Background There is a scarcity of validated rapid dietary screening tools for patient use in the clinical setting to improve health and reduce cardiovascular risk. The Healthy Eating Index (HEI) 2015 measures compliance with the 2015 to 2020 Dietary Guidelines for Americans but requires completion of an extensive diet assessment to compute, which is time consuming and impractical. The authors hypothesize that a 19-item dietary survey assessing consumption of common food groups known to affect health will be correlated with the HEI-2015 assessed by a validated food frequency questionnaire and can be further reduced without affecting validity. Methods and Results A 19-item Eating Assessment Tool (EAT) of common food groups was created through literature review and expert consensus. A cross-sectional survey was then conducted in adult participants from a preventive cardiology clinic or cardiac rehabilitation and in healthy volunteers (n=661, mean age, 36 years; 76% women). Participants completed an online 156-item food frequency questionnaire, which was used to calculate the HEI score using standard methods. The association between each EAT question and HEI group was analyzed by Kruskal-Wallis test. Linear regression models were subsequently used to identify univariable and multivariable predictors for HEI score for further reduction in the number of items. The final 9-item model of Mini-EAT was validated by 5-fold cross validation. The 19-item EAT had a strong correlation with the HEI score (r=0.73) and was subsequently reduced to the 9 items independently predictive of the HEI score: fruits, vegetables, whole grains, refined grains, fish or seafood, legumes/nuts/seeds, low-fat dairy, high-fat dairy, and sweets consumption, without affecting the predictive ability of the tool (r=0.71). Conclusions Mini-EAT is a 9-item validated brief dietary screener that correlates well with a comprehensive food frequency questionnaire. Future studies to test the Mini-EAT's validity in diverse populations and for development of clinical decision support systems to capture changes over time are needed.

International Clinical Research Center St Anne's University Hospital Brno Brno Czech Republic

Mayo Clinic Department of Cardiovascular Medicine Rochester MN

Mayo Clinic Division of Biomedical Statistics and Informatics Jacksonville FL

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