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The acceptability of and real time engagement with a context-aware smartphone smoking cessation app (Q Sense)

First published: 10 May 2019 | Last updated: 20 May 2019

Aims: We have developed a smartphone app (Q Sense) that uses real time logging to learn about the location and context of smoking behaviour leading up to a smoker’s quit date. Then, during their quit attempt, when the smoker enters or dwells in an identified smoking location (‘geofence’), the app triggers support messages, tailored to context data. We assessed the app’s acceptability among smokers and their speed of response to geofence-triggered support messages.

Design/Setting/Participants/Measurements: Smokers (N=43) were recruited through stop smoking services and online, and invited to use Q Sense pre and 4-weeks post quit-date. Data from three sources were analysed: the app, before and after surveys, and one-to-one interviews (n=9), analysed thematically.

Findings: Four participants withdrew. Of those followed up (30/39), three-quarters said they would use the app again. Only 13% felt the app was not easy to use, but almost half (47%) felt it was not always convenient to record smoking in real time, particularly when working and socialising. Few participants (13%) felt the messages inadvertently reminded them about smoking and 23% had some concerns about data privacy. Interviews indicated that participants considered the app a ‘friend’ or a convenient alternative to in-person support. Improvement suggestions included increased support duration and tailoring. 70% of eligible participants received geofence-triggered support messages and viewed messages were seen within a median of 4.5 minutes, significantly quicker than for routine daily messages (p<0.001).

Conclusion: Most participants considered the context-aware app to be acceptable and demonstrated relatively rapid engagement with context-triggered support.

Co-Authors

Miss Sarah Hopewell 1 Dr Neal Lathia 2 Mr Rik Schalbroeck 1 Dr Chloë Brown 2 Professor Cecilia Mascolo 2 Dr Andy McEwen 3 and Professor Stephen Sutton 1 1 Behavioural Science Group, University of Cambridge, Forvie Site, Cambridge, CB2 0SR, UK 2 Computer Laboratory, University of Cambridge, William Gates Building, Cambridge, CB3 0FD 3 Health Behaviour Research Centre, University College London, 1-19 Torrington Place, London, WC1E 6BT and the National Centre for Smoking Cessation and Training , 1-6 Yarmouth Place, London, W1J 7BU

Conflicts of interest:

Funding Sources: Medical Research Council, Society for the Study of Addiction

No conflict of interest

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Report Author

Dr Felix Naughton

Senior Research Associate, Behavioural Science Group, University of Cambridge


I am a health psychologist and addiction researcher with a primary research interest in investigating how new technologies, particularly mobile phones, can support smoking-related behaviour change. Mobile phone behaviour change interventions have real potential to make a significant public health impact given their wide-reach, low-cost and potential to intervene in real-time; our mobiles are rarely more than an arm’s reach away from us. The fellowship I have received from the SSA will enable me continue my work, with the help of colleagues and collaborators, to investigate the potential to address important gaps in NHS smoking cessation support with mobile phone-based smoking cessation interventions: https://vimeo.com/194363311

Support gap 1 – pregnant smokers have a narrow range of cessation support options

Most pregnant smokers are interested in receiving support to help them quit. However, only a small proportion engage with the NHS Stop Smoking Services. Mobile phone-based self-help support could bypass many of the barriers to accessing these services. During my PhD I developed and evaluated an automated SMS text message programme to help pregnant smokers quit smoking (MiQuit), which tailors self-help support to the individual’s characteristics. During the fellowship I will explore, with colleagues, who and how many pregnant smokers would make use of this type of support programme when made available in different real-world settings (antenatal, retail and online). Using data from a randomised controlled trial, I will also assess whether the MiQuit programme influences smoking behaviour in the way we expect e.g. via changing smoking-related beliefs. This will help us refine it to increase its effectiveness.

Support gap 2 – pregnant smokers have low adherence to Nicotine Replacement Therapy

While self-help can be effective for some pregnant smokers, for others nicotine withdrawal can significantly hamper their efforts to quit. Nicotine Replacement Therapy (NRT) is recommended for use in pregnancy by the National Institute for Health and Care Excellence (NICE) for those failing to quit using other methods. However, clinical trials have found that NRT without significant adherence support results in less than 20% of pregnant smokers using it continuously. I will explore why pregnant smokers discontinue using NRT by analysing one-to-one qualitative interview data and assess what types of adherence interventions might be effective by reviewing the literature. I will then design a prototype intervention for promoting adherence to NRT using tailored text messaging, which could be used independently or in combination with other support (e.g. MiQuit).

Support gap 3 – lack of support for smokers to manage cues to smoke from their environment

Most smoking cessation support available to smokers is not synchronised with when a smoker most needs advice or support. This is an important gap as triggers or cues to smoke from a smoker’s environment are implicated in almost half of all smoking lapses. With computer science colleagues, I have designed a smartphone app (Q Sense) which combines location sensor data from the phone with a smoking logging system to learn about the locations and context of individuals’ smoking behaviour. Q Sense then delivers real-time behavioural support triggered by a smoker’s proximity to a location the app has learned is high risk for smoking for that individual. During the fellowship I will coordinate studies exploring the feasibility and acceptability of Q Sense to inform its continued development and will analyse Q Sense data to expand on what little we know about the micro-environmental triggers of smoking.

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