The app is powered by a data analytics software platform that processes information from the sensors embedded in wearables. This software platform relies on an original algorithm to identify in real-time hand-to-mouth gestures that characterise smoking a cigarette.
The app not only records when the user smokes, but also uses cognitive behavioural therapy (CBT) to motivate the user to change their smoking habits. Using machine learning and big-data analytics, the app creates different tailored incentives for the user to help them quit, including financial – how much money they can save if they didn’t buy cigarettes, as well as emotional, rational and social incentives.
“When you place data in front of people about how much they actually smoke and the financial cost and health cost, without any external intervention we see people reducing their smoking volume,” said Co-founder and CEO of Somatix, Eran Ofir, during an interview with ISRAEL21c.
“We have found, as psychologists working with us predicted, that just the mindfulness effect of the app causes a decrease of 15 to 20% of smoking volume and applying CBT techniques adds another 40%,” continued Ofir.
Through the app users can and also view and share their smoking statistics with their doctors, get alerts, set goals, compare progress to others and receive credits and incentives if it’s linked to a health plan.
“The clinician can see everything about the habits of the person – how much and how long and where and when, how many cigarettes per day, week and month – without patients having to enter all that data themselves,” said Ofir.
A study published in the Oxford University Press journal Nicotine and Tobacco Research in November 2017 demonstrated positive results that monitoring and notifying smokers about smoking episodes immediately via the SmokeBeat app led to a reduction in smoking.
As part of the pilot study, 40 smokers, (nine women and 31 men) who expressed a goal to reduce or quit smoking were assigned randomly the SmokeBeat app for 30 days or to a wait-list control group. All participants completed questionnaires at baseline and at the end of the study, including their level of smoking during the test period. Smokers in the experimental group were notified whenever the SmokeBeat system detected a smoking episode and were asked to confirm or deny it.
According to the study, the SmokeBeat algorithm correctly detected more than 80% of the smoking episodes and produced few false alarms.
Another international study is currently underway using smokers in the US, Canada, France, Israel and Turkey. Once the clinical data is validated in early 2018, Somatix plans on marketing SmokeBeat to employee-benefits programmes, outpatient clinics and insurance companies as a tool to improve compliance with prescribed cessation therapies.