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Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method
Published on: February 19, 2021
Child meal microstructure and eating behaviors: A systematic review
Alaina L Pearce1, Maria C Cevallos1, Olivia Romano1
1Department of Nutritional Science, The Pennsylvania State University, University Park, PA, USA.
None:
Observational coding of children's eating behaviors and meal microstructure (e.g., bites, chews) provides an opportunity to assess complex eating styles that may relate to individual differences in energy intake and weight status. Across studies, however, similar terms are often defined differently, which complicates the interpretation and replication of coding protocols. Therefore, this study aimed to compile methods of coding meal microstructure in children. To limit bias and ensure a comprehensive review, a systematic search was conducted in January 2021 across three databases (PubMed, PsychInfo, Web of Science) resulting in 46 studies that coded at least one meal-related behavior in healthy children (i.e., no medical/psychological disorders) who were able to self-feed (i.e., no spoon-, breast-, or bottle-feeding). While the majority of studies had good interrater reliability, the details reported about study foods and the clarity of the definitions used for behavioral coding varied considerably. In addition to reported microstructure behaviors, a non-exhaustive review of individual differences was included. While few studies reported individual differences related to age and sex, there was a larger literature related to weight status that provided evidence for an 'obesogenic' style of eating characterized by larger Bites, faster Eating and Bite Rates, and shorter Meal Durations. However, some studies may not have been optimally designed or powered to detect individual differences because they did not set out a priori to examine them. Based on this systematic review, best practices for the field are recommended and include reporting more details about foods served and coded eating behaviors to improve reproducibility. These suggestions will improve the ability to examine patterns of individual differences across studies, which may help identify novel targets for intervention.
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