Initial Assessment of a Comprehensive Digital Smoking Cessation Program That Incorporates a Mobile App, Breath Sensor, and Coaching: Cohort Study

Effective Communication Supported by an App for Pregnant Women: Quantitative Longitudinal Study

BACKGROUND

In the medical field of obstetrics, communication plays a crucial role, and pregnant women, in particular, can benefit from interventions improving their self-reported communication behavior. Effective communication behavior can be understood as the correct transmission of information without misunderstanding, confusion, or losses. Although effective communication can be trained by patient education, there is limited research testing this systematically with an app-based digital intervention. Thus, little is known about the success of such a digital intervention in the form of a web-app, potential behavioral barriers for engagement, as well as the processes by which such a web-app might improve self-reported communication behavior.

OBJECTIVE

This study fills this research gap by applying a web-app aiming at improving pregnant women's communication behavior in clinical care. The goals of this study were to (1) uncover the potential risk factors for early dropout from the web-app and (2) investigate the social-cognitive factors that predict self-reported communication behavior after having used the web-app.

METHODS

In this study, 1187 pregnant women were recruited. They all started to use a theory-based web-app focusing on intention, planning, self-efficacy, and outcome expectancy to improve communication behavior. Mechanisms of behavior change as a result of exposure to the web-app were explored using stepwise regression and path analysis. Moreover, determinants of dropout were tested using logistic regression.

RESULTS

We found that dropout was associated with younger age (P=.014). Mechanisms of behavior change were consistent with the predictions of the health action process approach. The stepwise regression analysis revealed that action planning was the best predictor for successful behavioral change over the course of the app-based digital intervention (β=.331; P<.001). The path analyses proved that self-efficacy beliefs affected the intention to communicate effectively, which in turn, elicited action planning and thereby improved communication behavior (β=.017; comparative fit index=0.994; Tucker-Lewis index=0.971; root mean square error of approximation=0.055).

CONCLUSIONS

Our findings can guide the development and improvement of apps addressing communication behavior in the following ways in obstetric care. First, such tools would enable action planning to improve communication behavior, as action planning is the key predictor of behavior change. Second, younger women need more attention to keep them from dropping out. However, future research should build upon the gained insights by conducting similar internet interventions in related fields of clinical care. The focus should be on processes of behavior change and strategies to minimize dropout rates, as well as replicating the findings with patient safety measures.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT03855735; https://classic.clinicaltrials.gov/ct2/show/NCT03855735.

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

BACKGROUND

Digital therapies, especially smartphone apps for active and continuous smoking cessation support, are strongly emerging as an alternative smoking cessation therapy. In the Indian context, there is a growing interest in the use of app-based smoking cessation programs; however, there is limited evidence regarding their effectiveness in achieving long-term continuous abstinence.

OBJECTIVE

This study aimed to evaluate the long-term abstinence effect (up to 30-d abstinence postprogram completion) of a smartphone app, QuitSure, for smoking cessation in active smokers from India.

METHODS

In this prospective single-arm study, participants who signed up for the QuitSure app were enrolled in this study. The primary end point was the prolonged abstinence (PA) rate from weeks 1 to 4 (day 7 to day 30). Furthermore, data for withdrawal symptoms, relapse reasons, and reasons for not continuing the program were also assessed.

RESULTS

The quit rate was calculated considering only the participants who followed up and completed the survey sent to them (per protocol) at day 7 and at day 30, respectively. The PA rate at day 7 was found to be 64.5% (111/172; 95% CI 56% to 72%), and the PA rate at day 30 was found to be 55.8% (72/129; 95% CI 45% to 65%). Within the 7-day abstinence period, 60.4% (67/111) of the participants did not have any withdrawal symptoms. The most common mild withdrawal symptoms were mild sleep disturbance (21/111, 18.9%), mild digestive changes (19/111, 17.1%), and coughing (17/111, 15.3%). Severe withdrawal symptoms were rare, with only 5.4% (6/111) experiencing them. For those achieving 30-day postprogram abstinence, 85% (61/72) had no mild withdrawal symptoms, and 99% (71/72) had no severe withdrawal symptoms. Among successful quitters at day 7, a total of 72.1% (80/111) reported minimal to no cravings, which increased to 88% (63/72) at day 30. Furthermore, 78% (56/72) of those with PA at day 30 reported no change in weight or reduced weight. Among participants experiencing relapse, 48% (28/58) cited intense cravings, 28% (16/58) mentioned facing a tragedy, and 26% (15/58) reported relapsing due to alcohol consumption. The PA rates as a result of the QuitSure program were found to be better than those reported in the results of other smoking-cessation app programs' studies.

CONCLUSIONS

The QuitSure app yields high PA rates and ameliorates symptoms associated with smoking cessation. In order to obtain conclusive evidence regarding the effectiveness and efficacy of the QuitSure program, future research should include appropriate control measures. Nevertheless, the QuitSure program can serve as a valuable adjunct to a conventional smoking cessation treatment program to aid sustained abstinence.

Effect of digital health, biomarker feedback and nurse or midwife-led counselling interventions to assist pregnant smokers quit: a systematic review and meta-analysis

OBJECTIVE

To assess the effect of digital health (DH), biomarker feedback (BF) and nurse or midwife-led counselling (NoMC) interventions on abstinence in pregnant smokers during pregnancy and postpartum.

SETTINGS

Any healthcare setting servicing pregnant women, including any country globally.

PARTICIPANTS

Pregnant women of any social, ethnic or geographical background who smoke.

METHODS

We searched Embase, Medline, Web Of Science, Google Scholar, PsychINFO, CINAHL and PubMed between 2007 and November 2021. We included published original intervention studies in English with comparators (usual care or placebo). Two independent assessors screened and abstracted data. We performed a random-effects meta-analysis, assessed risk of bias with the Cochrane Tool and used Grading of Recommendations Assessment, Development and Evaluation to assess the quality of evidence.

RESULTS

We identified 57 studies and included 54 in the meta-analysis. Sixteen studies assessed DH (n=3961), 6 BF (n=1643), 32 NoMC (n=60 251), 1 assessed NoMC with BF (n=1120) and 2 NoMC with DH interventions (n=2107). DH interventions had moderate certainty evidence to achieve continuous abstinence (CA) at late pregnancy (4 studies; 2049 women; RR=1.98, 95% CI 1.08 to 3.64, p=0.03) and low certainty evidence to achieve point prevalence abstinence (PPA) postpartum (5 studies; 2238 women; RR=1.46, 95% CI 1.05 to 2.02, p=0.02). NoMC interventions had moderate certainty evidence to achieve PPA in late pregnancy (15 studies; 16 234 women; RR=1.54, 95% CI 1.16 to 2.06, p<0.01) and low certainty evidence to achieve PPA postpartum (13 studies; 5466 women; RR=1.79, 95% CI 1.14 to 2.83, p=0.01). Both DH and BF interventions did not achieve PPA at late pregnancy, nor NoMC interventions achieve CA postpartum. The certainty was reduced due to risk of bias, heterogeneity, inconsistency and/or imprecision.

CONCLUSION

NoMC interventions can assist pregnant smokers achieve PPA and DH interventions achieve CA in late pregnancy. These interventions may achieve other outcomes.

Effectiveness of Health Coaching in Smoking Cessation and Promoting the Use of Oral Smoking Cessation Drugs in Patients with Type 2 Diabetes: A Randomized Controlled Trial

INTRODUCTION

This study looked into the effectiveness of a 6 month health coaching intervention in smoking cessation and smoking reduction for patients with type 2 diabetes.

METHODS

The study was carried out via a two-armed, double-blind, randomized-controlled trial with 68 participants at a medical center in Taiwan. The intervention group received health coaching for 6 months, while the control group only received usual smoking cessation services; some patients in both groups participated in a pharmacotherapy plan. The health coaching intervention is a patient-centered approach to disease management which focuses on changing their actual behaviors. By targeting on achieving effective adult learning cycles, health coaching aims to help patients to establish new behavior patterns and habits.

RESULTS

In this study, the intervention group had significantly more participants who reduced their level of cigarette smoking by at least 50% than the control group (p = 0.030). Moreover, patients participating in the pharmacotherapy plan in the coaching intervention group had a significant effect on smoking cessation (p = 0.011), but it was insignificant in the control group.

CONCLUSIONS

Health coaching can be an effective approach to assisting patients with type 2 diabetes participating in a pharmacotherapy plan to reduce smoking and may help those who participate in pharmacotherapy plan to quit smoking more effectively. Further studies with higher-quality evidence on the effectiveness of health coaching in smoking cessation and the use of oral smoking cessation drugs in patients with type 2 diabetes are needed.

Technology and Mental Health: State of the Art for Assessment and Treatment

Technology is ubiquitous in society and is now being extensively used in mental health applications. Both assessment and treatment strategies are being developed and deployed at a rapid pace. The authors review the current domains of technology utilization, describe standards for quality evaluation, and forecast future developments. This review examines technology-based assessments of cognition, emotion, functional capacity and everyday functioning, virtual reality approaches to assessment and treatment, ecological momentary assessment, passive measurement strategies including geolocation, movement, and physiological parameters, and technology-based cognitive and functional skills training. There are many technology-based approaches that are evidence based and are supported through the results of systematic reviews and meta-analyses. Other strategies are less well supported by high-quality evidence at present, but there are evaluation standards that are well articulated at this time. There are some clear challenges in selection of applications for specific conditions, but in several areas, including cognitive training, randomized clinical trials are available to support these interventions. Some of these technology-based interventions have been approved by the U.S. Food and Drug administration, which has clear standards for which types of applications, and which claims about them, need to be reviewed by the agency and which are exempt.

Outcomes of a Comprehensive Mobile Smoking Cessation Program With Nicotine Replacement Therapy in Adult Smokers: Pilot Randomized Controlled Trial

BACKGROUND

Cigarette smoking remains the leading cause of preventable illness and death, underscoring ongoing need for evidence-based solutions. Pivot, a US Clinical Practice Guideline-based mobile smoking cessation program, comprises a personal carbon monoxide breath sensor; a smartphone app; in-app, text-based human-provided coaching; nicotine replacement therapy; and a moderated web-based community. Promising Pivot cohort studies have established the foundation for comparative assessment.

OBJECTIVE

This study aimed to compare engagement, retention, attitudes toward quitting smoking, smoking behavior, and participant feedback between Pivot and QuitGuide, a US Clinical Practice Guideline-based smoking cessation smartphone app from the National Cancer Institute.

METHODS

In this remote pilot randomized controlled trial, cigarette smokers in the United States were recruited on the web and randomized to Pivot or QuitGuide. Participants were offered 12 weeks of free nicotine replacement therapy. Data were self-reported via weekly web-based questionnaires for 12 weeks and at 26 weeks. Outcomes included engagement and retention, attitudes toward quitting smoking, smoking behavior, and participant feedback. The primary outcome was self-reported app openings at 12 weeks. Cessation outcomes included self-reported 7- and 30-day point prevalence abstinence (PPA), abstinence from all tobacco products, and continuous abstinence at 12 and 26 weeks. PPA and continuous abstinence were biovalidated via breath carbon monoxide samples.

RESULTS

Participants comprised 188 smokers (94 Pivot and 94 QuitGuide): mean age 46.4 (SD 9.2) years, 104 (55.3%) women, 128 (68.1%) White individuals, and mean cigarettes per day 17.6 (SD 9.0). Engagement via mean "total app openings through 12 weeks" (primary outcome) was Pivot, 157.9 (SD 210.6) versus QuitGuide, 86.5 (SD 66.3; P<.001). Self-reported 7-day PPA at 12 and 26 weeks was Pivot, 35% (33/94) versus QuitGuide, 28% (26/94; intention to treat [ITT]: P=.28) and Pivot, 36% (34/94) versus QuitGuide, 27% (25/94; ITT: P=.12), respectively. Self-reported 30-day PPA at 12 and 26 weeks was Pivot, 29% (27/94) versus QuitGuide, 22% (21/94; ITT: P=.32) and Pivot, 32% (30/94) versus QuitGuide, 22% (21/94; ITT: P=.12), respectively. The biovalidated abstinence rate at 12 weeks was Pivot, 29% (27/94) versus QuitGuide, 13% (12/94; ITT: P=.008). Biovalidated continuous abstinence at 26 weeks was Pivot, 21% (20/94) versus QuitGuide, 10% (9/94; ITT: P=.03). Participant feedback, including ease of setup, impact on smoking, and likelihood of program recommendation were favorable for Pivot.

CONCLUSIONS

In this randomized controlled trial comparing the app-based smoking cessation programs Pivot and QuitGuide, Pivot participants had higher engagement and biovalidated cessation rates and more favorable user feedback at 12 and 26 weeks. These findings support Pivot as an effective, durable mobile smoking cessation program.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04955639; https://clinicaltrials.gov/ct2/show/NCT04955639.

Delivery of smoking cessation treatment via live chat: An analysis of client-centered coaching skills and behavior change techniques

OBJECTIVE

This qualitative study explored whether evidence-based tobacco cessation treatment components can be deployed via web-based live chat coaching.

METHODS

N = 100 randomly selected chats were coded. Researchers used a structured coding guide to note the presence of 3 Motivational Interviewing (MI) skills and 61 behavior change techniques (BCTs).

RESULTS

MI skills were observed in 86% of chats: 31 chats incorporated one skill, 31 incorporated two, and 24 incorporated all three. Open-ended questions were most common (76%), followed by affirmations (47%) and reflective listening statements (38%). BCTs were observed in 100% of chats: 21% involved one-five BCTs, 69% involved six-10 BCTs, and 10% involved 11 or more BCTs. Mean number of BCTs per chat was 7.25 (SD=2.5; range 2-17). The most common BCTs were Social Support (99%), Reward/Threat (95%), Natural Consequences (82%), Regulation (82%), Goals/Planning (64%), and Self Belief (42%).

CONCLUSIONS

Tobacco cessation coaching using MI skills and evidence-based BCTs can be delivered via live chat. This synchronous modality allows the delivery of an intervention tailored to the user's motivations and goals.

PRACTICE IMPLICATIONS

Web-based live chat can broaden the reach of tobacco treatment specialists to deploy evidence-based counseling skills and behavior change techniques in personalized, accessible coaching.

Practice Quit Attempts: Scoping Review of a Novel Intervention Strategy

OBJECTIVE

Fostering practice quit attempts (PQAs)--that is, attempts to not smoke for a few hours or days, without pressure to permanently quit--represents a potential means to engage more individuals who smoke in efforts to change their smoking. However, little is known about interventions designed to foster PQAs. We aimed to identify the available evidence on PQA-focused intervention strategies and their impact on quit attempt and cessation outcomes.

METHOD

We conducted a scoping review of behavioral and pharmacological treatment studies targeting PQAs among adult cigarette smokers.

RESULTS

The systematic literature search yielded 3,879 articles, and the full-text review was narrowed to 86. Twenty-three studies were deemed relevant, and 5 were added through other sources, yielding 28 studies total. Fifteen studies included behavioral intervention techniques focused on the development and rehearsal of individualized coping skills, whereas eight studies provided brief advice/instruction. More than half of the PQA-focused interventions incorporated sampling of nicotine replacement products, through either guided or ad lib use. Five studies reported on PQA-focused digital health interventions that prompted brief abstinence challenges. Of eight large-scale controlled trials, six demonstrated an increase in quit attempt and cessation outcomes among the PQA-focused intervention group.

CONCLUSIONS

Fostering PQAs through behavioral and pharmacological interventions offers a promising technique for cessation induction that warrants future research.

Challenges in Participant Engagement and Retention using Mobile Health Apps: A Literature Review (Preprint)

Background

Mobile health (mHealth) apps are revolutionizing the way clinicians and researchers monitor and manage the health of their participants. However, many studies using mHealth apps are hampered by substantial participant dropout or attrition, which may impact the representativeness of the sample and the effectiveness of the study. Therefore, it is imperative for researchers to understand what makes participants stay with mHealth apps or studies using mHealth apps.

Objective

This study aimed to review the current peer-reviewed research literature to identify the notable factors and strategies used in adult participant engagement and retention.

Methods

We conducted a systematic search of PubMed, MEDLINE, and PsycINFO databases for mHealth studies that evaluated and assessed issues or strategies to improve the engagement and retention of adults from 2015 to 2020. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Notable themes were identified and narratively compared among different studies. A binomial regression model was generated to examine the factors affecting retention.

Results

Of the 389 identified studies, 62 (15.9%) were included in this review. Overall, most studies were partially successful in maintaining participant engagement. Factors related to particular elements of the app (eg, feedback, appropriate reminders, and in-app support from peers or coaches) and research strategies (eg, compensation and niche samples) that promote retention were identified. Factors that obstructed retention were also identified (eg, lack of support features, technical difficulties, and usefulness of the app). The regression model results showed that a participant is more likely to drop out than to be retained.

Conclusions

Retaining participants is an omnipresent challenge in mHealth studies. The insights from this review can help inform future studies about the factors and strategies to improve participant retention.

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

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

What Works Best to Engage Participants in Mobile App Interventions and e-Health: A Scoping Review

Background:

Despite the growing popularity of mobile app interventions, specific engagement components of mobile apps have not been well studied.

Methods:

The objectives of this scoping review are to determine which components of mobile health intervention apps encouraged or hindered engagement, and examine how studies measured engagement.

Results:

A PubMed search on March 5, 2020 yielded 239 articles that featured the terms engagement, mobile app/mobile health, and adult. After applying exclusion criteria, only 54 studies were included in the final analysis.

Discussion:

Common app components associated with increased engagement included: personalized content/feedback, data visualization, reminders/push notifications, educational information/material, logging/self-monitoring functions, and goal-setting features. On the other hand, social media integration, social forums, poor app navigation, and technical difficulties appeared to contribute to lower engagement rates or decreased usage. Notably, the review revealed a great variability in how engagement with mobile health apps is measured due to lack of established processes.

Conclusion:

There is a critical need for controlled studies to provide guidelines and standards to help facilitate engagement and its measurement in research and clinical trial work using mobile health intervention apps.

Remote Patient Monitoring and Incentives to Support Smoking Cessation Among Pregnant and Postpartum Medicaid Members: Three Randomized Controlled Pilot Studies

BACKGROUND

Smoking rates among low-income individuals, including those eligible for Medicaid, have not shown the same decrease that is observed among high-income individuals. The rate of smoking among pregnant women enrolled in Medicaid is almost twice that among privately insured women, which leads to significant disparities in birth outcomes and a disproportionate cost burden placed on Medicaid. Several states have identified maternal smoking as a key target for improving birth outcomes and reducing health care expenditures; however, efficacious, cost-effective, and feasible cessation programs have been elusive.

OBJECTIVE

This study aims to examine the feasibility, acceptability, and effectiveness of a smartwatch-enabled, incentive-based smoking cessation program for Medicaid-eligible pregnant smokers.

METHODS

Pilot 1 included a randomized pilot study of smartwatch-enabled remote monitoring versus no remote monitoring for 12 weeks. Those in the intervention group also received the SmokeBeat program. Pilot 2 included a randomized pilot study of pay-to-wear versus pay-to-quit for 4 weeks. Those in a pay-to-wear program could earn daily incentives for wearing the smartwatch, whereas those in pay-to-quit program could earn daily incentives if they wore the smartwatch and abstained from smoking. Pilot 3, similar to pilot 2, had higher incentives and a duration of 3 weeks.

RESULTS

For pilot 1 (N=27), self-reported cigarettes per week among the intervention group declined by 15.1 (SD 27) cigarettes over the study; a similar reduction was observed in the control group with a decrease of 17.2 (SD 19) cigarettes. For pilot 2 (N=8), self-reported cigarettes per week among the pay-to-wear group decreased by 43 cigarettes (SD 12.6); a similar reduction was seen in the pay-to-quit group, with an average of 31 (SD 45.6) fewer cigarettes smoked per week. For pilot 3 (N=4), one participant in the pay-to-quit group abstained from smoking for the full study duration and received full incentives.

CONCLUSIONS

Decreases in smoking were observed in both the control and intervention groups during all pilots. The use of the SmokeBeat program did not significantly improve cessation. The SmokeBeat program, remote cotinine testing, and remote delivery of financial incentives were considered feasible and acceptable. Implementation challenges remain for providing evidence-based cessation incentives to low-income pregnant smokers. The feasibility and acceptability of the SmokeBeat program were moderately high. Moreover, the feasibility and acceptability of remote cotinine testing and the remotely delivered contingent financial incentives were successful.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03209557; https://clinicaltrials.gov/ct2/show/NCT03209557.

Durability of Abstinence After Completing a Comprehensive Digital Smoking Cessation Program Incorporating a Mobile App, Breath Sensor, and Coaching: Cohort Study

BACKGROUND

Despite decreasing prevalence over the last several decades, cigarette smoking remains the leading cause of preventable death and disease, underscoring the need for innovative, effective solutions. Pivot is a novel, inclusive smoking cessation program designed for smokers along the entire spectrum of readiness to quit. Pivot leverages proven methods and technological advancements, including a personal portable breath carbon monoxide sensor, smartphone app, and in-app text-based coaching. We previously reported outcomes from the end of active Pivot program participation in 319 adult smokers. Herein, we report longer-term follow up in this cohort.

OBJECTIVE

The aim of this study was to assess and report participant outcomes 3 months after completion of Pivot, including smoking behavior, quit rates, continuous abstinence rates and durability, and predictors of abstinence.

METHODS

This prospective remote cohort study included US-based cigarette smokers aged 18 to 65 years who smoked ≥5 cigarettes per day (CPD). Three months after completion of active participation in Pivot, final follow-up data were collected via an online questionnaire. Outcomes included smoking behavior (CPD and quit attempts), self-reported quit rates (7- and 30-day point prevalence abstinence [PPA]), and continuous abstinence rates (proportion who achieved uninterrupted abstinence) and duration. Exploratory regression analyses were performed to identify baseline characteristics associated with achievement of 7-day PPA, 30-day PPA, and continuous abstinence.

RESULTS

A total of 319 participants completed onboarding (intention-to-treat [ITT]); 288/319 participants (90.3%) completed follow up (completers) at a mean of 7.2 (SD 1.2) months after onboarding. At final follow up, CPD were reduced by 52.6% (SE 2.1; P<.001) among all 319 participants, and most completers (152/288, 52.8%) reduced their CPD by at least 50%. Overall, most completers (232/288, 80.6%) made at least one quit attempt. Quit rates increased after the end of Pivot; using ITT analyses, 35.4% (113/319) achieved 7-day PPA and 31.3% (100/319) achieved 30-day PPA at final follow up compared with 32.0% (102/319) and 27.6% (88/319), respectively, at the end of the Pivot program. Continuous abstinence was achieved in about a quarter of those who onboarded (76/319, 23.8%) and in most who reported 30-day PPA at the end of Pivot (76/88, 86.4%), with a mean abstinence duration of 5.8 (SD 0.6) months. In exploratory regression analyses, lower baseline CPD, more positive baseline attitudes reflecting higher self-efficacy (higher confidence to quit and lower perceived difficulty of quitting), and higher education were associated with achieving abstinence.

CONCLUSIONS

This study provides the first longer-term outcomes of the Pivot smoking cessation program. At final follow up, quit rates increased and continuous abstinence was favorable; the majority who achieved abstinence at the end of Pivot sustained abstinence throughout follow up. Decreases in CPD persisted and most participants made a quit attempt. Overall, final follow-up outcomes were stable or improved when compared to previous outcomes from the end of the program. These findings validate earlier results, and suggest that Pivot is an effective and durable solution for smoking cessation.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03295643; https://clinicaltrials.gov/ct2/show/NCT03295643.

Digital Health in Cardiac Rehabilitation and Secondary Prevention: A Search for the Ideal Tool

Digital health is becoming more integrated in daily medical practice. In cardiology, patient care is already moving from the hospital to the patients' homes, with large trials showing positive results in the field of telemonitoring via cardiac implantable electronic devices (CIEDs), monitoring of pulmonary artery pressure via implantable devices, telemonitoring via home-based non-invasive sensors, and screening for atrial fibrillation via smartphone and smartwatch technology. Cardiac rehabilitation and secondary prevention are modalities that could greatly benefit from digital health integration, as current compliance and cardiac rehabilitation participation rates are low and optimisation is urgently required. This viewpoint offers a perspective on current use of digital health technologies in cardiac rehabilitation, heart failure and secondary prevention. Important barriers which need to be addressed for implementation in medical practice are discussed. To conclude, a future ideal digital tool and integrated healthcare system are envisioned. To overcome personal, technological, and legal barriers, technological development should happen in dialog with patients and caregivers. Aided by digital technology, a future could be realised in which we are able to offer high-quality, affordable, personalised healthcare in a patient-centred way.

Preliminary Outcomes of a Digital Therapeutic Intervention for Smoking Cessation in Adult Smokers: Randomized Controlled Trial

BACKGROUND

Tobacco smoking remains the leading cause of preventable death and disease worldwide. Digital interventions delivered through smartphones offer a promising alternative to traditional methods, but little is known about their effectiveness.

OBJECTIVE

Our objective was to test the preliminary effectiveness of Quit Genius, a novel digital therapeutic intervention for smoking cessation.

METHODS

A 2-arm, single-blinded, parallel-group randomized controlled trial design was used. Participants were recruited via referrals from primary care practices and social media advertisements in the United Kingdom. A total of 556 adult smokers (aged 18 years or older) smoking at least 5 cigarettes a day for the past year were recruited. Of these, 530 were included for the final analysis. Participants were randomized to one of 2 interventions. Treatment consisted of a digital therapeutic intervention for smoking cessation consisting of a smartphone app delivering cognitive behavioral therapy content, one-to-one coaching, craving tools, and tracking capabilities. The control intervention was very brief advice along the Ask, Advise, Act model. All participants were offered nicotine replacement therapy for 3 months. Participants in a random half of each arm were pseudorandomly assigned a carbon monoxide device for biochemical verification. Outcomes were self-reported via phone or online. The primary outcome was self-reported 7-day point prevalence abstinence at 4 weeks post quit date.

RESULTS

A total of 556 participants were randomized (treatment: n=277; control: n=279). The intention-to-treat analysis included 530 participants (n=265 in each arm; 11 excluded for randomization before trial registration and 15 for protocol violations at baseline visit). By the quit date (an average of 16 days after randomization), 89.1% (236/265) of those in the treatment arm were still actively engaged. At the time of the primary outcome, 74.0% (196/265) of participants were still engaging with the app. At 4 weeks post quit date, 44.5% (118/265) of participants in the treatment arm had not smoked in the preceding 7 days compared with 28.7% (76/265) in the control group (risk ratio 1.55, 95% CI 1.23-1.96; P<.001; intention-to-treat, n=530). Self-reported 7-day abstinence agreed with carbon monoxide measurement (carbon monoxide <10 ppm) in 96% of cases (80/83) where carbon monoxide readings were available. No harmful effects of the intervention were observed.

CONCLUSIONS

The Quit Genius digital therapeutic intervention is a superior treatment in achieving smoking cessation 4 weeks post quit date compared with very brief advice.

TRIAL REGISTRATION

International Standard Randomized Controlled Trial Number (ISRCTN) 65853476; https://www.isrctn.com/ISRCTN65853476.

Assessment of a Personal Interactive Carbon Monoxide Breath Sensor in People Who Smoke Cigarettes: Single-Arm Cohort Study

Background

Tobacco use is the leading cause of preventable morbidity and mortality. Existing evidence-based treatments are underutilized and have seen little recent innovation. The success of personal biofeedback interventions in other disease states portends a similar opportunity in smoking cessation. The Pivot Breath Sensor is a personal interactive FDA-cleared (over-the-counter) device that measures carbon monoxide (CO) in exhaled breath, enabling users to link their smoking behavior and CO values, and track their progress in reducing or quitting smoking.

Objective

The objective of this study is to assess the Pivot Breath Sensor in people who smoke cigarettes, evaluating changes in attitudes toward quitting smoking, changes in smoking behavior, and use experience.

Methods

US adults (18-80 years of age, ≥10 cigarettes per day [CPD]) were recruited online for this remote 12-week study. Participants completed a screening call, informed consent, and baseline questionnaire, and then were mailed their sensor. Participants were asked to submit 4 or more breath samples per day and complete questionnaires at 1-4, 8, and 12 weeks. Outcomes included attitudes toward quitting smoking (Stage of Change, success to quit, and perceived difficulty of quitting), smoking behavior (quit attempts, CPD reduction, and 7-, 30-day point prevalence abstinence [PPA]), and use experience (impact and learning).

Results

Participants comprised 234 smokers, mean age 39.9 (SD 11.3) years, 52.6% (123/234) female, mean CPD 20.3 (SD 8.0). The 4- and 12-week questionnaires were completed by 92.3% (216/234) and 91.9% (215/234) of participants, respectively. Concerning attitude outcomes, at baseline, 15.4% (36/234) were seriously thinking of quitting in the next 30 days, increasing to 38.9% (84/216) at 4 weeks and 47.9% (103/215) at 12 weeks (both P<.001). At 12 weeks, motivation to quit was increased in 39.1% (84/215), unchanged in 54.9% (118/215), and decreased in 6.0% (13/215; P<.001). Additional attitudes toward quitting improved from baseline to 12 weeks: success to quit 3.3 versus 5.0 (P<.001) and difficulty of quitting 2.8 versus 4.3 (P<.001). Regarding smoking behavior, at 4 weeks, 28.2% (66/234) had made 1 or more quit attempts (≥1 day of abstinence), increasing to 48.3% (113/234) at 12 weeks. At 4 weeks, 23.1% (54/234) had reduced CPD by 50% or more, increasing to 38.5% (90/234) at 12 weeks. At 12 weeks, CPD decreased by 41.1% from baseline (P<.001), and 7- and 30-day PPA were 12.0% (28/234) and 6.0% (14/234), respectively. Concerning use experience, 75.3% (171/227) reported the sensor increased their motivation to quit. More than 90% (>196/214) indicated the sensor taught them about their CO levels and smoking behavior, and 73.1% (166/227) reported that seeing their CO values made them want to quit smoking.

Conclusions

Use of the Pivot Breath Sensor resulted in a significant increase in motivation to quit, a reduction in CPD, and favorable quit attempt rates. These outcomes confer increased likelihood of quitting smoking. Accordingly, the results support a role for biofeedback via personal CO breath sampling in smoking cessation.

Trial Registration

ClinicalTrials.gov NCT04133064; https://clinicaltrials.gov/ct2/show/NCT04133064

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.

Who is Tracking Health on Mobile Devices: Behavioral Logfile Analysis in Hong Kong

Background

Health apps on mobile devices provide an unprecedented opportunity for ordinary people to develop social connections revolving around health issues. With increasing penetration of mobile devices and well-recorded behavioral data on such devices, it is desirable to employ digital traces on mobile devices rather than self-reported measures to capture the behavioral patterns underlying the use of mobile health (mHealth) apps in a more direct and valid way.

Objective

The objectives of this study were to (1) assess the demographic predictors of the adoption of mHealth apps; (2) investigate the temporal pattern underlying the use of mHealth apps; and (3) explore the impacts of demographic variables, temporal features, and app genres on the use of mHealth apps.

Methods

Logfile data of mobile devices were collected from a representative panel of about 2500 users in Hong Kong. Users’ mHealth app activities were analyzed. We first conducted a binary logistic regression analysis to uncover demographic predictors of users’ adoption status. Then we utilized a multilevel negative binomial regression to examine the impacts of demographic characteristics, temporal features, and app genres on mHealth app use.

Results

It was found that 27.5% of mobile device users in Hong Kong adopt at least one genre of mHealth app. Adopters of mHealth apps tend to be female and better educated. However, demographic characteristics did not showcase the predictive powers on the use of mHealth apps, except for the gender effect (B_female vs B_male=–0.18; P=.006). The use of mHealth apps demonstrates a significant temporal pattern, which is found to be moderately active during daytime and intensifying at weekends and at night. Such temporal patterns in mHealth apps use are moderated by individuals’ demographic characteristics. Finally, demographic characteristics were also found to condition the use of different genres of mHealth apps.

Conclusions

Our findings suggest the importance of dynamic perspective in understanding users’ mHealth app activities. mHealth app developers should consider more the demographic differences in temporal patterns of mHealth apps in the development of mHealth apps. Furthermore, our research also contributes to the promotion of mHealth apps by emphasizing the differences of usage needs for various groups of users.