Smoking environment cues reduce ability to resist smoking as measured by a delay to smoking task

How a 5-Day Stay in the Tobacco-Free Environment of the Stoptober House Supports Individuals to Quit Smoking: A Mixed Methods Pilot Study

INTRODUCTION

The Stoptober House is part of the annual national Stoptober smoking cessation campaign in the Netherlands. During the first week of October, 48 volunteers resided in the tobacco-free Stoptober House for 5 days and received smoking cessation counseling. This pilot study explored how the Stoptober House may have facilitated smoking cessation among participants.

METHODS

We included 48 individuals who were selected for the Stoptober House (intervention group) and 67 individuals who were not selected (control group). Surveys were conducted at baseline, immediately after 2 and 8 weeks of post-intervention. We compared self-reported abstinence, psychosocial mediators related to smoking cessation, and perceived active elements of the Stoptober House between the intervention and control groups using t/χ2 tests and linear mixed model (LMM) analysis. Sixteen semi-structured qualitative interviews were conducted to explore participants' perspectives on the elements contributing to their success in quitting smoking.

RESULTS

At 8 weeks of follow-up, a higher proportion of participants in the intervention group (24/48 [50%]) reported being abstinent compared to the control group (5/67 [7%]; p < 0.001). Among participants who reported making a quit attempt, 22/38 (57.9%) in the intervention group remained abstinent compared to 4/17 (23.5%) in the control group (p = 0.022). The intervention group also exhibited higher self-efficacy to quit smoking throughout the follow-up period and higher social support immediately after the Stoptober House. No significant differences were observed in other psychosocial factors. The interviews highlighted several perceived elements of the Stoptober House that contributed to smoking cessation success, including restricted smoking opportunities, access to smoking cessation counselors, and peer support.

CONCLUSION

This pilot study suggests that the Stoptober House provides support that can help people quit smoking. Further research is needed to confirm these findings and determine the cost-effectiveness of this intervention in promoting long-term abstinence among specific groups of smokers.

Effect of age of tobacco initiation and number of failed quit attempts on maintenance of tobacco abstinence

BACKGROUND

The decision to make a quit attempt is the first step toward the tobacco cessation process. It is well established in the literature that if someone does not take tobacco till the age of 21 years then his chances of remaining tobacco-free for life are higher than his counterparts who start tobacco at early developmental ages.

METHODOLOGY AND TOOLS

The present study was conducted among 400 university undergraduate students. A cross-sectional survey design was used, multi-stage sampling was done, and four colleges were selected via random sampling. The motivation to quit tobacco, tobacco craving, and maintenance of tobacco abstinence was assessed via contemplation ladder, tobacco craving questionnaire Short Form, and smoking abstinence questionnaire. To validate subjective data, a urine cotinine test was performed.

RESULTS

The age of tobacco initiation significantly impacts intentions to quit tobacco and tobacco craving levels. The number of unsuccessful quit attempts was also significantly associated with the maintenance of tobacco abstinence. The failed quit attempts play a vital role in altering tobacco cravings and make the withdrawals more complicated to handle for recent tobacco quitters.

Conditioned place preferences for virtual alcohol cues

This study examined whether a conditioned place preference (CPP) could be established for a virtual reality (VR) room that previously contained virtual alcohol stimuli. 298 undergraduates with varying levels of alcohol use completed six, three-minute conditioning sessions in which they were confined to one of two visually-distinct VR rooms: one of the VR rooms contained virtual alcohol cues (CS+) while the other VR room was neutral (CS-). Following conditioning, participants completed a three-minute test session during which they had unrestricted access to both VR rooms and neither room contained any alcohol-related cues. Although no virtual alcohol cues were present, participants with alcohol use (n = 248) spent significantly longer in CS+ relative to CS- compared to participants with alcohol non-use (n = 50) during the test session. This is the first study to show that a CPP can be established using virtual alcohol cues, in the absence of any actual alcohol administration. However, participants with alcohol use did not subjectively report enjoying CS+ more than CS- and explicitly chose CS- as their preferred room. Interestingly, these findings suggest that implicit and explicit measures of CPP may tap into distinct, separable processes and should be investigated further.

Modeling ability to resist alcohol in the human laboratory: A pilot study

Background

Roughly half of patients with alcohol use disorder prefer non-abstinence based approaches to treatment. However, only individuals who can limit their alcohol use after low-risk consumption are most likely to benefit from these approaches. This pilot study developed a laboratory-based intravenous alcohol self-administration paradigm to determine the characteristics of individuals who could successfully resist consuming alcohol after an initial exposure.

Methods

Seventeen non-treatment seeking heavy drinkers completed two versions of an intravenous alcohol self-administration paradigm designed to assess impaired control over alcohol use. In the paradigm, participants received a priming dose of alcohol and then entered a 120-min resist phase, in which they received monetary rewards if they resisted self-administering alcohol. We used Cox proportional hazards regression to determine the impact of craving and Impaired Control Scale scores on rate of lapse.

Results

64.7% of participants across both versions of the paradigm were unable to resist alcohol for the duration of the session. Craving at baseline (HR = 1.07, 95% CI 1.01-1.13, p = 0.02) and following priming (HR = 1.08, 95% CI 1.02-1.15, p = 0.01) were associated with rate of lapse. Individuals who lapsed endorsed greater attempts to control their drinking over the prior six months compared to individuals who resisted.

Conclusions

This study provides preliminary evidence that craving may be predictive of risk of lapse in individuals who are trying to limit alcohol intake after consuming a small initial amount of alcohol. Future studies should test this paradigm in a larger and more diverse sample.

Smokers’ Self-Report and Behavioral Reactivity to Combined Personal Smoking Cues (Proximal + Environment + People): A Pilot Study

Cue reactivity (CR) among smokers exposed to smoking-related stimuli, both proximal (e.g., cigarettes, lighter) and distal (environments, people), has been well-demonstrated. Furthermore, past work has shown that combining proximal smoking cues with smoking environment cues increases cue-provoked craving and smoking behavior above that elicited by either cue type alone. In this pilot study, we examined the impact of combining three personal cues, proximal + environment + people, on subjective and behavioral cue reactivity among smokers. To further understand the impact of this method, we also tested reactivity under the conditions of both smoking satiety and deprivation. In addition, we examined the extent to which cue-induced craving predicted immediate subsequent smoking. Fifteen smokers completed six sessions, of which two focused on the intake and development of personal cues and four involved personal cue reactivity sessions: (1) deprived, smoking cue combination, (2) deprived, nonsmoking cue combination, (3) sated, smoking combination, and (4) sated, nonsmoking cue combination. Cue-provoked craving was greater and smokers were quicker to light a cigarette and smoked more during their exposure to smoking rather than nonsmoking cues and in deprived compared to sated conditions, with no interaction between these variables. While deprived, greater cue-provoked craving in response to smoking cues was correlated with a quicker latency to light a cigarette. This work supports the feasibility of presenting three personal smoking-related combinations of cues within a cue reactivity paradigm and highlights the robust reactivity that this methodology can evoke in smokers.

Feasibility, Appropriateness, and Willingness to Use Virtual Reality as an Adjunct to Counseling among Addictions Counselors

INTRODUCTION

Research suggests that virtual reality (VR) experiences can be helpful as adjunctive tools in psychotherapy for some mental health conditions. VR is a computer-generated experience that produces a feeling of being immersed in a different environment. VR experiences could be useful in the treatment of substance use disorders, and several are currently being tested. However, few psychotherapists report using VR experiences in their practices, even when doing so is well-supported. Understanding key barriers and concerns about using VR among drug/alcohol counselors is important to ultimately encouraging adoption.

METHODS

Licensed counselors (N = 101) who provide treatment to clients with substance use disorders were recruited via email Listservs, professional organizations, and social media. Participants viewed a 15-minute educational video about VR and then completed a survey of their views about using it with their clients.

RESULTS

Most clinicians (82%) believed they would be likely to use a VR experience in drug/alcohol counseling, and 81% believed it would be appropriate for most of their clients. A minority (19%) noted important concerns, including that their clients may be skeptical of it (15%), cost (14%), and space (10%). Those who had cost and space concerns were less likely to report high use intentions (OR = 0.29 and OR = 031, both p < .05, respectively).

CONCLUSIONS

Findings suggest that addictions counselors are eager to use VR, but key barriers should be addressed. VR developers should incorporate features to encourage trust among users, design experiences for small spaces, and explore ways of supporting the purchase of VR systems for counselors.

Desire thinking as an underlying mechanism in alcohol use disorder and nicotine dependence

Desire thinking is an emerging construct in the addictive behaviours literature. No research, to date, has investigated its contribution to problematic alcohol use and nicotine dependence in patient samples when accounting for established predictors of addictive behaviours. The present study sought to clarify, in patient samples, the relative contribution of desire thinking in the associations between negative affect, impulsivity and thought suppression on the one hand and craving, problematic alcohol use and nicotine dependence on the other. To achieve this goal, two groups of individuals with alcohol use disorder (AUD) (n = 370; age range = 15-67 years) and nicotine dependence (n = 365; age range = 17-75 years) were selected, and measures of negative affect, impulsivity, thought suppression, craving, desire thinking, problematic alcohol use and nicotine dependence were completed by both groups. Results showed that in both groups, negative affect and thought suppression indirectly affected alcohol and nicotine craving, problematic alcohol use and nicotine dependence through the mediating role of desire thinking. The present study shows the independent role of desire thinking in predicting problematic alcohol use and nicotine dependence in patient samples, indicating its potential relevance for treatment.

Association of depression symptom level with smoking urges, cigarette withdrawal, and smoking reinstatement: A preliminary laboratory study

BACKGROUND

Cigarette smoking urges, withdrawal, and smoking reinstatement may be especially relevant to people with elevated depression symptoms who smoke. This laboratory study aimed to assess relations between depression symptom level and smoking urges for reward and relief, cigarette withdrawal, and smoking reinstatement in people who smoke cigarettes daily during acute abstinence and while smoking as usual.

METHODS

Participants with low (n = 51) or elevated (n = 29) baseline depression symptoms underwent two counterbalanced laboratory sessions (i.e., abstinent, non-abstinent). At each session, they completed subjective measures of smoking urges for reward and relief, and withdrawal. They also completed a laboratory smoking reinstatement task measuring whether they would delay smoking and the number of cigarettes smoked.

RESULTS

The elevated depression symptom group reported significantly higher withdrawal (p = .01) and smoked more cigarettes than the low depression symptoms group during the smoking reinstatement task self-administration period at the abstinent session (p = .04). Smoking urges for reward and relief were not significantly different by depression symptom group. There were no significant interactions of depression and abstinence with any outcomes.

CONCLUSIONS

As outcomes were measured at both an abstinent and non-abstinent session, findings identify factors for people with elevated depression symptoms who smoke which may drive smoking behavior and impede smoking cessation efforts. This study provides evidence that people with elevated depression symptoms who smoke may need additional/more pharmacological or behavioral smoking cessation aids targeted at reducing withdrawal and number of cigarettes smoked.

Prediction of Smoking Risk From Repeated Sampling of Environmental Images: Model Validation

Background

Viewing their habitual smoking environments increases smokers’ craving and smoking behaviors in laboratory settings. A deep learning approach can differentiate between habitual smoking versus nonsmoking environments, suggesting that it may be possible to predict environment-associated smoking risk from continuously acquired images of smokers’ daily environments.

Objective

In this study, we aim to predict environment-associated risk from continuously acquired images of smokers’ daily environments. We also aim to understand how model performance varies by location type, as reported by participants.

Methods

Smokers from Durham, North Carolina and surrounding areas completed ecological momentary assessments both immediately after smoking and at randomly selected times throughout the day for 2 weeks. At each assessment, participants took a picture of their current environment and completed a questionnaire on smoking, craving, and the environmental setting. A convolutional neural network–based model was trained to predict smoking, craving, whether smoking was permitted in the current environment and whether the participant was outside based on images of participants’ daily environments, the time since their last cigarette, and baseline data on daily smoking habits. Prediction performance, quantified using the area under the receiver operating characteristic curve (AUC) and average precision (AP), was assessed for out-of-sample prediction as well as personalized models trained on images from days 1 to 10. The models were optimized for mobile devices and implemented as a smartphone app.

Results

A total of 48 participants completed the study, and 8008 images were acquired. The personalized models were highly effective in predicting smoking risk (AUC=0.827; AP=0.882), craving (AUC=0.837; AP=0.798), whether smoking was permitted in the current environment (AUC=0.932; AP=0.981), and whether the participant was outside (AUC=0.977; AP=0.956). The out-of-sample models were also effective in predicting smoking risk (AUC=0.723; AP=0.785), whether smoking was permitted in the current environment (AUC=0.815; AP=0.937), and whether the participant was outside (AUC=0.949; AP=0.922); however, they were not effective in predicting craving (AUC=0.522; AP=0.427). Omitting image features reduced AUC by over 0.1 when predicting all outcomes except craving. Prediction of smoking was more effective for participants whose self-reported location type was more variable (Spearman ρ=0.48; P=.001).

Conclusions

Images of daily environments can be used to effectively predict smoking risk. Model personalization, achieved by incorporating information about daily smoking habits and training on participant-specific images, further improves prediction performance. Environment-associated smoking risk can be assessed in real time on a mobile device and can be incorporated into device-based smoking cessation interventions.

The impact of paternal alcohol, tobacco, caffeine use and physical activity on offspring mental health: a systematic review and meta-analysis

BACKGROUND

There is some evidence that paternal health behaviours during and around pregnancy could be associated with offspring health outcomes. However, the impact that paternal health behaviours during pregnancy can have on offspring mental health is understudied and remains unclear.

METHODS

We conducted a systematic review and meta-analysis of articles in PubMed describing studies of potentially modifiable paternal health behaviours (tobacco smoking, alcohol consumption, caffeine consumption and physical activity) in the prenatal period in relation to offspring mental health. GRADE was used to measure risk of bias.

RESULTS

Eight studies were included and categorized by paternal health behaviour and offspring mental health outcome investigated. The narrative synthesis provided evidence of association between paternal health behaviours around pregnancy and offspring mental health problems, with the strongest evidence shown for tobacco use. Grouped by analysis type, two separate meta-analyses showed evidence of paternal smoking during pregnancy being associated with greater odds of ADHD in offspring (OR 1.42, 95% CI 1.02-1.99; HR 1.28, 95% CI 1.19-1.39).

CONCLUSIONS

The small number of studies that have investigated paternal prenatal effects on offspring mental health, and the limited sample sizes of those studies, makes it challenging to draw firm conclusions. Although existing studies suggest that paternal tobacco smoking and alcohol consumption in the prenatal period are associated with poorer offspring mental health, (particularly hyperactivity/ADHD), further investigation of potential paternal effects is required, using methods that allow stronger inference to determine whether associations are causal.

Secondhand smoke exposure in European countries with different smoke-free legislation. Findings from the EUREST-PLUS ITC Europe Surveys

BACKGROUND

Exposure to secondhand smoke (SHS) poses serious and extensive health and economic-related consequences to European society and worldwide. Smoking bans are a key measure to reducing SHS exposure but have been implemented with varying levels of success. We assessed changes in the prevalence of self-reported SHS exposure and smoking behaviour in public places among smokers in six European countries and the influence of the country's type of smoking ban (partial or total ban) on such exposure and smoking behaviour.

METHODS

The EUREST-PLUS ITC Europe Surveys were conducted among adult smokers in Germany, Greece, Hungary, Poland, Romania, and Spain in 2016 (Wave 1, n=6,011) and 2018 (Wave 2, n=6,027). We used generalised estimating equations models to assess changes between Waves 1 and 2 and to test the interaction between the type of smoking ban and 1) self-reported SHS exposure, 2) self-reported smoking in public places.

RESULTS

A significant decrease in self-reported SHS exposure was observed in workplaces, from 19.1% in 2016 to 14.0% in 2018 (-5.1%; 95% CI: -8.0%;-2.2%). Self-reported smoking did not change significantly inside bars (22.7% in W2), restaurants (13.2% in W2) and discos/nightclubs (34.0% in W2). SHS exposure in public places was significantly less likely (OR=0.35; 95% CI: 0.26-0.47) in the countries with total bans as compared to those countries with partial bans.

CONCLUSION

The inverse association between smoking in public places and smoking bans indicates an opportunity for strengthening smoke-free legislation and protecting bystanders from exposure to SHS in public places.

The feasibility of an in-scanner smoking lapse paradigm to examine the neural correlates of lapses

Quitting smoking is notoriously difficult. Models of nicotine dependence posit that strength of cognitive control contributes to maintaining smoking abstinence during smoking cessation attempts. We examine the role for large-scale functional brain systems associated with cognitive control in smoking lapse using a novel adaption of a well-validated behavioral paradigm. We use data from 17 daily smokers (five females) after 12 h of smoking abstinence. Participants completed up to 10 sequential 5-min functional magnetic resonance imaging (fMRI) runs, within a single scanning session. After each run, participants decided whether to stay in the scanner in order to earn additional money or to terminate the session in order to smoke a cigarette (i.e., lapse) and forego additional monetary reward. Cox regression results indicate that decreased segregation of the default mode system from the frontoparietal system undermines the ability to resist smoking. This study demonstrates the feasibility of modifying an established behavioral model of smoking lapse behavior for use in the neuro imaging environment, and it provides initial evidence that this approach yields valuable information regarding fine-grained, time-varying changes in patterns of neural activity in the moments leading up to a decision to smoke. Specifically, results lend support to the hypothesis that the time-varying interplay between large-scale functional brain systems associated with cognitive control is implicated in smoking lapse behavior.

Are Machine Learning Methods the Future for Smoking Cessation Apps?

Smoking cessation apps provide efficient, low-cost and accessible support to smokers who are trying to quit smoking. This article focuses on how up-to-date machine learning algorithms, combined with the improvement of mobile phone technology, can enhance our understanding of smoking behaviour and support the development of advanced smoking cessation apps. In particular, we focus on the pros and cons of existing approaches that have been used in the design of smoking cessation apps to date, highlighting the need to improve the performance of these apps by minimizing reliance on self-reporting of environmental conditions (e.g., location), craving status and/or smoking events as a method of data collection. Lastly, we propose that making use of more advanced machine learning methods while enabling the processing of information about the user’s circumstances in real time is likely to result in dramatic improvement in our understanding of smoking behaviour, while also increasing the effectiveness and ease-of-use of smoking cessation apps, by enabling the provision of timely, targeted and personalised intervention.

Effectiveness of attentional bias modification training as add-on to regular treatment in alcohol and cannabis use disorder: A multicenter randomized control trial

Background

Attentional bias for substance-relevant cues has been found to contribute to the persistence of addiction. Attentional bias modification (ABM) interventions might, therefore, increase positive treatment outcome and reduce relapse rates. The current study investigated the effectiveness of a newly developed home-delivered, multi-session, internet-based ABM intervention, the Bouncing Image Training Task (BITT), as an add-on to treatment as usual (TAU).

Methods

Participants (N = 169), diagnosed with alcohol or cannabis use disorder, were randomly assigned to one of two conditions: the experimental ABM group (50%; TAU+ABM); or the control group (50%; split in two subgroups the TAU+placebo group and TAU-only group, 25% each). Participants completed baseline, post-test, and 6 and 12 months follow-up measures of substance use and craving allowing to assess long-term treatment success and relapse rates. In addition, attentional bias (both engagement and disengagement), as well as secondary physical and psychological complaints (depression, anxiety, and stress) were assessed.

Results

No significant differences were found between conditions with regard to substance use, craving, relapse rates, attentional bias, or physical and psychological complaints.

Conclusions

The findings may reflect unsuccessful modification of attentional bias, the BITT not targeting the relevant process (engagement vs. disengagement bias), or may relate to the diverse treatment goals of the current sample (i.e., moderation or abstinence). The current findings provide no support for the efficacy of this ABM approach as an add-on to TAU in alcohol or cannabis use disorder. Future studies need to delineate the role of engagement and disengagement bias in the persistence of addiction, and the role of treatment goal in the effectiveness of ABM interventions.

Addressing concerns about smoking cessation and mental health: theoretical review and practical guide for healthcare professionals

Smoking rates in people with depression and anxiety are twice as high as in the general population, even though people with depression and anxiety are motivated to stop smoking. Most healthcare professionals are aware that stopping smoking is one of the greatest changes that people can make to improve their health. However, smoking cessation can be a difficult topic to raise. Evidence suggests that smoking may cause some mental health problems, and that the tobacco withdrawal cycle partly contributes to worse mental health. By stopping smoking, a person's mental health may improve, and the size of this improvement might be equal to taking anti-depressants. In this theoretical review and practical guide we outline ways in which healthcare professionals can raise the topic of smoking compassionately and respectfully to encourage smoking cessation. We draw on evidence-based methods like cognitive behavioural therapy, and outline approaches that healthcare professionals can use to integrate these methods into routine care.

Trajectory of smoking behaviour during the first 6 months after diagnosis of lung cancer: A study from Taiwan

AIMS

To identify different classes of change pattern/ trajectory of tobacco smoking behaviour after diagnosis of lung cancer using multi-wave data and to explore factors associated with the class membership.

DESIGN

This is a multi-wave observational study.

METHODS

Smoking behaviour data were collected at diagnosis and then every month for 6 months from 133 newly diagnosed people with lung cancer who had recently quit smoking or continued to smoke at diagnosis. These patients were recruited from three medical centres and data were collected from May 2014 to January 2017. Smoking behaviour was assessed based on patients' self-reports on whether they smoked during the last month (yes/no) for a total of seven times. Mixture latent Markov model and logistic regression were used to analyse data.

RESULTS

Two latent classes of smoking trajectory were identified among recent quitters or current smokers of people with lung cancer, namely "perseverance for abstinence" and "indecisive for abstinence." Patients who were younger age (OR = 0.95, p = 0.026), exposure to second-hand smoke (OR = 3.35, p = 0.012) and lower self-efficacy for not smoking (OR = 0.96, p = 0.011) were more likely to belong to the class of "indecisive for abstinence."

CONCLUSIONS

Heterogeneous classes of smoking trajectory existed in newly diagnosed people with lung cancer. The risk factors associated with a less favourable smoking trajectory can be incorporated into tailored smoking-cessation programs for patients newly diagnosed with lung cancer.

IMPACT

The dynamic trajectory of smoking behaviour had not been adequately explored among newly diagnosed people with lung cancer. Two classes of smoking trajectory and the predictors associated with the class membership were identified. These findings suggest that the diagnosis of cancer is a teachable moment for smoking cessation. Patients with younger age, lower self-efficacy of not smoking and exposure to second-hand smoke at home need special attention.

Alcohol conditioned contexts enhance positive subjective alcohol effects and consumption

Associations between alcohol and the places it is consumed are important at all stages of alcohol abuse and addiction. However, it is not clear how the associations are formed in humans or how they influence drinking, and there are few effective strategies to prevent their pathological effects on alcohol use. We used a human laboratory model to study the effects of alcohol environments on alcohol consumption. Healthy regular binge drinkers completed conditioned place preference (CPP) with 0 vs. 80 mg/100 mL alcohol (Paired Group). Control participants (Unpaired Group) completed sessions without explicit alcohol-room pairings. After conditioning, participants completed alcohol self-administration in either the alcohol- or no alcohol-paired room. Paired group participants reported greater subjective stimulation and euphoria, and consumed more alcohol in the alcohol-paired room in comparison to the no alcohol-paired room, and controls tested in either room. Moreover, the strength of conditioning significantly predicted drinking; participants who exhibited the strongest CPP consumed the most alcohol in the alcohol-paired room. This is the first empirical evidence that laboratory-conditioned alcohol environments directly influence drinking. The results also confirm the viability of the model to examine the mechanisms by which alcohol environments stimulate drinking and to test strategies to counteract their influence on behavior.

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.

Differences in Magnitude of Cue Reactivity Across Durations of Smoking History: A Meta-analysis

BACKGROUND

Cue-elicited craving may vary due to duration of smoking history, increasing as more years of smoking strengthen associations between nicotine intake and cues. However, research on this relationship is virtually absent. This project assessed the relationship between cue reactivity and years of smoking.

METHODS

Data from 53 studies (68 effect sizes) were analyzed. Eligible studies were those measuring self-reported craving following cue exposure in nontreatment seeking smokers and reporting mean years smoking. Preliminary subgroup analyses identified methodological factors influencing cue-reactivity effect sizes; primary meta-regression analysis assessed differences across years smoking; exploratory analyses assessed potential for ceiling effects.

RESULTS

Effect sizes varied due to abstinence requirement and cue presentation modality, but not dependence severity. Unexpectedly, meta-regression analysis revealed a decline in effect sizes across years smoking. Exploratory analyses suggested declines may have been due to a ceiling effect in craving measurement for those with longer smoking histories-more experienced smokers reported higher levels of craving at baseline or following neutral cue exposure, but all reported similar levels of craving after smoking cue exposure.

CONCLUSIONS

Methodological factors and duration of smoking history influenced measurement of cue reactivity. Highlighted were important relationships between years smoking and magnitude of cue reactivity, depending on use of baseline or neutral cue comparisons. Further research is needed to assess differences in cue reactivity due to duration of smoking history using participant-level data, directly testing for ceiling effects. In addition, cue-reactivity studies are needed across young adults to assess onset of associations between nicotine intake and cues.

IMPLICATIONS

This meta-analysis project contributes to the cue-reactivity literature by reporting on the previously ignored relationship between duration of smoking history and magnitude of cue-elicited craving. Results suggest that declines in cue-reactivity effect sizes across years of smoking may have been due to study-level methodological factors, but not due to differences in sample-level dependence severity. Cue-reactivity effect sizes were stable across years of smoking in studies using a neutral cue comparison but declined sharply in studies when baseline assessment (typically coupled with an abstinence requirement) was used.

Considering Drug-Associated Contexts in Substance Use Disorders and Treatment Development

Environmental contexts that are reliably associated with the use of pharmacologically active substances are hypothesized to contribute to substance use disorders. In this review, we provide an updated summary of parallel preclinical and human studies that support this hypothesis. Research conducted in rats shows that environmental contexts that are reliably paired with drug use can renew extinguished drug-seeking behavior and amplify responding elicited by discrete, drug-predictive cues. Akin to drug-associated contexts, interoceptive drug stimuli produced by the psychopharmacological effects of drugs can also influence learning and memory processes that play a role in substance use disorders. Findings from human laboratory studies show that drug-associated contexts, including social stimuli, can have profound effects on cue reactivity, drug use, and drug-related cognitive expectancies. This translationally relevant research supports the idea that treatments for substance use disorders could be improved by considering drug-associated contexts as a factor in treatment interventions. We conclude this review with ideas for how to integrate drug-associated contexts into treatment-oriented research based on 4 approaches: pharmacology, brain stimulation, mindfulness-based relapse prevention, and cognitive behavioral group therapy. Throughout, we focus on alcohol- and tobacco-related research, which are two of the most prevalent and commonly misused drugs worldwide for which there are known treatments.

Lack of utility of cigarettes per day cutoffs for clinical and laboratory smoking research

BACKGROUND

Most clinical and laboratory smoking research studies require that participants smoke at a certain level to be eligible for enrollment. However, there is limited evidence that use of these cutoffs differentiates groups of smokers along clinically meaningful criteria.

METHODS

Using receiver operating characteristic curves, we analyzed data from daily smokers in the National Epidemiologic Study of Alcohol Use and Related Conditions - III (NESARC-III) to examine the utility of smoking rates for determining whether participants met DSM-5 criteria for tobacco use disorder, experienced nicotine withdrawal or had a history of failed quit attempts. We also examined whether relationships between these variables differed as a function of key sample characteristics.

RESULTS

Smoking rate exhibited a weak relationship with the presence of tobacco use disorder (AUC = 0.664), whether individuals experience nicotine withdrawal (AUC = 0.672) and whether individuals had a history of failed quit attempts (AUC = 0.578). The relationship between smoking rate and a history of failed quit attempts was weaker for women than men (p < .05). Otherwise, utility did not differ as a function of sex, race/ethnicity, education, income, or use of multiple tobacco products. Optimal cutoffs varied somewhat across indices, but the largest number of correct classifications occurred at very low smoking rates.

CONCLUSIONS

Researchers should consider abandoning the use of smoking rate cutoffs to determine study eligibility. If smoking rate cutoffs are used, a rationale should be presented along with justification for the specific cutoff chosen.

Identifying Smoking Environments From Images of Daily Life With Deep Learning

IMPORTANCE

Environments associated with smoking increase a smoker's craving to smoke and may provoke lapses during a quit attempt. Identifying smoking risk environments from images of a smoker's daily life provides a basis for environment-based interventions.

OBJECTIVE

To apply a deep learning approach to the clinically relevant identification of smoking environments among settings that smokers encounter in daily life.

DESIGN, SETTING, AND PARTICIPANTS

In this cross-sectional study, 4902 images of smoking (n = 2457) and nonsmoking (n = 2445) locations were photographed by 169 smokers from Durham, North Carolina, and Pittsburgh, Pennsylvania, areas from 2010 to 2016. These images were used to develop a probabilistic classifier to predict the location type (smoking or nonsmoking location), thus relating objects and settings in daily environments to established smoking patterns. The classifier combines a deep convolutional neural network with an interpretable logistic regression model and was trained and evaluated via nested cross-validation with participant-wise partitions (ie, out-of-sample prediction). To contextualize model performance, images taken by 25 randomly selected participants were also classified by smoking cessation experts. As secondary validation, craving levels reported by participants when viewing unfamiliar environments were compared with the model's predictions. Data analysis was performed from September 2017 to May 2018.

MAIN OUTCOMES AND MEASURES

Classifier performance (accuracy and area under the receiver operating characteristic curve [AUC]), comparison with 4 smoking cessation experts, contribution of objects and settings to smoking environment status (standardized model coefficients), and correlation with participant-reported craving.

RESULTS

Of 169 participants, 106 (62.7%) were from Durham (53 [50.0%] female; mean [SD] age, 41.4 [12.0] years) and 63 (37.3%) were from Pittsburgh (31 [51.7%] female; mean [SD] age, 35.2 [13.8] years). A total of 4902 images were available for analysis, including 3386 from Durham (mean [SD], 31.9 [1.3] images per participant) and 1516 from Pittsburgh (mean [SD], 24.1 [0.5] images per participant). Images were evenly split between the 2 classes, with 2457 smoking images (50.1%) and 2445 nonsmoking images (49.9%). The final model discriminated smoking vs nonsmoking environments with a mean (SD) AUC of 0.840 (0.024) (accuracy [SD], 76.5% [1.6%]). A model trained only with images from Durham participants effectively classified images from Pittsburgh participants (AUC, 0.757; accuracy, 69.2%), and a model trained only with images from Pittsburgh participants effectively classified images from Durham participants (AUC, 0.821; accuracy, 75.0%), suggesting good generalizability between geographic areas. Only 1 expert's performance was a statistically significant improvement compared with the classifier (α = .05). Median self-reported craving was significantly correlated with model-predicted smoking environment status (ρ = 0.894; P = .003).

CONCLUSIONS AND RELEVANCE

In this study, features of daily environments predicted smoking vs nonsmoking status consistently across participants. The findings suggest that a deep learning approach can identify environments associated with smoking, can predict the probability that any image of daily life represents a smoking environment, and can potentially trigger environment-based interventions. This work demonstrates a framework for predicting how daily environments may influence target behaviors or symptoms that may have broad applications in mental and physical health.

Combined Smoking Cues Enhance Reactivity and Predict Immediate Subsequent Smoking

Introduction

Cue reactivity (CR) research has reliably demonstrated robust cue-induced responding among smokers exposed to common proximal smoking cues (eg, cigarettes, lighter). More recent work demonstrates that distal stimuli, most notably the actual environments in which smoking previously occurred, can also gain associative control over craving. In the real world, proximal cues always occur within an environment; thus, a more informative test of how cues affect smokers might be to present these two cue types simultaneously.

Methods

Using a combined-cue counterbalanced CR paradigm, the present study tested the impact of proximal (smoking and neutral) + personal environment (smoking and nonsmoking places) pictorial cues, on smokers' subjective and behavioral CR; as well as the extent to which cue-induced craving predicts immediate subsequent smoking in a within-subjects design.

Results

As anticipated, the dual smoking cue combination (ProxS + EnvS) led to the greatest cue-induced craving relative to the other three cue combinations (ProxS + EnvN, ProxN + EnvS, and ProxN ± EnvN), ps < .004. Dual smoking cues also led to significantly shorter post-trial latencies to smoke, ps < .01. Overall CR difference score (post-trial craving minus baseline craving) was predictive of subsequent immediate smoking indexed by: post-trial latency to smoke [B = -2.69, SE = 9.02; t(143) = -2.98, p = .003]; total puff volume [B = 2.99, SE = 1.13; t(143) = 2.65, p = .009]; and total number of puffs [B = .053, SE = .027; t(143) = 1.95, p = .05].

Conclusions

The implications of these findings for better understanding the impact of cues on smoking behavior and cessation are discussed.

Implications

This novel cue reactivity study examined smokers' reactivity to combined proximal and distal smoking cues. Exposure to a combination of two smoking cues (proximal and environment) led to the greatest increases in cue-induced craving and smoking behavior compared to all other cue combinations. Further, the overall magnitude of cue-induced craving was found to significantly predict immediate subsequent smoking. This work provides new insight on how exposure to various cues and cue combinations directly affect smokers' craving and actual smoking behavior, as well as the relationship between those two indices of reactivity.

The effectiveness of attentional bias modification for substance use disorder symptoms in adults: a systematic review

BACKGROUND

Attentional bias modification (ABM) interventions have been developed to address addiction by reducing attentional bias for substance-related cues. This study provides a systematic review of the effectiveness of ABM interventions in decreasing symptoms of addictive behaviour, taking baseline levels of attentional bias and changes in attentional bias into account.

METHODS

We included randomised and non-randomised studies that investigated the effectiveness of ABM interventions in heavy-using adults and treatment-seeking individuals with symptoms of substance use disorder to manipulate attentional bias and to reduce substance use-related symptoms. We searched for relevant English peer-reviewed articles without any restriction for the year of publication using PsycINFO, PubMed, and ISI Web in August 2016. Study quality was assessed regarding reporting, external validity, internal validity, and power of the study.

RESULTS

Eighteen studies were included: nine studies reported on ABM intervention effects in alcohol use, six studies on nicotine use, and three studies on opiate use. The included studies differed with regard to type of ABM intervention (modified dot probe task n = 14; Alcohol Attention Control Training Programme n = 4), outcome measures, amount and length of provided sessions, and context (clinic versus laboratory versus home environment). The study quality mostly ranged from low average to high average (one study scored below the quality cut-off). Ten studies reported significant changes of symptoms of addictive behaviour, whereas eight studies found no effect of ABM interventions on symptoms. However, when restricted to multi-session ABM intervention studies, eight out of ten studies found effects on symptoms of addiction. Surprisingly, these effects on symptoms of addictive behaviour showed no straightforward relationship with baseline attentional bias and its change from baseline to post-test.

CONCLUSIONS

Despite a number of negative findings and the diversity of studies, multi-session ABM interventions, especially in the case of alcohol and when the Alcohol Attention Control Training Programme was used, appear to have positive effects on symptoms of addictive behaviour. However, more rigorous well-powered future research in clinical samples is needed before firm conclusions regarding the effectiveness of ABM interventions can be drawn.

SYSTEMATIC REVIEW REGISTRATION

Registration number PROSPERO: CRD42016046823.

Predicting Smoking Events with a Time-Varying Semi-Parametric Hawkes Process Model

Health risks from cigarette smoking - the leading cause of preventable death in the United States - can be substantially reduced by quitting. Although most smokers are motivated to quit, the majority of quit attempts fail. A number of studies have explored the role of self-reported symptoms, physiologic measurements, and environmental context on smoking risk, but less work has focused on the temporal dynamics of smoking events, including daily patterns and related nicotine effects. In this work, we examine these dynamics and improve risk prediction by modeling smoking as a self-triggering process, in which previous smoking events modify current risk. Specifically, we fit smoking events self-reported by 42 smokers to a time-varying semi-parametric Hawkes process (TV-SPHP) developed for this purpose. Results show that the TV-SPHP achieves superior prediction performance compared to related and existing models, with the incorporation of time-varying predictors having greatest benefit over longer prediction windows. Moreover, the impact function illustrates previously unknown temporal dynamics of smoking, with possible connections to nicotine metabolism to be explored in future work through a randomized study design. By more effectively predicting smoking events and exploring a self-triggering component of smoking risk, this work supports development of novel or improved cessation interventions that aim to reduce death from smoking.

A Control Theory Model of Smoking

We present a heuristic control theory model that describes smoking under restricted and unrestricted access to cigarettes. The model is based on the allostasis theory and uses a formal representation of a multiscale opponent process. The model simulates smoking behavior of an individual and produces both short-term ("loading up" after not smoking for a while) and long-term smoking patterns (e.g., gradual transition from a few cigarettes to one pack a day). By introducing a formal representation of withdrawal- and craving-like processes, the model produces gradual increases over time in withdrawal- and craving-like signals associated with abstinence and shows that after 3 months of abstinence, craving disappears. The model was programmed as a computer application allowing users to select simulation scenarios. The application links images of brain regions that are activated during the binge/intoxication, withdrawal, or craving with corresponding simulated states. The model was calibrated to represent smoking patterns described in peer-reviewed literature; however, it is generic enough to be adapted to other drugs, including cocaine and opioids. Although the model does not mechanistically describe specific neurobiological processes, it can be useful in prevention and treatment practices as an illustration of drug-using behaviors and expected dynamics of withdrawal and craving during abstinence.

Internet-based attentional bias modification training as add-on to regular treatment in alcohol and cannabis dependent outpatients: a study protocol of a randomized control trial

BACKGROUND

The automatic tendency to attend to and focus on substance-related cues in the environment (attentional bias), has been found to contribute to the persistence of addiction. Attentional bias modification (ABM) interventions might, therefore, contribute to treatment outcome and the reduction of relapse rates. Based on some promising research findings, we designed a study to test the clinical relevance of ABM as an add-on component of regular intervention for alcohol and cannabis patients.

DESIGN/METHODS

The current protocol describes a study which will investigate the effectiveness and cost-effectiveness of a newly developed home-delivered, multi-session, internet-based ABM (iABM) intervention as an add-on to treatment as usual (TAU). TAU consists of cognitive behavioural therapy-based treatment according to the Dutch guidelines for the treatment of addiction. Participants (N = 213) will be outpatients from specialized addiction care institutions diagnosed with alcohol or cannabis dependency who will be randomly assigned to one of three conditions: TAU + iABM; TAU + placebo condition; TAU-only. Primary outcome measures are substance use, craving, and rates of relapse. Changes in attentional bias will be measured to investigate whether changes in primary outcome measures can be attributed to the modification of attentional bias. Indices of cost-effectiveness and secondary physical and psychological complaints (depression, anxiety, and stress) are assessed as secondary outcome measures.

DISCUSSION

This randomized control trial will be the first to investigate whether a home-delivered, multi-session iABM intervention is (cost-) effective in reducing relapse rates in alcohol and cannabis dependency as an add-on to TAU, compared with an active and a waiting list control group. If proven effective, this ABM intervention could be easily implemented as a home-delivered component of current TAU.

TRIAL REGISTRATION

Netherlands Trial Register, NTR5497 , registered on 18th September 2015.