Imagine this scenario: Andrew is a smoker and is currently trying to quit. During his previous quit attempts he has really struggled when he spends time in places or with people where he would usually smoke. We catch up with Andrew one week after he started his quit attempt. After getting out of bed in the morning, Andrew checks his phone and there is a message congratulating him on being smoke free for one week. As Andrew parks his car at work later that morning he hears an alert from his phone, which is a message reminding him of his agreement not to smoke, not even a puff, and includes some advice on some things he can do instead of smoking when at work. After work, he goes for a pint with some colleagues in the pub closest to his workplace. As he crosses the road in front of the pub he hears his phone alert. He reads his message as he is waiting at the bar which warns him how socialising with his smoking colleagues is likely to make him want to smoke. The message also reminds him about his reasons for quitting and recommends that if he gets a craving to smoke he should do something like leave his colleagues to make a quick phone call until the craving goes.

So who is sending Andrew these messages? A supportive partner or close family member? A stop smoking advisor who knows Andrew’s routine inside and out? Or a mobile sensing app which identifies when Andrew is in a location where he used to regularly smoke, using sensors common to practically all smartphones, and then delivers advice and support in real-time.

You’ve guessed it, it was the app. This novel app, called Q Sense, is being developed by researchers in the Behavioural Science Group and the Computer Laboratory, University of Cambridge. It provides a smoking logging system, used in the lead up to the smoker’s specified quit date, to record the presence of several key environmental and psychological triggers in each of their smoking locations. Then come quit day, it uses this information to tailor advice messages which are delivered when the soon-to-be ex-smoker (fingers crossed) is in close proximity to one of these smoking locations.

As it happens, Andrew’s difficulties aren’t unique to him – almost half of all smoking lapses are attributed to cues to smoke from a person’s environment. Unfortunately the most commonly used smoking cessation medications, i.e. Varenicline/Champix and nicotine patches, do not appear to help smokers deal with these triggers, though there is some evidence that ‘acutely administered’ medications such as nicotine gum and lozenges can help. So with very little individualised support on offer to help smokers deal with these powerful situational cues, this issue represents a substantial missing part of the support jigsaw.

One of the most exciting aspects of mobile sensing is that it offers the opportunity to tailor the support given to people wanting to change their health behaviours, such as smoking, diet, physical activity and drinking, to what they are actually doing at any particular time. However, a major challenge now facing us is in knowing what advice is likely to be most effective at each particular time for each particular person.  Currently the science is way behind the technology. Perhaps it’s time that we use this technology to enable smokers to help the science element catch up?

What do you think? If you are a smoker/ex-smoker do you think this would help/have helped?

 

Felix Naughton