Mobile Health Intervention to Promote Positive Infant Health Outcomes in Guatemala
The objectives of this project are to develop and determine the effectiveness of an mHealth smartphone technology which can be used to engage primary caregivers directly in the active monitoring of their infants’ development, and to provide tailored feedback and support for the provision of nurturing care and positive infant developmental outcomes.
2021-2026 · NIH / NICHD (PI Beth Smith, Children’s Hospital Los Angeles) Longitudinal Study of Aging in Guatemala (ELEGUA): Pilot Survey
Pilot study for the Longitudinal Study of Aging in Guatemala (ELEGUA), a planned nationally representative, population-based aging cohort modeled on the Health and Retirement Study and Harmonized Cognitive Assessment Protocol.
2025–present · NIH/NIA (R21AG090819); University of Michigan Center for Global Health Equity Food Supplementation and Behavioral Counseling to Reduce the Double Burden of Malnutrition (K'ASLEM)
Randomized trial testing whether food supplementation plus nutrition and lifestyle counseling for pregnant women can improve both maternal weight and infant growth in Maya communities in Guatemala, where child undernutrition and adult obesity commonly occur side by side.
2024–present · NIH/NICHD (R01HD114708) Implementation of a National Model for Diabetes and Hypertension Treatment in Guatemala
This project is working in collaboration with the Ministry of Health and INCAP to scale up diabetes and hypertension treatment in public health facilities around the country.
2024–present · World Diabetes Foundation; NIH/NHLBI (K23HL161271, R01HL178752) Artificial Intelligence and Quality Improvement to Reduce Neonatal Mortality
This project builds on Wuqu' Kawoq's long standing Mobile Health Program, but now focusing on specific quality improvement initiatives, including enhanced real-time smartphone decision support, to detect neonatal complications and prevent neonatal deaths.
2024–present · Google Nonprofit Foundation AI-Driven Low-cost Ultrasound for Automated Quantification of Hypertension, Preeclampsia, and Fetal Growth Restriction
In low-resource settings, high rates of perinatal mortality are due in part to challenges accessing affordable technologies to screen and monitor high-risk maternal-fetal conditions, such as fetal growth restriction (FGR) and preeclampsia. To address these issues, we are using artificial intelligence to develop techniques for predicting FGR and preeclampsia from low-cost hand-held maternal and fetal Doppler recordings.
2023-2027 · NIH/NICHD