UCD Institute of Food & Health, Agriculture & Food Science Centre, University College Dublin, Belfield, Dublin 4, Republic of Ireland.
Public Health Nutr. 2013 May;16(5):848-57. doi: 10.1017/S1368980011002473. Epub 2011 Oct 13.
Pattern analysis of adolescent diets may provide an important basis for nutritional health promotion. The aims of the present study were to examine and compare dietary patterns in adolescents using cluster analysis and principal component analysis (PCA) and to examine the impact of the format of the dietary variables on the solutions.
Analysis was based on the Irish National Teens Food Survey, in which food intake data were collected using a semi-quantitative 7 d food diary. Thirty-two food groups were created and were expressed as either g/d or percentage contribution to total energy. Dietary patterns were identified using cluster analysis (k-means) and PCA.
Republic of Ireland, 2005-2006.
A representative sample of 441 adolescents aged 13-17 years.
Five clusters based on percentage contribution to total energy were identified, 'Healthy', 'Unhealthy', 'Rice/Pasta dishes', 'Sandwich' and 'Breakfast cereal & Main meal-type foods'. Four principal components based on g/d were identified which explained 28 % of total variance: 'Healthy foods', 'Traditional foods', 'Sandwich foods' and 'Unhealthy foods'.
A 'Sandwich' and an 'Unhealthy' pattern are the main dietary patterns in this sample. Patterns derived from either cluster analysis or PCA were comparable, although it appears that cluster analysis also identifies dietary patterns not identified through PCA, such as a 'Breakfast cereal & Main meal-type foods' pattern. Consideration of the format of the dietary variable is important as it can directly impact on the patterns obtained for both cluster analysis and PCA.
分析青少年饮食模式可为促进营养健康提供重要依据。本研究旨在通过聚类分析和主成分分析(PCA)来检验和比较青少年的饮食模式,并探讨饮食变量的格式对结果的影响。
分析基于爱尔兰国家青少年食品调查,该调查采用半定量 7 天食物日记收集食物摄入数据。共创建了 32 种食物组,以克/天或占总能量的百分比表示。采用聚类分析(k-均值)和 PCA 确定饮食模式。
爱尔兰共和国,2005-2006 年。
441 名年龄在 13-17 岁的青少年代表样本。
根据占总能量的百分比,确定了 5 个聚类,分别为“健康”、“不健康”、“米饭/面食”、“三明治”和“早餐谷物和主餐型食品”。基于克/天确定了 4 个主成分,解释了总方差的 28%:“健康食品”、“传统食品”、“三明治食品”和“不健康食品”。
在该样本中,“三明治”和“不健康”是主要的饮食模式。聚类分析或 PCA 得出的模式相似,但聚类分析似乎还可以识别 PCA 无法识别的饮食模式,如“早餐谷物和主餐型食品”模式。考虑饮食变量的格式很重要,因为它会直接影响聚类分析和 PCA 获得的模式。