Does the addition of a supportive chatbot promote user engagement with a smoking cessation app? An experimental study.
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ABSTRACT: Objective
The objective of this study was to assess whether a version of the Smoke Free app with a supportive chatbot powered by artificial intelligence (versus a version without the chatbot) led to increased engagement and short-term quit success.Methods
Daily or non-daily smokers aged ?18 years who purchased the 'pro' version of the app and set a quit date were randomly assigned (unequal allocation) to receive the app with or without the chatbot. The outcomes were engagement (i.e. total number of logins over the study period) and self-reported abstinence at a one-month follow-up. Unadjusted and adjusted negative binomial and logistic regression models were fitted to estimate incidence rate ratios (IRRs) and odds ratios (ORs) for the associations of interest.Results
A total of 57,214 smokers were included (intervention: 9.3% (5339); control: 90.7% (51,875). The app with the chatbot compared with the standard version led to a 101% increase in engagement (IRRadj?=?2.01, 95% confidence interval (CI)?=?1.92-2.11, p?vs. 3,704/51,875, ORadj?=?2.38, 95% CI?=?2.19-2.58, p?vs. 3,704/5,050, ORadj?=?1.36, 95% CI?=?1.16-1.61, p?ConclusionThe addition of a supportive chatbot to a popular smoking cessation app more than doubled user engagement. In view of very low follow-up rates, there is low quality evidence that the addition also increased self-reported smoking cessation.
SUBMITTER: Perski O
PROVIDER: S-EPMC6775545 | biostudies-literature | 2019 Jan-Dec
REPOSITORIES: biostudies-literature
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Does the addition of a supportive chatbot promote user engagement with a smoking cessation app? An experimental study.
Perski Olga O Crane David D Beard Emma E Brown Jamie J
Digital health 20190101
<h4>Objective</h4>The objective of this study was to assess whether a version of the Smoke Free app with a supportive chatbot powered by artificial intelligence (versus a version without the chatbot) led to increased engagement and short-term quit success.<h4>Methods</h4>Daily or non-daily smokers aged ≥18 years who purchased the 'pro' version of the app and set a quit date were randomly assigned (unequal allocation) to receive the app with or without the chatbot. The outcomes were engagement (i ...[more]
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Project description: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.
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Project description:IntroductionUsing 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.MethodsParticipants 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.ResultsThree 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).DiscussionWebQuit 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.ImplicationsThis 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.
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Project description:IntroductionMedication sampling is a clinically useful tool to engage smokers in the quitting process. Whether varenicline is suitable for sampling purposes is unclear. The purpose of this study was to examine the feasibility, uptake, and preliminary outcomes of varenicline sampling.MethodsSmokers (N = 99), both motivated to quit and not, were recruited and randomized to varenicline sampling versus not, with 12 week follow-up. The intervention consisted of mailing one-time samples of varenicline (lasting 2-4 wks), with minimally suggestive guidance on use.ResultsUptake of varenicline was strong, at 2 weeks (54% any use, 66% daily use) and 4 weeks (38%, 46%), with 58% of medication users seeking additional medication. Most users followed conventional titration patterns, self-titrating from 0.5 mg to 2 mg. Relative to control, varenicline sampling increased motivation (p = 0.006) and confidence to quit (p = 0.02), and decreased cigarette smoking (p = 0.02). Smokers receiving varenicline samples were significantly more likely to achieve 50% reduction in cigarettes per day (CPD), both immediately following the sampling exercise (Adjusted Odds Ratio [AOR] = 4.12; 95% CI: 1.39 to 12.17) and at final follow-up (AOR = 4.50; 95% CI: 1.56 to 13.01). Though cessation outcomes were not statistically significant, there was a 1.5 to 3-fold increase in quit attempts and abstinence from varenicline sampling throughout follow-up. These outcomes were comparable among smokers motivated to quit and not.ConclusionsUnguided, user-driven sampling of varenicline sampling is a concrete behavioral exercise that is feasible to do and seems to suggest clinical utility. Sampling is a pragmatic clinical approach to engage more smokers in quitting.ImplicationsUse of evidence-based pharmacotherapies for smoking cessation is low. Medication sampling is a pragmatic behavioral exercise that allows smokers to experience the benefits of using them, while promoting positive downstream effects towards quitting. While previous studies have shown that nicotine replacement therapy (NRT) sampling is viable and effective, whether this extends to varenicline is unclear. Results from this trial demonstrate that varenicline sampling is feasible, safe, and suggestive of clinically important steps toward quitting, deserving of a larger trial.Clinical trial registrationNCT #03742154.
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Project description:The use of mobile health applications (apps) especially in the area of lifestyle behaviors has increased, thus providing unprecedented opportunities to develop health programs that can engage people in real-time and in the real-world. Yet, relatively little is known about which factors relate to the engagement of commercially available apps for health behaviors. This exploratory study examined behavioral engagement with a weight loss app, Lose It! and characterized higher versus lower engaged groups. Cross-sectional, anonymized data from Lose It! were analyzed (n = 12,427,196). This dataset was randomly split into 24 subsamples and three were used for this study (total n = 1,011,008). Classification and regression tree methods were used to identify subgroups of user engagement with one subsample, and descriptive analyses were conducted to examine other group characteristics associated with engagement. Data mining validation methods were conducted with two separate subsamples. On average, users engaged with the app for 29 days. Six unique subgroups were identified, and engagement for each subgroup varied, ranging from 3.5 to 172 days. Highly engaged subgroups were primarily distinguished by the customization of diet and exercise. Those less engaged were distinguished by weigh-ins and the customization of diet. Results were replicated in further analyses. Commercially-developed apps can reach large segments of the population, and data from these apps can provide insights into important app features that may aid in user engagement. Getting users to engage with a mobile health app is critical to the success of apps and interventions that are focused on health behavior change.
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Project description:BackgroundMotivational messaging is a frequently used digital intervention to promote positive health behavior changes, including smoking cessation. Typically, motivational messaging systems have not actively sought feedback on each message, preventing a closer examination of the user-system engagement. This study assessed the granular user-system engagement around a recommender system (a new system that actively sought user feedback on each message to improve message selection) for promoting smoking cessation and the impact of engagement on cessation outcome.MethodsWe prospectively followed a cohort of current smokers enrolled to use the recommender system for 6 months. The system sent participants motivational messages to support smoking cessation every 3 days and used machine learning to incorporate user feedback (i.e., user's rating on the perceived influence of each message, collected on a 5-point Likert scale with 1 indicating strong disagreement and 5 indicating strong agreement on perceiving the influence on quitting smoking) to improve the selection of the following message. We assessed user-system engagement by various metrics, including user response rate (i.e., the percent of times a user rated the messages) and the perceived influence of messages. We compared retention rates across different levels of user-system engagement and assessed the association between engagement and the 7-day point prevalence abstinence (missing outcome = smoking) by using multiple logistic regression.ResultsWe analyzed data from 731 participants (13% Black; 73% women). The user response rate was 0.24 (SD = 0.34) and user-perceived influence was 3.76 (SD = 0.84). The retention rate positively increased with the user response rate (trend test P < 0.001). Compared with non-response, six-month cessation increased with the levels of response rates: low response rate (odds ratio [OR] = 1.86, 95% confidence interval [CI]: 1.07-3.23), moderate response rate (OR = 2.30, 95% CI: 1.36-3.88), high response rate (OR = 2.69, 95% CI: 1.58-4.58). The association between perceived message influence and the outcome showed a similar pattern.ConclusionsHigh user-system engagement was positively associated with both high retention rate and smoking cessation, suggesting that investigation of methods to increase engagement may be crucial to increase the impact of the recommender system for smoking cessation.Trial registrationRegistration Identifier: NCT03224520 . Registration date: July 21, 2017.
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Project description:Tobacco smoking is the leading cause of preventable death in the United States, and the annual economic burden attributable to smoking exceeds US $300 billion. Obstacles to smoking cessation include limited access and adherence to effective cessation interventions. Technology can help overcome these obstacles; many smartphone apps have been developed to aid smoking cessation, but few that conform to the US clinical practice guideline (USCPG) have been rigorously tested and reported in the literature. Clickotine is a novel smartphone app for smoking cessation, designed to deliver the essential features of the USCPG and engineered to engage smokers by personalizing intervention components.Our objective was to assess the engagement, efficacy, and safety of Clickotine in an initial, single-arm study. Outcomes measured were indicators of engagement with the smartphone app (number of app opens, number of interactions with the Clickotine program, and weeks active with Clickotine), cessation outcomes of 7- and 30-day self-reported abstinence from smoking, and negative health events.We recruited US residents between 18 and 65 years of age who owned an iPhone and smoked 5 or more cigarettes daily for the study via online advertising. Respondents were prescreened for eligibility by telephone and, if appropriate, directed to a Web portal to provide informed consent, confirm eligibility, and download the Clickotine app. Participants completed study assessments via the online portal at baseline and after 8 weeks. Data were collected in Amazon S3 with no manual data entry, and access to all data was maximally restrictive, logged, and auditable.A total of 416 participants downloaded the app and constituted the intention-to-treat (ITT) sample. On average, participants opened the Clickotine app 100.6 times during the 8-week study (median 69), logged 214.4 interactions with the Clickotine program (median 178), and remained engaged with Clickotine for 5.3 weeks (median 5). Among the ITT sample, 45.2% (188/416) reported 7-day abstinence and 26.2% (109/416) reported 30-day abstinence from smoking after 8 weeks. Completer analysis focused on 365 (87.7%) of the 416 enrolled participants who completed the 8-week questionnaire revealed that 51.5% (188/365) of completers reported 7-day abstinence and 29.9% (109/365) reported 30-day abstinence. Few adverse events, mostly consistent with nicotine withdrawal symptoms, were reported and overall no safety signal was detected.In this initial single-arm trial, Clickotine users appeared to demonstrate encouraging indicators of engagement in terms of the number of app opens, number of program interactions, and continued engagement over time. Clickotine users reported encouraging quit rates while reporting few adverse events. Future research is warranted to assess Clickotine's efficacy in a randomized controlled trial.Clinicaltrials.gov NCT02656745; https://clinicaltrials.gov/ct2/show/NCT02656745 (Archived by WebCite at http://www.webcitation.org/6peTT4x60).
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Project description:People living with HIV (PLWH) are more likely to smoke compared to HIV-uninfected counterparts, but little is known about smoking behaviors in sub-Saharan Africa. To address this gap in knowledge, we characterized smoking cessation patterns among people living with HIV (PLWH) compared to HIV-uninfected individuals in rural Uganda. PLWH were at least 40 years of age and on antiretroviral therapy for at least three years, and HIV-uninfected individuals were recruited from the clinical catchment area. Our primary outcome of interest was smoking cessation, which was assessed using an adapted WHO STEPS smoking questionnaire. We fit Cox proportional hazards models to compare time to smoking cessation between PLWH pre-care, PLWH in care, and HIV-uninfected individuals. We found that, compared to HIV-uninfected individuals, PLWH in care were less likely to have ever smoked (40% vs. 49%, p?=?0.04). The combined sample of 267 ever-smokers had a median age of 56 (IQR 49-68), 56% (n?=?150) were male, and 26% (n?=?70) were current smokers. In time-to-event analyses, HIV-uninfected individuals and PLWH prior to clinic enrollment ceased smoking at similar rates (HR 0.8, 95% CI 0.5-1.2). However, after enrolling in HIV care, PLWH had a hazard of smoking cessation over twice that of HIV-uninfected individuals and three times that of PLWH prior to enrollment (HR 2.4, 95% CI 1.3-4.6, p?=?0.005 and HR 3.0, 95% CI 1.6-5.5, p?=?0.001, respectively). In summary, we observed high rates of smoking cessation among PLWH after engagement in HIV care in rural Uganda. While we hypothesize that greater access to primary care services and health counseling might contribute, future studies should better investigate the mechanism of this association.
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