Purpose: Smoking during pregnancy and/or not breastfeeding have considerable negative health outcomes for the mother and infant. This descriptive and cross-sectional study determined the relationship between the prediction of smoking cessation success in pregnant women and their breastfeeding attrition prediction during lactation. The other aim of the study was to determine the predictor of smoking cessation success and the factors affecting breastfeeding attrition prediction.
Methods: The present study was conducted with 131 smoking pregnant women. Data were collected using the Personal Information Form, the Smoking Cessation Success Prediction Scale, and the Breastfeeding Attrition Prediction Tool.
Results: A statistically significant and positive correlation was revealed between the Smoking Cessation Success Prediction Scale and the positive breastfeeding attitude (r = 0.349, P < .01). Of the change in positive breastfeeding attitudes, 14.7% was explained by the prediction of smoking cessation success (adjusted R2 = 0.147).
Conclusion: The study revealed that the prediction of smoking cessation success increased with an increase in the positive breastfeeding attitude of smoking pregnant women.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1097/JPN.0000000000000703 | DOI Listing |
Front Public Health
January 2025
Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
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.
Front Public Health
January 2025
HEOA Group, School of Public Health, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
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.
Depress Anxiety
January 2025
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
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.
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 PDFBMC Public Health
January 2025
Department of Respiratory Medicine, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China.
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 PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!