Accurate dietary intake data are the basis for investigating diet-disease relationships. Data
coding is a critical step of generating dietary intake data for analyses in nutrition research.
However, there is currently no systematic method for assessing dietary intake data coding
process. The aim of this study was to explore discrepancies in dietary intake data coding
process through source data verification. A 1% random sample of paper-based diet history
records (source data) from participants (n=377) in a registered clinical trial was extracted as a
pilot audit to explore potential discrepancy types. Another 10% random sample (n=38) of
baseline dietary source data from the same trial was extracted developing the method. All
items listed in the source data underwent a 100% manual verification check with food output
data from FoodWorks software applied to the piloted discrepancy types. The identified
discrepancies were categorized into food groups based on modified major groups of
AUSNUT 2011–13. Free vegetables, meat, savory sauces and condiments, as well as cereals
were found to be more prone to coding discrepancies than other food groups. A more detailed
dietary intake data coding protocol is required prior to dietary data collection and coding
process to ensure data coding quality.