{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T20:35:04Z","timestamp":1779395704901,"version":"3.53.1"},"reference-count":28,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,2,17]],"date-time":"2020-02-17T00:00:00Z","timestamp":1581897600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s daily routine and predict smoking events. The model\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s20041099","type":"journal-article","created":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T03:20:03Z","timestamp":1582168803000},"page":"1099","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Towards a Smart Smoking Cessation App: A 1D-CNN Model Predicting Smoking Events"],"prefix":"10.3390","volume":"20","author":[{"given":"Maryam","family":"Abo-Tabik","sequence":"first","affiliation":[{"name":"Department of Computing and Mathematics, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester M15 6BH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas","family":"Costen","sequence":"additional","affiliation":[{"name":"Department of Computing and Mathematics, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester M15 6BH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Darby","sequence":"additional","affiliation":[{"name":"Department of Computing and Mathematics, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester M15 6BH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yael","family":"Benn","sequence":"additional","affiliation":[{"name":"Department of Psychology, Manchester Metropolitan University, Manchester M15 6GX, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,17]]},"reference":[{"key":"ref_1","unstructured":"NHS (2018). Adult Smoking Habits in the UK: 2016."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Abo-Tabik, M., Costen, N., Darby, J., and Benn, Y. (2019, January 19\u201323). Decision Tree Model of Smoking Behaviour. Proceedings of the 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation. IEEE, 2019, Leicester, UK.","DOI":"10.1109\/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00311"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e17","DOI":"10.2196\/mhealth.5181","article-title":"Design considerations for smoking cessation apps: Feedback from nicotine dependence treatment providers and smokers","volume":"4","author":"McClure","year":"2016","journal-title":"JMIR mHealth uHealth"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e106","DOI":"10.2196\/mhealth.5787","article-title":"A context-sensing mobile phone app (Q sense) for smoking cessation: A mixed-methods study","volume":"4","author":"Naughton","year":"2016","journal-title":"JMIR mHealth uHealth"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e275","DOI":"10.2196\/jmir.6307","article-title":"Using intensive longitudinal data collected via mobile phone to detect imminent lapse in smokers undergoing a scheduled quit attempt","volume":"18","author":"Businelle","year":"2016","journal-title":"J. Med. Internet Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1186\/s40814-017-0165-4","article-title":"MapMySmoke: Feasibility of a new quit cigarette smoking mobile phone application using integrated geo-positioning technology, and motivational messaging within a primary care setting","volume":"4","author":"Schick","year":"2018","journal-title":"Pilot Feasibility Stud."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.cmpb.2016.09.016","article-title":"Classifying smoking urges via machine learning","volume":"137","author":"Dumortier","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, J., Zhang, Z., and Huang, J. (2019, January 12\u201314). Prediction of Daily Smoking Behavior Based on Decision Tree Machine Learning Algorithm. Proceedings of the 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC), Beijing, China.","DOI":"10.1109\/ICEIEC.2019.8784698"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1111\/spc3.12071","article-title":"\u2018The Ostrich Problem\u2019: Motivated avoidance or rejection of information about goal progress","volume":"7","author":"Webb","year":"2013","journal-title":"Soc. Personal. Psychol. Compass"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Fibla, M.S., Bernardet, U., and Verschure, P.F. (2010, January 18\u201322). Allostatic control for robot behaviour regulation: An extension to path planning. Proceedings of the Intelligent Robots and Systems (IROS), 2010 IEEE\/RSJ International Conference on, Taipei, Taiwan.","DOI":"10.1109\/IROS.2010.5652866"},{"key":"ref_11","unstructured":"Hughes, J.M. (2010). Real World Instrumentation with Python: Automated Data Acquisition and Control Systems, O\u2019Reilly Media, Inc."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gutkin, B., and Ahmed, S.H. (2011). Computational Neuroscience of Drug Addiction, Springer Science & Business Media.","DOI":"10.1007\/978-1-4614-0751-5"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.drugalcdep.2007.01.008","article-title":"The simulation of addiction: Pharmacological and neurocomputational models of drug self-administration","volume":"90","author":"Ahmed","year":"2007","journal-title":"Drug Alcohol Depend."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1155\/2012\/817485","article-title":"From occasional choices to inevitable musts: A computational model of nicotine addiction","volume":"2012","author":"Metin","year":"2012","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1037\/0003-066X.35.8.691","article-title":"The opponent-process theory of acquired motivation: The costs of pleasure and the benefits of pain","volume":"35","author":"Solomon","year":"1980","journal-title":"Am. Psychol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bobashev, G., Holloway, J., Solano, E., and Gutkin, B. (2017). A Control Theory Model of Smoking. Methods Rep. (RTI Press), 2017.","DOI":"10.3768\/rtipress.2017.op.0040.1706"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Timms, K.P., Rivera, D.E., Collins, L.M., and Piper, M.E. (2013, January 17\u201319). Control systems engineering for understanding and optimizing smoking cessation interventions. Proceedings of the 2013 American Control Conference, Washington, DC, USA.","DOI":"10.1109\/ACC.2013.6580123"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/08898480009525471","article-title":"Reconstructing susceptible and recruitment dynamics from measles epidemic data","volume":"8","author":"Bobashev","year":"2000","journal-title":"Math. Population Stud."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1007\/s00521-018-3724-6","article-title":"Deep learning model for home automation and energy reduction in a smart home environment platform","volume":"31","author":"Popa","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zahid, M., Ahmed, F., Javaid, N., Abbasi, R.A., Kazmi, Z., Syeda, H., Javaid, A., Bilal, M., Akbar, M., and Ilahi, M. (2019). Electricity price and load forecasting using enhanced convolutional neural network and enhanced support vector regression in smart grids. Electronics, 8.","DOI":"10.3390\/electronics8020122"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_22","unstructured":"Kalchbrenner, N., Grefenstette, E., and Blunsom, P. (2020, February 15). A Convolutional Neural Network for Modelling Sentences. Available online: https:\/\/arxiv.org\/abs\/1404.2188."},{"key":"ref_23","unstructured":"Nguyen, T.H., and Grishman, R. (June, January 31). Relation extraction: Perspective from convolutional neural networks. Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing, Denver, Colorado."},{"key":"ref_24","unstructured":"Sorokin, M., Zhdanov, A., and Zhdanov, D. (2020, February 15). Recovery of Optical Parameters of a Scene Using Fully-Convolutional Neural Networks. Available online: http:\/\/ceur-ws.org\/Vol-2344\/short1.pdf."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Deka, B. (2019). Pattern Recognition and Machine Intelligence: 8th International Conference, PReMI 2019, Tezpur, India, December 17\u201320, 2019, Proceedings, Part I, Springer Nature.","DOI":"10.1007\/978-3-030-34872-4"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.knosys.2017.10.017","article-title":"Deep learning for freezing of gait detection in Parkinson\u2019s disease patients in their homes using a waist-worn inertial measurement unit","volume":"139","author":"Camps","year":"2018","journal-title":"Knowl. Based Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s12160-013-9486-6","article-title":"The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: Building an international consensus for the reporting of behavior change interventions","volume":"46","author":"Michie","year":"2013","journal-title":"Ann. Behav. Med."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.inffus.2006.10.009","article-title":"A new boosting algorithm for improved time-series forecasting with recurrent neural networks","volume":"9","author":"Assaad","year":"2008","journal-title":"Inf. Fusion"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/4\/1099\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:58:33Z","timestamp":1760173113000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/4\/1099"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,17]]},"references-count":28,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["s20041099"],"URL":"https:\/\/doi.org\/10.3390\/s20041099","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,17]]}}}