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Measurement Error Affecting Web- and Paper-Based Dietary Assessment Instruments: Insights From the Multi-Cohort Eating and Activity Study for Understanding Reporting Error
Abstract Few biomarker-based validation studies have examined error in online self-report dietary assessment instruments, and food records (FRs) have been considered less than food frequency questionnaires (FFQs) and 24-hour recalls (24HRs). We investigated measurement error in online and paper-base...
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Published in: | American journal of epidemiology 2022-05, Vol.191 (6), p.1125-1139 |
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creator | Kirkpatrick, Sharon I Troiano, Richard P Barrett, Brian Cunningham, Christopher Subar, Amy F Park, Yikyung Bowles, Heather R Freedman, Laurence S Kipnis, Victor Rimm, Eric B Willett, Walter C Potischman, Nancy Spielgelman, Donna Baer, David J Schoeller, Dale A Dodd, Kevin W |
description | Abstract
Few biomarker-based validation studies have examined error in online self-report dietary assessment instruments, and food records (FRs) have been considered less than food frequency questionnaires (FFQs) and 24-hour recalls (24HRs). We investigated measurement error in online and paper-based FFQs, online 24HRs, and paper-based FRs in 3 samples drawn primarily from 3 cohorts, comprising 1,393 women and 1,455 men aged 45–86 years. Data collection occurred from January 2011 to October 2013. Attenuation factors and correlation coefficients between reported and true usual intake for energy, protein, sodium, potassium, and respective densities were estimated using recovery biomarkers. Across studies, average attenuation factors for energy were 0.07, 0.07, and 0.19 for a single FFQ, 24HR, and FR, respectively. Correlation coefficients for energy were 0.24, 0.23, and 0.40, respectively. Excluding energy, the average attenuation factors across nutrients and studies were 0.22 for a single FFQ, 0.22 for a single 24HR, and 0.51 for a single FR. Corresponding correlation coefficients were 0.31, 0.34, and 0.53, respectively. For densities (nutrient expressed relative to energy), the average attenuation factors across studies were 0.37, 0.17, and 0.50, respectively. The findings support prior research suggesting different instruments have unique strengths that should be leveraged in epidemiologic research. |
doi_str_mv | 10.1093/aje/kwac026 |
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Few biomarker-based validation studies have examined error in online self-report dietary assessment instruments, and food records (FRs) have been considered less than food frequency questionnaires (FFQs) and 24-hour recalls (24HRs). We investigated measurement error in online and paper-based FFQs, online 24HRs, and paper-based FRs in 3 samples drawn primarily from 3 cohorts, comprising 1,393 women and 1,455 men aged 45–86 years. Data collection occurred from January 2011 to October 2013. Attenuation factors and correlation coefficients between reported and true usual intake for energy, protein, sodium, potassium, and respective densities were estimated using recovery biomarkers. Across studies, average attenuation factors for energy were 0.07, 0.07, and 0.19 for a single FFQ, 24HR, and FR, respectively. Correlation coefficients for energy were 0.24, 0.23, and 0.40, respectively. Excluding energy, the average attenuation factors across nutrients and studies were 0.22 for a single FFQ, 0.22 for a single 24HR, and 0.51 for a single FR. Corresponding correlation coefficients were 0.31, 0.34, and 0.53, respectively. For densities (nutrient expressed relative to energy), the average attenuation factors across studies were 0.37, 0.17, and 0.50, respectively. The findings support prior research suggesting different instruments have unique strengths that should be leveraged in epidemiologic research.</description><identifier>ISSN: 0002-9262</identifier><identifier>EISSN: 1476-6256</identifier><identifier>DOI: 10.1093/aje/kwac026</identifier><identifier>PMID: 35136928</identifier><language>eng</language><publisher>United States: Oxford University Press</publisher><subject>Attenuation ; Biomarkers ; Correlation coefficient ; Correlation coefficients ; Data collection ; Diet ; Energy ; Epidemiology ; Error analysis ; Food ; Nutrients ; Potassium ; Practice of Epidemiology ; Recovery (Medical)</subject><ispartof>American journal of epidemiology, 2022-05, Vol.191 (6), p.1125-1139</ispartof><rights>Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2022. This work is written by (a) US Government employee(s) and is in the public domain in the US. 2022</rights><rights>Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2022. This work is written by (a) US Government employee(s) and is in the public domain in the US.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c440t-15cca4ee5e468601bd1e1e5a42b0a0000d94cc7c816c687a2fb1067868ff84753</citedby><cites>FETCH-LOGICAL-c440t-15cca4ee5e468601bd1e1e5a42b0a0000d94cc7c816c687a2fb1067868ff84753</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,778,782,883,27907,27908</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35136928$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kirkpatrick, Sharon I</creatorcontrib><creatorcontrib>Troiano, Richard P</creatorcontrib><creatorcontrib>Barrett, Brian</creatorcontrib><creatorcontrib>Cunningham, Christopher</creatorcontrib><creatorcontrib>Subar, Amy F</creatorcontrib><creatorcontrib>Park, Yikyung</creatorcontrib><creatorcontrib>Bowles, Heather R</creatorcontrib><creatorcontrib>Freedman, Laurence S</creatorcontrib><creatorcontrib>Kipnis, Victor</creatorcontrib><creatorcontrib>Rimm, Eric B</creatorcontrib><creatorcontrib>Willett, Walter C</creatorcontrib><creatorcontrib>Potischman, Nancy</creatorcontrib><creatorcontrib>Spielgelman, Donna</creatorcontrib><creatorcontrib>Baer, David J</creatorcontrib><creatorcontrib>Schoeller, Dale A</creatorcontrib><creatorcontrib>Dodd, Kevin W</creatorcontrib><title>Measurement Error Affecting Web- and Paper-Based Dietary Assessment Instruments: Insights From the Multi-Cohort Eating and Activity Study for Understanding Reporting Error</title><title>American journal of epidemiology</title><addtitle>Am J Epidemiol</addtitle><description>Abstract
Few biomarker-based validation studies have examined error in online self-report dietary assessment instruments, and food records (FRs) have been considered less than food frequency questionnaires (FFQs) and 24-hour recalls (24HRs). We investigated measurement error in online and paper-based FFQs, online 24HRs, and paper-based FRs in 3 samples drawn primarily from 3 cohorts, comprising 1,393 women and 1,455 men aged 45–86 years. Data collection occurred from January 2011 to October 2013. Attenuation factors and correlation coefficients between reported and true usual intake for energy, protein, sodium, potassium, and respective densities were estimated using recovery biomarkers. Across studies, average attenuation factors for energy were 0.07, 0.07, and 0.19 for a single FFQ, 24HR, and FR, respectively. Correlation coefficients for energy were 0.24, 0.23, and 0.40, respectively. Excluding energy, the average attenuation factors across nutrients and studies were 0.22 for a single FFQ, 0.22 for a single 24HR, and 0.51 for a single FR. Corresponding correlation coefficients were 0.31, 0.34, and 0.53, respectively. For densities (nutrient expressed relative to energy), the average attenuation factors across studies were 0.37, 0.17, and 0.50, respectively. The findings support prior research suggesting different instruments have unique strengths that should be leveraged in epidemiologic research.</description><subject>Attenuation</subject><subject>Biomarkers</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Data collection</subject><subject>Diet</subject><subject>Energy</subject><subject>Epidemiology</subject><subject>Error analysis</subject><subject>Food</subject><subject>Nutrients</subject><subject>Potassium</subject><subject>Practice of Epidemiology</subject><subject>Recovery (Medical)</subject><issn>0002-9262</issn><issn>1476-6256</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kktv1DAQxy1ERZfCiTuyhFQhobS24zgJB6Rl6UtqBQIqjpbjTHazJHHqR9F-Jr4kzu5SAQdOHmt-858nQi8oOaGkTE_VGk6__1CaMPEIzSjPRSJYJh6jGSGEJSUT7BA9dW5NCKVlRp6gwzSjqShZMUM_b0C5YKGHweMza43F86YB7dthib9BlWA11PiTGsEm75WDGn9owSu7wXPnwLlt3NXgvA2T6d5On3a58g6fW9NjvwJ8EzrfJguzMjbmUFvpSXUes9y3foO_-FBvcBNz3w41WOejd4I-wxhDJmtb2TN00KjOwfP9e4Ruz8--Li6T648XV4v5daI5Jz6hmdaKA2TARSEIrWoKFDLFWUVUHAmpS651rgsqtChyxZqKEpEXomiagudZeoTe7XTHUPVQ69iXVZ0cbdvHxqVRrfzbM7QruTT3skzLlIhJ4PVewJq7AM7LvnUauk4NYIKTcSM5TVncQERf_YOuTbBDbC9SeVZwzriI1Jsdpa1xzkLzUAwlcjoCGY9A7o8g0i__rP-B_b31CBzvABPG_yr9AlF8vtk</recordid><startdate>20220520</startdate><enddate>20220520</enddate><creator>Kirkpatrick, Sharon I</creator><creator>Troiano, Richard P</creator><creator>Barrett, Brian</creator><creator>Cunningham, Christopher</creator><creator>Subar, Amy F</creator><creator>Park, Yikyung</creator><creator>Bowles, Heather R</creator><creator>Freedman, Laurence S</creator><creator>Kipnis, Victor</creator><creator>Rimm, Eric B</creator><creator>Willett, Walter C</creator><creator>Potischman, Nancy</creator><creator>Spielgelman, Donna</creator><creator>Baer, David J</creator><creator>Schoeller, Dale A</creator><creator>Dodd, Kevin W</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QP</scope><scope>7T2</scope><scope>7TK</scope><scope>7U7</scope><scope>7U9</scope><scope>C1K</scope><scope>H94</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20220520</creationdate><title>Measurement Error Affecting Web- and Paper-Based Dietary Assessment Instruments: Insights From the Multi-Cohort Eating and Activity Study for Understanding Reporting Error</title><author>Kirkpatrick, Sharon I ; Troiano, Richard P ; Barrett, Brian ; Cunningham, Christopher ; Subar, Amy F ; Park, Yikyung ; Bowles, Heather R ; Freedman, Laurence S ; Kipnis, Victor ; Rimm, Eric B ; Willett, Walter C ; Potischman, Nancy ; Spielgelman, Donna ; Baer, David J ; Schoeller, Dale A ; Dodd, Kevin W</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c440t-15cca4ee5e468601bd1e1e5a42b0a0000d94cc7c816c687a2fb1067868ff84753</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Attenuation</topic><topic>Biomarkers</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Data collection</topic><topic>Diet</topic><topic>Energy</topic><topic>Epidemiology</topic><topic>Error analysis</topic><topic>Food</topic><topic>Nutrients</topic><topic>Potassium</topic><topic>Practice of Epidemiology</topic><topic>Recovery (Medical)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kirkpatrick, Sharon I</creatorcontrib><creatorcontrib>Troiano, Richard P</creatorcontrib><creatorcontrib>Barrett, Brian</creatorcontrib><creatorcontrib>Cunningham, Christopher</creatorcontrib><creatorcontrib>Subar, Amy F</creatorcontrib><creatorcontrib>Park, Yikyung</creatorcontrib><creatorcontrib>Bowles, Heather R</creatorcontrib><creatorcontrib>Freedman, Laurence S</creatorcontrib><creatorcontrib>Kipnis, Victor</creatorcontrib><creatorcontrib>Rimm, Eric B</creatorcontrib><creatorcontrib>Willett, Walter C</creatorcontrib><creatorcontrib>Potischman, Nancy</creatorcontrib><creatorcontrib>Spielgelman, Donna</creatorcontrib><creatorcontrib>Baer, David J</creatorcontrib><creatorcontrib>Schoeller, Dale A</creatorcontrib><creatorcontrib>Dodd, Kevin W</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>American journal of epidemiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kirkpatrick, Sharon I</au><au>Troiano, Richard P</au><au>Barrett, Brian</au><au>Cunningham, Christopher</au><au>Subar, Amy F</au><au>Park, Yikyung</au><au>Bowles, Heather R</au><au>Freedman, Laurence S</au><au>Kipnis, Victor</au><au>Rimm, Eric B</au><au>Willett, Walter C</au><au>Potischman, Nancy</au><au>Spielgelman, Donna</au><au>Baer, David J</au><au>Schoeller, Dale A</au><au>Dodd, Kevin W</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Measurement Error Affecting Web- and Paper-Based Dietary Assessment Instruments: Insights From the Multi-Cohort Eating and Activity Study for Understanding Reporting Error</atitle><jtitle>American journal of epidemiology</jtitle><addtitle>Am J Epidemiol</addtitle><date>2022-05-20</date><risdate>2022</risdate><volume>191</volume><issue>6</issue><spage>1125</spage><epage>1139</epage><pages>1125-1139</pages><issn>0002-9262</issn><eissn>1476-6256</eissn><abstract>Abstract
Few biomarker-based validation studies have examined error in online self-report dietary assessment instruments, and food records (FRs) have been considered less than food frequency questionnaires (FFQs) and 24-hour recalls (24HRs). We investigated measurement error in online and paper-based FFQs, online 24HRs, and paper-based FRs in 3 samples drawn primarily from 3 cohorts, comprising 1,393 women and 1,455 men aged 45–86 years. Data collection occurred from January 2011 to October 2013. Attenuation factors and correlation coefficients between reported and true usual intake for energy, protein, sodium, potassium, and respective densities were estimated using recovery biomarkers. Across studies, average attenuation factors for energy were 0.07, 0.07, and 0.19 for a single FFQ, 24HR, and FR, respectively. Correlation coefficients for energy were 0.24, 0.23, and 0.40, respectively. Excluding energy, the average attenuation factors across nutrients and studies were 0.22 for a single FFQ, 0.22 for a single 24HR, and 0.51 for a single FR. Corresponding correlation coefficients were 0.31, 0.34, and 0.53, respectively. For densities (nutrient expressed relative to energy), the average attenuation factors across studies were 0.37, 0.17, and 0.50, respectively. The findings support prior research suggesting different instruments have unique strengths that should be leveraged in epidemiologic research.</abstract><cop>United States</cop><pub>Oxford University Press</pub><pmid>35136928</pmid><doi>10.1093/aje/kwac026</doi><tpages>15</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Attenuation Biomarkers Correlation coefficient Correlation coefficients Data collection Diet Energy Epidemiology Error analysis Food Nutrients Potassium Practice of Epidemiology Recovery (Medical) |
title | Measurement Error Affecting Web- and Paper-Based Dietary Assessment Instruments: Insights From the Multi-Cohort Eating and Activity Study for Understanding Reporting Error |
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