Comparar métodos

	Multiple-Pass 24-Hour Dietary Recall	Dietary Pattern Analysis
Campo	Food Agriculture Studies	Food Agriculture Studies
Familia	Process / pipeline	Process / pipeline
Año de origen	2008	2002
Autor original	Alanna J. Moshfegh and colleagues (USDA Agricultural Research Service)	Frank B. Hu; P. K. Newby & Katherine L. Tucker
Tipo	Structured multi-pass interview pipeline for quantitative dietary intake	Multivariate pipeline for deriving empirical dietary patterns from food intake
Fuente seminal	Moshfegh, A. J., Rhodes, D. G., Baer, D. J., Murayi, T., Clemens, J. C., Rumpler, W. V., Paul, D. R., Sebastian, R. S., Kuczynski, K. J., Ingwersen, L. A., Staples, R. C., & Cleveland, L. E. (2008). The US Department of Agriculture Automated Multiple-Pass Method reduces bias in the collection of energy intakes. The American Journal of Clinical Nutrition, 88(2), 324-332.	Hu, F. B. (2002). Dietary pattern analysis: a new direction in nutritional epidemiology. Current Opinion in Lipidology, 13(1), 3-9.
Alias	AMPM, Automated Multiple-Pass Method, 5-Pass 24-Hour Recall, 24-Hour Dietary Recall (Multiple-Pass)	Empirical Dietary Patterns, A Posteriori Dietary Patterns, Data-Driven Dietary Patterns, Eating Pattern Analysis
Relacionados	4	4
Resumen	The Multiple-Pass 24-Hour Dietary Recall is a structured interview method for measuring everything an individual ate and drank in the previous day, designed to maximize completeness and accuracy through several successive passes over the same day. Its definitive form is the USDA Automated Multiple-Pass Method (AMPM), a computerized five-step protocol used in What We Eat in America, the dietary component of NHANES. Validated against doubly labeled water by Moshfegh and colleagues in 2008, the AMPM was shown to reduce the bias in self-reported energy intake that plagues single-pass recalls, and the earlier 5-step protocol was validated against weighed observation by Conway and colleagues. By guiding respondents through a quick list, forgotten-food probes, time and occasion, detailed descriptions and amounts, and a final review, the method turns a notoriously error-prone task into a standardized, quantifiable dietary measurement.	Dietary pattern analysis is the nutritional-epidemiology application of multivariate statistics that identifies how foods are actually eaten together, summarizing the whole diet into a few empirical patterns rather than studying single nutrients in isolation. Introduced as a research direction by Frank Hu in his 2002 Current Opinion in Lipidology review and surveyed methodologically by Newby and Tucker in 2004, the approach takes a matrix of food-group intakes and applies factor (principal component) analysis, cluster analysis, or reduced-rank regression to extract a posteriori patterns such as a 'prudent' pattern rich in fruits, vegetables, and whole grains and a 'Western' pattern high in red meat and refined foods. While the underlying algebra is generic principal component or cluster analysis, what makes this a distinct method is its substantive construction: the input is the food-group intake matrix of the whole diet, and the output is interpretable eating patterns linked to disease.