A simple method for identification of misreporting of energy intake from infancy to school age: Results from a longitudinal study

Gomes, Delphina; Luque, Verónica; Xhonneux, Annick; Verduci, Elvira; Socha, Piotr; Koletzko, Berthold; Berger, Ursula; Grote, Veit

doi:10.1016/j.clnu.2017.05.003

Cited by 14 publications

(16 citation statements)

References 28 publications

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“…When children are the subjects of dietary assessment, the challenges are increased due to limited literacy, writing skills, food knowledge and often interest in taking part in dietary surveys coupled with the range of people responsible for their care and food provision [78]. However, in our study, the percentages of misreporting were low compared with other national and international studies [18,22,28,75]. Misreporting of dietary intake is a major issue in dietary recall methods as the 24-h DR [21].…”

Section: Strengths and Limitationscontrasting

confidence: 55%

“…The exclusion of over-and under-reporters from our analyses did not significantly modify the results of our study. Misreporting represent 15.3% and 16.4% for our SRS and AMS, respectively; this is lower than in other national [22,75] and European studies [18,28,76]. However, in accordance with the EFSA recommendations, we did not exclude potential misreporters from the analyses [31].…”

Section: Energy Misreportingmentioning

confidence: 61%

“…There are different dietary recall methods used in nutrition surveys, which are based on the assumption that reported dietary intake shows the habitual intake. The procedure of obtaining the habitual intake becomes more complex in young children, for whom dietary recall methods are conducted on proxy-reporters [28,29]. Therefore, the identification of the characteristics associated with misreporting (under-and over-reporting) of dietary intake is fundamental to obtaining results that represent the usual intake and that is physiologically plausible [30].…”

Section: Introductionmentioning

confidence: 99%

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Energy Intake, Macronutrient Profile and Food Sources of Spanish Children Aged One to <10 Years—Results from the EsNuPI Study †

Madrigal

Soto-Méndez²,

Hernández‐Ruiz³

et al. 2020

Nutrients

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The present study aimed to assess energy intake, nutrient profile and food sources in Spanish children participating in the EsNuPI (“Estudio Nutricional en Población Infantil Española”) study. Plausibility of energy intake and adequacy of nutrient intakes to international recommendations were analyzed in a final sample of 1448 subjects (728 boys and 720 girls) and one group representative of the 1 to <10 years old urban Spanish children (reference sample (n = 707)) who consumed milk and one of the same age who consumed adapted milk over the last year (adapted milk consumers sample (n = 741)) were compared. Both groups completed data of a face-to-face and a telephone 24-h dietary recalls. Both the reference and the adapted milk consumers samples reported an adequate daily energy intake (1503 kcal/day and 1404 kcal/day); and a high contribution to total energy from protein (16.5% and 15.6%) and fat (36.5% and 35.9%). Also, a high percentage of children from both samples were below the lower limit of the recommendations for carbohydrates (47.8% and 39.3%). As the percentage of plausible energy reporters was high for both groups (84.7% and 83.5%, respectively), data for the whole sample were analyzed. Milk and dairy, cereals, meat and derived products, fats and oils, bakery and pastry, fruits and vegetables contributed to about 80% of the total energy intake in both groups. However, the reference sample reported significantly more contribution to energy from cereals, meat and meat products, bakery and pastry and ready to cook/eat foods; meanwhile, the adapted milk consumers sample reported significantly more energy from milk and dairy products, fruits and eggs. Those results suggest that adapted milk consumers have better adherence to the food-based dietary guidelines. Further analyses are warranted to characterize food patterns and the quality of the diet in the EsNuPI study population.

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Section: Strengths and Limitationscontrasting

confidence: 55%

Section: Energy Misreportingmentioning

confidence: 61%

Section: Introductionmentioning

confidence: 99%

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Energy Intake, Macronutrient Profile and Food Sources of Spanish Children Aged One to <10 Years—Results from the EsNuPI Study †

Madrigal

Soto-Méndez²,

Hernández‐Ruiz³

et al. 2020

Nutrients

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“…It therefore seems timely to evaluate the association between ED and different body composition variables (i.e. BMI, adiposity) and cardiometabolic markers, taking into account levels of objectively measured physical activity, to ascertain whether ED is critical to body composition and whether physical activity modulates this association in children with overweight and obesity, a population in which EI under-reporting has been shown to be more prevalent and severe (24,25) . Consequently, the aims of the present study were (1) to evaluate the association of ED S and ED SL with adiposity and cardiometabolic risk in children with overweight and obesity, (2) to estimate the effect of under-reporting on the mentioned associations and (3) to assess whether the association between ED and body composition/cardiometabolic risk is influenced by levels of physical activity.…”

mentioning

confidence: 99%

Associations of dietary energy density with body composition and cardiometabolic risk in children with overweight and obesity: role of energy density calculations, under-reporting energy intake and physical activity

et al. 2019

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This study examined (1) the association of dietary energy density from solid (EDS) and solid plus liquids (EDSL) with adiposity and cardiometabolic risk factors (CRF) in children with overweight and obesity, (2) the effect of under-reporting on the mentioned associations and (3) whether the association between ED and body composition and CRF is influenced by levels of physical activity. In a cross-sectional design, 208 overweight and obese children (8–12-year-old; 111 boys) completed two non-consecutive 24 h recalls. ED was calculated using two different approaches: EDS and EDSL. Under-reporters were determined with the Goldberg method. Body composition, anthropometry and fasting blood sample measurements were performed. Moderate to vigorous physical activity (MVPA) was registered with accelerometers (7-d-register). Linear regressions were performed to evaluate the association of ED with the previously mentioned variables. Neither EDS nor EDSL were associated with body composition or CRF. However, when under-reporters were excluded, EDS was positively associated with BMI (P=0·019), body fat percentage (P=0·005), abdominal fat (P=0·008) and fat mass index (P=0·018), while EDSL was positively associated with body fat percentage (P=0·008) and fat mass index (P=0·026). When stratifying the group according to physical activity recommendations, the aforementioned associations were only maintained for non-compliers. Cluster analysis showed that the low-ED and high-MVPA group presented the healthiest profile for all adiposity and CRF. These findings could partly explain inconsistencies in literature, as we found that different ED calculations entail distinct results. Physical activity levels and excluding under-reporters greatly influence the associations between ED and adiposity in children with overweight and obesity.

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“…However, dietary data collected from such tools are error-prone (12,13). This error can mask true estimates of dietdisease associations, leading to untrustworthy conclusions about diet-disease relationships (14).…”

Section: Introductionmentioning

confidence: 99%

The Effect of Different Methods to Identify, and Scenarios Used to Address Energy Intake Misestimation on Dietary Patterns Derived by Cluster Analysis

Siou

Akawung

Solbak

et al. 2020

Preprint

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Background: All self-report dietary intake data are characterized by measurement error, and validation studies indicate that the estimation of energy intake (EI) is particularly affected. Methods: Using self-report food frequency and physical activity data from Alberta’s Tomorrow Project participants (n=9,847 men 16,241 women), we compared the revised-Goldberg and the predicted total energy expenditure methods in their ability to identify misreporters of EI. We also compared dietary patterns derived by k-means clustering under different scenarios where misreporters are included in the cluster analysis (Inclusion); excluded prior to completing the cluster analysis (ExBefore); excluded after completing the cluster analysis (ExAfter); and finally, excluded before the cluster analysis but added to the ExBefore cluster solution using the nearest neighbor method (InclusionNN). Results: The predicted total energy expenditure method identified a significantly higher proportion of participants as EI misreporters compared to the revised-Goldberg method (50% vs. 47%). k-means cluster analysis identified 3 dietary patterns: Healthy, Meats/Pizza and Sweets/Dairy. Among both men and women, participants assigned to dietary patterns changed substantially between ExBefore and ExAfter and also between the Inclusion and InclusionNN scenarios. Conclusions: Different scenarios used to account for EI misreporters influenced cluster analysis and hence the composition of the dietary patterns.

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A simple method for identification of misreporting of energy intake from infancy to school age: Results from a longitudinal study

Cited by 14 publications

References 28 publications

Energy Intake, Macronutrient Profile and Food Sources of Spanish Children Aged One to <10 Years—Results from the EsNuPI Study †

Energy Intake, Macronutrient Profile and Food Sources of Spanish Children Aged One to <10 Years—Results from the EsNuPI Study †

Associations of dietary energy density with body composition and cardiometabolic risk in children with overweight and obesity: role of energy density calculations, under-reporting energy intake and physical activity

The Effect of Different Methods to Identify, and Scenarios Used to Address Energy Intake Misestimation on Dietary Patterns Derived by Cluster Analysis

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