 0.969 (95% confidence interval [CI] = 0.961 to 0.978). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUC = 0.482-1.000). Individual-level algorithms could be constructed for 39/791 participants with sufficient data, with a median AUC of 0.938 (range: 0.518-1.000). Hybrid algorithms could be constructed for 184/791 participants and had a median AUC of 0.825 (range: 0.375-1.000). Using unprompted app data appeared feasible for constructing a high-performing group-level lapse classification algorithm but its performance was variable when applied to unseen individuals. Algorithms trained on each individual's dataset, in addition to hybrid algorithms trained on the group plus a proportion of each individual's data, had improved performance but could only be constructed for a minority of participants. This study used routinely collected data from a popular smartphone app to train and test a series of supervised machine learning algorithms to distinguish lapse from non-lapse events. Although a high-performing group-level algorithm was developed, it had variable performance when applied to new, unseen individuals. Individual-level and hybrid algorithms had somewhat greater performance but could not be constructed for all participants because of the lack of variability in the outcome measure. Triangulation of results with those from a prompted study design is recommended prior to intervention development, with real-world lapse prediction likely requiring a balance between unprompted and prompted app data.

Testing the Outcomes of a Smoking Cessation Smartphone App for Nondaily Smokers: Protocol for a Proof-of-concept Randomized Controlled Trial

Nondaily smoking is a widespread, increasingly prevalent pattern of smoking, particularly in ethnic minority and vulnerable populations. To date, no effective treatment approach for this type of smokers has been identified. This study aims to use a randomized controlled trial to evaluate proof-of-concept markers of the Smiling instead of Smoking (SiS) app, a smoking cessation smartphone app designed specifically for nondaily smokers. This app was developed iteratively and is now in its third version. Previous studies have demonstrated acceptability and feasibility when participants were onboarded in person (study 1) and remotely (study 2) and showed within-person changes in line with hypothesized mechanisms of change. This is the first randomized test of this app. In total, 225 adult nondaily smokers will be asked to undertake a quit attempt while using smoking cessation support materials for a period of 7 weeks. Participants will be randomized to use the SiS smartphone app, the National Cancer Institute smartphone app QuitGuide, or the National Cancer Institute smoking cessation brochure "Clearing the Air." Participants will take part in a 15-minute scripted onboarding phone call during which study staff will introduce participants to their support materials. Survey links will be sent 2, 6, 12, and 24 weeks after the participants' initially chosen quit date. The primary outcome is self-efficacy to remain abstinent from smoking at treatment end, measured using the Smoking Self-Efficacy Questionnaire. Secondary outcomes cover several domains relevant to treatment development and implementation: treatment acceptability (eg, satisfaction with smoking cessation support, measured using the Client Satisfaction Questionnaire, and app usability, measured using the System Usability Scale); treatment feasibility (eg, measured using the number of days participants used the SiS or QuitGuide app during the prescribed treatment period); and, in an exploratory way, treatment efficacy assessed using self-reported 30-day point prevalence abstinence. Recruitment began in January 2021 and ended June 2022. The final 24-week follow-up was completed in January 2023. This trial is funded by the American Cancer Society. This study is designed to test whether the prescribed use of the SiS app results in greater self-efficacy to abstain from smoking in nondaily smokers than commonly recommended alternative treatments and whether the SiS app treatment is acceptable and feasible. Positive results will mean that the SiS app warrants testing in a large-scale randomized controlled trial to test its effectiveness in supporting smoking cessation in nondaily smokers. The design of this study also provides insights into issues pertinent to smoking cessation smartphone app treatment development and implementation by measuring, in a randomized design, markers of treatment satisfaction, engagement with the technology and content of the treatment, and adherence to the treatment plan. ClinicalTrials.gov NCT04672239; https://clinicaltrials.gov/ct2/show/NCT04672239. DERR1-10.2196/40867.

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

BackgroundLittle is known about how individuals engage over time with smartphone app interventions and whether this engagement predicts health outcomes.ObjectiveIn 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.MethodsFunctional 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.ResultsFor 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.ConclusionsPatterns 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.

Feature-Level Analysis of a Smoking Cessation Smartphone App Based on a Positive Psychology Approach: Prospective Observational Study.

BackgroundSmoking cessation smartphone apps have emerged as highly accessible tools to support smoking cessation efforts. It is unknown how specific app features contribute to user engagement over time and relate to smoking outcomes.ObjectiveTo provide a feature-level analysis of the Smiling Instead of Smoking app (version 2) and to link feature use to subsequent smoking cessation.MethodsNondaily smokers (N=100) used the app for a period of 49 days (1 week before quitting and 6 weeks after quitting). Participants self-reported 30-day point-prevalence abstinence at the end of this period and at a 6-month follow up (the survey response rate was 94% and 89% at these points, respectively). Self-reported 30-day point prevalence abstinence rates were 40% at the end of treatment and 56% at the 6-month follow up. The app engaged users in both positive psychology content and traditional behavioral smoking cessation content. The app sent push notifications to prompt participants to complete prescribed content (ie, a “happiness exercise” every day and a “behavioral challenge” to use the app’s smoking cessation tools on 15 out of 49 days). Actions that participants took within the app were timestamped and recorded.ResultsParticipants used the app on 24.7 (SD 13.8) days out of the 49 prescribed days, interacting with the happiness content on more days than the smoking content (23.8, SD 13.8 days vs 17.8, SD 10.3 days; t99=9.28 [2-tailed]; P<.001). The prescribed content was frequently completed (45% of happiness exercises; 57% of behavioral challenges) and ad libitum tools were used on ≤7 days. Most participants used each ad libitum smoking cessation tool at least once, with higher use of personalized content (≥92% used “strategies,” “cigarette log,” “smoke alarms,” and “personal reasons”) than purely didactic content (79% viewed “benefits of quitting smoking”). The number of days participants used the app significantly predicted 30-day point-prevalence abstinence at the end of treatment (odds ratio [OR] 1.05, 95% CI 1.02-1.09; P=.002) and at the 6-month follow up (OR 1.04, 95% CI 1.008-1.07; P=.01). The number of days participants engaged with the happiness content significantly predicted smoking abstinence at the end of treatment (OR 1.05, 95% CI 1.02-1.08; P=.002) and at the 6-month follow up (OR 1.04, 95% CI 1.007-1.07; P=.02). This effect was not significant for the number of days participants engaged with the smoking cessation content of the app, either at the end of treatment (OR 1.04, 95% CI 0.996-1.08, P=.08) or at the 6-month follow up (OR 1.02, 95% CI 0.98-1.06; P=.29).ConclusionsGreater app usage predicted greater odds of self-reported 30-day point-prevalence abstinence at both the end of treatment and over the long term, suggesting that the app had a therapeutic benefit. Positive psychology content and prescriptive clarity may promote sustained app engagement over time.Trial RegistrationClinicalTrials.gov NCT03951766; https://clinicaltrials.gov/ct2/show/NCT03951766

Exploring Users' Experiences With a Quick-Response Chatbot Within a Popular Smoking Cessation Smartphone App: Semistructured Interview Study.

BackgroundEngagement with smartphone apps for smoking cessation tends to be low. Chatbots (ie, software that enables conversations with users) offer a promising means of increasing engagement.ObjectiveWe aimed to explore smokers’ experiences with a quick-response chatbot (Quit Coach) implemented within a popular smoking cessation app and identify factors that influence users’ engagement with Quit Coach.MethodsIn-depth, one-to-one, semistructured qualitative interviews were conducted with adult, past-year smokers who had voluntarily used Quit Coach in a recent smoking cessation attempt (5/14, 36%) and current smokers who agreed to download and use Quit Coach for a minimum of 2 weeks to support a new cessation attempt (9/14, 64%). Verbal reports were audio recorded, transcribed verbatim, and analyzed within a constructivist theoretical framework using inductive thematic analysis.ResultsA total of 3 high-order themes were generated to capture users’ experiences and engagement with Quit Coach: anthropomorphism of and accountability to Quit Coach (ie, users ascribing human-like characteristics and thoughts to the chatbot, which helped foster a sense of accountability to it), Quit Coach’s interaction style and format (eg, positive and motivational tone of voice and quick and easy-to-complete check-ins), and users’ perceived need for support (ie, chatbot engagement was motivated by seeking distraction from cravings or support to maintain motivation to stay quit).ConclusionsAnthropomorphism of a quick-response chatbot implemented within a popular smoking cessation app appeared to be enabled by its interaction style and format and users’ perceived need for support, which may have given rise to feelings of accountability and increased engagement.

Can Digital Health Solutions Fill in the Gap for Effective Guideline Implementation in Cardiovascular Disease Prevention: Hope or Hype?

To evaluate the use of digital health solutions in improving the implementation of cardiovascular disease (CVD) prevention guidelines and review current evidence supporting it. Healthy diet guideline recommendations can be improved by text-messaging programs and apps for reinforcing healthy food intake and reducing unhealthy nutrients purchase and consumption. Wearable activity trackers are also effective in increasing physical activity levels. Text-messaging programs for smoking cessation have demonstrated benefits in increasing quitting rates; however, evidence on smartphone apps for smoking cessation is still lacking. Smartphone apps have the potential to improve medication adherence; however, better quality evidence is needed. Digital pills are another promising digital solution to improve medication adherence. The use of digital health solutions in CVD prevention is an evolving field and, to date, there is an increasing body of evidence that supports that such technologies are effective in improving CVD prevention guideline implementation.

Feasibility and Acceptability of a Cognitive Behavioral Therapy-Based Smartphone App for Smoking Cessation in China: A Single-Group Cohort Study

BackgroundPrevious research has suggested that mobile phone applications (apps) may potentially increase quit rates. The purpose of this single-group cohort study sought to examine the feasibility and acceptability of a novel smartphone-based smoking cessation app designed for smoking cessation in China: smoking quit rate.MethodsA total of 180 smokers from two cities of mainland China with willingness to make a quit attempt were invited to this smoking cessation app program, a cognitive behavioral theory (CBT)-based smoking cessation intervention via a smartphone app. Participants received 37- to 44-day intervention (including 7- to 14-day pre-quit preparation and 33-day intervention from quit date). Feasibility and acceptability of the program, and smoking status were assessed at baseline stage (initial installation), pre-quit stage, and post-quit stage (days 7, 15, and 33 after quit date).ResultsA total of 163 (90.6%) participants completed the study. Among them, 76–89% of the participants logged into the app ≥1 time per day across stages (at baseline, during pre-quit stage, and on days 7, 15, and 33 of post-quit stage); approximately 90% of the participants were satisfied with the app across stages. A significant rise in self-reported overall satisfaction with the app is observed from baseline (93% at Time 1) to the end of the program (98% at Time 2, 33 days after quit date) (p = 0.021). Participants who believed/agreed this app can help them to quit smoking significantly increased from 69% at baseline to 97% at day 33 after quit date (p < 0.001). Participants were satisfied with most (80–90%) of the features, especially the information feature. Intention-to-treat analysis showed that the percentage of 33-day self-reported continuous prevalence abstinence was 63.9%, and 7-day point prevalence abstinence rate was 81.7, 87.2, and 77.8% on days 7, 15, and 33 after quit date, respectively.ConclusionsThis study demonstrated the feasibility and acceptability of the smartphone app intervention for smoking cessation and introduced a new digital treatment model, which is expected to overcome barriers facing accessing traditional in-person smoking cessation services and extend nationwide smoking cessation services in China.

A Quality Review of Smart-Phone Applications for Smoking Cessation

Smartphone apps for smoking cessation can provide a useful tool in helping individuals stop smoking. The aim of this study was to analyze the content of smoking cessations apps that claim to provide help in quitting smoking. It assesses the degree to which apps adhere to established evidence-based practices for smoking cessation, with the hope of identifying the best quality ones. Another aim was to identify potential apps that smoking cessation advisors may wish to recommend to clients. A search for smoking cessation apps on the iPhone and Android stores was done. Apps meeting inclusion criteria were downloaded. Their content was rated via a checklist devised from previous systematic reviews looking at the effectiveness of smartphone apps for smoking cessation. A total of 76 apps were included. Of these, only 5 apps incorporated advise from health care professionals, with most apps being devised by individuals whose credentials are unknown. It is unclear if these individual have any specific training in the delivery of smoking cessation interventions. Smartphone apps for smoking cessation would benefit from incorporating a theoretical framework in line with established guidelines. The testing of best quality apps in a randomized controlled trial and an evaluation of any long-term outcomes may help shed light on their clinical utility and effectiveness.

Smartband-Based Automatic Smoking Detection and Real-time Mindfulness Intervention: Protocol for a Feasibility Trial.

BackgroundSmoking is the leading cause of preventable death in the United States. Smoking cessation interventions delivered by smartphone apps are a promising tool for helping smokers quit. However, currently available smartphone apps for smoking cessation have not exploited their unique potential advantages to aid quitting. Notably, few to no available apps use wearable technologies, most apps require users to self-report their smoking, and few to no apps deliver treatment automatically contingent upon smoking.ObjectiveThis pilot trial tests the feasibility of using a smartband and smartphone to monitor and detect smoking and deliver brief mindfulness interventions in real time to reduce smoking.MethodsDaily smokers (N=100, ≥5 cigarettes per day) wear a smartband for 60 days to monitor and detect smoking, notify them about their smoking events in real time, and deliver real-time brief mindfulness exercises triggered by detected smoking events or targeted at predicted smoking events. Smokers set a quit date at 30 days. A three-step intervention to reduce smoking is tested. First, participants wear a smartband to monitor and detect smoking, and notify them of smoking events in real time to bring awareness to smoking and triggers for 21 days. Next, a “mindful smoking” exercise is triggered by detected smoking events to bring a clear recognition of the actual effects of smoking for 7 days. Finally, after their quit date, a “RAIN” (recognize, allow, investigate, nonidentification) exercise is delivered to predicted smoking events (based on the initial 3 weeks of tracking smoking data) to help smokers learn to work mindfully with cravings rather than smoke for 30 days. The primary outcomes are feasibility measures of treatment fidelity, adherence, and acceptability. The secondary outcomes are smoking rates at end of treatment.ResultsRecruitment for this trial started in May 2021 and will continue until November 2021 or until enrollment is completed. Data monitoring and management are ongoing for enrolled participants. The final 60-day end of treatment data is anticipated in January 2022. We expect that all trial results will be available in April 2022.ConclusionsFindings will provide data and information on the feasibility of using a smartband and smartphone to monitor and detect smoking and deliver real-time brief mindfulness interventions, and whether the intervention warrants additional testing for smoking cessation.Trial RegistrationClinicalTrials.gov NCT03995225; https://clinicaltrials.gov/ct2/show/NCT03995225International Registered Report Identifier (IRRID)DERR1-10.2196/32521

Mechanisms of Smartphone Apps for Cigarette Smoking Cessation: Results of a Serial Mediation Model From the iCanQuit Randomized Trial.

BackgroundEngagement with digital interventions is a well-known predictor of treatment outcomes, but this knowledge has had limited actionable value. Instead, learning why engagement with digital interventions impact treatment outcomes can lead to targeted improvements in their efficacy.ObjectiveThis study aimed to test a serial mediation model of an Acceptance and Commitment Therapy (ACT) smartphone intervention for smoking cessation.MethodsIn this randomized controlled trial, participants (N=2415) from 50 US states were assigned to the ACT-based smartphone intervention (iCanQuit) or comparison smartphone intervention (QuitGuide). Their engagement with the apps (primary measure: number of logins) was measured during the first 3 months, ACT processes were measured at baseline and 3 months (acceptance of internal cues to smoke, valued living), and smoking cessation was measured at 12 months with 87% follow-up retention.ResultsThere was a significant serial mediation effect of iCanQuit on smoking cessation through multiple indicators of intervention engagement (ie, total number of logins, total number of minutes used, and total number of unique days of use) and in turn through increases in mean acceptance of internal cues to smoke from baseline to 3 months. Analyses of the acceptance subscales showed that the mediation was through acceptance of physical sensations and emotions, but not acceptance of thoughts. There was no evidence that the effect of the iCanQuit intervention was mediated through changes in valued living.ConclusionsIn this first study of serial mediators underlying the efficacy of smartphone apps for smoking cessation, our results suggest the effect of the iCanQuit ACT-based smartphone app on smoking cessation was mediated through multiple indicators of engagement and in turn through increases in the acceptance of physical sensations and emotions that cue smoking.Trial RegistrationClinical Trials.gov NCT02724462; https://clinicaltrials.gov/ct2/show/NCT02724462

Smoking Cessation Apps: A Systematic Review of Format, Outcomes, and Features.

Smoking cessation interventions are effective, but they are not easily accessible for all treatment-seeking smokers. Mobile health (mHealth) apps have been used in recent years to overcome some of these limitations. Smoking cessation apps can be used in combination with a face-to-face intervention (FFSC-Apps), or alone as general apps (GSC-Apps). The aims of this review were (1) to examine the effects of FFSC-Apps and GSC-Apps on abstinence, tobacco use, and relapse rates; and (2) to describe their features. A systematic review was conducted following the internationally Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Of the total 6016 studies screened, 24 were included, of which nine used GSC-Apps and 15 FFSC-Apps. Eight studies reported significant differences between conditions in smoking cessation outcomes, with three of them being in favor of the use of apps, and two between different point-assessments. Concerning Apps features, most GSC-Apps included self-tracking and setting a quit plan, whereas most of the FFSC-Apps included self-tracking and carbon monoxide (CO) measures. Smartphone apps for smoking cessation could be promising tools. However, more research with an adequate methodological quality is needed to determine its effect. Nevertheless, smartphone apps’ high availability and attractiveness represent a great opportunity to reach large populations.