Literature DB >> 26468491

Addressing Current Criticism Regarding the Value of Self-Report Dietary Data.

Amy F Subar1, Laurence S Freedman2, Janet A Tooze3, Sharon I Kirkpatrick4, Carol Boushey5, Marian L Neuhouser6, Frances E Thompson7, Nancy Potischman8, Patricia M Guenther9, Valerie Tarasuk10, Jill Reedy7, Susan M Krebs-Smith7.   

Abstract

Recent reports have asserted that, because of energy underreporting, dietary self-report data suffer from measurement error so great that findings that rely on them are of no value. This commentary considers the amassed evidence that shows that self-report dietary intake data can successfully be used to inform dietary guidance and public health policy. Topics discussed include what is known and what can be done about the measurement error inherent in data collected by using self-report dietary assessment instruments and the extent and magnitude of underreporting energy compared with other nutrients and food groups. Also discussed is the overall impact of energy underreporting on dietary surveillance and nutritional epidemiology. In conclusion, 7 specific recommendations for collecting, analyzing, and interpreting self-report dietary data are provided: (1) continue to collect self-report dietary intake data because they contain valuable, rich, and critical information about foods and beverages consumed by populations that can be used to inform nutrition policy and assess diet-disease associations; (2) do not use self-reported energy intake as a measure of true energy intake; (3) do use self-reported energy intake for energy adjustment of other self-reported dietary constituents to improve risk estimation in studies of diet-health associations; (4) acknowledge the limitations of self-report dietary data and analyze and interpret them appropriately; (5) design studies and conduct analyses that allow adjustment for measurement error; (6) design new epidemiologic studies to collect dietary data from both short-term (recalls or food records) and long-term (food-frequency questionnaires) instruments on the entire study population to allow for maximizing the strengths of each instrument; and (7) continue to develop, evaluate, and further expand methods of dietary assessment, including dietary biomarkers and methods using new technologies.
© 2015 American Society for Nutrition.

Entities:  

Keywords:  dietary assessment; dietary surveillance; energy intake; measurement error; nutritional epidemiology; underreporting

Mesh:

Substances:

Year:  2015        PMID: 26468491      PMCID: PMC4656907          DOI: 10.3945/jn.115.219634

Source DB:  PubMed          Journal:  J Nutr        ISSN: 0022-3166            Impact factor:   4.798


  76 in total

1.  Implications of a new dietary measurement error model for estimation of relative risk: application to four calibration studies.

Authors:  V Kipnis; R J Carroll; L S Freedman; L Li
Journal:  Am J Epidemiol       Date:  1999-09-15       Impact factor: 4.897

Review 2.  Uses and limitations of statistical accounting for random error correlations, in the validation of dietary questionnaire assessments.

Authors:  Rudolf Kaaks; Pietro Ferrari; Antonio Ciampi; Martyn Plummer; Elio Riboli
Journal:  Public Health Nutr       Date:  2002-12       Impact factor: 4.022

3.  Taking advantage of the strengths of 2 different dietary assessment instruments to improve intake estimates for nutritional epidemiology.

Authors:  Raymond J Carroll; Douglas Midthune; Amy F Subar; Marina Shumakovich; Laurence S Freedman; Frances E Thompson; Victor Kipnis
Journal:  Am J Epidemiol       Date:  2012-01-24       Impact factor: 4.897

Review 4.  How accurate is self-reported dietary energy intake?

Authors:  D A Schoeller
Journal:  Nutr Rev       Date:  1990-10       Impact factor: 7.110

5.  Effect of measurement error on energy-adjustment models in nutritional epidemiology.

Authors:  V Kipnis; L S Freedman; C C Brown; A M Hartman; A Schatzkin; S Wacholder
Journal:  Am J Epidemiol       Date:  1997-11-15       Impact factor: 4.897

Review 6.  Considering the value of dietary assessment data in informing nutrition-related health policy.

Authors:  James R Hébert; Thomas G Hurley; Susan E Steck; Donald R Miller; Fred K Tabung; Karen E Peterson; Lawrence H Kushi; Edward A Frongillo
Journal:  Adv Nutr       Date:  2014-07-14       Impact factor: 8.701

7.  Measurement error in dietary assessment: an investigation using covariance structure models. Part I.

Authors:  M Plummer; D Clayton
Journal:  Stat Med       Date:  1993-05-30       Impact factor: 2.373

8.  Use of a urinary sugars biomarker to assess measurement error in self-reported sugars intake in the nutrition and physical activity assessment study (NPAAS).

Authors:  Natasha Tasevska; Douglas Midthune; Lesley F Tinker; Nancy Potischman; Johanna W Lampe; Marian L Neuhouser; Jeannette M Beasley; Linda Van Horn; Ross L Prentice; Victor Kipnis
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2014-09-18       Impact factor: 4.254

Review 9.  The Inadmissibility of What We Eat in America and NHANES Dietary Data in Nutrition and Obesity Research and the Scientific Formulation of National Dietary Guidelines.

Authors:  Edward Archer; Gregory Pavela; Carl J Lavie
Journal:  Mayo Clin Proc       Date:  2015-06-09       Impact factor: 7.616

10.  Low-energy reporting in women at risk for breast cancer recurrence. Women's Healthy Eating and Living Group.

Authors:  B J Caan; S W Flatt; C L Rock; C Ritenbaugh; V Newman; J P Pierce
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2000-10       Impact factor: 4.254

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  317 in total

1.  Diet Quality and Associations with Food Security among Women Eligible for Indiana Supplemental Nutrition Assistance Program-Education.

Authors:  Rebecca L Rivera; Yumin Zhang; Qi Wang; Melissa K Maulding; Janet A Tooze; Breanne N Wright; Bruce A Craig; Regan L Bailey; Heather A Eicher-Miller
Journal:  J Nutr       Date:  2020-08-01       Impact factor: 4.798

2.  The Carbon Isotope Ratios of Serum Amino Acids in Combination with Participant Characteristics can be Used to Estimate Added Sugar Intake in a Controlled Feeding Study of US Postmenopausal Women.

Authors:  Hee Young Yun; Lesley F Tinker; Marian L Neuhouser; Dale A Schoeller; Yasmin Mossavar-Rahmani; Linda G Snetselaar; Linda V Van Horn; Charles B Eaton; Ross L Prentice; Johanna W Lampe; Diane M O'Brien
Journal:  J Nutr       Date:  2020-10-12       Impact factor: 4.798

3.  Secular trends in regional differences in nutritional biomarkers and self-reported dietary intakes among American adults: National Health and Nutrition Examination Survey (NHANES) 1988-1994 to 2009-2010.

Authors:  Ashima K Kant; Barry I Graubard
Journal:  Public Health Nutr       Date:  2018-01-10       Impact factor: 4.022

4.  Race-dependent association of sulfidogenic bacteria with colorectal cancer.

Authors:  Cemal Yazici; Patricia G Wolf; Hajwa Kim; Tzu-Wen L Cross; Karin Vermillion; Timothy Carroll; Gaius J Augustus; Ece Mutlu; Lisa Tussing-Humphreys; Carol Braunschweig; Rosa M Xicola; Barbara Jung; Xavier Llor; Nathan A Ellis; H Rex Gaskins
Journal:  Gut       Date:  2017-02-02       Impact factor: 23.059

5.  Urinary metals and metal mixtures in midlife women: The Study of Women's Health Across the Nation (SWAN).

Authors:  Xin Wang; Bhramar Mukherjee; Stuart Batterman; Siobán D Harlow; Sung Kyun Park
Journal:  Int J Hyg Environ Health       Date:  2019-05-15       Impact factor: 5.840

6.  Healthy Eating among Mexican Immigrants: Migration in Childhood and Time in the United States.

Authors:  Jennifer Van Hook; Susana Quirós; Molly Dondero; Claire E Altman
Journal:  J Health Soc Behav       Date:  2018-07-24

7.  Response by Pase et al to Letters Regarding Article, "Sugar- and Artificially Sweetened Beverages and the Risks of Incident Stroke and Dementia. A Prospective Cohort Study".

Authors:  Matthew P Pase; Jayandra J Himali; Alexa Beiser; Sudha Seshadri; Paul F Jacques
Journal:  Stroke       Date:  2017-07-18       Impact factor: 7.914

Review 8.  STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1-Basic theory and simple methods of adjustment.

Authors:  Ruth H Keogh; Pamela A Shaw; Paul Gustafson; Raymond J Carroll; Veronika Deffner; Kevin W Dodd; Helmut Küchenhoff; Janet A Tooze; Michael P Wallace; Victor Kipnis; Laurence S Freedman
Journal:  Stat Med       Date:  2020-04-03       Impact factor: 2.373

9.  Older adults with obesity have higher risks of some micronutrient inadequacies and lower overall dietary quality compared to peers with a healthy weight, National Health and Nutrition Examination Surveys (NHANES), 2011-2014.

Authors:  Shinyoung Jun; Alexandra E Cowan; Anindya Bhadra; Kevin W Dodd; Johanna T Dwyer; Heather A Eicher-Miller; Jaime J Gahche; Patricia M Guenther; Nancy Potischman; Janet A Tooze; Regan L Bailey
Journal:  Public Health Nutr       Date:  2020-05-29       Impact factor: 4.022

Review 10.  Applications of the Healthy Eating Index for Surveillance, Epidemiology, and Intervention Research: Considerations and Caveats.

Authors:  Sharon I Kirkpatrick; Jill Reedy; Susan M Krebs-Smith; TusaRebecca E Pannucci; Amy F Subar; Magdalena M Wilson; Jennifer L Lerman; Janet A Tooze
Journal:  J Acad Nutr Diet       Date:  2018-09       Impact factor: 4.910

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