Cluster analysis and food group consumption in a national sample of Australian girls
ABSTRACT Food preferences develop early in life and track into later life. There is limited information on food consumption and dietary patterns in Australian girls. The present study aimed to: (i) determine the frequency of food groups consumed over 1day; (ii) identify dietary clusters based on food group consumption; and (iii) compare dietary intakes and activity variables between clusters.
A cross-sectional analysis of 9-16-year-old girls (n=1114) from the 2007 Australian National Children's Nutrition and Physical Activity Survey was performed.
Over the whole day, 30% of all girls consumed carbonated sugar drinks, 46% consumed take-away food, 56% consumed fruit, 70% consumed at least one vegetable, and 19% and 30% consumed white and/or red meat, respectively. K-means cluster analysis derived four clusters. Approximately one-third of girls were identified in a Meat and vegetable cluster; these girls had the highest intakes of red meat and vegetables, and tended to have higher intakes of fruit, whole grain breads, low fat yoghurt, and lower intakes of take-away foods and soft drinks. They also had the highest intakes of protein, fibre and micronutrients; and tended to perform more physical activity, compared to girls in the remaining clusters.
Girls identified in the Meat and vegetable cluster, on average, consumed more lean red meat, vegetables, fruits, and low-fat dairy products, and had a higher intakes of many nutrients. The high percentage of girls not identified in this cluster suggests the need to inform them on how to make healthy, nutrient dense food choices, and why they require increased nutrient intakes at this time.
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ABSTRACT: Aims: There is little data in Australian children assessing dietary fibre intakes or ready‐to‐eat cereal consumption. The aims were to: (i) assess the dietary fibre intake of 2‐ to 16‐year‐old children from the 2007 Australian National Children's Nutrition and Physical Activity Survey; (ii) determine the percentage of children meeting adequate fibre intake; and (iii) determine the contribution of breakfast and ready‐to‐eat cereal fibre intake.Methods: Secondary analysis of the 2007 Australian National Children's Nutrition and Physical Activity Survey.Results: On the day of the survey, the mean (SE) dietary fibre intake among all children at breakfast was 4.5 (0.1) g and total daily fibre intake was 20.7 (0.2) g. Fibre adequacy was achieved by 50% of Australian children. Forty‐nine percent of all children consumed ready‐to‐eat cereal, providing 2/3 of fibre intake at breakfast and 1/10 of total daily fibre intake. Consuming ready‐to‐eat cereal was a significant predictor of total daily fibre intake (β= 0.028). Over 60% of all children consumed ready‐to‐eat cereal in which at least a ‘source’ of fibre (1.5–3.0 g) was available; however, Conclusions: While ready‐to‐eat cereal was only consumed by close to half of all children, it was a significant contributor of fibre at both the breakfast meal occasion and over the whole day. Randomised controlled trials are necessary to identify whether increasing consumption of fibre from ready‐to‐eat cereal alters health outcomes such as anthropometric and biochemical end points.Nutrition & Dietetics 06/2013; 70(2-2):132-138. DOI:10.1111/j.1747-0080.2012.01640.x · 0.72 Impact Factor
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ABSTRACT: Objective The present study aimed to identify dietary patterns in 13-year-old adolescents and to evaluate their association with socio-economic and behavioural factors. Methods Data from 1489 adolescents born in 1990 and enrolled in schools of Porto, Portugal, in 2003/2004 (EPITeen) were analysed, after excluding those without information on the food frequency questionnaire (FFQ) and the outliers. The FFQ items were grouped into 14 food groups and dietary patterns were identified by cluster analysis (K-means). Their association with participants’ characteristics was computed using multinomial logistic regression. Results Four dietary patterns were identified. The pattern 'Healthier' (16.1%) was characterized by the highest consumption of seafood, soup, vegetables/legumes, fruit and added fats. The ’Dairy products’ (29.7%) showed the highest consumption of dairies. The pattern 'Fast food & sweets' (14.2%) presented the highest intake of fast food, sweets and pastry, soft drinks and coffee/tea. The 'Lower intake' (40.0%) was characterized by the lower consumption of the majority of food groups. A higher odds of belonging to the ‘Fast food & sweets’ pattern, compared to the pattern ‘Lower intake’, was found among adolescents spending more time watching TV at weekend (>360min: OR=2.09, 95%CI 1.23-3.57) and among those consuming 4 to 7 fried foods/week (OR=3.96, 95%CI 2.27-6.90). Adolescents with highly educated parents were less likely to belong to the ‘Fast food & sweets’. Conclusion Unhealthier behaviours and lower socioeconomic position were the main factors associated with the unhealthier dietary pattern (‘Fast-food & sweets’). This information should be considered in the development of health promotion interventions.Nutrition 07/2014; 31(1). DOI:10.1016/j.nut.2014.06.007 · 2.93 Impact Factor
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ABSTRACT: Introduction: Developing nutrition education interventions based on clusters of dietary patterns can only be done adequately when it is clear if distinctive clusters of dietary patterns can be derived and reproduced over time, if cluster membership is stable, and if it is predictable which type of people belong to a certain cluster. Hence, this study aimed to: (1) identify clusters of dietary patterns among Dutch adults, (2) test the reproducibility of these clusters and stability of cluster membership over time, and (3) identify sociodemographic predictors of cluster membership and cluster transition. Methods: This study had a longitudinal design with online measurements at baseline (N=483) and 6 months follow-up (N=379). Dietary intake was assessed with a validated food frequency questionnaire. A hierarchical cluster analysis was performed, followed by a K-means cluster analysis. Multinomial logistic regression analyses were conducted to identify the sociodemographic predictors of cluster membership and cluster transition. Results: At baseline and follow-up, a comparable three-cluster solution was derived, distinguishing a healthy, moderately healthy, and unhealthy dietary pattern. Male and lower educated participants were significantly more likely to have a less healthy dietary pattern. Further, 251 (66.2%) participants remained in the same cluster, 45 (11.9%) participants changed to an unhealthier cluster, and 83 (21.9%) participants shifted to a healthier cluster. Men and people living alone were significantly more likely to shift toward a less healthy dietary pattern. Conclusions: Distinctive clusters of dietary patterns can be derived. Yet, cluster membership is unstable and only few sociodemographic factors were associated with cluster membership and cluster transition. These findings imply that clusters based on dietary intake may not be suitable as a basis for nutrition education interventions.Appetite 07/2014; 82. DOI:10.1016/j.appet.2014.07.025 · 2.69 Impact Factor