Evidence map›Paper›PMID 30169740›Full record

Trial reportNicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco2019

COach2Quit: A Pilot Randomized Controlled Trial of a Personal Carbon Monoxide Monitor for Smoking Cessation.

Nandita Krishnan, Jessica L Elf, Sandy Chon, Jonathan E Golub

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 5 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

24 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
  2. Smoking Cessation Apps: A Systematic Review of Format, Outcomes, and Features.International journal of environmental research and public health · 2021
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  19. Mobile, Remote, and Individual Focused: Comparing Breath Carbon Monoxide Readings and Abstinence Between Smartphone-Enabled and Stand-Alone Monitors.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2021
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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.

Nandita KrishnanDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.
Jessica L ElfSchroeder Institute for Tobacco Research and Policy Studies, Washington, DC.
Sandy ChonCenter for Tuberculosis Research, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD.
Jonathan E GolubDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.

Funding

Institute for Clinical and Translational ResearchUL1TR001079 · NCATS · JOHNS HOPKINS UNIVERSITY · PI FORD, DANIEL ERNEST · 2013 to 2017
$60.1M
NCATS NIH HHS UL1 TR001079
6 · The paper itself

Abstract

introductionMobile phone-based messaging support and biomarker feedback independently show evidence of increasing an individual's likelihood of quitting smoking. However, the combination of these two strategies to facilitate smoking cessation has not been adequately explored.

methodsWe conducted a randomized controlled trial in Baltimore, Maryland, to assess the efficacy of COach2Quit, a smartphone application that provides exhaled carbon monoxide readings with message support. The primary outcome was self-reported and biochemically verified smoking cessation at 30-day follow-up. Secondary outcomes were reduction in smoking, motivation to quit, and engagement and satisfaction with COach2Quit. An intention-to-treat analysis was conducted.

resultsAdult smokers were randomized 1:1 to receive brief advice and COach2Quit (intervention, n = 50) or brief advice only (control, n = 52). Thirteen participants were lost to follow-up. At 30-day follow-up, one participant in each arm quit smoking. Median change in carbon monoxide levels (in parts per million (ppm)) (intervention: -3.0 [interquartile range (IQR) -12.0, 2.0]; control: -2.5 [IQR -9.0, 2.0]) and median change in number of cigarettes smoked per day (intervention: -5.5 [IQR -14.0, -1.0]; control: -6.0 [IQR -10.0, -2.0]) was similar between study arms. There was no significant difference in mean percent change in the Reasons for Quitting scale score (intervention: 6.3 [95% confidence interval = -2.2% to 14.8%]; control: -3.6 [95% confidence interval = -9.2% to 2.1%]). A majority (n = 32, 91%) of participants liked having COach2Quit to help them quit smoking.

conclusionsThere were no significant differences in smoking cessation, smoking reduction, and motivation to quit between study arms. However, high satisfaction with the COach2Quit application indicates its feasibility and acceptability as a smoking cessation tool. IMPLICATIONS: Smoking is the leading preventable cause of morbidity and mortality in the United States. Although counseling and pharmacotherapy are efficacious for smoking cessation, they are not easily accessible or desirable to all smokers, highlighting the need for identifying other interventions. There is evidence for the efficacy of mobile phone-based messaging support for smoking cessation. However, there is limited research on the efficacy of biomarker feedback, much less interventions that combine these two approaches. This research contributes to filling this gap and identifying novel interventions to facilitate smoking cessation.

Indexed as

Smoking CessationBaltimoreBiofeedback, PsychologyBiomarkersCarbon MonoxideCell PhoneFemaleHumansMaleMiddle AgedPilot ProjectsTobacco Use DisorderBiomarkersCarbon Monoxide

Identifiers

PMID30169740
PMCPMC6821366

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.