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Whitton C, Collins CE, Mullan BA, Rollo ME, Dhaliwal SS, Norman R, Boushey CJ, Delp EJ, Zhu F, McCaffrey TA, Kirkpatrick SI, Pollard CM, Healy JD, Hassan A, Garg S, Atyeo P, Mukhtar SA, Kerr DA. Accuracy of energy and nutrient intake estimation versus observed intake using four technology-assisted dietary assessment methods: a randomized crossover feeding study. Am J Clin Nutr 2024:S0002-9165(24)00456-8. [PMID: 38710447 DOI: 10.1016/j.ajcnut.2024.04.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 03/28/2024] [Accepted: 04/29/2024] [Indexed: 05/08/2024] Open
Abstract
BACKGROUND Technology-assisted 24-hour dietary recalls (24HR) have been widely adopted in population nutrition surveillance. Evaluations of 24HR inform improvements but direct comparisons of 24HR methods for accuracy in reference to a measure of true intake are rarely undertaken in a single study population. OBJECTIVE To compare the accuracy of energy and nutrient intake estimation of four technology-assisted dietary assessment methods relative to true intake across breakfast, lunch, and dinner. METHODS In a controlled feeding study with a crossover design, 152 participants (55% women; mean age 32y (SD 11); mean BMI 26 kg/m2 (SD 5)) were randomized to one of three separate feeding days to consume breakfast, lunch, and dinner, with unobtrusive weighing of foods and beverages consumed. Participants undertook a 24HR the following day (Automated Self-Administered Dietary Assessment Tool-Australia© (ASA24); Intake24-Australia©; mobile Food Record™ - Trained Analyst (mFR-TA); or Image-Assisted Interviewer-Administered 24-hour recall (IA-24HR)). When assigned to IA-24HR, participants referred to images captured of their meals using the mobile Food Record™ app. True and estimated energy and nutrient intakes were compared, and differences among methods were assessed using linear mixed models. RESULTS The mean difference between true and estimated energy intake as a percentage of true intake was 5.4% (95% CI 0.6, 10.2) using ASA24, 1.7% (95% CI -2.9, 6.3) using Intake24, 1.3% (95% CI -1.1, 3.8) using mFR-TA, and 15.0% (95% CI 11.6, 18.3) using IA-24HR. The variances of estimated and true energy intakes were statistically significantly different for all methods (P<0.01), apart from Intake 24 (P=0.1). Differential accuracy in nutrient estimation was present among the methods. CONCLUSIONS Under controlled conditions, Intake24, ASA24, and mFR-TA estimated average energy and nutrient intakes with reasonable validity, but intake distributions were estimated accurately by Intake24 only (energy and protein). This study may inform considerations regarding instruments of choice in future population surveillance. TRIAL REGISTRATION Australian New Zealand Clinical Trials Registry Number ACTRN12621000209897; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=381165&isReview=true.
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Affiliation(s)
- Clare Whitton
- School of Medical and Health Sciences, Edith Cowan University, 270 Joondalup Drive, Joondalup WA 6027, Australia; Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; Curtin Health Innovation Research Institute, Curtin University, Kent Street, GPO Box U1987, Perth 6845, Australia
| | - Clare E Collins
- School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia; Food and Nutrition Research Program, Hunter Medical Research Institute, New Lambton Heights, Newcastle, Australia
| | - Barbara A Mullan
- Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; Enable Institute, Curtin University, Perth, Australia
| | - Megan E Rollo
- School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia
| | - Satvinder S Dhaliwal
- Curtin Health Innovation Research Institute, Curtin University, Kent Street, GPO Box U1987, Perth 6845, Australia; Duke-NUS Medical School, National University of Singapore, 8 College Rd, Singapore 169857; Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Pulau Pinang, Malaysia; Singapore University of Social Sciences, 463 Clementi Road, Singapore 599494
| | - Richard Norman
- Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; Enable Institute, Curtin University, Perth, Australia
| | - Carol J Boushey
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Edward J Delp
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States
| | - Fengqing Zhu
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States
| | - Tracy A McCaffrey
- Department of Nutrition, Dietetics and Food, Monash University, Melbourne, Australia
| | | | - Christina M Pollard
- Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; Curtin Health Innovation Research Institute, Curtin University, Kent Street, GPO Box U1987, Perth 6845, Australia; Enable Institute, Curtin University, Perth, Australia
| | - Janelle D Healy
- Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; Curtin Health Innovation Research Institute, Curtin University, Kent Street, GPO Box U1987, Perth 6845, Australia
| | - Amira Hassan
- Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; Curtin Health Innovation Research Institute, Curtin University, Kent Street, GPO Box U1987, Perth 6845, Australia
| | - Shivangi Garg
- Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia
| | - Paul Atyeo
- Health Section, Health and Disability Branch, Australian Bureau of Statistics, Canberra, Australia
| | - Syed Aqif Mukhtar
- Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia
| | - Deborah A Kerr
- Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; Curtin Health Innovation Research Institute, Curtin University, Kent Street, GPO Box U1987, Perth 6845, Australia.
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Freedman LS, Wang CY, Commins J, Barrett B, Midthune D, Dodd KW, Carroll RJ, Kipnis V. Can sodium and potassium measured in timed voids be used as reference instruments for validating self-report instruments? Results from a urine calibration study. Am J Clin Nutr 2024; 119:1321-1328. [PMID: 38403166 DOI: 10.1016/j.ajcnut.2024.02.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 01/24/2024] [Accepted: 02/20/2024] [Indexed: 02/27/2024] Open
Abstract
BACKGROUND Sodium and potassium measured in 24-h urine collections are often used as reference measurements to validate self-reported dietary intake instruments. OBJECTIVES To evaluate whether collection and analysis of a limited number of urine voids at specified times during the day ("timed voids") can provide alternative reference measurements, and to identify their optimal number and timing. METHODS We used data from a urine calibration study among 441 adults aged 18-39 y. Participants collected each urine void in a separate container for 24 h and recorded the collection time. For the same day, they reported dietary intake using a 24-h recall. Urinary sodium and potassium were analyzed in a 24-h composite sample and in 4 timed voids (morning, afternoon, evening, and overnight). Linear regression models were used to develop equations predicting log-transformed 24-h urinary sodium or potassium levels using each of the 4 single timed voids, 6 pairs, and 4 triples. The equations also included age, sex, race, BMI (kg/m2), and log creatinine. Optimal combinations minimizing the mean squared prediction error were selected, and the observed and predicted 24-h levels were then used as reference measures to estimate the group bias and attenuation factors of the 24-h dietary recall. These estimates were compared. RESULTS Optimal combinations found were as follows: single voids-evening; paired voids-afternoon + overnight (sodium) and morning + evening (potassium); and triple voids-morning + evening + overnight (sodium) and morning + afternoon + evening (potassium). Predicted 24-h urinary levels estimated 24-h recall group biases and attenuation factors without apparent bias, but with less precision than observed 24-h urinary levels. To recover lost precision, it was estimated that sample sizes need to be increased by ∼2.6-2.7 times for a single void, 1.7-2.1 times for paired voids, and 1.5-1.6 times for triple voids. CONCLUSIONS Our results provide the basis for further development of new reference biomarkers based on timed voids. CLINICAL TRIAL REGISTRY clinicaltrials.gov as NCT01631240.
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Affiliation(s)
- Laurence S Freedman
- Information Management Services Inc., Rockville, MD, United States; Biostatistics and Biomathematics Unit, Gertner Institute for Epidemiology and Health Policy Research, Chaim Sheba Medical Center, Ramat Gan, Israel.
| | - Chia-Yih Wang
- Division of Health and Nutrition Examination Surveys, National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD, United States
| | - John Commins
- Information Management Services Inc., Rockville, MD, United States
| | - Brian Barrett
- Information Management Services Inc., Rockville, MD, United States
| | - Douglas Midthune
- Biometry Research Group, Division of Cancer Prevention, National Cancer Institute, Rockville, MD, United States
| | - Kevin W Dodd
- Biometry Research Group, Division of Cancer Prevention, National Cancer Institute, Rockville, MD, United States
| | - Raymond J Carroll
- Department of Statistics, Texas A&M University, College Station, TX, United States
| | - Victor Kipnis
- Biometry Research Group, Division of Cancer Prevention, National Cancer Institute, Rockville, MD, United States
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Prentice RL, Pettinger M, Zheng C, Neuhouser ML, Raftery D, Gowda GAN, Huang Y, Tinker LF, Howard BV, Manson JE, Van Horn L, Wallace R, Mossavar-Rahmani Y, Johnson KC, Snetselaar L, Lampe JW. Biomarkers for Components of Dietary Protein and Carbohydrate with Application to Chronic Disease Risk in Postmenopausal Women. J Nutr 2022; 152:1107-1117. [PMID: 35015878 PMCID: PMC8970980 DOI: 10.1093/jn/nxac004] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/09/2021] [Accepted: 01/04/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND We recently developed protein and carbohydrate intake biomarkers using metabolomics profiles in serum and urine, and used them to correct self-reported dietary data for measurement error. Biomarker-calibrated carbohydrate density was inversely associated with chronic disease risk, whereas protein density associations were mixed. OBJECTIVES To elucidate and extend this earlier work through biomarker development for protein and carbohydrate components, including animal protein and fiber. METHODS Prospective disease association analyses were undertaken in Women's Health Initiative (WHI) cohorts of postmenopausal US women, aged 50-79 y when enrolled at 40 US clinical centers. Biomarkers were developed using an embedded human feeding study (n = 153). Calibration equations for protein and carbohydrate components were developed using a WHI nutritional biomarker study (n = 436). Calibrated intakes were associated with chronic disease incidence in WHI cohorts (n = 81,954) over a 20-y (median) follow-up period, using HR regression methods. RESULTS Previously reported elevations in cardiovascular disease (CVD) with higher-protein diets tended to be explained by animal protein density. For example, for coronary heart disease a 20% increment in animal protein density had an HR of 1.20 (95% CI: 1.02, 1.42) relative to the HR for total protein density. In comparison, cancer and diabetes risk showed little association with animal protein density beyond that attributable to total protein density. Inverse carbohydrate density associations with total CVD were mostly attributable to fiber density, with a 20% increment HR factor of 0.89 (95% CI: 0.83, 0.94). Cancer risk showed little association with fiber density, whereas diabetes risk had a 20% increment HR of 0.93 (95% CI: 0.88, 0.98) relative to the HRs for total carbohydrate density. CONCLUSIONS In a population of postmenopausal US women, CVD risk was associated with high-animal-protein and low-fiber diets, cancer risk was associated with low-carbohydrate diets, and diabetes risk was associated with low-fiber/low-carbohydrate diets.
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Affiliation(s)
- Ross L Prentice
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- School of Public Health, University of Washington, Seattle, WA, USA
| | - Mary Pettinger
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Cheng Zheng
- Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE, USA
| | - Marian L Neuhouser
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- School of Public Health, University of Washington, Seattle, WA, USA
| | - Daniel Raftery
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA
| | - G A Nagana Gowda
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA
| | - Ying Huang
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- School of Public Health, University of Washington, Seattle, WA, USA
| | - Lesley F Tinker
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Barbara V Howard
- Department of Medicine, Georgetown University Medical Center, and MedStar Health Research Institute, Hyattsville, MD, USA
| | - JoAnn E Manson
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Linda Van Horn
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Robert Wallace
- College of Public Health, University of Iowa, Iowa City, IA, USA
| | - Yasmin Mossavar-Rahmani
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, NY, USA
| | - Karen C Johnson
- Department of Preventive Medicine, University of Tennessee Health Center, Memphis, TN, USA
| | - Linda Snetselaar
- College of Public Health, University of Iowa, Iowa City, IA, USA
| | - Johanna W Lampe
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- School of Public Health, University of Washington, Seattle, WA, USA
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Whitton C, Healy JD, Collins CE, Mullan B, Rollo ME, Dhaliwal SS, Norman R, Boushey CJ, Delp EJ, Zhu F, McCaffrey TA, Kirkpatrick SI, Atyeo P, Mukhtar SA, Wright JL, Ramos-García C, Pollard CM, Kerr DA. Accuracy and Cost-effectiveness of Technology-Assisted Dietary Assessment Comparing the Automated Self-administered Dietary Assessment Tool, Intake24, and an Image-Assisted Mobile Food Record 24-Hour Recall Relative to Observed Intake: Protocol for a Randomized Crossover Feeding Study. JMIR Res Protoc 2021; 10:e32891. [PMID: 34924357 PMCID: PMC8726032 DOI: 10.2196/32891] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 11/03/2021] [Accepted: 11/10/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The assessment of dietary intake underpins population nutrition surveillance and nutritional epidemiology and is essential to inform effective public health policies and programs. Technological advances in dietary assessment that use images and automated methods have the potential to improve accuracy, respondent burden, and cost; however, they need to be evaluated to inform large-scale use. OBJECTIVE The aim of this study is to compare the accuracy, acceptability, and cost-effectiveness of 3 technology-assisted 24-hour dietary recall (24HR) methods relative to observed intake across 3 meals. METHODS Using a controlled feeding study design, 24HR data collected using 3 methods will be obtained for comparison with observed intake. A total of 150 healthy adults, aged 18 to 70 years, will be recruited and will complete web-based demographic and psychosocial questionnaires and cognitive tests. Participants will attend a university study center on 3 separate days to consume breakfast, lunch, and dinner, with unobtrusive documentation of the foods and beverages consumed and their amounts. Following each feeding day, participants will complete a 24HR process using 1 of 3 methods: the Automated Self-Administered Dietary Assessment Tool, Intake24, or the Image-Assisted mobile Food Record 24-Hour Recall. The sequence of the 3 methods will be randomized, with each participant exposed to each method approximately 1 week apart. Acceptability and the preferred 24HR method will be assessed using a questionnaire. Estimates of energy, nutrient, and food group intake and portion sizes from each 24HR method will be compared with the observed intake for each day. Linear mixed models will be used, with 24HR method and method order as fixed effects, to assess differences in the 24HR methods. Reporting bias will be assessed by examining the ratios of reported 24HR intake to observed intake. Food and beverage omission and intrusion rates will be calculated, and differences by 24HR method will be assessed using chi-square tests. Psychosocial, demographic, and cognitive factors associated with energy misestimation will be evaluated using chi-square tests and multivariable logistic regression. The financial costs, time costs, and cost-effectiveness of each 24HR method will be assessed and compared using repeated measures analysis of variance tests. RESULTS Participant recruitment commenced in March 2021 and is planned to be completed by the end of 2021. CONCLUSIONS This protocol outlines the methodology of a study that will evaluate the accuracy, acceptability, and cost-effectiveness of 3 technology-enabled dietary assessment methods. This will inform the selection of dietary assessment methods in future studies on nutrition surveillance and epidemiology. TRIAL REGISTRATION Australian New Zealand Clinical Trials Registry ACTRN12621000209897; https://tinyurl.com/2p9fpf2s. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/32891.
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Affiliation(s)
- Clare Whitton
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Australia
| | - Janelle D Healy
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Australia
| | - Clare E Collins
- School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia
- Priority Research Centre in Physical Activity and Nutrition, University of Newcastle, Newcastle, Australia
| | - Barbara Mullan
- Enable Institute, Faculty of Health Sciences, Curtin University, Perth, Australia
| | - Megan E Rollo
- School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia
- Priority Research Centre in Physical Activity and Nutrition, University of Newcastle, Newcastle, Australia
| | - Satvinder S Dhaliwal
- Curtin Health Innovation Research Institute, Faculty of Health Sciences, Curtin University, Perth, Australia
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Pulau Pinang, Malaysia
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, Australia
| | - Richard Norman
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Australia
- Enable Institute, Faculty of Health Sciences, Curtin University, Perth, Australia
| | - Carol J Boushey
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, United States
| | - Edward J Delp
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States
| | - Fengqing Zhu
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States
| | - Tracy A McCaffrey
- Department of Nutrition, Dietetics and Food, Monash University, Melbourne, Australia
| | | | - Paul Atyeo
- Health Section, Health and Disability Branch, Australian Bureau of Statistics, Canberra, Australia
| | - Syed Aqif Mukhtar
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Australia
| | - Janine L Wright
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Australia
| | - César Ramos-García
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Australia
- Division of Health Sciences, Tonalá University Center, University of Guadalajara, Guadalajara, Mexico
| | - Christina M Pollard
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Australia
- Enable Institute, Faculty of Health Sciences, Curtin University, Perth, Australia
- Curtin Health Innovation Research Institute, Faculty of Health Sciences, Curtin University, Perth, Australia
| | - Deborah A Kerr
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Australia
- Curtin Health Innovation Research Institute, Faculty of Health Sciences, Curtin University, Perth, Australia
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Mossavar-Rahmani Y, Shaw PA, Wong WW, Sotres-Alvarez D, Gellman MD, Van Horn L, Stoutenberg M, Daviglus ML, Wylie-Rosett J, Siega-Riz AM, Ou FS, Prentice RL. Applying Recovery Biomarkers to Calibrate Self-Report Measures of Energy and Protein in the Hispanic Community Health Study/Study of Latinos. Am J Epidemiol 2015; 181:996-1007. [PMID: 25995289 PMCID: PMC4462334 DOI: 10.1093/aje/kwu468] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2014] [Accepted: 12/18/2014] [Indexed: 11/22/2022] Open
Abstract
We investigated measurement error in the self-reported diets of US Hispanics/Latinos, who are prone to obesity and related comorbidities, by background (Central American, Cuban, Dominican, Mexican, Puerto Rican, and South American) in 2010–2012. In 477 participants aged 18–74 years, doubly labeled water and urinary nitrogen were used as objective recovery biomarkers of energy and protein intakes. Self-report was captured from two 24-hour dietary recalls. All measures were repeated in a subsample of 98 individuals. We examined the bias of dietary recalls and their associations with participant characteristics using generalized estimating equations. Energy intake was underestimated by 25.3% (men, 21.8%; women, 27.3%), and protein intake was underestimated by 18.5% (men, 14.7%; women, 20.7%). Protein density was overestimated by 10.7% (men, 11.3%; women, 10.1%). Higher body mass index and Hispanic/Latino background were associated with underestimation of energy (P < 0.05). For protein intake, higher body mass index, older age, nonsmoking, Spanish speaking, and Hispanic/Latino background were associated with underestimation (P < 0.05). Systematic underreporting of energy and protein intakes and overreporting of protein density were found to vary significantly by Hispanic/Latino background. We developed calibration equations that correct for subject-specific error in reporting that can be used to reduce bias in diet-disease association studies.
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Affiliation(s)
- Yasmin Mossavar-Rahmani
- Correspondence to Dr. Yasmin Mossavar-Rahmani, Division of Health Promotion and Nutrition Research, Department of Epidemiology and Population Health, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Block Building, Room 339, Bronx, NY 10461 (e-mail: )
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Freedman LS, Commins JM, Moler JE, Willett W, Tinker LF, Subar AF, Spiegelman D, Rhodes D, Potischman N, Neuhouser ML, Moshfegh AJ, Kipnis V, Arab L, Prentice RL. Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for potassium and sodium intake. Am J Epidemiol 2015; 181:473-87. [PMID: 25787264 DOI: 10.1093/aje/kwu325] [Citation(s) in RCA: 182] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
We pooled data from 5 large validation studies (1999-2009) of dietary self-report instruments that used recovery biomarkers as referents, to assess food frequency questionnaires (FFQs) and 24-hour recalls (24HRs). Here we report on total potassium and sodium intakes, their densities, and their ratio. Results were similar by sex but were heterogeneous across studies. For potassium, potassium density, sodium, sodium density, and sodium:potassium ratio, average correlation coefficients for the correlation of reported intake with true intake on the FFQs were 0.37, 0.47, 0.16, 0.32, and 0.49, respectively. For the same nutrients measured with a single 24HR, they were 0.47, 0.46, 0.32, 0.31, and 0.46, respectively, rising to 0.56, 0.53, 0.41, 0.38, and 0.60 for the average of three 24HRs. Average underreporting was 5%-6% with an FFQ and 0%-4% with a single 24HR for potassium but was 28%-39% and 4%-13%, respectively, for sodium. Higher body mass index was related to underreporting of sodium. Calibration equations for true intake that included personal characteristics provided improved prediction, except for sodium density. In summary, self-reports capture potassium intake quite well but sodium intake less well. Using densities improves the measurement of potassium and sodium on an FFQ. Sodium:potassium ratio is measured much better than sodium itself on both FFQs and 24HRs.
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Freedman LS, Commins JM, Moler JE, Arab L, Baer DJ, Kipnis V, Midthune D, Moshfegh AJ, Neuhouser ML, Prentice RL, Schatzkin A, Spiegelman D, Subar AF, Tinker LF, Willett W. Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for energy and protein intake. Am J Epidemiol 2014; 180:172-88. [PMID: 24918187 DOI: 10.1093/aje/kwu116] [Citation(s) in RCA: 331] [Impact Index Per Article: 33.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
We pooled data from 5 large validation studies of dietary self-report instruments that used recovery biomarkers as references to clarify the measurement properties of food frequency questionnaires (FFQs) and 24-hour recalls. The studies were conducted in widely differing US adult populations from 1999 to 2009. We report on total energy, protein, and protein density intakes. Results were similar across sexes, but there was heterogeneity across studies. Using a FFQ, the average correlation coefficients for reported versus true intakes for energy, protein, and protein density were 0.21, 0.29, and 0.41, respectively. Using a single 24-hour recall, the coefficients were 0.26, 0.40, and 0.36, respectively, for the same nutrients and rose to 0.31, 0.49, and 0.46 when three 24-hour recalls were averaged. The average rate of under-reporting of energy intake was 28% with a FFQ and 15% with a single 24-hour recall, but the percentages were lower for protein. Personal characteristics related to under-reporting were body mass index, educational level, and age. Calibration equations for true intake that included personal characteristics provided improved prediction. This project establishes that FFQs have stronger correlations with truth for protein density than for absolute protein intake, that the use of multiple 24-hour recalls substantially increases the correlations when compared with a single 24-hour recall, and that body mass index strongly predicts under-reporting of energy and protein intakes.
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