Improving glycemic control among underserved patients with insulin-treated type 2 diabetes through nurse-led, app-based behavioral intervention

“治疗惰性”，定义为当患者的治疗目标不符合时，缺乏对治疗的及时调整 MET是导致2型糖尿病(T2D)预后不良的主要原因。对于LOW来说尤其如此 社会经济地位(SES)低的人群，他们经常面临有效的疾病自我管理的障碍 三个层次：(1)病人(例如，资源有限、健康素养低、获得护理的机会有限)；(2)临床医生 (例如，缺乏时间和/或文化敏感度差)；和(3)保健系统(例如，决策支持较差或缺乏 和有效的患者小组管理)。应对慢性病复杂挑战的一种方法 这三个级别的管理使用多级别健康信息技术(HIT)支持的行为 干预措施。这些干预措施结合了对临床工作流程的更改和自我管理支持，以帮助 患者通过电子方式跟踪糖尿病，将数据传输给临床医生，并接收反馈以调整治疗。 目前，存在最少的数据来通知这种多层次行为的最佳设计、实现和使用 干预措施，特别是针对低社会经济地位人群。我的职业目标是建立一个独立资助的 研究计划，使用支持长期有效的技术来帮助缩小健康差距 自我管理，改善结果。在这个项目中，我提出了一个为期5年的培训和研究计划， 基于APP的多水平干预，以改善低SES人群的T2D结局。我将发展和发展 试点测试护士主导的、基于APP的行为干预，包括三种循证干预：(1) 通过Epic电子健康记录中的患者门户网站MyChart进行A1C结果和目标设定的教育； (2)临床医生与患者合作制定的问题解决行动计划；以及(3)远程 通过最受欢迎的糖尿病应用OnTrack进行监测，以分析血糖并确定调整的需要 治疗。人口健康信息学、HIT实施科学、健康差距和 务实的试验不仅将使我能够完成拟议的项目，而且将为扩大 概念到其他疾病和临床用例。印第安纳大学和雷根斯特里夫研究所 信息学强国，外加生物医学信息学、HIT实施、健康方面的强大导师团队 差异和定性方法提供了一个特殊的科学环境。拟开展的工作包括： (1)事件发生时间分析，以了解与持续治疗相关的人群特征 惯性，用于指导干预措施的开发和调整[目标1]；(2)糖尿病护理团队的投入以适应 临床操作层面的干预和实施策略[目标2]；(3)焦点小组研究 让低SES患者帮助定制干预措施[目标3]；以及(4)在两个诊所进行的先导性研究(n=60，30 评估对胰岛素治疗的低SES患者进行干预的可行性 T2D[目标4]。该项目将支持未来的R01申请进行务实的试验，以评估其有效性 为患有T2D的低SES人群提供基于APP的多级别行为干预。

“Therapeutic inertia,” defined as a lack of timely adjustment to therapy when a patient’s treatment goals are not met, is a major cause of poor outcomes in Type 2 Diabetes (T2D). This is especially true for low socioeconomic status (low-SES) populations, who often face barriers to effective disease self-management at three levels: (1) patient (e.g., limited resources, low health literacy, and limited access to care); (2) clinician (e.g., lack of time and/or poor cultural sensitivity); and (3) health system (e.g., poor or absent decision support and effective patient panel management). One way to address the complex challenges of chronic disease management at these three levels is with multi-level health information technology (HIT)-supported behavioral interventions. Such interventions combine changes to clinical workflows and self-management support to help patients track diabetes electronically, transmit data to clinicians, and receive feedback for adjusting treatment. Currently, minimal data exist to inform optimal design, implementation, and use of such multi-level behavioral interventions, particularly for low-SES populations. My career goal is to establish an independently funded research program that helps decrease health disparities using technologies that support effective long-term self-management and improve outcomes. In this project, I propose a 5-year training and research plan for a multi-level app-based intervention to improve outcomes for T2D in low-SES populations. I will develop and pilot-test a nurse-led, app-based behavioral intervention consisting of three evidence-based interventions: (1) education on A1C results and goal setting via MyChart, the patient portal in the Epic electronic health record; (2) a problem-solving action plan developed by clinicians in collaboration with their patients; and (3) remote monitoring via OnTrack, a top-rated diabetes app, to analyze blood glucose and identify the need to adjust treatment. Training in population health informatics, HIT implementation science, health disparities, and pragmatic trials will not only allow me to complete the proposed project, but will set the stage for expanding the concept to other diseases and clinical use cases. Indiana University and the Regenstrief Institute, an informatics powerhouse, plus a strong team of mentors in biomedical informatics, HIT implementation, health disparity, and qualitative methods, provide an exceptional scientific environment. The proposed work includes: (1) time-to-event analysis to understand population characteristics associated with persistent therapeutic inertia, used to guide intervention development and tailoring [Aim 1]; (2) input from diabetes care team to adapt the intervention and implementation strategies to the clinical operations level [Aim 2]; (3) a focus group study with low-SES patients to help tailor the intervention [Aim 3]; and (4) a pilot study at two clinics (n=60, 30 patients per clinic) to assess the feasibility of the intervention for low-SES patients with insulin-treated T2D [Aim 4]. The project will support a future R01 application for a pragmatic trial to assess the effectiveness of a multi-level app-based behavioral intervention for low-SES populations with T2D.