Accruing evidence of an association between drinking and smoking relapse suggests that it is important to measure alcohol use in smoking cessation studies. However, most studies do not do so, often because of the extra time burden required for these assessments. Data from participants (N=634) in two smoking cessation clinical trials were used to examine the relationship between short and longer periods of monitoring for a number of Timeline Followback (TLFB) drinking metrics at baseline and during treatment. High intercorrelations were found between short (7 and 14 days) and longer (30 and 60 days) time windows for baseline drinking data. Intercorrelations between short (last 7 days of treatment) and longer (entire treatment period) time windows of drinking data during the smoking cessation treatment period were also mostly in the high range. Although total abstinence was significantly overestimated with shorter time windows, for those who were misclassified, percentage of days abstinent was high and percentage of heavy drinking days and number of drinks per drinking day were low during the longer period. Thus, a brief estimate of alcohol use over 7 days at baseline is likely to provide a representative assessment of percentage of days abstinent, percentage of heavy drinking days, and number of drinks per drinking day. To estimate abstinence at baseline and during treatment, however, a more comprehensive period of monitoring may be required.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2577001PMC
http://dx.doi.org/10.1016/j.addbeh.2008.07.007DOI Listing

Publication Analysis

Top Keywords

smoking cessation
16
time windows
12
estimate alcohol
8
cessation clinical
8
clinical trials
8
drinking
8
baseline treatment
8
intercorrelations short
8
days
8
short days
8

Similar Publications

Introduction: Smoking causes lung cancer and a wide range of acute and chronic diseases annually throughout the world. A fourth-generation behavioral framework, namely the Multi-Theory Model (MTM) of health behavior change was used to predict the initiation and maintenance of smoking cessation among health worker smokers.

Methods: A cross-sectional study of 170 smoking healthcare workers was conducted in Kabul.

View Article and Find Full Text PDF

Purpose: This study explored the effect of four different smoking statuses (non-smokers, moderate smokers, heavy smokers, and former smokers) on health-related quality of life (HRQOL) among residents aged 15 years and older in Sichuan Province, China with consideration of potential differences among age groups (young, middle-aged, and older adults).

Methods: The EQ-5D-5L utility index and EQ-VAS score were used to measure HRQOL. Self-reporting and salivary cotinine test were used to determine the smoking status of respondents, and the Tobit regression model was used to explore the relationship between smoking status and HRQOL.

View Article and Find Full Text PDF

Background: Individuals with mental health disorders face major barriers in accessing smoking cessation care, often due to the stigmas associated with mental disorders and addiction. Consequently, accessible population-based smoking cessation interventions are needed for this vulnerable group.

Objective: This secondary analysis utilized data from a 12-month randomized trial to examine whether an acceptance and commitment therapy-based app (iCanQuit) demonstrated greater efficacy, engagement, and satisfaction compared to a United States (US) Clinical Practice Guidelines-based app (QuitGuide) in helping adults with mental health disorders quit smoking.

View Article and Find Full Text PDF

Harnessing machine learning in contemporary tobacco research.

Toxicol Rep

June 2025

Division of Molecular Medicine, Bose Institute, Kolkata 700054, India.

Machine learning (ML) has the potential to transform tobacco research and address the urgent public health crisis posed by tobacco use. Despite the well-documented health risks, cessation rates remain low. ML techniques offer innovative solutions by analyzing vast datasets to uncover patterns in smoking behavior, genetic predispositions, and effective cessation strategies.

View Article and Find Full Text PDF

Background: Young chronic obstructive pulmonary disease (COPD) refers to people with COPD between the ages of 20 and 50 years. Current epidemiological studies focus on local geography, and there is a lack of global-level analysis. This study provides in-depth analyses of the disease burden of young COPD at global, regional, and national levels.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!