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Smoking Cessation

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Differences between Dual Users and Switchers Center around Vaping Behavior and Its Experiences Rather than Beliefs and Attitudes.

International journal of environmental research and public health
(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...

Predictors of adherence to nicotine replacement therapy: Machine learning evidence that perceived need predicts medication use.

Drug and alcohol dependence
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.

Effectiveness of a chat-bot for the adult population to quit smoking: protocol of a pragmatic clinical trial in primary care (Dejal@).

BMC medical informatics and decision making
BACKGROUND: The wide scale and severity of consequences of tobacco use, benefits derived from cessation, low rates of intervention by healthcare professionals, and new opportunities stemming from novel communications technologies are the main factors...

Towards a Smart Smoking Cessation App: A 1D-CNN Model Predicting Smoking Events.

Sensors (Basel, Switzerland)
Nicotine consumption is considered a major health problem, where many of those who wish to quit smoking relapse. The problem is that overtime smoking as behaviour is changing into a habit, in which it is connected to internal (e.g., nicotine level, c...

A Machine-Learning Approach to Predicting Smoking Cessation Treatment Outcomes.

Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
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...

Predicting the first smoking lapse during a quit attempt: A machine learning approach.

Drug and alcohol dependence
BACKGROUND: Just-in-time adaptive interventions (JITAI) aim to prevent smoking lapse using tailored support delivered via mobile technology in the moments when it is most needed. Effective smoking cessation JITAI rely on the development of accurate d...

Impact of deep learning-determined smoking status on mortality of cancer patients: never too late to quit.

ESMO open
BACKGROUND: Persistent smoking after cancer diagnosis is associated with increased overall mortality (OM) and cancer mortality (CM). According to the 2020 Surgeon General's report, smoking cessation may reduce CM but supporting evidence is not wide. ...

Individual Predictors of Response to A Behavioral Activation-Based Digital Smoking Cessation Intervention: A Machine Learning Approach.

Substance use & misuse
Depression is prevalent among individuals who smoke cigarettes and increases risk for relapse. A previous clinical trial suggests that Goal2Quit, a behavioral activation-based smoking cessation mobile app, effectively increases smoking abstinence an...

Testing a Machine Learning-Based Adaptive Motivational System for Socioeconomically Disadvantaged Smokers (Adapt2Quit): Protocol for a Randomized Controlled Trial.

JMIR research protocols
BACKGROUND: Individuals who are socioeconomically disadvantaged have high smoking rates and face barriers to participating in smoking cessation interventions. Computer-tailored health communication, which is focused on finding the most relevant messa...

Exploring the ChatGPT platform with scenario-specific prompts for vaping cessation.

Tobacco control
OBJECTIVE: To evaluate and start a discussion on the potential usefulness of applying Artificial Intelligence (AI)-driven natural language processing technology such as the ChatGPT in tobacco control efforts, specifically vaping cessation.