A systematic method to evaluate the dietary intake data coding process used in the research setting
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.