Predicting Smoking Abstinence via Mobile Monitoring of Stress and Social Context

描述（由申请人提供）：吸烟是一种主要的可预防的死亡原因，仅在美国每年就有五分之一的人死于吸烟。许多成年吸烟者想要或尝试戒烟，但由于缺乏最终导致成功戒烟的个性化戒烟治疗而无法做到。主要的障碍一直是技术的不可用性，可用于收集连续测量的压力环境在现场的预测禁欲和复发。没有这些测量，很难理解心理生理和生物行为的压力机制，可以预测烟雾消失。该应用程序提出使用一种新的现场部署工具，称为AutoSense，该工具是根据NIH的GEI计划开发的，用于收集个人心理生理压力测量值，并在个人的自然环境中调解社会和环境背景，以发现早期复发的特征。AutoSense由多个无线传感器组成（例如，ECG、呼吸、三轴加速度计），其将传感器测量结果无线传输到智能手机。精细的生理测量能够计算预测压力的生理测量，例如每分钟通气量和心率变异性。该项目使用复杂的机器学习方法，从生理测量中估计压力水平、吸烟事件和谈话事件。此外，加速度计用于识别姿势和身体活动的变化，移动的电话上的GPS测量值用于识别位置和交通方式。将招募72名想要戒烟的吸烟者。在退出日期前两周，他们将在现场佩戴AutoSense 24小时。戒烟行为治疗将在戒烟前到实验室就诊时开始。从退出日期开始，他们将在现场佩戴AutoSense 72小时。除了收集所有传感器的连续测量值外，吸烟和失效事件将自我报告。除了压力的生理测量外，还将收集唾液样本用于皮质醇评估。参与者将在戒烟日期后一周返回实验室报告，以确定吸烟/失效状态。这个项目的目的是确定心理生理措施的压力和环境线索，可以预测早期失效。首先，它将发现生理和激素措施的模式，以应对撤退压力和急性压力，这可能会预测在第一周内失效。其次，它将调查社会和环境背景的作用，如谈话行为（例如，会话的持续时间和频率），生活方式因素（例如，在坐姿和通勤中花费的时间）、位置（在家与办公室花费的时间）和室内/室外状态中调解压力和早期失效之间的关系。

DESCRIPTION (provided by applicant): Cigarette smoking is a leading preventable cause of death, responsible for one in five deaths annually in the United States alone. Many adult smokers want to or try to quit smoking, but, are unable to do so due to lack of personalized cessation treatments that ultimately leads to successful quitting. The primary hurdle has been the unavailability of technology that can be used to collect continuous measurements of stress contexts in field on predictors of abstinence and relapse. Without these measurements, it is difficult to understand the psychophysiological and biobehavioral stress mechanisms that may predict smoke lapse. This application proposes to use a new field deployable tool called AutoSense that was developed under NIH's GEI program to collect personal psychophysiological measures of stress and mediating social and environmental contexts in the natural environment of individuals to discover signatures of early relapse. AutoSense consists of several wireless sensors (e.g., ECG, respiration, triaxial accelerometer) attached to a chest band that transmit sensor measurements wirelessly to a smart phone. Fine-grained measurement of physiology enables computation of physiological measures that are predictive of stress such as minute ventilation and heart rate variability. The project uses sophisticated machine learning methods to estimate stress levels, smoking episodes and conversation episodes from physiological measurements. In addition, accelerometers are used to identify changes in posture and physical activity, and GPS measurements on the mobile phone are used to identify location and transportation modality. Seventy-two smokers who want to quit will be recruited. Two weeks prior to quit date, they will wear AutoSense for 24h in the field. Behavioral treatment for abstinence will begin in their visit to the lab prior to quitting. They wil wear AutoSense for 72h in the field, beginning on the quit date. In addition to collecting continuous measurements across all sensors, smoking and lapse events will be self-reported. Saliva samples will be collected for cortisol assessment, in addition to physiological measures of stress. Participants will report back to the lab one week after the quit date to determine smoking/lapse status. The aim of this project is to identify psychophysiological measures of stress and environmental cues that may predict early lapse. First, it will discover patterns in physiological and hormonal measures in response to withdrawal stress and acute stress that may predict lapse in the first week. Second, it will investigate the role of social and environmental context such as conversation behavior (e.g., duration and frequency of conversations), lifestyle factors (e.g., time spent in seated posture and in commuting), location (time spent at home vs. office), and indoor/outdoor status in mediating the relationship between stress and early lapse.