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Background and Aims: Pathways of transitioning from tobacco smoking to vaping after receiving an e‐cigarette‐based smoking cessation intervention have been minimally explored. Study aims: 1) identify pathways between intervention delivery and final follow‐up; 2) describe baseline and post‐intervention statistical data in relation to smoking/vaping behaviour of the different pathway groups; 3) explore qualitative participant perspectives contextualising pathway groups. Design: Embedded mixed‐methods analysis of data collected for the Cessation of Smoking Trial in the Emergency Department (COSTED) randomised controlled trial. Setting: Recruitment from 6 Emergency Departments (5 in England and 1 in Scotland) between January and August 2022. Participants: 366 adult smokers who were randomised to receive the COSTED intervention and provided data at 6‐month follow‐up. Qualitative subsample of 24 participants interviewed after follow‐up. Interventions: Brief smoking cessation advice, provision of an e‐cigarette starter kit and referral to the local Stop Smoking Service. Measurements: Descriptive statistical reporting of identified pathways and smoking/vaping behaviour at baseline and 6‐month follow‐up. Semi‐structured phone/video interviews analysed thematically. Findings 13.4% (n = 49) of participants quit smoking within 1 month of receiving the intervention, 19.1% (n = 70) quit between 1 and 6 months, 24.9% (n = 91) reduced cigarettes per day (CPD) by at least 50%, and 42.6% did not experience a significant smoking reduction. Approximately a third of participants who quit reported not vaping at follow‐up. Reporting dual use was associated with a reduction in CPD. Appoximately a third reported experimenting with a different device to the one provided as part of the intervention. Quitters reported themes of satisfaction with vaping, changes in environment facilitating quitting and motivation to quit. Conclusions: Dual use of cigarettes and e‐cigarettes can result in a reduction of smoking and may prelude quitting smoking. Sustained e‐cigarette use is not always necessary for quitting success. Success depends on personal context as well satisfaction with vaping. [ABSTRACT FROM AUTHOR]

Introduction: E-cigarettes have been shown to be effective for tobacco smoking cessation. Predicting those who are most likely to achieve smoking abstinence after receiving an e-cigarette based smoking cessation intervention could help to target interventions more efficiently. Methods: A secondary analysis of baseline characteristics of 505 people who received an emergency department based smoking cessation intervention incorporating brief advice, provision of an e-cigarette starter kit and referral to stop smoking services. Gender, ethnicity, age, employment status, deprivation, partner smoking status, cigarettes per day, motivation to quit, cigarette dependence and previous e-cigarette use were assessed as predictors of abstinence. Self-reported smoking status was collected 6 months after intervention delivery. Results: At 6 months 169/505 (33%) of those who received the intervention self-reported abstinence. The groups that were more likely to report having quit were females (37.4% of females vs 31.0% of males), older people (41.1% of over 50s vs 33.3% of under 35s), lighter smokers (36.4% of those who smoked less than 10 cigarettes per day vs 30.7% for those who smoked over 20) and more motivated quitters (35.6% for those with high motivation vs 29.2% for those with low motivation). However, in multiple logistic regression, when adjusting for the other factors, no factors significantly predicted smoking abstinence. Degree of nicotine dependence was very similar between those who quit and those who did not. Conclusion: The study found no baseline factors that could predict successful smoking cessation with e-cigarettes. Consequently, this study does not support the use of a targeted e-cigarette-based smoking cessation intervention, suggesting the adoption of a more universal approach. [ABSTRACT FROM AUTHOR]

Background: This paper reports on recruiting strategies in a study which aimed to examine the mechanism of intranasal oxytocin on cue-induced opiate craving and attentional bias in males using heroin in addition to substitute opiates from four UK community drug treatment services. Methods: Recruitment took place during and post-COVID-19 periods of social distancing and lockdowns. Caseworkers obtained consent to contact from interested service users before an initial telephone screen. People were then scheduled for in-person screens, typically within seven days of the initial telephone call. Subsequent visits took place within 30 days of the previous visit. Each visit lasted one hour and participants received one £20 voucher per completed visit. Results: Thirty participants were randomized from 113 referrals. We were unable to contact 36% (n = 41) of people. Of those eligible to start the study (n = 44), 68% (n = 30) agreed to start the study, retaining 82% (n = 24) to completion. Factors which positively influenced recruitment were having a research presence on site, the cultivation of relationships and demonstrating respect and gratitude toward the participants. Conclusions: These results support the feasibility of recruiting males currently using heroin in addition to substitute opiates utilizing a person-first approach with service users and staff. [ABSTRACT FROM AUTHOR]

Background and aims: The use of e‐cigarettes may influence later smoking uptake in young people. Evidence and gap maps (EGMs) are interactive on‐line tools that display the evidence and gaps in a specific area of policy or research. The aim of this study was to map clusters and gaps in evidence exploring the relationship between e‐cigarette use or availability and subsequent combustible tobacco use in people aged < 30 years. Methods: We conducted an EGM of primary studies and systematic reviews. A framework and an interactive EGM was developed in consultation with an expert advisory group. A systematic search of five databases retrieved 9057 records, from which 134 studies were included. Systematic reviews were appraised using AMSTAR‐2, and all included studies were coded into the EGM framework resulting in the interactive web‐based EGM. A descriptive analysis of key characteristics of the identified evidence clusters and gaps resulted in this report. Results: Studies were completed between 2015 and 2023, with the first systematic reviews being published in 2017. Most studies were conducted in western high‐income countries, predominantly the United States. Cohort studies were the most frequently used study design. The evidence is clustered on e‐cigarette use as an exposure, with an absolute gap identified for evidence looking into the availability of e‐cigarettes and subsequent cessation of cigarette smoking. We also found little evidence analysing equity factors, and little exploring characteristics of e‐cigarette devices. Conclusions: This evidence and gap map (EGM) offers a tool to explore the available evidence regarding the e‐cigarette use/availability and later cigarette smoking in people under the age of 30 years at the time of the search. The majority of the 134 reports is from high‐income countries, with an uneven geographic distribution. Most of the systematic reviews are of lower quality, suggesting the need for higher‐quality reviews. The evidence is clustered around e‐cigarette use as an exposure and subsequent frequency/intensity of current combustible tobacco use. Gaps in evidence focusing on e‐cigarette availability, as well as on the influence of equity factors may warrant further research. This EGM can support funders and researchers in identifying future research priorities, while guiding practitioners and policymakers to the current evidence base. [ABSTRACT FROM AUTHOR]

Background: Heroin is a substance with a unique social profile in that it is commonly used by individuals alone but there is a paucity of qualitative research exploring how social influences impact the continuation of heroin use, particularly when people are trying to stop using heroin. This study explored social determinants which influence the continuation of heroin use in males in UK community treatment who use illicit heroin alongside opioid replacement therapy. Design: Participants were self-selecting from an initial purposively recruited sample. Using Janis (1972) 8 symptoms of Groupthink as an a priori framework for analysis, the study method utilised qualitative interviews with fourteen males. The discussions were digitally-recorded, transcribed verbatim, and analysed thematically. Findings: Contrasting with the evidence base, the sample included people who transitioned from recreational drug use to dependent heroin use without experiencing trauma of any kind. Far from becoming socially isolated when actively using heroin, interviews identified a shift in social networks from networks built on shared moments to networks underpinned by transactional exchange. Components of Groupthink were identified when participants described belonging to heroin using networks and continued to use heroin whilst trying to abstain though individual accountability was central to the decision to continue to use heroin. Conclusions: The conflict between the individual goal of abstinence and the group goal of continuation suggests that social network interventions could be more successful if delivered to cohorts of people who buy heroin together. [ABSTRACT FROM AUTHOR]

Background and aims Methods Results Conclusions Youth use of disposable vapes has increased markedly in the United Kingdom in recent years, yet little is known about the motivations, experiences and perceptions of young people themselves. This study aimed to explore young people’s experiences and use of disposable vapes.This was a qualitative study recruiting young people reporting regularly vaping disposables, collecting data via dyad guided, individual and group interviews. Data analysis was theoretically informed by the Social Ecological Model. Inductive and deductive coding approaches were used, with resolution of coded interpretations by consensus.Twenty‐nine young people aged 16–20 years participated in qualitative interviews. At the individual level, participants discussed how characteristics of disposable vapes were important to them—particularly price, accessibility and the attractive designs, colours, names and flavours. Young people frequently engaged in both vaping and tobacco smoking, seeing the behaviours as interchangeable dependent on context, and having inaccurate relative harm perceptions of vaping compared with smoking. Experimentation was widespread and many used vapes as a way of managing stress and anxiety. Vaping was positioned as a social behaviour, common among peers. Parental influence on vaping behaviour was minimal, although vaping initiation could be influenced by family vaping norms. Culturally, vaping was a widespread normalized behaviour. Young people were aware of media reports and potential harms, but were less aware of smoking related harms as a consequence.Disposable vapes appear to be attractive and accessible to young people in the United Kingdom. Vaping is normalized in this population, despite being seen as potentially damaging to health, and vaping and smoking are engaged in interchangeably. Underage sales of vapes are reportedly widespread. Strict regulation, such as banning products or increasing prices, may prompt UK youth to switch from vaping to smoking. [ABSTRACT FROM AUTHOR]

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. Plain language summary: Smokers often fail to quit because of urges to smoke triggered by their surroundings (e.g. being around smokers). We developed a smartphone app ('Quit Sense') which learns about an individual's surroundings and locations where they smoke. During a quit attempt, Quit Sense uses in-built sensors to identify when smokers are in those locations and sends 'in the moment' advice to help prevent them from smoking. We ran a feasibility study to help plan for a future large study to see if Quit Sense helps smokers to quit. This feasibility study was designed to tell us how many participants complete study measures; recruitment costs; how many participants install and use Quit Sense; and estimate whether Quit Sense may help smokers to stop and how it might do this. We recruited 209 smokers using online adverts on Google search, Facebook and Instagram, costing £19 per participant. Participants then had an equal chance of receiving a web link to the National Health Service SmokeFree website ('usual care group') or receive that same web link plus a link to the Quit Sense app ('Quit Sense group'). Three-quarters of the Quit Sense group installed the app on their phone and half of these used the app for more than 1 week. We followed up 77% of participants at 6 months to collect study data, though only 39% of quitters returned a saliva sample for abstinence verification. At 6 months, more people in the Quit Sense group had stopped smoking (12%) than the usual care group (3%). It was not clear how the app helped smokers to quit based on study measures, though interviews found that the process of training the app helped people quit through learning about what triggered their smoking behaviour. The findings support undertaking a large study to tell us whether Quit Sense really does help smokers to quit. Scientific summary: Background: Three million UK smokers attempt to stop smoking each year, but over 80% relapse. A lapse (any smoking) early on in a quit attempt is strongly associated with subsequent relapse back to smoking. Approximately half of all lapses are due to environmental smoking cues which can elicit cravings to smoke, such as the presence of cigarettes or being in a location which a smoker associates with smoking. Few interventions are effective at targeting cue-induced cravings. Smokers are more likely to prevent lapses if they better understand their smoking cues and use lapse prevention strategies, such as controlled deep breathing or avoiding other smokers, to combat these. This would likely help them avoid or cope with cue-induced cravings, but smokers typically lack skills in applying these effectively. If smokers trying to quit used effective lapse prevention strategies, this would very likely increase their chances of success. With patient and public involvement, we developed, refined and piloted a theory-guided Just-In-Time Adaptive Intervention smartphone app called Quit Sense that provides support to help smokers manage environmental cues to smoke as they arise. Pilot studies have shown that Quit Sense can provide 'in the moment' support to smokers, including lapse prevention strategies, and that users will engage with the app and find it acceptable. As Quit Sense is such a novel intervention, before a 'definitive' randomised controlled trial (RCT) can be conducted to test effectiveness, it is important to first undertake a RCT to establish whether such a study might be feasible. Objectives: The main objective was to conduct a feasibility RCT of Quit Sense to inform a definitive effectiveness trial, by estimating: completion rates for the anticipated primary outcome for a full trial (6-month self-reported abstinence with biochemical validation, based on the Russell standard); usual care arm cessation rate; cost of recruitment using online advertising; rates of app installation, use and acceptability; completion of smoking cessation-related resource use and quality-of-life data; intervention effect on anticipated primary outcome; intervention effect on hypothesised mechanisms of action of app at 6 weeks post enrolment; participant views of the app, as part of a qualitative process evaluation. Design: A parallel, two-arm RCT with an embedded qualitative interview process evaluation was undertaken. The evaluation included a preliminary cost-effectiveness analysis and a nested randomised study [Study Within A Trial (SWAT)] assessing the effects of different financial incentives on 6-month follow-up rates. Setting: The study setting was online and trial procedures were primarily automated. Participants: Participants were recruited via online adverts on Google search, Facebook and Instagram, screened for eligibility on the study website and enrolled if they were regular smokers, aged 16 years and above, were willing to make a quit attempt and owned an Android smartphone. Interventions: After completing the baseline questionnaire participants were then randomly allocated to a 'usual care' arm (text message referral to NHS SmokeFree website) or a 'usual care' plus Quit Sense arm, via a text message invitation to install the Quit Sense app. The Quit Sense app required users to set a quit date and then, leading up to their quit date, alongside other support features, invited them to report their smoking in real time and indicate the presence of key environmental smoking cues using a smoking behaviour logging tool. Once their quit date arrived, the app used location sensing and what it had learnt about the individual's smoking behaviour to tailor the timing and content of support messages when they spent time in self-identified high-risk locations. The support and advice aimed to help smokers learn about their smoking behaviour, prepare for their quit attempt and promoted the use of effective lapse prevention strategies during their quit attempt. Main outcomes measures: All participants were contacted at 6-week and 6-month follow-up. This was done initially by text message with an embedded link to the follow-up questionnaires or, if no response, manually by telephone. As part of a SWAT , participants underwent secondary randomisation to receive either a £10 or £20 incentive for completion of the 6-month questionnaire. Four progression criteria and corresponding thresholds for seeking to undertake a definitive trial were set a priori. Criteria were met if each threshold was included in or lower than the 95% confidence interval (CI) of the estimate. Trial 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%), the app's effect on biochemically verified abstinence at 6 months (anticipated primary outcome of definitive trial) and hypothesised mechanisms of action. The qualitative process evaluation sought to identify participants' views of the trial and the Quit Sense app. The protocol and statistical and health economics analysis plan were pre-specified and published (open access). Results: Of the people screened (N = 323), 299 (93%) were eligible and 209 (65% of screened) were consented and randomised either to the usual care arm (n = 105) or to the Quit Sense arm (n = 104). Completion of self-reported smoking questions was 71% (149/209; 95% CI 65% to 77%) and 77% (160/209; 95% CI 71% to 82%) at 6 weeks and 6 months, respectively, and at 6 months 70% provided both cessation support resource use data and EuroQol-5 Dimensions five level (EQ-5D-5L) data. Among those self-reporting abstinence at 6 months follow-up, 39% (95% CI 24% to 54%) returned a viable sample for biochemical verification, although return rates were higher in the Quit Sense than the usual care arm (52% vs. 19%). Online recruitment was completed successfully within the planned 6-week period, split into two campaigns, with a per-participant cost of £19.20, split into advert (£5.82) and running costs (£13.38). In the Quit Sense arm, 75% (95% CI 67% to 83%) of participants installed the app and, of these, 100% set a quit date within the app and 51% (95% CI 39% to 63%) engaged with it for more than 1 week. At final follow-up, 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% CI 1.23 to 16.94). Similar effects were observed for biochemically verified secondary abstinence outcomes at 6-month follow-up and sensitivity analyses. Effect sizes were smaller and non-significant for self-report only abstinence and there was no evidence of a between-arm difference in abstinence at 6-week follow-up. Participants in the Quit Sense arm had higher rates of lapse avoidance in the first 2 weeks of their quit attempt or post enrolment compared to usual care though this was not statistically significant (29.6% vs. 19.2%; p = 0.14). There was no evidence of between-arm differences in hypothesised mechanisms of action of the app, including the mean frequency of lapse prevention strategy use [mean difference (MD) −0.07; p = 0.46], smoking cessation self-efficacy (MD 0.18; p = 0.39), strength (p = 0.23) and frequency (p = 0.83) of urges to smoke or the Wisconsin Inventory of Smoking Dependence Motives subscales of automaticity (p = 0.51) and associative processes (p = 0.58). The qualitative process evaluation identified several potential pathways to abstinence among Quit Sense arm participants. Interviewed participants reported finding the insights gained from engaging with the smoking behaviour learning tool before their quit attempt started particularly valuable. They reported that this reinforced their commitment to quit, helped them better understand the drivers of their smoking behaviour and challenged the need for them to smoke. Some also reported finding regular and location-specific support messages encouraging and motivational and that this reinforced the goal of quitting and made them feel equipped when they spent time in locations that they used to smoke in. Participants highlighted several factors which led to disengagement with the app, including relapse, no longer feeling they needed support, not finding the app met their needs and technical issues. Participants also provided suggestions for app improvement relating to logging smoking, adding gamification elements and improved support triggering. Interviewed participants from both arms highlighted how COVID-19 measures had affected their smoking behaviour and restricted the time they spent outside of their home, which would have had implications for location-based cessation support. In terms of the main cost-drivers, e-cigarettes/vaporisers and nicotine replacement therapy accounted for more than 70% of the total non-intervention costs. The total intervention cost (recruitment advertising and maintaining the Quit Sense app) was estimated to be £28.51 per participant. In both groups, the mean EQ-5D-5L score at both 6-week and 6-month follow-up was lower than that at baseline. A preliminary cost-effectiveness analysis using EQ-5D-5L score as the outcome estimated that the intervention was both more costly and less effective, compared to standard care, though there was no significant difference in cost or effect between the two groups. The SWAT analysis indicated that increasing the incentive for completing follow-up from £10 to £20 did not increase response rates at 6 months (74% vs. 79%; p = 0.36) but did reduce the proportion of participants requiring manual follow-up (62% vs. 46%; p = 0.018) and the median response time to responding (15 days vs. 7 days; p = 0.016). Conclusions: The Quit Sense RCT design and procedures demonstrated feasibility and generated preliminary efficacy evidence of the app on abstinence at 6-month follow-up, although how the app may achieve this is not clear. Three out of the four pre-specified feasibility progression criteria for moving to a definitive trial – completion rate of self-reported abstinence at final follow-up and the rate of Quit Sense installation and engagement – were met. The return of saliva samples among quitters was lower than anticipated, though this can likely be increased in a future trial through increased incentivisation and enhanced procedures. Progression to a definitive trial is warranted. 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. [ABSTRACT FROM AUTHOR]

The article discusses consensus, coherence, clarity, and consistency when talking about addiction. Topics include the differences of view that are hampering scientific progress, how the problem of conceptualizing addiction can be resolved, and addiction ontology terms relating to addiction including addictive disorder, substance dependence, and addictive behaviour.