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Letter to the Editor| Volume 90, ISSUE 12, P1736-1739, December 2015

A Discussion of the Refutation of Memory-Based Dietary Assessment Methods (M-BMs): The Rhetorical Defense of Pseudoscientific and Inadmissible Evidence

DOI:https://doi.org/10.1016/j.mayocp.2015.10.003
A Discussion of the Refutation of Memory-Based Dietary Assessment Methods (M-BMs): The Rhetorical Defense of Pseudoscientific and Inadmissible Evidence
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      To the Editor:
      We read the well-written editorial of Davy and Estabrooks
      • Davy B.M.
      • Estabrooks P.A.
      The Validity of Self-reported Dietary Intake Data: Focus on the “What We Eat In America” Component of the National Health and Nutrition Examination Survey Research Initiative.
      Mayo Clin Proc. 2015; 90: 845-847
      with considerable interest, hoping that they would “provide empirical evidence rather than rhetoric”
      • Archer E.
      • Pavela G.
      • Lavie C.J.
      The Inadmissibility of ‘What We Eat In America’ (WWEIA) and NHANES Dietary Data in Nutrition and Obesity Research and the Scientific Formulation of National Dietary Guidelines.
      Mayo Clin Proc. 2015; 90: 911-926
      to refute our conclusion that the data generated by memory-based dietary assessment methods (M-BMs) of nutrition epidemiology are pseudoscientific and inadmissible as scientific evidence.
      • Archer E.
      • Pavela G.
      • Lavie C.J.
      The Inadmissibility of ‘What We Eat In America’ (WWEIA) and NHANES Dietary Data in Nutrition and Obesity Research and the Scientific Formulation of National Dietary Guidelines.
      Mayo Clin Proc. 2015; 90: 911-926
      Our hope was in vain as Davy and Estabrooks provided an exemplar of the rhetorical defense of M-BMs and the unacceptable “status quo.”

      The Repeated Empirical Refutation of M-BMs

      In 2013, my colleagues and I demonstrated via 2 independent methods that approximately 55% to 88% of the caloric intake estimates of the National Health and Nutrition Examination Survey (NHANES) M-BMs (1971-2010) were physiologically implausible
      • Archer E.
      • Hand G.A.
      • Blair S.N.
      Validity of U.S. nutritional surveillance: National Health and Nutrition Examination Survey caloric energy intake data, 1971-2010.
      PLoS One. 2013; 8: e76632
      and often “incompatible with life.”
      • Ioannidis J.P.
      Implausible results in human nutrition research.
      BMJ. 2013; 347: f6698
      ,p7 Davy and Estabrooks admit that our results are “…well recognized and acknowledged…,”
      • Davy B.M.
      • Estabrooks P.A.
      The Validity of Self-reported Dietary Intake Data: Focus on the “What We Eat In America” Component of the National Health and Nutrition Examination Survey Research Initiative.
      Mayo Clin Proc. 2015; 90: 845-847
      ,p845 but follow with the contradictory statement “we believe that [these data] reflect a reasonable representation of usual dietary intake.”
      • Davy B.M.
      • Estabrooks P.A.
      The Validity of Self-reported Dietary Intake Data: Focus on the “What We Eat In America” Component of the National Health and Nutrition Examination Survey Research Initiative.
      Mayo Clin Proc. 2015; 90: 845-847
      These statements are logical contradictions and demonstrate the failure of nutrition epidemiologists to acknowledge the obvious: population-level physiologically implausible data are not a mere limitation of M-BMs; they are direct empirical refutation of these methods.
      Davy and Estabrooks cite Moshfegh et al
      • Moshfegh A.J.
      • Rhodes D.G.
      • Baer D.J.
      • et al.
      The US Department of Agriculture Automated Multiple-Pass Method reduces bias in the collection of energy intakes.
      Am J Clin Nutr. 2008; 88: 324-332
      as an example in which “the accuracy of dietary recalls can be substantially improved… [via the use of] well-trained research personnel.”
      • Davy B.M.
      • Estabrooks P.A.
      The Validity of Self-reported Dietary Intake Data: Focus on the “What We Eat In America” Component of the National Health and Nutrition Examination Survey Research Initiative.
      Mayo Clin Proc. 2015; 90: 845-847
      ,p845 Yet in this study, even with the use of highly trained research personnel, the ratio of reported energy intake to resting energy expenditure for the entire sample was 1.43,
      • Moshfegh A.J.
      • Rhodes D.G.
      • Baer D.J.
      • et al.
      The US Department of Agriculture Automated Multiple-Pass Method reduces bias in the collection of energy intakes.
      Am J Clin Nutr. 2008; 88: 324-332
      ,p329 a physiologically implausible value well below the 1.51 needed to be credible (as per Goldberg et al
      • Goldberg G.R.
      • Black A.E.
      • Jebb S.A.
      • et al.
      Critical evaluation of energy intake data using fundamental principles of energy physiology: 1, derivation of cut-off limits to identify under-recording.
      Eur J Clin Nutr. 1991; 45: 569-581
      ,p576). The implausible results of Moshfegh et al are due to obese men and women systematically underreporting by 620 and 524 kcal/d (20% and 21%), respectively, and overweight men and women systematically underreporting by 419 and 334 kcal/d (14% and 15%), respectively.
      • Moshfegh A.J.
      • Rhodes D.G.
      • Baer D.J.
      • et al.
      The US Department of Agriculture Automated Multiple-Pass Method reduces bias in the collection of energy intakes.
      Am J Clin Nutr. 2008; 88: 324-332
      The physiologically implausible data of Moshfegh et al did not represent a limitation of M-BM, but rather represent a “fatal flaw” and direct refutation.
      Unlike most research findings,
      • Ioannidis J.P.
      Why most published research findings are false.
      PLoS Med. 2005; 2: e124
      the lack of credibility of M-BM data has been replicated consistently over the past 3 decades.
      • Archer E.
      • Hand G.A.
      • Blair S.N.
      Validity of U.S. nutritional surveillance: National Health and Nutrition Examination Survey caloric energy intake data, 1971-2010.
      PLoS One. 2013; 8: e76632
      , 
      • Ferrari P.
      • Slimani N.
      • Ciampi A.
      • et al.
      Evaluation of under- and overreporting of energy intake in the 24-hour diet recalls in the European Prospective Investigation into Cancer and Nutrition (EPIC).
      Public Health Nutr. 2002; 5: 1329-1345
      , 
      • Lissner L.
      • Troiano R.P.
      • Midthune D.
      • et al.
      OPEN about obesity: recovery biomarkers, dietary reporting errors and BMI.
      Int J Obes (Lond). 2007; 31: 956-961
      We know of no other data collection tool (in any field of actual science) that demonstrates a significant decrement in performance each time it is used, yet that is precisely what was found in the Energetics study.
      • Arab L.
      • Wesseling-Perry K.
      • Jardack P.
      • Henry J.
      • Winter A.
      Eight self-administered 24-hour dietary recalls using the Internet are feasible in African Americans and Whites: the energetics study.
      J Am Diet Assoc. 2010; 110: 857-864
      Over the administration of eight 24-hour recalls, Arab et al
      • Arab L.
      • Wesseling-Perry K.
      • Jardack P.
      • Henry J.
      • Winter A.
      Eight self-administered 24-hour dietary recalls using the Internet are feasible in African Americans and Whites: the energetics study.
      J Am Diet Assoc. 2010; 110: 857-864
      reported a statistically significant decreasing trend for energy (−535 kcal/d; P<.001) that varied by macronutrient (protein: −22 g/d, −88 kcal/d; fat: −27 g/d, −243 kcal/d; carbohydrate: −54 g/d, −216 kcal/d). Given these results, we think that the Dietary Guidelines Advisory Committee’s (DGAC’s) statement, “repeated 24-hour recalls remain the backbone of dietary assessment and monitoring,”
      Dietary Guidelines Advisory Committee
      Scientific Report of the 2015 Dietary Guidelines Advisory Committee.
      U.S. Department of Health and Human Services and U.S. Department of Agriculture, Washington, DC2015
      ,Appendix E-4,p3 demonstrates that nutrition epidemiologists refuse to acknowledge the repeated, direct empirical refutation of M-BMs. As such, the DGAC’s request to “expand” the use of M-BM data collection
      Dietary Guidelines Advisory Committee
      Scientific Report of the 2015 Dietary Guidelines Advisory Committee.
      U.S. Department of Health and Human Services and U.S. Department of Agriculture, Washington, DC2015
      ,Appendix E-1,p1 is illogical at best.

      Altering Data When the Numbers “Don’t Add Up”

      Nutrition epidemiology often uses statistical machinations and post hoc data exclusions to “correct” or simply delete implausible data and alter results.
      • Willett W.C.
      • Howe G.R.
      • Kushi L.H.
      Adjustment for total energy intake in epidemiologic studies.
      Am J Clin Nutr. 1997; 65 (discussion 1229S-1231S): 1220S-1228S
      , 
      • Freedman L.S.
      • Carroll R.J.
      • Wax Y.
      Estimating the relation between dietary intake obtained from a food frequency questionnaire and true average intake.
      Am J Epidemiol. 1991; 134: 310-320
      , 
      • Huang T.T.
      • Roberts S.B.
      • Howarth N.C.
      • McCrory M.A.
      Effect of screening out implausible energy intake reports on relationships between diet and BMI.
      Obes Res. 2005; 13: 1205-1217
      , 
      • McCrory M.A.
      • McCrory M.A.
      • Hajduk C.L.
      • Roberts S.B.
      Procedures for screening out inaccurate reports of dietary energy intake.
      Public Health Nutr. 2002; 5: 873-882
      For example, Donin et al
      • Donin A.S.
      • Nightingale C.M.
      • Owen C.G.
      • et al.
      Dietary energy intake is associated with type 2 diabetes risk markers in children.
      Diabetes Care. 2014; 37: 116-123
      openly state before presenting their results that they removed “176 participants with implausible energy intakes” (ie, deleted ∼9% of their data).
      • Donin A.S.
      • Nightingale C.M.
      • Owen C.G.
      • et al.
      Dietary energy intake is associated with type 2 diabetes risk markers in children.
      Diabetes Care. 2014; 37: 116-123
      ,p1126 Similarly, Mendez et al
      • Mendez M.A.
      • Wynter S.
      • Wilks R.
      • Forrester T.
      Under- and overreporting of energy is related to obesity, lifestyle factors and food group intakes in Jamaican adults.
      Public Health Nutr. 2004; 7: 9-19
      ,p9 stated that “Adjusting for implausible reporting may help to reduce bias in diet-health outcome association.” Poslusna et al
      • Poslusna K.
      • Ruprich J.
      • de Vries J.H.
      • Jakubikova M.
      • van’t Veer P.
      Misreporting of energy and micronutrient intake estimated by food records and 24 hour recalls, control and adjustment methods in practice.
      Br J Nutr. 2009; 101: S73-S85
      ,pS73 state that “energy adjustment seems to be a good tool for practice to decrease an influence of misreporting,” and Willet et al
      • Willett W.C.
      • Sampson L.
      • Stampfer M.J.
      • et al.
      Reproducibility and validity of a semiquantitative food frequency questionnaire.
      Am J Epidemiol. 1985; 122: 51-65
      ,p61 suggested that “calorie-adjusted intakes are likely to be more appropriate with respect to public health policy…” These procedures are not merely correcting erroneous data entries or removing nonrepresentative data (ie, statistical outliers). The result of these machinations is to alter and/or delete the data of individuals most representative of the population of interest. For example, the US population is predominantly overweight and obese, and these individuals are the most likely to misreport. In other words, when the numbers did not add up, nutrition epidemiologists simply changed, ignored, or deleted the implausible data (regardless of the systematic biases they introduced) rather than acknowledge the invalidity of M-BMs. We are not aware of any research domain in which this type of data doctoring and consequent message distortion would be tolerated. We think that DGAC’s use of these manipulated data and consequent distorted messages to inform public health policy constitutes dubious scientific practices.

      Violations of Statistical Assumptions

      Since M-BMs were first refuted decades ago, numerous statistical data manipulation protocols have been developed
      • Dodd K.W.
      • Guenther P.M.
      • Freedman L.S.
      • et al.
      Statistical methods for estimating usual intake of nutrients and foods: a review of the theory.
      J Am Diet Assoc. 2006; 106: 1640-1650
      , 
      • Freedman L.S.
      • Midthune D.
      • Carroll R.J.
      • et al.
      Adjustments to improve the estimation of usual dietary intake distributions in the population.
      J Nutr. 2004; 134: 1836-1843
      and widely used despite the fact that the foundational assumptions of these methods are not met. For example, the National Cancer Institute’s method “…assumes that the 24-hour recall is an unbiased instrument for measuring usual food intake… and… provides an unbiased measure of the amount of food consumed on a consumption day.”
      National Cancer Institute
      Usual dietary intakes: the NCI method.
      http://appliedresearch.cancer.gov/diet/usualintakes/method.html
      Date: 2013
      Given the fallibility of human memory
      • Archer E.
      • Pavela G.
      • Lavie C.J.
      The Inadmissibility of ‘What We Eat In America’ (WWEIA) and NHANES Dietary Data in Nutrition and Obesity Research and the Scientific Formulation of National Dietary Guidelines.
      Mayo Clin Proc. 2015; 90: 911-926
      , 
      • Loftus E.
      Our changeable memories: legal and practical implications.
      Nat Rev Neurosci. 2003; 4: 231-234
      , 
      • Schacter D.L.
      • Slotnick S.D.
      The cognitive neuroscience of memory distortion.
      Neuron. 2004; 44: 149-160
      and the fact that M-BM data are not unbiased estimates of consumption,
      • Archer E.
      • Pavela G.
      • Lavie C.J.
      The Inadmissibility of ‘What We Eat In America’ (WWEIA) and NHANES Dietary Data in Nutrition and Obesity Research and the Scientific Formulation of National Dietary Guidelines.
      Mayo Clin Proc. 2015; 90: 911-926
      this assumption is violated and provides an exemplar of the GIGO (Garbage-In = Garbage-Out) principle. Therefore, DGAC’s statement, “usual intake distributions can be estimated based on statistical techniques…”
      Dietary Guidelines Advisory Committee
      Scientific Report of the 2015 Dietary Guidelines Advisory Committee.
      U.S. Department of Health and Human Services and U.S. Department of Agriculture, Washington, DC2015
      ,Appendix E-4,p2 is misleading, and the use of various energy adjustment
      • Freedman L.S.
      • Schatzkin A.
      • Midthune D.
      • Kipnis V.
      Dealing with dietary measurement error in nutritional cohort studies.
      J Natl Cancer Inst. 2011; 103: 1086-1092
      and calibration equations
      • Freedman L.S.
      • Commins J.M.
      • Moler J.E.
      • et al.
      Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for energy and protein intake.
      Am J Epidemiol. 2014; 180: 172-188
      should be a nonstarter, not government-funded standard practice.
      The American Statistical Association’s “Ethical Guidelines for Statistical Practice”

      Ethical guidelines for statistical practice. American Statistical Association web site. http://www.amstat.org/about/ethicalguidelines.cfm. Accessed July 1, 2015.

      clearly states that it is the statistician’s responsibility to “Report statistical and substantive assumptions…[and]…Clearly and fully report the steps taken to guard validity. Address the suitability of the analytic methods and their inherent assumptions…”

      Ethical guidelines for statistical practice. American Statistical Association web site. http://www.amstat.org/about/ethicalguidelines.cfm. Accessed July 1, 2015.

      If Davy and Estabrooks’ assertion that the biased and wholly implausible nature of M-BM data “are [is] well recognized and acknowledged by those utilizing these methods,”
      • Davy B.M.
      • Estabrooks P.A.
      The Validity of Self-reported Dietary Intake Data: Focus on the “What We Eat In America” Component of the National Health and Nutrition Examination Survey Research Initiative.
      Mayo Clin Proc. 2015; 90: 845-847
      ,p845 the continued use of statistical machinations despite the false assumptions and threats to validity constitute willfully specious statistical practices.

      To Present Mere Correlations as Evidence of Causation Is Not Sound Science

      In the 2015 DGAC report, the distinction between correlation and causation is either ignored or dismissed. For example, the words association, associated, and relationship are used more than 900 times in the 571-page DGAC text, whereas the words causal and causality are used fewer than 30 times and not once to describe an actual causal diet-health relationship.
      Dietary Guidelines Advisory Committee
      Scientific Report of the 2015 Dietary Guidelines Advisory Committee.
      U.S. Department of Health and Human Services and U.S. Department of Agriculture, Washington, DC2015
      Individuals practicing rigorous science understand that associations are not sufficient to demonstrate causation. Yet the DGAC generates national public health policy recommendations via mere statistical associations from physiologically implausible data while ignoring established causal factors for the development of chronic noncommunicable diseases.
      • Archer E.
      The Childhood Obesity Epidemic as a Result of Nongenetic Evolution: The Maternal Resources Hypothesis.
      Mayo Clin Proc. 2015; 90: 77-92
      The DGAC’s leap from questionable, confounded, and often clinically irrelevant correlations to dietary recommendations explains recent policy reversals
      Dietary Guidelines Advisory Committee
      Scientific Report of the 2015 Dietary Guidelines Advisory Committee.
      U.S. Department of Health and Human Services and U.S. Department of Agriculture, Washington, DC2015
      and demonstrates a lack of epistemic humility that has significant public health consequences.

      Misrepresentation of the Evidence

      Davy and Estabrooks
      • Davy B.M.
      • Estabrooks P.A.
      The Validity of Self-reported Dietary Intake Data: Focus on the “What We Eat In America” Component of the National Health and Nutrition Examination Survey Research Initiative.
      Mayo Clin Proc. 2015; 90: 845-847
      ,p845 state that the implausible and biased nature of M-BMs is “well recognized and acknowledged by those utilizing these methods.” If true, why are the terms misreporting, underreporting, and implausible not evident in the DGAC report with respect to dietary intake? The DGAC does use the term overreporting once, but only in reference to physical activity.
      Dietary Guidelines Advisory Committee
      Scientific Report of the 2015 Dietary Guidelines Advisory Committee.
      U.S. Department of Health and Human Services and U.S. Department of Agriculture, Washington, DC2015
      ,Part D,Chapter 7,p3 Nevertheless, if nutrition epidemiologists are well aware of the lack of credibility of M-BM data, then the 2015 DGAC’s declaration without caveat that the NHANES M-BM data “provide national and group level estimates of dietary intakes of the U.S. population, on a given day…”
      Dietary Guidelines Advisory Committee
      Scientific Report of the 2015 Dietary Guidelines Advisory Committee.
      U.S. Department of Health and Human Services and U.S. Department of Agriculture, Washington, DC2015
      ,Part C,Methodology,p13 is misleading to both the public and policymakers. It should be obvious that physiologically implausible estimates
      • Archer E.
      • Hand G.A.
      • Blair S.N.
      Validity of U.S. nutritional surveillance: National Health and Nutrition Examination Survey caloric energy intake data, 1971-2010.
      PLoS One. 2013; 8: e76632
      cannot be representative of national and group-level consumption. As such, DGAC’s statement is patently false and in direct violation of the US Department of Agriculture’s information quality guidelines that “ensure that the information they disseminate is substantively accurate, reliable, and unbiased and presented in an accurate, clear, complete, and unbiased manner.”
      Information quality activities
      United States Department of Agriculture web site.
      http://www.ocio.usda.gov/policy-directives-records-forms/information-quality-activities
      Date: 2015
      Furthermore, the DGAC falsely suggests that our data from the article of 2013
      • Archer E.
      • Hand G.A.
      • Blair S.N.
      Validity of U.S. nutritional surveillance: National Health and Nutrition Examination Survey caloric energy intake data, 1971-2010.
      PLoS One. 2013; 8: e76632
      ,Appendix E-4,p2 merely “discussed” the “strengths and shortcomings of [NHANES M-BMs].” Our article was an empirical falsification and refutation of the NHANES M-BMs and presented data on “28,993 men and 34,369 women, aged 20 to 74 years” to arrive at the conclusion that the dietary reports of “the majority of respondents (67.3% of women and 58.7% of men) were not physiologically plausible.”
      • Archer E.
      • Hand G.A.
      • Blair S.N.
      Validity of U.S. nutritional surveillance: National Health and Nutrition Examination Survey caloric energy intake data, 1971-2010.
      PLoS One. 2013; 8: e76632
      ,p1 At no time does the DGAC acknowledge, address, or attempt to refute our findings. As with other empirical refutations of M-BMs,
      • Ferrari P.
      • Slimani N.
      • Ciampi A.
      • et al.
      Evaluation of under- and overreporting of energy intake in the 24-hour diet recalls in the European Prospective Investigation into Cancer and Nutrition (EPIC).
      Public Health Nutr. 2002; 5: 1329-1345
      , 
      • Lissner L.
      • Troiano R.P.
      • Midthune D.
      • et al.
      OPEN about obesity: recovery biomarkers, dietary reporting errors and BMI.
      Int J Obes (Lond). 2007; 31: 956-961
      , 
      • Schoeller D.A.
      Limitations in the assessment of dietary energy intake by self-report.
      Metabolism. 1995; 44: 18-22
      the nutrition epidemiologic community and the DGAC simply ignored our results and conclusions.

      Conclusion

      The hypothesis that M-BMs can provide estimates of dietary intakes on the national and group level has been strongly refuted,
      • Archer E.
      • Hand G.A.
      • Blair S.N.
      Validity of U.S. nutritional surveillance: National Health and Nutrition Examination Survey caloric energy intake data, 1971-2010.
      PLoS One. 2013; 8: e76632
      and on the individual-level, M-BM–derived data cannot be falsified (ie, recalled memories are nonempirical and therefore are not subject to independent observation, measurement, and quantification).
      • Archer E.
      • Pavela G.
      • Lavie C.J.
      The Inadmissibility of ‘What We Eat In America’ (WWEIA) and NHANES Dietary Data in Nutrition and Obesity Research and the Scientific Formulation of National Dietary Guidelines.
      Mayo Clin Proc. 2015; 90: 911-926
      As such, these data are pseudoscientific and inadmissible in scientific research and the formulation of national dietary guidelines. Although the status quo is unacceptable, we do not fault Davy and Estabrooks for defending their discipline, which in our opinion promotes a failed research paradigm that lacks scientific rigor and skepticism. Nevertheless, it is time for the US Department of Agriculture, Centers for Disease Control and Prevention, and the National Institutes of Health to recognize and acknowledge the empirical refutation of M-BMs and reexamine the extensive utilization and funding of these data collection protocols.

      References

        • Davy B.M.
        • Estabrooks P.A.
        The Validity of Self-reported Dietary Intake Data: Focus on the “What We Eat In America” Component of the National Health and Nutrition Examination Survey Research Initiative.
        Mayo Clin Proc. 2015; 90: 845-847
        View in Article
        • Archer E.
        • Pavela G.
        • Lavie C.J.
        The Inadmissibility of ‘What We Eat In America’ (WWEIA) and NHANES Dietary Data in Nutrition and Obesity Research and the Scientific Formulation of National Dietary Guidelines.
        Mayo Clin Proc. 2015; 90: 911-926
        View in Article
        • Archer E.
        • Hand G.A.
        • Blair S.N.
        Validity of U.S. nutritional surveillance: National Health and Nutrition Examination Survey caloric energy intake data, 1971-2010.
        PLoS One. 2013; 8: e76632
        View in Article
        • Ioannidis J.P.
        Implausible results in human nutrition research.
        BMJ. 2013; 347: f6698
        View in Article
        • Moshfegh A.J.
        • Rhodes D.G.
        • Baer D.J.
        • et al.
        The US Department of Agriculture Automated Multiple-Pass Method reduces bias in the collection of energy intakes.
        Am J Clin Nutr. 2008; 88: 324-332
        View in Article
        • Goldberg G.R.
        • Black A.E.
        • Jebb S.A.
        • et al.
        Critical evaluation of energy intake data using fundamental principles of energy physiology: 1, derivation of cut-off limits to identify under-recording.
        Eur J Clin Nutr. 1991; 45: 569-581
        View in Article
        • Ioannidis J.P.
        Why most published research findings are false.
        PLoS Med. 2005; 2: e124
        View in Article
        • Ferrari P.
        • Slimani N.
        • Ciampi A.
        • et al.
        Evaluation of under- and overreporting of energy intake in the 24-hour diet recalls in the European Prospective Investigation into Cancer and Nutrition (EPIC).
        Public Health Nutr. 2002; 5: 1329-1345
        View in Article
        • Lissner L.
        • Troiano R.P.
        • Midthune D.
        • et al.
        OPEN about obesity: recovery biomarkers, dietary reporting errors and BMI.
        Int J Obes (Lond). 2007; 31: 956-961
        View in Article
        • Arab L.
        • Wesseling-Perry K.
        • Jardack P.
        • Henry J.
        • Winter A.
        Eight self-administered 24-hour dietary recalls using the Internet are feasible in African Americans and Whites: the energetics study.
        J Am Diet Assoc. 2010; 110: 857-864
        View in Article
        • Dietary Guidelines Advisory Committee
        Scientific Report of the 2015 Dietary Guidelines Advisory Committee.
        U.S. Department of Health and Human Services and U.S. Department of Agriculture, Washington, DC2015
        View in Article
        • Willett W.C.
        • Howe G.R.
        • Kushi L.H.
        Adjustment for total energy intake in epidemiologic studies.
        Am J Clin Nutr. 1997; 65 (discussion 1229S-1231S): 1220S-1228S
        View in Article
        • Freedman L.S.
        • Carroll R.J.
        • Wax Y.
        Estimating the relation between dietary intake obtained from a food frequency questionnaire and true average intake.
        Am J Epidemiol. 1991; 134: 310-320
        View in Article
        • Huang T.T.
        • Roberts S.B.
        • Howarth N.C.
        • McCrory M.A.
        Effect of screening out implausible energy intake reports on relationships between diet and BMI.
        Obes Res. 2005; 13: 1205-1217
        View in Article
        • McCrory M.A.
        • McCrory M.A.
        • Hajduk C.L.
        • Roberts S.B.
        Procedures for screening out inaccurate reports of dietary energy intake.
        Public Health Nutr. 2002; 5: 873-882
        View in Article
        • Donin A.S.
        • Nightingale C.M.
        • Owen C.G.
        • et al.
        Dietary energy intake is associated with type 2 diabetes risk markers in children.
        Diabetes Care. 2014; 37: 116-123
        View in Article
        • Mendez M.A.
        • Wynter S.
        • Wilks R.
        • Forrester T.
        Under- and overreporting of energy is related to obesity, lifestyle factors and food group intakes in Jamaican adults.
        Public Health Nutr. 2004; 7: 9-19
        View in Article
        • Poslusna K.
        • Ruprich J.
        • de Vries J.H.
        • Jakubikova M.
        • van’t Veer P.
        Misreporting of energy and micronutrient intake estimated by food records and 24 hour recalls, control and adjustment methods in practice.
        Br J Nutr. 2009; 101: S73-S85
        View in Article
        • Willett W.C.
        • Sampson L.
        • Stampfer M.J.
        • et al.
        Reproducibility and validity of a semiquantitative food frequency questionnaire.
        Am J Epidemiol. 1985; 122: 51-65
        View in Article
        • Dodd K.W.
        • Guenther P.M.
        • Freedman L.S.
        • et al.
        Statistical methods for estimating usual intake of nutrients and foods: a review of the theory.
        J Am Diet Assoc. 2006; 106: 1640-1650
        View in Article
        • Freedman L.S.
        • Midthune D.
        • Carroll R.J.
        • et al.
        Adjustments to improve the estimation of usual dietary intake distributions in the population.
        J Nutr. 2004; 134: 1836-1843
        View in Article
        • National Cancer Institute
        Usual dietary intakes: the NCI method.
        http://appliedresearch.cancer.gov/diet/usualintakes/method.html
        Date: 2013
        (Accessed June 29, 2015)
        View in Article
        • Loftus E.
        Our changeable memories: legal and practical implications.
        Nat Rev Neurosci. 2003; 4: 231-234
        View in Article
        • Schacter D.L.
        • Slotnick S.D.
        The cognitive neuroscience of memory distortion.
        Neuron. 2004; 44: 149-160
        View in Article
        • Freedman L.S.
        • Schatzkin A.
        • Midthune D.
        • Kipnis V.
        Dealing with dietary measurement error in nutritional cohort studies.
        J Natl Cancer Inst. 2011; 103: 1086-1092
        View in Article
        • Freedman L.S.
        • Commins J.M.
        • Moler J.E.
        • et al.
        Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for energy and protein intake.
        Am J Epidemiol. 2014; 180: 172-188
        View in Article

      27. Ethical guidelines for statistical practice. American Statistical Association web site. http://www.amstat.org/about/ethicalguidelines.cfm. Accessed July 1, 2015.

        View in Article
        • Archer E.
        The Childhood Obesity Epidemic as a Result of Nongenetic Evolution: The Maternal Resources Hypothesis.
        Mayo Clin Proc. 2015; 90: 77-92
        View in Article
        • Information quality activities
        United States Department of Agriculture web site.
        http://www.ocio.usda.gov/policy-directives-records-forms/information-quality-activities
        Date: 2015
        (Accessed July 2, 2015)
        View in Article
        • Schoeller D.A.
        Limitations in the assessment of dietary energy intake by self-report.
        Metabolism. 1995; 44: 18-22
        View in Article

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      Footnotes

      Grant Support: The work was supported by grant T32DK062710 (E.A.) from the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

      Potential Competing Interests: Dr Archer has received honoraria from the International Life Sciences Institute and the Coca Cola Company. Dr Lavie reports receiving consulting fees and speaking fees from the Coca Cola Company and writing a book on the obesity paradox with potential royalties. Dr Pavela reports no competing interests.

      Identification

      DOI: https://doi.org/10.1016/j.mayocp.2015.10.003

      Copyright

      © 2015 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.

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