A randomized controlled trial of smartphone-based mindfulness training for smoking cessation: a study protocol

Meditation for the primary and secondary prevention of cardiovascular disease

BACKGROUND

Interventions incorporating meditation to address stress, anxiety, and depression, and improve self-management, are becoming popular for many health conditions. Stress is a risk factor for cardiovascular disease (CVD) and clusters with other modifiable behavioural risk factors, such as smoking. Meditation may therefore be a useful CVD prevention strategy.

OBJECTIVES

To determine the effectiveness of meditation, primarily mindfulness-based interventions (MBIs) and transcendental meditation (TM), for the primary and secondary prevention of CVD.

SEARCH METHODS

We searched CENTRAL, MEDLINE, Embase, three other databases, and two trials registers on 14 November 2021, together with reference checking, citation searching, and contact with study authors to identify additional studies.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) of 12 weeks or more in adults at high risk of CVD and those with established CVD. We explored four comparisons: MBIs versus active comparators (alternative interventions); MBIs versus non-active comparators (no intervention, wait list, usual care); TM versus active comparators; TM versus non-active comparators.

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methods. Our primary outcomes were CVD clinical events (e.g. cardiovascular mortality), blood pressure, measures of psychological distress and well-being, and adverse events. Secondary outcomes included other CVD risk factors (e.g. blood lipid levels), quality of life, and coping abilities. We used GRADE to assess the certainty of evidence.

MAIN RESULTS

We included 81 RCTs (6971 participants), with most studies at unclear risk of bias. MBIs versus active comparators (29 RCTs, 2883 participants) Systolic (SBP) and diastolic (DBP) blood pressure were reported in six trials (388 participants) where heterogeneity was considerable (SBP: MD -6.08 mmHg, 95% CI -12.79 to 0.63, I2 = 88%; DBP: MD -5.18 mmHg, 95% CI -10.65 to 0.29, I2 = 91%; both outcomes based on low-certainty evidence). There was little or no effect of MBIs on anxiety (SMD -0.06 units, 95% CI -0.25 to 0.13; I2 = 0%; 9 trials, 438 participants; moderate-certainty evidence), or depression (SMD 0.08 units, 95% CI -0.08 to 0.24; I2 = 0%; 11 trials, 595 participants; moderate-certainty evidence). Perceived stress was reduced with MBIs (SMD -0.24 units, 95% CI -0.45 to -0.03; I2 = 0%; P = 0.03; 6 trials, 357 participants; moderate-certainty evidence). There was little to no effect on well-being (SMD -0.18 units, 95% CI -0.67 to 0.32; 1 trial, 63 participants; low-certainty evidence). There was little to no effect on smoking cessation (RR 1.45, 95% CI 0.78 to 2.68; I2 = 79%; 6 trials, 1087 participants; low-certainty evidence). None of the trials reported CVD clinical events or adverse events. MBIs versus non-active comparators (38 RCTs, 2905 participants) Clinical events were reported in one trial (110 participants), providing very low-certainty evidence (RR 0.94, 95% CI 0.37 to 2.42). SBP and DBP were reduced in nine trials (379 participants) but heterogeneity was substantial (SBP: MD -6.62 mmHg, 95% CI -13.15 to -0.1, I2 = 87%; DBP: MD -3.35 mmHg, 95% CI -5.86 to -0.85, I2 = 61%; both outcomes based on low-certainty evidence). There was low-certainty evidence of reductions in anxiety (SMD -0.78 units, 95% CI -1.09 to -0.41; I2 = 61%; 9 trials, 533 participants; low-certainty evidence), depression (SMD -0.66 units, 95% CI -0.91 to -0.41; I2 = 67%; 15 trials, 912 participants; low-certainty evidence) and perceived stress (SMD -0.59 units, 95% CI -0.89 to -0.29; I2 = 70%; 11 trials, 708 participants; low-certainty evidence) but heterogeneity was substantial. Well-being increased (SMD 0.5 units, 95% CI 0.09 to 0.91; I2 = 47%; 2 trials, 198 participants; moderate-certainty evidence). There was little to no effect on smoking cessation (RR 1.36, 95% CI 0.86 to 2.13; I2 = 0%; 2 trials, 453 participants; low-certainty evidence). One small study (18 participants) reported two adverse events in the MBI group, which were not regarded as serious by the study investigators (RR 5.0, 95% CI 0.27 to 91.52; low-certainty evidence). No subgroup effects were seen for SBP, DBP, anxiety, depression, or perceived stress by primary and secondary prevention. TM versus active comparators (8 RCTs, 830 participants) Clinical events were reported in one trial (201 participants) based on low-certainty evidence (RR 0.91, 95% CI 0.56 to 1.49). SBP was reduced (MD -2.33 mmHg, 95% CI -3.99 to -0.68; I2 = 2%; 8 trials, 774 participants; moderate-certainty evidence), with an uncertain effect on DBP (MD -1.15 mmHg, 95% CI -2.85 to 0.55; I2 = 53%; low-certainty evidence). There was little or no effect on anxiety (SMD 0.06 units, 95% CI -0.22 to 0.33; I2 = 0%; 3 trials, 200 participants; low-certainty evidence), depression (SMD -0.12 units, 95% CI -0.31 to 0.07; I2 = 0%; 5 trials, 421 participants; moderate-certainty evidence), or perceived stress (SMD 0.04 units, 95% CI -0.49 to 0.57; I2 = 70%; 3 trials, 194 participants; very low-certainty evidence). None of the trials reported adverse events or smoking rates. No subgroup effects were seen for SBP or DBP by primary and secondary prevention. TM versus non-active comparators (2 RCTs, 186 participants) Two trials (139 participants) reported blood pressure, where reductions were seen in SBP (MD -6.34 mmHg, 95% CI -9.86 to -2.81; I2 = 0%; low-certainty evidence) and DBP (MD -5.13 mmHg, 95% CI -9.07 to -1.19; I2 = 18%; very low-certainty evidence). One trial (112 participants) reported anxiety and depression and found reductions in both (anxiety SMD -0.71 units, 95% CI -1.09 to -0.32; depression SMD -0.48 units, 95% CI -0.86 to -0.11; low-certainty evidence). None of the trials reported CVD clinical events, adverse events, or smoking rates.

AUTHORS' CONCLUSIONS

Despite the large number of studies included in the review, heterogeneity was substantial for many of the outcomes, which reduced the certainty of our findings. We attempted to address this by presenting four main comparisons of MBIs or TM versus active or inactive comparators, and by subgroup analyses according to primary or secondary prevention, where there were sufficient studies. The majority of studies were small and there was unclear risk of bias for most domains. Overall, we found very little information on the effects of meditation on CVD clinical endpoints, and limited information on blood pressure and psychological outcomes, for people at risk of or with established CVD. This is a very active area of research as shown by the large number of ongoing studies, with some having been completed at the time of writing this review. The status of all ongoing studies will be formally assessed and incorporated in further updates.

A Network Analysis of Cigarette Craving

INTRODUCTION

Craving is considered a central process to addictive behavior including cigarette smoking, although the clinical utility of craving relies on how it is defined and measured. Network analysis enables examining the network structure of craving symptoms, identifying the most central symptoms of cigarette craving, and improving our understanding of craving and its measurement.

AIMS AND METHODS

This study used network analysis to identify the central symptoms of self-reported cigarette craving as measured by the Craving Experience Questionnaire, which assesses both craving strength and craving frequency. Data were obtained from baseline of a randomized controlled trial of mindfulness training for smoking cessation.

RESULTS

The most central symptoms in an overall cigarette craving network were the frequency of imagining its smell, imagining its taste, and intrusive thoughts. The most central symptoms of both craving frequency and craving strength sub-networks were imagining its taste, the urge to have it, and intrusive thoughts.

CONCLUSIONS

The most central craving symptoms reported by individuals in treatment for cigarette smoking were from the frequency domain, demonstrating the value of assessing craving frequency along with craving strength. Central craving symptoms included multisensory imagery (taste, smell), intrusive thoughts, and urge, providing additional evidence that these symptoms may be important to consider in craving measurement and intervention. Findings provide insight into the symptoms that are central to craving, contributing to a better understanding of cigarette cravings, and suggesting potential targets for clinical interventions.

IMPLICATIONS

This study used network analysis to identify central symptoms of cigarette craving. Both craving frequency and strength were assessed. The most central symptoms of cigarette craving were related to craving frequency. Central symptoms included multisensory imagery, intrusive thoughts, and urge. Central symptoms might be targeted by smoking cessation treatment.

Models for Zero-Inflated and Overdispersed Correlated Count Data: An Application to Cigarette Use

INTRODUCTION

Count outcomes in tobacco research are often analyzed with the Poisson distribution. However, they often exhibit features such as overdispersion (variance larger than expected) and zero inflation (extra zeros) that violate model assumptions. Furthermore, longitudinal studies have repeated measures that generate correlated counts. Failure to account for overdispersion, zero inflation, and correlation can yield incorrect statistical inferences. Thus, it is important to familiarize researchers with proper models for such data.

AIMS AND METHODS

Poisson and Negative Binomial models with correlated random effects with and without zero inflation are presented. The illustrative data comes from a study comparing a mindfulness training app (Craving to Quit [C2Q], n = 60) with a control app (experience sampling-only app, n = 66) on smoking frequency at 1, 3, and 6 months. Predictors include app, time, the app-by-time interaction, and baseline smoking. Each model is evaluated in terms of accounting for zero inflation, overdispersion, and correlation in the data. Emphasis is placed on evaluating model fit, subject-specific interpretation of effects, and choosing an appropriate model.

RESULTS

The hurdle Poisson model provided the best fit to the data. Smoking abstinence rates were 33%, 32%, and 28% at 1, 3, and 6 months, respectively, with variance larger than expected by a factor >7 at each follow-up. Individuals on C2Q were less likely to achieve abstinence across time but likely to smoke fewer cigarettes if smoking.

CONCLUSIONS

The models presented are specifically suited for analyzing correlated count outcomes and account for zero inflation and overdispersion. We provide guidance to researchers on the use of these models to better inform nicotine and tobacco research.

IMPLICATIONS

In tobacco research, count outcomes are often measured repeatedly on the same subject and thus correlated. Such outcomes often have many zeros and exhibit large variances relative to the mean. Analyzing such data require models specifically suited for correlated counts. The presented models and guidelines could improve the rigor of the analysis of correlated count data and thus increase the impact of studies in nicotine and tobacco research using such outcomes.

HIV patients’ perceptions of a potential multi-component mindfulness-based smoking cessation smartphone application intervention

Objectives

Cigarette smoking rates among people living with HIV (PLWH) in the US is triple that of the general population. PLWH smokers are a high-risk group for smoking-related health disparities and should be a prime focus for smoking cessation efforts. Our team has developed a novel evidence-based Mindfulness Training (MT) smoking cessation smartphone application (app), “Craving-to-Quit.” Using qualitative focus groups among PLWH smokers, this study aims to tailor and optimize the app’s content and design to PLWH’s unique psychosocial profile and needs.

Methods

We conducted 8 focus groups among PLWH smokers (n = 59; 47.5% females; ≥18 years) to gain insight into participants’ perceptions about the app, MT, and the feasibility and acceptability of adding two additional strategies (CM: Contingency Management; self-monitoring of anti-retroviral therapies intake [ART]) to further optimize the app. Participants were asked to practice MTs and watch videos from the app presented on a screen in the conference room to discuss their experience. Sessions were audio-taped, transcribed verbatim, and analyzed thematically using NVivo.

Results

Most participants were non-Hispanic black (67.8%), on a federal health insurance program (61.0%). Participants considered it easy to learn the app and thought that MT is helpful in reducing stress and motivating quit attempts and were supportive of adding CM and recommended providing $20-$50 weekly cash incentives to help in quitting. Participants felt that adding self-monitoring of ART is helpful but were concerned about confidentiality in case they lost their phone. Participants recommended making the app cost-free and adding information about smoking cessation medications and the negative effects of smoking among PLWH.

Conclusions

Findings will guide the development of a novel multi-component smoking cessation intervention app integrating MT, CM, and ART self-monitoring strategies. This intervention has the potential to address several barriers to quitting in PLWH. Further clinical research is needed to test this intervention.

App-Based Mindfulness Training Predicts Reductions in Smoking Behavior by Engaging Reinforcement Learning Mechanisms: A Preliminary Naturalistic Single-Arm Study

Mindfulness training (MT) has been shown to influence smoking behavior, yet the involvement of reinforcement learning processes as underlying mechanisms remains unclear. This naturalistic, single-arm study aimed to examine slope trajectories of smoking behavior across uses of our app-based MT craving tool for smoking cessation, and whether this relationship would be mediated by the attenuating impact of MT on expected reward values of smoking. Our craving tool embedded in our MT app-based smoking cessation program was used by 108 participants upon the experience of cigarette cravings in real-world contexts. Each use of the tool involved mindful awareness to the experience of cigarette craving, a decision as to whether the participant wanted to smoke or ride out their craving with a mindfulness exercise, and paying mindful attention to the choice behavior and its outcome (contentment levels felt from engaging in the behavior). Expected reward values were computed using contentment levels experienced from the choice behavior as the reward signal in a Rescorla−Wagner reinforcement learning model. Multi-level mediation analysis revealed a significant decreasing trajectory of smoking frequency across MT craving tool uses and that this relationship was mediated by the negative relationship between MT and expected reward values (all ps < 0.001). After controlling for the mediator, the predictive relationship between MT and smoking was no longer significant (p < 0.001 before and p = 0.357 after controlling for the mediator). Results indicate that the use of our app-based MT craving tool is associated with negative slope trajectories of smoking behavior across uses, mediated by reward learning mechanisms. This single-arm naturalistic study provides preliminary support for further RCT studies examining the involvement of reward learning mechanisms underlying app-based mindfulness training for smoking cessation.

Mindfulness for smoking cessation

BACKGROUND

Mindfulness-based smoking cessation interventions may aid smoking cessation by teaching individuals to pay attention to, and work mindfully with, negative affective states, cravings, and other symptoms of nicotine withdrawal. Types of mindfulness-based interventions include mindfulness training, which involves training in meditation; acceptance and commitment therapy (ACT); distress tolerance training; and yoga.

OBJECTIVES

To assess the efficacy of mindfulness-based interventions for smoking cessation among people who smoke, and whether these interventions have an effect on mental health outcomes.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group's specialised register, CENTRAL, MEDLINE, Embase, PsycINFO, and trial registries to 15 April 2021. We also employed an automated search strategy, developed as part of the Human Behaviour Change Project, using Microsoft Academic.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) and cluster-RCTs that compared a mindfulness-based intervention for smoking cessation with another smoking cessation programme or no treatment, and assessed smoking cessation at six months or longer. We excluded studies that solely recruited pregnant women.

DATA COLLECTION AND ANALYSIS

We followed standard Cochrane methods. We measured smoking cessation at the longest time point, using the most rigorous definition available, on an intention-to-treat basis. We calculated risk ratios (RRs) and 95% confidence intervals (CIs) for smoking cessation for each study, where possible. We grouped eligible studies according to the type of intervention and type of comparator. We carried out meta-analyses where appropriate, using Mantel-Haenszel random-effects models. We summarised mental health outcomes narratively.

MAIN RESULTS

We included 21 studies, with 8186 participants. Most recruited adults from the community, and the majority (15 studies) were conducted in the USA. We judged four of the studies to be at low risk of bias, nine at unclear risk, and eight at high risk. Mindfulness-based interventions varied considerably in design and content, as did comparators, therefore, we pooled small groups of relatively comparable studies. We did not detect a clear benefit or harm of mindfulness training interventions on quit rates compared with intensity-matched smoking cessation treatment (RR 0.99, 95% CI 0.67 to 1.46; I² = 0%; 3 studies, 542 participants; low-certainty evidence), less intensive smoking cessation treatment (RR 1.19, 95% CI 0.65 to 2.19; I² = 60%; 5 studies, 813 participants; very low-certainty evidence), or no treatment (RR 0.81, 95% CI 0.43 to 1.53; 1 study, 325 participants; low-certainty evidence). In each comparison, the 95% CI encompassed benefit (i.e. higher quit rates), harm (i.e. lower quit rates) and no difference. In one study of mindfulness-based relapse prevention, we did not detect a clear benefit or harm of the intervention over no treatment (RR 1.43, 95% CI 0.56 to 3.67; 86 participants; very low-certainty evidence). We did not detect a clear benefit or harm of ACT on quit rates compared with less intensive behavioural treatments, including nicotine replacement therapy alone (RR 1.27, 95% CI 0.53 to 3.02; 1 study, 102 participants; low-certainty evidence), brief advice (RR 1.27, 95% CI 0.59 to 2.75; 1 study, 144 participants; very low-certainty evidence), or less intensive ACT (RR 1.00, 95% CI 0.50 to 2.01; 1 study, 100 participants; low-certainty evidence). There was a high level of heterogeneity (I² = 82%) across studies comparing ACT with intensity-matched smoking cessation treatments, meaning it was not appropriate to report a pooled result. We did not detect a clear benefit or harm of distress tolerance training on quit rates compared with intensity-matched smoking cessation treatment (RR 0.87, 95% CI 0.26 to 2.98; 1 study, 69 participants; low-certainty evidence) or less intensive smoking cessation treatment (RR 1.63, 95% CI 0.33 to 8.08; 1 study, 49 participants; low-certainty evidence). We did not detect a clear benefit or harm of yoga on quit rates compared with intensity-matched smoking cessation treatment (RR 1.44, 95% CI 0.40 to 5.16; 1 study, 55 participants; very low-certainty evidence). Excluding studies at high risk of bias did not substantially alter the results, nor did using complete case data as opposed to using data from all participants randomised. Nine studies reported on changes in mental health and well-being, including depression, anxiety, perceived stress, and negative and positive affect. Variation in measures and methodological differences between studies meant we could not meta-analyse these data. One study found a greater reduction in perceived stress in participants who received a face-to-face mindfulness training programme versus an intensity-matched programme. However, the remaining eight studies found no clinically meaningful differences in mental health and well-being between participants who received mindfulness-based treatments and participants who received another treatment or no treatment (very low-certainty evidence).

AUTHORS' CONCLUSIONS

We did not detect a clear benefit of mindfulness-based smoking cessation interventions for increasing smoking quit rates or changing mental health and well-being. This was the case when compared with intensity-matched smoking cessation treatment, less intensive smoking cessation treatment, or no treatment. However, the evidence was of low and very low certainty due to risk of bias, inconsistency, and imprecision, meaning future evidence may very likely change our interpretation of the results. Further RCTs of mindfulness-based interventions for smoking cessation compared with active comparators are needed. There is also a need for more consistent reporting of mental health and well-being outcomes in studies of mindfulness-based interventions for smoking cessation.

Smartband-Based Automatic Smoking Detection and Real-time Mindfulness Intervention: Protocol for a Feasibility Trial

Background

Smoking is the leading cause of preventable death in the United States. Smoking cessation interventions delivered by smartphone apps are a promising tool for helping smokers quit. However, currently available smartphone apps for smoking cessation have not exploited their unique potential advantages to aid quitting. Notably, few to no available apps use wearable technologies, most apps require users to self-report their smoking, and few to no apps deliver treatment automatically contingent upon smoking.

Objective

This pilot trial tests the feasibility of using a smartband and smartphone to monitor and detect smoking and deliver brief mindfulness interventions in real time to reduce smoking.

Methods

Daily smokers (N=100, ≥5 cigarettes per day) wear a smartband for 60 days to monitor and detect smoking, notify them about their smoking events in real time, and deliver real-time brief mindfulness exercises triggered by detected smoking events or targeted at predicted smoking events. Smokers set a quit date at 30 days. A three-step intervention to reduce smoking is tested. First, participants wear a smartband to monitor and detect smoking, and notify them of smoking events in real time to bring awareness to smoking and triggers for 21 days. Next, a “mindful smoking” exercise is triggered by detected smoking events to bring a clear recognition of the actual effects of smoking for 7 days. Finally, after their quit date, a “RAIN” (recognize, allow, investigate, nonidentification) exercise is delivered to predicted smoking events (based on the initial 3 weeks of tracking smoking data) to help smokers learn to work mindfully with cravings rather than smoke for 30 days. The primary outcomes are feasibility measures of treatment fidelity, adherence, and acceptability. The secondary outcomes are smoking rates at end of treatment.

Results

Recruitment for this trial started in May 2021 and will continue until November 2021 or until enrollment is completed. Data monitoring and management are ongoing for enrolled participants. The final 60-day end of treatment data is anticipated in January 2022. We expect that all trial results will be available in April 2022.

Conclusions

Findings will provide data and information on the feasibility of using a smartband and smartphone to monitor and detect smoking and deliver real-time brief mindfulness interventions, and whether the intervention warrants additional testing for smoking cessation.

Trial Registration

ClinicalTrials.gov NCT03995225; https://clinicaltrials.gov/ct2/show/NCT03995225

International Registered Report Identifier (IRRID)

DERR1-10.2196/32521

Mindfulness and Behavior Change

Initiating and maintaining behavior change is key to the prevention and treatment of most preventable chronic medical and psychiatric illnesses. The cultivation of mindfulness, involving acceptance and nonjudgment of present-moment experience, often results in transformative health behavior change. Neural systems involved in motivation and learning have an important role to play. A theoretical model of mindfulness that integrates these mechanisms with the cognitive, emotional, and self-related processes commonly described, while applying an integrated model to health behavior change, is needed. This integrative review (1) defines mindfulness and describes the mindfulness-based intervention movement, (2) synthesizes the neuroscience of mindfulness and integrates motivation and learning mechanisms within a mindful self-regulation model for understanding the complex effects of mindfulness on behavior change, and (3) synthesizes current clinical research evaluating the effects of mindfulness-based interventions targeting health behaviors relevant to psychiatric care. The review provides insight into the limitations of current research and proposes potential mechanisms to be tested in future research and targeted in clinical practice to enhance the impact of mindfulness on behavior change.

Awareness, affect, and craving during smoking cessation: An experience sampling study

OBJECTIVE

Mindfulness has received attention in smoking cessation research, yet the mechanisms by which mindfulness may promote smoking cessation are not well understood. Mindfulness training may help individuals increase awareness and respond skillfully to processes that contribute to smoking, such as affective states and craving. This study used experience sampling (ES) to test how awareness was related to craving, positive and negative affect and smoking, in the moment, among smokers in treatment for smoking cessation.

METHOD

Participants (N = 228) were part of a clinical trial evaluating Craving to Quit, a smartphone app for mindfulness training for smoking cessation, compared to an app delivering only ES. All participants were asked to complete 22 days of ES, with up to 6 ES surveys per day, measuring awareness, craving, positive and negative affect and smoking. Data were analyzed using multilevel linear modeling.

RESULTS

Both at the within and between-person level, higher awareness was associated with higher positive affect, lower craving and lower negative affect. Lower within-person craving was associated with lower smoking. Within-person awareness, positive and negative affect were not significantly associated with smoking. At the between-person level, higher awareness and higher positive affect, and lower negative affect and lower craving were associated with lower smoking.

CONCLUSIONS

Awareness of current experience was related to key psychological variables linked to behavior change in smoking cessation, namely positive and negative affect and craving, among smokers trying to quit. Future studies should test whether learning to increase awareness, such as through mindfulness training, may benefit smokers in treatment. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Application of Automated Text Analysis to Examine Emotions Expressed in Online Support Groups for Quitting Smoking

Online support groups offer social support and an outlet for expressing emotions when dealing with health-related challenges. This study examines whether automated text analysis of emotional expressions using Linguistic Inquiry and Word Count (LIWC) can identify emotions related to abstinence expressed in online support groups for quitting smoking, suggesting promise for offering targeted mood management to members. The emotional expressions in 1 month of posts by members of 36 online support groups were related to abstinence at month end. Using the available LIWC dictionary, posts were scored for overall positive emotions, overall negative emotions, anxiety, anger, sadness, and an upbeat emotional tone. Greater expressions of negative emotions, and specifically anxiety, related to nonabstinence, while a more upbeat emotional tone related to abstinence. The results indicate that automated text analysis can identify emotions expressed in online support groups for quitting smoking and enable targeted delivery of mood management to group members.

Reach versus effectiveness: The design and protocol of randomized clinical trial testing a smartphone application versus in-person mindfulness-based smoking cessation intervention among young cancer survivors

Approximately 45% of young cancer survivors (18-40 years) are cigarette smokers. Continued smoking after cancer diagnosis leads to lower survival rates. A major logistical problem with smoking cessation efforts in this group is their geographic dispersion which makes them hard to reach. In addition, depression is a major predictor of smoking relapse and its rates are roughly twice as high in cancer survivors as the general population. Smartphone applications (apps) show promise in terms of efficacy, dissemination, and improving access to treatment. Mindfulness training (defined as maintaining attention on one's immediate experience and cultivating an attitude of acceptance toward this experience) is effective in improving smoking cessation outcomes by reducing psychological stress and controlling craving. Given that smartphone apps can address the issues of mobility and remote access, and mindfulness can address the high depression rate among cancer survivors, validating the feasibility and efficacy of a mindfulness-based smoking cessation intervention app in young cancer survivors is a high priority. Thus, the aims of the current study are: (1) test the feasibility, acceptability, and potential efficacy of the mindfulness-based smoking cessation app versus in-person mindfulness or usual care in a 3-arm pilot randomized clinical trial among young cancer survivors (n = 60; 18-40 years); and 2) conduct semi-structured exit interviews with participants in the two mindfulness groups to fine-tune the two active interventions based on feedback from participants. Findings will have implications for the development and dissemination of innovative and highly scalable tobacco cessation interventions designed for young cancer survivors.

Smoking cessation for improving mental health

BACKGROUND

There is a common perception that smoking generally helps people to manage stress, and may be a form of 'self-medication' in people with mental health conditions. However, there are biologically plausible reasons why smoking may worsen mental health through neuroadaptations arising from chronic smoking, leading to frequent nicotine withdrawal symptoms (e.g. anxiety, depression, irritability), in which case smoking cessation may help to improve rather than worsen mental health.

OBJECTIVES

To examine the association between tobacco smoking cessation and change in mental health.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group's Specialised Register, Cochrane Central Register of Controlled Trials, MEDLINE, Embase, PsycINFO, and the trial registries clinicaltrials.gov and the International Clinical Trials Registry Platform, from 14 April 2012 to 07 January 2020. These were updated searches of a previously-conducted non-Cochrane review where searches were conducted from database inception to 13 April 2012.  SELECTION CRITERIA: We included controlled before-after studies, including randomised controlled trials (RCTs) analysed by smoking status at follow-up, and longitudinal cohort studies. In order to be eligible for inclusion studies had to recruit adults who smoked tobacco, and assess whether they quit or continued smoking during the study. They also had to measure a mental health outcome at baseline and at least six weeks later.

DATA COLLECTION AND ANALYSIS

We followed standard Cochrane methods for screening and data extraction. Our primary outcomes were change in depression symptoms, anxiety symptoms or mixed anxiety and depression symptoms between baseline and follow-up. Secondary outcomes  included change in symptoms of stress, psychological quality of life, positive affect, and social impact or social quality of life, as well as new incidence of depression, anxiety, or mixed anxiety and depression disorders. We assessed the risk of bias for the primary outcomes using a modified ROBINS-I tool.  For change in mental health outcomes, we calculated the pooled standardised mean difference (SMD) and 95% confidence interval (95% CI) for the difference in change in mental health from baseline to follow-up between those who had quit smoking and those who had continued to smoke. For the incidence of psychological disorders, we calculated odds ratios (ORs) and 95% CIs. For all meta-analyses we used a generic inverse variance random-effects model and quantified statistical heterogeneity using I2. We conducted subgroup analyses to investigate any differences in associations between sub-populations, i.e. unselected people with mental illness, people with physical chronic diseases. We assessed the certainty of evidence for our primary outcomes (depression, anxiety, and mixed depression and anxiety) and our secondary social impact outcome using the eight GRADE considerations relevant to non-randomised studies (risk of bias, inconsistency, imprecision, indirectness, publication bias, magnitude of the effect, the influence of all plausible residual confounding, the presence of a dose-response gradient).

MAIN RESULTS

We included 102 studies representing over 169,500 participants. Sixty-two of these were identified in the updated search for this review and 40 were included in the original version of the review.  Sixty-three studies provided data on change in mental health, 10 were included in meta-analyses of incidence of mental health disorders, and 31 were synthesised narratively.  For all primary outcomes, smoking cessation was associated with an improvement in mental health symptoms compared with continuing to smoke: anxiety symptoms (SMD -0.28, 95% CI -0.43 to -0.13; 15 studies, 3141 participants; I2 = 69%; low-certainty evidence); depression symptoms: (SMD -0.30, 95% CI -0.39 to -0.21; 34 studies, 7156 participants; I2 = 69%' very low-certainty evidence);  mixed anxiety and depression symptoms (SMD -0.31, 95% CI -0.40 to -0.22; 8 studies, 2829 participants; I2 = 0%; moderate certainty evidence).  These findings were robust to preplanned sensitivity analyses, and subgroup analysis generally did not produce evidence of differences in the effect size among subpopulations or based on methodological characteristics. All studies were deemed to be at serious risk of bias due to possible time-varying confounding, and three studies measuring depression symptoms were judged to be at critical risk of bias overall. There was also some evidence of funnel plot asymmetry. For these reasons, we rated our certainty in the estimates for anxiety as low, for depression as very low, and for mixed anxiety and depression as moderate. For the secondary outcomes, smoking cessation was associated with an improvement in symptoms of stress (SMD -0.19, 95% CI -0.34 to -0.04; 4 studies, 1792 participants; I2 = 50%), positive affect (SMD 0.22, 95% CI 0.11 to 0.33; 13 studies, 4880 participants; I2 = 75%), and psychological quality of life (SMD 0.11, 95% CI 0.06 to 0.16; 19 studies, 18,034 participants; I2 = 42%). There was also evidence that smoking cessation was not associated with a reduction in social quality of life, with the confidence interval incorporating the possibility of a small improvement (SMD 0.03, 95% CI 0.00 to 0.06; 9 studies, 14,673 participants; I2 = 0%). The incidence of new mixed anxiety and depression was lower in people who stopped smoking compared with those who continued (OR 0.76, 95% CI 0.66 to 0.86; 3 studies, 8685 participants; I2 = 57%), as was the incidence of anxiety disorder (OR 0.61, 95% CI 0.34 to 1.12; 2 studies, 2293 participants; I2 = 46%). We deemed it inappropriate to present a pooled estimate for the incidence of new cases of clinical depression, as there was high statistical heterogeneity (I2 = 87%).

AUTHORS' CONCLUSIONS

Taken together, these data provide evidence that mental health does not worsen as a result of quitting smoking, and very low- to moderate-certainty evidence that smoking cessation is associated with small to moderate improvements in mental health.  These improvements are seen in both unselected samples and in subpopulations, including people diagnosed with mental health conditions. Additional studies that use more advanced methods to overcome time-varying confounding would strengthen the evidence in this area.

Evaluating the effectiveness of a 'Tobacco Monitor' App in reporting violations of tobacco policy in the community

It is of interest to evaluate the effectiveness of the "Tobacco Monitor" app in reporting violations of tobacco policy in the community. Hence, a study was conducted amongst the first and second-year undergraduate students of health science colleges of a University. Students were asked to register complaints related to tobacco violations on the tobacco monitor app. Registered complaints were verified by the National Forum for Tobacco Eradication (NFTE) and descriptive statistics were used in reporting the results. A total of 208 complaints on tobacco violation were registered through the Tobacco Monitor app, 163 valid complaints were identified and 45 reports were found invalid. 163 verified valid complaints by NFTE were transferred to the Non-Communicable Diseases (NCD) Cell, Maharashtra, India. It should be noted that anti-tobacco laws and national policies help to curb the menace of the tobacco epidemic to an extent. However, robust reporting and sustainable enforcement measures are required in implementing tobacco legislation effectively. We also report that youth are comfortable in using the Tobacco Monitor app for reporting violations on tobacco.

A Mobile Social Network-Based Smoking Cessation Intervention for Chinese Male Smokers: Protocol for a Pilot Randomized Controlled Trial

Background

Approximately 2 million Chinese people die annually from tobacco-related diseases, mostly men; yet, fewer than 8% of Chinese smokers ever receive any smoking cessation advice or support. A social network–based gamified smoking cessation intervention (SCAMPI: Smoking Cessation App for Chinese Male: Pilot Intervention) is designed to help Chinese male smokers to quit smoking.

Objective

This paper aims to present the protocol of a study examining the preliminary effectiveness of SCAMPI by comparing the prolonged abstinence rate of a group of users with a comparator group during a 6-week follow-up period.

Methods

A two-arm pilot randomized controlled trial was conducted to assess the preliminary effectiveness and acceptability of the SCAMPI program as a smoking cessation intervention. After initial web-based screening, the first 80 eligible individuals who had gone through the required registration process were registered as participants of the trial. Participants were randomly allocated to the intervention group (n=40) and the control group (n=40). Participants in the intervention group used the full version of the SCAMPI program, which is a Chinese smoking cessation program developed based on the Behavior Change Wheel framework and relevant smoking cessation and design guidelines with involvement of target users. The program delivers a range of smoking cessation approaches, including helping users to make quitting plans, calculator to record quitting benefits, calendar to record progress, gamification to facilitate quitting, providing information about smoking harms, motivational messages to help users overcome urges, providing standardized tests to users for assessing their levels of nicotine dependence and lung health, and providing a platform to encourage social support between users. Participants in the control group used the restricted version of the SCAMPI program (placebo app).

Results

Recruitment for this project commenced in January 2019 and proceeded until March 2019. Follow-up data collection was commenced and completed by June 2019. The primary outcome measure of the study was the 30-day bio-verified smoking abstinence at the 6-week follow-up (self-reported data verified by the Nicotine Cotinine Saliva Test). The secondary outcome measures of the study included participants’ cigarette consumption reduction (compared baseline daily cigarette consumption with end-of-trial daily cigarette consumption), participants’ 7-day smoking abstinence at 4-week and 6-week follow-up (self-reported), participants’ 30-day smoking abstinence at 6-week follow-up (self-reported data only), and participants’ acceptability and satisfaction levels of using the SCAMPI program (measured by the Mobile App Rating Scale questionnaire).

Conclusions

If the SCAMPI program is shown to be preliminary effective, the study will be rolled out to be a future trial with a larger sample size and longer follow-up (6 months) to identify if it is an effective social network–based tool to support Chinese male smokers to quit smoking.

Trial Registration

Australian New Zealand Clinical Trials Registry ACTRN12618001089224; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=375381

International Registered Report Identifier (IRRID)

RR1-10.2196/18071

Craving to Quit: A Randomized Controlled Trial of Smartphone App-Based Mindfulness Training for Smoking Cessation

INTRODUCTION

Mindfulness training may reduce smoking rates and lessen the association between craving and smoking. This trial tested the efficacy of mindfulness training via smartphone app to reduce smoking. Experience sampling (ES) was used to measure real-time craving, smoking, and mindfulness.

METHODS

A researcher-blind, parallel randomized controlled trial compared the efficacy of mobile mindfulness training with experience sampling (MMT-ES; Craving to Quit) versus experience sampling only (ES) to (1) increase 1-week point-prevalence abstinence rates at 6 months, and (2) lessen the association between craving and smoking. A modified intent-to-treat approach was used for treatment starters (MMT-ES n = 143; ES n = 182; 72% female, 81% white, age 41 ± 12 year).

RESULTS

No group difference was found in smoking abstinence at 6 months (overall, 11.1%; MMT-ES, 9.8%; ES, 12.1%; χ2(1) = 0.43, p = .51). From baseline to 6 months, both groups showed a reduction in cigarettes per day (p < .0001), craving strength (p < .0001) and frequency (p < .0001), and an increase in mindfulness (p < .05). Using ES data, a craving by group interaction was observed (F(1,3785) = 3.71, p = .05) driven by a stronger positive association between craving and cigarettes per day for ES (t = 4.96, p < .0001) versus MMT-ES (t = 2.03, p = .04). Within MMT-ES, the relationship between craving and cigarettes per day decreased as treatment completion increased (F(1,104) = 4.44, p = .04).

CONCLUSIONS

Although mindfulness training via smartphone app did not lead to reduced smoking rates compared with control, our findings provide preliminary evidence that mindfulness training via smartphone app may help lessen the association between craving and smoking, an effect that may be meaningful to support quitting in the longer term.

IMPLICATIONS

This is the first reported full-scale randomized controlled trial of any smartphone app for smoking cessation. Findings provide preliminary evidence that smartphone app-based MMT-ES may lessen the association between craving and smoking.

TRIAL REGISTRATION

Clinicaltrials.gov NCT02134509.

Virtual Reality Smartphone-Based Intervention for Smoking Cessation: Pilot Randomized Controlled Trial on Initial Clinical Efficacy and Adherence

BACKGROUND

Obstacles to current tobacco cessation programs include limited access and adherence to effective interventions. Digital interventions offer a great opportunity to overcome these difficulties, yet virtual reality has not been used as a remote and self-administered tool to help increase adherence and effectiveness of digital interventions for tobacco cessation.

OBJECTIVE

This study aimed to evaluate participant adherence and smoking cessation outcomes in a pilot randomized controlled trial of the digital intervention Mindcotine (MindCotine Inc) using a self-administered treatment of virtual reality combined with mindfulness.

METHODS

A sample of 120 participants was recruited in the city of Buenos Aires, Argentina (mean age 43.20 years, SD 9.50; 57/120, 47.5% female). Participants were randomly assigned to a treatment group (TG), which received a self-assisted 21-day program based on virtual reality mindful exposure therapy (VR-MET) sessions, daily surveys, and online peer-to-peer support moderated by psychologists, or a control group (CG), which received the online version of the smoking cessation manual from the Argentine Ministry of Health. Follow-up assessments were conducted by online surveys at postintervention and 90-day follow-up. The primary outcome was self-reported abstinence at postintervention, with missing data assumed as still smoking. Secondary outcomes included sustained abstinence at 90-day follow-up, adherence to the program, and readiness to quit.

RESULTS

Follow-up rates at day 1 were 93% (56/60) for the TG and 100% (60/60) for the CG. At postintervention, the TG reported 23% (14/60) abstinence on that day compared with 5% (3/60) in the CG. This difference was statistically significant (χ²₁=8.3; P=.004). The TG reported sustained abstinence of 33% (20/60) at 90 days. Since only 20% (12/60) of participants in the CG completed the 90-day follow-up, we did not conduct a statistical comparison between groups at this follow-up time point. Among participants still smoking at postintervention, the TG was significantly more ready to quit compared to the CG (TG: mean 7.71, SD 0.13; CG: mean 7.16, SD 0.13; P=.005). A total of 41% (23/56) of participants completed the treatment in the time frame recommended by the program.

CONCLUSIONS

Results provide initial support for participant adherence to and efficacy of Mindcotine and warrant testing the intervention in a fully powered randomized trial. However, feasibility of trial follow-up assessment procedures for control group participants needs to be improved. Further research is needed on the impact of VR-MET on long-term outcomes.

TRIAL REGISTRATION

ISRCTN Registry ISRCTN50586181; http://www.isrctn.com/ISRCTN50586181.

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.

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

Feasibility of a Smartphone App with Mindfulness Training for Adolescent Smoking Cessation: Craving to Quit (C2Q)-Teen

Objectives

The use of mobile technology for smoking cessation holds promise for adolescents, who do not typically access traditional treatments, but most are not grounded in theory or mechanism. Operant conditioning theory suggests an addictive smoking loop is formed between nicotine use and affective states, leading to habitual cue-induced craving and automatic behavior; mindfulness training may bring automated smoking behavior into awareness, so smokers may work mindfully with cravings. Mindfulness training delivered via smartphone technology therefore has potential to help adolescent smokers break this addictive loop and quit smoking. This pair-matched cluster-randomized controlled school-based pilot study evaluated program feasibility and preliminary smoking outcomes in relation to intervention engagement.

Methods

Six high schools were pair matched and randomly assigned to one of three interventions: (1) mindfulness training delivered via mobile smoking cessation application (Craving to Quit, C2Q), (2) NCI's QuitSTART smoking cessation application (NCI), and (3) written cessation materials (Materials). Adolescents (n = 146) smoking 5 or more cigarettes per day were recruited. Interventions were implemented over four weeks and study assessments were collected at baseline and 3- and 6- month follow-up, including self-reported 7-day point prevalence abstinence, program usage, smoking-related measures, and psychosocial factors.

Results

Overall cotinine-validated abstinence at 6 months was 15.8% and was similar between conditions. Odds of abstinence increased with each quartile increase in app/materials use with no significant differences between conditions (OR=1.60 (C2Q), 1.66 (Materials), and 2.69 (NCI)). Of participants still smoking at 6 months, for each quartile increase in engagement the number of cigarettes smoked in the previous 7 days showed a significantly greater decline in the C2Q condition (-5.71) compared to the Materials (-0.95) and NCI (+7.73) condition (p=0.02 for differences between conditions).

Conclusions

Cotinine-validated abstinence was similar between intervention conditions and tended to increase with greater engagement in each condition. Greater C2Q app engagement among continuing smokers was associated with a significantly greater decline in number of cigarettes smoked compared to the other conditions. The Craving to Quit (C2Q) mobile smoking cessation application with mindfulness training was feasible to use and has promise in assisting adolescents to quit or decrease cigarette smoking.

Clinical Trial Registration

Developing a Smartphone App with Mindfulness Training for Teen Smoking Cessation: ClinicalTrials.gov Identifier: NCT02218281.

Real-time video counselling for smoking cessation

BACKGROUND

Real-time video communication software such as Skype and FaceTime transmits live video and audio over the Internet, allowing counsellors to provide support to help people quit smoking. There are more than four billion Internet users worldwide, and Internet users can download free video communication software, rendering a video counselling approach both feasible and scalable for helping people to quit smoking.

OBJECTIVES

To assess the effectiveness of real-time video counselling delivered individually or to a group in increasing smoking cessation, quit attempts, intervention adherence, satisfaction and therapeutic alliance, and to provide an economic evaluation regarding real-time video counselling.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group Specialised Register, CENTRAL, MEDLINE, PubMed, PsycINFO and Embase to identify eligible studies on 13 August 2019. We searched the World Health Organization International Clinical Trials Registry Platform and ClinicalTrials.gov to identify ongoing trials registered by 13 August 2019. We checked the reference lists of included articles and contacted smoking cessation researchers for any additional studies.

SELECTION CRITERIA

We included randomised controlled trials (RCTs), randomised trials, cluster RCTs or cluster randomised trials of real-time video counselling for current tobacco smokers from any setting that measured smoking cessation at least six months following baseline. The real-time video counselling intervention could be compared with a no intervention control group or another smoking cessation intervention, or both.

DATA COLLECTION AND ANALYSIS

Two authors independently extracted data from included trials, assessed the risk of bias and rated the certainty of the evidence using the GRADE approach. We performed a random-effects meta-analysis for the primary outcome of smoking cessation, using the most stringent measure of smoking cessation measured at the longest follow-up. Analysis was based on the intention-to-treat principle. We considered participants with missing data at follow-up for the primary outcome of smoking cessation to be smokers.

MAIN RESULTS

We included two randomised trials with 615 participants. Both studies delivered real-time video counselling for smoking cessation individually, compared with telephone counselling. We judged one study at unclear risk of bias and one study at high risk of bias. There was no statistically significant treatment effect for smoking cessation (using the strictest definition and longest follow-up) across the two included studies when real-time video counselling was compared to telephone counselling (risk ratio (RR) 2.15, 95% confidence interval (CI) 0.38 to 12.04; 2 studies, 608 participants; I2 = 66%). We judged the overall certainty of the evidence for smoking cessation as very low due to methodological limitations, imprecision in the effect estimate reflected by the wide 95% CIs and inconsistency of cessation rates. There were no significant differences between real-time video counselling and telephone counselling reported for number of quit attempts among people who continued to smoke (mean difference (MD) 0.50, 95% CI -0.60 to 1.60; 1 study, 499 participants), mean number of counselling sessions completed (MD -0.20, 95% CI -0.45 to 0.05; 1 study, 566 participants), completion of all sessions (RR 1.13, 95% CI 0.71 to 1.79; 1 study, 43 participants) or therapeutic alliance (MD 1.13, 95% CI -0.24 to 2.50; 1 study, 398 participants). Participants in the video counselling arm were more likely than their telephone counselling counterparts to recommend the programme to a friend or family member (RR 1.06, 95% CI 1.01 to 1.11; 1 study, 398 participants); however, there were no between-group differences on satisfaction score (MD 0.70, 95% CI -1.16 to 2.56; 1 study, 29 participants).

AUTHORS' CONCLUSIONS

There is very little evidence about the effectiveness of real-time video counselling for smoking cessation. The existing research does not suggest a difference between video counselling and telephone counselling for assisting people to quit smoking. However, given the very low GRADE rating due to methodological limitations in the design, imprecision of the effect estimate and inconsistency of cessation rates, the smoking cessation results should be interpreted cautiously. High-quality randomised trials comparing real-time video counselling to telephone counselling are needed to increase the confidence of the effect estimate. Furthermore, there is currently no evidence comparing real-time video counselling to a control group. Such research is needed to determine whether video counselling increases smoking cessation.

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.

What Do Smokers Want in A Smartphone-Based Cessation Application?

Background

Fueled by rapid technological advances over the past decade, there is growing interest in the use of smartphones to aid in smoking cessation. Hundreds of applications have been developed for this purpose, but little is known about how these applications are accessed and used by smokers or what features smokers believe would be most useful.

Purpose

The present study sought to understand the prevalence of smartphone ownership and patterns of use among smokers as well as the perceived utility of various smartphone application features for smoking cessation that are currently in development or already available.

Methods

Daily cigarette smokers (n = 224) reported on smartphone ownership, their patterns of smartphone usage, and perceived utility of features. Features were ranked according to perceived utility and differences in both perceived utility and general smartphone use patterns were examined as a function of demographic and smoking-related variables.

Results

Most smokers (80.4%) own a smartphone, but experience with smoking cessation applications is extremely rare (6.1%). Ownership and patterns of usage differed as a function of demographic and smoking-related variables. Overall, gain-framed features were rated as most useful, while loss-framed and interpersonal features were rated as least useful.

Conclusions

Mobile health interventions have the potential to reach a large number of smokers but are currently underutilized. Additional effort is needed to ensure parity in treatment access. Gain-framed messages may be especially useful for engaging smokers, even if other features ultimately drive treatment effects.

Implications

This study describes patterns of smartphone usage among smokers and identifies the smartphone application features smokers believe would be most useful during a quit attempt. Findings indicate which subgroups of smokers are most likely to be reached with mobile health interventions and suggests that inclusion of specific features may be helpful for engaging smokers in the smoking cessation process.

Quitting starts in the brain: a randomized controlled trial of app-based mindfulness shows decreases in neural responses to smoking cues that predict reductions in smoking

Current treatments for smoking yield suboptimal outcomes, partly because of an inability to reduce cue-induced smoking. Mindfulness training (MT) has shown preliminary efficacy for smoking cessation, yet its neurobiological target remains unknown. Our prior work with nonsmokers indicates that MT reduces posterior cingulate cortex (PCC) activity. In individuals who smoke, the PCC, consistently a main hub of the "default mode network," activates in response to smoking cues. In this randomized controlled trial, we tested the effects of app-delivered MT on PCC reactivity to smoking cues and whether individual differences in MT-mediated PCC changes predicted smoking outcomes. Smoking cue-induced PCC reactivity was measured using functional magnetic resonance imaging at baseline and 1 month after receiving smartphone app-based MT (n = 33) vs. an active control (National Cancer Institute's QuitGuide, n = 34). Whether individual differences in treatment-related changes in PCC activity predicted smoking behavior was assessed. The MT group demonstrated a significant correlation between a reduction in PCC reactivity to smoking cues and a decline in cigarette consumption (r = 0.39, p = 0.02). No association was found in the control group (r = 0.08, p = 0.65). No effects of group alone were found in PCC or cigarette reduction. Post hoc analysis revealed this association is sex specific (women, r = 0.49, p = 0.03; men: r = -0.08, p = 0.79). This initial report indicates that MT specifically reduces smoking cue-induced PCC activity in a subject-specific manner, and the reduction in PCC activity predicts a concurrent decline in smoking. These findings link the hypothesized behavioral effects of MT for smoking to neural mechanisms particularly in women. This lays the groundwork for identifying individuals who may benefit from targeted digital therapeutic treatments such as smartphone-based MT, yielding improved clinical outcomes.

Effectiveness of Mindfulness-based Relapse Prevention on Quality of Life and Craving in Methadone-treated Patients: A Randomized Clinical Trial

Background

Quality of life (QOL) is always considered as a final consequence of clinical trials, interventions, and health care. The results of studies indicate that addiction leads to lower QOL. However, studies have been conducted on the effectiveness of mindfulness-based interventions on improving QOL. The aim of this study was to investigate the efficacy of mindfulness-based relapse prevention (MBRP) on QOL and craving in methadone-treated patients.

Methods

This study was conducted in Qom, Iran, in 2017. A sample of 70 methadone-treated patients were randomly selected and assigned to two groups (intervention and control). Participants in both groups completed the 36-item Short Form (SF-36) QOL Questionnaire and Craving Beliefs Questionnaire (CBQ) at the beginning of the study (pre-test), 8 weeks after the study (post-test), and two months after the study (follow up). In this study, the experimental group received 8 training sessions on mindfulness prevention, while the control group did not receive general information about addiction and did not receive any psychological intervention. Finally, data of 63 patients were analyzed with the SPSS software, chi-square test, t-test, and repeated-measures ANOVA.

Findings

The results of repeated-measures ANOVA showed that there was no significant difference between intervention and control groups in the pre-test, but MBRP in the intervention group significantly increased the scores of QOL and decreased the scores of craving, significantly (P < 0.001).

Conclusion

The findings of present study indicate that MBRP training can increase the psychological and physical health in dependent methadone-treated patients and decrease craving. These findings suggest that mindfulness training can be used as an effective intervention for improving QOL and reducing craving.

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.

Smartphone-based emotion recognition skills training for alexithymia - A randomized controlled pilot study

BACKGROUND

Neurobiological studies suggest that deficits in emotion recognition are common phenomena in alexithymia. Thus, effective treatments for alexithymia often include skills training in the domain of emotion recognition. Given that smartphone-based interventions (SBIs) offering skills training have been shown to be promising adjuncts to psychological treatments, a blended SBI facilitating the training of emotional skills might be effective in reducing alexithymia.

METHODS

In this pilot trial, N = 29 individuals reporting elevated alexithymia levels were randomly assigned to a blended SBI including a psychoeducation session and 14 days of training with the mindtastic alexithymia app (MT-ALEX) or a psychoeducation-only control condition. Primary outcome was emotion recognition skills as assessed in a computer-based two-choice task paradigm.

RESULTS

On average, participating in the SBI was associated with a significant increase in computer-assessed emotion recognition skills compared to the control condition (d = 0.97).

CONCLUSIONS

Study findings provide preliminary evidence that SBIs can improve emotion recognition skills in alexithymic individuals. Research using larger samples and targeting clinical populations is necessary to further evaluate the potential of MT-ALEX.

Kick.it: The development of an evidence-based smoking cessation smartphone app

Currently, the evidence for mobile health (mHealth) smoking cessation interventions is limited and heterogeneous, warranting the need for innovative rigorously developed solutions. The aim of this study was to describe the development of a smoking cessation smartphone application (app) developed using evidence-based principles. The app (Kick.it) was designed using the Intervention Mapping framework, incorporating an extensive literature review and qualitative study, in combination with the Behavioural Change Taxonomy v1, the Theoretical Domains Framework, and the Persuasive System Design framework. Kick.it provides quit smoking education, skills training, motivational content and self-regulation functionality for smokers, as well as their social support network. By logging cravings and cigarettes smoked, users will create their own smoking profile, which will be used to provide tailored interventions. It hosts a social network to allow 24/7 social support and provides in-app tools to help with urges to smoke. The app aims to motivate smokers to retry if they slip-up or relapse, allowing them to learn from previous smoking cessation attempts. Rather than basing the app on a singular behavioral change approach, Kick.it will use elements stemming from a variety of behavioral approaches by combining methods of multiple psychological theories. The use of best-practice intervention development frameworks in conjunction with evidence-based behavioral change techniques is expected to result in a smartphone app that has an optimal chance of helping people to quit smoking.

Effect of a Mobile Phone Intervention on Quitting Smoking in a Young Adult Population of Smokers: Randomized Controlled Trial

Background

Digital mobile technology presents a promising medium for reaching young adults with smoking cessation interventions because they are the heaviest users of this technology.

Objective

The aim of this study was to determine the efficacy of an evidence-informed smartphone app for smoking cessation, Crush the Crave (CTC), on reducing smoking prevalence among young adult smokers in comparison with an evidence-informed self-help guide, On the Road to Quitting (OnRQ).

Methods

A parallel, double-blind, randomized controlled trial with 2 arms was conducted in Canada to evaluate CTC. In total, 1599 young adult smokers (aged 19 to 29 years) intending to quit smoking in the next 30 days were recruited online and randomized to receive CTC or the control condition OnRQ for a period of 6 months. The primary outcome measure was self-reported continuous abstinence at the 6-month follow-up.

Results

Overall follow-up rates were 57.41% (918/1599) and 60.48% (967/1599) at 3 and 6 months, respectively. Moreover, 45.34% (725/1599) of participants completed baseline, 3-, and 6-month follow-up. Intention-to-treat analysis (last observation carried forward) showed that continuous abstinence (N=1599) at 6 months was not significantly different at 7.8% (64/820) for CTC versus 9.2% (72/779) for OnRQ (odds ratio; OR 0.83, 95% CI 0.59-1.18). Similarly, 30-day point prevalence abstinence at 6 months was not significantly different at 14.4% (118/820) and 16.9% (132/779) for CTC and OnRQ, respectively (OR 0.82, 95% CI 0.63-1.08). However, these rates of abstinence were favorable compared with unassisted 30-day quit rates of 11.5% among young adults. Secondary measures of quit attempts and the number of cigarettes smoked per day at 6-month follow-up did not reveal any significant differences between groups. For those who completed the 6-month follow-up, 85.1% (359/422) of young adult smokers downloaded CTC as compared with 81.8% (346/423) of OnRQ, χ²_1(N=845)=1.6, P=.23. Furthermore, OnRQ participants reported significantly higher levels of overall satisfaction (mean 3.3 [SD 1.1] vs mean 2.6 [SD 1.3]; t₆₄₄=6.87, P<.001), perceived helpfulness (mean 5.8 [SD 2.4] vs mean 4.3 [SD 2.6], t₆₅₇=8.0, P<.001), and frequency of use (mean 3.6 [SD 1.2] vs mean 3.2 [SD 1.1], t₆₈₃=5.7, P<.001) compared with CTC participants.

Conclusions

CTC was feasible for delivering cessation support but was not superior to a self-help guide in helping motivated young adults to quit smoking. CTC will benefit from further formative research to address satisfaction and usage. As smartphone apps may not serve as useful alternatives to printed self-help guides, there is a need to conduct further research to understand how digital mobile technology smoking cessation interventions for smoking cessation can be improved.

Trial Registration

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

Can Mindfulness Address Maladaptive Eating Behaviors? Why Traditional Diet Plans Fail and How New Mechanistic Insights May Lead to Novel Interventions

Emotional and other maladaptive eating behaviors develop in response to a diversity of triggers, from psychological stress to the endless external cues in our modern food environment. While the standard approach to food- and weight-related concerns has been weight-loss through dietary restriction, these interventions have produced little long-term benefit, and may be counterproductive. A growing understanding of the behavioral and neurobiological mechanisms that underpin habit formation may explain why this approach has largely failed, and pave the way for a new generation of non-pharmacologic interventions. Here, we first review how modern food environments interact with human biology to promote reward-related eating through associative learning, i.e., operant conditioning. We also review how operant conditioning (positive and negative reinforcement) cultivates habit-based reward-related eating, and how current diet paradigms may not directly target such eating. Further, we describe how mindfulness training that targets reward-based learning may constitute an appropriate intervention to rewire the learning process around eating. We conclude with examples that illustrate how teaching patients to tap into and act on intrinsic (e.g., enjoying healthy eating, not overeating, and self-compassion) rather than extrinsic reward mechanisms (e.g., weighing oneself), is a promising new direction in improving individuals' relationship with food.

Current, future and potential use of mobile and wearable technologies and social media data in the ABCD study to increase understanding of contributors to child health

Mobile and wearable technologies and novel methods of data collection are innovating health-related research. These technologies and methods allow for multi-system level capture of data across environmental, physiological, behavioral, and psychological domains. In the Adolescent Brain Cognitive Development (ABCD) Study, there is great potential for harnessing the acceptability, accessibility, and functionality of mobile and social technologies for in-vivo data capture to precisely measure factors, and interactions between factors, that contribute to childhood and adolescent neurodevelopment and psychosocial and health outcomes. Here we discuss advances in mobile and wearable technologies and methods of analysis of geospatial, ecologic, social network and behavioral data. Incorporating these technologies into the ABCD study will allow for interdisciplinary research on the effects of place, social interactions, environment, and substance use on health and developmental outcomes in children and adolescents.

Combining ecological momentary assessment with objective, ambulatory measures of behavior and physiology in substance-use research

Whereas substance-use researchers have long combined self-report with objective measures of behavior and physiology inside the laboratory, developments in mobile/wearable electronic technology are increasingly allowing for the collection of both subjective and objective information in participants' daily lives. For self-report, ecological momentary assessment (EMA), as implemented on contemporary smartphones or personal digital assistants, can provide researchers with near-real-time information on participants' behavior and mood in their natural environments. Data from portable/wearable electronic sensors measuring participants' internal and external environments can be combined with EMA (e.g., by timestamps recorded on questionnaires) to provide objective information useful in determining the momentary context of behavior and mood and/or validating participants' self-reports. Here, we review three objective ambulatory monitoring techniques that have been combined with EMA, with a focus on detecting drug use and/or measuring the behavioral or physiological correlates of mental events (i.e., emotions, cognitions): (1) collection and processing of biological samples in the field to measure drug use or participants' physiological activity (e.g., hypothalamic-pituitary-adrenal axis activity); (2) global positioning system (GPS) location information to link environmental characteristics (disorder/disadvantage, retail drug outlets) to drug use and affect; (3) ambulatory electronic physiological monitoring (e.g., electrocardiography) to detect drug use and mental events, as advances in machine learning algorithms make it possible to distinguish target changes from confounds (e.g., physical activity). Finally, we consider several other mobile/wearable technologies that hold promise to be combined with EMA, as well as potential challenges faced by researchers working with multiple mobile/wearable technologies simultaneously in the field.

A randomised active-controlled trial to examine the effects of an online mindfulness intervention on executive control, critical thinking and key thinking dispositions in a university student sample

BACKGROUND

Arguments for including mindfulness instruction in higher education have included claims about the benefits of mindfulness practice for critical thinking. While there is theoretical support for this claim, empirical support is limited. The aim of this study was to test this claim by investigating the effects of an online mindfulness intervention on executive function, critical thinking skills and associated thinking dispositions.

METHOD

Participants recruited from a university were randomly allocated, following screening, to either a mindfulness meditation group or a sham meditation group. Both the researchers and the participants were blind to group allocation. The intervention content for both groups was delivered through the Headspace online application, an application which provides guided meditations to users. Both groups were requested to complete 30 guided mindfulness meditation sessions across a 6 week period. Primary outcome measures assessed mindfulness, executive functioning, critical thinking, actively open-minded thinking and need for cognition. Secondary outcome measures assessed wellbeing, positive and negative affect, and real-world outcomes.

RESULTS

In a series of full-information maximum likelihood analyses, significant increases in mindfulness dispositions and critical thinking scores were observed in both the mindfulness meditation and sham meditation groups. However, no significant effects of group allocation were observed for either primary or secondary measures. Furthermore, mediation analyses testing the indirect effect of group allocation through executive functioning performance did not reveal a significant result and moderation analyses showed that the effect of the intervention did not depend on baseline levels of the key thinking dispositions, actively open-minded thinking and need for cognition.

CONCLUSION

No evidence was found to suggest that engaging in guided mindfulness practice for 6 weeks using the online intervention method applied in this study improves critical thinking performance. While further research is warranted, claims regarding the benefits of mindfulness practice for critical thinking should be tempered in the meantime.

TRIAL REGISTRATION

The study was initially registered in the AEA Social Science Registry before the recruitment was initiated (RCT ID: AEARCTR-0000756; 14/11/2015) and retrospectively registered in the ISRCTN registry ( RCT ID: ISRCTN16588423 ) in line with requirements for publishing the study protocol.

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.

Effects of a brief mindfulness-meditation intervention on neural measures of response inhibition in cigarette smokers

Research suggests that mindfulness-practices may aid smoking cessation. Yet, the neural mechanisms underlying the effects of mindfulness-practices on smoking are unclear. Response inhibition is a main deficit in addiction, is associated with relapse, and could therefore be a candidate target for mindfulness-based practices. The current study hence investigated the effects of a brief mindfulness-practice on response inhibition in smokers using behavioral and electroencephalography (EEG) measures. Fifty participants (33 females, mean age 20 years old) underwent a protocol of cigarette exposure to induce craving (cue-exposure) and were then randomly assigned to a group receiving mindfulness-instructions or control-instructions (for 15 minutes approximately). Immediately after this, they performed a smoking Go/NoGo task, while their brain activity was recorded. At the behavioral level, no group differences were observed. However, EEG analyses revealed a decrease in P3 amplitude during NoGo vs. Go trials in the mindfulness versus control group. The lower P3 amplitude might indicate less-effortful response inhibition after the mindfulness-practice, and suggest that enhanced response inhibition underlies observed positive effects of mindfulness on smoking behavior.

Mindfulness-based interventions for addictive behaviors: Implementation issues on the road ahead

Over the past 35 years, mindfulness meditation practices have increasingly been integrated into Western medical settings. Research into the benefits of mindfulness-based interventions (MBIs) continues to expand, such that there are currently more than a dozen different protocolled MBIs for patients suffering from a variety of physical and psychological disorders. In the last decade, a number of MBIs specifically designed to treat addictive behaviors have been developed and tested. This review first provides a brief overview of the current state of the science with respect to the efficacy of MBIs for addictive behaviors, and some of the proposed mechanisms underlying the efficacy of MBIs. Second, the review highlights unresolved implementation issues and provides suggestions for how future research can address the implementation challenges to advance the delivery of MBIs. Specifically, this review focuses on the lack of clear empirical guidelines in the following areas: (a) effective training for MBI treatment providers; (b) adaptations of the traditional 2-hr closed-cohort group format; (c) delivery of MBIs in 1-on-1 treatment contexts; (d) delivery of MBIs at different points in the change process; (e) delivery of MBIs via technology-based platforms; and (f) facilitation of precision medicine in the delivery of MBIs. Specific research directions are suggested with an eye toward a meaningful increase in access to MBIs for front-line clinicians and clients. (PsycINFO Database Record

Neuroscience-informed psychoeducation for addiction medicine: A neurocognitive perspective

Psychoeducation (PE) is defined as an intervention with systematic, structured, and didactic knowledge transfer for an illness and its treatment, integrating emotional and motivational aspects to enable patients to cope with the illness and to improve its treatment adherence and efficacy. PE is considered an important component of treatment in both medical and psychiatric disorders, especially for mental health disorders associated with lack of insight, such as alcohol and substance use disorders (ASUDs). New advancements in neuroscience have shed light on how various aspects of ASUDs may relate to neural processes. However, the actual impact of neuroscience in the real-life clinical practice of addiction medicine is minimal. In this chapter, we provide a perspective on how PE in addiction medicine can be informed by neuroscience in two dimensions: content (knowledge we transfer in PE) and structure (methods we use to deliver PE). The content of conventional PE targets knowledge about etiology of illness, treatment process, adverse effects of prescribed medications, coping strategies, family education, and life skill training. Adding neuroscience evidence to the content of PE could be helpful in communicating not only the impact of drug use but also the beneficial impact of various treatments (i.e., on brain function), thus enhancing motivation for compliance and further destigmatizing their symptoms. PE can also be optimized in its "structure" by implicitly and explicitly engaging different neurocognitive processes, including salience/attention, memory, and self-awareness. There are many interactions between these two dimensions, structure and content, in the delivery of neuroscience-informed psychoeducation (NIPE). We explore these interactions in the development of a cartoon-based NIPE to promote brain recovery during addiction treatment as a part of the brain awareness for addiction recovery initiative.

Innovative approaches to support smoking cessation for individuals with mental illness and co-occurring substance use disorders

INTRODUCTION

Tobacco remains the leading preventable cause of death in the US, accounting for over 520,000 deaths annually. While the smoking prevalence has declined over the past 50 years, those with mental illness and addictive disorders continue to smoke at high levels and with significant tobacco-related health problems. Areas covered: This review highlights the epidemiology, contributing factors, and evidence-base for intervening upon tobacco use in those with mental illness and addictive disorders. Historically underprioritized, a growing body of literature supports treating tobacco within mental health and addiction treatment settings. Critically, treating tobacco use appears to support, and not harm, mental health recovery and sobriety. This review also summarizes novel, emerging approaches to mitigate the harms of cigarette smoking. Expert commentary: People with mental illness and addictive disorders have a high prevalence of tobacco use with serious health harms. Treating tobacco use is essential. Evidence-based strategies include individual treatments that are stage-matched to readiness to quit and combine cessation medications with behavioral therapies, supported by smoke-free policies in treatment settings and residential environments. Emerging approaches, with a focus on harm reduction, are electronic nicotine delivery systems and tobacco regulatory efforts to reduce the nicotine content in cigarettes, thereby reducing their addiction potential.

Testing a mobile mindful eating intervention targeting craving-related eating: feasibility and proof of concept

Theoretically driven smartphone-delivered behavioral interventions that target mechanisms underlying eating behavior are lacking. In this study, we administered a 28-day self-paced smartphone-delivered intervention rooted in an operant conditioning theoretical framework that targets craving-related eating using mindful eating practices. At pre-intervention and 1-month post-intervention, we assessed food cravings among adult overweight or obese women (N = 104; M age = 46.2 ± 14.1 years; M BMI = 31.5 ± 4.5) using ecological momentary assessment via text message (SMS), self-reported eating behavior (e.g., trait food craving), and in-person weight. Seventy-eight participants (75.0%) completed the intervention within 7 months ('all completers'), and of these, 64 completed the intervention within 3 months ('timely completers'). Participants experienced significant reductions in craving-related eating (40.21% reduction; p < .001) and self-reported overeating behavior (trait food craving, p < .001; other measures ps < .01). Reductions in trait food craving were significantly correlated with weight loss for timely completers (r = .30, p = .020), this pattern of results was also evident in all completers (r = .22, p = .065). Taken together, results suggest that smartphone-delivered mindful eating training targeting craving-related eating may (1) target behavior that impacts a relative metabolic pathway, and (2) represent a low-burden and highly disseminable method to reduce problematic overeating among overweight individuals. ClinicalTrials.gov registration: NCT02694731.

Reducing procrastination using a smartphone-based treatment program: A randomized controlled pilot study

BACKGROUND

Procrastination affects a large number of individuals and is associated with significant mental health problems. Despite the deleterious consequences individuals afflicted with procrastination have to bear, there is a surprising paucity of well-researched treatments for procrastination. To fill this gap, this study evaluated the efficacy of an easy-to-use smartphone-based treatment for procrastination.

METHOD

N = 31 individuals with heightened procrastination scores were randomly assigned to a blended smartphone-based intervention including two brief group counseling sessions and 14 days of training with the mindtastic procrastination app (MT-PRO), or to a waitlist condition. MT-PRO fosters the approach of functional and the avoidance of dysfunctional behavior by systematically utilizing techniques derived from cognitive bias modification approaches, gamification principles, and operant conditioning. Primary outcome was the course of procrastination symptom severity as assessed with the General Procrastination Questionnaire.

RESULTS

Participating in the smartphone-based treatment was associated with a significantly greater reduction of procrastination than was participating in the control condition (η²  = .15).

CONCLUSION

A smartphone-based intervention may be an effective treatment for procrastination. Future research should use larger samples and directly compare the efficacy of smartphone-based interventions and traditional interventions for procrastination.

TControl: A mobile app to follow up tobacco-quitting patients

BACKGROUND AND OBJECTIVE

Tobacco smoking is a major risk factor for a wide range of respiratory and circulatory diseases in active and passive smokers. Well-designed campaigns are raising awareness to the problem and an increasing number of smokers seeks medical assistance to quit their habit. In this context, there is the need to develop mHealth Apps that assist and manage large smoke quitting programs in efficient and economic ways.

OBJECTIVES

Our main objective is to develop an efficient and free mHealth app that facilitates the management of, and assistance to, people who want to quit smoking. As secondary objectives, our research also aims at estimating the economic effect of deploying that App in the public health system.

METHODS

Using JAVA and XML we develop and deploy a new free mHealth App for Android, called TControl (Tobacco-quitting Control). We deploy the App at the Tobacco Unit of the Santa Maria Hospital in Lleida and determine its stability by following the crashes of the App. We also use a survey to test usability of the app and differences in aptitude for using the App in a sample of 31 patients. Finally, we use mathematical models to estimate the economic effect of deploying TControl in the Catalan public health system.

RESULTS

TControl keeps track of the smoke-quitting users, tracking their status, interpreting it, and offering advice and psychological support messages. The App also provides a bidirectional communication channel between patients and clinicians via mobile text messages. Additionally, registered patients have the option to interchange experiences with each other by chat. The App was found to be stable and to have high performances during startup and message sending. Our results suggest that age and gender have no statistically significant effect on patient aptitude for using TControl. Finally, we estimate that TControl could reduce costs for the Catalan public health system (CPHS) by up to € 400M in 10 years.

CONCLUSIONS

TControl is a stable and well behaved App, typically operating near optimal performance. It can be used independent of age and gender, and its wide implementation could decrease costs for the public health system.

Perceptions Toward a Smoking Cessation App Targeting LGBTQ+ Youth and Young Adults: A Qualitative Framework Analysis of Focus Groups

BACKGROUND

The prevalence of smoking among lesbian, gay, bisexual, trans, queer, and other sexual minority (LGBTQ+) youth and young adults (YYA) is significantly higher compared with that among non-LGBTQ+ persons. However, in the past, interventions were primarily group cessation classes that targeted LGBTQ+ persons of all ages. mHealth interventions offer an alternate and modern intervention platform for this subpopulation and may be of particular interest for young LGBTQ+ persons.

OBJECTIVE

This study explored LGBTQ+ YYA (the potential users') perceptions of a culturally tailored mobile app for smoking cessation. Specifically, we sought to understand what LGBTQ+ YYA like and dislike about this potential cessation tool, along with how such interventions could be improved.

METHODS

We conducted 24 focus groups with 204 LGBTQ+ YYA (aged 16-29 years) in Toronto and Ottawa, Canada. Participants reflected on how an app might support LGBTQ+ persons with smoking cessation. Participants indicated their feelings, likes and dislikes, concerns, and additional ideas for culturally tailored smoking cessation apps. Framework analysis was used to code transcripts and identify the overarching themes.

RESULTS

Study findings suggested that LGBTQ+ YYA were eager about using culturally tailored mobile apps for smoking cessation. Accessibility, monitoring and tracking, connecting with community members, tailoring, connecting with social networks, and personalization were key reasons that were valued for a mobile app cessation program. However, concerns were raised about individual privacy and that not all individuals had access to a mobile phone, users might lose interest quickly, an app would need to be marketed effectively, and app users might cheat and lie about progress to themselves. Participants highlighted that the addition of distractions, rewards, notifications, and Web-based and print versions of the app would be extremely useful to mitigate some of their concerns.

CONCLUSIONS

This study provided insight into the perspectives of LGBTQ+ YYA on a smoking cessation intervention delivered through a mobile app. The findings suggested a number of components of a mobile app that were valued and those that were concerning, as well as suggestions on how to make a mobile app cessation program successful. App development for this subpopulation should take into consideration the opinions of the intended users and involve them in the development and evaluation of mobile-based smoking cessation programs.

Functional Connectivity During Exposure to Favorite-Food, Stress, and Neutral-Relaxing Imagery Differs Between Smokers and Nonsmokers

INTRODUCTION

Tobacco-use disorder is a complex condition involving multiple brain networks and presenting with multiple behavioral correlates including changes in diet and stress. In a previous functional magnetic resonance imaging (fMRI) study of neural responses to favorite-food, stress, and neutral-relaxing imagery, smokers versus nonsmokers demonstrated blunted corticostriatal-limbic responses to favorite-food cues. Based on other recent reports of alterations in functional brain networks in smokers, the current study examined functional connectivity during exposure to favorite-food, stress, and neutral-relaxing imagery in smokers and nonsmokers, using the same dataset.

METHODS

The intrinsic connectivity distribution was measured to identify brain regions that differed in degree of functional connectivity between groups during each imagery condition. Resulting clusters were evaluated for seed-to-voxel connectivity to identify the specific connections that differed between groups during each imagery condition.

RESULTS

During exposure to favorite-food imagery, smokers versus nonsmokers showed lower connectivity in the supramarginal gyrus, and differences in connectivity between the supramarginal gyrus and the corticostriatal-limbic system. During exposure to neutral-relaxing imagery, smokers versus nonsmokers showed greater connectivity in the precuneus, and greater connectivity between the precuneus and the posterior insula and rolandic operculum. During exposure to stress imagery, smokers versus nonsmokers showed lower connectivity in the cerebellum.

CONCLUSIONS

These findings provide data-driven insights into smoking-related alterations in brain functional connectivity patterns related to appetitive, relaxing, and stressful states.

IMPLICATIONS

This study uses a data-driven approach to demonstrate that smokers and nonsmokers show differential patterns of functional connectivity during guided imagery related to personalized favorite-food, stress, and neutral-relaxing cues, in brain regions implicated in attention, reward-related, emotional, and motivational processes. For smokers, these differences in connectivity may impact appetite, stress, and relaxation, and may interfere with smoking cessation.

Prioritizing the mHealth Design Space: A Mixed-Methods Analysis of Smokers' Perspectives

Background

Smoking remains the leading cause of preventable disease and death in the United States. Therefore, researchers are constantly exploring new ways to promote smoking cessation. Mobile health (mHealth) technologies could be effective cessation tools. Despite the availability of commercial quit-smoking apps, little research to date has examined smokers’ preferred treatment intervention components (ie, design features). Honoring these preferences is important for designing programs that are appealing to smokers and may be more likely to be adopted and used.

Objective

The aim of this study was to understand smokers’ preferred design features of mHealth quit-smoking tools.

Methods

We used a mixed-methods approach consisting of focus groups and written surveys to understand the design preferences of adult smokers who were interested in quitting smoking (N=40). Focus groups were stratified by age to allow differing perspectives to emerge between older (>40 years) and younger (<40 years) participants. Focus group discussion included a “blue-sky” brainstorming exercise followed by participant reactions to contrasting design options for communicating with smokers, providing social support, and incentivizing program use. Participants rated the importance of preselected design features on an exit survey. Qualitative analyses examined emergent discussion themes and quantitative analyses compared feature ratings to determine which were perceived as most important.

Results

Participants preferred a highly personalized and adaptive mHealth experience. Their ideal mHealth quit-smoking tool would allow personalized tracking of their progress, adaptively tailored feedback, and real-time peer support to help manage smoking cravings. Based on qualitative analysis of focus group discussion, participants preferred pull messages (ie, delivered upon request) over push messages (ie, sent automatically) and preferred interaction with other smokers through closed social networks. Preferences for entertaining games or other rewarding incentives to encourage program use differed by age group. Based on quantitative analysis of surveys, participants rated the importance of select design features significantly differently (P<.001). Design features rated as most important included personalized content, the ability to track one’s progress, and features designed to help manage nicotine withdrawal and medication side effects. Design features rated least important were quit-smoking videos and posting on social media. Communicating with stop-smoking experts was rated more important than communicating with family and friends about quitting (P=.03). Perceived importance of various design features varied by age, experience with technology, and frequency of smoking.

Conclusions

Future mHealth cessation aids should be designed with an understanding of smokers’ needs and preferences for these tools. Doing so does not guarantee treatment effectiveness, but balancing user preferences with best-practice treatment considerations could enhance program adoption and improve treatment outcomes. Grounded in the perspectives of smokers, we identify several design considerations, which should be prioritized when designing future mHealth cessation tools and which warrant additional empirical validation.

Evaluating an Adaptive and Interactive mHealth Smoking Cessation and Medication Adherence Program: A Randomized Pilot Feasibility Study

BACKGROUND

Mobile health (mHealth) interventions hold great promise for helping smokers quit since these programs can have wide reach and facilitate access to comprehensive, interactive, and adaptive treatment content. However, the feasibility, acceptability, and effectiveness of these programs remain largely untested.

OBJECTIVE

To assess feasibility and acceptability of the My Mobile Advice Program (MyMAP) smoking cessation program and estimate its effects on smoking cessation and medication adherence to inform future research planning.

METHODS

Sixty-six smokers ready to quit were recruited from a large regional health care system and randomized to one of two mHealth programs: (1) standard self-help including psychoeducational materials and guidance how to quit smoking or (2) an adaptive and interactive program consisting of the same standard mHealth self-help content as controls received plus a) real-time, adaptively tailored advice for managing nicotine withdrawal symptoms and medication side-effects and b) asynchronous secure messaging with a cessation counselor. Participants in both arms were also prescribed a 12-week course of varenicline. Follow-up assessments were conducted at 2 weeks post-target quit date (TQD), 3 months post-TQD, and 5 months post-TQD. Indices of program feasibility and acceptability included acceptability ratings, utilization metrics including use of each MyMAP program component (self-help content, secure messaging, and adaptively tailored advice), and open-ended feedback from participants. Smoking abstinence and medication adherence were also assessed to estimate effects on these treatment outcomes.

RESULTS

Utilization data indicated the MyMAP program was actively used, with higher mean program log-ins by experimental than control participants (10.6 vs 2.7, P<.001). The majority of experimental respondents thought the MyMAP program could help other people quit smoking (22/24, 92%) and consistently take their stop-smoking medication (17/22, 97%) and would recommend the program to others (20/23, 87%). They also rated the program as convenient, responsive to their needs, and easy to use. Abstinence rates at 5-month follow-up were 36% in the experimental arm versus 24% among controls (odds ratio 1.79 [0.61-5.19], P=.42). Experimental participants used their varenicline an average of 46 days versus 39 among controls (P=.49). More than two-thirds (22/33, 67%) of experimental participants and three-quarters (25/33, 76%) of controls prematurely discontinued their varenicline use (P=.29).

CONCLUSIONS

The MyMAP intervention was found to be feasible and acceptable. Since the study was not powered for statistical significance, no conclusions can be drawn about the program's effects on smoking abstinence or medication adherence, but the overall study results suggest further evaluation in a larger randomized trial is warranted.

CLINICALTRIAL

ClinicalTrials.gov NCT02136498; https://clinicaltrials.gov/ct2/show/NCT02136498 (Archived by WebCite at http://www.webcitation.org/6jT3UMFLj).

A randomized controlled pilot study of CBT-I Coach: Feasibility, acceptability, and potential impact of a mobile phone application for patients in cognitive behavioral therapy for insomnia

There has been growing interest in utilizing mobile phone applications (apps) to enhance traditional psychotherapy. Previous research has suggested that apps may facilitate patients' completion of cognitive behavioral therapy for insomnia (CBT-I) tasks and potentially increase adherence. This randomized clinical trial pilot study ( n = 18) sought to examine the feasibility, acceptability, and potential impact on adherence and sleep outcomes related to CBT-I Coach use. All participants were engaged in CBT-I, with one group receiving the app as a supplement and one non-app group. We found that patients consistently used the app as intended, particularly the sleep diary and reminder functions. They reported that it was highly acceptable to use. Importantly, the app did not compromise or undermine benefits of cognitive behavioral therapy for insomnia and patients in both groups had significantly improved sleep outcomes following treatment.

Update on Smoking Cessation: E-Cigarettes, Emerging Tobacco Products Trends, and New Technology-Based Interventions

Tobacco use disorders (TUDs) continue to be overly represented in patients treated in mental health and addiction treatment settings. It is the most common substance use disorder (SUD) and the leading cause of health disparities and increased morbidity/mortality amongst individuals with a psychiatric disorder. There are seven Food and Drug Administration (FDA) approved medications and excellent evidence-based psychosocial treatment interventions to use in TUD treatment. In the past few years, access to and use of other tobacco or nicotine emerging products are on the rise, including the highly publicized electronic cigarette (e-cigarette). There has also been a proliferation of technology-based interventions to support standard TUD treatment, including mobile apps and web-based interventions. These tools are easily accessed 24/7 to support outpatient treatment. This update will review the emerging products and counter-measure intervention technologies, including how clinicians can integrate these tools and other community-based resources into their practice.

Are Smoking Cessation Treatments Associated with Suicidality Risk? An Overview

Risk of suicidality during smoking cessation treatment is an important, but often overlooked, aspect of nicotine addiction research and treatment. We explore the relationship between smoking cessation interventions and suicidality and explore common treatments, their associated risks, and effectiveness in promoting smoking reduction and abstinence. Although active smokers have been reported to have twofold to threefold increased risk of suicidality when compared to nonsmokers,1–4 research regarding the safest way to stop smoking does not always provide clear guidelines for practitioners wishing to advise their patients regarding smoking cessation strategies. In this article, we review pharmacological and cognitive behavioral therapy (CBT) options that are available for people seeking to quit smoking, focusing on the relationship between the ability of these therapies to reduce smoking behavior and promote abstinence and suicidality risks as assessed by reported suicidality on validated measures, reports of suicidal ideation, behaviors, actual attempts, or completed suicides. Pharmacotherapies such as varenicline, bupropion, and nicotine replacement, and CBTs, including contextual CBT interventions, have been found to help reduce smoking rates and promote and maintain abstinence. Suicidality risks, while present when trying to quit smoking, do not appear to demonstrate a consistent or significant rise associated with use of any particular smoking cessation pharmacotherapy or CBT/contextual CBT intervention reviewed.

Mobile phone-based interventions for smoking cessation

BACKGROUND

Access to mobile phones continues to increase exponentially globally, outstripping access to fixed telephone lines, fixed computers and the Internet. Mobile phones are an appropriate and effective option for the delivery of smoking cessation support in some contexts. This review updates the evidence on the effectiveness of mobile phone-based smoking cessation interventions.

OBJECTIVES

To determine whether mobile phone-based smoking cessation interventions increase smoking cessation in people who smoke and want to quit.

SEARCH METHODS

For the most recent update, we searched the Cochrane Tobacco Addiction Group Specialised Register in April 2015. We also searched the UK Clinical Research Network Portfolio for current projects in the UK, and the ClinicalTrials.gov register for ongoing or recently completed studies. We searched through the reference lists of identified studies and attempted to contact the authors of ongoing studies. We applied no restrictions on language or publication date.

SELECTION CRITERIA

We included randomised or quasi-randomised trials. Participants were smokers of any age who wanted to quit. Studies were those examining any type of mobile phone-based intervention for smoking cessation. This included any intervention aimed at mobile phone users, based around delivery via mobile phone, and using any functions or applications that can be used or sent via a mobile phone.

DATA COLLECTION AND ANALYSIS

Review authors extracted information on risk of bias and methodological details using a standardised form. We considered participants who dropped out of the trials or were lost to follow-up to be smoking. We calculated risk ratios (RR) and 95% confidence intervals (CI) for each included study. Meta-analysis of the included studies used the Mantel-Haenszel fixed-effect method. Where meta-analysis was not possible, we presented a narrative summary and descriptive statistics.

MAIN RESULTS

This updated search identified 12 studies with six-month smoking cessation outcomes, including seven studies completed since the previous review. The interventions were predominantly text messaging-based, although several paired text messaging with in-person visits or initial assessments. Two studies gave pre-paid mobile phones to low-income human immunodeficiency virus (HIV)-positive populations - one solely for phone counselling, the other also included text messaging. One study used text messages to link to video messages. Control programmes varied widely. Studies were pooled according to outcomes - some providing measures of continuous abstinence or repeated measures of point prevalence; others only providing 7-day point prevalence abstinence. All 12 studies pooled using their most rigorous 26-week measures of abstinence provided an RR of 1.67 (95% CI 1.46 to 1.90; I(2) = 59%). Six studies verified quitting biochemically at six months (RR 1.83; 95% CI 1.54 to 2.19).

AUTHORS' CONCLUSIONS

The current evidence supports a beneficial impact of mobile phone-based smoking cessation interventions on six-month cessation outcomes. While all studies were good quality, the fact that those studies with biochemical verification of quitting status demonstrated an even higher chance of quitting further supports the positive findings. However, it should be noted that most included studies were of text message interventions in high-income countries with good tobacco control policies. Therefore, caution should be taken in generalising these results outside of this type of intervention and context.