How do smokers use a smoking cessation text messaging intervention?

Engagement With a Mobile Phone-Based Life Skills Intervention for Adolescents and Its Association With Participant Characteristics and Outcomes: Tree-Based Analysis

Background

Mobile phone–delivered life skills programs are an emerging and promising way to promote mental health and prevent substance use among adolescents, but little is known about how adolescents actually use them.

Objective

The aim of this study is to determine engagement with a mobile phone–based life skills program and its different components, as well as the associations of engagement with adolescent characteristics and intended substance use and mental health outcomes.

Methods

We performed secondary data analysis on data from the intervention group (n=750) from a study that compared a mobile phone–based life skills intervention for adolescents recruited in secondary and upper secondary school classes with an assessment-only control group. Throughout the 6-month intervention, participants received 1 SMS text message prompt per week that introduced a life skills topic or encouraged participation in a quiz or individual life skills training or stimulated sharing messages with other program participants through a friendly contest. Decision trees were used to identify predictors of engagement (use and subjective experience). The stability of these decision trees was assessed using a resampling method and by graphical representation. Finally, associations between engagement and intended substance use and mental health outcomes were examined using logistic and linear regression analyses.

Results

The adolescents took part in half of the 50 interactions (mean 23.6, SD 15.9) prompted by the program, with SMS text messages being the most used and contests being the least used components. Adolescents who did not drink in a problematic manner and attended an upper secondary school were the ones to use the program the most. Regarding associations between engagement and intended outcomes, adolescents who used the contests more frequently were more likely to be nonsmokers at follow-up than those who did not (odds ratio 0.86, 95% CI 0.76-0.98; P=.02). In addition, adolescents who read the SMS text messages more attentively were less likely to drink in a problematic manner at follow-up (odds ratio 0.43, 95% CI 1.29-3.41; P=.003). Finally, participants who used the program the most and least were more likely to increase their well-being from baseline to 6-month follow-up compared with those with average engagement (βs=.39; t₅₈₆=2.66; P=.008; R²=0.24).

Conclusions

Most of the adolescents participating in a digital life skills program that aimed to prevent substance use and promote mental health engaged with the intervention. However, measures to increase engagement in problem drinkers should be considered. Furthermore, efforts must be made to ensure that interventions are engaging and powerful across different educational levels. First results indicate that higher engagement with digital life skills programs could be associated with intended outcomes. Future studies should apply further measures to improve the reach of lower-engaged participants at follow-up to establish such associations with certainty.

Associations Between Engagement and Outcomes in the SmokefreeTXT Program: A Growth Mixture Modeling Analysis

INTRODUCTION

Smoking continues to be a leading cause of preventable death. Mobile health (mHealth) can extend the reach of smoking cessation programs; however, user dropout, especially in real-world implementations of these programs, limit their potential effectiveness. Research is needed to understand patterns of engagement in mHealth cessation programs.

METHODS

SmokefreeTXT (SFTXT) is the National Cancer Institute's 6-8 week smoking cessation text-messaging intervention. Latent growth mixture modeling was used to identify unique classes of engagement among SFTXT users using real-world program data from 7090 SFTXT users. Survival analysis was conducted to model program dropout over time by class, and multilevel modeling was used to explore differences in abstinence over time.

RESULTS

We identified four unique patterns of engagement groups. The largest percentage of users (61.6%) were in the low-engagers declining group; these users started off with low level of engagement and their engagement decreased over time. Users in this group were more likely to drop out from the program and less likely to be abstinent than users in the other groups. Users in the high engagers-maintaining group (ie, the smallest but most engaged group) were less likely to be daily smokers at baseline and were slightly older than those in the other groups. They were most likely to complete the program and report being abstinent.

CONCLUSIONS

Our findings show the importance of maintaining active engagement in text-based cessation programs. Future research is needed to elucidate predictors of the various levels of engagement, and to assess whether strategies aimed at increasing engagement result in higher abstinence rates.

IMPLICATIONS

The current study enabled us to investigate differing engagement patterns in non-incentivized program participants, which can help inform program modifications in real-world settings. Lack of engagement and dropout continue to impede the potential effectiveness of mHealth interventions, and understanding patterns and predictors of engagement can enhance the impact of these programs.

Mobile phone text messaging and app-based interventions for smoking cessation

BACKGROUND

Mobile phone-based smoking cessation support (mCessation) offers the opportunity to provide behavioural support to those who cannot or do not want face-to-face support. In addition, mCessation can be automated and therefore provided affordably even in resource-poor settings. This is an update of a Cochrane Review first published in 2006, and previously updated in 2009 and 2012.

OBJECTIVES

To determine whether mobile phone-based smoking cessation interventions increase smoking cessation rates in people who smoke.

SEARCH METHODS

For this update, we searched the Cochrane Tobacco Addiction Group's Specialised Register, along with clinicaltrials.gov and the ICTRP. The date of the most recent searches was 29 October 2018.

SELECTION CRITERIA

Participants were smokers of any age. Eligible interventions were those testing any type of predominantly mobile phone-based programme (such as text messages (or smartphone app) for smoking cessation. We included randomised controlled trials with smoking cessation outcomes reported at at least six-month follow-up.

DATA COLLECTION AND ANALYSIS

We used standard methodological procedures described in the Cochrane Handbook for Systematic Reviews of Interventions. We performed both study eligibility checks and data extraction in duplicate. We performed meta-analyses of the most stringent measures of abstinence at six months' follow-up or longer, using a Mantel-Haenszel random-effects method, pooling studies with similar interventions and similar comparators to calculate risk ratios (RR) and their corresponding 95% confidence intervals (CI). We conducted analyses including all randomised (with dropouts counted as still smoking) and complete cases only.

MAIN RESULTS

This review includes 26 studies (33,849 participants). Overall, we judged 13 studies to be at low risk of bias, three at high risk, and the remainder at unclear risk. Settings and recruitment procedures varied across studies, but most studies were conducted in high-income countries. There was moderate-certainty evidence, limited by inconsistency, that automated text messaging interventions were more effective than minimal smoking cessation support (RR 1.54, 95% CI 1.19 to 2.00; I² = 71%; 13 studies, 14,133 participants). There was also moderate-certainty evidence, limited by imprecision, that text messaging added to other smoking cessation interventions was more effective than the other smoking cessation interventions alone (RR 1.59, 95% CI 1.09 to 2.33; I² = 0%, 4 studies, 997 participants). Two studies comparing text messaging with other smoking cessation interventions, and three studies comparing high- and low-intensity messaging, did not show significant differences between groups (RR 0.92 95% CI 0.61 to 1.40; I² = 27%; 2 studies, 2238 participants; and RR 1.00, 95% CI 0.95 to 1.06; I² = 0%, 3 studies, 12,985 participants, respectively) but confidence intervals were wide in the former comparison. Five studies compared a smoking cessation smartphone app with lower-intensity smoking cessation support (either a lower-intensity app or non-app minimal support). We pooled the evidence and deemed it to be of very low certainty due to inconsistency and serious imprecision. It provided no evidence that smartphone apps improved the likelihood of smoking cessation (RR 1.00, 95% CI 0.66 to 1.52; I² = 59%; 5 studies, 3079 participants). Other smartphone apps tested differed from the apps included in the analysis, as two used contingency management and one combined text messaging with an app, and so we did not pool them. Using complete case data as opposed to using data from all participants randomised did not substantially alter the findings.

AUTHORS' CONCLUSIONS

There is moderate-certainty evidence that automated text message-based smoking cessation interventions result in greater quit rates than minimal smoking cessation support. There is moderate-certainty evidence of the benefit of text messaging interventions in addition to other smoking cessation support in comparison with that smoking cessation support alone. The evidence comparing smartphone apps with less intensive support was of very low certainty, and more randomised controlled trials are needed to test these interventions.

The Time-Varying Relations Between Risk Factors and Smoking Before and After a Quit Attempt

Introduction

Intensive longitudinal data (ILD) collected with ecological momentary assessments (EMAs) can provide a rich resource for understanding the relations between risk factors and smoking in the time surrounding a cessation attempt.

Methods

Participants (N = 142) were smokers seeking treatment at a safety-net hospital smoking cessation clinic who were randomly assigned to receive standard clinic care (ie, counseling and cessation medications) or standard care plus small financial incentives for biochemically confirmed smoking abstinence. Participants completed EMAs via study provided smartphones several times per day for 14 days (1 week prequit through 1 week postquit). EMAs assessed current contextual factors including environmental (eg, easy access to cigarettes, being around others smoking), cognitive (eg, urge to smoke, stress, coping expectancies, cessation motivation, cessation self-efficacy, restlessness), behavioral (ie, recent smoking and alcohol consumption), and affective variables. Temporal relations between risk factors and smoking were assessed using a logistic time-varying effect model.

Results

Participants were primarily female (57.8%) and Black (71.8%), with an annual household income of <$20000 per year (71.8%), who smoked 17.6 cigarettes per day (SD = 8.8). Individuals assigned to the financial incentives group had decreased odds of smoking compared with those assigned to usual care beginning 3 days before the quit attempt and continuing throughout the first week postquit. Environmental, cognitive, affective, and behavioral variables had complex time-varying impacts on smoking before and after the scheduled quit attempt.

Conclusions

Knowledge of time-varying effects may facilitate the development of interventions that target specific psychosocial and behavioral variables at critical moments in the weeks surrounding a quit attempt.

Implications

Previous research has examined time-varying relations between smoking and negative affect, urge to smoke, smoking dependence, and certain smoking cessation therapies. We extend this work using ILD of unexplored variables in a socioeconomically disadvantaged sample of smokers seeking cessation treatment. These findings could be used to inform ecological momentary interventions that deliver treatment resources (eg, video- or text-based content) to individuals based upon critical variables surrounding their attempt.

Uptake of Tailored Text Message Smoking Cessation Support in Pregnancy When Advertised on the Internet (MiQuit): Observational Study

BACKGROUND

Smoking in pregnancy is a major public health concern. Pregnant smokers are particularly difficult to reach, with low uptake of support options and few effective interventions. Text message-based self-help is a promising, low-cost intervention for this population, but its real-world uptake is largely unknown.

OBJECTIVE

The objective of this study was to explore the uptake and cost-effectiveness of a tailored, theory-guided, text message intervention for pregnant smokers ("MiQuit") when advertised on the internet.

METHODS

Links to a website providing MiQuit initiation information (texting a short code) were advertised on a cost-per-click basis on 2 websites (Google Search and Facebook; £1000 budget each) and free of charge within smoking-in-pregnancy webpages on 2 noncommercial websites (National Childbirth Trust and NHS Choices). Daily budgets were capped to allow the Google and Facebook adverts to run for 1 and 3 months, respectively. We recorded the number of times adverts were shown and clicked on, the number of MiQuit initiations, the characteristics of those initiating MiQuit, and whether support was discontinued prematurely. For the commercial adverts, we calculated the cost per initiation and, using quit rates obtained from an earlier clinical trial, estimated the cost per additional quitter.

RESULTS

With equal capped budgets, there were 812 and 1889 advert clicks to the MiQuit website from Google (search-based) and Facebook (banner) adverts, respectively. MiQuit was initiated by 5.2% (42/812) of those clicking via Google (95% CI 3.9%-6.9%) and 2.22% (42/1889) of those clicking via Facebook (95% CI 1.65%-2.99%). Adverts on noncommercial webpages generated 53 clicks over 6 months, with 9 initiations (9/53, 17%; 95% CI 9%-30%). For the commercial websites combined, mean cost per initiation was £24.73; estimated cost per additional quitter, including text delivery costs, was £735.86 (95% CI £227.66-£5223.93). Those initiating MiQuit via Google were typically very early in pregnancy (median gestation 5 weeks, interquartile range 10 weeks); those initiating via Facebook were distributed more evenly across pregnancy (median gestation 16 weeks, interquartile range 14 weeks).

CONCLUSIONS

Commercial online adverts are a feasible, likely cost-effective method for engaging pregnant smokers in digital cessation support and may generate uptake at a faster rate than noncommercial websites. As a strategy for implementing MiQuit, online advertising has large reach potential and can offer support to a hard-to-reach population of smokers.

Engagement Within a Mobile Phone-Based Smoking Cessation Intervention for Adolescents and its Association With Participant Characteristics and Outcomes

BACKGROUND

Although mobile phone-delivered smoking cessation programs are a promising way to promote smoking cessation among adolescents, little is known about how adolescents might actually use them.

OBJECTIVE

The aim of this study was to determine adolescents' trajectories of engagement with a mobile phone-delivered smoking cessation program over time and the associations these trajectories have with baseline characteristics and treatment outcomes.

METHODS

We performed secondary data analysis on a dataset from a study that compared a mobile phone-delivered integrated smoking cessation and alcohol intervention with a smoking cessation only intervention for adolescents recruited in vocational and upper secondary school classes (N=1418). Throughout the 3-month intervention, participants in both intervention groups received one text message prompt per week that either assessed smoking-related target behaviors or encouraged participation in a quiz or a message contest. Sequence analyses were performed to identify engagement trajectories. Analyses were conducted to identify predictors of engagement trajectory and associations between engagement trajectories and treatment outcomes.

RESULTS

Three engagement trajectories emerged: (1) stable engagement (646/1418, 45.56%), (2) decreasing engagement (501/1418, 35.33%), and (3) stable nonengagement (271/1418, 19.11%). Adolescents who were younger, had no immigrant background, perceived more benefits of quitting smoking, and reported binge drinking preceding the baseline assessment were more likely to exhibit stable engagement. Due to different reach of more engaged and less engaged participants at follow-up, three statistical models (complete-cases, last-observation-carried-forward, and multiple imputation) for the associations of engagement trajectory and smoking outcome were tested. For 7-point smoking abstinence, no association was revealed to be statistically significant over all three models. However, decreasing engagement with the program was associated over all three models, with greater reductions in daily tobacco use than nonengagement.

CONCLUSIONS

The majority of tobacco-smoking adolescents engaged extensively with a mobile phone-based smoking cessation program. However, not only stable engagement but also decreasing engagement with a program might be an indicator of behavioral change. Measures to avoid nonengagement among adolescents appear especially necessary for older smokers with an immigrant background who do not drink excessively. In addition, future studies should not only examine the use of specific program components but also users' engagement trajectories to better understand the mechanisms behind behavioral change.

An Ecological Momentary Intervention for Smoking Cessation: Evaluation of Feasibility and Effectiveness

BACKGROUND

Despite substantial public health progress in reducing the prevalence of smoking in the United States overall, smoking among socioeconomically disadvantaged adults remains high.

OBJECTIVE

To determine the feasibility and preliminary effectiveness of a novel smartphone-based smoking cessation app designed for socioeconomically disadvantaged smokers.

METHODS

Participants were recruited from a safety-net hospital smoking cessation clinic in Dallas, Texas, and were followed for 13 weeks. All participants received standard smoking cessation clinic care (ie, group counseling and cessation pharmacotherapy) and a smartphone with a novel smoking cessation app (ie, Smart-T). The Smart-T app prompted 5 daily ecological momentary assessments (EMAs) for 3 weeks (ie, 1 week before cessation and 2 weeks after cessation). During the precessation period, EMAs were followed by messages that focused on planning and preparing for the quit attempt. During the postcessation period, participant responses to EMAs drove an algorithm that tailored messages to the current level of smoking lapse risk and currently present lapse triggers (eg, urge to smoke, stress). Smart-T offered additional intervention features on demand (eg, one-click access to the tobacco cessation quitline; "Quit Tips" on coping with urges to smoke, mood, and stress).

RESULTS

Participants (N=59) were 52.0 (SD 7.0) years old, 54% (32/59) female, and 53% (31/59) African American, and 70% (40/57) had annual household income less than US $16,000. Participants smoked 20.3 (SD 11.6) cigarettes per day and had been smoking for 31.6 (SD 10.9) years. Twelve weeks after the scheduled quit date, 20% (12/59) of all participants were biochemically confirmed abstinent. Participants responded to 87% of all prompted EMAs and received approximately 102 treatment messages over the 3-week EMA period. Most participants (83%, 49/59) used the on-demand app features. Individuals with greater nicotine dependence and minority race used the Quit Tips feature more than their counterparts. Greater use of the Quit Tips feature was linked to nonabstinence at the 2 (P=.02), 4 (P<.01), and 12 (P=.03) week follow-up visits. Most participants reported that they actually used or implemented the tailored app-generated messages and suggestions (83%, 49/59); the app-generated messages were helpful (97%, 57/59); they would like to use the app in the future if they were to lapse (97%, 57/59); and they would like to refer friends who smoke to use the Smart-T app (85%, 50/59). A minority of participants (15%, 9/59) reported that the number of daily assessments (ie, 5) was "too high."

CONCLUSIONS

This novel just-in-time adaptive intervention delivered an intensive intervention (ie, 102 messages over a 3-week period), was well-liked, and was perceived as helpful and useful by socioeconomically disadvantaged adults who were seeking smoking cessation treatment. Smartphone apps may be used to increase treatment exposure and may ultimately reduce tobacco-related health disparities among socioeconomically disadvantaged adults.

Using Intensive Longitudinal Data Collected via Mobile Phone to Detect Imminent Lapse in Smokers Undergoing a Scheduled Quit Attempt

Background

Mobile phone‒based real-time ecological momentary assessments (EMAs) have been used to record health risk behaviors, and antecedents to those behaviors, as they occur in near real time.

Objective

The objective of this study was to determine if intensive longitudinal data, collected via mobile phone, could be used to identify imminent risk for smoking lapse among socioeconomically disadvantaged smokers seeking smoking cessation treatment.

Methods

Participants were recruited into a randomized controlled smoking cessation trial at an urban safety-net hospital tobacco cessation clinic. All participants completed in-person EMAs on mobile phones provided by the study. The presence of six commonly cited lapse risk variables (ie, urge to smoke, stress, recent alcohol consumption, interaction with someone smoking, cessation motivation, and cigarette availability) collected during 2152 prompted or self-initiated postcessation EMAs was examined to determine whether the number of lapse risk factors was greater when lapse was imminent (ie, within 4 hours) than when lapse was not imminent. Various strategies were used to weight variables in efforts to improve the predictive utility of the lapse risk estimator.

Results

Participants (N=92) were mostly female (52/92, 57%), minority (65/92, 71%), 51.9 (SD 7.4) years old, and smoked 18.0 (SD 8.5) cigarettes per day. EMA data indicated significantly higher urges (P=.01), stress (P=.002), alcohol consumption (P<.001), interaction with someone smoking (P<.001), and lower cessation motivation (P=.03) within 4 hours of the first lapse compared with EMAs collected when lapse was not imminent. Further, the total number of lapse risk factors present within 4 hours of lapse (mean 2.43, SD 1.37) was significantly higher than the number of lapse risk factors present during periods when lapse was not imminent (mean 1.35, SD 1.04), P<.001. Overall, 62% (32/52) of all participants who lapsed completed at least one EMA wherein they reported ≥3 lapse risk factors within 4 hours of their first lapse. Differentially weighting lapse risk variables resulted in an improved risk estimator (weighted area=0.76 vs unweighted area=0.72, P<.004). Specifically, 80% (42/52) of all participants who lapsed had at least one EMA with a lapse risk score above the cut-off within 4 hours of their first lapse.

Conclusions

Real-time estimation of smoking lapse risk is feasible and may pave the way for development of mobile phone‒based smoking cessation treatments that automatically tailor treatment content in real time based on presence of specific lapse triggers. Interventions that identify risk for lapse and automatically deliver tailored messages or other treatment components in real time could offer effective, low cost, and highly disseminable treatments to individuals who do not have access to other more standard cessation treatments.

Does Short Message Service Increase Adherence to Smoking Cessation Clinic Appointments and Quitting Smoking?

BACKGROUND

Using innovative and scientific methods increases the rate of quitting in smokers. Short message service (SMS) is a communication tool widely used and well integrated in many people's daily lives. To increase adherence to appointments in smoking cessation clinics (SCC), it is thought that increased compliance could be achieved by falling outside the traditional methods. SMS has been shown to increase the compliance of patients with SCC appointments.

AIMS

In this study, we aimed to evaluate the effect of SMS in the compliance of patients with SCC follow-up visits and smoking cessation success.

STUDY DESIGN

Case-control study.

METHODS

Our study was a controlled, open, prospective study. We enrolled 436 cases applied to SCC of Yedikule Training and Research Hospital between 01.10.2013-30.06.2014 and agreed to follow-up with SMS. SMS was sent to the patients to remind them of appointments at the SCC and to query their smoking state.

RESULTS

Two hundred-and-eighty seven (65.8%) of the patients were male and 149 (34.2%) were female. The mean age was 45±12 years. In this study, 296 (67.9%) patients had graduated from primary school. Our patients' smoking state was queried by telephone at the 6-month follow-up and we contacted 348 patients. According to this, 88 (25.3%) patients were not smoking, and 260 (74.7%) patients were smokers. Therefore, the smoking cessation rate was 24% (n=60) in patients who did not respond to SMS reminders at all, and 28.6% (n=28) in patients answering any SMS at least once (p=0.377). Smoking cessation rate of the patients invited by SMS but who did not attend any control visits was 19.1%, and it was 34.5% in patients coming to a control visit at least once. This difference was statistically significant (p=0.001).

CONCLUSION

In our study, there was increased success of smoking cessation in patients coming to control visits. We think that this may result from the possibly increased compliance to SCC appointments following reminders by SMS, and that this may also increase smoking cessation success.

Delivering "Just-In-Time" Smoking Cessation Support Via Mobile Phones: Current Knowledge and Future Directions

Smoking lapses early on during a quit attempt are highly predictive of failing to quit. A large proportion of these lapses are driven by cravings brought about by situational and environmental cues. Use of cognitive-behavioral lapse prevention strategies to combat cue-induced cravings is associated with a reduced risk of lapse, but evidence is lacking in how these strategies can be effectively promoted. Unlike most traditional methods of delivering behavioral support, mobile phones can in principle deliver automated support, including lapse prevention strategy recommendations, Just-In-Time (JIT) for when a smoker is most vulnerable, and prevent early lapse. JIT support can be activated by smokers themselves (user-triggered), by prespecified rules (server-triggered) or through sensors that dynamically monitor a smoker's context and trigger support when a high risk environment is sensed (context-triggered), also known as a Just-In-Time Adaptive Intervention (JITAI). However, research suggests that user-triggered JIT cessation support is seldom used and existing server-triggered JIT support is likely to lack sufficient accuracy to effectively target high-risk situations in real time. Evaluations of mobile phone cessation interventions that include user and/or server-triggered JIT support have yet to adequately assess whether this improves management of high risk situations. While context-triggered systems have the greatest potential to deliver JIT support, there are, as yet, no impact evaluations of such systems. Although it may soon be feasible to learn about and monitor a smoker's context unobtrusively using their smartphone without burdensome data entry, there are several potential advantages to involving the smoker in data collection.

IMPLICATIONS

This commentary describes the current knowledge on the potential for mobile phones to deliver automated support to help smokers manage or cope with high risk environments or situations for smoking, known as JIT support. The article categorizes JIT support into three main types: user-triggered, server-triggered, and context-triggered. For each type of JIT support, a description of the evidence and their potential to effectively target specific high risk environments or situations is described. The concept of unobtrusive sensing without user data entry to inform the delivery of JIT support is finally discussed in relation to potential advantages and disadvantages for behavior change.

Text2Quit: an analysis of participant engagement in the mobile smoking cessation program

BACKGROUND

Text2Quit, an interactive text-messaging program aimed at smoking cessation, has been shown to increase quit rates, but engagement has not been thoroughly explored. Understanding the program features associated with engagement and behavior change is integral for effective program design.

OBJECTIVES

This study explored participants' interaction with the Text2Quit text-messaging program and associations between engagement and smoking cessation.

METHODS

The study included the 262 participants who received the Text2Quit intervention. Self-reported engagement measures, primarily usage of Text2Quit keywords and survey responses, were collected through computer records of participant use. Demographic variables and self-reported smoking abstinence were recorded in surveys at baseline and 6-month assessment.

RESULTS

The majority of participants (73%) maintained their subscription during the 6-month intervention. On average, participants received 210.51 text messages, 23.75 emails, and logged into the web portal 1.94 times. Being female was predictive of engagement with the program (β = 15.39). Program engagement, measured by the keyword PLEDGE (p = .002) and the Smokefree Status at 7 Days survey (p < .001) were associated with 6-month abstinence; use of keywords SMOKED (p < .001), RELAPSE (p = .007), and STOP (p = .023) were inversely related to abstinence. While abstainers (N = 83, 31%) stayed enrolled longer and engaged with the program more frequently, program "dose" was not predictive of smoking cessation.

CONCLUSIONS

Using interactive tools such as pledges and reporting on smoking status were predictive of cessation. Further study of program features is required to understand how to optimally design text messaging programs.

Design Considerations for Smoking Cessation Apps: Feedback From Nicotine Dependence Treatment Providers and Smokers

Background

Hundreds of smoking cessation apps are commercially available, but most are not theory-based or designed to take advantage of mobile technology in ways that could make them more engaging and possibly more effective. Considering input from both clinical experts (who understand best practice nicotine dependence treatment requirements) to inform appropriate content and from smokers (the end users) to express their preferences is important in designing these programs in the future.

Objective

To assess and compare the opinions of nicotine dependence treatment providers and smokers regarding the design of future smoking cessation apps.

Methods

We surveyed providers (n=264) and smokers who own smartphones (n=40) to assess their opinions on the importance of 21 app design features. Features represented 5 domains: cost, reputation, privacy and security, content and user experience, and communication. Domains were chosen to reflect best practice treatment, leverage mobile technology to support smoking cessation, and elicit important user preferences. Data were collected between June and July 2015.

Results

Most providers agreed that mHealth apps hold promise for helping people quit smoking (203/264, 76.9%) and would recommend them to their clients/patients (201/264, 76.1%), especially if the app were empirically validated (236/264, 89.4%). Few providers believe effective cessation apps currently exist (112/264, 42.4%). Few smokers (5/40, 13%) had ever downloaded a smoking cessation app; of the ones who had not, most said they would consider doing so (29/35, 83%). Both respondent groups indicated the following features were very to extremely important to include in cessation apps: free or low cost, keeps information private, matches individual needs and interests, adapts as one’s needs and interests change, helps to manage nicotine withdrawal symptoms and medication side effects, and allows users to track their progress. Providers and smokers also indicated gaming and social media connectivity were less important than other features. Despite these similarities, the groups had significantly different opinions about the relative importance of various features. In particular, providers rated privacy as the most important feature, whereas smokers rated low cost and the ability to adaptively tailor content as the most important features.

Conclusions

Smoking cessation apps hold great promise as intervention tools but only if they engage users and appropriately treat nicotine dependence. Intervention development should take into consideration the perspectives of both treatment experts and smokers. This paper highlights important perspectives from each of these groups to be considered when designing future app-based smoking cessation programs.

Developing and Pretesting a Text Messaging Program for Health Behavior Change: Recommended Steps

Background

A growing body of evidence demonstrates that text messaging-based programs (short message service [SMS]) on mobile phones can help people modify health behaviors. Most of these programs have consisted of automated and sometimes interactive text messages that guide a person through the process of behavior change.

Objective

This paper provides guidance on how to develop text messaging programs aimed at changing health behaviors.

Methods

Based on their collective experience in designing, developing, and evaluating text messaging programs and a review of the literature, the authors drafted the guide. One author initially drafted the guide and the others provided input and review.

Results

Steps for developing a text messaging program include conducting formative research for insights into the target audience and health behavior, designing the text messaging program, pretesting the text messaging program concept and messages, and revising the text messaging program.

Conclusions

The steps outlined in this guide may help in the development of SMS-based behavior change programs.

Adaptation and uptake evaluation of an SMS text message smoking cessation programme (MiQuit) for use in antenatal care

OBJECTIVES

To adapt a tailored short message service (SMS) text message smoking cessation intervention (MiQuit) for use without active health professional endorsement in routine antenatal care settings, to estimate 'real-world' uptake and test the feasibility of its use.

DESIGN

Single-site service evaluation.

SETTING

A Nottinghamshire (UK) antenatal clinic.

PARTICIPANTS

Pregnant women accessing the antenatal clinic (N=1750) over 6 months.

INTERVENTION

A single-sheet A5 leaflet provided in the women's maternity notes folder describing the MiQuit text service. Similar materials were left on clinic desks and noticeboards.

OUTCOME MEASURES

MiQuit activation requests and system interactions were logged for two time frames: 6 months (strict) and 8 months (extended). Local hospital data were used to estimate the denominator of pregnant smokers exposed to the materials.

RESULTS

During the strict and extended time frames, 13 and 25 activation requests were received, representing 3% (95% CI 2% to 5%) and 4% (95% CI 3% to 6%) of estimated smokers, respectively. Only 11 (44%) of the 25 requesting activation sent a correctly formatted initiation text. Of those activating MiQuit, and invited to complete tailoring questions (used to tailor support), 6 (67%) completed all 12 questions by text or website and 5 (56%) texted a quit date to the system. Of the 11 activating MiQuit, 5 (45%, 95% CI 21% to 72%) stopped the programme prematurely.

CONCLUSIONS

A low-intensity, cheap cessation intervention promoted at very low cost, resulted in a small but potentially impactful uptake rate by pregnant smokers.