Manchester Metropolitan University's Research Repository

    Are Machine Learning Methods the Future for Smoking Cessation Apps?

    Abu-Tabik, Maryam, Benn, Yael ORCID logoORCID: https://orcid.org/0000-0001-7482-5927 and Costen, Nicholas ORCID logoORCID: https://orcid.org/0000-0001-9454-8840 (2021) Are Machine Learning Methods the Future for Smoking Cessation Apps? Sensors, 21 (4254). ISSN 1424-8220

    Published Version
    Available under License Creative Commons Attribution.

    Download (228kB) | Preview


    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.

    Impact and Reach


    Activity Overview
    6 month trend
    6 month trend

    Additional statistics for this dataset are available via IRStats2.


    Repository staff only

    Edit record Edit record