The long-term goal of the proposed line of research is to develop technology-based interventions that increase smoking cessation rates by delivering just-in-time support to minimize the likelihood that smoking triggers lead to smoking lapses. Towards this objective we propose to use continuous physiological and environmental time- series data obtained from sensors embedded in widely used consumer wearable technology to construct a predictive model for detecting antecedents of smoking events (e.g., stressors, smoking cues, etc). In this project, we will conduct an observational study of smokers during an ad libitum smoking period and during a period in which they attempt to quit smoking. During the observation period, smokers will wear three devices capable of collecting physiological and motion data. We hypothesize that consumer wearable technology can reliably capture physiological response to events that precede smoking during the ad libitum smoking period and precede lapse during the cessation period. Demonstrating that imminent smoking can be predicted would lead to the development and testing of just-in-time interventions that can be delivered via customized messaging on devices such as smartphones or smartwatches.
Current methods for smoking cessation result in low long-term cessation rates necessitating novel approaches to improve cessation success. The proposed project will use data from consumer wearable devices to identify physiological patterns that occur prior to smoking episodes in order to enable just-in-time delivery of interventions aimed at preventing relapse during smoking cessation attempts. The successful accomplishment of this project will lay the foundation for testing a broad range of behavioral and therapeutic interventions aimed at minimizing the likelihood that smoking triggers lead to smoking lapses and thereby improving the chances of successful smoking cessation.
