[Tabacco harm reduction: the need for a paradigm shift in German tobacco control policy]. / Tabak Harm Reduction: Die Notwendigkeit eines Paradigmenwechsels in der deutschen Tabakkontrollpolitik.

The consumption of tobacco continues to be the greatest preventable health risk in Germany. Every year, more than 127,000 people die prematurely as a result of using tobacco - one in five new cases of cancer is a consequence of smoking. Germany's current tobacco control policy, consisting of advertising bans, the promotion of smoke-free environments, and an abstinence-only paradigm, are only able to initiate slow change. Other countries have shown how the number of smokers can be reduced and deaths prevented by integrating harm reduction as a complementary measure and providing a much more active support for people who want to quit. This article first presents the current situation and current measures in Germany. Then recommendations for changes to the current tobacco control policy in Germany are derived from a look at the United Kingdom, which is considered a pioneer throughout Europe with its tobacco control policy, as well as a summary of the current state of research.

Comparative Effectiveness of Mobile Health-Based Comprehensive Smoking Cessation Modalities and Traditional Clinic-Delivered Treatments - Beijing Municipality, China, May 2022 to April 2024.

What is already known about this topic?: Most Chinese smokers have not accessed professional help due to a lack of sufficient smoking cessation services. Mobile health (mHealth) can mitigate obstacles related to time and transportation, thereby providing effective support for smokers seeking to quit. What is added by this report?: This study offers real-world evidence supporting the effectiveness of mHealth-based comprehensive smoking cessation interventions. The findings indicate that these modalities can significantly enhance abstinence rates, albeit to a lesser extent compared to traditional clinic-based treatments. Adherence to the intervention was identified as a critical factor influencing the effectiveness of smoking cessation strategies. What are the implications for public health practice?: The mHealth-based comprehensive smoking cessation modalities, with or without mailing cessation medications, present a promising approach to enhancing access to and utilization of smoking cessation services. This strategy addresses the significant challenge of limited smoking cessation resources in China.

Real-World Treatment Adherence to Smoking Cessation Medications and Association with Treatment Outcome - 27 Centers, China, 2017-2022.

What is already known about this topic?: Medication non-adherence significantly impedes smoking cessation efforts. While effective smoking cessation medications are accessible in China, real-world adherence and its correlation with treatment outcomes remain largely unexplored. What is added by this report?: From December 2017 to January 2022, 1,120 participants were recruited from 27 centers in all seven geographical regions of China. Results revealed that only 38.2% of smokers adhered to the prescribed 12-week smoking cessation treatment and those with a higher education level were more likely to be adherent. In addition, smokers with good adherence were more likely to successfully quit smoking. What are the implications for public health practice?: The data obtained could enhance our understanding of smoking cessation practices in China. Improving adherence is a crucial strategy to enhance the effectiveness of smoking cessation treatments and management provided to smokers.

The trajectory of smoking cessation after treatment and its related factors in Taiwan.

Smoking has multiple negative effects on health; therefore, the Taiwanese government provides smoking cessation clinics to smokers. This study aimed to explore the trajectory of smoking cessation after smokers received treatment and the variables related to different trajectories. A retrospective longitudinal study was conducted, in which 735 adult smokers who received smoking cessation medications were recruited. The participants' demographic characteristics, chronic diseases, smoking characteristics, and cigarette dependence were collected from chart review. The amount of smoking was collected at baseline, and at 1 week, 1 month, 3 months, and 6 months after treatment. The Proc Traj procedure for group-based modeling and multinomial logistic regression were used for statistical analysis. Three trajectories were identified: early quitters (28.03%), late quitters (11.43%) and reducers (60.54%). Compared with early quitters, reducers were younger and had a higher probability of severe cigarette dependence. Compared with early quitters, late quitters had a higher number of taking smoking cessation medications. The findings revealed that approximately 60% of participants who received smoking cessation treatment could not completely quit smoking, and that age, number of medications taken, and cigarette dependence were significant predictors of different trajectories.

Individual Predictors of Response to A Behavioral Activation-Based Digital Smoking Cessation Intervention: A Machine Learning Approach.

Background: Depression is prevalent among individuals who smoke cigarettes and increases risk for relapse. A previous clinical trial suggests that Goal2Quit, a behavioral activation-based smoking cessation mobile app, effectively increases smoking abstinence and reduces depressive symptoms. Objective: Secondary analyses were conducted on these trial data to identify predictors of success in depression-specific digitalized cessation interventions. Methods: Adult who smoked cigarettes (age = 38.4 ± 10.3, 53% women) were randomized to either use Goal2Quit for 12 weeks (N = 103), paired with a 2-week sample of nicotine replacement therapy (patch and lozenge) or to a Treatment-As-Usual (TAU) control (N = 47). The least absolute shrinkage and selection operator was utilized to identify a subset of baseline variables predicting either smoking or depression intervention outcomes. The retained predictors were then fitted via linear regression models to determine relations to each intervention outcome. Results: Relative to TAU, only individuals who spent significant time using Goal2Quit (56 ± 46 min) were more likely to reduce cigarette use by at least 50% after 12 weeks, whereas those who spent minimal time using Goal2Quit (10 ± 2 min) did not exhibit significant changes. An interaction between educational attainment and treatment group revealed that, as compared to TAU, only app users with an educational degree beyond high school exhibited significant reductions in depression. Conclusions: The findings highlight the importance of tailoring depression-specific digital cessation interventions to individuals' unique engagement needs and educational level. This study provides a potential methodological template for future research aimed at personalizing technology-based treatments for cigarette users with depressive symptoms.

Factors associated with smoking cessation in Chinese psychiatric professionals: A cross-sectional survey.

INTRODUCTION: Healthcare workers are integral to public smoking cessation; however, their own smoking behavior can create a significant obstacle to intervening in patients' cessation efforts. Conversely, their success in quitting can enhance their ability to support patients. Research on smoking behavior, particularly smoking cessation among Chinese psychiatric professionals is limited. This study addresses this gap by examining the factors associated with smoking cessation in this population, providing insights for targeted tobacco control policies. METHODS: A cross-sectional survey was conducted, targeting psychiatric professionals including psychiatrists and psychiatric nurses, in 41 tertiary psychiatric hospitals in China. From January to March 2021, a WeChat-based questionnaire was distributed to collect demographic, occupational, and health-behaviors (including smoking) data. Statistical analyses, including the chi-squared test and adjusted binary logistic regression analysis, were conducted to identify the factors associated with smoking cessation. RESULTS: Among the 12762 psychiatric professionals who participated in the survey, 11104 (87.0%) were non-smokers, 1196 (9.4%) were current smokers, and 462 (3.6%) were ex-smokers. Several factors were found to be associated with smoking cessation. Women had a higher prevalence of ex-smokers than men (AOR=1.88; 95% CI: 1.332-2.666, p<0.001). Compared to East China, the prevalence of ex-smokers among participants in Central and Northeast China was lower. Older age (≥50 years), higher level of education (Master's degree or higher), and non-drinkers, showed a higher likelihood of being ex-smokers. Notably, compared to current smokers, ex-smokers reported a lower prevalence of burnout (AOR=0.70; 95% CI: 0.552-0.892, p=0.004). CONCLUSIONS: Smoking cessation interventions or health promotion programs should also focus on gender, age, education level, region, alcohol use, and burnout to effectively address smoking cessation within this specific professional group.

Digital Smoking Cessation Intervention for Cancer Survivors: Analysis of Predictors and Moderators of Engagement and Outcome Alongside a Randomized Controlled Trial.

BACKGROUND: Recent studies have shown positive, though small, clinical effects of digital smoking cessation (SC) interventions for cancer survivors. However, research on associations among participant characteristics, intervention engagement, and outcomes is limited. OBJECTIVE: This study aimed to explore the predictors and moderators of engagement and outcome of MyCourse-Quit Smoking (in Dutch: "MijnKoers-Stoppen met Roken"), a digital minimally guided intervention for cancer survivors. METHODS: A secondary analysis of data from the randomized controlled trial was performed. The number of cigarettes smoked in the past 7 days at 6-month follow-up was the primary outcome measure. We analyzed interactions among participant characteristics (11 variables), intervention engagement (3 variables), and outcome using robust linear (mixed) modeling. RESULTS: In total, 165 participants were included in this study. Female participants accessed the intervention less often than male participants (B=-11.12; P=.004). A higher Alcohol Use Disorders Identification Test score at baseline was associated with a significantly higher number of logins (B=1.10; P<.001) and diary registrations (B=1.29; P<.001). A higher Fagerström Test for Nicotine Dependence score at baseline in the intervention group was associated with a significantly larger reduction in tobacco use after 6 months (B=-9.86; P=.002). No other associations and no moderating effects were found. CONCLUSIONS: Overall, a limited number of associations was found between participant characteristics, engagement, and outcome, except for gender, problematic alcohol use, and nicotine dependence. Future studies are needed to shed light on how this knowledge can be used to improve the effects of digital SC programs for cancer survivors. TRIAL REGISTRATION: Netherlands Trial register NTR6011/NL5434; https://onderzoekmetmensen.nl/nl/trial/22832.

Smartphone-Based Survey and Message Compliance in Adults Initially Unready to Quit Smoking: Secondary Analysis of a Randomized Controlled Trial.

BACKGROUND: Efficacy of smartphone-based interventions depends on intervention content quality and level of exposure to that content. Smartphone-based survey completion rates tend to decline over time; however, few studies have identified variables that predict this decline over longer-term interventions (eg, 26 weeks). OBJECTIVE: This study aims to identify predictors of survey completion and message viewing over time within a 26-week smoking cessation trial. METHODS: This study examined data from a 3-group pilot randomized controlled trial of adults who smoke (N=152) and were not ready to quit smoking within the next 30 days. For 182 days, two intervention groups received smartphone-based morning and evening messages based on current readiness to quit smoking. The control group received 2 daily messages unrelated to smoking. All participants were prompted to complete 26 weekly smartphone-based surveys that assessed smoking behavior, quit attempts, and readiness to quit. Compliance was operationalized as percentages of weekly surveys completed and daily messages viewed. Linear regression and mixed-effects models were used to identify predictors (eg, intervention group, age, and sex) of weekly survey completion and daily message viewing and decline in compliance over time. RESULTS: The sample (mean age 50, SD 12.5, range 19-75 years; mean years of education 13.3, SD 1.6, range 10-20 years) was 67.8% (n=103) female, 74.3% (n=113) White, 77% (n=117) urban, and 52.6% (n=80) unemployed, and 61.2% (n=93) had mental health diagnoses. On average, participants completed 18.3 (71.8%) out of 25.5 prompted weekly surveys and viewed 207.3 (60.6%) out of 345.1 presented messages (31,503/52,460 total). Age was positively associated with overall weekly survey completion (P=.003) and daily message viewing (P=.02). Mixed-effects models indicated a decline in survey completion from 77% (114/148) in the first week of the intervention to 56% (84/150) in the last week of the intervention (P<.001), which was significantly moderated by age, sex, ethnicity, municipality (ie, rural/urban), and employment status. Similarly, message viewing declined from 72.3% (1533/2120) in the first week of the intervention to 44.6% (868/1946) in the last week of the intervention (P<.001). This decline in message viewing was significantly moderated by age, sex, municipality, employment status, and education. CONCLUSIONS: This study demonstrated the feasibility of a 26-week smartphone-based smoking cessation intervention. Study results identified subgroups that displayed accelerated rates in the decline of survey completion and message viewing. Future research should identify ways to maintain high levels of interaction with mobile health interventions that span long intervention periods, especially among subgroups that have demonstrated declining rates of intervention engagement over time. TRIAL REGISTRATION: ClinicalTrials.gov NCT03405129; https://clinicaltrials.gov/ct2/show/NCT03405129.

Reasons for continued usage of ENDS: Differences by device and liquid characteristics among US adults.

INTRODUCTION: Motivations for using electronic nicotine delivery systems (ENDS) include quitting or reducing cigarette smoking, flavor, and addiction. This study examines whether the primary reason for continued ENDS usage changes over time, and its association with device and liquid characteristics. METHODS: Data are from a longitudinal cohort study and include 526 US adults (≥21 years) using ENDS frequently (≥5 days/week) as self-reported, and uploaded photos of their most used ENDS devices and liquids and self-reported primary reason for continued ENDS usage in wave 2 (December 2020-April 2021) and wave 5 (February-April 2023). Device-liquid grouping was defined by device (disposable/disposable pod/refillable pod/tank, adjustable/no adjustable settings) and liquid (salt/freebase) characteristics. A device was classified as having adjustable settings if it allowed users to modify the power, coil, or airflow. Data were analyzed using multivariable logistic regressions and McNemar tests. RESULTS: From wave 2 to 5, the primary reason for continued ENDS usage significantly changed, with more participants reporting addiction (29.2% vs 34.6%, p<0.001); and significantly more participants used disposable devices (salt, no adjustable settings) (7.9% vs 25.2%, p<0.001). Compared to those using tanks (freebase, adjustable settings), participants using devices with nicotine salt liquids were more likely to report addiction (AOR>2; 95% CI: 1.12-8.19); and participants using disposable devices (salt, no adjustable settings) were less likely to report quitting/reducing smoking as the primary ENDS use reason after controlling for smoking status and sociodemographic characteristics (AOR<0.6; 95% CI: 0.14-0.995). CONCLUSIONS: Over a 2.5-year period, the proportion of participants continuing to use ENDS at least 5 days/week because of addiction grew, and participants' motivations varied by device-liquid grouping. Restrictions on nicotine salts may disproportionately impact those using ENDS because of addiction; and regulations targeting tanks with freebase liquids may disproportionately impact those using ENDS for smoking cessation/reduction.

Reduced nicotine in cigarettes in a marketplace with alternative nicotine systems: randomized clinical trial.

Background: Reducing cigarette addictiveness has the potential to avert millions of yearly tobacco-related deaths worldwide. Substantially reducing nicotine in cigarettes decreases cigarette consumption, but no large clinical trial has determined the effects of reduced-nicotine cigarettes when other nicotine-containing products are available. The aim of this study was to examine the effects of reduced-nicotine cigarettes in the context of the availability of alternative nicotine delivery systems. Methods: In a U.S. six-site, open-label, parallel-arm study, smokers were randomized for twelve weeks to an experimental marketplace containing cigarettes with either 0.4 mg or 15.8 mg nicotine per gram of tobacco; all had access to non-combusted alternative nicotine delivery systems (e.g., e-cigarettes; medicinal nicotine). Group differences in the primary outcomes (cigarettes per day, number of smoke-free days) were examined using linear and negative binomial regression, respectively (Trial Registration: NCT03272685). Findings: Among 438 randomized participants (mean [standard deviation (SD), range] age, 44.5 [11.9, 20-73] years, 225 [51.4%] women, 282 [64.4%] White and 339 [77.4%] trial completers), those in the 0.4 mg vs. 15.8 mg nicotine cigarette condition experienced significantly lower cigarettes per day at the end of intervention (mean [SD], 7.05 [7.88] vs. 12.95 [9.07], adjusted mean difference, -6.21 [95% CI, -7.66 to -4.75], P < 0.0001) and greater smoke-free days during intervention (mean [SD], 18.59 [27.97] vs. 5.06 [13.77], adjusted rate ratio, 4.25 [95% CI, 2.58-6.98], P < 0.0001). Interpretation: A reduced-nicotine cigarette standard in the context of access to other non-combusted nicotine products has the potential to benefit public health. Funding: U.S. NIH/FDA U54DA03165.

Towards AI-Driven Healthcare: Systematic Optimization, Linguistic Analysis, and Clinicians' Evaluation of Large Language Models for Smoking Cessation Interventions.

Creating intervention messages for smoking cessation is a labor-intensive process. Advances in Large Language Models (LLMs) offer a promising alternative for automated message generation. Two critical questions remain: 1) How to optimize LLMs to mimic human expert writing, and 2) Do LLM-generated messages meet clinical standards? We systematically examined the message generation and evaluation processes through three studies investigating prompt engineering (Study 1), decoding optimization (Study 2), and expert review (Study 3). We employed computational linguistic analysis in LLM assessment and established a comprehensive evaluation framework, incorporating automated metrics, linguistic attributes, and expert evaluations. Certified tobacco treatment specialists assessed the quality, accuracy, credibility, and persuasiveness of LLM-generated messages, using expert-written messages as the benchmark. Results indicate that larger LLMs, including ChatGPT, OPT-13B, and OPT-30B, can effectively emulate expert writing to generate well-written, accurate, and persuasive messages, thereby demonstrating the capability of LLMs in augmenting clinical practices of smoking cessation interventions.

The efficacy and acceptability of pharmacological monotherapies and e-cigarette on smoking cessation: a systemic review and network meta-analysis.

Background and aims: Several pharmacological interventions, such as nicotine replacement therapy (NRT), varenicline, and bupropion, have been approved for clinical use of smoking cessation. E-cigarettes (EC) are increasingly explored by many RCTs for their potentiality in smoking cessation. In addition, some RCTs are attempting to explore new drugs for smoking cessation, such as cytisine. This network meta-analysis (NMA) aims to investigate how these drugs and e-cigarettes compare regarding their efficacy and acceptability. Materials and methods: This systematic review and NMA searched all clinical studies on smoking cessation using pharmacological monotherapies or e-cigarettes published from January 2011 to May 2022 using MEDLINE, COCHRANE Library, and PsychINFO databases. NRTs were divided into transdermal (TDN) and oronasal nicotine (ONN) by administrative routes, thus 7 network nodes were set up for direct and indirect comparison. Two different indicators measured the efficacy: prevalent and continuous smoking abstinence. The drop-out rates measured the acceptability. Results: The final 40 clinical studies included in this study comprised 77 study cohorts and 25,889 participants. Varenicline is more effective intervention to assist in smoking cessation during 16-32 weeks follow-up, and is very likely to prompt dropout. Cytisine shows more effectiveness in continuous smoking cessation but may also lead to dropout. E-cigarettes and oronasal nicotine are more effective than no treatment in encouraging prevalent abstinence, but least likely to prompt dropout. Finally, transdermal nicotine delivery is more effective than no treatment in continuous abstinence, with neither significant effect on prevalent abstinence nor dropout rate. Conclusion: This review suggested and agreed that Varenicline, Cytisine and transdermal nicotine delivery, as smoking cessation intervention, have advantages and disadvantages. However, we had to have reservations about e-cigarettes as a way to quit smoking in adolescents.

Sex differences in tobacco use, attempts to quit smoking, and cessation among dual users of cigarettes and e-cigarettes: Longitudinal findings from the US Population Assessment of Tobacco and Health Study.

SIGNIFICANCE: A growing number of adults use more than one tobacco product, with dual use of cigarettes and e-cigarettes being the most common combination. Monitoring sex disparities in tobacco use is a public health priority. However, little is known regarding whether dual users differ by sex. METHODS: Data came from Waves 4-6 (12/2016-11/2021) of the Population Assessment of Tobacco and Health Study, a US nationally-representative longitudinal survey. This analysis included current adult dual users of cigarettes and e-cigarettes. We used weighted generalized estimating equations to assess the association between sex and (1) making a cigarette quit attempt (n = 1882 observations from n = 1526 individuals) and (2) smoking cessation (n = 2081 observations from n = 1688 individuals) across two wave pairs, adjusting for age, education, ethnicity, time-to-first cigarette after waking, and e-cigarette use frequency. RESULTS: Among US dual users, 14.1% (95% Confidence Intervals [Cl] = 11.9-16.4) of females and 23.4% (20.0-26.9) of males were young adults (aged 18-24), 11.7% (9.2-14.2) of females and 14.4% (11.6-17.2) of males had

Evaluation of a regional tobacco control programme (Greater Manchester's Making Smoking History) on quitting and smoking in England 2014 to 2022: a time-series analysis.

INTRODUCTION: This study aimed to assess the impact of Greater Manchester's Making Smoking History programme - a region-wide smoking cessation programme launched in January 2018 - on key smoking and quitting outcomes. METHODS: Data were from a nationally-representative monthly survey, 2014-2022 (n=171,281). We used interrupted time-series analyses (Autoregressive Integrated Moving Average [ARIMA] and generalised additive models [GAM]) to examine regional differences between Greater Manchester and the rest of England, before and during the programme's first five years. Outcomes were rates of quit attempts and overall quits among smokers, quit success rates among smokers who tried to quit (pre-registered outcomes), and current smoking prevalence among adults (unregistered outcome). RESULTS: Results showed mixed effects of the programme on quitting. Primary ARIMA models showed comparative reductions in quit success rates (change in quarterly difference between regions = -11.03%; 95%CI -18.96;-3.11) and overall quit rates in Greater Manchester compared with the rest of England (-2.56%; 95%CI -4.95;-0.18), and no significant change in the difference in the quit attempt rate (+2.95%; 95%CI -11.64;17.54). These results were not consistently observed across sensitivity analyses or GAM analyses. Exploratory ARIMA models consistently showed smoking prevalence in Greater Manchester declined more quickly than in the rest of England following initiation of the programme (-2.14%; 95%CI -4.02;-0.27). CONCLUSIONS: The first five years of Greater Manchester's Making Smoking History programme did not appear to be associated with substantial increases in quitting activity. However, exploratory analyses showed a significant reduction in the regional smoking rate, over and above changes in the rest of England over the same period. IMPLICATIONS: Taken together, these results show a relative decline in smoking prevalence in Greater Manchester but equivocal data on quitting, introducing some uncertainty. It is possible the programme has reduced smoking prevalence in the absence of any substantial change in quitting activity by changing norms around smoking and reducing uptake, or by reducing the rate of late relapse. It is also possible that an undetected effect on quitting outcomes has still contributed to the programme's impact on reducing prevalence to some degree. It will be important to evaluate the overall impact of the programme over a longer timeframe.

Smoking patterns and the intention to quit in German cancer patients: a cross-sectional study.

BACKGROUND: Continued smoking after a cancer diagnosis can be associated with lower treatment tolerance, poorer outcomes, and reduced quality of life compared to non-smoking cancer patients or to those who have quit. Yet about 60% of patients continue to smoke after being diagnosed and find it difficult to quit. To address this problem, it is necessary to identify current and past smoking patterns (e.g., frequency of use, types of tobacco products) and determine whether there is motivation to quit. Similarly, factors associated with continued smoking should be identified. These data will provide the basis for the development of smoking cessation programs tailored to the needs of cancer patients. METHODS: A questionnaire was distributed to cancer patients older than 18 years in a German Comprehensive Cancer Center. Participating cancer patients were divided into three main groups: 1) patients who stopped smoking before being diagnosed with cancer (Ex-before); 2) patients who stopped smoking after a cancer diagnosis (Ex-after); and 3) patients who currently smoke cigarettes (CS). Sociodemographic, medical, and psychosocial data were collected, as well as smoking patterns and the motivation to quit smoking. RESULTS: About half of patients (51%) who smoked before diagnosis continue to smoke after a cancer diagnosis. Being diagnosed with a tobacco-related cancer type was associated with a decreased probability of continued smoking. Patients with tobacco-related tumors and receiving positive support in burdensome situations were more likely to have a higher cigarette dependence. Of all CS, 59.1% had intention to quit, and 22.7% reported having taken action to quit. The support by a smoking cessation program was considered important. CS were willing to spend up to €100 for support and were open to multiple sessions per week, group sessions, one-on-one sessions and/or online support. CONCLUSION: These findings underscore the importance of educating cancer patients about the consequences of smoking and to provide them with support to quit. Identified risk factors may further help to recognize cancer patients with high risk of continued smoking after diagnosis. TRIAL REGISTRATION: The study was registered at OSF ( https://osf.io/3c9km ) and published as a study protocol at " https://bmjopen.bmj.com/content/13/4/e069570 ".

Effect of narrative videos and mini-VR games in nursing smoking cessation training on empathy and self-efficacy of smoking cessation counseling: A randomized controlled trial.

BACKGROUND: Empathy and self-efficacy for smoking cessation counseling can be enhanced through smoking cessation training. Narrative videos and virtual reality (VR) games have been applied in medical education, but their application in smoking cessation training is limited and understudied. OBJECTIVES: To evaluate the effect of smokers' narrative videos and mini-VR games on nursing students' empathy towards smokers (State Empathy Scale), confidence in practicing empathy, self-efficacy in smoking cessation counseling, and learning satisfaction. DESIGN: An open-labeled randomized controlled trial, registration number: NCT05440877 (ClinicalTrials.gov). SETTING AND PARTICIPANTS: Master of nursing students from the University in Hong Kong who enrolled in a smoking cessation course. METHODS: All students attended a tutorial including case-based discussions and role-play. The intervention group could additionally access the narrative videos and mini-VR games of smokers' cases. Linear mixed models and Cohen's d were used to evaluate the intervention effect on the self-reported learning outcomes after the intervention. RESULTS: 26 students enrolled in this trial, with 13 in each trial arm. All completed the trial. Post-test state empathy scores were significantly higher in the control group than in the intervention group (Cohen's d = 0.814, p = 0.049). No significant group differences were observed in the change of confidence in practicing empathy (ß = -11.154, p = 0.073), self-efficacy (ß = 4.846, p = 0.096), and students' learning satisfaction (Cohen's d = 0.041, p = 0.917). Both groups showed a significant increase in self-efficacy post-test (p < 0.001). CONCLUSIONS: Narrative videos showing smoking scenes and reluctance to quit, and our mini-VR games may weaken nursing students' empathy towards smokers. Smoking cessation training involving narrative videos should be modified. Debriefing and guidance to understand smokers' difficulties and express empathy are needed. VR games for smoking cessation training can involve more frame stories and challenging tasks to increase engagement.

Does e-cigarette use predict short-term smoking cessation behaviors among adults who smoke in Mexico? A longitudinal study.

INTRODUCTION: This study assessed quit attempts among adults who use cigarettes either exclusively or with e-cigarettes in Mexico, where non-daily smoking predominates. METHODS: An open cohort of Mexican adults who smoke was surveyed every four months from November 2018 to March 2021. Participants followed to the next survey were analyzed (n = 2220 individuals, 4560 observations). Multinomial logistic models regressed smoking quit attempts reported at the followup survey (ref = no attempt; tried to quit; sustained attempt of ≥30 days) on e-cigarette use frequency (none = ref; 1-2 days/week; ≥3 days/week), adjusting for sociodemographics and smoking-related variables. Additional models subdivided e-cigarette users by intentions to quit smoking in the next six months (i.e., yes/no), use of nicotine (i.e., yes/no), and vaping device used (i.e., open/closed). RESULTS: At 4-month follow-up, 32.7 % had tried to quit, and 2.9 % had quit for 30 days or more. Compared to those who smoke exclusively, occasional, and frequent e-cigarette users were more likely to try to quit (Adjusted Relative Risk Ratio or ARRR = 1.26 and 1.66, respectively) but no more likely to sustain their quit attempt. Among those who intended to quit smoking, e-cigarette users were no more likely to either try or sustain quit attempts. Furthermore, use of e-cigarettes with nicotine (59.6 %) was unassociated with cessation except that those who frequently used e-cigarettes with nicotine were more likely to try to quit than those who smoke exclusively (ARRR = 1.88). Device type used was not differentially associated with outcomes. CONCLUSIONS: Mexicans who smoke and use e-cigarettes appear more likely than those who smoke exclusively to try to quit but not to sustain abstinence.

Brain Glucose Metabolism as a Readout of the Central Nervous System Impact of Cigarette Smoke Exposure and Withdrawal and the Effects of NFL-101, as an Immune-Based Drug Candidate for Smoking Cessation Therapy.

Neuroimaging biomarkers are needed to investigate the impact of smoking withdrawal on brain function. NFL-101 is a denicotinized aqueous extract of tobacco leaves currently investigated as an immune-based smoking cessation therapy in humans. However, the immune response to NFL-101 and its ability to induce significant changes in brain function remain to be demonstrated. Brain glucose metabolism was investigated using [18F]fluoro-deoxy-glucose ([18F]FDG) PET imaging in a mouse model of cigarette smoke exposure (CSE, 4-week whole-body inhalation, twice daily). Compared with control animals, the relative uptake of [18F]FDG in CSE mice was decreased in the thalamus and brain stem (p < 0.001, n = 14 per group) and increased in the hippocampus, cortex, cerebellum, and olfactory bulb (p < 0.001). NFL-101 induced a humoral immune response (specific IgGs) in mice and activated human natural-killer lymphocytes in vitro. In CSE mice, but not in control mice, single-dose NFL-101 significantly increased [18F]FDG uptake in the thalamus (p < 0.01), thus restoring normal brain glucose metabolism after 2-day withdrawal in this nicotinic receptor-rich region. In tobacco research, [18F]FDG PET imaging provides a quantitative method to evaluate changes in the brain function associated with the withdrawal phase. This method also showed the CNS effects of NFL-101, with translational perspectives for future clinical evaluation in smokers.