COach2Quit: A Pilot Randomized Controlled Trial of a Personal Carbon Monoxide Monitor for Smoking Cessation

Evaluation of effectiveness and acceptability of a psychological treatment for smoking cessation combined with a smartphone App: A pilot study

Despite the increasing number of mobile-based interventions to quit smoking over the last years, few studies have investigated the efficacy of smoking cessation interventions blended with smartphone Apps. The present pilot study aims to examine the preliminary effectiveness and acceptability of a cognitive-behavioral treatment combined with a smartphone App, compared to the same psychological treatment without the App. The sample comprised 206 treatment-seeking smokers, who were assigned to: 1) an experimental group receiving a cognitive-behavioral intervention combined with the "Non Fumo" App (n = 102), and 2) a control group receiving only the cognitive-behavioral intervention to quit smoking (n = 104). Results concerning the primary outcomes showed no significant differences between conditions in point-prevalence abstinence rates at 12-month follow-up (35.30 % in the experimental group vs. 31.70 % in the control group) and in treatment acceptability. Regarding the secondary outcomes, both groups obtained similar point-prevalence abstinence rates at the end of treatment (61.80 % vs. 65.40 %), at 3-month (42.20 % vs. 45.20 %, respectively) and 6-month follow-ups (37.30 % vs. 37.50 %). No significant differences were found between conditions in prolonged abstinence rates at 6-month (35.3 % vs. 35.6 %) and 12-month follow-ups (30.4 % vs. 26.9 %). Overall, good abstinence rates and treatment acceptability were obtained, although there were no significant differences between conditions. More research is needed to establish clear conclusions about the efficacy of psychological smoking cessation treatments blended with smartphone Apps.

Additive effects of adjunctive app-based interventions for mental disorders - A systematic review and meta-analysis of randomised controlled trials

Background

It is uncertain whether app-based interventions add value to existing mental health care.

Objective

To examine the incremental effects of app-based interventions when used as adjunct to mental health interventions.

Methods

We searched PubMed, PsycINFO, Scopus, Web of Science, and Cochrane Library databases on September 15th, 2023, for randomised controlled trials (RCTs) on mental health interventions with an adjunct app-based intervention compared to the same intervention-only arm for adults with mental disorders or respective clinically relevant symptomatology. We conducted meta-analyses on symptoms of different mental disorders at postintervention. PROSPERO, CRD42018098545.

Results

We identified 46 RCTs (4869 participants). Thirty-two adjunctive app-based interventions passively or actively monitored symptoms and behaviour, and in 13 interventions, the monitored data were sent to a therapist. We found additive effects on symptoms of depression (g = 0.17; 95 % CI 0.02 to 0.33; k = 7 comparisons), anxiety (g = 0.80; 95 % CI 0.06 to 1.54; k = 3), mania (g = 0.2; 95 % CI 0.02 to 0.38; k = 4), smoking cessation (g = 0.43; 95 % CI 0.29 to 0.58; k = 10), and alcohol use (g = 0.23; 95 % CI 0.08 to 0.39; k = 7). No significant effects were found on symptoms of depression within a bipolar disorder (g = -0.07; 95 % CI -0.37 to 0.23, k = 4) and eating disorders (g = -0.02; 95 % CI -0.44 to 0.4, k = 3). Studies on depression, mania, smoking, and alcohol use had a low heterogeneity between the trials. For other mental disorders, only single studies were identified. Only ten studies had a low risk of bias, and 25 studies reported insufficient statistical power.

Discussion

App-based interventions may be used to enhance mental health interventions to further reduce symptoms of depression, anxiety, mania, smoking, and alcohol use. However, the effects were small, except for anxiety, and limited due to study quality. Further high-quality research with larger sample sizes is warranted to better understand how app-based interventions can be most effectively combined with established interventions to improve outcomes.

Ecological momentary interventions for smoking cessation: a systematic review and meta-analysis

BACKGROUND AND OBJECTIVES

Tobacco use is an important cause of preventable mortality and morbidity worldwide. Only 7% of smokers successfully quit annually, despite numerous evidence-based smoking cessation treatments. An important reason for failure is barriers to accessing appropriate smoking cessation interventions, which can be minimized by technology-delivered interventions, such as ecological momentary interventions. Ecological momentary interventions provide the right type and intensity of treatment in real time, based on ecological momentary assessments of relevant variables. The aim of this review was to assess the effectiveness of ecological momentary interventions in smoking cessation.

METHODS

We searched MEDLINE, Scopus, CENTRAL, psychINFO, and ProQuest without applying any filters on 19 September, 2022. One author screened search results for obvious irrelevant and duplicate studies. The remaining studies were independently reviewed by two authors to exclude irrelevant studies, and then they extracted data from the included studies. We collated study findings, transformed data into a common rubric, and calculated a weighted treatment effect across studies using Review Manager 5.

FINDINGS

We analyzed 10 studies with a total of 2391 participants. Assessment methods included exhaled CO analyzers, bidirectional SMS, data input in apps, and hand movement detection. Interventions were based on acceptance and commitment therapy and cognitive behavioral therapy. Smoking abstinence was significantly higher in participants of intervention groups compared to control groups (RR = 1.24; 95% CI 1.07-1.44, P = 0.004; I2 = 0%).

CONCLUSION

Ecological momentary intervention is a novel area of research in behavioral science. The results of this systematic review based on the available literature suggest that these interventions could be beneficial for smoking cessation.

Influence of Milk on Exhaled Carbon Monoxide (CO) Measurement by Portable CO Monitors

Background

A portable breath carbon monoxide (CO) monitor has a high cross-sensitivity to hydrogen (H₂). This study examined the influences of H₂ after consuming milk on the detected CO values using three types of portable CO monitors.

Materials and Methods

Exhaled breath from seven participants (four healthy nonsmokers and three smokers with otherwise unknown comorbidities) was collected in sampling bags. The participants then consumed 200 mL of milk, and the exhaled breath of each was collected in separate bags every 30 minutes until 9 hours later. CO and H₂ in the bag were measured using a gas chromatograph as a reference analyzer, and CO was also measured using three types of portable CO monitors.

Results

After consuming milk, H₂ levels were significantly higher, and CO levels were not significantly elevated as measured by the reference analyzer. However, CO levels in monitors A and B were significantly elevated, even though participants did not smoke. The H₂ levels in the reference analyzer significantly increased and reached a maximum 4.5 hours after consuming milk. The difference in CO levels between the reference analyzer and each monitor increased significantly after 5 or 5.5 hours.

Conclusions

This study suggested that the breath CO monitors with a cross-sensitivity to H₂ responded to H₂ as CO in the exhaled gas and measured higher than actual values after milk consumption. The extent of the influence of H₂ differed depending on the type of CO monitor. It is necessary to consider milk consumption when assessing the smoking status of people using portable CO monitors.

Development, validation and transfer to clinical practice of a mobile application for the treatment of smoking

OBJECTIVE

The main objective is to transfer to clinical practice a new smoking cessation application ("Vive sin Tabaco" a) in all health centers of the public Basque Health Service.

DESIGN

An implementation study of a smoking cessation program previously validated. After implementation, a retrospective study has been carried out to evaluate its use under normal conditions. SITE: The process of transfer to clinical practice has been held in several phases; first a pilotage in four health centers of Alava and subsequently, when all reported incidents were resolved, it was extended to all health centers of the Basque Health Service.

INTERVENTION AND MAIN MEASUREMENT

Development of "Vive sin Tabaco"; a corporate tool for smoking cessation, and its transfer to clinical practice. All interested health care workers received training on how to use the application. User manuals for both patients and professionals were developed. Smoking cessation rates at 12 months during implementation were also collected.

RESULTS

The percentage of patients of post pilot phase who quit smoking at 12 months was 14.1%.

CONCLUSIONS

The conception of "Vive sin tabaco" as a corporate tool for smoking cessation, available in all health centers of Basque Health Service, has been long and arduous, and has required the participation of health professionals and patients as end-users in order to obtain a tool that adapts to their expectations and guarantees greater usability and satisfaction. This application is being effective as an adjuvant tool to health advice.

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

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.

Feasibility of a Smoking Cessation Smartphone App (Quit with US) for Young Adult Smokers: A Single Arm, Pre-Post Study

While smartphone applications (apps) have been shown to enhance success with smoking cessation, no study has been conducted among young adult smokers aged 18-24 years in Thailand. Quit with US was developed based on the 5 A's model and self-efficacy theory. This single arm, pre-post study was conducted aiming to assess results after using Quit with US for 4 weeks. The primary outcome was a biochemically verified 7-day point prevalence of smoking abstinence. The secondary outcomes included smoking behaviors, knowledge and attitudes toward smoking and smoking cessation, and satisfaction and confidence in the smartphone app. A total number of 19 young adult smokers were included; most participants were males (68.4%) with the mean (SD) age of 20.42 (1.46) years. After 4 weeks of study, the primary outcome demonstrated a smoking cessation rate of 31.6%. All 19 participants expressed better smoking behaviors and better knowledge and attitudes toward smoking and smoking cessation. Further, they were satisfied with the smartphone app design and content and expressed confidence in using it. These findings provided preliminary evidence that Quit with US was found to be a potentially effective smoking cessation smartphone app for young adult smokers.

An innovation involving self-surveillance and serious gaming to increase smoking quit rate: Protocol for a pilot randomized controlled trial

Smoking is a health hazard. Current smoking cessation measures such as behavioral change counselling by trained professionals, nicotine replacement therapy and medications have limited success. Smoking intensity is assessed using a portable device to measure the smokers’ exhaled breath carbon monoxide (eCO) level. A systematic review suggests the potential of serious gaming to increase smoking quit rate. However, the related studies were unable to explain and determine the effect gamification on smoking cessation.

A handy personalized eCO measurement device linked to a smart-phone applications (app) has been developed (integrated STEADES-2 system). This novel system incorporates app-based video and print learning resources, authentication function and gamification using the eCO data as game element. Trained multidisciplinary healthcare professionals access the STEADES-2 data to monitor smoking status and support smokers via asynchronous virtual coaching. The pilot randomized controlled trial will enroll 20 smokers to use the STEADES-2 system (intervention group) and another 20 to the existing smoking cessation programme (control group) in primary care. The primary feasibility outcomes will include the recruitment response rate, the smokers’ usability of the STEADES-2 system, their self eCO monitoring, frequencies of participation in the serious games and interactions with their virtual coaches. Their smoking literacy, utility and experience of the STEADES-2 system are other outcomes. Smokers in both groups will be compared on their cigarette abstinence as secondary outcome based on eCO levels and urine cotinine test (primary outcomes after 12 weeks).

The results will be disseminated via conferences and publications.

Can You Believe Your Patients If They Say They Have Quit Smoking?

OBJECTIVES

To determine the reliability of self-reported smoking status in the orthopaedic trauma population and determine if certain patient factors might predispose inaccurate self-reported smoking cessation.

DESIGN

Prospective.

SETTING

Level I trauma center.

PATIENTS

Two hundred forty-seven orthopaedic trauma patients were included in the study.

INTERVENTION

In-office measurement of exhaled carbon monoxide (CO).

MAIN OUTCOME MEASUREMENTS

Self-reported smoking cessation with exhaled CO measurements.

RESULTS

A total of 906 self-reported surveys were completed over 4 follow-up visits. Of the responses indicating smoking cessation (n = 174), 12.6% [95% confidence interval (CI), 0.081-0.185] reported smoking cessation with positive CO readings, suggesting inaccurate self-reporting of smoking status. Over 20% of those patients inaccurately reporting abstinence did so more than once. The odds of inaccurate self-reporting was 3 times higher in patients with no insurance or government insurance [odds ratio (OR), 3.5; 95% CI, 1.1-11.0; P = 0.043] and in the unemployed (OR, 3.3; 95% CI, 0.97-8.57; P = 0.049).

CONCLUSIONS

Self-reported smoking status in the orthopaedic population is fairly reliable, with 13% of patient's inaccurately self-reporting smoking cessation despite knowing their smoking status was being measured. Clinicians should be aware of the potential for inaccuracy in self-reported smoking cessation, particularly in patients with the identified socioeconomic factors. Point-of-care testing before elective trauma procedures to confirm smoking status might have a role if the procedure outcome is highly dependent on smoking status.

LEVEL OF EVIDENCE

Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence.

Mobile, Remote, and Individual Focused: Comparing Breath Carbon Monoxide Readings and Abstinence Between Smartphone-Enabled and Stand-Alone Monitors

INTRODUCTION

Newly available, smartphone-enabled carbon monoxide (CO) monitors are lower in cost than traditional stand-alone monitors and represent a marked advancement for smoking research. New products are promising, but data are needed to compare breath CO readings between smartphone-enabled and stand-alone monitors. The purpose of this study was to (1) determine the agreement between the mobile iCO (Bedfont Scientific Ltd) with two other monitors from the same manufacturer (Micro+ pro and Micro+ basic) and (2) determine optimal, monitor-specific, cotinine-confirmed abstinence cutoff values.

METHODS

Adult (≥18) smokers (n = 26) and nonsmokers (n = 21) provided three breath CO samples (using three different monitors) in each of 10 sessions, and urine cotinine was measured for gold standard determination of abstinence. CO comparisons (N = 437) were analyzed using regression-based Bland-Altman Analysis of Agreement; receiver operating characteristics curves were used to determine optimal abstinence cutoffs.

RESULTS

Bland-Altman analyses indicated that the iCO monitor provided higher CO results than both Micro+ monitors. Sensitivity and specificity analyses showed that the optimal CO cutoff for determining abstinence was <3 ppm for the Micro+ pro (88% sensitivity, 93% specificity) and Micro+ basic (83% sensitivity, 98% specificity), but was higher for the iCO (<6 ppm; 73% sensitivity, 100% specificity).

CONCLUSIONS

Relative to both Micro+ monitors, the smartphone-enabled iCO provided systematically higher CO values and required a higher cutoff to reliably determine smoking abstinence. This does not indicate that CO values obtained using the iCO are not valid; instead, these results suggest that monitor-specific abstinence cutoffs are needed to ensure accurate bioverification of smoking status.

IMPLICATIONS

Results from this study indicate that CO values from the smartphone-enabled iCO should not be used interchangeably with the stand-alone Micro+ pro and Micro+ basic, particularly when lower CO values (<10 ppm) are critical (ie, determination of abstinence vs confirming smoking status for study inclusion). Optimal CO cutoffs recommended for determining abstinence on Micro+ and iCO monitors are at <3 and <6 ppm, respectively.

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

Smartphone health apps for tobacco Cessation: A systematic review

BACKGROUND

Given the low retention and lack of persistent support by traditional tobacco cessation programs, evidence-based smartphone app-supported interventions can be an important tobacco control component. The objective of this systematic review was to identify and evaluate the types of studies that use smartphone apps for interventions in tobacco cessation.

METHODS

We conducted a systematic review of PubMed (1946-2019), EMBASE (1974-2019), and PsycINFO (1806-2019) databases with keywords related to smartphone-supported tobacco cessation. Included articles were required to meet 3 baseline screening criteria: 1) be written in English, 2) include an abstract, and 3) be a full, peer-reviewed manuscript. The criteria for the second level of review were: 1) primary outcome of tobacco cessation, 2) intervention study, and 3) smartphone app as primary focus of study.

RESULTS

Of 1973 eligible manuscripts, 18 met inclusion criteria. Most studies (n = 17) recruited adult participants (18 + years); one included teens (16 + years). Tobacco cessation was usually self-reported (n = 11), compared to biochemical verification (n = 3) or both (n = 4). There were 11 randomized controlled trials, 4 of which reported statistically significant results, and 7 single-arm trials that reported a mean abstinence rate of 33.9%.

DISCUSSION

The majority of studies that use tobacco cessation apps as an intervention delivery modality are mostly at the pilot/feasibility stage. The growing field has resulted in studies that varied in methodologies, study design, and inclusion criteria. More consistency in intervention components and larger randomized controlled trials are needed for tobacco cessation smartphone apps.

Inventory and Analysis of Controlled Trials of Mobile Phone Applications Targeting Substance Use Disorders: A Systematic Review

Background: Less than 20% of people with addictions have access to adequate treatment. Mobile health could improve access to care. No systematic review evaluates effectiveness of mobile health applications for addiction. Objectives: First aim was to describe controlled trials evaluating the effectiveness of smartphone applications targeting substance use disorders and addictive behaviors. Secondly, we aimed to understand how the application produced changes in behavior and craving management. Method: A systematic review based on PRISMA recommendations was conducted on MEDLINE, CENTRAL, and PsycINFO. Studies had to be controlled trials concerning addictive disorders (substance/behavior), mobile application-based interventions, assessing effectiveness or impact of those applications upon use, published after 2008. Relevant information was systematically screened for synthesis. Quality and risk of bias were evaluated with JADAD score. Results: Search strategy retrieved 22 articles (2014-2019) corresponding to 22 applications targeting tobacco, alcohol, other substances and binge eating disorder. Control groups had access to usual treatments or a placebo-application or no treatment. Eight applications showed reduced use. Most of the applications informed about risks of use and suggestions for monitoring use. Twelve applications managed craving. Discussion: Heterogeneity limited study comparisons. Duration of studies was too short to predict sustainable results. A reduction of craving seemed related to a reduction in use. Conclusion: There is a lack of robust and comparable studies on mHealth applications for addiction treatment. Such applications could become significant contributors in clinical practice in the future so longer-termed double-blind studies are needed. Targeting craving to prevent relapse should be systematic.

Mobile Apps to Reduce Tobacco, Alcohol, and Illicit Drug Use: Systematic Review of the First Decade

Background

Mobile apps for problematic substance use have the potential to bypass common barriers to treatment seeking. Ten years following the release of the first app targeting problematic tobacco, alcohol, and illicit drug use, their effectiveness, use, and acceptability remains unclear.

Objective

This study aims to conduct a systematic literature review of trials evaluating mobile app interventions for problematic tobacco, alcohol, and illicit drug use.

Methods

The review was conducted according to recommended guidelines. Relevant databases were searched, and articles were included if the mobile app study was a controlled intervention trial and reported alcohol, tobacco, or illicit drug consumption as outcomes.

Results

A total of 20 studies met eligibility criteria across a range of substances: alcohol (n=11), tobacco (n=6), alcohol and tobacco (n=1), illicit drugs (n=1), and illicit drugs and alcohol (n=1). Samples included the general community, university students, and clinical patients. The analyzed intervention sample sizes ranged from 22 to 14,228, and content was considerably diverse, from simple stand-alone apps delivering self-monitoring or psychoeducation to multicomponent apps with interactive features and audio content, or used as adjuncts alongside face-to-face treatment. Intervention duration ranged from 1 to 35 weeks, with notifications ranging from none to multiple times per day. A total of 6 of the 20 app interventions reported significant reductions in substance use at post or follow-up compared with a comparison condition, with small to moderate effect sizes. Furthermore, two other app interventions reported significant reductions during the intervention but not at post treatment, and a third reported a significant interaction of two app intervention components.

Conclusions

Although most app interventions were associated with reductions in problematic substance use, less than one-third were significantly better than the comparison conditions at post treatment. A total of 5 out of the 6 apps that reported intervention effects targeted alcohol (of those, one targeted alcohol and illicit drugs and another alcohol and tobacco) and 1 targeted tobacco. Moreover, 3 out of 6 apps included feedback (eg, personalized) and 2 had high risk of bias, 1 some risk, and 3 low risk. All 6 apps included interventions of 6 weeks or longer. Common study limitations were small sample sizes; risk of bias; lack of relevant details; and, in some cases, poorly balanced comparison conditions. Appropriately powered trials are required to understand which app interventions are most effective, length of engagement required, and subgroups most likely to benefit. In sum, evidence to date for the effectiveness of apps targeting problematic substance use is not compelling, although the heterogeneous comparison conditions and trial designs across studies limit the ability to compare efficacy between apps. We discuss potential approaches that can help ascertain whether the promise of mobile app interventions for problematic substance use can be fulfilled.

Digital Health and Addiction

Digital technologies are rapidly changing how we understand and promote health. A robust and growing line of research has examined how digital health may enhance our understanding and treatment of addiction. This manuscript highlights innovations in the application of digital health approaches to addiction medicine, with a particular emphasis on advances in (1) real-time measurement of drug use events, (2) real-time measurement of the confluence of factors that surround drug use events, and (3) research examining how real-time measurement can inform responsive, in-the-moment interventions to prevent and treat substance use disorder. Although this manuscript focuses on addiction medicine as one exemplar of the striking impact of digital health, science-based digital health offers generalizable solutions to scaling-up unprecedented models of precision healthcare delivery across a broad spectrum of diseases across the globe.

Mobile Apps for Health Behavior Change in Physical Activity, Diet, Drug and Alcohol Use, and Mental Health: Systematic Review

Background

With a growing focus on patient interaction with health management, mobile apps are increasingly used to deliver behavioral health interventions. The large variation in these mobile health apps—their target patient group, health behavior, and behavioral change strategies—has resulted in a large but incohesive body of literature.

Objective

This systematic review aimed to assess the effectiveness of mobile apps in improving health behaviors and outcomes and to examine the inclusion and effectiveness of behavior change techniques (BCTs) in mobile health apps.

Methods

PubMed, EMBASE, CINAHL, and Web of Science were systematically searched for articles published between 2014 and 2019 that evaluated mobile apps for health behavior change. Two authors independently screened and selected studies according to the eligibility criteria. Data were extracted and the risk of bias was assessed by one reviewer and validated by a second reviewer.

Results

A total of 52 randomized controlled trials met the inclusion criteria and were included in the analysis—37 studies focused on physical activity, diet, or a combination of both, 11 on drug and alcohol use, and 4 on mental health. Participant perceptions were generally positive—only one app was rated as less helpful and satisfactory than the control—and the studies that measured engagement and usability found relatively high study completion rates (mean 83%; n=18, N=39) and ease-of-use ratings (3 significantly better than control, 9/15 rated >70%). However, there was little evidence of changed behavior or health outcomes.

Conclusions

There was no strong evidence in support of the effectiveness of mobile apps in improving health behaviors or outcomes because few studies found significant differences between the app and control groups. Further research is needed to identify the BCTs that are most effective at promoting behavior change. Improved reporting is necessary to accurately evaluate the mobile health app effectiveness and risk of bias.

The mobile-phone-based iCOTM Smokerlyzer®: Comparison with the piCO+ Smokerlyzer® among smokers undergoing methadone-maintained therapy

INTRODUCTION

The mobile-phone-based Bedfont iCO^TM Smokerlyzer^® is of unknown validity and reproducibility compared to the widely-used piCO⁺ Smokerlyzer^®. We aimed to compare the validity and reproducibility of the iCO^TM Smokerlyzer^® with the piCO⁺ Smokerlyzer^® among patients reducing or quitting tobacco smoking.

METHODS

Methadone-maintained therapy (MMT) users from three centers in Malaysia had their exhaled carbon monoxide (eCO) levels recorded via the piCO⁺ and iCO^TM Smokerlyzers^®, their nicotine dependence assessed with the Malay version of the Fagerström Test for Nicotine Dependence (FTND-M), and daily tobacco intake measured via the Opiate Treatment Index (OTI) Tobacco Q-score. Pearson partial correlations were used to compare the eCO results of both devices, as well as the corresponding FTND-M scores.

RESULTS

Among the 146 participants (mean age 47.9 years, 92.5% male, and 73.3% Malay ethnic group) most (55.5%) were moderate smokers (6-19 cigarettes/day). Mean eCO categories were significantly correlated between both devices (r=0.861, p<0.001), and the first and second readings were significantly correlated for each device (r=0.94 for the piCO⁺ Smokerlyzer^®, p<0.001; r=0.91 for the iCO^TM Smokerlyzer^®, p<0.001). Exhaled CO correlated positively with FTND-M scores for both devices. The post hoc analysis revealed a significantly lower iCO^TM Smokerlyzer^® reading of 0.82 (95% CI: 0.69–0.94, p<0.001) compared to that of the piCO⁺ Smokerlyzer^®, and a significant intercept of -0.34 (95% CI: -0.61 – -0.07, p=0.016) on linear regression analysis, suggesting that there may be a calibration error in one or more of the iCO^TM Smokerlyzer^® devices.

CONCLUSIONS

The iCO^TM Smokerlyzer^® readings are highly reproducible compared to those of the piCO⁺ Smokerlyzer^®, but calibration guidelines are required for the mobile-phone-based device. Further research is required to assess interchangeability.

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

BACKGROUND

Cigarette smoking is the leading cause of preventable morbidity and mortality, excess health care expenditure, and lost work productivity. Otherwise effective evidence-based treatments have had limited success owing to challenges with access, engagement, and scale. Pivot is a comprehensive digital smoking cessation program that incorporates a Food and Drug Administration-cleared carbon monoxide breath sensor, smartphone app, and text-based human coaching.

OBJECTIVE

This initial evaluation of Pivot aimed to assess participant engagement, changes in attitudes toward quitting, and changes in smoking behavior.

METHODS

US cigarette smokers aged 18 to 65 years who smoked ≥5 cigarettes per day (CPD) were recruited online. Participants completed a screening call, electronic informed consent, registration, and onboarding before beginning Pivot. Pivot includes 5 sequential stages (Explore, Build, Mobilize, Quit, and Secure), taking 14.5 to 18.5 weeks to complete. Data were collected via app and online questionnaires. Outcomes included engagement and retention (ie, weeks of active engagement and Pivot stage progression); attitudes toward quitting (ie, quit readiness, quit confidence, and expected difficulty maintaining quit); and smoking behavior (ie, quit attempts, cigarette reduction, and abstinence (7- and 30-day point prevalence abstinence [PPA]).

RESULTS

A total of 319 participants completed onboarding (intention-to-treat [ITT] sample); 272/319 participants (85.3%) completed the end-of-Pivot questionnaire (study completer sample). Most (212/319, 66.5%) were not ready to quit in the next 30 days at baseline. On average, participants actively engaged in the program for a mean 12.4 (SD 7.1) weeks. Pivot stage completion rates were Explore: 88.7% (283/319), Build: 57.4% (183/319), Mobilize: 43.6% (139/319), Quit: 41.1% (131/319), and Secure: 39.5% (126/319). Repeated measures linear mixed model analyses demonstrated positive changes in attitudes from baseline to Mobilize (pre-Quit): increased confidence to quit (4.2 to 7.4, P<.001) and decreased expected difficulty maintaining quit (3.1 to 6.8, P<.001). The quit attempt rate (ie, those making ≥1 quit attempt lasting ≥1 day) was 79.4% (216/272, completer). At the end of Pivot, 7-day PPA rates were 32.0% (102/319, ITT) and 37.5% (102/272, completer); 30-day PPA rates were 27.6% (88/319, ITT) and 32.4% (88/272, completer). Moreover, 30-day PPA rates were comparable among those ready and not ready to quit in the next 30 days at baseline. Of those not achieving abstinence, 25.9% (44/170, completer) achieved ≥50% reduction in CPD by study end.

CONCLUSIONS

This study evaluated Pivot's initial performance with comparable quit rates among those ready and not ready to quit in the next 30 days at entry. The present data, considered with the program's accessibility, innovation, evidence-based foundation, and design for all smokers, suggest Pivot has the potential to address limitations of reach and scale and thereby advance smoking cessation efforts.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03295643; https://clinicaltrials.gov/ct2/show/NCT03295643 (Archived by WebCite at http://www.webcitation.org/75TiNe6BE).