Automated Physiological Assessment of Chronic Pain in Daily Life

日常生活中慢性疼痛的自动生理评估

• 批准号: 10369031
• 负责人: Marta Ceko
• 金额: $ 19.17万
• 依托单位: UNIVERSITY OF COLORADO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-09 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10369031
• 关键词: Adult; American; Behavioral; Cognitive; Complement; Computer Models; Data; Detection; Devices; Diagnostic; Ear; Electroencephalogram; Electromyography; Electrooculogram; Electrooculography; Emotional; Emotions; Epidemic; Face; Galvanic Skin Response; Goals; Grant; Heart Rate; Hour; Informal Social Control; Intervention; Knowledge; Life; Machine Learning; Measures; Methods; Modeling; Monitor; Nature; Noise; Opioid; Pain; Pain Disorder; Pain intensity; Pain management; Patient Monitoring; Patient Self-Report; Patients; Pattern; Persistent pain; Pharmacological Treatment; Pharmacology; Phase; Physiological; Physiology; Population; Prevention; Process; Psychological Factors; Psychophysiology; Regulation; Research; Risk Factors; Sampling; Signal Transduction; System; Techniques; Testing; Time; Translating; United States; Validation; Work; Wrist; base; chronic back pain; chronic pain; chronic painful condition; clinical pain; clinical predictors; experience; field study; improved; improved outcome; machine learning method; machine learning model; machine learning prediction; open source; opioid abuse; opioid misuse; opioid use; pain patient; pain reduction; pain self-management; predictive modeling; prescription opioid; psychologic; response; smartphone Application; sociodemographics; wearable device

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.

美国正处于慢性疼痛和阿片类药物滥用的双重流行之中，大约有100万人。20%的 每年有超过40，000人因滥用阿片类药物而丧生。慢性背痛（CBP） 最常见的疼痛障碍，也是开阿片类药物的主要原因之一。有助于减少 因此，没有阿片类药物的CBP疼痛是紧迫的。一种有前途的阿片类药物替代品是心理干预 减少疼痛强度，干扰和负面情绪，而不仅仅是针对身体疼痛强度， 就像许多传统的药理学方法一样。然而，这些干预措施往往不是暂时的， 与疼痛发作一致。 我们建议通过捕获持续的临床疼痛， 临床疼痛以及相关的生理波动和心理过程。我们将全面发展 从收集的生理体征中自动实时检测N=80例CBP患者的持续疼痛， 日常生活我们将记录多个生理信号（脑电图（EEG），面部 肌电图（EMG）、眼电图（EOG）、皮电活动（EDA）和心率（HR））。 两个可穿戴设备，一个戴在耳朵周围（Earable），一个戴在手腕周围（Empatica）。感测 该系统将与经验采样方法（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。我们的目标1是建立计算 基于生理学的模型，可以预测现实生活中的临床疼痛。为了实现这一目标，我们将应用机器学习 疼痛自我报告之前的生理数据，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发展。我们在目标2中的目标是实地测试这些计算模型， 一组新的N=20名CBP患者。 这项拟议中的工作将首次在现实生活中自主监测临床疼痛。如果 可以在疼痛之前的生理模式中捕获患者的真实疼痛体验， 跟踪生理学有相当大的潜力，以提高心理治疗的疗效，通过 提供触发及时干预的信号。总的来说，拟议项目将有助于基本的 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并减少阿片类药物的使用。