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Purpose: This 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., Methods: Information 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., Results: In 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., Conclusions: Austrians 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., (© 2023. The Author(s).)

Sleep impairment is highly prevalent in night shift workers, but evidence on the association of former night shift work (NSW) and its metrics (duration and frequency) in relation to sleep complaints is lacking. We evaluated the association of former and current NSW with chronic insomnia or circadian rhythm sleep disorder in a sample of the general worker (GW) population and in hospital workers (HW) in Austria. Information on sleep, NSW history, sociodemographic, and lifestyle factors was collected through an online cross-sectional survey in a representative sample of GW ( N = 1,004) and a sample of HW ( N = 799) between 2017 and 2019. Multi-variable adjusted logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for various measures of sleep (including chronic insomnia, daytime sleepiness, sleep duration, napping habits) and doctor-diagnosed chronic insomnia across NSW exposure (never night shift work; ever; ever/former; ever/current) and related metrics (cumulative duration, average frequency), compared to never NSW. Effect modification by chronotype and daytime napping was investigated. Former NSW was associated with higher odds of chronic insomnia in both samples (GW: OR = 2.28, 95% CI = 1.07-4.83; HW: OR = 1.17, 95% CI = 0.60-2.27). Chronic insomnia odds tended to increase among current night shift workers (HW: OR = 1.50, 95% CI = 0.79-2.83), compared to day workers. Higher NSW frequency (shifts/month) was associated with higher chronic insomnia odds in former night shift workers in both samples (GW: OR per shift/month  = 1.06, 95% CI = 1.00-1.12; HW: OR per shift/month  = 1.12, 95% CI = 1.00-1.25). Former NSW was also associated with increased daytime sleepiness among GW (OR = 2.26, 95% CI 1.28-3.99). Associations were more pronounced among early chronotypes and participants who reported no daytime naps. Our results suggest that NSW is associated with chronic insomnia even in the years after cessation of involvement in working it.