Automated Physiological Assessment of Chronic Pain in Daily Life

美国正处于慢性疼痛和阿片类药物滥用的双重流行之中，约有3%。20%的 每年有40,000多人死于阿片类药物滥用。慢性背痛是 最常见的疼痛障碍，也是开阿片类药物的主要原因之一。帮助减少 因此，没有阿片类药物的CBP疼痛是紧迫的。一种有希望的阿片类药物替代方案是心理干预 减少疼痛强度、干扰和负面情绪，而不仅仅是针对身体上的疼痛强度 就像许多传统的药理学方法一样。然而，这些干预通常不是临时性的。 与疼痛发作相一致。 我们建议通过捕获正在进行的疼痛来建立正在进行的临床疼痛的诊断生理标记 临床疼痛及其相关的生理波动和心理过程。我们将全面发展 根据收集的生理体征自动实时检测N=80名CBP患者的持续疼痛 日常生活。我们将记录多种生理信号(脑电、面部 肌电(EMG)、眼电(EOG)、皮肤电活动(EDA)和心率(HR)) 两个可穿戴设备，一个戴在耳朵上(可穿戴)，一个戴在手腕上(Empatica)。感官 系统将与体验采样法(ESM)智能手机应用程序集成，以收集疼痛评级 以及与疼痛发作相关的心理过程。我们在目标1中的目标是建立计算性 基于生理学的模型，可以预测现实生活中的临床疼痛。为了实现这一点，我们将应用机器学习 疼痛自我报告前的生理数据技术，以建立持续疼痛的预测模型， 这些计算模型的最终目标是能够在最需要的时候触发心理干预， 我们的目标是在未来的研究中进行开发。我们在目标2中的目标是在 一组新的N=20例CBP患者。 这项拟议的工作将首次提供对现实生活中临床疼痛的自主监测。如果 患者的真实疼痛体验可以在疼痛之前的生理模式中捕获，然后自动进行 对生理学的追踪有相当大的潜力来提高心理治疗的有效性，通过 提供信号以触发及时干预。总体而言，拟议的项目将对 关于现实生活中疼痛的心理生理迹象的科学知识，并为翻译奠定基础 努力改善疼痛自我管理的结果，减少阿片类药物的使用。

The United States is in the midst of dual epidemics of chronic pain and opioid abuse, with approx. 20% of the population in persistent pain, and over 40,000 lives lost each year to opioid misuse. Chronic back pain (CBP) is the most common pain disorder and one of the major reasons for prescribing opioids. Strategies to help reduce CBP pain without opioids are therefore urgent. A promising opioid alternative are psychological interventions that reduce pain intensity, interference and negative emotions, and do not just target the physical pain intensity as many of the traditional pharmacological approaches do. However, these interventions are not often temporally aligned with pain episodes. We propose to establish diagnostic physiological markers of ongoing clinical pain by capturing ongoing clinical pain and the associated physiological fluctuations and psychological processes. We will develop fully automated real-time detection of ongoing pain in N=80 CBP patients from physiological signs collected in everyday life. We will record multiple physiological signals (electroencephalogram (EEG), facial electromyography (EMG), electrooculography (EOG), electrodermal activity (EDA), and heart rate (HR)) from two wearable device, one worn around the ears (Earable) and one worn around the wrist (Empatica). The sensing system will be integrated with an experience sampling method (ESM) smartphone app to collect ratings of pain and psychological processes associated with pain episodes. Our goal in Aim 1 is to establish computational physiology-based models that can predict clinical pain in real-life. To achieve this, we will apply machine-learning techniques to physiological data preceding pain self-reports to build predictive models of ongoing pain, with the ultimate goal for these computational models to be able to trigger psychological interventions when needed most, which we aim to develop in our future research. Our goal in Aim 2 is to field-test these computational models in a new group of N=20 CBP patients. The proposed work will afford, for the first time, autonomous monitoring of clinical pain in real-life. If the real-life pain experience of patients can be captured in physiological patterns preceding pain, then automated tracking of physiology has considerable potential to improve the efficacy of psychological treatments, by providing signals to trigger just-in-time interventions. Overall, the proposed project will contribute fundamental scientific knowledge about psycho-physiological signs of real-life pain and lay the groundwork for translational efforts to improve outcomes of pain self-management and reduce opioid use.