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Temporal eating patterns: a latent class analysis approach

BACKGROUND: There is some evidence that large energy intakes towards the end of the day are associated with adverse health outcomes, however, studies of temporal eating patterns across the day are rare. This study examines the temporal eating patterns of Australian adults using latent class analysis...

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Autores principales: Leech, Rebecca M., Worsley, Anthony, Timperio, Anna, McNaughton, Sarah A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5219683/
https://www.ncbi.nlm.nih.gov/pubmed/28061795
http://dx.doi.org/10.1186/s12966-016-0459-6
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author Leech, Rebecca M.
Worsley, Anthony
Timperio, Anna
McNaughton, Sarah A.
author_facet Leech, Rebecca M.
Worsley, Anthony
Timperio, Anna
McNaughton, Sarah A.
author_sort Leech, Rebecca M.
collection PubMed
description BACKGROUND: There is some evidence that large energy intakes towards the end of the day are associated with adverse health outcomes, however, studies of temporal eating patterns across the day are rare. This study examines the temporal eating patterns of Australian adults using latent class analysis (LCA), as a novel approach. METHODS: Dietary data (n = 2402 men and n = 2840 women, ≥19 years) from two 24-h recalls collected during the 2011–12 Australian National Nutrition and Physical Activity Survey were analyzed. LCA was performed to identify distinct temporal eating patterns based on whether or not an eating occasion (EO) occurred within each hour of the day. F and adjusted-chi(2) tests assessed differences in sociodemographic and eating patterns (e.g., meal, snack and EO frequency) between latent classes. RESULTS: Three patterns, labelled “Conventional” (men: 43%, women: 41%), “Later lunch” (men: 34%, women: 34%) and “Grazing” (men: 23%, women: 25%) were identified. Men and women with a “Grazing” pattern were significantly younger (P < 0.001) and a higher proportion were from major cities (P < 0.01) and were not married (men only, P = 0.01), compared to the “Conventional” and “Later lunch” patterns. The “Grazing” pattern was also characterized by a higher EO frequency (P < 0.01) and snack frequency (P < 0.001) and consumption of a higher proportion of total energy intake from snacks but a lower proportion of total energy intake from meals (P < 0.001). CONCLUSIONS: This study identified three distinct temporal eating patterns in adults that varied by age, EO frequency, snack frequency and energy intake pattern. LCA is a useful approach to capture differences in EO timing across the day. Future research should examine associations between temporal eating patterns and health.
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spelling pubmed-52196832017-01-10 Temporal eating patterns: a latent class analysis approach Leech, Rebecca M. Worsley, Anthony Timperio, Anna McNaughton, Sarah A. Int J Behav Nutr Phys Act Research BACKGROUND: There is some evidence that large energy intakes towards the end of the day are associated with adverse health outcomes, however, studies of temporal eating patterns across the day are rare. This study examines the temporal eating patterns of Australian adults using latent class analysis (LCA), as a novel approach. METHODS: Dietary data (n = 2402 men and n = 2840 women, ≥19 years) from two 24-h recalls collected during the 2011–12 Australian National Nutrition and Physical Activity Survey were analyzed. LCA was performed to identify distinct temporal eating patterns based on whether or not an eating occasion (EO) occurred within each hour of the day. F and adjusted-chi(2) tests assessed differences in sociodemographic and eating patterns (e.g., meal, snack and EO frequency) between latent classes. RESULTS: Three patterns, labelled “Conventional” (men: 43%, women: 41%), “Later lunch” (men: 34%, women: 34%) and “Grazing” (men: 23%, women: 25%) were identified. Men and women with a “Grazing” pattern were significantly younger (P < 0.001) and a higher proportion were from major cities (P < 0.01) and were not married (men only, P = 0.01), compared to the “Conventional” and “Later lunch” patterns. The “Grazing” pattern was also characterized by a higher EO frequency (P < 0.01) and snack frequency (P < 0.001) and consumption of a higher proportion of total energy intake from snacks but a lower proportion of total energy intake from meals (P < 0.001). CONCLUSIONS: This study identified three distinct temporal eating patterns in adults that varied by age, EO frequency, snack frequency and energy intake pattern. LCA is a useful approach to capture differences in EO timing across the day. Future research should examine associations between temporal eating patterns and health. BioMed Central 2017-01-07 /pmc/articles/PMC5219683/ /pubmed/28061795 http://dx.doi.org/10.1186/s12966-016-0459-6 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Leech, Rebecca M.
Worsley, Anthony
Timperio, Anna
McNaughton, Sarah A.
Temporal eating patterns: a latent class analysis approach
title Temporal eating patterns: a latent class analysis approach
title_full Temporal eating patterns: a latent class analysis approach
title_fullStr Temporal eating patterns: a latent class analysis approach
title_full_unstemmed Temporal eating patterns: a latent class analysis approach
title_short Temporal eating patterns: a latent class analysis approach
title_sort temporal eating patterns: a latent class analysis approach
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5219683/
https://www.ncbi.nlm.nih.gov/pubmed/28061795
http://dx.doi.org/10.1186/s12966-016-0459-6
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