Improving representation of non-Hispanic Black and Hispanic study participants in a trial of virtual reality for chronic lower back pain

项目总结 尽管数字健康技术现在广泛用于治疗和监测 在数字健康的应用方面，在接受和有效性方面存在种族和民族差异 干预措施。与我们的父母Hear研究有关，该研究检查了数字健康干预(虚拟 现实)对于疼痛管理，最近的数据表明，非西班牙裔黑人和西班牙裔患者 不太可能获得数字健康信息，而更有可能报告严重疼痛 非西班牙裔白人。除了在获取和采用数字健康干预措施方面存在差异外， 种族和族裔少数群体在疼痛发生率和报告方面存在着众所周知的差异。 例如，对2000名患者的研究发现，非西班牙裔黑人和西班牙裔患者 与非西班牙裔白人相比，更有可能报告剧烈疼痛。关于这种多样性的拟议研究， 纳入和参与我们的NIH Hear研究，评估治疗性药物的作用 慢性下腰痛(CLBP)的VR将提供一个促进多样性和包容性的框架 在我们的医疗中心和其他地方为未来的数字健康试验做出努力。此外，它还将加强 通过寻求增加历史上接触较少的参与者的比例来进行家长研究 并熟悉数字技术，同时提高整体参与者的种族和民族 多样性。使用NIH阶段模型支持的行为干预发展 人工智能(AI)驱动的队列构建工具的进展，我们提出了有意义的 提高非西班牙裔黑人和西班牙裔参与者在我们母公司NIH Hear的参保率 通过实现两个目标进行研究：(1)为非西班牙裔黑人和西班牙裔定制招聘材料 与具有代表性的患者咨询委员会合作的慢性下腰痛患者；以及(2)过度抽样 非西班牙裔黑人和西班牙裔家长研究参与者使用人工智能驱动的队列建立 电子健康记录中包含的工具。

PROJECT SUMMARY Although digital health technologies are now widely available for both therapeutic and monitoring applications, there are racial and ethnic disparities in uptake and effectiveness of digital health interventions. Of relevance to our parent HEAL study examining a digital health intervention (virtual reality) for pain management, recent data indicate that non-Hispanic Black and Hispanic patients are less likely to have access to digital health information and more likely to report severe pain than non-Hispanic whites. In addition to disparities in access and adoption of digital health interventions, there are known disparities in the incidence and reporting of pain by racial and ethnic minorities. For example, study of 2000 patients found that non-Hispanic Black and Hispanic patients were more likely to report severe pain than non-Hispanic whites. The proposed study for this Diversity, Inclusion and Engagement Supplement to our NIH HEAL study evaluating the role of therapeutic VR for chronic lower back pain (cLBP) will provide a framework to advance diversity and inclusion efforts for future digital health trials at our medical center and beyond. Further, it will enhance the parent study by seeking to increase the proportion of participants with historically less access to and familiarity with digital technologies while enhancing overall participant racial and ethnic diversity. Using the NIH Stage Model for Behavioral Intervention Development supported by advances in artificial intelligence (AI)-driven cohort building tools, we propose to meaningfully improve enrollment rates of non-Hispanic Black and Hispanic participants in our parent NIH HEAL study by achieving two aims: (1) tailor recruitment materials for non-Hispanic Black and Hispanic patients with cLBP in partnership with representative patient advisory boards; and (2) oversample non-Hispanic Black and Hispanic participants in parent study using an AI-driven cohort building tool housed within the electronic health record.