Structure of dietary measurement error: results of the OPEN biomarker study.

By V. Kipnis, A. Subar, D. Midthune, L. Freedman, R. Ballard‐Barbash, R. Troiano, S. Bingham, D. Schoeller, A. Schatzkin, Raymond J. Carroll. American Journal of Epidemiology, 2003

872 citations recorded by the source corpus.

Subfields: Nutrition and Dietetics, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/b48c7aa494ff8c8383712b185a03e8935263a863

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Nutrition and Dietetics

1. F. Thompson
2. A. Subar
3. H. Boeing
4. M. Schulze
5. G. Hoffmann
6. L. Schwingshackl

In Public Health, Environmental and Occupational Health

1. A. Subar
2. S. Krebs-Smith
3. F. Thompson
4. K. Dodd
5. S. Kirkpatrick
6. P. Guenther
7. H. Boeing
8. David Ruppert

Citations in the corpus

• The Automated Self-Administered 24-Hour Dietary Recall (ASA24): A Resource for Researchers, Clinicians and Educators from the National Cancer Institute (2012, methodology): “ASA24 is based upon the USDA Automated Multiple-Pass Method (AMPM), which has been validated and shown to accurately estimate mean total energy and protein intakes compared to recovery biomarkers (1,3).”
• Nonlinear and Nonparametric Regression and Instrumental Variables (2004, methodology): “The OPEN (observing protein and energy intake) Study (Subar et al. 2003; Kipnis et al. 2003a ) is the first large study using biomarkers to investigate the properties of self-reporting instruments for measuring nutrient intakes.”
• Dietary Assessment Methodology (2017, methodology): “4 [109]—indicating that energy adjustment may alleviate some of the error inherent in food frequency instruments.”
• Fruit and vegetable intake and incidence of type 2 diabetes mellitus: systematic review and meta-analysis (2010, background): “These errors in measurement can underestimate true interactions between diet and disease.”
• Statistical methods for estimating usual intake of nutrients and foods: a review of the theory. (2006, background): “Accordingly, the distributions estimated from 24-hour recalls—after adjustment for within-person variation using a method similar to that described in Figure 3—agreed with the biomarker-derived distributions much more closely than did the distributions estimated from FFQs.”
• Application of a new statistical method to derive dietary patterns in nutritional epidemiology. (2004, background): “Assessing dietary intake by food frequency questionnaire is subject to considerable measurement error, as demonstrated by the OPEN Study (53).”
• Higher diet quality is associated with decreased risk of all-cause, cardiovascular disease, and cancer mortality among older adults. (2014, methodology): “Limitations include the assessment of diet with an FFQ, a tool that is known to contain nondifferential measurement error, although energy adjustment may serve to mitigate some of this error (44,45).”
• BEST PRACTICES FOR USING PHYSICAL ACTIVITY MONITORS IN POPULATION-BASED RESEARCH (2012, positive): “Errors at the individual level lead to misclassification of exposure, resulting in loss of statistical power and attenuation of the effect sizes (14).”
• Relative validity of a semi-quantitative food-frequency questionnaire in an elderly Mediterranean population of Spain (2010, neutral): “It has been suggested that the measurement errors of FFQ and reference methods may be independent((25)), but there is some disagreement on this point((26,27)).”
• Addressing Current Criticism Regarding the Value of Self-Report Dietary Data. (2015, background): “EIs are derived from the reporting of foods and beverages by using 1 of 3 instruments: FFQs, 24-h dietary recalls (24HRs), and food records (FRs).”
• Misreporting of energy and micronutrient intake estimated by food records and 24 hour recalls, control and adjustment methods in practice (2009, neutral): “However, micronutrients were not studied((57)).”