Abstract
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.
Highlights
Smoking is considered a major public health problem with over 1.3 billion smokers worldwide [1]
This article explored methods for developing smoking cessation apps, highlighting the need to improve our understanding of smokers’ craving factors, in particular as they change through the quitting period, in order to improve targeted and timely interventions
We further explored how data that enable the mapping of smoking and craving patterns may be collected, as well as the importance of sharing existing datasets
Summary
Smoking is considered a major public health problem with over 1.3 billion smokers worldwide [1]. Typical smartphones are equipped with multiple sensors that are able to collect information such as the user location (Global Positioning System, GPS), identify other people who are near the user (by detecting Bluetooth signals), identifying users’ movements (walking/driving, etc., via onboard accelerometers) and potentially additional information (such as specific arm movements), if connected to other devices such as smartwatches, which have shown an accelerating increase in use around the world [11] Combining this information with smoking events can enable the characterisation of smoking behaviours to be generated for individual smokers [12]. This article provides an overview of up-to-date methods and techniques that have been used to implement smoking cessation apps, highlights the gaps in current approaches and suggests methods that can improve interventions and users’ experience by making better use of advances in mobile technology and machine learning models
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