A Diet Quality Tool for use in weight management presented on 14 May, 2014 Brisbane Convention Centre
This paper emphasises the value of food-level analysis via the use of a diet quality tool, the Food Choices Score, and 17 food categories for the analysis of dietary change in intervention trials. The Food Choices Score is specifically designed for weight loss in the research setting.
Cluster analysis as a tool for analysing dietary data from intervention trials
Baseline diets are rarely the focus of dietary interventions, but this analysis demonstrates they may be relevant in weight loss success. Cluster analysis performed at baseline may be useful prior to assigning participants to groups within an intervention, as the baseline diet, with high intakes of non-core foods and drinks (Cluster 2) allowed participants to reduce their kilojoule intake more than others (Cluster 1) who were closer to dietary guidelines. Cluster 2 subsequently went on to lose more weight.
Food, Nutrition and Health written by a team from the University of Wollongong and edited by Linda Tapsell, explores the study of food, nutrition and the lifecycle. The book provides a clear framework for understanding and working with nutrition science and key concepts are built upon in more detail to progressively develop knowledge, foster research skills and encourage critical thinking. See my chapters : Chapter 8 Nutrition During the Lifecycle: Pregnancy and Lactation and Chapter 9: Nutrition During the Lifecycle: Infancy and Childhood
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Cluster analysis is a statistical technique for grouping individuals with similar dietary features. In this study, cluster analysis, was conducted using diet history data from two clinical trials at baseline and then weight loss outcomes at 3 months were reviewed based on these clusters (n=231).
The cluster solution was analysed using defined food groups and the change in these groups over time. Usually, this sort of research method is performed at a population level – so it is novel within the context of an intervention trial.
Two distinct dietary patterns were identified : Subjects in Cluster 1 reported
food patterns characterised by higher intakes of low-fat dairy and unsaturated oils and margarine and were generally more closely aligned to food choices encouraged in national dietary guidelines. Subjects in Cluster 2 reported a dietary pattern characterised by
very high intakes of non-core foods and drinks, higher- and medium-fat dairy foods, fatty meats and alcohol.
At 3 months, Cluster 2 subjects reported greater reductions in energy intake (-5317 kJ; P<0.001) and greater weight loss (-5.6 kg; P<0.05) compared with Cluster 1.
What it means for clinical practice:
Overweight subjects with reported dietary patterns similar to dietary guidelines at baseline may have more difficulty in reducing energy intake than those with poor dietary patterns. This makes life difficult for practitioners! On the other hand, if a diet history is able to identify, quantify and correctly target the the types of non-core foods and drinks this yields large reductions in energy and, as in this study, more successful weight loss.
This is a modified extract from my first paper ‘Baseline dietary patterns are a significant consideration in correcting dietary exposure for weight loss.’ which is now published online in EJCN http://www.nature.com/doifinder/10.1038/ejcn.2013.26 in the Advance Online Publication (AOP) service.
Dietary pattern studies are traditionally the domain of epidemiological research. Rather than being focused on nutrients, these studies focus on foods. From a clinical dietetic perspective, there is a need to explore the effects of changing food and dietary patterns of individuals. This will help provide evidence for clinical practice where dietitians give advice about foods. My aim is to identify patterns of food choice behaviour in the context of a clinical weight loss trial.
My paper in this area will be published soon.
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