Combined Smoking Cues Enhance Reactivity and Predict Immediate Subsequent Smoking

A smoking cessation smartphone app that delivers real-time 'context aware' behavioural support: the Quit Sense feasibility RCT

Background

During a quit attempt, cues from a smoker's environment are a major cause of brief smoking lapses, which increase the risk of relapse. Quit Sense is a theory-guided Just-In-Time Adaptive Intervention smartphone app, providing smokers with the means to learn about their environmental smoking cues and provides 'in the moment' support to help them manage these during a quit attempt.

Objective

To undertake a feasibility randomised controlled trial to estimate key parameters to inform a definitive randomised controlled trial of Quit Sense.

Design

A parallel, two-arm randomised controlled trial with a qualitative process evaluation and a 'Study Within A Trial' evaluating incentives on attrition. The research team were blind to allocation except for the study statistician, database developers and lead researcher. Participants were not blind to allocation.

Setting

Online with recruitment, enrolment, randomisation and data collection (excluding manual telephone follow-up) automated through the study website.

Participants

Smokers (323 screened, 297 eligible, 209 enrolled) recruited via online adverts on Google search, Facebook and Instagram.

Interventions

Participants were allocated to 'usual care' arm (n = 105; text message referral to the National Health Service SmokeFree website) or 'usual care' plus Quit Sense (n = 104), via a text message invitation to install the Quit Sense app.

Main outcome measures

Follow-up at 6 weeks and 6 months post enrolment was undertaken by automated text messages with an online questionnaire link and, for non-responders, by telephone. Definitive trial progression criteria were met if a priori thresholds were included in or lower than the 95% confidence interval of the estimate. Measures included health economic and outcome data completion rates (progression criterion #1 threshold: ≥ 70%), including biochemical validation rates (progression criterion #2 threshold: ≥ 70%), recruitment costs, app installation (progression criterion #3 threshold: ≥ 70%) and engagement rates (progression criterion #4 threshold: ≥ 60%), biochemically verified 6-month abstinence and hypothesised mechanisms of action and participant views of the app (qualitative).

Results

Self-reported smoking outcome completion rates were 77% (95% confidence interval 71% to 82%) and health economic data (resource use and quality of life) 70% (95% CI 64% to 77%) at 6 months. Return rate of viable saliva samples for abstinence verification was 39% (95% CI 24% to 54%). The per-participant recruitment cost was £19.20, which included advert (£5.82) and running costs (£13.38). In the Quit Sense arm, 75% (95% CI 67% to 83%; 78/104) installed the app and, of these, 100% set a quit date within the app and 51% engaged with it for more than 1 week. The rate of 6-month biochemically verified sustained abstinence, which we anticipated would be used as a primary outcome in a future study, was 11.5% (12/104) in the Quit Sense arm and 2.9% (3/105) in the usual care arm (estimated effect size: adjusted odds ratio = 4.57, 95% CIs 1.23 to 16.94). There was no evidence of between-arm differences in hypothesised mechanisms of action. Three out of four progression criteria were met. The Study Within A Trial analysis found a £20 versus £10 incentive did not significantly increase follow-up rates though reduced the need for manual follow-up and increased response speed. The process evaluation identified several potential pathways to abstinence for Quit Sense, factors which led to disengagement with the app, and app improvement suggestions.

Limitations

Biochemical validation rates were lower than anticipated and imbalanced between arms. COVID-19-related restrictions likely limited opportunities for Quit Sense to provide location tailored support.

Conclusions

The trial design and procedures demonstrated feasibility and evidence was generated supporting the efficacy potential of Quit Sense.

Future work

Progression to a definitive trial is warranted providing improved biochemical validation rates.

Trial registration

This trial is registered as ISRCTN12326962.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme (NIHR award ref: 17/92/31) and is published in full in Public Health Research; Vol. 12, No. 4. See the NIHR Funding and Awards website for further award information.

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.

Effects of Smoking Social Cues on Inhibitory Control in Smokers: An Event-Related Potential Study

Objective

Reduced inhibitory control is a general characteristic of smokers and becomes increasingly pronounced in smoking-related contexts. However, research has rarely considered differences in the effects of various smoking-related cues. To fill this research gap, this study compared the effects of smoking object-related and smoking social-related cues on inhibitory control in smokers.

Methods

We used a visual Go/NoGo paradigm with three types of long-lasting backgrounds (neutral, smoking object, and smoking social background) to record the error rates, reaction times, and amplitudes of the N2 and P3 event-related potentials (ERPs) by 25 smokers and 25 non-smokers.

Results

(1) Smokers displayed smaller NoGo-N2 amplitudes than controls under the neutral background; (2) smokers displayed smaller NoGo-N2 amplitudes under the smoking social background and smoking object background than they did under the neutral background; (3) relative to neutral and smoking object backgrounds, smokers displayed higher commission error rates, shorter reaction times, and larger NoGo-P3 amplitudes under smoking social background.

Conclusion

Smoking-related stimuli impair inhibitory control in smokers, especially when these stimuli are socially related.

VR for Studying the Neuroscience of Emotional Responses

Emotions are frequently considered as the driving force of behavior, and psychopathology is often characterized by aberrant emotional responding. Emotional states are reflected on a cognitive-verbal, physiological-humoral, and motor-behavioral level but to date, human research lacks an experimental protocol for a comprehensive and ecologically valid characterization of such emotional states. Virtual reality (VR) might help to overcome this situation by allowing researchers to study mental processes and behavior in highly controlled but reality-like laboratory settings. In this chapter, we first elucidate the role of presence and immersion as requirements for eliciting emotional states in a virtual environment and discuss different VR methods for emotion induction. We then consider the organization of emotional states on a valence continuum (i.e., from negative to positive) and on this basis discuss the use of VR to study threat processing and avoidance as well as reward processing and approach behavior. Although the potential of VR has not been fully realized in laboratory and clinical settings yet, this technological tool can open up new avenues to better understand the neurobiological mechanisms of emotional responding in healthy and pathological conditions.

Augmented reality for extinction of cue-provoked urges to smoke: Proof of concept

OBJECTIVE

Cue-exposure therapy (CET) aims to extinguish conditioned cue reactivity (CR) to aid in smoking cessation. A key disadvantage of extant CET is its limited ability to generalize extinction to the real world. Our team developed a set of augmented reality smoking-related and neutral cues that can appear in real-time in smokers' natural environments as viewed through a smartphone screen. Prior to deployment as a clinical tool, the present study tested the ability of AR smoking cues to extinguish CR in a controlled laboratory study with an AR smartphone application developed for this project. We hypothesized that daily smokers who completed a single session of cue exposure with AR smoking cues (extinction condition) would demonstrate lower cue-provoked urge to smoke at posttest compared to those who viewed AR neutral cues (control condition).

METHOD

Daily smokers (N = 129, 46.5% female, Mage = 47.6, Mcigarettes/day = 19.1) in acute abstinence were randomized to either the extinction or control condition comprising 28 AR trials.

RESULTS

As hypothesized, we found a Time × Condition interaction indicating that posttest urge ratings were lower in the extinction condition than in the control condition (p = .034). A secondary hypothesis that participants in the extinction condition would show a longer latency to smoke when provided a cigarette was not supported.

CONCLUSIONS

These laboratory findings provide evidence supporting the potential clinical efficacy of AR cues for cue-exposure trials, setting the stage for testing in smokers' naturalistic environments. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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.

Assessing the attentional bias of smokers in a virtual reality anti-saccade task using eye tracking

INTRODUCTION

Cognitive biases (among them attentional bias, AB) are considered an important factor in the development, maintenance, and recurrence of addiction. However, traditional paradigms to measure AB have been criticized regarding external validity and methodical issues. Therefore, and because the neurophysiological correlates of anti-saccade tasks are known, we implemented a novel smoking anti-saccade task in virtual reality (VR) to measure AB and inhibitory control in different contexts and with higher ecological validity.

METHODS

Smokers (n = 20) and non-smokers (n = 20) were tested on a classic pro- and anti-saccade task, a VR anti-saccade task and a VR attention fixation task (all containing smoking-related and neutral stimuli) while eye-tracking data was collected. Two VR contexts (park and office room) were applied.

RESULTS

Saccade latencies were significantly higher for the smoking group in the VR anti-saccade task. However, this effect did not differ between smoking-related and neutral stimuli, thus overall no AB was observed. Instead, AB was only present in the park context. Additionally, saccade latencies and error rates were significantly higher in the park context.

CONCLUSIONS

Results indicate impaired inhibitory control in smokers relative to non-smokers. The lack of evidence for a general AB might be due to the lower severity of smoking dependence in the smoking sample. Instead, results suggest context specificity of AB. Implications for smoking cessation interventions in the field of inhibitory control training and attention bias modification are discussed.

Neuroimaging of situational urgency and incontinence provoked by personal urgency cues

INTRODUCTION

Situational triggers for urinary urgency and incontinence (UUI) such as "latchkey incontinence" and running water are often reported clinically, but no current clinical tools exist to directly address symptoms of UUI provoked by environmental stimuli. Previously we have shown that urgency and leakage can be reproduced during urodynamic studies with exposure to personal urgency-related images. Here we investigate the neural signatures associated with such situational triggers to inform potential therapies for reducing reactivity to these personal urgency-related cues among women with situational UUI.

METHOD

We recruited 23 women with situational UUI who took photographs of their personal "urgency trigger" and "safe" situations and were exposed to them in a magnetic resonance imaging (MRI) scanner. We identified brain areas that were more active during urgency versus safe image exposure.

RESULTS

We found that, during urgency image exposure, main components of the attention network and decision-related processes, the middle and medial frontal gyri, were more active (p < 0.01). In addition, areas well known to be involved in the continence mechanism, such as the cingulate and parahippocampal areas, were also more active during urgency image exposure.

CONCLUSION

Exposure to personal situational urgency images activated different areas of the brain compared with safe environments, highlighting the complex brain mechanisms that provoke real-world urgency. Using brain and behavioral-based therapies which target the attentional areas identified here and extinguish cue reactivity might reduce symptom burden in this subset of UUI sufferers.

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.

Quantification of Smoking Characteristics Using Smartwatch Technology: Pilot Feasibility Study of New Technology

Background

While there have been many technological advances in studying the neurobiological and clinical basis of tobacco use disorder and nicotine addiction, there have been relatively minor advances in technologies for monitoring, characterizing, and intervening to prevent smoking in real time. Better understanding of real-time smoking behavior can be helpful in numerous applications without the burden and recall bias associated with self-report.

Objective

The goal of this study was to test the validity of using a smartwatch to advance the study of temporal patterns and characteristics of smoking in a controlled laboratory setting prior to its implementation in situ. Specifically, the aim was to compare smoking characteristics recorded by Automated Smoking PerceptIon and REcording (ASPIRE) on a smartwatch with the pocket Clinical Research Support System (CReSS) topography device, using video observation as the gold standard.

Methods

Adult smokers (N=27) engaged in a video-recorded laboratory smoking task using the pocket CReSS while also wearing a Polar M600 smartwatch. In-house software, ASPIRE, was used to record accelerometer data to identify the duration of puffs and interpuff intervals (IPIs). The recorded sessions from CReSS and ASPIRE were manually annotated to assess smoking topography. Agreement between CReSS-recorded and ASPIRE-recorded smoking behavior was compared.

Results

ASPIRE produced more consistent number of puffs and IPI durations relative to CReSS, when comparing both methods to visual puff count. In addition, CReSS recordings reported many implausible measurements in the order of milliseconds. After filtering implausible data recorded from CReSS, ASPIRE and CReSS produced consistent results for puff duration (R²=.79) and IPIs (R²=.73).

Conclusions

Agreement between ASPIRE and other indicators of smoking characteristics was high, suggesting that the use of ASPIRE is a viable method of passively characterizing smoking behavior. Moreover, ASPIRE was more accurate than CReSS for measuring puffs and IPIs. Results from this study provide the foundation for future utilization of ASPIRE to passively and accurately monitor and quantify smoking behavior in situ.

5-HT2A and 5-HT2C receptors as potential targets for the treatment of nicotine use and dependence

Nicotine use and dependence, typically achieved through cigarette smoking, but increasingly through vape products, is the leading cause of preventable death today. Despite a recognition that many current smokers would like to quit, the success rate at doing so is low and indicative of the persistent nature of nicotine dependence and the high urge to relapse. There are currently three main forms of pharmacotherapy approved as aids to treat nicotine dependence: a variety of nicotine replacement products (NRT's), the mixed NA/DA reuptake inhibitor bupropion (Zyban®), and the preferential nicotinic α4β2 receptor agonist drug, varenicline (Chantix®); the latter being generally recognized to be the most effective. However, each of these approaches afford only limited efficacy, and various other pharmacological approaches are being explored. This chapter focusses on approaches targeted to the serotonin (5-HT) system, namely, selective serotonin reuptake inhibitors (SSRI's) which served a pioneer role in the investigation of serotoninergic modulators in human smoking cessation trials; and secondly drugs selectively interacting with the 5-HT2A and 5-HT2C receptor systems. From an efficacy perspective, measured as smoking abstinence, the 5-HT2A agonist psychedelics, namely psilocybin, seem to show the most promise; although as the article highlights, these findings are both preliminary and there are significant challenges to the route to approval, and therapeutic use of this class should they reach approval status. Additional avenues include 5-HT2C receptor agonists, which until recently was pioneered by lorcaserin, and 5-HT2A receptor antagonists represented by pimavanserin. Each of these approaches has distinct profiles across preclinical tests of nicotine dependence, and may have therapeutic potential. It is anticipated as diagnostic and predictive biomarkers emerge, they may provide opportunities for subject stratification and opportunities for personalizing smoking cessation treatment. The clinical assessment of SSRI, 5-HT2A and/or 5-HT2C receptor-based treatments may be best served by this process.

Dissociable mesolimbic dopamine circuits control responding triggered by alcohol-predictive discrete cues and contexts

Context can influence reactions to environmental cues and this elemental process has implications for substance use disorder. Using an animal model, we show that an alcohol-associated context elevates entry into a fluid port triggered by a conditioned stimulus (CS) that predicted alcohol (CS-triggered alcohol-seeking). This effect persists across multiple sessions and, after it diminishes in extinction, the alcohol context retains the capacity to augment reinstatement. Systemically administered eticlopride and chemogenetic inhibition of ventral tegmental area (VTA) dopamine neurons reduce CS-triggered alcohol-seeking. Chemogenetically silencing VTA dopamine terminals in the nucleus accumbens (NAc) core reduces CS-triggered alcohol-seeking, irrespective of context, whereas silencing VTA dopamine terminals in the NAc shell selectively reduces the elevation of CS-triggered alcohol-seeking in an alcohol context. This dissociation reveals new roles for divergent mesolimbic dopamine circuits in the control of responding to a discrete cue for alcohol and in the amplification of this behaviour in an alcohol context.

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.

Specific Relapse Predictors: Could Cognitive-Behavioral Treatment for Smoking Cessation Be Improved?

Relapse remains a frequent and complex phenomenon that is not yet well understood. An under-researched area of study that may provide relevant information concerns the assessment of specific post-treatment variables, rather than the composite measures commonly used to predict smoking relapse. The current study sought to examine the effects of post-treatment smoking-related variables, including withdrawal symptomatology, abstinence self-efficacy, and smoking urgency in negative-affect situations and smoking relapse at the 3 month follow-up. The sample comprised 130 participants who achieved abstinence for at least 24 h through a cognitive-behavioral smoking cessation treatment. Regression analysis was conducted for both composite measures and specific subscales and items. Data showed that composite measures of tobacco withdrawal, self-efficacy, and smoking urgency in negative-affect situations were not significant predictors of smoking relapse. However, the analysis including subscales, and specific items showed that lower self-efficacy in negative-affect-related situations (OR = 1.36) and three withdrawal symptoms—irritability/frustration/anger (OR = 2.99), restlessness/impatience (OR = 1.87), and craving (OR = 2.31)—were significant predictors of relapse. These findings offer new insights into the role of different smoking-related post-treatment variables in short-term relapse. Considering and specifically targeting these variables after achieving abstinence may potentially contribute to reducing smoking relapse.

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.

Nicotine in thirdhand smoke residue predicts relapse from smoking cessation: A pilot study

INTRODUCTION

Thirdhand smoke (THS) residue lingers for months in homes of former smokers and may play a role in relapse after smoking cessation. This study examined the association between THS pollution as measured by the level of nicotine in house dust and continued abstinence from smoking.

METHODS

Participants were 65 cigarette smokers who reported they were enrolled in any type of smoking cessation program, had set a specific date to quit, and had biochemical verification of continuous abstinence at 1-week (W1), 1-month (M1), 3-months (M3), or 6-months (M6) after their quit date. House dust samples collected at baseline before quitting were analyzed for nicotine concentration (μg/g) and nicotine loading (μg/m²) using liquid chromatography-tandem mass spectrometry (LC-MS/MS).

RESULTS

Controlling for age, gender, overall and indoor smoking rates, and years lived in their home, dust nicotine concentration and loading predicted abstinence at W1, M1, M3, and M6. A 10-fold increase in dust nicotine loading and concentration were associated with approximately 50% lower odds of remaining abstinent.

CONCLUSIONS

Findings suggest nicotine in house dust may play a role in facilitating relapse after smoking cessation. Additional research is warranted to investigate the causal role of THS residue in homes of former smokers on cravings and continued abstinence.

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.

A cross-sectional study of the relationship of proximal smoking environments and cessation history, plans, and self-efficacy among low-income smokers

Introduction

Proximal environments could facilitate smoking cessation among low-income smokers by making cessation appealing to strive for and tenable.

Aims

We sought to examine how home smoking rules and proximal environmental factors such as other household members' and peers' smoking behaviors and attitudes related to low-income smokers' past quit attempts, readiness, and self-efficacy to quit.

Methods

This analysis used data from Offering Proactive Treatment Intervention (OPT-IN) (randomized control trial of proactive tobacco cessation outreach) baseline survey, which was completed by 2,406 participants in 2011/12. We tested the associations between predictors (home smoking rules and proximal environmental factors) and outcomes (past-year quit attempts, readiness to quit, and quitting self-efficacy).

Results

Smokers who lived in homes with more restrictive household smoking rules, and/or reported having 'important others' who would be supportive of their quitting, were more likely to report having made a quit attempt in the past year, had greater readiness to quit, and greater self-efficacy related to quitting.

Conclusions

Adjustments to proximal environments, including strengthening household smoking rules, might encourage cessation even if other household members are smokers.

Evaluating the Evidence on Sitting, Smoking, and Health: Is Sitting Really the New Smoking?

Sitting has frequently been equated with smoking, with some sources even suggesting that smoking is safer than sitting. This commentary highlights how sitting and smoking are not comparable. The most recent meta-analysis of sedentary behavior and health outcomes reported a hazard ratio of 1.22 (95% confidence interval [CI] = 1.09, 1.41) for all-cause mortality. The relative risk (RR) of death from all causes among current smokers, compared with those who have never smoked, is 2.80 (95% CI = 2.72, 2.88) for men and 2.76 for women (95% CI = 2.69, 2.84). The risk is substantially higher for heavy smokers (> 40 cigarettes per day: RR = 4.08 [95% CI = 3.68, 4.52] for men, and 4.41 [95% CI = 3.70, 5.25] for women). These estimates correspond to absolute risk differences of more than 2000 excess deaths from any cause per 100 000 persons per year among the heaviest smokers compared with never smokers, versus 190 excess deaths per 100 000 persons per year when comparing people with the highest volume of sitting with the lowest. Conflicting or distorted information about health risks related to behavioral choices and environmental exposures can lead to confusion and public doubt with respect to health recommendations.