Assessing diet in a university student population: a longitudinal food card transaction data approach

Abstract

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.

Dietary Patterns Derived from UK Supermarket Transaction Data with Nutrient and Socioeconomic Profiles

“…Being data driven, using detailed food categories, they are more difficult to compare to patterns observed in other studies, generated from subjects with different consumption or purchasing behaviours. That said, there are common themes in our patterns seen in many other studies, for example: presence of a carnivore pattern, a sweet tooth/snacking pattern and a meat alternative, or vegetarian pattern [ 32 , 33 ]. Our patterns differ from many previous studies, due to the inclusion of alcohol in the cluster generating process, which we believe to be a strength as alcohol contributes to energy intake.…”

A systematic review of supermarket automated electronic sales data for population dietary surveillance

“… 62 , 68 , 69 Transaction data have a number of strengths. Large data volumes enable data-driven exploration of dietary patterns 55–57 , 82 to better understand food-purchase behaviors and identify intervention target groups. Furthermore, continuous data collection permits observation and control for day to day, 83 week by week, 38 and seasonal variation in dietary choices, 59 which cannot be revealed in such detail by cross-sectional dietary surveys.…”

Effectiveness of a Smartphone App (e-12HR) in Improving Adherence to the Mediterranean Diet in Spanish University Students by Age, Gender, Field of Study, and Body Mass Index: A Randomized Controlled Trial

“…According to age group, gender, and BMI, the results were inconsistent. With respect to age group, various studies have shown that AMD increased as age did [ 18 , 25 ], but another study found no difference with age group [ 21 ]. In terms of gender, one study shows that females had higher adherence than males [ 4 ], while another study found that females had poorer adherence than males [ 21 ].…”