Evidence map›Paper›PMID 37455014›Full record

ArticleAddiction (Abingdon, England)2023

E-cigarette support for smoking cessation: Identifying the effectiveness of intervention components in an on-line randomized optimization experiment.

Catherine Kimber, Vassilis Sideropoulos, Sharon Cox, Daniel Frings, Felix Naughton, Jamie Brown, Hayden McRobbie, Lynne Dawkins

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Addiction (Abingdon, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 pooled it
0.9field-weighted citation impact, top 27% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 2 syntheses or guidelines pooled it, 5 citations in OpenAlex.

  1. Electronic cigarettes for smoking cessation.The Cochrane database of systematic reviews · 2025
    Pooled it
  2. Electronic cigarettes for smoking cessation.The Cochrane database of systematic reviews · 2024
    Pooled it
  3. Observational
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 5 institutions in 2 countries.

Catherine KimberLondon South Bank University, London, UK.ORCID 0000-0001-9994-8640
Vassilis SideropoulosIOE, UCL's Faculty of Education and Society, University College London, London, UK.
Sharon CoxDepartment of Behavioural Science and Health, University College London, London, UK.ORCID 0000-0001-8494-5105
Daniel FringsLondon South Bank University, London, UK.
Felix NaughtonSchool of Health Sciences, University of East Anglia, Norwich, UK.ORCID 0000-0001-9790-2796
Jamie BrownDepartment of Behavioural Science and Health, University College London, London, UK.ORCID 0000-0002-2797-5428
Hayden McRobbieNational Drug and Alcohol Research Centre, University of New South Wales, Sydney, NSW, Australia.ORCID 0000-0002-7777-1845
Lynne DawkinsLondon South Bank University, London, UK.ORCID 0000-0003-1236-009X
London South Bank University · GBUniversity College London · GBFaculty (United Kingdom) · GBUniversity of East Anglia · GBUNSW Sydney · AU

Funding

Cancer Research UK PRCRPG-Nov21\100 002Medical Research Council MR/T002352/1
6 · The paper itself

Abstract

AIMS, DESIGN AND

settingThe aim of this study was to determine which combination(s) of five e-cigarette-orientated intervention components, delivered on-line, affect smoking cessation. An on-line (UK) balanced five-factor (2 × 2 × 2 × 2 × 2 = 32 intervention combinations) randomized factorial design guided by the multi-phase optimization strategy (MOST) was used.

participantsA total of 1214 eligible participants (61% female; 97% white) were recruited via social media.

interventionsThe five on-line intervention components designed to help smokers switch to exclusive e-cigarette use were: (1) tailored device selection advice; (2) tailored e-liquid nicotine strength advice; (3): tailored e-liquid flavour advice; (4) brief information on relative harms; and (5) text message (SMS) support. MEASUREMENTS: The primary outcome was 4-week self-reported complete abstinence at 12 weeks post-randomization. Primary analyses were intention-to-treat (loss to follow-up recorded as smoking). Logistic regressions modelled the three- and two-way interactions and main effects, explored in that order.

findingsIn the adjusted model the only significant interaction was a two-way interaction, advice on flavour combined with text message support, which increased the odds of abstinence (odds ratio = 1.55, 95% confidence interval = 1.13-2.14, P = 0.007, Bayes factor = 7.25). There were no main effects of the intervention components.

conclusionsText-message support with tailored advice on flavour is a promising intervention combination for smokers using an e-cigarette in a quit attempt.

Indexed as

Electronic Nicotine Delivery SystemsSmoking CessationBayes TheoremFemaleHumansMaleSmokingTobacco SmokingDigital interventionse-cigarettesmulti-phase optimization strategy (MOST)nicotinesmoking cessationsmoking reductiontailored advicetobaccovaping

Identifiers

PMID37455014
PMCPMC10952247
OpenAlexW4384465866

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.