Literature DB >> 31415971

An electronic, smart lighter to measure cigarette smoking: A pilot study to assess feasibility and initial validity.

Rachel L Tomko1, Erin A McClure2, Patrick A Cato2, Julie B Wang3, Matthew J Carpenter4, Joshua L Karelitz5, Brett Froeliger6, Michael E Saladin7, Kevin M Gray2.   

Abstract

Understanding variability in smoking patterns may inform smoking cessation interventions. Retrospective reports of cigarettes smoked per day may be biased and typically do not provide temporal precision regarding when cigarettes are smoked. However, real-time, user-initiated tracking, such as logging each time a cigarette is smoked, can be burdensome over long time frames. In this study, adult, non-treatment seeking daily smokers (N = 22) used an electronic, smart lighter to light and timestamp cigarettes for 14 days. Participants reported number of cigarettes smoked per day (CPD) via a mobile device (daily diary) and retrospectively reported CPD at the end of the study using the Timeline Followback (TLFB). Self-reported lighter satisfaction and adherence varied with 68% of participants reporting that they liked using the lighter and participants reporting using the lighter for 92% of cigarettes smoked, on average. Lighter-estimated CPD did not differ from daily diary-estimated CPD, but was significantly lower than TLFB estimates. The lighter resulted in greater day-to-day variability relative to other methods and fewer rounded cigarette counts (digit bias) relative to the TLFB. The lighter appears to be feasible for capturing data on smoking patterns in daily smokers. Though false positive cigarettes are likely low, additional technologies that augment data captured from the lighter may be necessary to reduce false negatives (missed cigarettes) and alternative lighter designs may appeal more to certain smokers.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Ambulatory assessment; Cigarettes; Daily diary; Methodology; Technology; Timeline followback

Mesh:

Year:  2019        PMID: 31415971      PMCID: PMC6708757          DOI: 10.1016/j.addbeh.2019.106052

Source DB:  PubMed          Journal:  Addict Behav        ISSN: 0306-4603            Impact factor:   3.913


  34 in total

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3.  Validation of the timeline follow-back in the assessment of adolescent smoking.

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Review 4.  Does smoking reduction increase future cessation and decrease disease risk? A qualitative review.

Authors:  John R Hughes; Matthew J Carpenter
Journal:  Nicotine Tob Res       Date:  2006-12       Impact factor: 4.244

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Authors:  Siddharth Chandra; Saul Shiffman; Deborah M Scharf; Qianyu Dang; William G Shadel
Journal:  Exp Clin Psychopharmacol       Date:  2007-02       Impact factor: 3.157

6.  Agreement between methods of measurement with multiple observations per individual.

Authors:  J Martin Bland; Douglas G Altman
Journal:  J Biopharm Stat       Date:  2007       Impact factor: 1.051

7.  Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

Authors:  Paul A Harris; Robert Taylor; Robert Thielke; Jonathon Payne; Nathaniel Gonzalez; Jose G Conde
Journal:  J Biomed Inform       Date:  2008-09-30       Impact factor: 6.317

8.  Patterns of intermittent smoking: An analysis using Ecological Momentary Assessment.

Authors:  Saul Shiffman; Thomas R Kirchner; Stuart G Ferguson; Deborah M Scharf
Journal:  Addict Behav       Date:  2009-01-31       Impact factor: 3.913

9.  The Fagerström Test for Nicotine Dependence: a revision of the Fagerström Tolerance Questionnaire.

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Journal:  Br J Addict       Date:  1991-09

10.  Patterns of smoking and methadone dose in drug treatment patients.

Authors:  Kimber P Richter; Ashley K Hamilton; Sandra Hall; Delwyn Catley; Lisa S Cox; James Grobe
Journal:  Exp Clin Psychopharmacol       Date:  2007-04       Impact factor: 3.157

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  5 in total

1.  Making pharmacotherapy trials for substance use disorder more efficient: Leveraging real-world data capture to maximize power and expedite the medication development pipeline.

Authors:  Ryan W Carpenter; Lindsay M Squeglia; Noah N Emery; Erin A McClure; Kevin M Gray; Robert Miranda; Rachel L Tomko
Journal:  Drug Alcohol Depend       Date:  2020-02-05       Impact factor: 4.492

2.  Mobile, Remote, and Individual Focused: Comparing Breath Carbon Monoxide Readings and Abstinence Between Smartphone-Enabled and Stand-Alone Monitors.

Authors:  Breanna M Tuck; Joshua L Karelitz; Rachel L Tomko; Jennifer Dahne; Patrick Cato; Erin A McClure
Journal:  Nicotine Tob Res       Date:  2021-03-19       Impact factor: 4.244

3.  Comparing video observation to electronic topography device as a method for measuring cigarette puffing behavior.

Authors:  Melissa Mercincavage; Joshua L Karelitz; Catherine L Kreider; Valentina Souprountchouk; Benjamin Albelda; Andrew A Strasser
Journal:  Drug Alcohol Depend       Date:  2021-02-17       Impact factor: 4.492

4.  Measurement of cigarette smoking: Comparisons of global self-report, returned cigarette filters, and ecological momentary assessment.

Authors:  Jenny E Ozga; Colleen Bays; Ilana Haliwa; Nicholas J Felicione; Stuart G Ferguson; Geri Dino; Melissa D Blank
Journal:  Exp Clin Psychopharmacol       Date:  2021-02-25       Impact factor: 3.492

5.  Characterizing and Modeling Smoking Behavior Using Automatic Smoking Event Detection and Mobile Surveys in Naturalistic Environments: Observational Study.

Authors:  DongHui Zhai; Ruud van Stiphout; Giuseppina Schiavone; Walter De Raedt; Chris Van Hoof
Journal:  JMIR Mhealth Uhealth       Date:  2022-02-18       Impact factor: 4.947

  5 in total

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