Source: International Atomic Energy Agency – IAEA
A new equation is helping scientists check the reliability of people’s reports about what they eat, supporting better nutrition research.
A new equation, developed using data from an IAEA nutrition database, is helping researchers assess the accuracy of self-reported dietary information in studies and surveys.
This equation, developed using machine learning, has revealed that close to a third of records in two widely used nutritional datasets were likely to be misreported, according to a recent scientific article published in Nature Food.
This revelation underlines the need for better methods to measure what people really eat.
Nutritional epidemiology, a field that examines the link between diet and human diseases, commonly relies on tools such as questionnaires and food diaries to assess dietary intake. However, these methods are prone to misreporting, as participants may inaccurately estimate portion sizes, misremember what they ate, intentionally misstate their consumption, or even alter their eating habits during the reporting period.
“Many nutritional epidemiology studies that try to link dietary exposure to disease outcomes are based on unreliable data, which can explain why many findings contradict each other,” said John Speakman, one of the paper’s authors and a professor at the Shenzhen Institute of Advanced Technology in China and the University of Aberdeen in the United Kingdom.
While the issue of misreporting and its impact on metabolic research has been recognized since the 1980s, studies continue to use these tools due to their perceived utility and the lack of practical and accessible alternatives for collecting dietary data.