From paper surveys to chatbots: Advancing digital monitoring of diet quality in Guatemala
The diet of Guatemalans has deep roots in the Mayan worldview, which relied on the milpa system composed of maize, beans, and squash—foods that continue to be consumed daily by the population. However, between May and August 2025, it was estimated that nearly 19% of the Guatemalan population was experiencing an acute food insecurity crisis, struggling to access not only these traditional foods but also others essential for diet diversification. This situation stems from a combination of socioeconomic and environmental factors, ranging from rising prices of the basic food basket to extreme climate conditions that lead to crop losses and, therefore, reduced food reserves.
From paper surveys to chatbots: Advancing digital monitoring of diet quality in Guatemala
The diet of Guatemalans has deep roots in the Mayan worldview, which relied on the milpa system composed of maize, beans, and squash—foods that continue to be consumed daily by the population. However, between May and August 2025, it was estimated that nearly 19% of the Guatemalan population was experiencing an acute food insecurity crisis, struggling to access not only these traditional foods but also others essential for diet diversification. This situation stems from a combination of socioeconomic and environmental factors, ranging from rising prices of the basic food basket to extreme climate conditions that lead to crop losses and, therefore, reduced food reserves.
To understand the dynamics of these factors and quantify their impact on food availability, access, and consumption, it is essential to have dynamic and timely information systems that allow for monitoring the quality and variety of what people actually eat. Although Guatemala has the National Food and Nutrition Security Information System (SIINSAN) and other monitoring tools, there is still no real-time system that supports timely decision-making at the local level.
In the search for solutions to improve data collection on diet quality, a crowdsourcing-based study in Rwanda was identified. It consisted of sending participants the Diet Quality Questionnaire (DQQ) through automated calls (IVR) and text messages (SMS). The study generated approximately 1,800 weekly responses over one year, demonstrating the capacity and versatility of mobile phones to collect data at scale and at low cost.