{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T17:36:58Z","timestamp":1783100218382,"version":"3.54.6"},"reference-count":89,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T00:00:00Z","timestamp":1624320000000},"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>Smoking cessation apps provide efficient, low-cost and accessible support to smokers who are trying to quit smoking. This article focuses on how up-to-date machine learning algorithms, combined with the improvement of mobile phone technology, can enhance our understanding of smoking behaviour and support the development of advanced smoking cessation apps. In particular, we focus on the pros and cons of existing approaches that have been used in the design of smoking cessation apps to date, highlighting the need to improve the performance of these apps by minimizing reliance on self-reporting of environmental conditions (e.g., location), craving status and\/or smoking events as a method of data collection. Lastly, we propose that making use of more advanced machine learning methods while enabling the processing of information about the user\u2019s circumstances in real time is likely to result in dramatic improvement in our understanding of smoking behaviour, while also increasing the effectiveness and ease-of-use of smoking cessation apps, by enabling the provision of timely, targeted and personalised intervention.<\/jats:p>","DOI":"10.3390\/s21134254","type":"journal-article","created":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T22:10:59Z","timestamp":1624399859000},"page":"4254","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Are Machine Learning Methods the Future for Smoking Cessation Apps?"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7067-6853","authenticated-orcid":false,"given":"Maryam","family":"Abo-Tabik","sequence":"first","affiliation":[{"name":"Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M1 5GD, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7482-5927","authenticated-orcid":false,"given":"Yael","family":"Benn","sequence":"additional","affiliation":[{"name":"Department of Psychology, Manchester Metropolitan University, Manchester M15 6GX, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9454-8840","authenticated-orcid":false,"given":"Nicholas","family":"Costen","sequence":"additional","affiliation":[{"name":"Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M1 5GD, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,22]]},"reference":[{"key":"ref_1","unstructured":"WHO (2021, June 18). Tobacco, Leading Cause of Death, Illness and Impoverishment. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/tobacco\/."},{"key":"ref_2","unstructured":"CDC (2021, June 18). Smoking Cessation: Fast Facts, Available online: https:\/\/www.cdc.gov\/tobacco\/data_statistics\/fact_sheets\/cessation\/smoking-cessation-fast-facts\/index.html."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1093\/ntr\/ntp198","article-title":"Do smokers crave cigarettes in some smoking situations more than others? Situational correlates of craving when smoking","volume":"12","author":"Dunbar","year":"2010","journal-title":"Nicotine Tob. Res."},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1037\/1064-1297.15.1.67","article-title":"Daily smoking patterns, their determinants, and implications for quitting","volume":"15","author":"Chandra","year":"2007","journal-title":"Exp. Clin. Psychopharmacol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.drugalcdep.2015.07.673","article-title":"Does laboratory cue reactivity correlate with real-world craving and smoking responses to cues?","volume":"155","author":"Shiffman","year":"2015","journal-title":"Drug Alcohol Depend."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Shiffman, S., Dunbar, M.S., Li, X., Scholl, S.M., Tindle, H.A., Anderson, S.J., and Ferguson, S.G. (2014). Smoking patterns and stimulus control in intermittent and daily smokers. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0089911"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.addbeh.2016.12.007","article-title":"Smoking environment cues reduce ability to resist smoking as measured by a delay to smoking task","volume":"67","author":"Stevenson","year":"2017","journal-title":"Addict. Behav."},{"key":"ref_9","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_10","unstructured":"Statista (2021, June 18). Number of Smartphone Users Worldwide from 2016 to 2026. Available online: https:\/\/www.statista.com\/statistics\/330695\/number-of-smartphone-users-worldwide\/."},{"key":"ref_11","unstructured":"Statista (2021, June 18). Smartwatches\u2014Statistics and Facts. Available online: https:\/\/www.statista.com\/topics\/4762\/smartwatches\/."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"120835","DOI":"10.1109\/ACCESS.2020.3006163","article-title":"A new approach for smoking event detection using a variational autoencoder and neural decision forest","volume":"8","author":"Fan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1016\/j.simpat.2009.09.002","article-title":"Mobile health monitoring system based on activity recognition using accelerometer","volume":"18","author":"Hong","year":"2010","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1750","DOI":"10.1109\/JBHI.2017.2649602","article-title":"Automatic prediction of health status using smartphone-derived behavior profiles","volume":"21","author":"Kelly","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, F., Wang, R., Zhou, X., and Campbell, A.T. (2014, January 16). My smartphone knows I am hungry. Proceedings of the 2014 Workshop on Physical Analytics, Bretton Woods, NH, USA.","DOI":"10.1145\/2611264.2611270"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e21","DOI":"10.2196\/humanfactors.8905","article-title":"Development of a Just-in-Time adaptive mHealth intervention for insomnia: Usability study","volume":"5","author":"Pulantara","year":"2018","journal-title":"JMIR Hum. Factors"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1007\/s11695-013-1010-3","article-title":"Obesity surgery smartphone apps: A review","volume":"24","author":"Stevens","year":"2014","journal-title":"Obes. Surg."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1002\/eat.22386","article-title":"Development of a smartphone application for eating disorder self-monitoring","volume":"48","author":"Tregarthen","year":"2015","journal-title":"Int. J. Eat. Disord."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1258\/jtt.2011.101102","article-title":"Smartphone applications for pain management","volume":"17","author":"Rosser","year":"2011","journal-title":"J. Telemed. Telecare"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1027\/1016-9040\/a000351","article-title":"Integrating behavioral science with mobile (mhealth) technology to optimize health behavior change interventions","volume":"24","author":"Walsh","year":"2019","journal-title":"Eur. Psychol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ferdous, R., Osmani, V., and Mayora, O. (2015, January 20\u201323). Smartphone app usage as a predictor of perceived stress levels at workplace. Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), Istanbul, Turkey.","DOI":"10.4108\/icst.pervasivehealth.2015.260192"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1007\/s13142-015-0352-x","article-title":"Characterising smoking cessation smartphone applications in terms of behaviour change techniques, engagement and ease-of-use features","volume":"6","author":"Ubhi","year":"2016","journal-title":"Transl. Behav. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.drugalcdep.2014.07.006","article-title":"Randomized, controlled pilot trial of a smartphone app for smoking cessation using acceptance and commitment therapy","volume":"143","author":"Bricker","year":"2014","journal-title":"Drug Alcohol Depend."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1651","DOI":"10.1093\/ntr\/ntt054","article-title":"I am your smartphone, and I know you are about to smoke: The application of mobile sensing and computing approaches to smoking research and treatment","volume":"15","author":"McClernon","year":"2013","journal-title":"Nicotine Tob. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1016\/j.amepre.2013.07.008","article-title":"A content analysis of popular smartphone apps for smoking cessation","volume":"45","author":"Abroms","year":"2013","journal-title":"Am. J. Prev. Med."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1007\/s13142-017-0492-2","article-title":"A systematic review of smartphone applications for smoking cessation","volume":"7","author":"Haskins","year":"2017","journal-title":"Transl. Behav. Med."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"L\u00fcscher, J., Berli, C., Schwaninger, P., and Scholz, U. (2019). Smoking cessation with smartphone applications (SWAPP): Study protocol for a randomized controlled trial. BMC Public Health, 19.","DOI":"10.1186\/s12889-019-7723-z"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e17","DOI":"10.2196\/jmir.3479","article-title":"A mobile app to aid smoking cessation: Preliminary evaluation of SmokeFree28","volume":"17","author":"Ubhi","year":"2015","journal-title":"J. Med. Internet Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e031204","DOI":"10.1136\/bmjopen-2019-031204","article-title":"Design, development and randomised controlled trial of a smartphone application,\u2018QinTB\u2019, for smoking cessation in tuberculosis patients: Study protocol","volume":"9","author":"Lin","year":"2019","journal-title":"BMJ Open"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e12694","DOI":"10.2196\/12694","article-title":"Impact of a novel smartphone app (CureApp Smoking Cessation) on nicotine dependence: Prospective single-arm interventional pilot study","volume":"7","author":"Masaki","year":"2019","journal-title":"JMIR mHealth uHealth"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1016\/j.pec.2017.11.006","article-title":"Missing the mark for patient engagement: mHealth literacy strategies and behavior change processes in smoking cessation apps","volume":"101","author":"Paige","year":"2018","journal-title":"Patient Educ. Couns."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"12","DOI":"10.18332\/tpc\/70088","article-title":"Effectiveness of mobile apps for smoking cessation: A review","volume":"3","author":"Regmi","year":"2017","journal-title":"Tob. Prev. Cessat."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1007\/s40429-019-00248-0","article-title":"Mobile applications for the treatment of tobacco use and dependence","volume":"6","author":"Vilardaga","year":"2019","journal-title":"Curr. Addict. Rep."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1037\/0022-006X.65.2.292.a","article-title":"Remember that? A comparison of real-time versus retrospective recall of smoking lapses","volume":"65","author":"Shiffman","year":"1997","journal-title":"J. Consult. Clin. Psychol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.addbeh.2017.10.026","article-title":"An ecological momentary intervention for smoking cessation: The associations of just-in-time, tailored messages with lapse risk factors","volume":"78","author":"Stevens","year":"2018","journal-title":"Addict. Behav."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1093\/ntr\/ntx225","article-title":"The time-varying relations between risk factors and smoking before and after a quit attempt","volume":"20","author":"Koslovsky","year":"2018","journal-title":"Nicotine Tob. Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.1093\/ntr\/nty142","article-title":"Level of Alcohol Consumption and Successful Smoking Cessation","volume":"21","author":"Lynch","year":"2019","journal-title":"Nicotine Tob. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1037\/hea0000797","article-title":"Momentary precipitants connecting stress and smoking lapse during a quit attempt","volume":"38","author":"Cambron","year":"2019","journal-title":"Health Psychol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.amepre.2013.05.016","article-title":"Geospatial exposure to point-of-sale tobacco: Real-time craving and smoking-cessation outcomes","volume":"45","author":"Kirchner","year":"2013","journal-title":"Am. J. Prev. Med."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","unstructured":"Stankevich, E., Paramonov, I., and Timofeev, I. (2012, January 5\u20139). Mobile phone sensors in health applications. Proceedings of the 12th Conference of Open Innovations Association (FRUCT), Oulu, Finland.","DOI":"10.23919\/FRUCT.2012.8122097"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Acien, A., Morales, A., Vera-Rodriguez, R., and Fierrez, J. (2020, January 13\u201317). Smartphone sensors for modeling human-computer interaction: General outlook and research datasets for user authentication. Proceedings of the IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain.","DOI":"10.1109\/COMPSAC48688.2020.00-81"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3374","DOI":"10.1109\/ACCESS.2020.3045935","article-title":"Smartphone Sensing for the Well-being of Young Adults: A Review","volume":"9","author":"Meegahapola","year":"2020","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.compbiomed.2018.09.025","article-title":"The mobile sleep lab app: An open-source framework for mobile sleep assessment based on consumer-grade wearable devices","volume":"103","author":"Burgdorf","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_47","unstructured":"Li, P., Abdel-Aty, M., Cai, Q., and Islam, Z. (2020). A Deep Learning Approach to Detect Real-Time Vehicle Maneuvers Based on Smartphone Sensors. IEEE Trans. Intell. Transp. Syst."},{"key":"ref_48","unstructured":"Milette, G., and Stroud, A. (2012). Professional Android Sensor Programming, John Wiley & Sons."},{"key":"ref_49","unstructured":"Nagpal, V. (2016). Android Sensor Programming By Example, Packt Publishing Ltd."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.jbi.2017.12.008","article-title":"Systematic review of smartphone-based passive sensing for health and wellbeing","volume":"77","author":"Cornet","year":"2018","journal-title":"J. Biomed. Inform."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.drugpo.2016.05.013","article-title":"Realising the technological promise of smartphones in addiction research and treatment: An ethical review","volume":"36","author":"Capon","year":"2016","journal-title":"Int. J. Drug Policy"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"e12649","DOI":"10.2196\/12649","article-title":"Passive sensing of health outcomes through smartphones: Systematic review of current solutions and possible limitations","volume":"7","author":"Trifan","year":"2019","journal-title":"JMIR mHealth uHealth"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"e23","DOI":"10.2196\/mhealth.3209","article-title":"A mobile app offering distractions and tips to cope with cigarette craving: A qualitative study","volume":"2","author":"Ploderer","year":"2014","journal-title":"JMIR mHealth uHealth"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1940-0640-7-9","article-title":"A randomized trial evaluating an mHealth system to monitor and enhance adherence to pharmacotherapy for alcohol use disorders","volume":"7","author":"Stoner","year":"2012","journal-title":"Addict. Sci. Clin. Pract."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1109\/JBHI.2015.2446195","article-title":"Automatic Stress Detection in Working Environments From Smartphones\u2019 Accelerometer Data: A First Step","volume":"20","author":"Osmani","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.chb.2016.10.027","article-title":"Patterns of behavior change in students over an academic term: A preliminary study of activity and sociability behaviors using smartphone sensing methods","volume":"67","author":"Harari","year":"2017","journal-title":"Comput. Hum. Behav."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2279","DOI":"10.1109\/TMC.2018.2797901","article-title":"DrinkSense: Characterizing youth drinking behavior using smartphones","volume":"17","author":"Santani","year":"2018","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"e15610","DOI":"10.2196\/15610","article-title":"Reducing drinking among people experiencing homelessness: Protocol for the development and testing of a just-in-time adaptive intervention","volume":"9","author":"Businelle","year":"2020","journal-title":"JMIR Res. Protoc."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.gaitpost.2017.11.019","article-title":"Using phone sensors and an artificial neural network to detect gait changes during drinking episodes in the natural environment","volume":"60","author":"Suffoletto","year":"2018","journal-title":"Gait Posture"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.addbeh.2017.11.039","article-title":"Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions","volume":"83","author":"Bae","year":"2018","journal-title":"Addict. Behav."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.geoderma.2017.06.020","article-title":"Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques","volume":"305","author":"Chen","year":"2017","journal-title":"Geoderma"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1148\/rg.2017160130","article-title":"Machine learning for medical imaging","volume":"37","author":"Erickson","year":"2017","journal-title":"Radiographics"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.eswa.2011.07.035","article-title":"Decision tree models for characterizing smoking patterns of older adults","volume":"39","author":"Moon","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Mak, K.K., Lee, K., and Park, C. (2019). Applications of machine learning in addiction studies: A systematic review. Psychiatry Res.","DOI":"10.1016\/j.psychres.2019.03.001"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.1007\/s10899-014-9490-1","article-title":"Using opinions and knowledge to identify natural groups of gambling employees","volume":"31","author":"Gray","year":"2015","journal-title":"J. Gambl. Stud."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1016\/j.addbeh.2012.05.010","article-title":"Improved methods to identify stable, highly heritable subtypes of opioid use and related behaviors","volume":"37","author":"Sun","year":"2012","journal-title":"Addict. Behav."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1111\/biom.12792","article-title":"Bayesian variable selection for multistate Markov models with interval-censored data in an ecological momentary assessment study of smoking cessation","volume":"74","author":"Koslovsky","year":"2018","journal-title":"Biometrics"},{"key":"ref_68","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_69","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 IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC), Beijing, China.","DOI":"10.1109\/ICEIEC.2019.8784698"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1093\/ntr\/nty259","article-title":"A machine-learning approach to predicting smoking cessation treatment outcomes","volume":"22","author":"Coughlin","year":"2020","journal-title":"Nicotine Tob. Res."},{"key":"ref_71","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 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld\/SCALCOM\/UIC\/ATC\/CBDCom\/IOP\/SCI), Leicester, UK.","DOI":"10.1109\/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00311"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Gavankar, S.S., and Sawarkar, S.D. (2017, January 7\u20139). Eager decision tree. Proceedings of the 2nd International Conference for Convergence in Technology (I2CT), Mumbai, India.","DOI":"10.1109\/I2CT.2017.8226246"},{"key":"ref_73","unstructured":"Breiman, L., Friedman, J., Stone, C.J., and Olshen, R.A. (1984). Classification and Regression Trees, CRC Press."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"27435","DOI":"10.1109\/ACCESS.2020.2971693","article-title":"Identification of walker identity using smartphone sensors: An experiment using ensemble learning","volume":"8","author":"Angrisano","year":"2020","journal-title":"IEEE Access"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Bouktif, S., Fiaz, A., Ouni, A., and Serhani, M.A. (2018). Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches. Energies, 11.","DOI":"10.3390\/en11071636"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Ord\u00f3\u00f1ez, F.J., and Roggen, D. (2016). Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors, 16.","DOI":"10.3390\/s16010115"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Zahid, M., Ahmed, F., Javaid, N., Abbasi, R.A., Zainab Kazmi, H.S., 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_78","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_79","doi-asserted-by":"crossref","unstructured":"Abo-Tabik, M., Costen, N., Darby, J., and Benn, Y. (2020). Towards a smart smoking cessation app: A 1D-CNN model predicting smoking events. Sensors, 20.","DOI":"10.3390\/s20041099"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Benouis, M., Abo-Tabik, M., Benn, Y., Salmon, O., Barret-Chapman, A., and Costen, N. (2019, January 19\u201323). Behavioural Smoking Identification via Hand-Movement Dynamics. Proceedings of the IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld\/SCALCOM\/UIC\/ATC\/CBDCom\/IOP\/SCI), Leicester, UK.","DOI":"10.1109\/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00309"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1093\/ntr\/nty008","article-title":"StopWatch: The preliminary evaluation of a smartwatch-based system for passive detection of cigarette smoking","volume":"21","author":"Skinner","year":"2019","journal-title":"Nicotine Tob. Res."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"e189","DOI":"10.2196\/mhealth.9035","article-title":"Detecting smoking events using accelerometer data collected via smartwatch technology: Validation study","volume":"5","author":"Cole","year":"2017","journal-title":"JMIR mHealth uHealth"},{"key":"ref_83","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_84","doi-asserted-by":"crossref","unstructured":"Davagdorj, K., Lee, J.S., Pham, V.H., and Ryu, K.H. (2020). A comparative analysis of machine learning methods for class imbalance in a smoking cessation intervention. Appl. Sci., 10.","DOI":"10.3390\/app10093307"},{"key":"ref_85","unstructured":"Hochreiter, S., and Schmidhuber, J. (1996, January 2\u20135). LSTM Can Solve Hard Long Time Lag Problems. Proceedings of the 9th International Conference on Neural Information Processing Systems, Denver, CO, USA."},{"key":"ref_86","unstructured":"Singh, D., Merdivan, E., Psychoula, I., Kropf, J., Hanke, S., Geist, M., and Holzinger, A. (September, January 29). Human activity recognition using recurrent neural networks. Proceedings of the International Cross-Domain Conference for Machine Learning and Knowledge Extraction, Reggio, Italy."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhong, K., Zhang, J., Sun, Q., and Zhao, X. (2016, January 24\u201325). LSTM networks for mobile human activity recognition. Proceedings of the International Conference on Artificial Intelligence: Technologies and Applications, Bangkok, Thailand.","DOI":"10.2991\/icaita-16.2016.13"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.cmpb.2019.05.004","article-title":"A new approach for arrhythmia classification using deep coded features and LSTM networks","volume":"176","author":"Yildirim","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1071\/BI9570484","article-title":"Simulation of genetic systems by automatic digital computers I. Introduction","volume":"10","author":"Fraser","year":"1957","journal-title":"Aust. J. Biol. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4254\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:20:34Z","timestamp":1760163634000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4254"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,22]]},"references-count":89,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["s21134254"],"URL":"https:\/\/doi.org\/10.3390\/s21134254","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,22]]}}}