Evidence map›Paper›PMID 30535269›Full record

Trial reportNicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco2020

Does Smoking Intensity Predict Cessation Rates? A Study of Light-Intermittent, Light-Daily, and Heavy Smokers Enrolled in Two Telephone-Based Counseling Interventions.

Katherine Ni, Binhuan Wang, Alissa R Link, Scott E Sherman

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 3 pooled it
–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

16 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. A meta-analysis of smoking and fracture risk to update the FRAX® tool.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026
    Pooled it
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  11. Birth Cohort‒Specific Smoking Patterns by Family Income in the U.S.American journal of preventive medicine · 2023
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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

4 authors.

Katherine NiDepartment of Medicine, New York University School of Medicine, New York, NY.
Binhuan WangDepartment of Population Health, New York University School of Medicine, New York, NY.
Alissa R LinkDepartment of Population Health, New York University School of Medicine, New York, NY.
Scott E ShermanDepartment of Medicine, New York University School of Medicine, New York, NY.

Funding

Effectiveness of smoking-cessation interventions for urban hospital patientsU01HL105229 · NHLBI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI SHERMAN, SCOTT E · 2010 to 2014
$5.1M
Midcareer Investigator Award in Patient-Oriented Research for Dr. Scott ShermanK24DA038345 · NIDA · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI SHERMAN, SCOTT E · 2014 to 2018
$923k
NHLBI NIH HHS U01 HL105229NIDA NIH HHS K24 DA038345
6 · The paper itself

Abstract

introductionThough many interventions have been shown to be effective in helping smokers quit, outcomes may differ between light and heavy smokers. We identified differences in baseline characteristics and post-intervention cessation rates among smoker groups at two safety-net hospitals.

methodsWe retrospectively analyzed cessation rates in 1604 patients randomized to either a quitline referral (1-2 telephone counseling sessions) or intensive counseling program (seven telephone sessions). Participants were stratified into light-intermittent (smoked on ≤24 of last 30 days), light-daily (smoked on >24/30 days, 1-9 cigarettes per day [CPD]), or heavy smokers (smoked on >24/30 days, ≥10 CPD). We compared baseline characteristics between smoker types using chi-squared tests, then identified predictors of 30-day abstinence using a multivariable model.

resultsCompared with light-daily and light-intermittent smokers, heavy smokers were more likely to be white, male, concomitant e-cigarette users, to have high-risk alcohol use, to have used quitting aids previously, to have current or lifetime substance use (excluding cannabis), and have lower confidence in quitting. However, in multivariable analysis, smoker type was not significantly associated with cessation. The statistically significant predictors of cessation at 6 months were higher confidence in quitting and enrollment in the intensive counseling intervention.

conclusionsSmoker type (light-intermittent, light-daily, or heavy) does not independently predict success in a cessation program. However, smoker type is strongly associated with patients' confidence in quitting, which may be one predictor of cessation. IMPLICATIONS: This study of two safety-net hospitals emphasizes that the number of cigarettes smoked per day does not independently predict smoking cessation. Additionally, heavy smokers are at highest risk for the detrimental health effects of tobacco, yet have lower confidence and motivation to quit. Confidence in quitting may be one factor that affects cessation rates; however, further study is needed to identify which other attributes predict cessation. These findings suggest that smoker type may still be a useful proxy for predicting cessation and that interventions specifically designed for and validated in heavy smokers are needed to better aid these individuals.

Indexed as

CounselingFemaleHealth BehaviorHumansMaleMiddle AgedMotivationRetrospective StudiesSmokersSmokingSmoking CessationTelephone

Identifiers

PMID30535269
PMCPMC7297095

What OpenQuestion holds

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Registered trials

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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.