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

> **NIH NIH R33** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2024 · $169,896

## Abstract

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 Gl...

## Key facts

- **NIH application ID:** 10893389
- **Project number:** 5R33HD103058-05
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Jacquelyn K Patterson
- **Activity code:** R33 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $169,896
- **Award type:** 5
- **Project period:** 2022-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10893389

## Citation

> US National Institutes of Health, RePORTER application 10893389, An Integrated mHealth Strategy to Improve Newborn Resuscitation in Low and Lower-Middle Income Countries (5R33HD103058-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10893389. Licensed CC0.

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