Background:
“SmokeBeat,” developed by SomatixTM, a big data analytics software company, is a body motion detection application. It detects and records real-time hand-to-mouth movements, and differentiates with high precision smoking a cigarette from other hand-to-mouth gestures such as eating, drinking, shaving, teeth brushing etc. In addition to its ability to detect smoking episodes, SmokeBeat has a potential as a smoking cessation app.

Methods:
Automated detection of smoking behavior not requiring smokers to cooperate: hand gestures of smoking are detected by a wearable, and data are transferred to the individual's smartphone and from there to the BMD™ (Body Motion Data) platform. The smoking behavior of a given individual is followed in real time by a researcher or a health care provider. SmokeBeat can provide feedback to the individual smoker about his/her current smoking behavior and can utilize cognitive-behavior therapy techniques (coping tactics, reminders, real time cost of smoking, etc) to help smokers quit.

Results:
40 smokers who expressed a wish to reduce or quit smoking were randomly assigned to use the SmokeBeat app for 30 days or to a wait-list control group. Smokers in the experimental condition were notified whenever the SmokeBeat system detected a smoking episode and were asked to confirm or deny it. The SmokeBeat algorithm correctly detected over 80% of the smoking episodes and produced very few false alarms. According to both self-report and detection of smoking episodes by the SmokeBeat system, smokers in the experimental condition showed a significant decline in smoking rate over the 30-day trial (p < .001). There was no change in the smoking rate of the control group.

Conclusions:
The real-time automatic monitoring and notification feature of SmokeBeat may follow up real time cigarette use and potentially facilitate smoking reduction or even cessation in motivated smokers.