Literature DB >> 18388890

Prevalence and characteristics of energy underreporting in African-American girls.

Jennifer Q Lanctot1, Robert C Klesges, Michelle B Stockton, Lisa M Klesges.   

Abstract

OBJECTIVE: To determine the frequency and characteristics of energy intake underreporting in African-American preadolescent girls as part of the Girls health Enrichment Multi-site Studies (GEMS). METHODS AND PROCEDURES: Energy intake was summarized using the Nutrition Data System for Research software and computed as a 3-day average of 24-h dietary recalls. Physical activity was assessed by an accelerometer, basal metabolic rate (BMR) was estimated using the World Health Organization's prediction equation, and underreporting of caloric intake was based on the Goldberg equation.
RESULTS: Using a conservative criterion for determining energy underreporting, we classified 54.8% of the girls as underreporters; 45.2% were classified as plausible reporters. Factors related to underreporting included higher BMI (beta = -0.506, P < or = 0.001), older age (beta = -0.159, P = 0.001), greater unhealthy eating behaviors (beta = -0.118, P = 0.025), and higher self-efficacy for diet (beta = -0.098, P = 0.033). DISCUSSION: Underreporting of dietary intake, specifically energy, is common in African-American preadolescent girls and can be partially explained by weight status and psychosocial variables. The extent of dietary underreporting in specific and high-risk populations is largely unknown and could be evaluated by routinely including a report of such an index in future research studies.

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Mesh:

Year:  2008        PMID: 18388890     DOI: 10.1038/oby.2008.222

Source DB:  PubMed          Journal:  Obesity (Silver Spring)        ISSN: 1930-7381            Impact factor:   5.002


  15 in total

1.  Dietary energy density is associated with body weight status and vegetable intake in U.S. children.

Authors:  Jacqueline A Vernarelli; Diane C Mitchell; Terryl J Hartman; Barbara J Rolls
Journal:  J Nutr       Date:  2011-11-02       Impact factor: 4.798

2.  Use of accelerometer data in prediction equations for capturing implausible dietary intakes in adolescents.

Authors:  Sabrina E Noel; Calum Mattocks; Pauline Emmett; Chris J Riddoch; Andrew R Ness; P K Newby
Journal:  Am J Clin Nutr       Date:  2010-09-29       Impact factor: 7.045

3.  Self-reported vs. actual energy intake in youth with and without loss of control eating.

Authors:  Laura E Wolkoff; Marian Tanofsky-Kraff; Lauren B Shomaker; Merel Kozlosky; Kelli M Columbo; Camden A Elliott; Lisa M Ranzenhofer; Robyn L Osborn; Susan Z Yanovski; Jack A Yanovski
Journal:  Eat Behav       Date:  2010-09-18

4.  Mindfulness and laboratory eating behavior in adolescent girls at risk for type 2 diabetes.

Authors:  Shelly K Annameier; Nichole R Kelly; Amber B Courville; Marian Tanofsky-Kraff; Jack A Yanovski; Lauren B Shomaker
Journal:  Appetite       Date:  2018-01-31       Impact factor: 3.868

5.  Misreport of energy intake assessed with food records and 24-h recalls compared with total energy expenditure estimated with DLW.

Authors:  T S Lopes; R R Luiz; D J Hoffman; E Ferriolli; K Pfrimer; A S Moura; R Sichieri; R A Pereira
Journal:  Eur J Clin Nutr       Date:  2016-06-08       Impact factor: 4.016

6.  20-Year trends in dietary and meal behaviors were similar in U.S. children and adolescents of different race/ethnicity.

Authors:  Ashima K Kant; Barry I Graubard
Journal:  J Nutr       Date:  2011-08-24       Impact factor: 4.798

7.  Comparative effects of whey and casein proteins on satiety in overweight and obese individuals: a randomized controlled trial.

Authors:  S Pal; S Radavelli-Bagatini; M Hagger; V Ellis
Journal:  Eur J Clin Nutr       Date:  2014-05-07       Impact factor: 4.016

Review 8.  Energy intake misreporting among children and adolescents: a literature review.

Authors:  Sarah G Forrestal
Journal:  Matern Child Nutr       Date:  2010-08-23       Impact factor: 3.092

9.  Serum carbon isotope values change in adults in response to changes in sugar-sweetened beverage intake.

Authors:  Tala H I Fakhouri; A Hope Jahren; Lawrence J Appel; Liwei Chen; Reza Alavi; Cheryl A M Anderson
Journal:  J Nutr       Date:  2014-04-09       Impact factor: 4.798

10.  Comparison of estimated energy intake using Web-based Dietary Assessment Software with accelerometer-determined energy expenditure in children.

Authors:  Anja Biltoft-Jensen; Mads F Hjorth; Ellen Trolle; Tue Christensen; Per B Brockhoff; Lene F Andersen; Inge Tetens; Jeppe Matthiessen
Journal:  Food Nutr Res       Date:  2013-12-17       Impact factor: 3.894

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