ArticleSensors (Basel, Switzerland)2020
Towards a Smart Smoking Cessation App: A 1D-CNN Model Predicting Smoking Events.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- Neuropharmacology of Nicotine Addiction and Therapeutic Strategies for Smoking Cessation.Biology · 2026Review
- Smartphone movement data can reliably predict smoking lapses and cravings to enable timely smoking cessation support.Scientific reports · 2026Article
- Relative importance of temporal and location features in predicting smoking events.NPJ digital medicine · 2025Article
- Harnessing machine learning in contemporary tobacco research.Toxicology reports · 2025Review
- Unveiling the Influence of AI on Advancements in Respiratory Care: Narrative Review.Interactive journal of medical research · 2024Review
- Article
- Towards Predicting Smoking Events for Just-in-time Interventions.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Using Smartphone Survey and GPS Data to Inform Smoking Cessation Intervention Delivery: Case Study.JMIR mHealth and uHealth · 2023Article
- Classification of Lapses in Smokers Attempting to Stop: A Supervised Machine Learning Approach Using Data From a Popular Smoking Cessation Smartphone App.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2023Article
- Predicting food craving in everyday life through smartphone-derived sensor and usage data.Frontiers in digital health · 2023Article
- The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN).Sensors (Basel, Switzerland) · 2022Article
- Prediction of Pulmonary Function Parameters Based on a Combination Algorithm.Bioengineering (Basel, Switzerland) · 2022Article
- Prediction of Smoking Risk From Repeated Sampling of Environmental Images: Model Validation.Journal of medical Internet research · 2021Article
- Are Machine Learning Methods the Future for Smoking Cessation Apps?Sensors (Basel, Switzerland) · 2021Article
- Trends, Technologies, and Key Challenges in Smart and Connected Healthcare.IEEE access : practical innovations, open solutions · 2021Article
- Radiomics for Gleason Score Detection through Deep Learning.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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, craving) and external (action, time, location) triggers. Smoking cessation apps have proved their efficiency to support smoking who wish to quit smoking. However, still, these applications suffer from several drawbacks, where they are highly relying on the user to initiate the intervention by submitting the factor the causes the urge to smoke. This research describes the creation of a combined Control Theory and deep learning model that can learn the smoker's daily routine and predict smoking events. The model's structure combines a Control Theory model of smoking with a 1D-CNN classifier to adapt to individual differences between smokers and predict smoking events based on motion and geolocation values collected using a mobile device. Data were collected from 5 participants in the UK, and analysed and tested on 3 different machine learning model (SVM, Decision tree, and 1D-CNN), 1D-CNN has proved it's efficiency over the three methods with average overall accuracy 86.6%. The average MSE of forecasting the nicotine level was (0.04) in the weekdays, and (0.03) in the weekends. The model has proved its ability to predict the smoking event accurately when the participant is well engaged with the app.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.