Digital Markers in Relapse and Recovery

关于复发的文献仍然充斥着方法上的不一致，在复发的定义、评估方法和解决复发相关因素的模型方面表现出广泛的差异。收集复发数据的传统方法包括：1）回顾性审查，促使参与者选择复发和先前因素的实例; 2）前瞻性报告，在基线或间隔收集潜在的先前信息，然后评估与检测到的复发的关联;以及3）近实时报告，其中参与者报告或以电子方式提示接近实际复发事件的因素。由于数据收集方面的挑战，在利用日常生活中的行为观察的研究中存在着显着的差距。 最近的进展重申，近实时报告提供了一个最佳策略，因为复发脆弱性因素，如自我效能，药物线索，焦虑，压力，药物渴望和社会支持可以在短短几分钟内演变。认识到这些挑战，我们的项目利用实时报告来检测和预测参加物质使用治疗计划和康复者的复发。 随着公共卫生研究和实践开始利用通信媒体的变革，我们的项目一直处于最前沿，采用创新工具来仔细检查社交媒体语言和数字设备（包括智能手机和可穿戴设备）生成的数据。今年，我们通过采用先进的数据分析技术，更深入地研究物质使用治疗和长期康复的数字印记，取得了重大进展。通过自然语言处理和机器学习，我们制作了预测复发和长期恢复的预测模型，通过提供详细行为数据的被动测量技术增强了我们的能力，通常超过了传统方法。 在这些努力的同时，今年突出的一个工具性出版物是我们实验室的Giorgi等人的工作，标题为“从多模式社交媒体和心理自我报告数据预测美国县阿片类药物中毒死亡率。“这项研究通过采用多模态数据集，包括Twitter语言和心理测量自我报告，解决了美国严重的阿片类药物中毒死亡率危机。该研究强调了社交媒体数据的实用性，表明Twitter语言比社会人口统计学或医疗保健获取等传统因素更能指示阿片类药物中毒死亡率。这说明了利用社交媒体中的自然语言作为预测社区阿片类药物中毒和加深我们对该流行病多方面性质的理解的关键监测工具的潜力。 大多数复发预防策略历来受到限制，依赖于通常通过调查和访谈获得的一小部分可用数据。相比之下，我们的方法利用了大量的数据。我们不是仅仅关注评估复发风险的最新测量方法，而是利用复发脆弱性因素的动态性质，结合各种康复旅程的见解来完善我们的预测。这一奋进的高潮是开发动态的实时预测，以适应复发风险的快速变化。 我们实验室雄心勃勃的长期目标正在实现：我们正在开创一个自动化的、不间断的系统，该系统监测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一系列数字源，以预测每日复发脆弱性评分。在此基础上，我们制定了开发复发脆弱性反馈工具的路线，旨在为广大受众提供服务，包括成瘾治疗提供者，正在接受治疗的人和康复中的个人。这一开创性的举措有望在临床研究和实践中实现范式转变，为针对高危患者的自动干预奠定基础。 其他一些值得注意的文章包括基于人工智能的分析Curtis B等人，2023年，强调了社交媒体语言在确定成瘾治疗辍学方面的预测能力。此外，我们对媒体作用的探索哈比卜·DRS等人，2023年和语言学方法与公共卫生的交叉Lane JM et al.，2023年为我们的研究叙事增添了新的维度。

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.