Literature DB >> 11237930

Use of a triaxial accelerometer to validate reported food intakes.

A H Goris1, E P Meijer, A Kester, K R Westerterp.   

Abstract

BACKGROUND: An easy and cheap method for validating reported energy intake (EI) is needed.
OBJECTIVE: Reported EI was compared with calculated energy expenditure (EE(calc)) and with energy expenditure measured by the doubly labeled water method (EE(DLW)).
DESIGN: EE was calculated on the basis of basal metabolic rate (BMR) measured with the ventilated-hood technique and physical activity (PA) measured with a triaxial accelerometer (EE(VH+PA)) and on the basis of BMR estimated by using World Health Organization equations and PA (EE(WHO+PA)): EE(calc) = -1.259 + 1.55 x BMR + 0.076 x counts/min (r(2) = 0.90, P = 0.0001). Subjects [n = 12 men and 12 women aged 60 +/- 3 y; body mass index (in kg/m(2)): 26 +/- 4] reported their food intakes for 7 d and EE(DLW), EE(VH+PA), and EE(WHO+PA) were assessed over the same 7 d.
RESULTS: Reported EI (9.0 +/- 2.1 MJ/d) was lower (P: < 0.0001) than were EE(DLW) (11.3 +/- 2.3 MJ/d), EE(VH+PA) (10.8 +/- 1.7 MJ/d), and EE(WHO+PA) (10.8 +/- 1.8 MJ/d). Underreporting was 19.4 +/- 14.0%, 16.7 +/- 13.6%, and 16.4 +/- 15.5% on the basis of EE(DLW), EE(VH+PA), and EE(WHO+PA), respectively. The difference of 2.7 +/- 8.0% between EE(DLW) and EE(VH+PA) was not related to the average of both percentages and was not significantly different from zero. The percentage of underreporting calculated with EE(WHO+PA) was not significantly different from that calculated with EE(DLW).
CONCLUSIONS: The use of a combination of BMR (measured or estimated) and PA is a good method for validating reported EI. There was no significant difference between the percentage of underreporting calculated with EE(VH+PA), EE(WHO+PA), or EE(DLW).

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Year:  2001        PMID: 11237930     DOI: 10.1093/ajcn/73.3.549

Source DB:  PubMed          Journal:  Am J Clin Nutr        ISSN: 0002-9165            Impact factor:   7.045


  7 in total

1.  Energy intake estimation from counts of chews and swallows.

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2.  Valuing the Diversity of Research Methods to Advance Nutrition Science.

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Journal:  Adv Nutr       Date:  2022-08-01       Impact factor: 11.567

Review 3.  Validity of activity monitors in health and chronic disease: a systematic review.

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Journal:  Int J Behav Nutr Phys Act       Date:  2012-07-09       Impact factor: 6.457

4.  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

5.  Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor.

Authors:  Xin Yang; Abul Doulah; Muhammad Farooq; Jason Parton; Megan A McCrory; Janine A Higgins; Edward Sazonov
Journal:  Sci Rep       Date:  2019-01-10       Impact factor: 4.379

6.  Estimates of adherence and error analysis of physical activity data collected via accelerometry in a large study of free-living adults.

Authors:  David R Paul; Matthew Kramer; Kim S Stote; Karen E Spears; Alanna J Moshfegh; David J Baer; William V Rumpler
Journal:  BMC Med Res Methodol       Date:  2008-06-09       Impact factor: 4.615

7.  Influence of cardiorespiratory fitness and physical activity levels on cardiometabolic risk factors during menopause transition: A MONET study.

Authors:  Joseph Abdulnour; Sahar Razmjou; Éric Doucet; Pierre Boulay; Martin Brochu; Rémi Rabasa-Lhoret; Jean-Marc Lavoie; Denis Prud'homme
Journal:  Prev Med Rep       Date:  2016-06-29
  7 in total

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