Design and Feasibility of a Mobile Mental Health Stigma Reducing Intervention towards Optimization of Care for Black Adults with Depression and Anxiety

项目摘要/摘要 这份K23提案旨在帮助佩德森博士实现她成为独立人士的长期目标 具有开发和测试干预措施以减少心理健康污名的专业知识的调查员，增加 参与精神健康服务，并改善精神健康结果。候选人和她的导师 我制定了全面的培训计划：1)建立以用户为中心的设计和数字方面的专业知识 心理健康干预开发，以便她可以在干预设计的早期就让最终用户参与进来 在这项研究和未来的试验中都有过程；2)扩大她在社区参与研究方面的培训，以 解决健康差距并促进对精神健康服务的参与；以及3)提高她在 为未来的R01应用进行临床试验和临床试验数据的量化分析，重点是 实施和测试全能的反污名接触干预措施。抗病毒药物的有效性和精确度 在服务不足的黑人成年人中改善心理健康结果的污名干预措施非常有限 并代表着严重的公共卫生差距。研究表明，耻辱会导致与初选有关的残疾 精神疾病的症状，并增加与精神疾病有关的发病率和过早死亡率。 与白人成年人相比，患有精神疾病的黑人成年人有更多的慢性病，也更严重 陈述时的疾病。Meta分析一直表明，面对面和基于视频的接触 与患有精神疾病的人在一起可以减少耻辱。最近的研究表明，接触交付是有区别的 显示了基于视频的接触的效果大小与面对面接触相当。数字心理健康也 提供了一个平台，以克服早期访问、实施和可扩展性方面的障碍。强劲的初步数据 将精神疾病污名和医疗不信任确定为关键干预目标，应在 以提高黑人成年人的精神卫生服务利用率。为了解决文献中的这些空白， 提出了以下具体目标：1)将使用以用户为中心的设计方法来开发自 管理的、基于视频的移动应用程序，以减少黑人成年人的污名和医疗不信任；2)试点 将采用随机试验设计评估其可行性和可接受性，并检验其初步疗效。 一款自我管理的、基于视频的移动应用程序在减少患有精神疾病的黑人成年人中的污名 中度至重度抑郁或焦虑；以及3)因果中介分析将被用来估计 预期/制定/内化的污名和医疗不信任的变化在多大程度上调解 干预对一次和二次结局的影响。这一提议的关键创新之处在于，它将 成为第一个通过有针对性的方法减少精神健康污名的流动健康干预措施 整合用户特征(如种族、民族和宗教信仰)，同时解决医疗不信任问题 强大的以用户为中心的设计方法，并以服务利用率为目标。

Project Summary/Abstract This K23 proposal is designed to help Dr. Pederson achieve her long-term goal of becoming an independent investigator with expertise in developing and testing interventions that reduce mental health stigma, increase engagement in mental health services, and improve mental health outcomes. The candidate and her mentors have developed a comprehensive training plan: 1) to build her expertise in user-centered design and digital mental health intervention development, so that she can engage end-users early in the intervention design process both in this study and in future trials; 2) to expand her training in community engaged research to address health disparities and promote engagement in mental health services; and 3) to bolster her skills in the conduct of clinical trials and quantitative analysis of clinical trial data for a future R01 application centered on implementing and testing a fully powered anti-stigma contact intervention. The efficacy and precision of anti- stigma interventions to improve mental health outcomes among underserved Black adults are grossly limited and represent a critical public health gap. Studies show stigma compounds disabilities related to the primary symptoms of mental illness, and increases morbidity and premature mortality related to mental illness. Compared with white adults, Black adults with mental illness have more chronic disease, and more severe illness at presentation. Meta-analyses have consistently shown that both face-to-face and video based contact with individuals with mental illness can reduce stigma. Recent studies that distinguished contact delivery showed effect size for video-based contact to be comparable to face-to-face contact. Digital mental health also offers a platform to overcome barriers to early access, implementation, and scalability. Strong preliminary data identify mental illness stigma and medical mistrust as critical intervention targets that should be addressed in order to increase mental health service utilization among Black adults. To address these gaps in the literature, the following specific aims are proposed: 1) a user-centered design approach will be used to develop a self- administered, video-based mobile app to reduce stigma and medical mistrust among Black adults; 2) a pilot randomized trial design will be used to assess the feasibility and acceptability, and test the preliminary efficacy, of a self-administered, video-based mobile app in reducing mental illness stigma among Black adults with moderate to severe depression or anxiety; and 3) a causal mediation analysis will be used to estimate the extent to which changes in anticipated/enacted/internalized stigma and medical mistrust mediate the intervention’s effect on the primary and secondary outcomes. The key innovation of this proposal is that it will be the first mobile health intervention focused on mental health stigma reduction using a targeted approach by integrating user characteristics (such as race, ethnicity and religiosity), while addressing medical mistrust using a robust user-centered design approach and targeting service utilization.