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

减少非医疗阿片类药物的使用：自动适应的移动医疗干预措施

• 批准号: 9416993
• 负责人: Amy S B Bohnert
• 金额: $ 53.72万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-01 至 2022-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9416993
• 关键词: Abnormal coordination; Accident and Emergency department; Acute; Address; Adult; Artificial Intelligence; Automobile Driving; Behavior; Caring; Clinical; Complement; Emergency Care; Emergency Department patient; Emergency department visit; Ensure; Future; Guidelines; Health Technology; Individual; Injury; Intervention; Interview; Lead; Learning; Medical; Methodology; Monitor; Opioid; Opioid Analgesics; Outcome; Outpatients; Overdose; Pain; Participant; Patients; Play; Process; Psychological reinforcement; Psychotherapy; Public Health; Randomized; Randomized Clinical Trials; Randomized Controlled Trials; Recording of previous events; Reporting; Research; Research Methodology; Resources; Risk Behaviors; Role; Safety; Severities; Site; Surveys; System; Telephone; Testing; Therapeutic; Time; Treatment Efficacy; United States National Institutes of Health; Voice; Work; adverse outcome; base; behavioral outcome; brief motivational intervention; clinical practice; drugged driving; experience; high risk; improved; individualized medicine; innovation; intervention effect; learning progression; mHealth; mobile computing; motivational intervention; multidisciplinary; new technology; nonmedical use; opioid misuse; opioid therapy; opioid use; overdose risk; post intervention; prescription opioid; public health priorities; public health relevance; recruit; response; screening; skills; success; treatment as usual

 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.

 描述（由申请人提供）：近年来在美国，与类阿片处方有关的问题，包括非医疗使用和过量用药，增加到历史上前所未有的水平，构成了一场公共卫生危机。急诊科（ED）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相关不良后果的高风险人群。一半的艾德就诊是因为疼痛，三分之一的艾德就诊 导致开了阿片类药物此外，在我们的试点工作中，在艾德研究中心接受调查的患者中有四分之一报告在前三个月使用了非医疗阿片类药物。尽管这个问题很重要，但在艾德就诊后减少非医疗阿片类药物使用的策略还没有得到很好的研究。我们最近的一项试验是对那些患有抑郁症的病人进行激励干预。 与对照条件相比，治疗师进行的艾德治疗导致艾德就诊后非医疗用途适度减少。然而，干预措施无法解决由于艾德遭遇而处方的阿片类药物对艾德后阿片类药物使用行为的影响。该项目将通过移动的技术调整干预措施，以便在艾德访视后交付，从而直接解决ED提供的阿片类药物的使用问题。将在艾德访视期间招募患者（n = 600）进行适应性干预的随机对照试验，该试验基于在前三个月内非药物使用阿片类药物并由艾德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疗阿片类药物使用和疼痛水平，并提供干预内容。干预将包括几个强度不同的潜在行动：仅评估，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是未知的，并且可能因患者而异，因此该项目将使用一种称为强化学习（RL）的人工智能策略。RL系统将不断从类似患者在类似情况下的先前行动的成功中“学习”，以便选择最有可能减少每个参与者在每次通话期间使用非医疗阿片类药物的行动。将通过定性访谈对随机对照试验进行补充，以便为以后的实施提供信息。具体目标是：（1）通过RL优化干预强度和持续时间，调整和加强现有的动机干预，以减少艾德访视后的非医疗阿片类药物使用;（2）检查干预对艾德访视后6个月内非医疗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、过量风险行为和随后的阿片类药物相关艾德访视的影响。次要目标是：（1）检查非医疗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因素。该项目将使用高度创新的人工智能策略来解决一个非常重要的问题，即非医疗阿片类药物的使用。最终，这项研究可以导致阿片类药物相关危害的减少，并推动移动的健康领域向前发展。