Smoking is the leading cause of preventable death in the US. Effective smoking cessation interventions are available but underutilized. Smoking cessation interventions delivered by smartphone apps are a promising tool for helping smokers quit. Delivery of treatments via smartphone apps may maximize the likelihood of use by smokers and the potential impact on smoking behavior. However, currently available smartphone apps for smoking cessation have not exploited their unique potential advantages to aid quitting. Notably, no available apps utilize wearable technologies; all current apps require users to self-report their smoking; and no apps deliver treatment automatically contingent upon smoking. Therefore, this pilot trial will test the feasibility of using a smartband to detect and track smoking and deliver brief smoking cessation interventions by smartphone app in real time. The interventions to be delivered will be brief mindfulness exercises that have been previously shown to reduce craving and smoking. This trial uses SmokeBeat, a novel mobile technology platform that uses multimodal data from wristband sensors to monitor and detect smoking, notify smokers about their smoking in real time and deliver real time interventions triggered by detected smoking episodes. SmokeBeat also applies machine learning to smoking tracking data to identify individual smoking patterns and deliver real time interventions targeted at predicted smoking episodes. This trial tests a three-step intervention to reduce smoking, in which smokers first become aware of their smoking and triggers by tracking smoking; then gain a clear recognition of the actual effects of smoking by ?mindful smoking?; and finally learn to work mindfully with cravings rather than smoke. Briefly, daily smokers (N=200, ?5 cig/day) will wear a smartband to detect and notify them of smoking for 21 days and obtain individual smoking profiles; detected smoking will then trigger a ?mindful smoking? exercise for the next 7 days leading up to their quit date at 30 days; after which another mindfulness exercise (?RAIN?: recognize, accept, investigate and note cravings rather than smoke) will be delivered prior to each predicted smoking episode according to their individual smoking profile for 30 days post-quit.
Aim 1 will be to determine treatment fidelity. Fidelity measures will be: (1) percent of smoking episodes correctly detected; (2) percent of ?mindful smoking? exercises correctly triggered by smoking; and (3) users? real time ratings of how timely ?RAIN? was delivered to predicted smoking episodes.
Aim 2 will be to determine adherence to treatment. Adherence measures will be: (1) percent of time spent wearing the smartband; (2) percent of smoking notifications answered; (3) percent of ecological momentary assessment (EMA) ratings (e.g., timeliness and others) answered; and (4) percent of mindfulness exercises completed.
Aim 3 will be to determine the acceptability of treatment. Acceptability measures will be: (1) average helpfulness ratings after each mindfulness exercise; (2) feedback on user experience surveys. Overall: this project tests a highly innovative technology-based mindfulness intervention for smoking cessation.

Public Health Relevance

Recent developments in mobile technology suggest new and innovative ways to deliver effective smoking cessation treatments. This project will test the feasibility of using a smartband to automatically monitor and detect smoking and deliver real time mindfulness interventions via smartphone app to reduce smoking. This pilot trial will provide important data and information to inform a larger clinical efficacy trial.

National Institute of Health (NIH)
National Center for Complementary & Alternative Medicine (NCCAM)
Planning Grant (R34)
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Special Emphasis Panel (ZAT1)
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Clark, David
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Yale University
Schools of Medicine
New Haven
United States
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