Introduction: While large proportions of smokers attempt to quit, rates of relapse remain high and identification of valid prognostic markers is of high priority. Delayed reward discounting (DRD) is a behavioral economic index of impulsivity that has been associated with smoking cessation, albeit inconsistently. This systematic review sought to synthesize the empirical findings on DRD as a predictor of smoking cessation treatment outcome, to critically appraise the quality of the literature, and to propose directions for future research.
Aims And Methods: A total of 734 articles were identified, yielding k = 14 studies that met the eligibility criteria. The Quality in Prognosis Studies (QUIPS) tool was used to assess methodological quality of the included studies.
Results: Individual study methods were highly heterogeneous, including substantial variation in research design, DRD task, clinical subpopulation, and treatment format. The predominant finding was that steeper DRD (higher impulsivity) was associated with significantly worse smoking cessation outcomes (10/14 studies). Negative results tended to be in pregnant and adolescent subpopulations. The QUIPS results suggested low risk of bias across studies; 11/14 studies were rated as low risk of bias for 5/6 QUIPS domains.
Conclusions: This review revealed consistent low-bias evidence for impulsive DRD as a negative prognostic predictor of smoking cessation treatment outcome in adults. However, methodological heterogeneity was high, precluding meta-analysis and formal tests of small study bias. The prospects of targeting impulsive DRD as a potentially modifiable risk factor or providing targeted treatment for smokers exhibiting high levels of discounting are discussed.
Implications: These findings indicate consistent evidence for DRD as a negative prognostic factor for smoking cessation outcome in adults. As such, DRD may be a useful as a novel treatment target or for identifying high-risk populations requiring more intensive treatment.
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http://dx.doi.org/10.1093/ntr/ntab052 | DOI Listing |
Radiologie (Heidelb)
January 2025
Klinik für Diagnostische, und Interventionelle Neuroradiologie, Universitätsklinikum des Saarlandes, Kirrberger Straße, 66424, Homburg-Saar, Deutschland.
Stroke is one of the most common causes of disability in older adults. It remains a common cause of death and permanent functional limitation in individuals who are older than 80 years. Approximately 50% of all strokes occur in people over the age of 75, and 30% in those over 85.
View Article and Find Full Text PDFFront 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.
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