An Integrated mHealth Strategy to Improve Newborn Resuscitation in Low and Lower-Middle Income Countries

项目摘要/摘要 每年有近100万新生儿死于出生时呼吸不畅。几乎所有这些死亡都发生在 低收入和中低收入国家(LMIC)。这些死亡是在救生袋面罩通风时造成的 (BMV)延迟或中断。基于模拟的培训通常用于改善BMV，但在 业绩保持不变。用反馈策略改善BMV有很强的科学前提。在……里面 随机模拟试验，在BMV(实时指导)期间和BMV(汇报)改善后的反馈 性能。床边复苏期间的反馈可能会减少延迟和中断的BMV，但需要 专业临床医生-教育家。移动医疗(MHealth)技术可以实施和评估 LMICs的床边反馈策略。这项研究的总体目标是通过以下方式降低新生儿死亡率 使用创新的mHealth应用程序通过床边反馈改善LMIC的BMV 还活着。这项研究的具体目标是1)开发LiveBorn，这是一款mHealth应用程序，以改进 BMV，2)设计和评估针对LiveBorn的反馈策略的可行性，3)评估 在一项混合随机试验中出生。这项提案将在金沙萨的10个医疗机构进行， 刚果民主共和国(刚果(金))的助产士。LiveBorn将使用以下工具识别抑郁新生儿 来自新的心率监测器的心率和观察者输入的提供者行动的数据。经过比较后 行动到推荐的护理，LiveBorn将提供实时指导和支持汇报。活生生 将通过科学严谨的过程来开发，包括形成性研究、技术开发 以及可用性测试。针对LiveBorn反馈的集成mHealth策略(一个用于实时指导 一个用于汇报情况)将与来自两个设施的刚果助产士合作设计，使用试验 改进了实践。最终战略将在为期3个月的可行性测试中进行评估，为 混合随机试验。在一项混合随机试验中，8个设施点将被随机聚类为实时 指导或向LiveBorn汇报情况。在一段时间的基线数据收集之后，助产士将实施他们的 分配给LiveBorn的反馈策略。LiveBorn在BMV上的反馈效果将是 比较基线和干预数据进行评估。如果对LiveBorn的反馈有效，则相对的 将评估实时指导与汇报的有效性。主要结果将是到了 启动BMV。二次结局将中断BMV和24小时新生儿死亡率。可行性和 对LiveBorn的反馈的可接受性将使用混合方法进行评估。这项研究将 由五个机构强有力的合作执行：北卡罗来纳大学教堂山分校(UNC)， 刚果民主共和国的金沙萨公共卫生学院(KSPH)、拉达尔全球卫生组织、RTI国际和 杰皮亚戈。KSPH进行移动健康研究的能力将通过制定一个 移动健康实施科学课程和建立KSPH-UNC实施科学核心。

Project Summary/Abstract Almost one million newborns die each year from failure to breathe at birth. Nearly all of these deaths occur in low and lower-middle income countries (LMICs). These deaths result when life-saving bag mask ventilation (BMV) is delayed or interrupted. Simulation-based training is commonly used to improve BMV, but gaps in performance remain. There is strong scientific premise for improving BMV with feedback strategies. In randomized simulation trials, feedback during BMV (real-time guidance) and after BMV (debriefing) improves performance. Feedback during bedside resuscitations may reduce delayed and interrupted BMV, but requires expert clinician-educators. Mobile health (mHealth) technology could enable implementation and evaluation of feedback strategies at the bedside in LMICs. The overall goal of this study is to reduce newborn mortality by improving BMV in LMICs through bedside feedback using an innovative mHealth application called LIVEBORN. The specific aims of this study are to 1) develop LIVEBORN, an mHealth application to improve BMV, 2) design and evaluate feasibility of feedback strategies for LIVEBORN, and 3) evaluate effectiveness of LIVEBORN in a hybrid, randomized trial. This proposal will take place in 10 health facilities in Kinshasa, Democratic Republic of the Congo (DRC) with midwives. LIVEBORN will identify depressed newborns using heart rate from a new heart rate monitor and data on provider action’s entered by an observer. After comparing actions to recommended care, LIVEBORN will deliver real-time guidance and support debriefing. LIVEBORN will be developed through a scientifically rigorous process involving formative research, technical development and usability testing. Integrated mHealth strategies for feedback with LIVEBORN (one for real-time guidance and one for debriefing) will be designed in collaboration with Congolese midwives from two facilities using trials of improved practices. The final strategies will be evaluated in a 3-month feasibility test in preparation for a hybrid, randomized trial. In a hybrid, randomized trial, eight facilities will be cluster randomized to real-time guidance or debriefing with LIVEBORN. After a period of baseline data collection, midwives will implement their assigned feedback strategy with LIVEBORN. The effectiveness of feedback with LIVEBORN on BMV will be evaluated comparing baseline and intervention data. If feedback with LIVEBORN is effective, the relative effectiveness of real-time guidance versus debriefing will be evaluated. The primary outcome will be the time to initiation of BMV. Secondary outcomes will be interrupted BMV and 24-hour newborn mortality. Feasibility and acceptability of feedback with LIVEBORN will be evaluated using a mixed methods approach. This study will be executed by a strong collaboration of five institutions: the University of North Carolina at Chapel Hill (UNC), the Kinshasa School of Public Health (KSPH) in the DRC, Laerdal Global Health, RTI International and Jhpiego. KSPH’s capacity to conduct mHealth research will be strengthened through the development of an mHealth Implementation Science course and establishment of a KSPH-UNC Implementation Science Core.