Hojjatinia Sahar, Daly Elyse R, Hnat Timothy, Hossain Syed Monowar, Kumar Santosh, Lagoa Constantino M, Nahum-Shani Inbal, Samiei Shahin Alan, Spring Bonnie, Conroy David E
School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, 16802, USA.
Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
NPJ Digit Med. 2021 Nov 23;4(1):162. doi: 10.1038/s41746-021-00532-2.
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.
自我报告表明压力会增加吸烟风险；然而，来自传感器的密集数据能够更细致入微地理解在吸烟事件前后的瞬间压力情况。识别压力与吸烟反应的个性化动态模型有助于改进对压力后吸烟反应的特征描述，但用于识别这些模型的技术需要密集的纵向数据。本研究利用可穿戴传感技术以及压力和吸烟的数字标记方面的进展，通过考虑吸烟事件之前、期间和之后的压力，来识别压力与吸烟系统动态的个人特定模型。成年吸烟者（n = 45）在尝试戒烟前三天佩戴了AutoSense胸带（呼吸感应体积描记器、心电图、加速度计），并在每只手腕上佩戴了MotionSense（加速度计、陀螺仪）。将分钟级吸烟事件的几率与分钟级压力概率进行回归分析，以识别吸烟对压力反应的个人特定动态模型。对持续压力事件的模拟脉搏反应显示，对于所有参与者来说，在模拟压力事件开始后不久或有延迟时，吸烟几率会出现一致的增加模式。此后，约一半的参与者（49%）吸烟概率会大幅下降。传感器检测到的压力概率表明存在吸烟易感性，这可作为即时干预的定制变量，以支持戒烟尝试。
