The feasibility of analysing food consumption combinations of weight loss clinical trials Vivienne GUAN, Yasmine PROBST, Elizabeth NEALE, Allison MARTIN and Linda TAPSELL School of Medicine, Faculty of Science, Medicine and Health, University of Wollongong, Australia
Overweight and Obesity Overweight & obesity A global epidemic No reported successful population-based case 1 Food consumption combinations based on meals Manage overweight and obesity
Food intake assessment and data Detailed food intake data Advanced analytical methods A detailed dietary assessment method Diet history interview 2 Food items Food quantity Food frequency (Defined time period) Combined food intake data
Analytical method Food items Associations Food consumption combinations based on meals Association rules Apriori algorithm 3 Frequency item sets Association rules (support, confidence and lift)
Analytical method Food intake data Previous studies 4,5 Food intake data Daily intake data Pre-defined food groups/combinations VS. food items
Aim To explore the feasibility of using detailed food intake data to investigate food consumption combinations based on meals
Objectives 1. To determine whether food consumption combinations can be identified 2. To examine challenges
Sample and Food intake data A 10% random sample (n=62) From 3 registered clinical trials (n=617) A 10-15% sample Test the feasibility of a study 6 Food intake data Baseline paper-based diet history records Reflect combined 7 days intake Food item check list
Food consumption combinations The nested hierarchical food groups of the 2011 13 AHS food classification system 7 Food consumption combinations Consumed at the same time/occasion Groups by meals The USDA Food Combination Codes 8
The nested hierarchical food groups At the major food group level Identified At the sub-major & minor food group levels Breakfast, lunch, mid-meals and beverages Identified Dinner Unidentified (n=13/21%) Variations in meat The food classification system
USDA Food Combination Codes Breakfast 84% (n=52) Cereal with additions 55% (n=34) Bread/baked products with additions Lunch 92% (n=57) Sandwiches Dinner Variations in food consumption combinations
Challenge Assessing the combination of foods & beverages 60 50 40 30 20 10 0 30 53 Beverages reporting With food Alone With food and alone 13 30 Food frequency checklist 8 With food, alone and in the food checklist Number of cases
Conclusion Sufficiently detailed information Breakfast, lunch, mid-meals and beverages Additional food combination codes categories Challenges Meat-containing food consumption combinations Grouping beverage consumption
Next step Complexity of preparing food intake data Usual weekly food intake Define meals Beverage Applications Meal-based food consumption habits Data entry and analysis
Next step
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Acknowledgements The HEAL study team The SMART study team The HealthTrack study team
Vivienne Guan Contact details xg885@uowmail.edu.au