Cigarette smoking is the leading preventable cause of death and disability in the U.S.;worldwide it results in >5,000,000 premature deaths each year. The majority of smokers express a desire to quit, and each year more than half will. However, less than 7% of those who quit will achieve long-term abstinence. Much is known about the immediate and predictive antecedents of the first cigarette following a quit attempt (i.e. lapse), which include situations (e.g. presence of other smokers) and activities (e.g. alcohol consumption). While ideally this information could be used to develop a system that detects these predictive antecedents in real-time and provides just-in-time intervention;to date no such systems have been developed. The overarching goal of our research program is to develop and test such a system that uses smartphone technology. The system developed here can be incorporated into diverse application platforms that target the modification of smoking and multiple other health related behaviors including drug and alcohol use, food consumption and physical activity.
Despite the fact that smoking is a known cause of death and disability, many smokers find it very difficult to quit and those who do quit often return to smoking (i.e. lapse). Although the situational factors leading up to a lapse are well documented, treatments designed to interrupt these situations before relapse occurs have not been forthcoming. Here, we propose to develop a smartphone-based system that can detect these situations in real-time. Once developed, the sensor system can be incorporated into mobile smoking cessation applications designed to help smokers avoid relapse.
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|McClernon, F Joseph; Roy Choudhury, Romit (2013) I am your smartphone, and I know you are about to smoke: the application of mobile sensing and computing approaches to smoking research and treatment. Nicotine Tob Res 15:1651-4|