BACKGROUND: Nonadherence to smoking cessation medication is a frequent problem. Identifying pre-quit predictors of nonadherence may help explain nonadherence and suggest tailored interventions to address it.
International journal of environmental research and public health
Dec 23, 2017
(1) Background: Many smokers completely switch to vaping (switchers), whereas others use e-cigarettes (e-cigs) alongside tobacco cigarettes (dual users). To the extent that dual users substantially lower the number of cigarettes, they will reduce hea...
BACKGROUND: Outside health care, content tailoring is driven algorithmically using machine learning compared to the rule-based approach used in current implementations of computer-tailored health communication (CTHC) systems. A special class of machi...
BACKGROUND: This study examined the long-term efficacy of individualized counseling letters that targeted either smoking abstinence or reducing the number of cigarettes smoked per day to promote future cessation.
International journal of medical informatics
Oct 1, 2025
BACKGROUND: Tobacco use remains a significant global health challenge, contributing substantially to preventable morbidity and mortality. Despite established interventions, outcomes vary due to scalability issues, resource constraints, and limited re...
Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
Mar 16, 2020
AIMS: Most cigarette smokers want to quit smoking and more than half make an attempt every year, but less than 10% remain abstinent for at least 6 months. Evidence-based tobacco use treatment improves the likelihood of quitting, but more than two-thi...
Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
Jan 4, 2019
INTRODUCTION: Machine learning algorithms such as elastic net regression and backward selection provide a unique and powerful approach to model building given a set of psychosocial predictors of smoking lapse measured repeatedly via ecological moment...
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