Evaluation of the dietary intake data coding process in a clinical setting: Implications for research practice

Abstract

Background High quality dietary intake data is required to support evidence of diet-disease relationships exposed in clinical research. Source data verification may be a useful quality assurance method in this setting. The present pilot study aimed to apply source data verification to evaluate the quality of the data coding process for dietary intake in a clinical trial and to explore potential barriers to data quality in this setting. Methods Using a sample of 20 cases from a clinical trial, source data verification was conducted between three sets of data derived documents: transcripts of audio-recorded diet history interviews, matched paper-based diet history forms and outputs from nutrition analysis software. The number of cases and rates of discrepancies between documents were calculated. A total of five in-depth interviews with dietitians collecting and coding dietary data were thematically analysed. Results Some 2024 discrepancies were identified. The highest discrepancy rate was 57.49%, and occurred between diet history interviews and nutrition analysis software outputs. Sources of the discrepancies included both quantities and frequencies of food intake. The highest discrepancy rate was for the food group “vegetable products and dishes”. In-depth interviews implicated recall bias of trial participants as a cause of discrepancies, but dietitians also acknowledged a possible subconscious influence of having to code reported foods into nutrition analysis software programs. Conclusion The accuracy of dietary intake data appeared to depend on the level of detailed food data required. More support for participants on reporting consumption, and incorporating supportive tools to guide estimates of food quantities may facilitate a more consistent coding process and improve data quality. This pilot study offers a novel method and an overview of dietary intake data coding measurement errors. These findings may warrant further investigation in a larger sample.

Current Developments in Digital Quantitative Volume Estimation for the Optimisation of Dietary Assessment

“…This is further amplified by the complexity of Southeast Asian diets, and the reliance on food consumed outside the home can make it difficult for individuals to be aware of all the ingredients in the food [32,44]. Mixed dishes are featured heavily in Southeast Asian cuisine and are known to be especially difficult to quantify given the different proportions of ingredients used in different settings [54]. The shared or multi-course meals common in Asian demographics may also further complicate the quantification of consumed foods, and respondents may leave out many details when attempting to record their intake [44].…”

“…Significant errors can also occur at the point of coding, especially in the case of open-ended diet records [33,[51][52][53][54][55]. These errors include: misinterpretation of portion sizes; unclear or inconsistent food names, particularly in the case of ethnic foods; or lack of detail with regards to food preparation methods and specific ingredients [54]. All of these factors require a degree of professional judgement on the part of the coder when the food records are interpreted, and this can differ between nutrition professionals [54].…”

“…These errors include: misinterpretation of portion sizes; unclear or inconsistent food names, particularly in the case of ethnic foods; or lack of detail with regards to food preparation methods and specific ingredients [54]. All of these factors require a degree of professional judgement on the part of the coder when the food records are interpreted, and this can differ between nutrition professionals [54]. Guan et al found a 26% discrepancy rate when food record entries were verified against their respective FoodWorks analyses entered by coders, indicating that a certain degree of subjective extrapolation was required to match paper records with information available in the database [54].…”

“…All of these factors require a degree of professional judgement on the part of the coder when the food records are interpreted, and this can differ between nutrition professionals [54]. Guan et al found a 26% discrepancy rate when food record entries were verified against their respective FoodWorks analyses entered by coders, indicating that a certain degree of subjective extrapolation was required to match paper records with information available in the database [54]. The choice of analysis software and nutrient database can also add another layer of complication, with different tools having different values for similar items.…”

“…Despite 7-day Diet Records being considered one of the better-regarded methods for dietary assessment, the number of participants that researchers are able to recruit is limited not only because of the difficulties faced by respondents, but due to the time-consuming nature of participant training and data processing [33,47]. At the end of studies, researchers may be presented with huge amounts of data to transcribe, and depending on the dietary complexity of the target group and the length of the study, this can put a significant strain on resources [54,56].…”