Evidence map›Paper›PMID 41458239›Full record

ArticleBMJ public health2025

Smoking reduction trajectories and their association with smoking cessation: a secondary analysis of longitudinal clinical trial data.

Anthony Barrows, Elias Klemperer, Hugh Garavan, Nicholas Allgaier, Nicola Lindson, Gemma Taylor

Abstract read
In one paragraph

Article in BMJ public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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

6 authors.

Anthony BarrowsPsychiatry, University of Vermont, Burlington, Vermont, USA.ORCID https://orcid.org/0000-0003-1941-4660
Elias KlempererPsychiatry, University of Vermont, Burlington, Vermont, USA.
Hugh GaravanPsychiatry, University of Vermont, Burlington, Vermont, USA.
Nicholas AllgaierPsychiatry, University of Vermont, Burlington, Vermont, USA.
Nicola LindsonUniversity of Oxford, Oxford, UK.
Gemma TaylorCentre for Public Health, Population Health Sciences, Bristol Medical School, Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK.

Funding

Vermont Center on Behavior and HealthP30GM149331 · NIGMS · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI Stephen T Higgins · 2023 to 2026
$5.6M
NIGMS NIH HHS P30 GM149331
6 · The paper itself

Abstract

Introduction: Tobacco smoking remains the leading cause of preventable death worldwide. Smoking reduction can be recommended to people unmotivated to quit, but evidence on trajectories of reduction and associated outcomes is mixed. Methods: In a secondary analysis of five randomised, placebo-controlled trials of nicotine replacement therapy, we used latent class analysis and elastic net regression to determine latent smoking trajectories using cigarettes-per-day (CPD) across 26 weeks. Participants were adults who smoked daily without intention to quit in the next month. We used predictive modelling and receiver operator characteristic area under-the-curve (AUC) to assess smoking cessation after 1 year. Results: Participants (n=2066) smoked a mean 27.26±9.74 CPD at baseline. Three distinct smoking patterns emerged: Class 1 (n=186, 10%) achieved the greatest reduction in CPD (2-week mean 57% reduction) with subsequent reduction; Class 2 (n=803, 45%) saw a 2-week mean 50% reduction and remained at that level and Class 3 (n=794, 45%) reduced by a 2-week mean of 22% and returned to near-baseline CPD. Older, male participants with lower anxiety and lower nicotine dependence were more likely to be in Class 1. Abstinence rates at 1 year (~50 weeks after reduction) were 37.6% for Class 1, 4.2% for Class 2 and 2.3% for Class 3.Using latent class assignment as a predictor improved prediction of smoking cessation at 1 year follow-up over prediction using baseline characteristics by 14.4% (AUC=0.776±0.010, p=0.002). Those who reduced their CPD minimally were nearly 90% less likely to achieve cessation than those who reduced by over 50% (ORs: Class 2=0.111±0.013, Class 3=0.070±0.005). Conclusions: Findings suggest adults who are unmotivated to quit at baseline but reduce their smoking by more than half are most likely to achieve smoking cessation. A lack of early reduction success could indicate that greater support is needed to help people to quit.

Indexed as

methodsPublic HealthSociodemographic Factors

Identifiers

PMID41458239
PMCPMC12742168

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.