A systematic review of predictors of vaping cessation among young people.

INTRODUCTION: Understanding the factors influencing vaping cessation among young people is crucial for targeted interventions. This review aimed to summarize the individual and environmental factors that predict vaping cessation related behaviours in the young population. METHODS: We systematically searched five databases for studies investigating predictors of vaping cessation behaviours among young people aged 10-35 years. Studies that examined predictors of cessation of cigarettes, other tobacco products, cannabis vaping, and studies evaluating efficacy of cessation interventions were excluded. Quality in Prognosis Studies tool was used to assess risk of bias. RESULTS: We found 24 studies analyzing predictors of intention to quit vaping (n=15), quit attempts (n=11), and vaping abstinence (n=7). Most studies had low risk of bias, except for study attrition. We identified 107 predictors and grouped them into 'probable', 'possible', 'insufficient evidence', 'probably unrelated', and 'inconsistent direction' categories. For 'probable' predictors, we found 11 for intention to quit, 8 for quit attempts and 5 for vaping abstinence. Overall, harm perception of vaping, current other tobacco products use, frequency of use, and level of nicotine dependence were common 'probable' predictors across three outcomes, with low harm perception of vaping, dual use, and poly tobacco use associated with decreased intention to quit and quit attempts in younger population (~10-19 years). CONCLUSIONS: Predictive modelling studies investigating vaping cessation related behaviours among young people is still limited. Future research should specifically study the natural history of vaping in youth in different jurisdictions, populations, and age groups to expand our knowledge on this area. IMPLICATIONS: We identified and categorized predictors of intention to quit vaping, quit attempts, and vaping abstinence among young people. While the 'probable' predictors can inform public health and policymakers to plan targeted vaping cessation programs for high-risk populations, raising public harm perception of vaping and encouraging to quit other tobacco products might increase intention to quit and quit attempts among younger population. However, the 'possible', 'insufficient evidence' and 'inconsistent direction' predictors needs further testing by future prospective longitudinal research. Additionally, we emphasized the significance of appropriate study designs, conducting research across various jurisdictions, and different population groups to obtain comprehensive insights.

Machine learning applications in tobacco research: a scoping review.

OBJECTIVE: Identify and review the body of tobacco research literature that self-identified as using machine learning (ML) in the analysis. DATA SOURCES: MEDLINE, EMABSE, PubMed, CINAHL Plus, APA PsycINFO and IEEE Xplore databases were searched up to September 2020. Studies were restricted to peer-reviewed, English-language journal articles, dissertations and conference papers comprising an empirical analysis where ML was identified to be the method used to examine human experience of tobacco. Studies of genomics and diagnostic imaging were excluded. STUDY SELECTION: Two reviewers independently screened the titles and abstracts. The reference list of articles was also searched. In an iterative process, eligible studies were classified into domains based on their objectives and types of data used in the analysis. DATA EXTRACTION: Using data charting forms, two reviewers independently extracted data from all studies. A narrative synthesis method was used to describe findings from each domain such as study design, objective, ML classes/algorithms, knowledge users and the presence of a data sharing statement. Trends of publication were visually depicted. DATA SYNTHESIS: 74 studies were grouped into four domains: ML-powered technology to assist smoking cessation (n=22); content analysis of tobacco on social media (n=32); smoker status classification from narrative clinical texts (n=6) and tobacco-related outcome prediction using administrative, survey or clinical trial data (n=14). Implications of these studies and future directions for ML researchers in tobacco control were discussed. CONCLUSIONS: ML represents a powerful tool that could advance the research and policy decision-making of tobacco control. Further opportunities should be explored.

An overview of systematic reviews on predictors of smoking cessation among young people.

Understanding the factors that influence smoking cessation among young people is crucial for planning targeted cessation approaches. The objective of this review was to comprehensively summarize evidence for predictors of different smoking cessation related behaviors among young people from currently available systematic reviews. We searched six databases and reference lists of the included articles for studies published up to October 20, 2023. All systematic reviews summarizing predictors of intention to quit smoking, quit attempts, or smoking abstinence among people aged 10-35 years were included. We excluded reviews on effectiveness of smoking cessation intervention; smoking prevention and other smoking behaviors; cessation of other tobacco products use, dual use, and polysubstance use. We categorized the identified predictors into 5 different categories for 3 overlapping age groups. JBI critical appraisal tool and GRADE-CERqual approach were used for quality and certainty assessment respectively. A total of 11 systematic reviews were included in this study; all summarized predictors of smoking abstinence/quit attempts and two also identified predictors of intention to quit smoking. Seven reviews had satisfactory critical appraisal score and there was minimal overlapping between the reviews. We found 4 'possible' predictors of intention to quit smoking and 119 predictors of smoking abstinence/quit attempts. Most of these 119 predictors were applicable for ~10-29 years age group. We had moderate confidence on the 'probable', 'possible', 'insufficient evidence', and 'inconsistent direction' predictors and low confidence on the 'probably unrelated' factors. The 'probable' predictors include a wide variety of socio-demographic factors, nicotine dependence, mental health, attitudes, behavioral and psychological factors, peer and family related factors, and jurisdictional policies. These predictors can guide improvement of existing smoking cessation interventions or planning of new targeted intervention programs. Other predictors as well as predictors of intention to quit smoking need to be further investigated among adolescents and young adults separately.

Scoping review of guidance on cessation interventions for electronic cigarettes and dual electronic and combustible cigarettes use.

BACKGROUND: Although evidence-based smoking cessation guidelines are available, the applicability of these guidelines for the cessation of electronic cigarette and dual e-cigarette and combustible cigarette use is not yet established. In this review, we aimed to identify current evidence or recommendations for cessation interventions for e-cigarette users and dual users tailored to adolescents, youth and adults, and to provide direction for future research. METHODS: We systematically searched MEDLINE, Embase, PsycINFO and grey literature for publications that provided evidence or recommendations on vaping cessation for e-cigarette users and complete cessation of cigarette and e-cigarette use for dual users. We excluded publications focused on smoking cessation, harm reduction by e-cigarettes, cannabis vaping, and management of lung injury associated with e-cigarette or vaping use. Data were extracted on general characteristics and recommendations made in the publications, and different critical appraisal tools were used for quality assessment. RESULTS: A total of 13 publications on vaping cessation interventions were included. Most articles were youth-focused, and behavioural counselling and nicotine replacement therapy were the most recommended interventions. Whereas 10 publications were appraised as "high quality" evidence, 5 articles adapted evidence from evaluation of smoking cessation. No study was found on complete cessation of cigarettes and e-cigarettes for dual users. INTERPRETATION: There is little evidence in support of effective vaping cessation interventions and no evidence for dual use cessation interventions. For an evidence-based cessation guideline, clinical trials should be rigorously designed to evaluate the effectiveness of behavioural interventions and medications for e-cigarette and dual use cessation among different subpopulations.

Are vaporizers a lower-risk alternative to smoking cannabis?

Cannabis use is associated with various adverse physical and mental health outcomes as well as increased risk of motor vehicle collision. Many organizations and the "Lower-Risk Cannabis Use Guidelines" have recommended to use cannabis vaporizers instead of smoking to reduce the associated health risk. This commentary draws attention to the present evidence regarding harm reduction potential of cannabis vaping. Cannabis vaporizer use can reduce the emission of carbon monoxide, chronic respiratory symptoms, and exposure to several toxins while producing similar subjective effects and blood THC concentration compared with smoking cannabis, holding potential for harm reduction among habitual cannabis smokers. However, new cannabis users, regardless of method of administration of cannabis, may experience intense subjective effects and cognitive impairment with increased susceptibility to dependence. Hence, policy makers should consider limiting access to cannabis among young people and adopting strategies to reduce impaired driving under influence of cannabis. Future research should focus on impact of switching from cannabis smoking to dried herb vaping using cannabis vaporizers among chronic cannabis smokers, and long-term outcomes of medical cannabis vaping, and further explore association of vaping-associated lung injury with THC-containing e-liquids.

RéSUMé: L'usage du cannabis est associé à une panoplie de résultats de santé physique et mentale indésirables et à un risque accru de collision entre véhicules automobiles. De nombreux organismes, ainsi que les « Recommandations canadiennes pour l'usage du cannabis à moindre risque ¼, recommandent d'utiliser un vaporisateur au lieu de fumer le cannabis afin d'en réduire les risques pour la santé. Notre commentaire attire l'attention sur les preuves actuelles concernant le potentiel de réduction des méfaits du vapotage du cannabis. L'utilisation d'un vaporisateur de cannabis peut réduire l'émission de monoxyde de carbone, les symptômes respiratoires chroniques et l'exposition à plusieurs toxines tout en produisant des effets subjectifs et une concentration de THC dans le sang semblables à ceux du cannabis fumé, ce qui pourrait réduire les méfaits chez les fumeurs réguliers de cannabis. Par contre, les nouveaux consommateurs de cannabis, peu importe la méthode d'administration du cannabis choisie, peuvent éprouver des effets subjectifs intenses et une détérioration cognitive, ainsi qu'une susceptibilité accrue à la dépendance. Les responsables des politiques devraient donc songer à limiter l'accès des jeunes au cannabis et adopter des stratégies pour réduire la conduite avec facultés affaiblies par cette drogue. Des études futures devraient porter sur les conséquences, pour les fumeurs réguliers de cannabis, de vapoter l'herbe séchée à l'aide d'un vaporisateur au lieu de fumer le cannabis, et sur les effets à long terme du vapotage du cannabis médical, et explorer plus avant l'association entre les lésions pulmonaires associées au vapotage et les liquides à vapoter contenant du THC.

Smartphone Apps for Vaping Cessation: Quality Assessment and Content Analysis.

BACKGROUND: As the prevalence of electronic cigarette (e-cigarette) use, or vaping, continues to grow, particularly among young people, so does the need for research and interventions to address vaping. OBJECTIVE: This study examines the quality of free vaping cessation apps, their contents and features, popularity among users, and adherence to evidence-based principles. METHODS: A systematic search of existing apps for vaping cessation was conducted in December 2020. Eligible apps were free, in English, and included features specifically targeting vaping cessation. Each app included in the analysis was used daily for at least seven consecutive days, assessed using the Mobile App Rating Scale, and rated by at least two authors (AK, EL, or SS) based on adherence to evidence-based practices. Intraclass correlation coefficient (ICC) estimates were computed to assess interrater reliability (excellent agreement; ICC 0.92; 95% CI 0.78-0.98). RESULTS: A total of 8 apps were included in the quality assessment and content analysis: 3 were developed specifically for vaping cessation and 5 focused on smoking cessation while also claiming to address vaping cessation. The mean of app quality total scores was 3.66 out of 5. Existing vaping cessation apps employ similar approaches to smoking cessation apps. However, they are very low in number and have limited features developed specifically for vaping cessation. CONCLUSIONS: Given the lack of vaping cessation interventions at a time when they are urgently needed, smartphone apps are potentially valuable tools. Therefore, it is recommended that these apps apply evidence-based practices and undergo rigorous evaluations that can assess their quality, contents and features, and popularity among users. Through this process, we can improve our understanding of how apps can be effective in helping users quit vaping.

Correlates of wanting to seek help for mental health and substance use concerns by sexual and gender minority young adults during the COVID-19 pandemic: A machine learning analysis.

The COVID-19 pandemic has worsened the mental health and substance use challenges among many people who are Two Spirit, lesbian, gay, bisexual, transgender, queer, questioning, and intersex (2SLGBTQI+). We aimed to identify the important correlates and their effects on the predicted likelihood of wanting to seek help among 2SLGBTQI+ young adults for mental health or substance use concerns during the pandemic. A cross-sectional survey was conducted in 2020-2021 among 2SLGBTQI+ young adults aged 16-29 living in two Canadian provinces (Ontario and Quebec). Among 1414 participants, 77% (n = 1089) wanted to seek help for their mental health or substance use concerns during the pandemic, out of these, 69.8% (n = 760) reported delay in accessing care. We built a random forest (RF) model to predict the status of wanting to seek help, which achieved moderately high performance with an area under the receiver operating characteristic curve (AUC) of 0.85. The top 10 correlates of wanting to seek help were worsening mental health, age, stigma and discrimination, and adverse childhood experiences. The interactions of adequate housing with certain sexual orientations, gender identities and mental health challenges were found to increase the likelihood of wanting to seek help. We built another RF model for predicting risk of delay in accessing care among participants who wanted to seek help (n = 1089). The model identified a similar set of top 10 correlates of delay in accessing care but lacked adequate performance (AUC 0.61). These findings can direct future research and targeted prevention measures to reduce health disparities for 2SLGBTQI+ young adults.

Correlates of past year suicidal thoughts among sexual and gender minority young adults: A machine learning analysis.

Sexual and gender minority populations are at elevated risk of experiencing suicidal thoughts and attempting suicide. The COVID-19 pandemic exacerbated mental health and substance use challenges among this population. We aimed to examine the relative importance and effects of intersectional factors and strong interactions associated with the risk of suicidal thoughts among Canadian lesbian, gay, bisexual, transgender, queer, questioning, intersex and Two Spirit (LGBTQI2S+) young adults. A cross-sectional online survey was conducted among LGBTQI2S + participants aged 16-29 years living in two Canadian provinces (Ontario, Quebec). Among 1414 participants (mean age 21.90 years), 61% (n = 857) participants reported suicidal thoughts in last 12 months. We built a random forest model to predict the risk of having past year suicidal thoughts, which achieved high performance with an area under the receiver operating characteristic curve (AUC) of 0.84. The top 10 correlates identified were: seeking help from health professionals for mental health or substance use issues since the start of the pandemic, current self-rated mental health status, insulted by parents or adults in childhood, ever heard that being identifying as LGBTQI2S+ is not normal, age in years, past week feeling depressed, lifetime diagnosis of mental illness, lifetime diagnosis of depressive disorder, past week feeling sad, ever pretended to be straight or cisgender to be accepted. The increase in the risk of suicidal thoughts for those having mental health challenges or facing minority stressors is more pronounced in those living in urban areas or being unemployed than those living in rural areas or being employed.

Machine Learning Applications in Mental Health and Substance Use Research Among the LGBTQ2S+ Population: Scoping Review.

BACKGROUND: A high risk of mental health or substance addiction issues among sexual and gender minority populations may have more nuanced characteristics that may not be easily discovered by traditional statistical methods. OBJECTIVE: This review aims to identify literature studies that used machine learning (ML) to investigate mental health or substance use concerns among the lesbian, gay, bisexual, transgender, queer or questioning, and two-spirit (LGBTQ2S+) population and direct future research in this field. METHODS: The MEDLINE, Embase, PubMed, CINAHL Plus, PsycINFO, IEEE Xplore, and Summon databases were searched from November to December 2020. We included original studies that used ML to explore mental health or substance use among the LGBTQ2S+ population and excluded studies of genomics and pharmacokinetics. Two independent reviewers reviewed all papers and extracted data on general study findings, model development, and discussion of the study findings. RESULTS: We included 11 studies in this review, of which 81% (9/11) were on mental health and 18% (2/11) were on substance use concerns. All studies were published within the last 2 years, and most were conducted in the United States. Among mutually nonexclusive population categories, sexual minority men were the most commonly studied subgroup (5/11, 45%), whereas sexual minority women were studied the least (2/11, 18%). Studies were categorized into 3 major domains: web content analysis (6/11, 54%), prediction modeling (4/11, 36%), and imaging studies (1/11, 9%). CONCLUSIONS: ML is a promising tool for capturing and analyzing hidden data on mental health and substance use concerns among the LGBTQ2S+ population. In addition to conducting more research on sexual minority women, different mental health and substance use problems, as well as outcomes and future research should explore newer environments, data sources, and intersections with various social determinants of health.