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Estimation of underreporting of energy intake using different methods in a subsample of the ELSA-Brasil study.

Priscila Santana OliveiraJéssica LevyEduardo De CarliIsabela Judith Martins BensenorPaulo Andrade LotufoRosangela Alves PereiraEdna Massae YokooRosely SichieriSandra Patrícia CrispimLeticia Oliveira Cardoso
Published in: Cadernos de saude publica (2022)
Existing methods for assessing food consumption are subject to measurement errors, especially the underreporting of energy intake, characterized by reporting energy intake below the minimum necessary to maintain body weight. This study aimed to compare the identification of energy intake underreporters using different predictive equations and instruments to collect dietary data. The study was conducted with 101 selected participants in the third wave of the Longitudinal Study of Adult Health (ELSA-Brasil) at the University Hospital of the University of São Paulo. For the dietary assessment, we applied a food frequency questionnaire (FFQ), two 24-hour diet recall (24hR) using the GloboDiet software, and two 24hR using the Brasil-Nutri software. The energy intake underreport obtained from the FFQ was 13%, 16%, and 1% using the equations proposed by Goldberg et al. (1991), Black (2000), and McCrory et al. (2002), respectively. With these same equations, the 24hR described an underreport of 9.9%, 14.9%, and 0.9% respectively with the GloboDiet software and 14.7%, 15.8%, and 1.1% respectively with the Brasil-Nutri software. We verified a low prevalence of underreported energy intake among the three self-report-based dietary data collection methods (FFQ, 24hR with GloboDiet, and Brasil-Nutri). Though no statistically significant differences were found among three methods, the equations for each method differed among them. The agreement of energy intake between the methods was very similar, but the best was between GloboDiet and Brasil-Nutri.
Keyphrases
  • weight gain
  • body weight
  • public health
  • blood pressure
  • data analysis
  • mental health
  • electronic health record
  • machine learning
  • young adults
  • weight loss
  • patient safety
  • big data
  • body mass index