Digital Therapeutic Development of Virtual Cognitive-Affective Training for Opioid Use Disorder

美国正在经历阿片类药物危机，估计有250万美国人 阿片类药物使用障碍（OUD）阿片类药物使用障碍（MOUD），如丁丙诺啡， 目前，这是对OUD最有效的干预形式。尽管MOUD的有效性已得到证实，但近一半的 在六个月内开始复发的人。因此，新颖有效的行为抑制剂， 需要MOUD来改善治疗结果。正念导向的恢复增强（更多），a 通过NIDA资助的治疗开发过程产生的认知情感训练干预， 显示出显著的前景，在四项随机对照试验（RCT）中证明了治疗效果， 减少阿片类药物的使用和滥用，调节药物线索反应期间的神经生理反应， 自然奖励处理然而，MORE干预需要大量的人际互动， 因此资源密集。此外，鉴于COVID-19大流行，OUD患者可能对 由于病毒传播的风险，参与面对面的干预。为了克服这些实施障碍， 并增加获得治疗的机会，拟议的项目将开发和测试一个虚拟现实版本的 MORE干预（MORE-VR）建立在BehaVR LLC的VR现有商用平台上， 动态体验引擎（DXE）。DXE使用机器学习将生物识别技术集成到VR中 个性化成瘾治疗的经验，提高患者参与度并优化 临床干预。在第一阶段，我们将在单臂临床研究中开发和测试MORE-VR原型 OUD患者在阿片类药物治疗计划（OTP）中接受丁丙诺啡，重点关注患者安全性， 订婚如果原型被认为具有足够的可用性和净推荐值，以及 在渴望和情感状态的近端测量中产生改善，项目将进展到阶段 二.在第二阶段，我们将首先根据第一阶段的反馈开发MORE-VR最小可行计划 研究以优化干预，然后照常进行MORE-VR与MOUD治疗的RCT。我们 稳健和无偏见的研究设计将临床结果测量与生化指标进行三角测量。 禁欲验证，生态瞬时评估（EMA）和神经生理学评估， 线索反应性。此外，我们还将MORE-VR计划整合到OTP操作环境中， 快速医疗保健互操作性资源接口可从电子病历中获取患者数据 （EMR），并将患者使用、评估和生物特征数据导出回EMR，以创建患者 记录并告知临床医生患者的进展情况。第一阶段和第二阶段的活动将有助于确定 监管途径，并建立监管可行性，以先于美国食品药品监督管理局（FDA） 批准MORE-VR系统为II类医疗器械。总之，这个项目将翻译一个证据- 基于生物行为的治疗方法转化为OUD的创新数字治疗方法。

Project Summary: The US is experiencing an opioid crisis, with an estimated 2.5 million Americans meeting full criteria for opioid use disorder (OUD). Medications for opioid use disorder (MOUD) like buprenorphine are currently the most effective form of intervention for OUD. In spite of the proven efficacy of MOUD, nearly half of people who begin a MOUD relapse within six months. As such, novel and efficacious behavioral adjuncts to MOUD are needed to improve treatment outcomes. Mindfulness-Oriented Recovery Enhancement (MORE), a cognitive-affective training intervention generated through a NIDA-funded treatment development process has shown significant promise, demonstrating therapeutic effects in four randomized controlled trials (RCTs) by reducing opioid use and misuse and modulating neurophysiological responses during drug cue-reactivity and natural reward processing. However, the MORE intervention requires significant human interaction and is therefore resource intensive. Further, in light of the COVID-19 pandemic, people with OUD may be reticent to engage in face-to-face interventions due to the risk of viral spread. To overcome these implementation barriers and increase access to treatment, the proposed project will develop and test a virtual reality version of the MORE intervention (MORE-VR) built on BehaVR LLC's VR existing, commercially available platform, the Dynamic eXperience Engine (DXE). The DXE uses machine learning to integrate biometrics into the VR experience to personalize addictions treatment, boosting patient engagement and optimizing the efficacy of clinical intervention. In Phase I, we will develop and test the MORE-VR prototype in a single arm clinical study of OUD patients receiving buprenorphine in an opioid treatment program (OTP), focusing on patient safety and engagement. If the prototype is perceived to have adequate usability and Net Promoter Scores, as well as to produce improvements in proximal measures of craving and affective state, the project will progress to Phase II. In Phase II, we will first develop the MORE-VR Minimal Viable Program using feedback from our Phase I study to optimize the intervention, and then conduct a RCT of MORE-VR vs. MOUD treatment as usual. Our robust and unbiased research design will triangulate clinical outcome measurement with biochemical verification of abstinence, ecological momentary assessments (EMA), and neurophysiological assessment of cue-reactivity. Further, we will integrate the MORE-VR Program into the OTP operational environment, building Fast Healthcare Interoperability Resources interfaces to ingest patient data from electronic medical records (EMRs) and to export patient utilization, assessment, and biometric data back to EMRs to create patient records and inform clinicians of patient progress. Activities in Phase I and Phase II will help define the regulatory pathway and establish regulatory feasibility to precede Food and Drug Administration (FDA) approval of the MORE-VR system as a Class II medical device. In sum, this project will translate an evidence- based biobehavioral treatment approach into an innovative digital therapeutic for OUD.