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亡。许多成年吸烟者想要或尝试戒烟，但由于缺乏个性化的戒烟治疗，最终导致成功戒烟，因此无法做到这一点。主要的障碍是没有技术可以用来收集现场压力环境的连续测量数据，以预测戒毒和复发。没有这些测量，很难理解可能预测烟雾消失的心理生理和生物行为应激机制。该应用程序建议使用一种名为AutoSense的新现场可部署工具，该工具是在NIH的GEI项目下开发的，用于收集个人心理生理压力测量，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由几个无线传感器组成（例如，心电图、呼吸、三轴加速度计），连接在胸带上，将传感器的测量数据无线传输到智能手机上。细粒度的生理测量能够计算出预测压力的生理测量，如微小通气和心率变异性。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、吸烟次数和谈话次数。此外，加速度计被用来识别姿势和身体活动的变化，手机上的GPS测量被用来识别位置和交通方式。72名想戒烟的人将被招募。在戒烟日期前两周，他们将在现场佩戴AutoSense 24小时。戒断行为治疗将在他们戒烟前到实验室进行。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数据外，吸烟和失误事件将自我报告。除了生理压力测量外，唾液样本将被收集用于皮质醇评估。参与者将在戒烟日期一周后返回实验室，以确定吸烟/失效状态。这个项目的目的是确定心理生理的压力和环境线索，可能预测早期失效。首先，它将发现对戒断压力和急性压力反应的生理和激素测量模式，这些模式可能会预测第一周的失效。其次，它将调查社会和环境背景的作用，如谈话行为（如，谈话的持续时间和频率），生活方式因素（如，坐着的姿势和通勤的时间），地点（花在家里和办公室的时间），以及室内/室外状态在调节压力和早期痴呆之间的关系。

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.