Reducing Non-Medical Opioid Use: An automatically adaptive mHealth Intervention

 描述(申请人提供)：近年来，在美国，与阿片类药物处方相关的问题，包括非医疗用途和过量使用，增加到历史上前所未有的水平，并代表着公共健康危机。急诊科在阿片类药物处方中发挥着重要作用，特别是对阿片类药物不良后果的高危人群。一半的急诊室就诊是因为痛苦的情况，三分之一的急诊室就诊是因为痛苦的情况 导致开出阿片类药物。此外，在我们的试点工作中，在ED研究地点接受调查的患者中，有四分之一报告在前三个月使用非医用阿片类药物。尽管这个问题很重要，但在急诊室就诊后减少非医用阿片类药物使用的策略尚未得到很好的研究。我们最近进行了一项激励性干预的试验，试验对象是 与对照条件相比，在急诊室就诊后，由治疗师进行的ED导致非医疗用途的适度减少。然而，干预措施无法解决由于ED遭遇而开出的阿片类药物对ED后阿片类药物使用行为的影响。该项目将通过移动技术将干预措施调整为在急诊科就诊后提供，以便直接解决急诊科提供的阿片类药物的使用问题。患者(n=600)将在急诊科就诊期间被招募参加适应干预的随机对照试验，其基础是在前三个月内曾非医学使用阿片类药物，并由急诊科处方者给予阿片类药物。在干预条件下，交互式语音应答呼叫将反复评估非医用阿片类药物的使用和疼痛程度，并提供干预内容。干预将包括几种不同强度的潜在行动：仅评估、一条简短消息、扩展消息或通过电话与治疗师联系。由于最有帮助的干预强度尚不清楚，而且可能因患者而异，该项目将使用一种名为强化学习(RL)的人工智能策略。RL系统将不断“学习”以前在类似情况下对相似患者采取的行动的成功经验，以便为每个参与者选择最有可能在每次通话中减少非医用阿片类药物使用的行动。区域工作队将得到定性访谈的补充，以便为以后的执行提供信息。具体目的是：(1)调整和加强现有的动机干预措施，以减少ED就诊后非医用阿片类药物的使用；(2)通过RL优化干预强度和持续时间；(2)检查干预对ED就诊后6个月内非医用阿片类药物使用水平的影响；(3)检查干预对阿片类药物使用后驾驶、过量危险行为和随后与阿片类药物相关的ED就诊的影响。次要目标是：(1)检查非医用阿片类药物使用基线水平高和低的参与者之间干预效果的差异；(2)了解实施的障碍和促进者。该项目将使用一种高度创新的战略，人工智能，以解决一个非常重要的问题，非医用阿片类药物的使用。最终，这项研究可以减少阿片类药物相关的危害，并推动移动卫生领域的发展。

 DESCRIPTION (provided by applicant): In recent years in the U.S., problems associated with opioid prescriptions, including non-medical use and overdose, increased to historically unprecedented levels and represent a public health crisis. Emergency departments (EDs) play an important role in opioid prescribing, particularly to individuals at high risk for adverse opioi-related outcomes. Half of all ED visits are for a painful condition, and one third of all ED visits result in an opioid being prescribed. Moreover, in our pilot work, a quarter of patients surveyed at the ED study site reported non-medical opioid use in the prior three months. Despite the importance of this problem, strategies to reduce non-medical opioid use after an ED visit have not been well-studied. Our recent trial of a motivational intervention delivered to patients in the ED by a therapist resulted in modest reductions in non- medical use after the ED visit compared to a control condition. However, the intervention was unable to address the implications of opioids prescribed as a result of the ED encounter on post-ED opioid use behavior. This project will adapt the intervention for delivery after the ED visit through mobile technology in order to directly address the use of ED-provided opioids. Patients (n=600) will be recruited during an ED visit for a randomized controlled trial of the adapted intervention based on having used opioids non-medically in the prior three months and being given an opioid by an ED prescriber. In the intervention condition, interactive voice response calls will repeatedly assess non-medical opioid use and pain level and deliver intervention content. The intervention will include several potential actions that vary in intensity: assessment only, a brief message, extended messaging, or connection to a therapist by phone. Because the most helpful intensity of intervention is unknown and likely to vary between patients, the project will use an artificial intelligence stratey called reinforcement learning (RL). The RL system will continuously "learn" from the success of prior actions in similar situations with similar patients in order to select the action most likelyto reduce non-medical opioid use for each participant during each call. The RCT will be complemented by qualitative interviews to inform later implementation. The specific aims are to: (1) Adapt and enhance an existing motivational intervention to decrease non-medical opioid use after an ED visit by optimizing intervention intensity and duration through RL; (2) Examine the impact of the intervention on non-medical opioid use level during the six months post-ED visit; (3) Examine the impact of the intervention on driving after opioid use, overdose risk behaviors, and subsequent opioid-related ED visits. Secondary Aims are: (1) to examine differences in intervention effects between participants with high and low baseline levels of non-medical opioid use; and (2) to understand barriers and facilitators of implementation. This project will use a highly innovative strategy, artificial intelligence, to address a highly significant problem, non-medical opioid use. Ultimately, this study can lead to reductions in opioid- related harms and move forward the field of mobile health.