Objective: This tool is designed for resource poor settings to effectively collect and analyse dietary intake data. It can be used in small or large projects, at local, regional and national level to collect accurate and timely data on dietary patterns for monitoring purposes or for assessing effectiveness of implemented programs over time.

Challenge addressed: Traditionally, dietary assessment at population-level is done in national surveys using the 24h dietary recall interview method, that is recorded on paper. Later on the interviews are retyped to electronic format and calculations for food and nutrient intakes done using a program that normally requires a license. This process is laborious and costly, thus there is a clear need for open-source tools that can leverage collecting dietary data in resource poor settings.

Opportunity and purpose: This tool makes it possible to significantly reduce the time spend on data clearance and food and nutrient calculations. The tool has been successfully tested in a survey of 800 mothers and small children (aged 6-23 months) in Kenya in 2021. During the data collection, data clearance and calculations were done on daily basis, and the food and nutrient intakes were ready for further analyses only two weeks after the data collection ended. Currently, the tool uses Kenyan food composition tables in excel form enriched with some commonly appearing foods from other composition tables, namely USDA food composition database and the Finnish Fineli® composition database. For portion size estimation, the tool includes the use of Photographic Food Atlas for Kenyans that has been validated among Kenyan women in reproductive age. The Atlas is freely available online at https://kenfinedura.com/research/367-2/.

The tool can be adopted in other countries than Kenya with some prerequisites. the local food composition tables/database that contains the information of energy and nutrient contents of foods and dishes needs to be at a certain level of completeness. Second, there needs to be a standard tool for portion size estimation that can be used in the field work effectively, such as a collection of portion size pictures of most commonly used foods.

Technological considerations: In the core of this digital dietary assessment tool is Open Data Kit (ODK) platform which is a freely available application that runs on mobile devices (https://docs.getodk.org/getting-started/). In addition of being free of charge, ODK can be used to collect data offline. To record data from 24h dietary recall interview on ODK, the interview structure was built in an XLSForm standard that is used by the ODK ecosystem. The XLSForm for 24h dietary recall for adult women is provided here. The XLSForm can be uploaded to a cloud service, such as ONA (www.ona.io) in order to connect it with the ODK collection application on a mobile device. After the data has been collected using ODK, the data can be sent to the cloud service and downloaded from there. Next, data clearance and food and nutrient calculations can be done using the provided R code. R-software and its integrated development environment RStudio are freely available online (https://posit.co/download/rstudio-desktop/).

Contact information

For assistance in getting to use the tool, please contact Dr. Noora Kanerva

Email: noora.kanerva@helsinki.fi

Address: University of Helsinki| Department of Food and Nutrition, P.O. Box 66 (Agnes Sjöberginkatu 2)| FIN-00014 University of Helsinki

Linear programming is a mathematical optimization technique that can be used to formulate food products that meet nutritional targets while remaining affordable and culturally acceptable. In the context of the InnoFoodAfrica project, linear programming is being used to fill nutrient gaps in African and European diets. By creating food products that meet these nutrient gaps, the project aims to prevent malnutrition, even in times of crisis such as droughts or migration.

To accomplish this, the project will conduct a food consumption survey in African countries to gather data on current dietary patterns. The survey data will be used to model African diets and create food products that meet nutrient targets while remaining culturally acceptable and affordable. Additionally, existing food consumption databases will be used to model European diets, specifically the Mediterranean and Baltic Sea diets. The project will evaluate the potential contribution of innovative African foods to European diets through potential exports.

The modelling exercise will consider additional constraints such as environmental and health impacts to ensure that the food products developed are sustainable and promote good health. The data generated by the modelling exercise will be used to develop practical tools for implementation and development of country-specific Food-Based Dietary Guidelines. These guidelines will be especially useful in addressing issues such as small children malnutrition and adult obesity risk.

In summary, the InnoFoodAfrica project is using linear programming techniques to create food products that meet nutritional targets, remain affordable and culturally acceptable, and have minimal environmental and health impacts. The project is bench marking against European diets to evaluate the potential contribution of innovative African foods in European diets through potential exports. The data generated by the project will be used to develop practical tools for implementation and development of country-specific Food-Based Dietary Guidelines, with a focus on preventing malnutrition and promoting good health.

Based on the InnoFoodAfrica food consumption survey conducted in Kenya, a health promotion project was designed to address the growing problem of overweight and obesity among adult women. Even though this project plan has a specific focus, it gives concrete examples of how to adopt the steps of intervention planning into other similar projects. The health promotion module provides information and guidance on how to conduct a needs assessment and behavioral and environmental assessments. It also provides guidance on how to prepare matrices of change, illustrates how to select theory-informed interventions, then provides step by step guide on how to define program components, planning, implementation, and evaluation.

Contact information

Email: noora.kanerva@helsinki.fi mikael.fogelholm@helsinki.fi

Address: University of Helsinki| Department of Food and Nutrition, P.O. Box 66 (Agnes Sjöberginkatu 2)| FIN-00014 University of Helsinki