ArticleJournal of smoking cessation2018
Personalized Intervention Program: Tobacco Treatment for Patients at Risk for Lung Cancer.
Article in Journal of smoking cessation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- Effect of a Personalized Tobacco Treatment Intervention on Smoking Abstinence in Individuals Eligible for Lung Cancer Screening.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2024Trial
- Cost-Effectiveness of Smoking Cessation Interventions Integrated Into Lung Cancer Screening.JAMA network open · 2026Article
- Pre- and post-intervention survey on lung cancer awareness among adults in selected communities in KwaZulu-Natal, South Africa: A quasi-experimental study.Journal of public health in Africa · 2023Article
- Quit4hlth: a preliminary investigation of tobacco treatment with gain-framed and loss-framed text messages for quitline callers.Journal of smoking cessation · 2020Article
- The worldwide burden of smoking-related oral cancer deaths.Clinical and experimental dental research · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundLung cancer screening and tobacco treatment for patients at high-risk for lung cancer may greatly reduce mortality from smoking, and there is an urgent need to improve smoking cessation therapies for this population.
aimsThe purpose of this study is to test the efficacy of two separate, sequential interventions to promote tobacco cessation/reduction compared to standard care in smokers considered high-risk for lung cancer.
methodsThe study will recruit 276 current smokers attending a lung cancer screening clinic or considered high-risk for lung cancer based on age and smoking history across two sites. Patients first will be randomized to either standard tobacco treatment (8 weeks of nicotine patch and five individual counselling sessions) or standard tobacco treatment plus personalized gain-framed messaging. At the 8-week visit, all patients will be re-randomized to receive biomarker feedback or no biomarker feedback. Repeated assessments during treatment will be used to evaluate changes in novel biomarkers: skin carotenoids, lung function, and plasma bilirubin that will be used for biomarker feedback. We hypothesize that personalized gain-framed messages and receiving biomarker feedback related to tobacco cessation/reduction will improve quit rates and prevent relapse compared to standard care. Primary outcomes include 7-day point-prevalence abstinence verified with expired carbon monoxide at 8 weeks and mean cigarettes per day in the past week at 6 months.
conclusionsStudy findings will inform the development of novel interventions for patients at risk for lung cancer to improve smoking cessation rates.
Identifiers
What OpenQuestion holds
Registered trials
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.