Introduction: Smartphone applications (apps) for smoking cessation are becoming increasingly available, but their efficacy remains to be demonstrated. We conducted a pilot study of SmokeBeat, a novel app designed for use with smartwatches and wristbands. SmokeBeat is powered by a data analytics software platform that processes information from the sensors embedded in wearables. It relies on an original algorithm to identify in real time the hand-to-mouth gestures that characterize smoking a cigarette. We examined whether merely monitoring and notifying smokers on smoking episodes in real time via the SmokeBeat app would lead to reduction in smoking.
Methods: Forty smokers (9 women and 31 men) who expressed a wish to reduce or quit smoking were randomly assigned to using 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. Smokers in the experimental condition were notified whenever the SmokeBeat system detected a smoking episode and were asked to confirm or deny it.
Results: 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.
Conclusion: These preliminary results suggest that automatic monitoring of smoking episodes and alerting the smoker in real time may facilitate smoking reduction in motivated smokers.
Implications: Raising the awareness of smokers to the act of smoking in real time, as the SmokeBeat app is able to do, can counter the automaticity of the smoking habit. Bringing smoking under conscious awareness may benefit smokers who are motivated to reduce or quit smoking to gain better control of their smoking behavior and reduce cigarette intake.