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

项目摘要 尽管数字健康技术现在广泛用于治疗和监测 在数字医疗的应用方面，在数字医疗的吸收和有效性方面存在种族和民族差异 干预措施。与我们的母公司HEAL研究相关，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，更有可能报告严重疼痛， 非西班牙裔白人除了在获得和采用数字健康干预措施方面的差距外， 已知少数种族和族裔在疼痛的发生率和报告方面存在差异。 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 比非西班牙裔白人更容易报告严重疼痛。针对这一多样性的拟议研究， 纳入和参与补充我们的NIH HEAL研究评估的作用，治疗 慢性下背痛（cLBP）的VR将为促进多样性和包容性提供一个框架 为我们的医疗中心和其他地方的未来数字健康试验做出努力。此外，它还将增强 通过寻求增加参与者的比例与历史上较少获得父母的研究 和熟悉数字技术，同时提高整体参与者的种族和民族 多样性使用NIH行为干预发展阶段模型， 人工智能（AI）驱动的队列构建工具的进步，我们建议有意义地 提高非西班牙裔黑人和西班牙裔参与者在我们的母体NIH HEAL中的入学率 研究通过实现两个目标：（1）为非西班牙裔黑人和西班牙裔美国人量身定制招聘材料 与代表性患者咨询委员会合作的cLBP患者;和（2）过采样 非西班牙裔黑人和西班牙裔参与者在父母研究中使用AI驱动的队列构建 电子健康记录中的工具。

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.