Evidence map›Paper›PMID 35284022›Full record

ArticleJournal of smoking cessation2022

Modeling Health Event Impact on Smoking Cessation.

Edwin D Boudreaux, Erin O'Hea, Bo Wang, Eugene Quinn, Aaron L Bergman, Beth C Bock, Bruce M Becker

Abstract read
In one paragraph

Article in Journal of smoking cessation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Edwin D BoudreauxDepartments of Emergency Medicine, Psychiatry, And Quantitative Health Sciences, University of Massachusetts Medical School, Worcester MA, USA.ORCID https://orcid.org/0000-0002-3223-6371
Erin O'HeaDepartment of Psychology, Stonehill College, Easton MA, USA.ORCID https://orcid.org/0000-0002-3531-4275
Bo WangDepartment of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester MA, USA.ORCID https://orcid.org/0000-0001-5132-7685
Eugene QuinnDepartment of Mathematics, Stonehill College, Easton MA, USA.ORCID https://orcid.org/0000-0002-6987-5273
Aaron L BergmanDepartments of Emergency Medicine and Psychiatry, University of Massachusetts Medical School, Worcester MA, USA.ORCID https://orcid.org/0000-0002-7592-1432
Beth C BockThe Miriam Hospital, Department of Psychiatry & Human Behavior, Warren Alpert School of Medicine, Brown University, Providence RI, USA.ORCID https://orcid.org/0000-0002-4530-7618
Bruce M BeckerBehavioral and Social Science, The School of Public Health, Brown University, Providence RI, USA.ORCID https://orcid.org/0000-0001-6264-1004

Funding

The Sentinel Events Model: A Dynamic Model of Substance Use CessationR01DA023170 · NIDA · UNIV OF MED/DENT NJ-R W JOHNSON MED SCH · PI BOUDREAUX, EDWIN D · 2008 to 2012
$2.4M
NIDA NIH HHS R01 DA023170
6 · The paper itself

Abstract

Background: This study examined how cognitive and affective constructs related to an acute health event predict smoking relapse following an acute cardiac health event. Methods: Participants were recruited from emergency departments and completed cognitive and emotional measures at enrollment and ecological momentary assessments (EMA) for 84 days postvisit. Results: Of 394 participants, only 35 (8.9%) remained abstinent 84 days postvisit. Time to relapse was positively associated with age, actual illness severity, self-efficacy, and quit intentions. Conclusions: Older, seriously ill patients with strong confidence and intentions to quit smoking remain abstinent longer after discharge, but most still relapse within three months.

Identifiers

PMID35284022
PMCPMC8898873

What OpenQuestion holds

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LicenceCC BY
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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.