Dynamic models of stress-smoking responses based on high-frequency sensor data

Abstract

Background and Aims: Self-reports indicate that stress increases 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 episodes. Design: Observational study. Methods: 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. Findings: 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%). Conclusions: 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.

Dynamical Outcome Modeling of Fragmented Intensive Longitudinal Data

“…Moreover, such a framework should allow for undetermined and/or variable lags. For example, a stress event not only has an immediate effect on the probability of smoking but it can also have a lingering effect that increases the probability of smoking for some time interval following the stress event (Hojjatinia, Daly, et al, 2021). One should note that at this point, there has been some attempts to extend existing approaches in order to deal with these delayed effects.…”