SOAR: Smartphones for Opioid Addiction Recovery

项目总结 超过200万美国人患有阿片使用障碍(OUD)，另有900万人滥用阿片类药物。 阿片成瘾的治疗方法是存在的，但当受试者在治疗期间使用非法阿片类药物时，有效性会受到影响 治疗。治疗期间的重复使用率可能很高，因此减少治疗期间非法阿片类药物的使用 最近成为NIDA的一个主要政策目标。提高再使用率不仅会损害治疗效果， 但这种行为预示着，而且很可能会导致治疗退出。在NIDA基础科学R01的支持下， 我们开发了一套易于使用的工具，预测阿片类药物重复使用事件的准确度大约是 任何现有工具。我们开发的5分钟电池表明了患者重复使用的数字概率 在未来7-10天内使用非法鸦片类药物。在试点队列中，我们成功地将这种电池移植到了商业 智能手机平台，并表现出100%的保留率和85%的合规性(中位数合规性&95%) 使用期限长达4个月。在一项对大部分无家可归的Moud患者的调查中，我们发现85%的人已经 有适当的智能手机和数据合同，可以使用这个平台作为他们治疗的一部分。在一项调查中 在我们的治疗医生中，我们发现我们的系统及其提供的重复使用预测都非常高 所实施的可取和可用的。最后，我们在对CTN-0051数据的重新分析中发现，动态 这种剂量可以降低复发率。我们在这个中等规模的临床试验中的主要目标是测试 假设临床医生使用我们移动系统的输出来调整丁丙诺啡和美沙酮 与提供照常护理的医生相比，配药实现了较低的阿片类药物重用率。我们的次要目标是 考虑到可用性和规模性，为临床医生检查此解决方案的可用性和可取性 部署。我们的第三个也是最后一个目标是从多个方面衡量此解决方案的成本效益 透视。如果我们成功了，就有可能采用算法和基于测量的方法 使用美沙酮和丁丙诺啡治疗可降低患者的再使用率和复发率 我们的病人。

PROJECT SUMMARY Over 2 million Americans suffer from Opioid Use Disorder (OUD) and another 9 million misuse opioids. Treatments for opioid addiction exist, but effectiveness is compromised when subjects use illicit opiates during treatment. Reuse rates during treatment can be high, and reducing illicit opiate use during treatment has thus recently become a major NIDA policy goal. Elevated reuse rates not only compromise treatment effectiveness, but this behavior predicts, and likely drives, treatment dropout. With the support of a NIDA basic science R01, we developed a set of easy-to-use instruments that predict opioid reuse events with about twice the accuracy of any existing tool. The 5-minute battery we developed indicates the numerical probability that a patient will reuse illicit opiates within the next 7-10 days. In a pilot cohort, we successfully migrated this battery to a commercial smartphone platform, and demonstrated 100% retention and >85% compliance (median compliance > 95%) over a use period of up to 4 months. In a survey of our largely homeless MOUD patients we found that 85% already had smartphones and data contracts appropriate for using this platform as a part of their treatment. In a survey of OUD treatment physicians, we found that our system and the reuse prediction it provides, was both highly desirable and usable as implemented. Finally, we found in a reanalysis of data from CTN-0051 that dynamic dosing of this very kind reduces relapse rates. Our primary goal in this mid-scale clinical trial is to test the hypothesis that clinicians who use the output of our mobile system to adjust buprenorphine and methadone dosing achieve lower opiate reuse rates than physicians who provide care-as-usual. Our secondary goal is to examine the usability and desirability of this solution for clinicians with an eye to usability and large-scale deployment. Our third and final goal is to measure the cost-effectiveness of this solution from multiple perspectives. If we are successful it will be possible to employ an algorithmic and measurement-based approach to OUD treatment with methadone and buprenorphine which reduces reuse rates and relapse rates amongst OUD patients.