# Mixed-Methods Evaluation of Mobile Health Adaptive Learning Training for Pediatric Healthcare Workers in Tanzania

> **NIH NIH F32** · STANFORD UNIVERSITY · 2023 · $2,500

## Abstract

PROJECT ABSTRACT
Preventable illness in low- and middle-income countries (LMICs) contributes to millions of pediatric deaths
each year. Evidence-based guidelines (EBG) created by the World Health Organization are shown to improve
outcomes for pediatric patients in LMICs and reduce this amenable burden of disease. Utilization of existing
EBG is highly variable among healthcare workers (HCWs), however, and contributes to increased mortality.
Mobile technology has transformed many aspects of public health, but is yet to be fully leveraged for HCW
training. Adaptive learning, which uses electronic algorithms to deliver individualized content to learners,
promotes increased learning efficiency in high-resource settings and could strengthen mobile training platforms
in LMICs. To evaluate the efficacy of these tools in addressing the existing educational gap in LMICs, we
designed an adaptive electronic learning curriculum based on existing EBG for the management pediatric
illness in LMICs. We hypothesize that an adaptive electronic learning curriculum will increase HCW knowledge
of EBG for the care of serious illness in children and that knowledge of learner perceptions will inform the
creation and implementation of future electronic learning interventions.
We will conduct a mixed-methods study among a cohort of pediatric HCWs in Mwanza, Tanzania. Quantitative
study design will be prospective randomized parallel-group double- blinded with an allocation ratio of 1:1. All
participants will complete an electronic learning curriculum on priority content areas as defined by local
stakeholders. Participants in the intervention arm will receive an adaptive electronic learning curriculum, and
controls will receive a non-adaptive electronic learning curriculum. Knowledge acquisition will be measured
using standard mean effect size comparing pre- and post-curriculum knowledge assessments. Semi-structured
and group interviews with a random sample of quantitative participants will be used to determine HCW habits
and perceptions relating to electronic learning interventions. Successful completion of the proposed research
will serve as the foundation for the development of innovative solutions and low-cost implementation strategies
aimed at improving the care of seriously ill children worldwide.

## Key facts

- **NIH application ID:** 10863717
- **Project number:** 3F32HD106683-01A1S1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Zachary Haines Smith
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $2,500
- **Award type:** 3
- **Project period:** 2023-06-20 → 2023-07-09

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10863717

## Citation

> US National Institutes of Health, RePORTER application 10863717, Mixed-Methods Evaluation of Mobile Health Adaptive Learning Training for Pediatric Healthcare Workers in Tanzania (3F32HD106683-01A1S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10863717. Licensed CC0.

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