Digital Markers in Relapse and Recovery

复发和恢复中的数字标记

基本信息

批准号：
10001918
负责人：
Brenda Curtis
金额：
$ 44.35万
依托单位：
NATIONAL INSTITUTE ON DRUG ABUSE
依托单位国家：
美国
项目类别：
财政年份：
资助国家：
美国
起止时间：
至
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10001918
关键词：
Alcohol or Other Drugs use Anxiety Area Behavior Behavioral Cellular Phone Clinical Research Communications Media Cues Data Data Analytics Data Collection Feedback Future Goals Health Personnel Individual Interview Language Left Life Link Literature Machine Learning Measurement Measures Methodology Methods Modeling Monitor Natural Language Processing Nature Participant Patients Pattern Pharmaceutical Preparations Prevention approach Recovery Relapse Reporting Research Risk Self Efficacy Social support Source Stress Surveys System Techniques Telephone Testing Time Treatment outcome Variant addiction base behavior observation clinical practice digital disorder later incidence prevention drug craving experience improved insight novel strategies predictive modeling prospective public health research relapse prediction relapse risk sensor social media theories tool treatment program wearable device

项目摘要

The literature on relapse includes numerous methodological inconsistencies, with wide variation in the definition of relapse, assessment methodologies, and models of relapse-related factors. Traditional methodologies for collecting data on relapse included: 1) retrospective reviews in which participants are asked to recall instances of relapse and the factors preceding them; 2) prospective reports, in which information about potential antecedents is collected at baseline or periodically and then examined for association with a detected relapse; and 3) near real-time reports, in which participants are asked or electronically prompted to report on factors near the actual time of relapse. The lack of research using behavioral observation of daily life is mainly because collecting this data has been almost impossible. However, near real-time reports are optimal because relapse vulnerability factors such as self-efficacy, drug cues, anxiety, stress, drug craving, and social support can change over a period of a few minutes. In the context of these challenges, this project will real-time reports as a tool to detect and predict relapse in patients attending substance use treatment programs and in patients who are in recovery. Public health research and practice are just beginning to taken advantage of emerging changes in communication media by using methods and tools that analyze social media language and data generated from smartphones and wearable devices. This project will adapt advanced data analytic techniques to examine the digital footprints left by individuals in substance use treatment and in those who are experiencing long-term recovery. We will use natural language processing and machine learning techniques to build models that predict future relapse and long-term recovery. Passive measurement will be used to capture behavioral data at a finer level of detail than is typically achieved using conventional methods. The majority of relapse prevention approaches utilize only a fraction of the available information about a participant typically gathered through surveys and interviews. Even when relapse risk is measured repeatedly over time, relapse vulnerability is typically based on the last available measurement. However, this approach discards valuable information on the dynamically changing nature of relapse vulnerability factors and does not use information from other patients in recovery to improve predictions. This project will result in the dynamic, real-time predictions of relapse vulnerability linked to rapid changes in relapse risk. Our long-term goal in this lab is to develop an automated, continuous system for monitoring digital sources (social media language, smartphone phone sensor data, data from wearable devices) to forecast daily relapse vulnerability scores. We will then develop a relapse vulnerability feedback tool to be used by addiction treatment providers, people in treatment, and people in recovery. This will enable the use of novel approaches to clinical research and practice by developing applications that automatically intervene when a patient is at risk.

有关复发的文献包括许多方法上的不一致，在复发，评估方法和复发相关因素模型的定义方面存在很大差异。收集复发数据的传统方法包括：1）回顾性评论，要求参与者回忆复发实例及其先前的因素； 2）前瞻性报告，其中在基线或定期收集有关潜在前因的信息，然后检查与检测到的复发相关； 3）接近实时报告，其中要求参与者或以电子方式提示参与者报告实际复发时间附近的因素。缺乏使用行为观察日常生活的研究主要是因为收集这些数据几乎是不可能的。但是，近乎实时的报告是最佳的，因为复发脆弱性因素，例如自我效能感，药物提示，焦虑，压力，渴望和社会支持可能会在几分钟内发生变化。在这些挑战的背景下，该项目将实时报告作为检测和预测参加药物治疗计划的患者以及正在康复的患者的复发的工具。公共卫生研究和实践刚刚通过使用分析从智能手机和可穿戴设备产生的社交媒体语言和数据的方法和工具来利用通信媒体中新兴的变化。该项目将调整高级数据分析技术，以检查个人在药物使用治疗中留下的数字足迹以及经历长期恢复的人。我们将使用自然语言处理和机器学习技术来构建预测未来复发和长期恢复的模型。被动测量将用于比使用常规方法通常实现的详细信息捕获行为数据。大多数预防复发方法仅利用有关参与者的可用信息的一小部分，通常是通过调查和访谈聚集的。即使随着时间的推移反复测量复发风险，复发脆弱性通常也基于上一次可用的测量。但是，这种方法丢弃了有关复发脆弱性因素动态变化性质的有价值信息，并且不会在康复中使用其他患者的信息来改善预测。该项目将产生与复发风险快速变化有关的复发脆弱性的动态实时预测。我们在本实验室中的长期目标是开发一种自动连续的系统，用于监视数字源（社交媒体语言，智能手机传感器数据，可穿戴设备的数据），以预测每日复发脆弱性分数。然后，我们将开发一种复发脆弱性反馈工具，以由成瘾治疗提供者，治疗人员和康复者使用。这将通过开发在患者处于危险中自动干预的应用来使用新颖的临床研究和实践方法。