iTransform: Wearable Biosensors to Detect the Evolution of Opioid Tolerance in Opioid Naïve Individuals

iTransform：可穿戴生物传感器检测阿片类药物耐受性的演变

• 批准号: 9889092
• 负责人: STEPHANIE P CARREIRO
• 金额: $ 18.86万
• 依托单位: UNIV OF MASSACHUSETTS MED SCH WORCESTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9889092
• 关键词: Accident and Emergency department; Acute; Age; Algorithms; Analgesics; Area; Behavior Therapy; Behavioral; Big Data; Biological Markers; Biometry; Biosensing Techniques; Biosensor; Characteristics; Consent; Data; Data Analyses; Data Analytics; Data Collection; Data Science; Data Set; Development; Devices; Doctor of Philosophy; Dose; Drug abuse; Ensure; Evaluation; Event; Evolution; Fracture; Funding; Galvanic Skin Response; Gender; Genetic; Goals; Heroin Abuse; Immersion; Individual; Ingestion; Intervention; Investigation; Italy; K-Series Research Career Programs; Knowledge; Machine Learning; Measurement; Measures; Medical; Mentored Patient-Oriented Research Career Development Award; Mentors; Mentorship; Methods; Monitor; Morbidity - disease rate; Motion; Opiate Addiction; Opioid; Opioid Analgesics; Overdose; Pain; Participant; Patients; Pattern; Pattern Recognition; Pharmaceutical Preparations; Physiological; Physiology; Pilot Projects; Population; Predictive Analytics; Productivity; Protocols documentation; Public Health; Records; Relapse; Reporting; Research; Research Personnel; Research Project Grants; Research Training; Risk; Risk Factors; Safety; Science; Scientist; Signal Transduction; Skin Temperature; Source; Substance Use Disorder; Substance abuse problem; Teacher Professional Development; Techniques; Technology; Testing; Therapeutic; Therapeutic Effect; Therapeutic Uses; Time; Training; United States National Institutes of Health; Work; Wrist; addiction; analytical method; base; behavioral health; career; comparative; data integrity; data management; digital; experience; high risk; innovation; insight; learning algorithm; mHealth; machine learning algorithm; machine learning method; mortality; novel; opiate tolerance; opioid abuse; opioid mortality; opioid overdose; opioid therapy; opioid use; prediction algorithm; prescription opioid; prevent; programs; recruit; response; sensor; signal processing; skills; social; success; supervised learning; tool

PROJECT SUMMARY The integrated research and training plans outlined in this K23 submission will prepare me for a career as a clinician-scientist conducting translational substance abuse research. My career goal is to perform hypothesis- driven original research investigations directed toward reducing morbidity and mortality from opioid overdose. In this proposal, I intend to deploy wearable biosensors (small devices that continuously record physiology) to study the effects of therapeutic administration of opioid analgesics. I have already studied wearable biosensors in individuals receiving opioids; my preliminary data demonstrates that opioid-tolerant individuals have different biometric signals than non-tolerant individuals. This observation suggests that biosensors can be used to identify the onset of tolerance, an important event that correlates with higher doses of opioid analgesics, and higher risk of death from opioid overdose. Biosensor data management and analysis requires signal processing, data analytic, and machine learning techniques; these approaches are beyond the areas of traditional medical training. My short-term goal is to utilize this K23 award to fill my knowledge gaps in wearable biosensing and advanced data analysis so that I can generate ever more innovative responses to the problem of opioid prescribing, tolerance, misuse, addiction, and overdose. To optimize this important line of investigation, I have developed a training plan that includes: 1) completing a PhD through the Millennium PhD program; 2) expanding my skills in wearable biosensing and behavioral health-based research; 3) developing an understanding of signal processing and machine learning; 4) developing data analytic and data science skills; and 5) expanding my research presentation and dissemination skills. I will achieve these goals through directed coursework, focused seminars, and practical experience. My mentorship team of expert investigators who will ensure my productivity and success includes E. Boyer (primary mentor), D. Smelson, J. Fang, and P. Indic (secondary mentors), and D. Ganesan (advisor) My research plan has three specific aims: 1) to deploy a wearable biosensor technology to detect digital biomarkers associated with the initiation of opioid analgesic therapy in an opioid naïve population; 2) to use signal-processing analytics to identify transitions in digital biomarkers with progressive opioid use and to identify individual characteristics associated with this transition; and, 3) to apply and explore supervised learning algorithms that can predict transitions in digital biomarkers that herald the onset of opioid tolerance. To identify dynamic patterns in response to opioids, I will study the digital biomarkers of opioid-naïve patients with acute fractures who are prescribed opioid analgesics. Results will be used to develop “big data” approaches to apply predictive algorithms to identify the onset of opioid tolerance. This work has the potential to prevent development of problematic opioid use and will provide the basis for subsequent R01 submissions to implement sensor-based interventions triggered by the onset of tolerance in individuals receiving opioid analgesics.

项目摘要 本k23提交中概述的综合研究和培训计划将使我为自己的职业做好准备 临床科学家进行转化药物滥用研究。我的职业目标是进行假设 - 驱动的原始研究调查是针对降低阿片类药物过量的发病率和死亡率的。在 该建议，我打算部署可穿戴的生物传感器（不断记录生理学的小型设备）来研究 阿片类镇痛药治疗施用的影响。我已经研究了可穿戴的生物传感器 接受阿片类药物的个人；我的初步数据表明，阿片类药物耐受性的人有不同的 比非耐受个体的生物识别信号。该观察结果表明生物传感器可用于识别 耐受性的发作，这是一个与较高剂量的阿片类镇痛药相关的重要事件，风险更高 阿片类药物过量的死亡。生物传感器数据管理和分析需要信号处理，数据 分析和机器学习技术；这些方法超出了传统医学领域 训练。我的短期目标是利用该K23奖，以填补我在可穿戴生物传感和 高级数据分析，以便我可以对阿片类药物问题产生更具创新的回应 处方，宽容，滥用，成瘾和过量。为了优化这一重要的调查，我有 制定了一个培训计划，其中包括：1）通过千年博士学位完成博士学位； 2）扩展 我在可穿戴生物传感和基于行为健康的研究方面的技能； 3）对信号的理解 处理和机器学习； 4）开发数据分析和数据科学技能； 5）扩大我的 研究演示和传播技巧。我将通过有定向课程实现这些目标，集中于 半手和实践经验。我的专家调查员团队将确保我的生产力 成功包括E. Boyer（主要导师），D。Smelson，J。Fang和P. AIND（次要导师）和 D. Ganesan（顾问）我的研究计划具有三个具体目标：1）部署可穿戴生物传感器技术 检测与Ooid幼体中Ooid镇痛治疗的倡议相关的数字生物标志物； 2）使用信号处理分析来识别具有渐进式阿片类药物使用和的数字生物标志物中的过渡 确定与此过渡相关的个人特征； 3）申请和探索监督 学习可以预测数字生物标志物中的过渡的学习算法，这些算法预示了阿片类药物耐受性的发作。到 确定对阿片类药物的动态模式，我将研究未经阿片类药物患者的数字生物标志物 处方阿片类镇痛药的急性骨折。结果将用于开发“大数据”方法 应用预测算法来识别阿片类药物耐受性的发作。这项工作有可能预防 开发有问题的阿片类药物使用，并将为随后的R01提交提供基础 基于传感器的干预措施是由接受阿片类镇痛药的个体的公差发作引发的。