Digital Markers in Relapse and Recovery

复发和恢复中的数字标记

• 批准号: 10928582
• 负责人: Brenda Curtis
• 金额: $ 236.54万
• 依托单位: NATIONAL INSTITUTE ON DRUG ABUSE
• 依托单位国家: 美国
• 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/10928582
• 关键词: Address; Anxiety; Area; Behavior; Behavioral; Cellular Phone; Clinical Research; Communications Media; Communities; County; Cues; Data; Data Analytics; Data Collection; Data Reporting; Development; Devices; Digital biomarker; Dimensions; Dropout; Epidemic; Event; Exhibits; Feedback; Foundations; Goals; Harvest; Health Personnel; Individual; Interview; Language; Life; Linguistics; Literature; Machine Learning; Measurement; Measures; Methodology; Methods; Modeling; Monitor; Natural Language Processing; Nature; Output; Participant; Patient Self-Report; Patients; Pattern; Pharmaceutical Preparations; Prevention strategy; Psychometrics; Public Health; Publications; Recovery; Relapse; Reporting; Research; Risk; Role; Self Efficacy; Social support; Source; Stress; Surveys; System; Techniques; Testing; Time; Treatment outcome; Twitter; Variant; Work; addiction; automated intervention; behavior observation; clinical practice; digital; drug craving; health care availability; imprint; innovation; insight; long term recovery; mortality; multimodal data; multimodality; natural language; novel; opioid overdose; predictive modeling; prospective; psychologic; public health research; relapse prediction; relapse prevention; relapse risk; sensor; social media; sociodemographics; substance use treatment; theories; tool; treatment program; wearable device

The literature on relapse continues to be rife with methodological inconsistencies, exhibiting broad variations in the definition of relapse, assessment methodologies, and models addressing relapse-related factors. Traditional methodologies to gather data on relapse encompass: 1) retrospective reviews prompting participants to recollect instances of relapse and preceding factors; 2) prospective reports collecting potential antecedent information at baseline or at intervals and later assessing the association with detected relapses; and 3) near real-time reports where participants report or are prompted electronically on factors proximate to the actual relapse event. A notable gap exists in research utilizing behavioral observation in daily life due to the challenges posed in data collection. Recent advancements reaffirm that near real-time reports offer an optimal strategy, as relapse vulnerability factors like self-efficacy, drug cues, anxiety, stress, drug craving, and social support can evolve within mere minutes. Recognizing these challenges, our project has leveraged real-time reports to detect and predict relapse in individuals attending substance use treatment programs and those in recovery. As public health research and practice begin to harness the transformative changes in communication media, our project has been at the forefront, employing innovative tools to scrutinize social media language and data generated from digital devices, including smartphones and wearables. This year, we made significant strides by adapting advanced data analytics to delve deeper into the digital imprints of those in substance use treatment and long-term recovery. Through natural language processing and machine learning, we've crafted predictive models that forecast relapse and long-term recovery, enhancing our capabilities through passive measurement techniques that offer detailed behavioral data, often surpassing traditional methods. In tandem with these efforts, an instrumental publication that stood out this year is the work in our lab by Giorgi et al., titled "Predicting U.S. county opioid poisoning mortality from multi-modal social media and psychological self-report data." This research addressed the severe opioid poisoning mortality crisis in the U.S. by employing a multi-modal dataset, including Twitter language and psychometric self-reports. Highlighting the utility of social media data, the study showed that Twitter language was more indicative of opioid poisoning mortality than traditional factors like socio-demographics or healthcare access. This illuminates the potential of harnessing natural language from social media as a pivotal surveillance tool in predicting community opioid poisonings and deepening our understanding of the epidemic's multifaceted nature. Most relapse prevention strategies have historically been restricted, relying on a sliver of available data typically harvested through surveys and interviews. By contrast, our approach utilizes a wealth of data. Instead of focusing solely on the most recent measurement for assessing relapse risk, we harness the dynamic nature of relapse vulnerability factors, combining insights from various recovery journeys to refine our predictions. The culmination of this endeavor is the development of dynamic, real-time predictions that stay attuned to swift alterations in relapse risk. Our lab's ambitious long-term goal is crystallizing: we are pioneering an automated, ceaseless system that monitors a spectrum of digital sources from social media language to smartphone sensor data and wearable device outputs to predict daily relapse vulnerability scores. Building on this, we have charted the course to develop a feedback tool for relapse vulnerability, aiming to serve a wide audience, including addiction treatment providers, those under treatment, and individuals in recovery. This trailblazing initiative promises a paradigm shift in clinical research and practice, laying the foundation for automated interventions targeting patients at heightened risk. A few other notable articles include an AI-based analysis Curtis B et al., 2023 that underscored the predictive capabilities of social media language in determining addiction treatment dropouts. Furthermore, our explorations into the role of media Habib DRS et al., 2023 and the intersection of linguistic methodologies with public health Lane JM et al., 2023 have added novel dimensions to our research narrative.

有关复发的文献仍然充斥着方法论上的不一致，在复发，评估方法和解决与复发相关因素的模型的定义中表现出广泛的差异。传统方法收集有关复发数据的数据：1）回顾性评论促使参与者回忆起复发和先前的因素； 2）前瞻性报告在基线或间隔收集潜在的先行信息，然后评估与检测到的复发的关联； 3）接近实时报告，参与者报告或以电子方式提示与实际复发事件有关的因素。由于数据收集中带来的挑战，在利用日常生活中行为观察的研究中存在一个显着的差距。 最近的进步重申，近实时报告提供了最佳策略，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，可以在短短的几分钟内发展。认识到这些挑战，我们的项目利用了实时报告来检测和预测参加药物使用治疗计划的个人以及正在康复的人的复发。 随着公共卫生研究和实践开始利用通信媒体的变革性变化，我们的项目一直处于最前沿，采用创新的工具来仔细研究来自数字设备（包括智能手机和可穿戴设备）的社交媒体语言和数据。今年，我们通过调整先进的数据分析来深入研究药物使用治疗和长期恢复的数字烙印，从而取得了长足的进步。通过自然语言处理和机器学习，我们制定了预测模型，这些模型预测了复发和长期恢复，通过被动测量技术增强了我们的能力，这些技术提供详细的行为数据，通常超过传统方法。 与这些努力同时，Giorgi等人在我们的实验室中引人注目的工具出版物的标题为“预测来自多模式社交媒体和心理自我报告数据的美国县阿片类药物中毒死亡率”。这项研究通过采用多模式数据集（包括Twitter语言和心理学自我报告）来解决美国严重的阿片类中毒死亡率危机。该研究强调了社交媒体数据的实用性，表明Twitter语言比传统因素（如社会人口统计学或医疗保健访问）更能表明阿片类中毒死亡率。这阐明了从社交媒体中利用自然语言作为一种关键监视工具的潜力，以预测社区阿片类药物中毒并加深我们对流行病的多方面性质的理解。 历史上，大多数预防复发策略都受到限制，依赖于通常通过调查和访谈收获的大量可用数据。相比之下，我们的方法利用了大量数据。我们不仅专注于评估复发风险的最新测量值，而是利用复发脆弱性因素的动态性质，结合了各种恢复旅程的见解以完善我们的预测。这项努力的高潮是动态，实时预测的发展，这些预测始终迅速改变复发风险。 我们实验室的雄心勃勃的长期目标是结晶：我们正在开创一种自动化，不断的系统，该系统可监视从社交媒体语言到智能手机传感器数据和可穿戴设备输出的数字资源范围，以预测每日复发脆弱性得分。在此基础上，我们制定了一门课程，以开发一种反馈工具，以便为广泛的受众提供复发脆弱性，包括成瘾治疗提供者，正在接受治疗的人和康复中的个人。这项开拓性的计划有望在临床研究和实践中范式转移，为针对风险增加的患者的自动干预奠定了基础。 其他一些值得注意的文章包括基于AI的分析Curtis B等，2023年，强调了社交媒体语言在确定成瘾治疗辍学时的预测能力。此外，我们对媒体Habib Drs等人的作用的探索，以及2023年，以及语言方法与公共卫生Lane Lane JM等，2023的交集为我们的研究叙述增加了新的维度。

项目成果

Covid-19 and alcohol associated liver disease.

• DOI: 10.1016/j.dld.2022.07.007
• 发表时间: 2022-11
• 期刊: DIGESTIVE AND LIVER DISEASE
• 影响因子: 4.5
• 作者: Deutsch-Link, Sasha;Curtis, Brenda;Singal, Ashwani K.
• 通讯作者: Singal, Ashwani K.

A daily diary study into the effects on mental health of COVID-19 pandemic-related behaviors.

一项每日日记研究，探讨与 COVID-19 大流行相关的行为对心理健康的影响。

• DOI: 10.1017/s0033291721001896
• 发表时间: 2023
• 期刊: Psychological medicine
• 影响因子: 6.9
• 作者: Shaw,Philip;Blizzard,Sam;Shastri,Gauri;Kundzicz,Paul;Curtis,Brenda;Ungar,Lyle;Koehly,Laura
• 通讯作者: Koehly,Laura

Author Correction: Opioid death projections with AI-based forecasts using social media language.

• DOI: 10.1038/s41746-023-00793-z
• 发表时间: 2023-03-17
• 期刊: NPJ digital medicine
• 影响因子: 15.2

Dynamics of Sadness by Race, Ethnicity, and Income Following George Floyd's Death.

• DOI: 10.1016/j.ssmmh.2022.100134
• 发表时间: 2022-12
• 期刊: SSM-MENTAL HEALTH
• 作者: Lin, Jielu;Shaw, Philip;Curtis, Brenda;Ungar, Lyle;Koehly, Laura
• 通讯作者: Koehly, Laura

COVID-related social determinants of substance use disorder among diverse U.S. racial ethnic groups.

• DOI: 10.1016/j.socscimed.2022.115599
• 发表时间: 2023-01
• 期刊: SOCIAL SCIENCE & MEDICINE
• 影响因子: 5.4
• 作者: Tao, Xiangyu;Liu, Tingting;Fisher, Celia B.;Giorgi, Salvatore;Curtis, Brenda
• 通讯作者: Curtis, Brenda