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Enregistrement W4406121349 · doi:10.1002/14651858.cd016058.pub2

Interventions for quitting vaping

2025· review· en· W4406121349 sur OpenAlexfundno aff
Ailsa R. Butler, Nicola Lindson, Jonathan Livingstone‐Banks, Caitlin Notley, Tari Turner, Nancy A. Rigotti, Thomas Fanshawe, Lynne Dawkins, Rachna Begh, Angela Difeng Wu, Leonie S. Brose, Monserrat Conde, Erikas Simonavičius, Jamie Hartmann‐Boyce

Notice bibliographique

RevueCochrane Database of Systematic Reviews · 2025
Typereview
Langueen
DomaineMedicine
ThématiqueSmoking Behavior and Cessation
Établissements canadiensnon disponible
Organismes subventionnairesEvidence Synthesis ProgrammeMedical Research CouncilHealth CanadaCancer Research UKNational Institutes of HealthOxford Health NHS Foundation TrustNational Institute for Health and Care ResearchDepartment of Health and Social Care
Mots-clésPsychological interventionMedicinePsycINFOAbstinenceSmoking cessationNicotine replacement therapyMEDLINENicotineRandomized controlled trialMeta-analysisIntervention (counseling)Environmental healthFamily medicinePsychiatrySurgeryInternal medicine

Résumé

récupéré en direct d'OpenAlex

RATIONALE: There is limited guidance on the best ways to stop using nicotine-containing vapes (otherwise known as e-cigarettes) and ensure long-term abstinence, whilst minimising the risk of tobacco smoking and other unintended consequences. Treatments could include pharmacological interventions, behavioural interventions, or both. OBJECTIVES: To conduct a living systematic review assessing the benefits and harms of interventions to help people stop vaping compared to each other or to placebo or no intervention. To also assess how these interventions affect the use of combustible tobacco, and whether the effects vary based on participant characteristics. SEARCH METHODS: We searched the following databases from 1 January 2004 to 24 April 2024: CENTRAL; MEDLINE; Embase; PsycINFO; ClinicalTrials.gov (through CENTRAL); World Health Organization International Clinical Trials Registry Platform (through CENTRAL). We also searched the references of eligible studies and abstracts from the Society for Research on Nicotine and Tobacco 2024 conference, and contacted study authors. ELIGIBILITY CRITERIA: Randomised controlled trials (RCTs) recruiting people of any age using nicotine-containing vapes, regardless of tobacco smoking status. Studies had to test an intervention designed to support people to quit vaping, and plan to measure at least one of our outcomes. OUTCOMES: Critical outcomes: vaping cessation; change in combustible tobacco use at six months or longer; number of participants reporting serious adverse events (SAEs) at one week or longer. RISK OF BIAS: We used the Cochrane RoB 1 tool to assess bias in the included studies. SYNTHESIS METHODS: We followed standard Cochrane methods for screening and data extraction. We grouped studies by comparisons and outcomes reported, and calculated individual study and pooled effects, as appropriate. We used random-effects Mantel-Haenszel methods to calculate risk ratios (RR) with 95% confidence intervals (CI) for dichotomous outcomes. We used random-effects inverse variance methods to calculate mean differences and 95% CI for continuous outcomes. We assessed the certainty of the evidence using the GRADE approach. INCLUDED STUDIES: Nine RCTs, representing 5209 participants motivated to stop using nicotine-containing vapes at baseline, are included. In six studies, participants were abstinent from smoking tobacco cigarettes at baseline, although most studies included some participants who had previously smoked. Eight studies included participants aged 18 or older, three included only young adults (18 to 24 years), and one included 13- to 17-year-olds only. We judged three studies at low risk, three at high risk, and three at unclear risk of bias. SYNTHESIS OF RESULTS: = 0%; 2 studies, 4091 participants). The one study investigating nicotine/vaping behaviour reduction did not report on SAEs. One of the studies investigating text message-based interventions did report on SAEs; however, zero events were reported in both study arms (508 participants; low-certainty evidence due to imprecision). No studies reported change in combustible tobacco smoking at six-month follow-up or longer. AUTHORS' CONCLUSIONS: There is low-certainty evidence that text message-based interventions designed to help people stop nicotine vaping may help more youth and young adults to successfully stop than no/minimal support, and low-certainty evidence that varenicline may also help people quit vaping. Data exploring the effectiveness of combination NRT, cytisine, and nicotine/vaping behaviour reduction are inconclusive due to risk of bias and imprecision. Most studies that measured SAEs reported none; however, more data are needed to draw clear conclusions. Of note, data from studies investigating these interventions for quitting smoking have not demonstrated serious concerns about SAEs. No studies assessed the change in combustible tobacco smoking, including relapse to or uptake of tobacco smoking, at six-month follow-up or longer. It is important that future studies measure this so the complete risk profile of relevant interventions can be considered. We identified 20 ongoing RCTs. Their incorporation into the evidence base and the continued identification of new studies is imperative to inform clinical and policy guidance on the best ways to stop vaping. Therefore, we will continue to update this review as a living systematic review by running searches monthly and updating the review when relevant new evidence that will strengthen or change our conclusions emerges. FUNDING: Cancer Research UK (PRCPJT-Nov22/100012). National Institute of Health Research (NIHR206123) REGISTRATION: Protocol available via DOI: 10.1002/14651858.CD016058.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,022
score de la tête « metaresearch » (Gemma)0,069
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,037
Score d'incertitude au seuil0,124

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0220,069
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0040,007
Bibliométrie0,0070,003
Études des sciences et des technologies0,0010,002
Communication savante0,0030,003
Science ouverte0,0020,004
Intégrité de la recherche0,0060,005
Charge utile insuffisante (le modèle a refusé de juger)0,0370,003

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,279
Tête enseignante GPT0,489
Écart entre enseignants0,210 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeRevue systématique
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations18
Publié2025
Routes d'admission1
Résumé présentoui

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