Are Machine Learning Methods the Future for Smoking Cessation Apps?
| Autor: | Maryam Abo-Tabik, Nicholas Costen, Yael Benn |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
020205 medical informatics
medicine.medical_treatment mobile computing Mobile computing Craving 02 engineering and technology TP1-1185 Machine learning computer.software_genre Biochemistry Quit smoking GeneralLiterature_MISCELLANEOUS smoking Analytical Chemistry 03 medical and health sciences 0302 clinical medicine Intervention (counseling) 0202 electrical engineering electronic engineering information engineering medicine Humans 030212 general & internal medicine Electrical and Electronic Engineering Instrumentation Smokers Data collection business.industry Chemical technology deep learning Mobile Applications Atomic and Molecular Physics and Optics smoking cessation machine learning Mobile phone Perspective Smoking cessation Artificial intelligence medicine.symptom smoking cessation apps Psychology business computer |
| Zdroj: | Sensors, Vol 21, Iss 4254, p 4254 (2021) Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| Popis: | 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’s 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. |
| Databáze: | OpenAIRE |
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