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

改善低收入和中低收入国家新生儿复苏的综合移动医疗战略

• 批准号: 10704761
• 负责人: Jacquelyn K Patterson
• 金额: $ 17.23万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10704761
• 关键词: Address; Birth; Breathing; Caring; Cessation of life; Clinical; Collaborations; Data; Data Collection; Democratic Republic of the Congo; Depressed mood; Development; Effectiveness; Ensure; Evaluation; Event; Faculty; Failure; Feedback; Focus Groups; Future; Goals; Grant; Health Technology; Health care facility; Heart Rate; Hour; Hybrids; Institution; International; Interruption; Intervention; Life; Manikins; Masks; Methods; Midwife; Mobile Health Application; Neonatal Mortality; Newborn Infant; North Carolina; Performance; Phase; Preparation; Process; Provider; Public Health Schools; Randomized; Recommendation; Research; Resource-limited setting; Resuscitation; Technology; Testing; Time; Training; Universities; acceptability and feasibility; design; digital; digital tool; effectiveness evaluation; experience; feasibility testing; global health; heart rate monitor; implementation science; improved; innovation; low and middle-income countries; mHealth; mortality risk; neonatal resuscitation; novel; pilot test; primary outcome; programs; randomized trial; relative effectiveness; secondary outcome; simulation; tool; usability; ventilation

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.

项目摘要/摘要 每年将近一百万新生儿因出生时未能呼吸而死。几乎所有这些死亡都发生在 中低收入国家（LMIC）。当挽救生命的袋子面具通风时，这些死亡会导致这些死亡 （BMV）被延迟或中断。基于模拟的培训通常用于改善BMV，但差距 表现仍然存在。通过反馈策略改善BMV有很强的科学前提。在 随机模拟试验，BMV期间的反馈（实时指导）和BMV（汇报）后改进 表现。床边复苏期间的反馈可能会减少延迟和中断的BMV，但需要 专家临床教育者。移动健康（MHealth）技术可以实施和评估 LMICS床边的反馈策略。这项研究的总体目标是通过 使用创新的MHealth应用程序通过床头反馈来改善LMIC的BMV 活出生。这项研究的具体目的是1）开发LiveBorn，这是一种改进的MHealth应用程序 BMV，2）设计和评估活产反馈策略的可行性，3）评估有效性 在一项混合，随机试验中活体。该提案将在金沙萨的10个医疗机构中进行 刚果民主共和国（DRC）与助产士。 Liveborn将使用 来自观察者输入的新的心率监测器和有关提供者行动的数据的心率。比较后 推荐护理的行动，LiveBorn将提供实时指导并支持汇报。活出生 将通过涉及形成性研究，技术发展的科学严格过程开发 和可用性测试。 LiveBorn的综合MHealth策略（用于实时指导） 一个用于汇报）将与来自两个设施的刚果助产士合作设计 改进的实践。最终策略将在为期3个月的可行性测试中评估，以准备 混合，随机试验。在一项混合，随机试验中，将八个设施聚集到实时 Liveborn的指导或汇报。经过一段时间的基线数据收集，助产士将实施他们的 通过LiveBorn分配反馈策略。对Liveborn对BMV的反馈的有效性将是 评估了比较基线和干预数据。如果对Liveborn的反馈有效，则亲戚 将评估实时指导与汇报的有效性。主要结果将是时间 BMV的启动。次要结果将被中断BMV和24小时新生儿死亡率。可行性和 将使用混合方法方法评估反馈的可接受性。这项研究会 由五个机构的强大合作执行：北卡罗来纳大学教堂山（UNC）， Kinshasa公共卫生学院（KSPH），DRC，Laerdal Global Health，RTI International和 jpego。 KSPH的进行MHealth研究的能力将通过开发 MHealth实施科学课程和建立KSPH-UNC实施科学核心。