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.
This proposal will develop and test LIVEBORN, an innovative mobile health application to reduce newborn mortality by improving bag mask ventilation in low-resource settings. LIVEBORN will collect real-time data on resuscitation events and provide automated feedback both during and after a resuscitation. This research seeks to shift the paradigm for improving bag mask ventilation from simulation-based training to complementary, guided bedside practice.
