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

Circadian misalignment through night shift work has been associated with various chronic disease outcomes [1‐6]. Recently, this research has been expanded to identify other sources of circadian disruption in the general population that may result from mistimed exposures, such as sleeping or eating at the “wrong” time [7‐9]. More concretely, the adherence to a more diurnal eating pattern has been associated with a lower cancer risk [10‐12] and an improvement of metabolic function and weight loss [13]. Moreover, individual aspects associated with a diurnal eating pattern, such as eating breakfast or abstaining from eating at night, have been associated with better sleep quality and longer sleep duration [8]. On the contrary, individuals experience a reduction of sleep duration and an increase of daytime sleepiness during Ramadan, a period of 29–30 days, during which healthy adult Muslims abstain from eating and drinking from dawn to sunset [14]. These observations stress the importance of evaluating timing (in addition to quality, quantity and frequency) of diet in epidemiological studies.

In terms of exposure assessment, most epidemiological studies have characterized meal-timing through questionnaires and have tended to analyse single meal-timing aspects, e.g. dinner time [10] or breakfast skipping [15‐20]. However, different meal-timing aspects, such as nighttime fasting interval, interval between the last eating occasion and bed time or breakfast skipping, are interrelated and their effects could overlap. Because of that, Khanna et al. [21] argued that analysing meal-timing behaviours, rather than isolated aspects, using cluster analysis might be a more suitable method of assessing the effects of meal-timing.

Moreover, meal-timing patterns have not been previously described in detail in the Austrian population, and we hypothesized that dietary patterns, especially the eating frequency, could have been modified in Austria as a consequence of the COVID-19 mitigation measures and lockdown, as reported for other populations [22‐26]. Furthermore, although evidence starts to emerge that meal-timing might have an influence on health outcomes, little is known in relation to sleep outcomes.

Therefore, the first aim of this study was to describe meal-timing and meal frequency, as well as fasting patterns, and to define predominant meal-timing behaviours in the Austrian population. Secondly, we aimed to compare meal-timing patterns before and during the first COVID-19 mitigation measures and evaluate potential effects of the lockdown on meal-timing habits in Austria. Lastly, we explored the cross-sectional association of meal-timing behaviours and chronic insomnia, chronic disease prevalence and self-rated health-status.

In this study, we provide a description of meal-timing patterns in the Austrian population in 2017 (prepandemic) and in 2020 (during the first COVID-19 mitigation measures), and explored the associations between meal-timing patterns and health outcomes. In both surveys, participants reported eating between 7:30 (median breakfast time) and 18:30 (median dinnertime) or, for those who reported having a snack after dinner, around 21:00 (median snack after dinner time) during the week. Lunch was eaten around 12:30 (median) and breakfast was skipped by about 25% of Austrians on weekdays. In 2020, all main meals were eaten later on weekends than during the week. We observed long nighttime fasting periods and a frequency of 3 eating occasions a day in both surveys. We found correlation between meal-timing variables, and therefore, performed cluster analysis in each survey to group participants according to different meal timing behavioural patterns. The results of the cluster analysis were similar in 2017 and 2020: one group (A17 or A20) was formed by the majority of the participants, who reported long fasting periods and early mealtimes; the remaining participants comprised groups B17 and B20, characterized by even longer fasting intervals, later mealtimes and a high proportion of breakfast skippers.

Huseinovic et al. [29] described a north–south gradient of meal-times in Europe, with Scandinavian countries eating earlier and Mediterranean countries later. According to this, meal-times in the Austrian population are similar to those reported for countries like Germany or Denmark. On the other hand, differences in meal-timing between countries could also be explained by differences in longitude between countries within the same time zone. Austria is located in the extreme east of the Central European time zone, with earlier sunrise and sunset throughout the year. This could explain why Austrian meal-times are earlier compared to countries that are in the same latitude but situated in the western extreme of their time zone and, thus, experience later sun time (e.g. France, Spain). Huseinovic et al. [29] also showed that countries in Central and Northern Europe tend to consume more calories later in the day than earlier on. This is in line with our finding that around one quarter of the Austrians skip breakfast during the week and around one fifth during weekends. However, this proportion is strikingly higher than the one described in a cross-sectional study in Poland during the COVID-19 mitigation measures [22]. In this study, with a younger study population than ours and an under-representation of men, and in which individuals working on a regular basis during the lockdown were excluded, only 1.2% of the participants reported never eating breakfast.

The number of eating occasions (median = 3 in both surveys) reported in our samples was rather low, compared to the numbers described in other countries. Based on self-reported data, Americans have about 5.6 meals a day [30] and a study carried out in five European countries reported an eating frequency that ranked between 4.3 (France) and 7.1 (The Netherlands) eating occasions/day [31]. According to Huseinovic et al. [29], people living in Northern and Central Europe eat more frequently than those in southern, Mediterranean countries do. The low eating frequency reported in our study might be partly explained by the way we assessed exposure. In our survey, information on maximal 6 eating episodes was obtained (3 main meals and 3 snacks) and participants could not report having two or more snacks between two main meals or after dinner, which could have resulted in underreporting of eating occasions.

Suprisingly, meal-timing and frequency during the COVID-19 mitigation measures was very similar to in 2017. The results on eating frequency oppose studies from other countries that have described an increase in snacking frequency during lockdowns [23, 25]. In a survey conducted in a representative sample of the population of Jordan [24], participants were more likely to have breakfast, lunch and dinner during the lockdown than before mitigation measures. Furthermore, a study conducted among students in Peru [26] found delays in meal-times and eating-midpoint and a reduction of the nighttime fasting interval after 12 weeks of lockdown measures. Some of these inconsistent findings might be due to differences in the formulation of the meal-timing questions and the fact that we compared the results of the 2020 survey with prepandemic information collected in a different population sample and, thus, intra-individual comparisons could not be performed. Besides, lockdown measures differed among countries and these differences might also explain the discrepancies with our findings.

We also explored the interrelationship between meal-timing and frequency variables and observed moderate correlation between the different variables considered in our study. The association between longer nighttime fasting and skipping breakfast or having an earlier last meal of the day is evident, and this interdependence of meal-timing aspects has also been described previously [32, 33]. Therefore, there has been a claim for analysis focusing on behaviours rather than isolated aspects [21]. This was our motivation for using cluster analysis to group participants according to their combined patterns of meal-timing. Indeed, by doing so, we identified two distinct meal-timing clusters in both surveys that were associated with different insomnia and chronic disease prevalences.

Our exploratory analysis across clusters revealed that insomnia symptoms were more prevalent among participants in groups B17 and B20. Prevalence of depression was also consistently higher in clusters B than A. Besides, in both surveys, participants with later mealtimes and longer fasting periods were slightly more likely to report being obese. There was also an association of meal-timing behaviours with self-rated health status, which was weaker in 2017 than in 2020. These results suggest that longer fasting intervals are not beneficial under all circumstances and that, in everyday life, longer fasting intervals might be the consequence of skipping mealtimes earlier in the day, such as breakfast. Therefore, some of the beneficial health effects usually attributed to fasting in controlled conditions might be reduced or reverted in real-life settings.

Althoug any study had evaluated meal-timing behavioural clusters in relation to chronic diseases before, several studies have analysed the association between individual aspects of meal-timing and chronic disease outcomes. Traditionally, eating breakfast and having several smaller meals in a day have been considered healthy behaviours [34]. Indeed, recent meta-analyses of observational studies concluded that skipping breakfast might be associated with higher risk of diabetes [35], depression [36], overweight and obesity [37]. However, this last assumption has been challenged by a meta-analysis of randomized trials, in which breakfast skipping was associated with modest weight loss [38]. Moreover, Schwingshackl et al. [39] showed that the evidence from randomized control trials did not support the belief that a higher meal frequency contributes to weight control. Concurrently, there has been a shift of paradigm and evidence from experimental studies is emerging, showing prolonged fasting intervals (≥ 12 h), like the ones reported in our samples, might be a successful strategy to reduce body weight [40] or blood pressure [41]. On top of that, not only the duration of the fasting window, but also aligning the eating window with the day and the fasting window with the night might contribute to better health outcomes, through the synchronization of central and peripheral clocks [34]. Eating during the dark phase might also negatively affect sleep, as signalled by a meta-analysis of studies conducted during Ramadan [14]. However, it is unclear how all these interrelated meal-timing aspects interact with each other and affect human health, and studies analysing the combined effects of fasting and meal-frequency and timing are lacking.

To our knowledge, this is the first study to analyse meal-timing as a behaviour, rather than isolated aspects of meal-timing, using cluster analysis to characterize meal-timing behaviours in real-life settings. It is also the first study to describe meal-timing in a representative sample of the Austrian population. Another strength is that our results are consistent in the two samples analysed. The inclusion of two identical surveys administered at two time points in representative samples of Austrians, allowed the comparison of data obtained before and during the pandemic. The analysis was carefully adjusted for a wide range of potential confounders. An additional strength is the definition used for chronic insomnia, which was based on criteria suggested by the ISCD-III [27]. Our analysis also has several limitations. First, as this is a cross sectional study, we are unable to comment on the direction of the described associations. Second, we performed several statistical analysis and, therefore, our results on the association between meal-timing and health outcomes are subject to multiple testing errors. This was an exploratory analysis and results should be interpreted with caution. Third, our surveys did not collect information on food content, liquid intake or diet type. Fourth, we might have underestimated the number of eating occasions, as a maximum of one snack could be reported between two main meals in our surveys. Last, information on meal-times was self-reported. Studies using objective meal-timing measures show that human eating patterns are erratic and not well captured through self-reports [42, 43], so under- or over-reporting of eating occasions cannot be discarded in our study and the long fasting intervals observed in our sample might be a consequence of this. Moreover, the questions used for exposure assessment had not been validated against objective measures, as, to our knowledge, at the time the surveys were conducted, no validated meal-timing questionnaire existed. This lack of validated tools also limits the comparability of our results with other studies. Recently, some studies have been published showing moderate agreement between short meal-timing questionnaires and the Automated Self-Administered 24-h recall (ASA24®) Dietary Assessment tool [44] and prospective food records [45]. Future studies on meal-timing with improved and validated reporting methods are, therefore, necessary to confirm our results.

In conclusion, our results suggest that Austrians have earlier meal-times, longer fasting intervals and lower eating frequency than most European countries, although these results might be partly due to an under-reporting of eating occasions. In Austria, meal-timing habits barely changed during the first COVID-19 mitigation measures. Individual meal-timing aspects were highly correlated between them and cluster analysis revealed two well-differentiated groups with different meal-timing behaviours in both surveys. Future epidemiologic studies with improved reporting (e.g. validated questionnaires and objective measures of meal-timing) and analytical methods (e.g. cluster analysis of behavioural patterns) are warranted to evaluate the impact of meal-timing on chronic disease risk.