Helping smokers at times of need using smartphones
Health Psychologist Felix Naughton (School of Health Sciences, University of East Anglia) discusses the potential of context-aware smartphone apps in helping smokers quit smoking
Comprehensive tobacco control policies include smoking cessation treatment, but there is a key factor that explains why many smokers fail to quit smoking that almost all treatments fail to adequately address. Ask a smoker when is the most difficult time for them to resist smoking and they'll most likely describe a specific time and situation. When they're stressed at work or when they are with their friends or family that smoke. Such specific situations contain cues, sometimes outside the individual's awareness, that generate urges to smoke. These 'cue-induced' urges develop as a result of learned associations between cue (e.g. friend) and consequence (smoking) and account for roughly half of all lapses to smoking when people are trying to quit. A key study showed how half of all such lapses occur rapidly, within 11 minutes, after urge onset. All of this might not seem very surprising but it might be more surprising to learn that most formal smoking cessation treatments do not directly address these types of urges and high risk moments. Nicotine patches and Varenicline (Champix), the most commonly used smoking cessation medications, don't help in these situations. Faster acting nicotine replacements like gum and sprays can help, but these are less commonly used and often used inadequately. Studies have identified some effective psychological and behavioural strategies to help avoid or cope with cue-induced urges, such as avoiding other smokers, giving yourself a 'pep talk' ("I can do this") or going for a quick walk. But the problem is that relatively few smokers trying to quit make use of effective strategies when they are most at risk of smoking. However, technology may be able to plug this gap.
With all this in mind, we (a team of health psychologists and computer scientists at the University of Cambridge and University of East Anglia) developed an experimental context-aware smartphone app called Q Sense. The app is designed for smokers motivated to quit, who start using it approximately one-week before their nominated quit date. During this time, they log each time they smoke in real time, including the presence of key situational and psychological cues to smoke. At the same time, their smartphone captures the physical location of each smoking episode, using location sensors (e.g. GPS). If the person reports smoking twice in the same place, the app creates a 'geofence', a virtual perimeter, around that location. When their quit date arrives, the app triggers support messages in real time if and when they enter or spend time in any created geofences. Importantly, the advice and support Q Sense delivers is tailored to the smoking cues they are likely to be exposed to, based on what it learnt about their smoking behaviour via the logging system.
In a feasibility study, we found smokers were engaged in the process of teaching the app about their smoking. It took them approximately 13 seconds each time to report smoking, including the
presence/absence of situational cues, and for the phone to capture their location. There were times when the participants did not report smoking, either because they forgot, did not want to appear rude when around others or were constrained to use their phone (e.g. driving). Somewhat surprisingly, the participants did not indicate any privacy concerns about the location data the app was collecting, though the trustworthiness of the app developer was critical to this.
In a second larger study exploring the acceptability of Q Sense, we found that over three-quarters of participants said they would use the app again. One important question this study addressed was how long it took smokers to engage with real time support messages when in a high risk situation for smoking. Or put another way, it's one thing to have the technology to deliver support messages according to real time activities, but if people do not view those messages in a timely manner relative to exposure to smoking cues, this would not represent a very time-sensitive intervention. The study found that participants viewed 56% of the location triggered messages. So we were not always able to reach the participants. But for those messages they viewed, most were viewed within 5 minutes after delivery. So when people did engage, they engaged quickly. This is probably the first data indicating the speed of engagement of real time support delivered by a health app and shows we can support people quickly when they are at risk of unhealthy behaviours, though not all the time. The next important question for Q Sense is whether it changes smoking behaviour and promotes abstinence. Answering this question will require a large randomised controlled trial. But knowing we can reach people and deliver tailored support to them when they may be stressed at work or socialising with friends, and at high risk of smoking, is an important and exciting first step. A key benefit of apps like Q Sense, if proven to be effective, is their low cost and scalability, making them accessible to anyone with a smartphone across the globe.