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: 10260542
• 负责人: Jacquelyn K Patterson
• 金额: $ 18万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-09 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10260542
• 关键词: Address; Birth; 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; Periodicity; Phase; Preparation; Process; Provider; Public Health Schools; Randomized; Research; Resources; Resuscitation; Savings; Testing; Time; Training; Universities; acceptability and feasibility; base; design; digital; effectiveness evaluation; experience; feasibility testing; global health; heart rate monitor; implementation science; improved; innovation; low and middle-income countries; low income country; mHealth; mortality risk; neonatal resuscitation; novel; 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.

项目总结/摘要 每年有近100万新生儿死于出生时呼吸困难。几乎所有这些死亡都发生在 低收入和中低收入国家（LMIC）。这些死亡的结果时，救生袋面罩通风 (BMV)延迟或中断。基于模拟的培训通常用于提高BMV，但 业绩依然。运用反馈策略提高BMV具有很强的科学性。在 随机模拟试验，BMV（实时指导）期间和BMV（汇报）后的反馈改善 性能床旁复苏期间的反馈可以减少延迟和中断的BMV，但需要 临床教育专家移动的健康（mHealth）技术可以实现和评估 反馈策略在LMIC的床边。这项研究的总体目标是通过以下措施降低新生儿死亡率： 通过使用创新的移动健康应用程序（称为 活的。这项研究的具体目的是1）开发LIVEBORN，一个移动健康应用程序，以改善 BMV，2）设计并评估LIVEBORN反馈策略的可行性，3）评估 LIVEBORN在一项混合随机试验中。这一提议将在金沙萨的10个保健设施进行， 刚果民主共和国（DRC），有助产士。LIVEBORN将使用以下方法识别抑郁新生儿 来自新心率监视器的心率和由观察者输入的关于提供者动作的数据。比较后 根据建议的护理措施，LIVEBORN将提供实时指导和支持汇报。活产 将通过一个科学严谨的过程，包括形成研究，技术开发， 和可用性测试。与LIVEBORN集成的移动健康反馈策略（一个用于实时指导 将与来自两个设施的刚果助产士合作， 改进的做法。最终的战略将在为期3个月的可行性测试中进行评估， 混合随机试验在一项混合随机试验中，8个设施将被随机分组至实时 或以其为指导，或以其为实践。经过一段时间的基线数据收集，助产士将实施他们的 与LIVEBORN一起分配反馈策略。LIVEBORN对BMV的反馈的有效性将是 比较基线和干预数据进行评估。如果与LIVEBORN的反馈是有效的， 将评价实时指导与汇报的有效性。主要结果将是 启动BMV。次要结局将是中断BMV和24小时新生儿死亡率。可行性和 将使用混合方法评估LIVEBORN反馈的可接受性。本研究将 由五个机构的强有力的合作执行：北卡罗来纳州查佩尔山大学， 刚果民主共和国金沙萨公共卫生学院（KSPH）、挪度全球健康、RTI国际和 Jhpiego. KSPH进行移动健康研究的能力将通过开发一个 移动健康实施科学课程，并建立KSPH-ESTA实施科学核心。