Evidence map›Paper›PMID 36864319›Full record

ArticleEuropean journal of nutrition2023

Meal-timing patterns and chronic disease prevalence in two representative Austrian studies.

Isabel Santonja, Leonie H Bogl, Jürgen Degenfellner, Gerhard Klösch, Stefan Seidel, Eva Schernhammer, Kyriaki Papantoniou

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Article in European journal of nutrition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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0cells of the map it votes in
5citing papers in PubMed
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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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3 · Its place in the literature

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5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Isabel SantonjaDepartment of Epidemiology, Center for Public Health, Medical University of Vienna, Vienna, Austria. isabel.santonja@meduniwien.ac.at.ORCID http://orcid.org/0000-0002-6736-7988
Leonie H BoglDepartment of Epidemiology, Center for Public Health, Medical University of Vienna, Vienna, Austria.
Jürgen DegenfellnerDepartment of Epidemiology, Center for Public Health, Medical University of Vienna, Vienna, Austria.
Gerhard KlöschDepartment of Neurology, Medical University of Vienna, Vienna, Austria.
Stefan SeidelDepartment of Neurology, Medical University of Vienna, Vienna, Austria.
Eva SchernhammerDepartment of Epidemiology, Center for Public Health, Medical University of Vienna, Vienna, Austria.
Kyriaki PapantoniouDepartment of Epidemiology, Center for Public Health, Medical University of Vienna, Vienna, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study aimed at describing meal-timing patterns using cluster analysis and explore their association with sleep and chronic diseases, before and during COVID-19 mitigation measures in Austria.

methodsInformation was collected in two surveys in 2017 (N = 1004) and 2020 (N = 1010) in representative samples of the Austrian population. Timing of main meals, nighttime fasting interval, last-meal-to-bed time, breakfast skipping and eating midpoint were calculated using self-reported information. Cluster analysis was applied to identify meal-timing clusters. Multivariable-adjusted logistic regression models were used to study the association of meal-timing clusters with prevalence of chronic insomnia, depression, diabetes, hypertension, obesity and self-rated bad health status.

resultsIn both surveys, median breakfast, lunch and dinner times on weekdays were 7:30, 12:30 and 18:30. One out of four participants skipped breakfast and the median number of eating occasions was 3 in both samples. We observed correlation between the different meal-timing variables. Cluster analysis resulted in the definition of two clusters in each sample (A17 and B17 in 2017, and A20 and B20 in 2020). Clusters A comprised most respondents, with fasting duration of 12-13 h and median eating midpoint between 13:00 and 13:30. Clusters B comprised participants reporting longer fasting intervals and later mealtimes, and a high proportion of breakfast skippers. Chronic insomnia, depression, obesity and self-rated bad health-status were more prevalent in clusters B.

conclusionsAustrians reported long fasting intervals and low eating frequency. Meal-timing habits were similar before and during the COVID-19-pandemic. Besides individual characteristics of meal-timing, behavioural patterns need to be evaluated in chrono-nutrition epidemiological studies.

Indexed as

COVID-19Sleep Initiation and Maintenance DisordersAustriaBreakfastChronic DiseaseFeeding BehaviorHumansMealsObesityPrevalenceChrono-nutritionCluster analysisMeal-timingNighttime fastingTime-restricted eating

Identifiers

PMID36864319
PMCPMC9980854

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.