Researchers propose novel model to screen misreporting in dietary surveys

An international team led by Prof. John Speakman from the Shenzhen Institutes of Advanced Technology of the Chinese Academy of Sciences derived a predictive model by combining classical statistics and machine learning for total energy expenditure, providing a more objective way to assess the validity of food intake records.

The study was published in Nature Food on Jan. 13.

Nutritional epidemiology aims to link dietary exposures to chronic disease, but in the past methods for evaluating dietary intake greatly depended on the subjects’ ability to note down or recall what they have eaten or they are eating using tools such as food frequency questionnaires, 24 hour recall interviews and food diaries.

It is well known that such tools are inaccurate because people may forget, or may even falsify their reports. Increasing amounts of inaccurate data (here termed dietary misreporting) will mislead the decision of nutritional strategy and policy.

In this study, researchers used an isotope-based method called the doubly-labeled water technique, which directly measures the individual’s energy needs. They pulled together more than 6,000 measurements in total and used classical statistics and machine-learning-based approaches to derive a predictive model which was then validated in about 600 additional subjects.

The resultant equations are currently the most accurate method to estimate energy requirements without making an actual measurement.

To demonstrate the effectiveness of this model, researchers applied it to two large surveys of food intake data: National Health and Nutrition Examination Survey (NHANES) in the U.S. and National Diet and Nutrition Survey (NDNS) in the UK. They found that 48% of food intake records in NHANES and 54% in NDNS had unrealistically low levels of energy intake.

“This new model suggests that we should throw out large amounts of data, and nutritionists using dietary instruments may be unwilling to do that. However, continuing on just publishing erroneous data because it is too painful to acknowledge it’s flawed, probably isn’t the best way forward for nutrition science. I think as we go forward into the future many widely held beliefs that have been based on these problematical methods will need to be revised,” said Prof. John Speakman.

An international team led by Prof. John Speakman from the Shenzhen Institutes of Advanced Technology of the Chinese Academy of Sciences derived a predictive model by combining classical statistics and machine learning for total energy expenditure, providing a more objective way to assess the validity of food intake records.

The study was published in Nature Food on Jan. 13.

Nutritional epidemiology aims to link dietary exposures to chronic disease, but in the past methods for evaluating dietary intake greatly depended on the subjects’ ability to note down or recall what they have eaten or they are eating using tools such as food frequency questionnaires, 24 hour recall interviews and food diaries.

It is well known that such tools are inaccurate because people may forget, or may even falsify their reports. Increasing amounts of inaccurate data (here termed dietary misreporting) will mislead the decision of nutritional strategy and policy.

In this study, researchers used an isotope-based method called the doubly-labeled water technique, which directly measures the individual’s energy needs. They pulled together more than 6,000 measurements in total and used classical statistics and machine-learning-based approaches to derive a predictive model which was then validated in about 600 additional subjects.

The resultant equations are currently the most accurate method to estimate energy requirements without making an actual measurement.

To demonstrate the effectiveness of this model, researchers applied it to two large surveys of food intake data: National Health and Nutrition Examination Survey (NHANES) in the U.S. and National Diet and Nutrition Survey (NDNS) in the UK. They found that 48% of food intake records in NHANES and 54% in NDNS had unrealistically low levels of energy intake.

“This new model suggests that we should throw out large amounts of data, and nutritionists using dietary instruments may be unwilling to do that. However, continuing on just publishing erroneous data because it is too painful to acknowledge it’s flawed, probably isn’t the best way forward for nutrition science. I think as we go forward into the future many widely held beliefs that have been based on these problematical methods will need to be revised,” said Prof. John Speakman.