Starting university is an important time with respect to dietary changes. This study reports a novel approach to assessing student diet by utilising student-level food transaction data to explore dietary patterns. First-year students living in catered accommodation at the University of Leeds (UK) received pre-credited food cards for use in university catering facilities. Food card transaction data were obtained for semester 1, 2016 and linked with student age and sex. k-Means cluster analysis was applied to the transaction data to identify clusters of food purchasing behaviours. Differences in demographic and behavioural characteristics across clusters were examined using χ2 tests. The semester was divided into three time periods to explore longitudinal changes in purchasing patterns. Seven dietary clusters were identified: 'Vegetarian', 'Omnivores', 'Dieters', 'Dish of the Day', 'Grab-and-Go', 'Carb Lovers' and 'Snackers'. There were statistically significant differences in sex (P < 0·001), with women dominating the Vegetarian and Dieters, age (P = 0·003), with over 20s representing a high proportion of the Omnivores and time of day of transactions (P < 0·001), with Dieters and Snackers purchasing least at breakfast. Many students (n 474, 60·4 %) changed dietary cluster across the semester. This study demonstrates that transactional data present a feasible method for dietary assessment, collecting detailed dietary information over time and at scale, while eliminating participant burden and possible bias from self-selection, observation and attrition. It revealed that student diets are complex and that simplistic measures of diet, focusing on narrow food groups in isolation, are unlikely to adequately capture dietary behaviours.
Keywords: Big data; Diet; Dietary patterns; Students; Transactions.