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

Identifying Patterns of Smoking Cessation App Feature Use That Predict Successful Quitting: Secondary Analysis of Experimental Data Leveraging Machine Learning

BACKGROUND

Leveraging free smartphone apps can help expand the availability and use of evidence-based smoking cessation interventions. However, there is a need for additional research investigating how the use of different features within such apps impacts their effectiveness.

OBJECTIVE

We used observational data collected from an experiment of a publicly available smoking cessation app to develop supervised machine learning (SML) algorithms intended to distinguish the app features that promote successful smoking cessation. We then assessed the extent to which patterns of app feature use accounted for variance in cessation that could not be explained by other known predictors of cessation (eg, tobacco use behaviors).

METHODS

Data came from an experiment (ClinicalTrials.gov NCT04623736) testing the impacts of incentivizing ecological momentary assessments within the National Cancer Institute's quitSTART app. Participants' (N=133) app activity, including every action they took within the app and its corresponding time stamp, was recorded. Demographic and baseline tobacco use characteristics were measured at the start of the experiment, and short-term smoking cessation (7-day point prevalence abstinence) was measured at 4 weeks after baseline. Logistic regression SML modeling was used to estimate participants' probability of cessation from 28 variables reflecting participants' use of different app features, assigned experimental conditions, and phone type (iPhone [Apple Inc] or Android [Google]). The SML model was first fit in a training set (n=100) and then its accuracy was assessed in a held-aside test set (n=33). Within the test set, a likelihood ratio test (n=30) assessed whether adding individuals' SML-predicted probabilities of cessation to a logistic regression model that included demographic and tobacco use (eg, polyuse) variables explained additional variance in 4-week cessation.

RESULTS

The SML model's sensitivity (0.67) and specificity (0.67) in the held-aside test set indicated that individuals' patterns of using different app features predicted cessation with reasonable accuracy. The likelihood ratio test showed that the logistic regression, which included the SML model-predicted probabilities, was statistically equivalent to the model that only included the demographic and tobacco use variables (P=.16).

CONCLUSIONS

Harnessing user data through SML could help determine the features of smoking cessation apps that are most useful. This methodological approach could be applied in future research focusing on smoking cessation app features to inform the development and improvement of smoking cessation apps.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04623736; https://clinicaltrials.gov/study/NCT04623736.

User Engagement Clusters of an 8-Week Digital Mental Health Intervention Guided by a Relational Agent (Woebot): Exploratory Study

BACKGROUND

With the proliferation of digital mental health interventions (DMHIs) guided by relational agents, little is known about the behavioral, cognitive, and affective engagement components associated with symptom improvement over time. Obtaining a better understanding could lend clues about recommended use for particular subgroups of the population, the potency of different intervention components, and the mechanisms underlying the intervention's success.

OBJECTIVE

This exploratory study applied clustering techniques to a range of engagement indicators, which were mapped to the intervention's active components and the connect, attend, participate, and enact (CAPE) model, to examine the prevalence and characterization of each identified cluster among users of a relational agent-guided DMHI.

METHODS

We invited adults aged 18 years or older who were interested in using digital support to help with mood management or stress reduction through social media to participate in an 8-week DMHI guided by a natural language processing-supported relational agent, Woebot. Users completed assessments of affective and cognitive engagement, working alliance as measured by goal and task working alliance subscale scores, and enactment (ie, application of therapeutic recommendations in real-world settings). The app passively collected data on behavioral engagement (ie, utilization). We applied agglomerative hierarchical clustering analysis to the engagement indicators to identify the number of clusters that provided the best fit to the data collected, characterized the clusters, and then examined associations with baseline demographic and clinical characteristics as well as mental health outcomes at week 8.

RESULTS

Exploratory analyses (n=202) supported 3 clusters: (1) "typical utilizers" (n=81, 40%), who had intermediate levels of behavioral engagement; (2) "early utilizers" (n=58, 29%), who had the nominally highest levels of behavioral engagement in week 1; and (3) "efficient engagers" (n=63, 31%), who had significantly higher levels of affective and cognitive engagement but the lowest level of behavioral engagement. With respect to mental health baseline and outcome measures, efficient engagers had significantly higher levels of baseline resilience (P<.001) and greater declines in depressive symptoms (P=.01) and stress (P=.01) from baseline to week 8 compared to typical utilizers. Significant differences across clusters were found by age, gender identity, race and ethnicity, sexual orientation, education, and insurance coverage. The main analytic findings remained robust in sensitivity analyses.

CONCLUSIONS

There were 3 distinct engagement clusters found, each with distinct baseline demographic and clinical traits and mental health outcomes. Additional research is needed to inform fine-grained recommendations regarding optimal engagement and to determine the best sequence of particular intervention components with known potency. The findings represent an important first step in disentangling the complex interplay between different affective, cognitive, and behavioral engagement indicators and outcomes associated with use of a DMHI incorporating a natural language processing-supported relational agent.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05672745; https://classic.clinicaltrials.gov/ct2/show/NCT05672745.

A systematic review and meta-analysis of self-guided online acceptance and commitment therapy as a transdiagnostic self-help intervention

Online Acceptance and Commitment Therapy (ACT) interventions use websites and smartphone apps to deliver ACT exercises and skills. The present meta-analysis provides a comprehensive review of online ACT self-help interventions, characterizing the programs that have been studied (e.g. platform, length, content) and analyzing their efficacy. A transdiagnostic approach was taken, including studies that addressed a range of targeted problems and populations. Multi-level meta-analyses were used to nest multiple measures of a single construct within their respective studies. A total of 53 randomized controlled trials were included (n = 10,730). Online ACT produced significantly greater outcomes than waitlist controls at post-treatment for anxiety, depression, quality of life, psychological flexibility, and all assessed outcomes (i.e. omnibus effect), which were generally maintained at follow-up. However, only psychological flexibility and all assessed outcomes at post-treatment were found to be significantly greater for online ACT when compared to active controls, with no significant follow-up effects. Overall, these results further clarify that ACT can be effectively delivered in an online format to target a wide range of mental health concerns, although it is less clear if and when online ACT is more efficacious than other online interventions.

Feature-level analysis of a smoking cessation smartphone app that uses a positive psychology approach (Preprint)

Background

Smoking 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.

Objective

To provide a feature-level analysis of the Smiling Instead of Smoking app (version 2) and to link feature use to subsequent smoking cessation.

Methods

Nondaily 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.

Results

Participants 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).

Conclusions

Greater 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 Registration

ClinicalTrials.gov NCT03951766; https://clinicaltrials.gov/ct2/show/NCT03951766

Motivations Toward Using Digital Health and Exploring the Possibility of Using Digital Health for Mental Health in Bangladesh University Students: Cross-sectional Questionnaire Study

Background

Digital health is efficacious for the management and prevention of mental health (MH) problems. It is particularly helpful for the young adult population, who appreciate the autonomy digital health provides, and in low-income countries, where the prevalence of MH problems is high but the supply of professionals trained in MH is low.

Objective

The objectives of this study are 2-fold: to determine whether university students in Bangladesh find using digital health for MH promotion acceptable and to examine motivational factors for using digital health for MH.

Methods

This study used a cross-sectional survey to examine the likelihood that university students in Bangladesh (n=311) would use different forms of digital health platforms for MH promotion and assessed drivers of intention to use and actual use of digital health generally and digital health for MH through the lens of the Technology Acceptance Model. The results provided evidence that the university student population in Bangladesh is likely to use digital health to promote their MH.

Results

Social influence (adjusted odds ratio [aOR] 1.68, 95% CI 1.40-2.01; P<.001), ease of use (aOR 1.85, 95% CI 1.35-2.53; P<.001), and perceived usefulness (aOR 4.12, 95% CI 1.79-9.51; P=.001) of digital health were found to be significant drivers of the intention to use general digital health, and having an intention to use digital health (aOR 2.10, 95% CI 1.17-3.78; P=.01) had the greatest influence on actual use of digital health. Social influence (aOR 1.71, 95% CI 1.43-2.04; P<.001), perceived usefulness (aOR 8.92, 95% CI 4.18-19.04; P<.001), and use of general digital health (aOR 2.16, 95% CI 1.18-3.97; P=.01) were associated with higher intention to use digital health for MH. The use of general digital health (aOR 4.19, 95% CI 2.37-7.41; P<.001) was associated with the actual use of digital health for MH, as were greater non–stigma-related barriers to using traditional clinical MH services (aOR 2.05, 95% CI 1.10-3.80; P=.02).

Conclusions

Overall, we see that the use of digital health for MH is acceptable in this population and can be helpful for students who perceive barriers to receiving traditional care. We also gain insight into how to promote the intention to use digital health, which in turn promotes the actual use of digital health.

NoFumo+: A Clinical Trial of an mHealth for Smoking Cessation with Hospitalized Patients

Smartphone applications (apps) improve accessibility to smoking cessation treatments. The NoFumo+ app administers a cognitive behavioral therapy program for smoking cessation. This study evaluates the efficacy of NoFumo+ for quitting smoking or reducing cigarette consumption versus the usual information-based treatment. A clinical trial was conducted with 99 hospitalized smokers, 54 pseudo-randomly assigned to the app treatment and 45 to the usual treatment. The two groups had homogeneous baseline characteristics to ensure comparability. Abstinence was evaluated at post-treatment (two months) and at a six-month follow-up. The results obtained indicate that participants who receive the usual treatment are 5.40 times more likely to continue smoking than those who undergo the app treatment (95% CI = [1.35; 20.15]). Participants who do not succeed in quitting smoking with the app manage to decrease their habitual consumption. Users who successfully complete treatment with NoFumo+ access all its contents and use the chat, but without requesting professional support. There is not enough empirical evidence to attribute this success to any specific element of the app. NoFumo+ achieves better abstinence rates than the usual information-based treatments, and the goal of generalizing its use to the non-hospitalized smoking population may be achievable in the future.

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

BACKGROUND

Young adults with serious mental illness (SMI) have higher smoking rates and lower cessation rates than young adults without SMI. Scalable interventions such as smartphone apps with evidence-based content (eg, the National Cancer Institute's [NCI's] QuitGuide and quitSTART) could increase access to potentially appealing and effective treatment for this group but have yet to be tested in this population.

OBJECTIVE

The goal of this user-centered design study is to determine the user experience (including usability and acceptability) of 2 widely available apps developed by the NCI-QuitGuide and quitSTART-among young adult tobacco users with SMI.

METHODS

We conducted usability and acceptability testing of QuitGuide and quitSTART among participants with SMI aged between 18 and 35 years who were stable in community mental health treatment between 2019 and 2020. Participants were randomly assigned to use QuitGuide or quitSTART on their smartphones. App usability was evaluated at baseline and following a 2-week field test of independent use via a video-recorded task completion protocol. Using a mixed method approach, we triangulated 4 data sources: nonparticipant observation, open-ended interviews, structured interviews (including the System Usability Scale [SUS]), and backend app use data obtained from the NCI. Quantitative data were analyzed using descriptive statistics, and qualitative data were analyzed using thematic analysis.

RESULTS

Participants were 17 smokers who were not interested in quitting, with a mean age of 29 (SD 4) years; 41% (n=7) presented with psychotic disorders. Participants smoked an average of 15 (SD 7) cigarettes per day. The mean SUS scores for QuitGuide were similar at visits one and two (mean 64, SD 18 and mean 66, SD 18, respectively). The mean SUS scores for quitSTART numerically increased from visit one (mean 55, SD 20) to visit two (mean 64, SD 16). Acceptability scores followed the same pattern. Observed task completion rates were at least 75% (7/9 for QuitGuide, 6/8 for quitSTART) for both apps at both visits for all but 2 tasks. During the 13-day trial period, QuitGuide and quitSTART users interacted with their assigned app on an average of 4.6 (SD 2.8) days versus 10.8 (SD 3.5) days, for a mean total of 5.6 (SD 3.8) interactions versus 41 (SD 26) interactions, and responded to a median of 1 notification (range 0-8) versus 18.5 notifications (range 0-37), respectively. Qualitative comments indicated moderate to high satisfaction overall but also included concerns about the accuracy of the apps' feedback.

CONCLUSIONS

Both QuitGuide and quitSTART had acceptable levels of usability and mixed levels of acceptability among young adults with SMI. The higher level of engagement with quitSTART suggests that quitSTART may be a favorable tool for young adult smokers with SMI. However, clinical support or coaching may be needed to overcome initial usability issues.

Acceptability and Usability of Mobile Apps for Smoking Cessation Among Young Adults With Psychotic Disorders and Other Serious Mental Illness

Background: Young adults with serious mental illness (SMI) are over twice as likely to smoke cigarettes than those in the general population, but little research has evaluated the efficacy of interventions for this group. While smartphone apps are a promising tool to address this need, their usability should be evaluated among young adults with psychotic disorders, whose symptoms and cognitive impairments may be a barrier to app use. Methods: We compared usability and acceptability of National Cancer Institute apps (QuitGuide and quitSTART) between young adult smokers with SMI psychotic disorders and other SMI diagnoses. We evaluated objective app usability at the initial study visit and following 2 weeks of independent use via a video-recorded task-completion protocol. Perceptions of usability and acceptability were assessed with semi-structured interviews. Engagement was assessed with backend app use data. Results: Participants had a mean age of 29 years old (SD = 4). Of the participants without psychotic disorders (n = 10), all were diagnosed with SMI post-traumatic stress disorder (SMI-PTSD). QuitGuide objective task completion rates were high and similar between diagnosis groups, whereas quitSTART task completion was initially lower among users with psychotic disorder compared to users with SMI-PTSD at Visit 1, and improved by Visit 2. Mean app interactions, mean days of use, and median completed notifications were dramatically higher among quitSTART users compared to QuitGuide users. Compared to quitSTART users with SMI-PTSD, quitSTART users with psychotic disorders had similar daily app interactions over the first week of use (mean 3.8 ± 2.4 interactions), and numerically lower mean daily app interactions during the second week (1.9 ± 1.5 vs. 3.4 ± 2.5), whereas completed notifications remained stable among quitSTART users in both diagnosis groups over time. Qualitative comments indicated general acceptability of both apps among both diagnosis groups. Conclusions: Both QuitGuide and quitSTART were usable and appealing among young adult smokers with psychotic disorders and SMI-PTSD, although quitSTART engendered a dramatically greater level of engagement compared to QuitGuide. Initial coaching to support initiation and app notifications to promote prolonged engagement may be important for young adult smokers with psychotic disorders. Replication and efficacy testing for quitSTART is warranted.

Perceptions of Mobile Apps for Smoking Cessation Among Young People in Community Mental Health Care: Qualitative Study

BACKGROUND

Young adults with serious mental illness are over twice as likely to have tobacco use disorder than those in the general population and are less likely to utilize proven treatment methods during quit attempts. However, little research has evaluated the efficacy of interventions for this group. Smartphone apps may be an underutilized tool for tobacco use disorder among young adults with serious mental illness.

OBJECTIVE

The aim of this study was to explore attitudes toward smoking cessation apps and preferences regarding app design in young adult smokers with serious mental illness.

METHODS

Five focus groups involving 25- to 35-year-old adults with serious mental illness receiving treatment at a community mental health center were conducted between May 2019 and August 2019. Three researchers independently coded transcripts and identified themes using thematic analysis.

RESULTS

Participants (n=22) were individuals who smoke daily: 10 (46%) self-identified as female, 18 (82%) self-identified as White, and 9 (41%) had psychotic disorders. Key themes that emerged included a general interest in using health apps; a desire for apps to provide ongoing motivation during a quit attempt via social support, progress tracking, and rewards; a desire for apps to provide distraction from smoking; concerns about app effectiveness due to a lack of external accountability; and concerns that apps could trigger cravings or smoking behavior by mentioning cigarettes or the act of smoking.

CONCLUSIONS

Apps have the potential to support smoking cessation or reduction efforts among young adults with serious mental illness. However, they may require tailoring, optimization, and clinical support to effectively promote cessation in this population.

Formative, multimethod case studies of learn to quit, an acceptance and commitment therapy smoking cessation app designed for people with serious mental illness

Despite public health efforts, individuals with serious mental illness (SMI) still have very high rates of tobacco smoking. Innovative approaches to reach this population are needed. These series of case studies aimed to descriptively evaluate the usability, user experience (UX), and user engagement (UE) of Learn to Quit (LTQ), an acceptance and commitment therapy smoking cessation app designed for people with SMI, and to compare it with an app designed for the general population, NCI (National Cancer Institute) QuitGuide (QG). Both apps were combined with nicotine replacement therapy and technical coaching. Inspired by the ORBIT model, we implemented two case studies with crossover AB interventions, two B-phase training designs, and three bi-phasic AB single-case designs with Start-Point and Order randomization (A = QG, B = LTQ). Study outcomes were measured using the System Usability Scale, UX interviews, and background analytics. LTQ's usability levels were above the standard cutoff and on average higher than QG. UX outcomes suggested the relative benefits of LTQ's visual design, gamification and simple design structure. LTQ's overall UE was high; the app was opened for an average of 14 min per day (vs. QG: 7 min). However, users showed low levels of UE with each of the app's tracking feature. Measures of psychiatric functioning suggested the safety of LTQ in people with SMI. LTQ appears to be a usable and engaging smoking cessation app in people with SMI. An optimized version of LTQ should be tested in a Phase II study.

Intensive longitudinal methods for studying the role of self-regulation strategies in substance use behavior change

Purpose of review

Many psychosocial interventions for substance use disorders (SUDs) focus on teaching self-regulation strategies. Research using intensive longitudinal methods (ILM), such as ecological momentary assessment and daily diaries, is critical for elucidating if and how these strategies function as mechanisms of change among individuals with SUDs. We review this emerging area of research.

Recent findings

We found a small number of studies using ILM to study self-regulation strategies in SUD (n=18 studies), with most conducted among college student drinkers (n=9) and cigarette smokers (n=7), and few among treatment-engaged individuals, and those with other drug use disorders. There is preliminary evidence that the use of specific self-regulation strategies commonly taught in psychosocial interventions for SUDs (i.e., cognitive reappraisal, problem solving, stimulus control, harm reduction) is associated with decreased momentary or daily substance use, at the within-person level.

Summary

There is a need for further ILM research on self-regulation strategies as mechanisms of substance use behavior change. Such research can inform the development, refinement, and personalization of interventions that teach self-regulation strategies, including mobile interventions that facilitate strategy use in the moment. One key next step is developing psychometrically validated ILM assessments of self-regulation strategy use.

The Stop-Tabac smartphone application for smoking cessation: study protocol for a randomized controlled trial in the general population

Background

Smartphone-based support can reach thousands of smokers and help those who would otherwise try to quit smoking by themselves with little chance of success. Nicotine medications double the chances of quitting smoking, but few smokers use them, and they often use them for too short a time and at an insufficient dose. It is therefore important to increase access to support for smoking cessation and compliance with nicotine therapy. The objectives of this study are to assess whether the Stop-Tabac application (app) is effective for smoking cessation and to examine whether the outcome is influenced by the personal characteristics of participants.

Methods

Trial design: this is a two-arm, parallel-group, superiority, individually randomized, “placebo” controlled trial in 5200 smokers, with follow up after 1 week, 1 month and 6 months. The participants are adult daily smokers (N = 5200) enrolled on the Internet, living in France or Switzerland. The intervention is the Stop-tabac fully-automated app for smartphones, which was launched in 2012 and continuously improved thereafter. It includes fact sheets; calculators of cigarettes not smoked, money saved, and years of life gained; an interactive “coach” that provides automated, individually tailored counseling messages based on the user’s personal profile, sent regularly for 6 months; immediate feedback during episodes of craving and tobacco withdrawal symptoms; a discussion forum (“The Tribe”) where participants provide and receive social support; a quiz that informs users in a playful way; and a module on nicotine therapy that includes personalized feedback and follow up. The outcome is self-reported smoking cessation after 6 months (no puff of tobacco in the past 4 weeks), and after 1 week and 1 month (no puff in the past 7 days). Participants will be randomized automatically based on a list of random numbers. Participants, assistants in charge of collecting follow-up data and data analysts will be blinded to allocation. Funding is provided by the Swiss National Science Foundation, CHF 194,942 (EUR 182,200, USD 200,700), grant 32003_179369. JFE’s salary is paid by the University of Geneva, YK’s salary is paid by the Lausanne University Hospitals.

Discussion

There is little evidence from randomized trials of the impact of health apps in general and of smoking cessation apps in particular. This study will fill this gap.

Trial registration

ISRCTN Registry: ISRCTN11318024. Registered on 17 May 2018.

Offering an app to book cervical screening appointments: A service evaluation

OBJECTIVE

To assess the feasibility of offering women who are overdue for cervical screening the use of a smartphone app to book their appointment.

METHODS

Women who were at least six months overdue for cervical screening in three general practice surgeries in a deprived East London borough were identified from practice records. Staff sent batches of text messages informing women that they were overdue for screening, and inviting them to download an app to book their appointment.

RESULTS

Across the three practices, 2632 eligible women were identified. Valid mobile phone numbers were available for 1465 women. One woman had opted out of receiving text messages, so messages were sent to 1464 women. Of these, 158 (11%) booked a screening appointment within five months. The majority of these women booked without using the app (72%; 113/158); just over a quarter booked via the app (28%; 45/158).

CONCLUSIONS

Just over 10% of cervical screening non-attenders booked an appointment in response to a text message with a link to a downloadable app; however, only one in four of these women booked using the app. This suggests that the text message reminder was likely to have been the key 'active ingredient' for most women, rather than the app itself. Future research could explore the optimal message for a text reminder in this context and evaluate the inclusion of a link to existing online booking systems.

Cognitive Behavioral and Mindfulness-Based Interventions for Smoking Cessation: a Review of the Recent Literature

PURPOSE OF REVIEW

Cigarette smoking is the primary cause of cancer and is the leading preventable cause of morbidity and mortality. Cognitive behavioral therapy (CBT) is one of the most well-established and efficacious interventions for smoking cessation. The study of mindfulness-based interventions (MBIs) has increased exponentially in recent years, showing efficacy for smoking cessation as well. This review highlights research from the past 5 years examining CBT and MBIs for smoking cessation.

RECENT FINDINGS

Both CBT and MBIs are efficacious for special populations (e.g., low SES; pregnant smokers) and have shown initial efficacy when delivered via mhealth/ehealth. CBT has shown efficacy when combined with another behavioral treatment (e.g., ACT). Continued research is needed on CBT and MBIs that have high potential for scalability. Understanding whether they are beneficial for certain populations (e.g., cancer survivors), along with determining for whom CBT vs MBIs are most effective, is also needed.

Efficacy of the Ascure Smoking Cessation Program: Retrospective Study

BACKGROUND

Smoking cessation helps extend a healthy life span and reduces medical expenses. However, the standard 12-week smoking cessation program in Japan has several notable problems. First, only 30% of participants complete this program. Second, participants may choose not to participate unless they have a strong motivation to quit smoking, such as health problems. Third, the program does not provide enough support during the period between clinical visits and after 12 weeks.

OBJECTIVE

This study examined the efficacy of the 24-week ascure program to address the problems of accessibility and continuous support. The program combines online mentoring, over-the-counter pharmacotherapy, and a smartphone app.

METHODS

Using a retrospective study design, we investigated data for 177 adult smokers who were enrolled in the ascure smoking cessation program between August 2017 and August 2018. The primary outcomes were continuous abstinence rates (CARs) during weeks 9-12 and weeks 21-24. To confirm smoking status, we performed salivary cotinine testing at weeks 12 and 24. We also evaluated the program adherence rate. Finally, we performed exploratory analysis to determine the factors associated with continuous abstinence at weeks 21-24 to provide insights for assisting with long-term continuous abstinence.

RESULTS

The CARs of all participants for weeks 9-12 and weeks 21-24 were 48.6% (95% CI 41.2-56.0) and 47.5% (95% CI 40.0-54.8), respectively. Program adherence rates were relatively high throughout (72% at week 12 and 60% at week 24). In the analysis of the factors related to the CAR at weeks 21-24, the number of entries in the app's digital diary and number of educational videos watched during the first 12 weeks were significant factors.

CONCLUSIONS

The ascure program achieved favorable CARs, and participants showed high adherence. Proactive usage of the smartphone app may help contribute to smoking cessation success in the long-term.

Mobile device applications and treatment of autism spectrum disorder: a systematic review and meta-analysis of effectiveness

OBJECTIVE

The current study was performed to assess the evidence for effects of therapeutic intervention with mobile device applications (apps) for individuals with autism spectrum disorder (ASD).

DESIGN

The main methodology of the current study was systematic review with meta-analysis.

SETTING

Only randomised controlled trials (RCTs) for mobile device apps for individuals with ASD were considered for review in the current study.

PATIENTS

The target population was individuals clinically diagnosed with ASD.

INTERVENTIONS

Applications that are operable on a smart (mobile) device and interactive with users.

MAIN OUTCOME MEASURES

The main outcomes were based on standardised mean differences in pretrial and post-trial scales in each control and intervention group.

RESULTS

Out of a total of 1100 studies (after duplicate removal), 7 RCTs were selected for final analysis. Of the seven studies, two RCTs were further analysed for effects based on the visual and fine motor subscales of the Mullen Scales of Early Learning, which favoured the intervention groups (standardised mean difference (SMD)=0.41, 95% CI 0.03 to 0.80; SMD=0.41, 95% CI 0.03 to 0.80), without either having any heterogeneity (p>0.1) or publication bias.

CONCLUSIONS

Although it is still early to draw a conclusion, available studies are showing promise for use of mobile device apps for treatment of individuals with ASD. More well-designed and large-scale studies focused on improving behavioural symptoms of ASD are warranted.

PROSPERO REGISTRATION NUMBER

CRD42019128362.

A Mobile Health Solution Complementing Psychopharmacology-Supported Smoking Cessation: Randomized Controlled Trial

Background

Smoking cessation is a persistent leading public health challenge. Mobile health (mHealth) solutions are emerging to improve smoking cessation treatments. Previous approaches have proposed supporting cessation with tailored motivational messages. Some managed to provide short-term improvements in smoking cessation. Yet, these approaches were either static in terms of personalization or human-based nonscalable solutions. Additionally, long-term effects were neither presented nor assessed in combination with existing psychopharmacological therapies.

Objective

This study aimed to analyze the long-term efficacy of a mobile app supporting psychopharmacological therapy for smoking cessation and complementarily assess the involved innovative technology.

Methods

A 12-month, randomized, open-label, parallel-group trial comparing smoking cessation rates was performed at Virgen del Rocío University Hospital in Seville (Spain). Smokers were randomly allocated to a control group (CG) receiving usual care (psychopharmacological treatment, n=120) or an intervention group (IG) receiving psychopharmacological treatment and using a mobile app providing artificial intelligence–generated and tailored smoking cessation support messages (n=120). The secondary objectives were to analyze health-related quality of life and monitor healthy lifestyle and physical exercise habits. Safety was assessed according to the presence of adverse events related to the pharmacological therapy. Per-protocol and intention-to-treat analyses were performed. Incomplete data and multinomial regression analyses were performed to assess the variables influencing participant cessation probability. The technical solution was assessed according to the precision of the tailored motivational smoking cessation messages and user engagement. Cessation and no cessation subgroups were compared using t tests. A voluntary satisfaction questionnaire was administered at the end of the intervention to all participants who completed the trial.

Results

In the IG, abstinence was 2.75 times higher (adjusted OR 3.45, P=.01) in the per-protocol analysis and 2.15 times higher (adjusted OR 3.13, P=.002) in the intention-to-treat analysis. Lost data analysis and multinomial logistic models showed different patterns in participants who dropped out. Regarding safety, 14 of 120 (11.7%) IG participants and 13 of 120 (10.8%) CG participants had 19 and 23 adverse events, respectively (P=.84). None of the clinical secondary objective measures showed relevant differences between the groups. The system was able to learn and tailor messages for improved effectiveness in supporting smoking cessation but was unable to reduce the time between a message being sent and opened. In either case, there was no relevant difference between the cessation and no cessation subgroups. However, a significant difference was found in system engagement at 6 months (P=.04) but not in all subsequent months. High system appreciation was reported at the end of the study.

Conclusions

The proposed mHealth solution complementing psychopharmacological therapy showed greater efficacy for achieving 1-year tobacco abstinence as compared with psychopharmacological therapy alone. It provides a basis for artificial intelligence–based future approaches.

Trial Registration

ClinicalTrials.gov NCT03553173; https://clinicaltrials.gov/ct2/show/NCT03553173

International Registered Report Identifier (IRRID)

RR2-10.2196/12464

A Behavioral Activation Mobile Health App for Smokers With Depression: Development and Pilot Evaluation in a Single-Arm Trial

Background

The integration of Behavioral Activation Treatment for Depression (BAT-D) into smoking cessation interventions is a promising approach to address depression as a barrier to quitting. However, this approach has only been tested as a face-to-face intervention, which has low reach.

Objective

The aims of the study were to develop a BAT-D mobile health app with high potential reach and determine its feasibility, acceptability, and preliminary effects on theory-based behavioral processes of behavioral activation, reduced depressive symptoms, and smoking cessation.

Methods

Following a user-centered design process consisting of competitive analysis, focus groups, and prototype testing, we conducted a single-arm pilot trial of Actify!, a BAT-D app for depressed smokers. Participants used SmokefreeTXT along with Actify! to provide cessation content that had not yet been built into the app for this initial phase of pilot testing. Participants in the trial were current, daily smokers with mild to moderate depressive symptoms. We examined use outcomes for all enrolled participants and process and cessation outcomes at 6 weeks postenrollment for study completers (16/17, 94% retention).

Results

Regarding acceptability, average number of log-ins per participant was 16.6 (SD 13.7), and 63% (10/16) reported being satisfied overall with the app. Posttreatment interviews identified some usability challenges (eg, high perceived burden of planning and scheduling values-based activities). There was a significant decrease in depressive symptoms from baseline to follow-up (mean change in Patient Health Questionnaire–9 scores was –4.5, 95% CI –7.7 to –1.3; P=.01). Additionally, carbon monoxide (CO)-confirmed, 7-day point prevalence abstinence (PPA) at 6-week follow-up was 31% (5/16), and the 30-day PPA was 19% (3/16).

Conclusions

Results demonstrate promising engagement with Actify! and potential for impact on theory-based change processes and cessation outcomes. Preliminary quit rates compare favorably to previous trials of smoking cessation apps for the general population (ie, short-term, self-reported 30-day quit rates in the 8% to 18% range) and a previous trial of face-to-face BAT-D for depressed smokers (ie, CO-confirmed, 7-day PPA rate of 17% at end of treatment).

Mental Health Apps in China: Analysis and Quality Assessment

BACKGROUND

Mental disorders have been a great burden on health care systems, affecting the quality of life of millions of people worldwide. Developing countries, including China, suffer from the double burden of both the increasing mental health issues in population and the deficiency in mental health care resources. The use of mobile health technologies, especially for mobile phone apps, can be a possible solution.

OBJECTIVE

This review aimed to describe the features and assess the quality of mental health apps in major mobile phone app markets in China and further discuss the priorities for mental health app development.

METHODS

Keywords including psychology, psychological health, psychological hygiene, psychological health service(s), mental, mental health, mental hygiene, mental health service(s), depression, and anxiety were searched in Chinese in 3 Android app markets (Baidu Mobile Assistant, Tencent MyApp, and 360 Mobile Assistant) and iOS App Store independently. Mental health apps were then selected according to established criteria for in-depth analysis and quality assessment by the Mobile App Rating Scale.

RESULTS

In total, 63 of 997 mental health apps were analyzed in depth, of which 78% (49/63) were developed by commercial entities for general population, 17% (11/63) were for patients or clients of specialized psychiatric hospitals or counseling agencies, 3% (2/63) were by government or local Centers for Disease Control and Prevention for general information, and 2% (1/63) for students of a university. Major built-in features of the apps included counseling services, mental health education, and self-assessment of mental health status by validated self-rating scales. The overall quality score of the MH apps was acceptable.

CONCLUSIONS

Mental health apps are emerging in the area of mobile health in China. Popular mental health apps usually provide a synthetic platform organizing resources of information, knowledge, counseling services, self-tests, and management for the general population with mental health-related inquiries. The quality of the apps was rated as acceptable on average, suggesting some space for improvement. Official guidelines and regulations are urgently required for the field in the future.

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.

Design and Development of Smoking Cessation Apps Based on Smokers' and Providers' Perspectives in China: Survey Study

BACKGROUND

Although there are more than 60 smartphone apps for smoking cessation in China, many of them do not include the content and features that health care professionals and smokers prefer-which may make them impractical, unengaging, and ineffective. Therefore, we investigated both health care providers' and smokers' preferences for features of future smoking cessation apps.

OBJECTIVE

This study aimed to investigate Chinese health care providers' and smokers' desired features of a smoking cessation app, with the goal of providing design recommendations for app designers and researchers.

METHODS

Both Chinese smokers who own smartphones (n=357) and Chinese health care providers (n=224) responded to a survey collecting data on their sociodemographic characteristics and opinions on the importance of 20 smoking cessation app design features studied in previous US research.

RESULTS

Chinese health care providers expressed strong support of smoking cessation apps on a number of attitude indicators (range 153/224, 68.3% to 204/224, 91.1%). They rated nearly all (18/20) features as very or extremely important (range 52.2%-83.4%) and rated nearly all features (17/20) as more important than the smokers did. More than 60% of smokers rated the following 4 features as very or extremely important: allow sharing the process of smoking cessation with family members and friends (216/319, 67.7%), helping smokers track their progress (such as the amount of smoking per day; 213/319, 66.8%), helping with the side effects of medications and nicotine withdrawal symptoms (201/319, 63.0%), and adapting to ongoing needs and interests of smokers (194/319, 60.8%). Contrary to a similar study of US smokers and health care providers, Chinese smokers and providers rated reputation and ability to communicate with family members and friends as important features, whereas Chinese smokers rated privacy and security as less important.

CONCLUSIONS

The design of future smoking cessation and health behavior change apps should consider perspectives of both providers and smokers as well as the role of culture.

A Pilot Randomized Controlled Trial of a Web-Based Growth Mindset Intervention to Enhance the Effectiveness of a Smartphone App for Smoking Cessation

Background

Although smartphone apps have shown promise for smoking cessation, there is a need to enhance their low engagement rates. This study evaluated the application of the growth mindset theory, which has demonstrated the potential to improve persistence in behavior change in other domains, as a means to improve engagement and cessation.

Objective

This study aimed to explore the feasibility, utility, and efficacy of a Web-based growth mindset intervention for addiction when used alongside a smoking cessation app.

Methods

Daily smokers (N=398) were all recruited on the Web and randomly assigned to receive either a cessation app alone or the app plus a Web-delivered growth mindset intervention. The primary outcome was engagement, that is, the number of log-ins to the smoking cessation app. The secondary outcome was 30-day point prevalence abstinence at 2-month follow-up collected through a Web-based survey.

Results

The 2-month outcome data retention rate was 91.5% (364/398). In addition, 77.9% (310/398) of the participants in the experimental arm viewed at least 1 page of their growth mindset intervention, and 21.1% (84/398) of the group viewed all the growth mindset intervention. The intention-to-treat analysis did not show statistically significant differences between the experimental and comparison arms on log-ins to the app (19.46 vs 21.61; P=.38). The experimental arm had cessation rates, which trended higher than the comparison arm (17% vs 13%; P=.10). The modified intent-to-treat analysis, including only participants who used their assigned intervention at least once (n=115 in experimental group and n=151 in the control group), showed that the experimental arm had a similar number of log-ins (32.31 vs 28.48; P=.55) but significantly higher cessation rates (21% vs 13%; P=.03) than the comparison arm.

Conclusions

A growth mindset intervention for addiction did not increase engagement rates, although it may increase cessation rates when used alongside a smartphone app for smoking cessation. Future research is required to refine the intervention and assess efficacy with long-term follow-up to evaluate the efficacy of the mindset intervention.

Trial Registration

ClinicalTrials.gov NCT03174730; https://clinicaltrials.gov/ct2/show/NCT03174730

Evaluating the Feasibility and Effectiveness of an Australian Safety Planning Smartphone Application: A Pilot Study Within a Tertiary Mental Health Service

OBJECTIVE

The purpose of this study was to examine the feasibility and effectiveness of a suicide prevention smartphone application.

METHOD

Thirty-six non-Aboriginal Australians aged between 16 and 42 years (67% female) were recruited from a tertiary mental health service where they were receiving treatment for suicide risk. Participants were asked to use the BeyondNow safety planning smartphone application to manage their suicide safety plan during a 2-month trial, as an adjunct to treatment as usual. A survey battery designed to measure feasibility and effectiveness of the smartphone app plus treatment as usual intervention was completed at baseline and follow-up.

RESULTS

A vast majority of participants used the app to view and edit their safety plans and reported that the app was easy to use. A reduction was observed in participant severity and intensity of suicide ideation, and suicide-related coping increased significantly. No significant changes were observed in suicide resilience.

CONCLUSIONS

The BeyondNow safety planning smartphone application was shown to be feasible and effective as an adjunct to mental health treatment among patients at risk of suicide.

Mobile Applications for the Treatment of Tobacco Use and Dependence

PURPOSE OF REVIEW

Smoking remains a leading preventable cause of premature death in the world; thus, developing effective and scalable smoking cessation interventions is crucial. This review uses the Obesity-Related Behavioral Intervention Trials (ORBIT) model for early phase development of behavioral interventions to conceptually organize the state of research of mobile applications (apps) for smoking cessation, briefly highlight their technical and theory-based components, and describe available data on efficacy and effectiveness.

RECENT FINDINGS

Our review suggests that there is a need for more programmatic efforts in the development of mobile applications for smoking cessation, though it is promising that more studies are reporting early phase research such as user-centered design. We identified and described the app features used to implement smoking cessation interventions, and found that the majority of the apps studied used a limited number of mechanisms of intervention delivery, though more effort is needed to link specific app features with clinical outcomes. Similar to earlier reviews, we found that few apps have yet been tested in large well-controlled clinical trials, although progress is being made in reporting transparency with protocol papers and clinical trial registration.

SUMMARY

ORBIT is an effective model to summarize and guide research on smartphone apps for smoking cessation. Continued improvements in early phase research and app design should accelerate the progress of research in mobile apps for smoking cessation.

Technical Guidance for Clinicians Interested in Partnering With Engineers in Mobile Health Development and Evaluation

The explosion of mobile health (mHealth) interventions has prompted significant investment and exploration that has extended past industry into academia. Although research in this space is emerging, it focuses on the clinical and population level impact across different populations. To realize the full potential of mHealth, an intimate understanding of how mHealth is being used by patients and potential differences in usage between various demographic groups must also be prioritized. In this viewpoint, we use our experiences in building an mHealth intervention that incorporates an iOS app, Bluetooth-enabled blood pressure cuff, and Apple Watch to share knowledge on (1) how user interaction data can be tracked in the context of health care privacy laws, (2) what is required for effective, nuanced communication between clinicians and engineers to design mHealth interventions that are patient-centered and have high clinical impact, and (3) how to handle and set up a process to handle user interaction data efficiently.

Reducing Primary Care Attendance Intentions for Pediatric Respiratory Tract Infections

PURPOSE

The aim of this study was to evaluate a theory and evidence-based, parent-targeted online intervention, combining microbiological local syndromic surveillance data, symptom information, and home-care advice, to reduce primary care attendance for self-limiting, low-risk pediatric respiratory tract infections (RTIs).

METHODS

The effect of this novel intervention on primary care attendance intentions was evaluated in an online experimental study. A representative sample of mothers (n = 806) was randomly assigned to receive the intervention material before (intervention) or after (control) answering questions concerning attendance intentions for an RTI illness scenario and mediating factors. Both groups provided feedback on the material. Group comparisons, linear regression, and path analyses were conducted.

RESULTS

Intervention participants reported lower attendance intentions compared with control participants (d = 0.69, 95% CI, 0.55-0.83), an effect that remained when controlling for demographic and clinical characteristics (B = -1.62, 95% CI, -1.97 to -1.30). The path model highlighted that the intervention effect (B = -0.33, 95% CI, -0.40 to -0.26) was mostly indirect and mediated by infection and antibiotic knowledge, symptom severity concerns, and social norm perceptions concerning attendance. Information on when to attend was rated as the most important intervention component 227 times, followed by symptoms rated 186 times. Information on circulating viruses was rated as least important 274 times.

CONCLUSIONS

The intervention was effective in reducing primary care attendance intentions by increasing knowledge, lowering attendance motivation, and reducing the need for additional resources. The contribution of individual intervention components and effects on behavioral outcomes requires further testing.

Comparison of Developers' and End-Users' Perspectives About Smoking Cessation Support Through the Crush the Crave App

Background

High smoking rates among end-users, combined with their high rates of app use, render this age group as a particularly captive audience for quit smoking apps. There is emerging evidence that apps are an effective way to support smoking cessation among end-users. How the expectations behind the design of apps align with the needs and preferences of end-users, and if this differs by gender, is poorly understood, limiting the ability to evaluate and scale these interventions.

Objective

The objective of this qualitative case study was to detail how the overall design approach of Crush the Crave (CTC), a quit smoking app that targets end-users, compares with young adult women’s and men’s perspectives and experiences, with consideration for the influence of gender.

Methods

Semistructured interviews were conducted with 15 developers involved in the development of CTC and 31 young adult CTC users. Data were analyzed inductively to derive thematic findings of the perceived pros and cons of CTC by both developers and end-users. Findings were grouped under 4 categories (1) technology and platforms utilized for the app, (2) foundation of app content, (3) underlying focus of the app, and (4) look, feel and functionality of the app.

Results

Under the category, technology and platforms utilized for the app, it was found that both developers and end-users agreed that apps aligned with the needs and preferences of young adult smokers. Major limitations with the technology identified by end-users were the frequent “glitches” and requirement for internet or data. For the category, foundation of app content,developers agreed that the strength of CTC was in its strong evidence-base. What mattered to end-users, however, was that the content was packaged positively, focusing on the benefits of quitting versus the consequences of smoking. It was found under the category, underlying focus of the app, that the individually-led focus of the app resonated with both developers and end-users, especially young men. Under the final category, look, feel and functionality of the app, it was found that developers were very positive about the app's aesthetics but end-users thought that the aesthetics incited a negative effect. Also, while end-users found it easy to use, they did not find the app intuitive. Finally, end-users thought that, because the app functions were largely based on a user’s quit date versus their ongoing efforts, this often lent to unmeaningful data.

Conclusions

The current study findings highlight the importance of understanding multiple perspectives of stakeholders involved in a mobile-based intervention. By gathering the viewpoints of developers and end-users, both problematic and effective approaches that underlie development goals were revealed as a means of informing the development, implementation, and evaluation of future electronic health (eHealth) interventions.

Does the addition of a supportive chatbot promote user engagement with a smoking cessation app? An experimental study

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 < .001). The one-month follow-up rate was 10.6% (intervention: 19.9% (1,061/5,339); control: 9.7% (5,050/51,875). Smokers allocated to the intervention had greater odds of quit success (missing equals smoking: 844/5,339 vs. 3,704/51,875, ORadj = 2.38, 95% CI = 2.19-2.58, p < .001; follow-up only: 844/1,061 vs. 3,704/5,050, ORadj = 1.36, 95% CI = 1.16-1.61, p < .001).

CONCLUSION

The 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.

Theoretical Advancements in mHealth: A Systematic Review of Mobile Apps

There are now few hundred thousand healthcare apps, yet there is a gap in our understanding of the theoretical mechanisms for which, and how, technological features translate into improved healthcare outcomes. In particular, the technological convergence, within mobile health (mHealth) apps, of the processes of mass and interpersonal communication, and human-computer interaction requires greater parsing in the literature. This paper analyzed 85 empirical studies on mHealth apps using the Input-Mechanism-Output model. We found in the literature that, firstly, there is a greater emphasis on technological inputs (87%) of accessibility, usability, usage, and data quality, than health outputs (52%) such as system process efficiencies and individual level behavioral or health outcomes. Secondly, there is little evidence of explanatory mechanisms (19%) of how the effects of mHealth apps are achieved. While we believe that successful apps would require research that incorporates technological inputs, theoretical mechanisms and health outputs, such studies are a rarity (n = 3). There is a minor increase in rigor with randomized control trials (n = 5), and a preponderance of discussion around social influence (n = 8) and gamification (n = 7), albeit in a scattered manner. We discuss the implications of the trend towards socialization and gamification findings in terms of future research, particularly in terms of study design guided by theoretical mechanisms.

QUALITY AND USE OF FREE SMOKING CESSATION APPS FOR SMARTPHONES

OBJECTIVES

Smartphone Apps are one of the tools available to support patients who wish to quit smoking. Content analysis studies have indicated multiple deficiencies within these Apps including minimal use of evidence-based research and Nicotine Dependence Treatment Provider (NDTP) in App development. The aim of this study was to determine quality and features of smoking cessation Apps available on Android® and iOS® platforms.

METHODS

The first fifty free smoking cessation Apps available for download using the search term smoking cessation on Google Play Store and Apple App Store were chosen. Each of these Apps was analyzed and categorized based on ratings, target audience age, language, and a variety of tracking functionalities noted on the Apps. Indications and suggestions regarding either the use of NDTP or evidence-based behavior change protocols were noted.

RESULTS

There were no significant differences in the features of smoking cessation Apps on Android and iOS. Only 15 percent of all Apps analyzed on both platforms indicated some involvement of NDTP and there was no difference between the two platforms. More than 50 percent of Apps studied were downloaded over half a million times and the average user rating was 3.89/5.00 for Android and 3.72/5.00 for iOS with no significant difference.

CONCLUSIONS

Most smoking cessation Apps in both platforms offer basic tracking functionalities with limited motivational tips. Only a handful of Apps have moved beyond this role and while their development is applaudable much innovation remains.

Comparing in-the-moment skill coaching effects from tailored versus non-tailored acceptance and commitment therapy mobile apps in a non-clinical sample

Mobile apps are promising for teaching how to practice psychological skills in high-risk and in vivo momentary situations, but there has been minimal research on the immediate effects of app-based skill coaching on mental health in-the-moment. This study analyzed the mobile app data in a non-clinical sample of 39 adults participating in a larger randomized controlled trial, with participants randomized to an acceptance and commitment therapy (ACT) mobile app that tailors skill coaching based on in-the-moment variables (n = 17) or an app that provides randomly selected skill coaching (n = 22). Data were collected before and after each ACT skill coaching session on proximal outcome (depression, anxiety, and engagement in meaningful activity) and ACT process variables. Multilevel models indicated significant immediate improvements on average following ACT skill coaching sessions on all proximal outcome and ACT process variables, although with relatively small effects ranging between 0.17 and 0.27 SD units change. Larger immediate pre-to-post effects from ACT coaching sessions were found for anxiety, experiential avoidance, and cognitive fusion in the tailored app versus random app condition. Overall, results suggest that an ACT app can have immediate, in-the-moment effects on psychological functioning, which may be enhanced by tailoring skills to current context.

The Crush the Crave Quit Smoking App and Young Adult Smokers: Qualitative Case Study of Affordances

Background

Mobile phone apps have emerged as a promising way to reach young adult smokers, given their high mobile phone ownership rates and openness to receiving cessation support via digital technologies. Although emerging evidence indicates that quit smoking apps are an effective way to reduce smoking among young adults, lacking is formative evaluative research that captures the perspectives of end-users.

Objective

The objective of this study was to contribute insights toward understanding how young adults interact with the Crush the Crave (CTC) app, and how this interaction shapes young adults’ smoking cessation experiences and practices, with consideration of the influence of gender.

Methods

Semistructured interviews were conducted with 31 young adult CTC end-users. Guided by sociomateriality theory and an affordances approach, data were inductively analyzed to derive thematic findings in relation to the impacts of CTC on quit efforts, and to expose the underlying affordances (mechanisms) that lend to these outcomes. Findings were grouped according to the 4 design components of CTC: credibility, social support, task support, and dialogue support.

Results

The credibility component of CTC played an important role in harnessing the trust of young adults because it afforded them promise in relation to its potential effectiveness in assisting them with quitting smoking. The social support component lent to various end-user practices and experiences that rendered this aspect as the weakest component in supporting quit efforts. Although most functions situated in the task and dialogue support components were found to be helpful, there were a few affordances in CTC that resulted in negative experiences, notably weaning from smoking. Gender-related influences were also evident. For example, young men preferred to control and self-manage their quitting and, therefore, did not engage with functions that afforded journaling or reminding to stay on track. Women, in contrast, were more likely to benefit from these affordances.

Conclusions

An affordances approach is productive for gaining an in-depth understanding of how mobile apps interact with end-users to lend to particular outcomes. The study findings have implications for developing and improving apps for helping young adults quit smoking, as well as apps that target other health behaviors. Productive affordances may also serve as a framework for leveraging apps for smoking cessation.

Identification of Users for a Smoking Cessation Mobile App: Quantitative Study

BACKGROUND

The number of mobile apps that support smoking cessation is growing, indicating the potential of the mobile phone as a means to support cessation. Knowledge about the potential end users for cessation apps results in suggestions to target potential user groups in a dissemination strategy, leading to a possible increase in the satisfaction and adherence of cessation apps.

OBJECTIVE

This study aimed to characterize potential end users for a specific mobile health (mHealth) smoking cessation app.

METHODS

A quantitative study was conducted among 955 Dutch smokers and ex-smokers. The respondents were primarily recruited from addiction care facilities and hospitals through Web-based media via websites and forums. The respondents were surveyed on their demographics, smoking behavior, and personal innovativeness. The intention to use and the attitude toward a cessation app were determined on a 5-point Likert scale. To study the association between the characteristics and intention to use and attitude, univariate and multivariate ordinal logistic regression analyses were performed.

RESULTS

The multivariate ordinal logistic regression showed that the number of previous quit attempts (odds ratio [OR] 4.1, 95% CI 2.4-7.0, and OR 3.5, 95% CI 2.0-5.9) and the score on the Fagerstrom Test of Nicotine Dependence (OR 0.8, 95% CI 0.8-0.9, and OR 0.8, 95% CI 0.8-0.9) positively correlates with the intention to use a cessation app and the attitude toward cessation apps, respectively. Personal innovativeness also positively correlates with the intention to use (OR 0.3, 95% CI 0.2-0.4) and the attitude towards (OR 0.2, 95% CI 0.1-0.4) a cessation app. No associations between demographics and the intention to use or the attitude toward using a cessation app were observed.

CONCLUSIONS

This study is among the first to show that demographic characteristics such as age and level of education are not associated with the intention to use and the attitude toward using a cessation app when characteristics related specifically to the app, such as nicotine dependency and the number of quit attempts, are present in a multivariate regression model. This study shows that the use of mHealth apps depends on characteristics related to the content of the app rather than general user characteristics.

The Development and Evaluation of Online Smoking Cessation Services: A Narrative Literature Review

Introduction:The use of quitlines for smoking cessation has contracted, with service providers adapting through the development of comprehensive interactive online smoking cessation services. The primary aim of this review is to investigate the continuum of online cessation services, innovations in design and service components, measures used in formative, process and outcome evaluations, as well as evidence of effectiveness.

Methods:This review includes the peer-reviewed literature, scholarly articles and the grey literature material. Databases searched included: PubMed, Google Scholar, SCOPUS and the Cochrane Collaboration.

Results/findings:56 academic journal articles and the 5 grey literature reports met the inclusion criteria for this review. Developmental stages of online/combination services included: static websites, tailored feedback, email and text services, interactive components, social media, pharmacological offers; as well as social and professional support mechanisms. Innovations in online smoking cessation include: chat rooms, new recruitment strategies, mobile apps, service tailoring and messaging support groups. Online cessation services were significantly cheaper and more popular than quitlines; however, abstinence rates appear higher amongst quitline users.

Conclusions:Three likely catalysts for the shift from quitlines to online services are the rapid development of technology, increased internet access and the general movement of the goods and services sector to digital channels. The challenge for online cessation service providers is to leverage their comparative cost advantage and develop strategies that keep pace, engage users and increase service effectiveness.

Implications:Our paper synthesises a wide-range of the literature that evaluates the effectiveness and scope of online smoking cessation programs. Through applying this literature to the stages of evaluation framework, we also provide one of the first detailed roadmaps towards developing comprehensive evaluation methodology for online smoking cessation services.

A systematic review of smartphone applications for smoking cessation

Tobacco use is the leading cause of preventable disease and death in the USA. However, limited data exists regarding smoking cessation mobile app quality and intervention effectiveness. Innovative and scalable interventions are needed to further alleviate the public health implications of tobacco addiction. The proliferation of the smartphone and the advent of mobile phone health interventions have made treatment more accessible than ever. The purpose of this review was to examine the relation between published scientific literature and available commercial smartphone health apps for smoking cessation to identify the percentage of scientifically supported apps that were commercially available to consumers and to determine how many of the top commercially available apps for smoking cessation were supported by the published scientific literature. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, apps were reviewed in four phases: (1) identified apps from the scientific literature, (2) searched app stores for apps identified in the literature, (3) identified top apps available in leading app stores, and (4) determined which top apps available in stores had scientific support. Seven articles identified six apps with some level of scientific support, three (50%) were available in at least one app store. Conversely, among the top 50 apps suggested by each of the leading app stores, only two (4%) had any scientific support. While half of the scientifically vetted apps remain available to consumers, they are difficult to find among the many apps that are identified through app store searches.

Crush the Crave: Development and Formative Evaluation of a Smartphone App for Smoking Cessation

Background

Emerging evidence supports the use of smartphone apps for smoking cessation, especially in young adults given their high smoking rates and high smartphone ownership rates. Although evaluative evidence is encouraging for supporting smoking cessation, there remains a paucity of research describing the design and development processes of mobile health (mHealth) interventions.

Objective

The aim of this paper was to describe the process of developing Crush the Crave (CTC), an evidence-informed app to support smoking cessation in young adults, and the results of a formative evaluation of app usage behavior, as part of a broader program of research that seeks to establish the effectiveness of the CTC app.

Methods

The Spiral Technology Action Research (STAR) 5-cycle model (listen, plan, do, act, and study) was employed to guide the development, implementation, and dissemination of CTC. The approach to development and formative evaluation included focus groups with young adult smokers (n=78) across 2 phases, analysis of the content of existing apps, 2 sessions with content experts, and Google Analytics to assess user behavior during a 12-month pilot.

Results

LISTEN—focus groups revealed young adult smoker preferences of (1) positive reinforcement, (2) personalization, (3) social support, (4) quit support, (5) tracking the behavior, and (6) tracking quit benefits. PLAN—informed by evidence for smoking cessation, young adult preferences and an assessment of popular cessation apps, content experts produced a mind map and a storyboard describing app content and structure. DO—focus groups with young adult smokers provided feedback on the first version of the app with opinions on content and suggestions for improvement such as providing alerts and distractions from craving. ACT—refinements were made, and app content was organized using the 4 key design components informed by principles of persuasive technology for behavior change: credibility, task support, dialogue support, and social support. CTC was launched in April 2013 and piloted from the period July 2013 to June 2014 where 1987 Android users had 18,567 sessions, resulting in 59,384 page views and 89.58% (1780/1987) of users returning within the same day to use CTC. STUDY—a pragmatic randomized controlled trial of CTC was launched in August 2014 to demonstrate that including mHealth technology as a population-based intervention can help young adult smokers to quit. The results of this phase will be presented in a subsequent publication.

Conclusions

CTC is one of the first smoking cessation apps designed to meet the needs of young adult smokers. The development was informed by the inclusion of young adults in the design and the systematic application of multiple stakeholder input, scientific evidence, and theory. The STAR model approach was followed from the beginning of intervention development, which should facilitate optimization of mHealth interventions in the future.

Trial Registration

ClinicalTrials.gov NCT01983150; http://clinicaltrials.gov/ct2/show/NCT01983150 (Archived by WebCite at http://www.webcitation.org/6VGyc0W0i)

User-Centered Design of Learn to Quit, a Smoking Cessation Smartphone App for People With Serious Mental Illness

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.

If we build it, will they come? Issues of engagement with digital health interventions for trauma recovery

Exposure to traumatic events is extremely common with nearly 75% reported to have experienced one or more traumatic events worldwide. A significant number of those exposed will develop posttraumatic stress disorder (PTSD) along with depression, anxiety, and substance use disorders. Globally, trauma-related mental health disorders are the leading cause of global disability burden, and many of these disorders are caused, or worsened, by exposure to wars, natural and human-caused disasters, and other traumatic events. Significant barriers to treatment exist including logistical, geographical, financial, stigma, and other attitudinal challenges. One opportune approach to overcoming these barriers is the provision of mental health interventions via technology that can be readily standardized for wide dissemination of evidence-based care. However, engagement with technology-based interventions is a concern and limited participation and high attrition rates are common. This may be especially true for trauma survivors who often experience symptoms of avoidance and hyperarousal. Engagement is regarded as an essential component of intervention efficacy and has been demonstrated to be associated with more positive clinical outcomes, yet theoretically based research in this area is sparse. This review focuses on the complex issue of engagement with digital health interventions (DHIs). Specifically, we review the conceptualization and measurement of engagement, predictors of engagement, and importantly, the relationship of engagement with intervention effectiveness. Finally, a theoretically based model of engagement is proposed that considers the unique challenges of trauma recovery. This review is not intended to provide a systematic or exhaustive set of recommendations, rather it is intended to highlight the challenges of engagement research including its definition, measurement, and modeling. Future engagement research that includes valid and reliable measures of engagement will enable consistent exploration of engagement predictors that can then inform methods for increasing engagement and, ultimately, intervention effectiveness.

User Participation and Engagement With the See Me Smoke-Free mHealth App: Prospective Feasibility Trial

BACKGROUND

The See Me Smoke-Free (SMSF) mobile health (mHealth) app was developed to help women quit smoking by targeting concerns about body weight, body image, and self-efficacy through cognitive behavioral techniques and guided imagery audio files addressing smoking, diet, and physical activity. A feasibility trial found associations between SMSF usage and positive treatment outcomes. This paper reports a detailed exploration of program use among eligible individuals consenting to study participation and completing the baseline survey (participants) and ineligible or nonconsenting app installers (nonparticipants), as well as the relationship between program use and treatment outcomes.

OBJECTIVE

The aim of this study was to determine whether (1) participants were more likely to set quit dates, be current smokers, and report higher levels of smoking at baseline than nonparticipants; (2) participants opened the app and listened to audio files more frequently than nonparticipants; and (3) participants with more app usage had a higher likelihood of self-reported smoking abstinence at follow up.

METHODS

The SMSF feasibility trial was a single arm, within-subjects, prospective cohort study with assessments at baseline and 30 and 90 days post enrollment. The SMSF app was deployed on the Google Play Store for download, and basic profile characteristics were obtained for all app installers. Additional variables were assessed for study participants. Participants were prompted to use the app daily during study participation. Crude differences in baseline characteristics between trial participants and nonparticipants were evaluated using t tests (continuous variables) and Fisher exact tests (categorical variables). Exact Poisson tests were used to assess group-level differences in mean usage rates over the full study period using aggregate Google Analytics data on participation and usage. Negative binomial regression models were used to estimate associations of app usage with participant baseline characteristics after adjustment for putative confounders. Associations between app usage and self-reported smoking abstinence were assessed using separate logistic regression models for each outcome measure.

RESULTS

Participants (n=151) were more likely than nonparticipants (n=96) to report female gender (P<.02) and smoking in the 30 days before enrollment (P<.001). Participants and nonparticipants opened the app and updated quit dates at the same average rate (rate ratio [RR] 0.98; 95% CI 0.92-1.04; P=.43), but participants started audio files (RR 1.07; 95% CI 1.00-1.13; P<.04) and completed audio files (RR 1.11; 95% CI 1.03-1.18; P<.003) at significantly higher rates than nonparticipants. Higher app usage among participants was positively associated with some smoking cessation outcomes.

CONCLUSIONS

This study suggests potential efficacy of the SMSF app, as increased usage was generally associated with higher self-reported smoking abstinence. A planned randomized controlled trial will assess the SMSF app's efficacy as an intervention tool to help women quit smoking.

User Preferences for Mobile Health Interventions: A Survey among Intermittent Claudication Patients and Their Physical Therapists

BACKGROUND

Smartphone apps provide novel ways for triggering lifestyle change by coupling objective measurements of health behavior with tailored feedback. Little is known about end-user preferences regarding the content of mobile health (mHealth) interventions. The aim of this study was to assess smartphone use and preferences regarding app content among intermittent claudication patients and their treating physical therapists.

METHODS

A cross-sectional survey was sent via an internal email system to 1,514 physical therapists specialized in treating patients with intermittent claudication. They were asked to complete one questionnaire themselves and administer a second to their intermittent claudication patients currently under treatment. Data on participant characteristics and smartphone use were collected from all respondents. The preferred app components were obtained from participants owning a smartphone. Binary logistic regression analysis was used to explore the adjusted association between age and attained educational level, and smartphone use.

RESULTS

The response rate of therapists was 40.8% (617/1,514), and a total of 488 patients completed the survey. After excluding incomplete forms, a total of 615 physical therapist forms and 483 patient forms were analyzed. Overall, 40.6% of patients and 95% of therapists owned a smartphone. Higher educational level was associated with smartphone ownership (adjusted odds ratio = 2.46, 95% confidence interval (CI) = 1.41-4.27, P = 0.001). Compared to patients aged ≥75 years, lower age was associated with higher odds of owning a smartphone (adjusted odds ratios for patients aged ≤54 years = 21.27, 95% CI = 6.82-66.30, P < 0.001; aged 55-64 years = 4.76, 95% CI = 2.52-9.00, P < 0.001; and aged 65-74 years = 2.58, 95% CI = 1.54-4.33, P < 0.001). The most preferred app components for intermittent claudication patients in possession of a smartphone included monitoring treadmill-measured walking distances (71%), global positioning system tracking of walks (50%), and daily physical activity monitoring (49%). Physical therapists were most interested in global positioning system tracking of walks (89%), daily physical activity monitoring (82%), keeping track of treadmill-measured walking distance (79%), help with smoking cessation (65%).

CONCLUSIONS

Smartphone ownership is associated with younger age and a higher educational level in patients with intermittent claudication. This study provides a framework of end-user preferences regarding desired features to guide the development of an app to potentiate health outcomes of intermittent claudication treatment.

Free smoking cessation mobile apps available in Australia: a quality review and content analysis

OBJECTIVES

This review aimed to identify free, high-quality, smoking cessation mobile applications (apps) that adhere to Australian smoking cessation treatment guidelines.

METHODS

A systematic search of smoking cessation apps was conducted using Google. The technical quality of relevant apps was rated using the Mobile Application Rating Scale. The content of apps identified as high quality was assessed for adherence to smoking cessation treatment guidelines.

RESULTS

112 relevant apps were identified. The majority were of poor technical quality and only six 'high-quality' apps were identified. These apps adhered to Australian treatment guidelines in part. The efficacy of two apps had been previously evaluated.

CONCLUSIONS

In lieu of more substantial research in this area, it is suggested that the high-quality apps identified in this review may be more likely than other available apps to encourage smoking cessation. Implications for public health: Smoking cessation apps have the potential to address many barriers that prevent smoking cessation support being provided; however few high-quality smoking cessation apps are currently available in Australia, very few have been evaluated and the app market is extremely volatile. More research to evaluate smoking cessation apps, and sustained funding for evidence-based apps, is needed.

Choosing between responsive-design websites versus mobile apps for your mobile behavioral intervention: presenting four case studies

Both mobile apps and responsive-design websites (web apps) can be used to deliver mobile health (mHealth) interventions, but it can be difficult to discern which to use in research. The goal of this paper is to present four case studies from behavioral interventions that developed either a mobile app or a web app for research and present an information table to help researchers determine which mobile option would work best for them. Four behavioral intervention case studies (two developed a mobile app, and two developed a web app) presented include time, cost, and expertise. Considerations for adopting a mobile app or a web app-such as time, cost, access to programmers, data collection, security needs, and intervention components- are presented. Future studies will likely integrate both mobile app and web app modalities. The considerations presented here can help guide researchers on which platforms to choose prior to starting an mHealth intervention.

Effectiveness of Mobile Apps for Smoking Cessation: A Review

INTRODUCTION

Smartphone-based smoking cessation interventions are increasingly used around the world. However, the effects of smartphone applications on applicability and efficacy on cessation rate and prevention of relapses are not often evaluated. Therefore, this review aims to assess the evidence on effectiveness of smartphone applications as an intervention tool for smoking cessation support.

METHODS

We conducted the search using Ovid Medline/PubMed, CENTRAL and Scopus databases dated (January 2007-June 2016). Inclusion criteria include randomized control trials or intervention studies with mobile applications that offer smoking cessation support. Two assessors independently extracted and evaluated the data from each included study.

RESULTS

The review of eight selected studies illustrate the use of smartphone applications in increasing quit rates among smokers, however adherence to app features influences quit rates. Audiovisual features followed by a quit plan, tracking progress and sharing features are most accepted and utilised app features. However, inconsistency was observed in their association with abstinence or quit rate. App engagement features increase the statistical significance in the quit rate. Development of smartphone applications was supported by behavior change theories in all studies nevertheless; heterogeneous forms of intervention were adopted within studies. Similarly, reduction in relapse attributed to enhanced discussion among quitters using social media applications was observed.

CONCLUSIONS

Quality evidence is warranted with large sample size to measure effect size of the intervention. Future research on effectiveness and efficacy of smartphone alone and comparisons with other mHealth interventions, such as text messaging would be useful.

Design Considerations for mHealth Programs Targeting Smokers Not Yet Ready to Quit: Results of a Sequential Mixed-Methods Study

Background

Mobile health (mHealth) smoking cessation programs are typically designed for smokers who are ready to quit smoking. In contrast, most smokers want to quit someday but are not yet ready to quit. If mHealth apps were designed for these smokers, they could potentially encourage and assist more people to quit smoking. No prior studies have specifically examined the design considerations of mHealth apps targeting smokers who are not yet ready to quit.

Objective

To inform the user-centered design of mHealth apps for smokers who were not yet ready to quit by assessing (1) whether these smokers were interested in using mHealth tools to change their smoking behavior; (2) their preferred features, functionality, and content of mHealth programs addressing smoking; and (3) considerations for marketing or distributing these programs to promote their uptake.

Methods

We conducted a sequential exploratory, mixed-methods study. Qualitative interviews (phase 1, n=15) were completed with a demographically diverse group of smokers who were smartphone owners and wanted to quit smoking someday, but not yet. Findings informed a Web-based survey of smokers from across the United States (phase 2, n=116). Data were collected from April to September, 2016.

Results

Findings confirmed that although smokers not yet ready to quit are not actively seeking treatment or using cessation apps, most would be interested in using these programs to help them reduce or change their smoking behavior. Among phase 2 survey respondents, the app features, functions, and content rated most highly were (1) security of personal information; (2) the ability to track smoking, spending, and savings; (3) content that adaptively changes with one’s needs; (4) the ability to request support as needed; (5) the ability to earn and redeem awards for program use; (6) guidance on how to quit smoking; and (7) content specifically addressing management of nicotine withdrawal, stress, depression, and anxiety. Results generally did not vary by stage of change for quitting smoking (precontemplation vs contemplation). The least popular feature was the ability to share progress via social media. Relevant to future marketing or distribution considerations, smokers were price-sensitive and valued empirically validated programs. Program source, expert recommendations, and user ratings were also important considerations.

Conclusions

Smokers who are not yet ready to quit represent an important target group for intervention. Study findings suggest that many of these individuals are receptive to using mHealth tools to reduce or quit smoking, despite not having made a commitment to quit yet. The preferences for specific mHealth intervention features, functionality, and content outlined in this paper can aid addiction treatment experts, design specialists, and software developers interested in creating engaging interventions for smokers who want to quit in the future but are not yet committed to this important health goal.

Smokers' and drinkers' choice of smartphone applications and expectations of engagement: a think aloud and interview study

BACKGROUND

Public health organisations such as the National Health Service in the United Kingdom and the National Institutes of Health in the United States provide access to online libraries of publicly endorsed smartphone applications (apps); however, there is little evidence that users rely on this guidance. Rather, one of the most common methods of finding new apps is to search an online store. As hundreds of smoking cessation and alcohol-related apps are currently available on the market, smokers and drinkers must actively choose which app to download prior to engaging with it. The influences on this choice are yet to be identified. This study aimed to investigate 1) design features that shape users' choice of smoking cessation or alcohol reduction apps, and 2) design features judged to be important for engagement.

METHODS

Adult smokers (n = 10) and drinkers (n = 10) interested in using an app to quit/cut down were asked to search an online store to identify and explore a smoking cessation or alcohol reduction app of their choice whilst thinking aloud. Semi-structured interview techniques were used to allow participants to elaborate on their statements. An interpretivist theoretical framework informed the analysis. Verbal reports were audio recorded, transcribed verbatim and analysed using inductive thematic analysis.

RESULTS

Participants chose apps based on their immediate look and feel, quality as judged by others' ratings and brand recognition ('social proof'), and titles judged to be realistic and relevant. Monitoring and feedback, goal setting, rewards and prompts were identified as important for engagement, fostering motivation and autonomy. Tailoring of content, a non-judgmental communication style, privacy and accuracy were viewed as important for engagement, fostering a sense of personal relevance and trust. Sharing progress on social media and the use of craving management techniques in social settings were judged not to be engaging because of concerns about others' negative reactions.

CONCLUSIONS

Choice of a smoking cessation or alcohol reduction app may be influenced by its immediate look and feel, 'social proof' and titles that appear realistic. Design features that enhance motivation, autonomy, personal relevance and credibility may be important for engagement.

The Ethical Use of Mobile Health Technology in Clinical Psychiatry

The rapid rise of mobile health technologies, such as smartphone apps and wearable sensors, presents psychiatry with new tools of potential value in caring for patients. Novel diagnostic and therapeutic applications of these technologies have been developed in private industry and utilized in mental health, although these methods do not yet constitute standard of care. In this article, we provide an ethical perspective on the practical use of this novel modality by psychiatrists. We propose that in the present context of limited scientific research and regulatory oversight, mobile technologies should serve to enhance the psychiatrist-patient relationship, rather than replace it, to minimize potential clinical and ethical harm to vulnerable patients. We analyze areas of possible ethical tension between clinical practice and the consumer-driven mobile industry, and develop a decision-tree model for implementing ethical safeguards in practice, focused on managing risk to the therapeutic relationship, informed consent, confidentiality, and mutual alignment of treatment goals and expectations.