Why the study?
Intensive sensor data can provide a more nuanced understanding of stress surrounding smoking events, and identifying personalized dynamic models requires intensive longitudinal data.
Can high-frequency wearable sensor data identify person-specific dynamic models of stress-smoking responses in adult smokers?
Can high-frequency wearable sensor data identify person-specific dynamic models of stress-smoking responses in adult smokers?
High-frequency sensor data can identify personalized dynamic models of stress-smoking responses, revealing that stress increases the immediate or delayed odds of smoking, which could inform just-in-time interventions for smoking cessation.
Supports wearable-derived person-specific stress-smoking models; hypothesis-generating for just-in-time interventions, needs prospective trials.
Self-reports indicate that stress increases the risk for smoking; however, intensive data from sensors can provide a more nuanced understanding of stress in the moments leading up to and following smoking events. Identifying personalized dynamical models of stress-smoking responses can improve characterizations of smoking responses following stress, but techniques used to identify these models require intensive longitudinal data. This study leveraged advances in wearable sensing technology and digital markers of stress and smoking to identify person-specific models of stress and smoking system dynamics by considering stress immediately before, during, and after smoking events. Adult smokers (n = 45) wore the AutoSense chestband (respiration-inductive plethysmograph, electrocardiogram, accelerometer) with MotionSense (accelerometers, gyroscopes) on each wrist for three days prior to a quit attempt. The odds of minute-level smoking events were regressed on minute-level stress probabilities to identify person-specific dynamic models of smoking responses to stress. Simulated pulse responses to a continuous stress episode revealed a consistent pattern of increased odds of smoking either shortly after the beginning of the simulated stress episode or with a delay, for all participants. This pattern is followed by a dramatic reduction in the probability of smoking thereafter, for about half of the participants (49%). Sensor-detected stress probabilities indicate a vulnerability for smoking that may be used as a tailoring variable for just-in-time interventions to support quit attempts.
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Hojjatinia et al. (2021) studied this question.
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