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.

项目总结