Áine P Hearty1, Michael J Gibney. 1. UCD Institute of Food & Health, Agriculture & Food Science Centre, University College Dublin, Belfield, Dublin 4, Republic of Ireland. Aine.Hearty@ucd.ie
Abstract
OBJECTIVE: 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. DESIGN: 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. SETTING: Republic of Ireland, 2005-2006. SUBJECTS: A representative sample of 441 adolescents aged 13-17 years. RESULTS: 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'. CONCLUSIONS: 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.
OBJECTIVE: 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. DESIGN: 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. SETTING: Republic of Ireland, 2005-2006. SUBJECTS: A representative sample of 441 adolescents aged 13-17 years. RESULTS: 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'. CONCLUSIONS: 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.
Authors: Vanessa M B Andrade; Mônica L P de Santana; Kiyoshi F Fukutani; Artur T L Queiroz; Maria B Arriaga; Maria Ester P Conceição-Machado; Rita de Cássia R Silva; Bruno B Andrade Journal: Nutrients Date: 2019-08-19 Impact factor: 5.717
Authors: Vanessa M B Andrade; Mônica L P de Santana; Kiyoshi F Fukutani; Artur T L Queiroz; Maria B Arriaga; Nadjane F Damascena; Rodrigo C Menezes; Catarina D Fernandes; Maria Ester P Conceição-Machado; Rita de Cássia R Silva; Bruno B Andrade Journal: Nutrients Date: 2020-06-04 Impact factor: 5.717
Authors: Patricia Bodega; Juan Miguel Fernández-Alvira; Gloria Santos-Beneit; Amaya de Cos-Gandoy; Rodrigo Fernández-Jiménez; Luis Alberto Moreno; Mercedes de Miguel; Vanesa Carral; Xavier Orrit; Isabel Carvajal; Carolina E Storniolo; Anna Tresserra-Rimbau; Mónica Doménech; Ramón Estruch; Rosa María Lamuela-Raventós; Valentín Fuster Journal: Nutrients Date: 2019-09-26 Impact factor: 5.717