In this new publication, “D2A: a community-led smartphone tool for malnutrition screening in Kenya”, Ravi Bhavnani and Nina Sophia Link examine the potential of smartphone-based tools to strengthen child acute malnutrition screening in low-resource settings. Based on a seven-month pilot study with 180 households in West Pokot County, Kenya, the article introduces and evaluates the D2A (“Data to Analysis”) smartphone application, designed to enable mothers and caregivers to self-report Family MUAC (Mid-Upper Arm Circumference) and key household-level indicators of malnutrition risk. 

The study assesses the app along three dimensions: accuracy, acceptance, and cost. The findings suggest that app-based self-collection of Family MUAC can achieve similar classification accuracy to conventional paper-based nutrition screening, while also offering a lower-cost alternative when implemented over time. The study further shows that households were able to use the app successfully after brief training, and that support from Community Health Volunteers improved survey completion rates. More broadly, the article contributes to ongoing research on how low-cost digital tools can support more frequent, timely, and locally led nutrition monitoring—particularly in remote or difficult-to-access areas where conventional data collection is often costly or easily disrupted. The publication also speaks to wider efforts to improve real-time information for early warning, targeting, and intervention in child nutrition. 

This research forms part of the broader AIMM project (Artificial Intelligence for Monitoring Malnutrition), which explores how smartphone-based and AI-supported tools can improve the monitoring of child nutrition in resource-constrained settings.

Published: 27 February 2026