Capturing Complexity: A Latent Variable Mixture Modelling Approach to Examining Foods Consumed at Eating Occasions and Associations with adiposity (OR06-01-19)
Year: 2019
Type: article
Abstract: Understanding how foods are consumed together in eating occasions (EOs) may be useful when developing messages and strategies for nutrition promotion. However, examining the many combinations of food consumed at EOs requires novel analytic approaches to capture this complexity. We applied a latent variable mixture modelling approach to understand how foods are consumed in relation to each other at breakfast EOs in adults, and examined associations with adiposity. Dietary intake at breakfast (n = 8361 EO) was assessed via 24-h recall during the 2011–12 Australian National Nutrition and Physical Activity Survey (n = 3545 men, n = 4127 women, ⩾19 y). Two-part latent variable mixture modelling, appropriate for use with semi-continuous data, was used to determine distinct breakfast food profiles based on 35 food groups. F and adjusted-chi2 tests assessed differences in socio-demographic factors and regression models, adjusted for covariates, were used to estimate differences in adiposity measures (BMI, waist circumference [WC]) between the latent breakfast profiles. Five distinct breakfast food profiles were found. Three were similar for men and women. These were labelled: “Wholegrain cereals and milks” (men: 16%, women: 17%), “Protein-foods” (men and women: 11%) and “Mixed cereals and milks” (men: 33%, women: 37%). Two “Breads and spreads” profiles were also found that were differentiated by their accompanying beverages (men) or type of grain (women). BMI and WC were lowest among men and women with a “Wholegrain cereals and milks” profile, and were significantly lower when compared to the “Mixed cereals and milks” pattern (women only, P < 0.01) and the “Breads and spreads” profiles (men only, P < 0.01). We identified five breakfast food profiles in adults that varied by socio-demographic factors and adiposity. Latent variable mixture modelling is a useful approach to capture the complexity of food combinations at EO. RM Leech is financially supported by an Alfred Deakin Postdoctoral Research Fellowship and SA McNaughton is supported by a National Health and Medical Research Council Career Development Fellowship (ID1104636). All authors had no financial relationships with any organisations that might have an interest in the presented work in the previous three years; no other relationships or activities that could appear to have influenced the presented work.
Authors: Rebecca Leech, Carol Boushey, Sarah McNaughton
Institution: University of Hawaii Cancer Center
Language: English
Subfield: Nutrition and Dietetics
Field: Nursing
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $5,000
OpenAlex ID: W2951794432