Dataset Information

Comparison of self-reported dietary intakes from the Automated Self-Administered 24-h recall, 4-d food records, and food-frequency questionnaires against recovery biomarkers.

ABSTRACT: Background:A limited number of studies have evaluated self-reported dietary intakes against objective recovery biomarkers. Objective:The aim was to compare dietary intakes of multiple Automated Self-Administered 24-h recalls (ASA24s), 4-d food records (4DFRs), and food-frequency questionnaires (FFQs) against recovery biomarkers and to estimate the prevalence of under- and overreporting. Design:Over 12 mo, 530 men and 545 women, aged 50-74 y, were asked to complete 6 ASA24s (2011 version), 2 unweighed 4DFRs, 2 FFQs, two 24-h urine collections (biomarkers for protein, potassium, and sodium intakes), and 1 administration of doubly labeled water (biomarker for energy intake). Absolute and density-based energy-adjusted nutrient intakes were calculated. The prevalence of under- and overreporting of self-report against biomarkers was estimated. Results:Ninety-two percent of men and 87% of women completed ?3 ASA24s (mean ASA24s completed: 5.4 and 5.1 for men and women, respectively). Absolute intakes of energy, protein, potassium, and sodium assessed by all self-reported instruments were systematically lower than those from recovery biomarkers, with underreporting greater for energy than for other nutrients. On average, compared with the energy biomarker, intake was underestimated by 15-17% on ASA24s, 18-21% on 4DFRs, and 29-34% on FFQs. Underreporting was more prevalent on FFQs than on ASA24s and 4DFRs and among obese individuals. Mean protein and sodium densities on ASA24s, 4DFRs, and FFQs were similar to biomarker values, but potassium density on FFQs was 26-40% higher, leading to a substantial increase in the prevalence of overreporting compared with absolute potassium intake. Conclusions:Although misreporting is present in all self-report dietary assessment tools, multiple ASA24s and a 4DFR provided the best estimates of absolute dietary intakes for these few nutrients and outperformed FFQs. Energy adjustment improved estimates from FFQs for protein and sodium but not for potassium. The ASA24, which now can be used to collect both recalls and records, is a feasible means to collect dietary data for nutrition research.

SUBMITTER: Park Y

PROVIDER: S-EPMC5972568 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Comparison of self-reported dietary intakes from the Automated Self-Administered 24-h recall, 4-d food records, and food-frequency questionnaires against recovery biomarkers.

Park Yikyung Y Dodd Kevin W KW Kipnis Victor V Thompson Frances E FE Potischman Nancy N Schoeller Dale A DA Baer David J DJ Midthune Douglas D Troiano Richard P RP Bowles Heather H Subar Amy F AF

The American journal of clinical nutrition 20180101 1

<h4>Background</h4>A limited number of studies have evaluated self-reported dietary intakes against objective recovery biomarkers.<h4>Objective</h4>The aim was to compare dietary intakes of multiple Automated Self-Administered 24-h recalls (ASA24s), 4-d food records (4DFRs), and food-frequency questionnaires (FFQs) against recovery biomarkers and to estimate the prevalence of under- and overreporting.<h4>Design</h4>Over 12 mo, 530 men and 545 women, aged 50-74 y, were asked to complete 6 ASA24s ...[more]

PMID: 29381789

Similar Datasets

Project description:BackgroundThe Automated Self-Administered 24-hour Recall (ASA24), a freely available Web-based tool, was developed to enhance the feasibility of collecting high-quality dietary intake data from large samples.ObjectiveThe purpose of this study was to assess the criterion validity of ASA24 through a feeding study in which the true intake for 3 meals was known.DesignTrue intake and plate waste from 3 meals were ascertained for 81 adults by inconspicuously weighing foods and beverages offered at a buffet before and after each participant served him- or herself. Participants were randomly assigned to complete an ASA24 or an interviewer-administered Automated Multiple-Pass Method (AMPM) recall the following day. With the use of linear and Poisson regression analysis, we examined the associations between recall mode and 1) the proportions of items consumed for which a match was reported and that were excluded, 2) the number of intrusions (items reported but not consumed), and 3) differences between energy, nutrient, food group, and portion size estimates based on true and reported intakes.ResultsRespondents completing ASA24 reported 80% of items truly consumed compared with 83% in AMPM (P = 0.07). For both ASA24 and AMPM, additions to or ingredients in multicomponent foods and drinks were more frequently omitted than were main foods or drinks. The number of intrusions was higher in ASA24 (P < 0.01). Little evidence of differences by recall mode was found in the gap between true and reported energy, nutrient, and food group intakes or portion sizes.ConclusionsAlthough the interviewer-administered AMPM performed somewhat better relative to true intakes for matches, exclusions, and intrusions, ASA24 performed well. Given the substantial cost savings that ASA24 offers, it has the potential to make important contributions to research aimed at describing the diets of populations, assessing the effect of interventions on diet, and elucidating diet and health relations. This trial was registered at clinicaltrials.gov as NCT00978406.

Project description:BackgroundAlthough prior evidence indicates that water intake is important for health, the ability to accurately measure community-dwelling intake is limited. Only a few studies have evaluated self-reported water intake against an objective recovery biomarker.ObjectivesThe aim was to compare preformed water intakes (all sources including food) by multiple Automated Self-Administered 24-h recalls (ASA24s), food frequency questionnaires (FFQs), and 4-d food records (4DFRs) against a recovery biomarker, doubly labeled water (DLW), to assess measurement error.MethodsOver 1 y, 1082 women and men (50%), aged 50 to 74 y, were asked to complete 6 ASA24s, 2 FFQs, 2 unweighted 4DFRs, and an administration of DLW (n = 686). Geometric means of water intake by self-report tools were compared with DLW. Attenuation factors and correlation coefficients between self-reported and the recovery biomarker (DLW) were estimated.ResultsMean water intakes by DLW were 2777 mL/d (interquartile range, 2350 to 3331) in women and 3243 mL/d (interquartile range, 2720 to 3838) in men. Compared with DLW, water intake was underestimated by 18% to 31% on ASA24s and 43% to 44% on 4DFRs. Estimated geometric means from FFQs differed from DLW by -1% to +13%. For a single ASA24, FFQ, and 4DFR, attenuation factors were 0.28, 0.27, and 0.32 and correlation coefficients were 0.46, 0.48, and 0.49, respectively. Repeated use of 6 ASA24s, 2 FFQs, and 2 4DFRs improved attenuation factors to 0.43, 0.32, and 0.39 and correlation coefficients to 0.58, 0.53, and 0.54, respectively.ConclusionsFFQs may better estimate population means for usual water intake compared with ASA24 and 4DFR. Similar attenuation factors and correlation coefficients across all self-report tools indicate that researchers have 3 feasible options if the goal is understanding intake-disease relationships. The findings are useful for planning future nutrition studies that set policy priorities for populations and to understand the health impact of water. This trial was registered at clinicaltrials.gov as NCT03268577.

Project description:BACKGROUND:Despite the increasing use of medical records to measure quality of care, studies have shown that their validity is suboptimal. The objective of this study is to assess the concordance of cardiovascular care processes evaluated through medical record review and patient self-administered questionnaires (SAQs) using ten quality indicators (TRANSIT indicators). These indicators were developed as part of a participatory research program (TRANSIT study) dedicated to TRANSforming InTerprofessional clinical practices to improve cardiovascular disease (CVD) prevention in primary care. METHODS:For every patient participating in the TRANSIT study, the compliance to each indicator (individual scores) as well as the mean compliance to all indicators of a category (subscale scores) and to the complete set of ten indicators (overall scale score) were established. Concordance between results obtained using medical records and patient SAQs was assessed by prevalence-adjusted bias-adjusted kappa (PABAK) coefficients as well as intraclass correlation coefficients (ICCs) and 95% confidence intervals (95% CI). Generalized linear mixed models (GLMM) were used to identify patients' sociodemographic and clinical characteristics associated with agreement between the two data sources. RESULTS:The TRANSIT study was conducted in a primary care setting among patients (n?=?759) with multimorbidity, at moderate (16%) and high risk (83%) of cardiovascular diseases. Quality of care, as measured by the TRANSIT indicators, varied substantially between medical records and patient SAQ. Concordance between the two data sources, as measured by ICCs (95% CI), was poor for the subscale (0.18 [0.08-0.27] to 0.46 [0.40-0.52]) and overall (0.46 [0.40-0.53]) compliance scale scores. GLMM showed that agreement was not affected by patients' characteristics. CONCLUSIONS:In quality improvement strategies, researchers must acknowledge that care processes may not be consistently recorded in medical records. They must also be aware that the evaluation of the quality of care may vary depending on the source of information, the clinician responsible of documenting the interventions, and the domain of care.

Project description:BackgroundAutomated dietary assessment tools such as ASA24® are useful for collecting 24-hour recall data in large-scale studies. Modifications made during manual data cleaning may affect nutrient intakes.ObjectivesWe evaluated the effects of modifications made during manual data cleaning on nutrient intakes of interest: energy, carbohydrate, total fat, protein, and fiber.MethodsDifferences in mean intake before and after data cleaning modifications for all recalls and average intakes per subject were analyzed by paired t-tests. The Chi-squared test was used to determine whether unsupervised recalls had more open-ended text responses that required modification than supervised recalls. We characterized food types of text response modifications. Correlations between predictive energy requirements, measured total energy expenditure (TEE), and mean energy intake from raw and modified data were examined.ResultsAfter excluding 11 recalls with invalidating technical errors, 1499 valid recalls completed by 393 subjects were included in this analysis. We found significant differences before and after modifications for energy, carbohydrate, total fat, and protein intakes for all recalls (P < 0.05). Limiting to modified recalls, there were significant differences for all nutrients of interest, including fiber (P < 0.02). There was not a significantly greater proportion of text responses requiring modification for home compared with supervised recalls (P = 0.271). Predicted energy requirements correlated highly with TEE. There was no significant difference in correlation of mean energy intake with TEE for modified compared with raw data. Mean intake for individual subjects was significantly different for energy, protein, and fat intakes following cleaning modifications (P < 0.001).ConclusionsManual modifications can change mean nutrient intakes for an entire cohort and individuals. However, modifications did not significantly affect the correlation of energy intake with predictive requirements and measured expenditure. Investigators can consider their research question and nutrients of interest when deciding to make cleaning modifications.

Project description:BackgroundA dietary screener questionnaire (DSQ) was used to assess dietary outcomes among children in the Healthy Communities Study (HCS), a study of the relationships between programs and policies to prevent child obesity and child diet, physical activity and weight outcomes.MethodsTo compare dietary intake estimates derived from the DSQ against those from the Automated Self-Administered 24-Hour Recalls for Children (ASA24-Kids) among children, a measurement error model, using structural equation modelling, was utilized to estimate slopes, deattenuated correlation coefficients, and attenuation factors by age and sex, ethnicity, and BMI status.Participants/settingA randomly selected sub-sample of HCS participants aged 4-15 years in 130 communities throughout the U.S. who completed the DSQ and up to two ASA24-Kids recalls (n?=?656;13% of HCS participants).ResultsFor most nutrient/foods examined, the DSQ yielded larger mean intake estimates than the ASA24-Kids, and agreement between the two measures varied by food/nutrient, age and sex, ethnicity, and BMI category. Deattenuated correlation coefficients of 0.4 or greater were observed for added sugars from SSBs (0.54), fruits and vegetables (0.40), and dairy foods (0.50). Lower deattenuated correlation coefficients were seen for total added sugars (0.37), whole grains (0.34), and fiber (0.34). Attenuation factors were most severe for total added sugars intake among overweight children, and for several other dietary outcomes among children aged 9-11 years.ConclusionsThe DSQ was found to be a tool with acceptable agreement with the ASA24-Kids for measuring multiple dietary outcomes of interest in the HCS, although there may be potential due to measurement error to underestimate results (bias towards the null). In future studies, measurement error modelling and regression calibration may be possible solutions to correct for bias due to measurement error in most food/nutrient intake estimates from the DSQ when used among children.

Project description:Web-based instruments are being increasingly used in nutrition epidemiology and surveillance. However, the extent to which dietary intake estimates derived from web-based 24-h recalls such as the R24W are consistent with data derived from more traditional interviewer-administered 24-h recalls (TRAD) remains uncertain. Our objective was to compare dietary intake estimates obtained using the R24W and a TRAD instrument in population-based samples from the province of Québec in Canada. This comparison of dietary assessment methods was based on data from two sample survey studies in adults (18-65 years). The R24W was used in a sample of 1,147 French-speaking adults from five regions of Québec as part of the PREDISE (PRÉDicteurs Individuels, Sociaux et Environnementaux) study. The TRAD was used in a sample of 875 French-speaking adults from the Canadian Community Health Survey 2015 located in the same five regions. Characteristics of both samples were matched through selection and weighting (language, sex, age, region, education, body mass index, weekend day, and season of survey). Mean and usual intake data of each sample were compared. The plausibility of reported energy intakes was compared using predictive equations of the Institute of Medicine. Mean servings/day from the R24W were higher than with TRAD for vegetables and fruit (+11%, P = 0.003), grain products (+7%, P = 0.06), milk and alternatives (+21%, P < 0.001), and meat and alternatives (+18%, P = 0.001). Intake of low nutritive value foods was also 28% higher with the R24W than with TRAD (mean difference +164 kcal; 95% CI, 107-222). As a result, total energy intakes from the R24W compared with TRAD were 18% higher in women (mean difference +325 kcal; 95% CI, 243-407) and 15% higher in men (mean difference +361 kcal; 95% CI 232-490). The prevalence of underreporting of energy intakes was 10% lower with the R24W than with TRAD (prevalence ratio 0.90; 95% CI, 0.86-0.94). In conclusion, differences between dietary assessment methods in the context of population-based surveys on nutrition have potentially important consequences on the quality of the data and should be carefully considered in future surveys and surveillance strategies.

Dataset Information

Comparison of self-reported dietary intakes from the Automated Self-Administered 24-h recall, 4-d food records, and food-frequency questionnaires against recovery biomarkers.

Publications

Comparison of self-reported dietary intakes from the Automated Self-Administered 24-h recall, 4-d food records, and food-frequency questionnaires against recovery biomarkers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets