Evidence map›Paper›PMID 31807784›Full record

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

Real-World Effectiveness of Smoking Cessation Strategies for Young and Older Adults: Findings From a Nationally Representative Cohort.

Shannon Lea Watkins, Johannes Thrul, Wendy Max, Pamela M Ling

Abstract read
In one paragraph

Article 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 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 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

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

4 authors.

Shannon Lea WatkinsDepartment of Community and Behavioral Health, University of Iowa College of Public Health, Iowa City, IA.
Johannes ThrulDepartment of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Wendy MaxInstitute for Health & Aging, School of Nursing, University of California San Francisco, San Francisco, CA.
Pamela M LingCenter for Tobacco Control Research and Education and Division of General Internal Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA.

Funding

Training and Education p489-500P50CA180890 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GLANTZ, STANTON ARNOLD · 2013 to 2018
$20.5M
PROJECT 5: IMPACT OF CHANGING TOBACCO PRODUCT USE ON HEALTHCARE COSTS FOR GENERAL AND VULNERABLE POPULATIONSU54HL147127 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LING, PAMELA MAY · 2018 to 2022
$20.4M
NCI NIH HHS P50 CA180890NHLBI NIH HHS U54 HL147127
6 · The paper itself

Abstract

introductionYoung adults have high combustible cigarette and e-cigarette use rates, and low utilization of evidence-based smoking cessation strategies compared to older adults. It is unknown whether young adults who try to quit smoking without assistance, with evidence-based strategies, or with e-cigarettes, are equally successful compared to older adults. AIMS AND

methodsThis analysis used a population-based sample from the Population Assessment of Tobacco and Health study of young adult (aged 18-24, n = 745) and older adult (aged 25-64, n = 2057) established cigarette smokers at Wave 1 (2013-2014) who reported having made a quit attempt at Wave 2 (2014-2015). Cessation strategies were: behavioral therapy, pharmacotherapy, product substitution, 2+ strategies, and unassisted. Logistic regression estimated associations between cessation strategy and short-term cessation status at Wave 2 (quit, no quit); multinomial logistic regression predicted long-term cessation patterns at Waves 2 and 3 (sustained quit, temporary quit, delayed quit, no quit).

resultsNo cessation strategy (ref: unassisted) significantly predicted short-term cessation. No cessation strategy (ref: unassisted) significantly predicted long-term cessation patterns for young adults. Substitution with e-cigarettes predicted short-term cessation for older daily smokers of ≥5 cigarettes/day (adjusted odds ratio [AOR]: 1.70; 95% confidence interval: 1.08, 2.67) but did not predict long-term cessation patterns.

conclusionsDespite differences in cessation strategy use between young and older adult smokers, strategy effectiveness largely did not differ by age group. No strategy examined, including e-cigarettes, was significantly associated with successful cessation for young adults. More work is needed to identify effective interventions that help young adult smokers quit. IMPLICATIONS: (1) Neither behavioral support, pharmacotherapy, nor product substitution was associated with short-term cessation for young or older adults compared to quitting unassisted. (2) Neither behavioral support, pharmacotherapy, nor product substitution was associated with longer-term cessation for young or older adults compared to quitting unassisted. (3) Substitution with e-cigarettes predicted short-term cessation for older daily smokers of ≥5 cigarettes/day but was not associated with longer-term cessation.

Indexed as

AdolescentAdultBehavior TherapyCohort StudiesElectronic Nicotine Delivery SystemsFemaleHealth BehaviorHumansMaleMiddle AgedPrognosisSmokersSmokingSmoking CessationUnited StatesYoung Adult

Identifiers

PMID31807784
PMCPMC7443598

What OpenQuestion holds

Textmetadata
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.