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Academic Fellowship: Q Sense: A context aware smartphone sensing app for smoking cessation

First published: 29 March 2019 | Last updated: 20 May 2019

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Dr Felix Naughton

Senior Research Associate, Behavioural Science Group, University of Cambridge

I am a health psychologist and Senior Research Associate based within the Behavioural Science Group at the University of Cambridge. My primary research interest is in investigating how new technologies, particularly mobile phones, can support smoking-related behaviour change. I currently hold an SSA fellowship that enables me, with colleagues and collaborators, to investigate the potential of mobile phone-based smoking cessation interventions to address important gaps in NHS smoking cessation support. This work mainly focuses on impact and implementation evaluations of a tailored text message cessation support system for pregnant smokers (MiQuit), an effectiveness evaluation of a tailored text message cessation support system for use alongside routine NHS smoking cessation support (iQuit in Practice), the feasibility and acceptability of a context aware smartphone sensing app for smoking cessation (Q Sense) and the development of a mobile phone based Nicotine Replacement Therapy adherence support system for pregnant smokers.

Context aware smartphone apps for behaviour change have become a reality due to a rapidly growing smartphone market and the presence of sensors on smartphones as standard. In the field of smoking behaviour, such apps have the potential to address a major gap in treatment: addressing cues from an individual smoker’s environment which bring about cravings to smoke. Such instances are estimated to account for almost half of all smoking lapses. This talk will describe a novel smoking cessation sensing app (Q Sense). Q Sense builds on two theory-guided tailored SMS text message support systems for smoking cessation (MiQuit and iQuit) and combines their existing tailored message libraries with a smartphone sensing system. A core feature of the app is the delivery of real time behavioural support triggered by a smoker’s proximity to a pre-identified high risk location for a cue-induced craving episode, using location sensors in their smartphone and geofencing. The findings from a mixed methods feasibility study (funded by the Medical Research Council and supported by the SSA) will be drawn on to provide insight into smokers’ usage (quantitative) and experiences (qualitative) of the Q Sense app. This study aimed to assess the feasibility of user-initiated data recording, accuracy of the app’s identification of high risk situations (geofences), experiences of geofence triggered support delivery and views on technological and data privacy issues.

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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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