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

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.

Acceptability of smartphone technology to interrupt sedentary time in adults with diabetes.

Breaking up sitting time with light- or moderate-intensity physical activity may help to alleviate some negative health effects of sedentary behavior, but few studies have examined ways to effectively intervene. This feasibility study examined the acceptability of a new technology (NEAT!) developed to interrupt prolonged bouts (≥20 min) of sedentary time among adults with type 2 diabetes. Eight of nine participants completed a 1-month intervention and agreed that NEAT! made them more conscious of sitting time.