Predicting Smoking Abstinence via Mobile Monitoring of Stress and Social Context

描述(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死亡是由吸烟造成的。许多成年吸烟者想要或试图戒烟，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对戒酒和复发预测因素的压力背景的连续测量。如果没有这些测量，就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，该工具是根据NIH的GEI计划开发的，用于收集个人压力的心理生理测量，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由几个连接到胸带上的无线传感器(例如，心电、呼吸、三轴加速计)组成，这些传感器将传感器测量数据无线传输到智能手机。精细的生理学测量可以计算预测压力的生理测量，如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，根据生理测量来估计压力水平、吸烟时间和交谈时间。此外，加速计用于识别姿势和体力活动的变化，移动电话上的GPS测量用于识别位置和交通方式。72名想要戒烟的吸烟者将被招募。在戒烟日期前两周，他们将在现场佩戴AutoSense 24小时。戒断的行为治疗将在他们戒烟前访问实验室开始。他们将在现场佩戴AutoSense达72小时，从戒烟之日开始。除了收集所有传感器的连续测量外，吸烟和失误事件将自我报告。除了对压力进行生理测量外，还将收集唾液样本进行皮质醇评估。参与者将在戒烟日期一周后返回实验室报告，以确定吸烟/戒烟情况。这个项目的目的是确定压力和环境线索的心理生理学指标，以预测早期的失误。首先，它将发现应对戒断压力和急性压力的生理和荷尔蒙措施的模式，这些模式可能会在第一周预测失败。其次，它将调查社会和环境背景在调节压力和早逝之间的关系方面的作用，如交谈行为(例如，交谈的持续时间和频率)、生活方式因素(例如，坐着和通勤的时间)、地点(在家和办公室的时间)以及室内/室外状态。 与公共健康相关：通过识别吸烟的心理生理和生物行为预测因素，并通过检测自然环境中的吸烟线索，该项目将使个性化戒烟干预措施的开发和评估成为可能，这些干预措施可以在智能手机上实时实施，这可能被证明在防止复发方面是有效的。鉴于吸烟对吸烟者和非吸烟者的不利影响，这一进步将显著提高生活质量，并减轻公众健康负担。

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. PUBLIC HEALTH RELEVANCE: By identifying psychophysiological and biobehavioral predictors of smoking lapse and by detecting smoking cues in the natural environment, this project will enable the development and evaluation of personalized cessation interventions that can be administered in real-time on smart phones, which could prove potent in preventing relapse. Given the adverse impact of smoking on smokers and non-smokers, such advancement will significantly improve the quality of life and reduce public health burden.