Abstract
Purpose
Young adults eat erratically and later in the day which may impact weight and cardiometabolic health. This cross-sectional study examined relationships between chrononutritional patterns and diet quality in two young adult populations: a university and community sample.
Methods
Three days of dietary data were collected including food images captured using wearable cameras. Chrononutritional variables were extracted: time of first and last eating occasions, caloric midpoint (time at which 50% of daily energy was consumed), number of eating occasions per day, eating window, day-to-day variability of the above metrics, and evening eating (≥20:00h). The Healthy Eating Index for Australian Adults scored diet quality. Statistical analyses controlled for gender, body mass index, and socio-economic status.
Results
No significant associations between chrononutritional patterns and diet quality were found for all participants (n = 95). However, differences in diet quality were found between university (n = 54) and community (n = 41) samples with average diet quality scores of 59.1 (SD 9.7) and 47.3 (SD 14.4), respectively. Of those who extended eating ≥20:00 h, university participants had better diet quality (62.9±SE 2.5 vs. 44.3±SE 2.3, p < 0.001) and discretionary scores (7.9±SE 0.9 vs. 1.6±SE 0.6, p < 0.001) than community participants. University participants consumed predominately healthful dinners and fruit ≥20:00h whereas community participants consumed predominately discretionary foods.
Conclusion
For the general young adult population, meal timing needs to be considered. Food choices made by this cohort may be poorer during evenings when the desire for energy-dense nutrient-poor foods is stronger. However, meal timing may be less relevant for young adults who already engage in healthy dietary patterns.
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Introduction
Despite continuous efforts to create healthier food environments, the obesity epidemic has worsened over the past decade with almost half of the young adult population in Australia now being affected by overweight and obesity [1]. Although genetic, environmental, and hormonal factors have an influence on body weight, diet, specifically excessive caloric intake, is the greatest contributor to weight gain [2]. In recent years, there has been a growing body of evidence in the area of chrononutrition which points towards the role of the timing of food intake on food choices [3, 4], caloric intake [3, 5] and its effects body weight [3, 5] and cardiometabolic health [5].
Young adults’ eating patterns are less structured [6,7,8,9] compared with older cohorts [10] with a substantial proportion of their energy intake occurring later in the day [8, 11]. Most published epidemiological studies suggest that such eating patterns have implications on body weight [3, 5] and cardiometabolic health [12,13,14,15,16,17,18,19,20,21,22] including increased blood pressure, poorer glycaemic control [5, 16], elevated high-sensitivity C-reactive protein [17], higher fatal cancer risk [23], and increased adiposity [12, 16]. Randomized crossover trials have found that a regular meal pattern where participants were provided six meals a day for 14 days had a beneficial impact on peak insulin and fasting total and low-density lipoprotein cholesterol levels when compared to an irregular meal pattern varying from three to nine meals a day within the same duration of time [22, 24]. However, it is unclear whether the effects of temporal eating patterns such as meal timing variability on weight and cardiometabolic health are independent of food choice, diet quality, and other dietary behaviours [19, 25].
Current literature examining the associations between temporal eating patterns and diet quality have had contrasting findings – whilst some suggest that grazing [9, 19] and late eating [3, 26, 27] are linked with lower diet quality, others suggest the opposite [27,28,29]. Most of these studies used nationally-representative data [9, 19, 26, 28, 29] and grazing was usually measured as the frequency of food consumption with [9, 19, 28] or without [26, 27, 29] consideration of the timing of these eating occasions. In our study, we aimed to explore how chrononutritional patterns such as meal timing variability and evening eating relate to diet quality. Our secondary aim was to compare these relationships between two groups of young adults. One group was a sample of university students and the other was a community sample who had eating behaviours that were more in line with the general Australian young adult population, that is, lower vegetable [30] and higher discretionary food consumption [31]. We hypothesised that individuals with greater meal timing variability and those who engaged in evening eating would have lower diet quality and that this relationship would be stronger in the community sample.
Materials and methods
Data collection
University sample
The university sample included students interested in their nutritional intake and quality. Interest in nutritional intake was ensured by offering participants feedback on their diet quality by Accredited Practising Dietitians as non-monetary incentive. Participants were recruited between October 2021 and May 2022 using methods such as word-of-mouth, including physical flyers around university campus, advertisements and invitations on university websites, and social media posts. To be eligible, participants had to be aged between 19 and 30 years inclusive, own a smartphone or digital camera, and must not have ever had an eating disorder or concerns about disordered eating. Participants completed a basic demographic questionnaire, providing information such as gender (female, male, other), height (cm), weight (kg), residential postcode, and highest degree or level of school completed (year 11 or below, year 12, Certificate III or IV, diploma or advanced diploma, bachelor’s degree, graduate certificate or graduate diploma, master’s degree or doctoral degree). Participants’ self-reported weight (kg) and height (cm) were used to calculate body mass index (BMI) (underweight < 18.5 kg/m2, healthy weight 18.5–24.9 kg/m2, overweight 25.0–29.9 kg/m2, obese ≥ 30.0 kg/m2), which has been found to be sufficiently accurate [32]. Socio-economic status was determined using the Socio-Economic Indexes for Areas 2016 Index of Relative Socio-economic Advantage and Disadvantage [33] based on participants’ postcodes. The top five deciles were labelled as high, and the bottom five deciles were labelled as low.
Each participant captured images of all foods and drinks consumed on three days using their smartphone. Images were captured immediately prior to the meal, snack or beverage so that the time stamp on the image reflected the start time of consumption. On the same three days, participants also kept a record of their intake on a commercial smartphone application (app) [34] or a paper-based food diary. On the app, participants recorded all foods and drinks consumed and amounts consumed. If a food or beverage they consumed was not on the app’s database, the closest substitute was used at the participant’s discretion. After completion, data on the app were emailed directly to researchers in a format that could readily be imported into the Foodworks Professional nutrient analysis software [35] and analysed using the Australian Food and Nutrient Database (AUSNUT 2011-13) [36, 37]. For participants who used a paper-based food diary, data were manually entered into the Foodworks Professional nutrient analysis software by researchers. This component of the study was approved by the University of Sydney Human Research Ethics Committee on the 9th of March 2022 (2021/513).
Community sample
The community sample included young adults from the sub-study (n = 133) [38] of a larger cross-sectional MYMeals project (n = 1001) [39]. Data from this group were collected in a similar manner to that of the university sample but used automated wearable cameras instead of user-initiated smartphone cameras and researcher-administered 24-hour recalls instead of food diaries recorded by participants.
Participants were aged 18–30 years, consumed foods or beverages prepared outside the home at least once a week, owned a smartphone, and were able to read and write English. Participants who were pregnant, lactating or had ever had an eating disorder were excluded. Recruitment and data collection methods have previously been described in the MYMeals study protocol [40]. In brief, participants in the sub-study wore an Autographer camera on a lanyard around the neck for all waking hours over three days. The camera automatically captured images from a first-person perspective every 30 s. On the same three days, they completed daily 24-hour dietary recall interviews with research dietitians via the Australian version of the Automated Self-Administered 24-hour recall Australia program [41]. Demographics were collected similarly to the university sample. Participants received an AUD $100 voucher as monetary incentive if they completed the study and returned the camera upon completion.
All images captured by the Autographer camera were coded by Accredited Practising Dietitians for the presence or absence of food and beverages, and matched with their 24-hour recall data. Two researchers independently matched the two sources (V.C. and A.D.) [42]. This component of the study was approved by the University of Sydney Human Research Ethics Committee on the 15th of July 2016 (2016/546).
Data analysis
After the food images were matched with the food diaries and 24-hour recalls, data from participants with no unmatched main meals over the three days of data collection and no more than one unmatched snack or beverage per day were included for analysis. The data of all other participants were excluded.
The times of eating occasions were collated using the time and date stamps available from the food images. An eating occasion was defined as the consumption of any food or beverage with ≥210 kJ of energy [7]. Images captured within ≤ 15 min of each other were combined to form one eating occasion as per previous studies [7, 8] and the time of the latest image taken prior to food consumption was used to label the eating occasion. Each day was defined as the 24-hour period from 00:00 h to 23:59 h the same calendar day.
Chrononutritional variables
Eleven metrics measuring chrononutritional patterns were extracted – five eating pattern metrics, five meal timing variability metrics, and evening eating.
Eating pattern metrics including the chronological clock time of the first and last eating occasions, caloric midpoint, number of eating occasions per day, and eating window were extracted for all days and averaged over the three days of data collection. The caloric midpoint was defined as the time at which 50% of daily energy was consumed. The eating window was defined as the duration from the start time of the first eating occasion to the start time of the last eating occasion of the same day.
The variability of the above eating pattern metrics across the three days of data collection were used to measure meal timing variability: standard deviation of the first eating occasion (SD First), standard deviation of the last eating occasion (SD Last), standard deviation of the caloric midpoint (SD Caloric Midpoint), coefficient of variation of the daily number of eating occasions (CV Eating Occasions), and coefficient of variation of the eating window (CV Eating Window). These metrics were selected based on the methods of previous studies that used standard deviation to measure the variability of the first and last eating occasions and caloric midpoint [12, 16, 43] and coefficient of variation to measure the variability of the daily number of eating occasions and eating window [15]. The cut-offs used to categorize standard deviation and coefficient of variation scores as low, moderate, high, and very high variability were based on the difference in hours or number of eating occasions (Table 1). For example, participants with low variability in SD First varied in meal timing by less than two hours over the three days of data collection. An example of this is the consumption of the first meal or snack at 09:00 h on the first day, 10:00 h on the second day, and 10:45 h on the third day. These cut-offs were adapted from the methods of a previous study measuring meal timing variability in young adults [6].
Evening eating was defined as continuing to eat at or after 20:00 h [44, 45], as determined using food image time stamps. Images with time stamps ≥20:00 h were further examined and labelled by food type as well as by food group, that is, predominately foods from the five food groups (grains, vegetables, fruits, dairy, lean meat and alternatives); or predominately discretionary, that is, foods and drinks that are high in saturated fat, added sugar, added salt, and/or alcohol that should only be consumed sometimes and in small amounts [46, 47]. If an eating occasion consisted of both five food group and discretionary foods, the one that provided more energy was used to label the eating occasion.
Diet quality
Diet quality was measured using the Healthy Eating Index for Australian Adults (HEIFA-2013), one of the best performing diet quality indices used in Australian adults [48] based on an inventory of diet quality indices construction criteria [49, 50]. It is a validated, gender-specific tool that assesses adherence to the Australian Dietary Guidelines [51]. The scoring system for this tool has been described elsewhere [52]. Briefly, the index consisted of 11 components: one for each of the five food groups; one for discretionary foods; four for specific nutrients (fatty acids, added sugar, sodium, alcohol), and one for water intake. Each component was scored a maximum of 10 points except for water intake and alcohol, which were scored a maximum of five. This totalled to an overall maximum score of 100. A higher score indicated a closer adherence to the dietary guidelines. For three of the five food group components (grains, vegetables, fruit), five of the 10 points were assigned to the adequate consumption of these food groups and the other five were assigned to the number of serves of wholegrains or how much variety was present in the types of fruit and vegetables consumed. For the fatty acid component, five of the 10 points were assigned to minimising saturated fat intake, and the other five were assigned to the adequate consumption of poly- and monounsaturated fats.
Within each of the 11 components, points were given incrementally for specified increases or decreases in number of serves consumed. Components where higher scores were given for a lower consumption were discretionary, saturated fat, sodium, added sugar, and alcohol. Increments and serves were different for each component and for different genders. For example, for the lean meat and alternatives component, males consuming ≥3.0 serves earned 10/10 points, 2.5 to < 3.0 serves earned 8/10, 2.0 to < 2.5 serves earned 6/10, 1.5 to < 2.0 serves earned 4/10, 1.0 to < 1.5 serves earned 2/10, and ≤0.5 earned 0/10. For females, ≥2.5 serves earned 10/10 points, 2.0 to < 2.5 serves earned 8/10, 1.5 to < 2.0 serves earned 6/10, 1.0 to < 1.5 serves earned 4/10, 0.5 to < 1.0 serve earned 2/10, and 0.0 serves earned 0/10. This scoring system was used on each day of the participants’ 24-hour recalls or food diaries via the Foodworks output and averaged across the three days of data collection to provide an overall diet quality score, as well as scores for individual diet quality components.
Statistical analysis
Statistical analyses were conducted using SPSS software, v27.0 for Windows (IBM, Armonk, NY, USA) [53]. Descriptive statistics (frequency, mean, standard deviation, and percentage (%)) were used to summarise sample characteristics, chrononutritional variables, and diet quality. Differences between university and community groups in chrononutritional variables and diet quality were determined using the t-test for normally distributed data and the Mann-Whitney U test for non-normal data. Normality was determined using the Shapiro-Wilk test. Linear regression was used to identify associations between chrononutritional variables and diet quality, including overall diet quality and individual diet quality components (discretionary, total vegetable, total fruit). Univariate general linear models tested for differences in diet quality between participants who continued to eat at or after 20:00 h and participants who concluded eating by 20:00h, as well as between university and community participants who continued to eat at or after 20:00h. In the linear regression and univariate general linear model, analyses were adjusted for gender, BMI, and socio-economic status. A p value of ≤ 0.05 was considered statistically significant.
Results
A total of 95 participants were included in the analysis; 54 in the university sample and 41 in the community sample. Participant characteristics can be found in Table 2. A total of 1411 eating occasions across 285 days were included for analysis.
Diet quality scores, and chrononutritional variables are summarised in Table 3. For the combined sample, the average overall diet quality score was 54.0, discretionary component score was 5.4/10, and fruit and vegetable component scores were both 2.8/5. All meal timing variability metrics scored high to very high except SD First and SD Last, which were moderate.
Compared to the university sample, overall diet quality and discretionary component scores of the community sample were significantly lower by 11.8 and 5.6 points respectively. The time of the first eating occasion of the community sample was significantly earlier than that of the university sample by 46 min, and a significantly higher number of eating occasions were recorded in the community sample. No significant differences were found between the groups in any of the meal timing variability metrics.
Figure 1 shows the distribution of participants in each variability category for the meal timing variability metrics. The overall proportion of participants in each variability category was similar for both groups except for caloric midpoint – whilst 72% of the university group fell into the very high variability for the caloric midpoint category, only 56% of the community group fell into the very high variability category for this metric.
Table 4 shows the correlations between the chrononutritional variables (except for evening eating as this was a categorical variable) and diet quality for all participants after controlling for gender, BMI, and socio-economic status. Overall diet quality was not significantly correlated with any chrononutritional variable. However, some significant correlations were found between chrononutritional variables and individual diet quality components; for example, an earlier first eating occasion and a longer daily eating window were associated with higher fruit and vegetable scores and more discretionary food intake, and more eating occasions per day were associated with more discretionary food and vegetable intake. Further, there were no significant correlations between eating pattern and meal timing variability metrics with overall diet quality when the university and community groups were analysed separately (Table S1).
Evening eating
Table 5 shows differences in diet quality between participants who continued to eat at or after 20:00 h and those who had finished eating by that time. No significant differences in overall diet quality and individual diet quality component scores were found in the combined sample. However, significant differences were found in the university sample, where individuals who continued to eat at or after 20:00 h had significantly higher overall diet quality and fruit component scores than those who did not eat after 20:00 h.
In addition, university participants who continued to eat at or after 20:00 h had significantly better diet quality (62.9 ± SE 2.5 vs. 44.3 ± SE 2.3, p < 0.001) and discretionary scores (7.9 ± SE 0.9 vs. 1.6 ± SE 0.6, p < 0.001) than community participants who ate continued to eat at or after 20:00 h when adjusted for gender, BMI, and socio-economic status.
As shown in Table 6, the majority of the food choices made by university participants in the evenings were from the five food groups, with a large proportion of this being healthful dinners and fruit or fruit products. In contrast, the majority of the food choices made by community participants in the evenings were discretionary.
Discussion
The primary aim of this study was to examine the associations between chrononutritional patterns and diet quality in two groups of young adults. Chrononutritional variables included eating pattern metrics, meal timing variability metrics, and evening eating. Contrary to our initial hypothesis, there were no significant associations between any of the chrononutritional variables and overall diet quality, although some chrononutritional variables were significantly correlated with individual diet quality components. For example, an earlier first eating occasion and a longer daily eating window were associated with higher fruit and vegetable scores and more discretionary food intake. More eating occasions per day was also associated with more discretionary food and vegetable intake. However, these associations are likely simply due to having more opportunities to consume foods and beverages, resulting in an increase in consumption of all food groups used to measure the individual diet quality components.
The secondary aim of the study was to compare chrononutritional variable and diet quality associations between the two groups of young adults. One was a university sample interested in their nutritional intake and the other was a community sample with eating behaviours more in line with the general Australian young adult population. There were no significant associations between any of the chononutritional variables and overall diet quality for either group except for their evening food choices. Whilst participants in the university sample had better diet quality than the community sample overall, those who continued to eat at or after 20:00 h saw further significant improvements to their diet quality. This improvement in diet quality was not seen in evening eaters from the community sample. This was due to the university group choosing predominately foods from the five food groups, in particular evening meals (dinner) and fruit, and less discretionary choices than the community sample in the evenings. This finding was also in contrast to our initial hypothesis.
Evenings are a time when the desire for energy-dense nutrient-poor foods is stronger [28, 54,55,56]. This may in part be due to factors related to having a late chronotype, a dominant form of sleep regimen for young adults [57], such as decreased self-control [58] or sleep deprivation [59]. Studies have shown that late night eating events were more likely to consist of discretionary snacks [60, 61] due to an increased desire for these foods despite lowest hunger levels at this time [55]. Systematic and scoping reviews examining the role of chronotype on meal timing and dietary intake have also found a link between late chronotypes and a high consumption of nutritionally poor or high fat foods [3], as well as the tendency to eat main meals later in the day compared to early or intermediate chronotypes [3, 62]. This can lead to adverse health outcomes such as weight gain and poorer glycemic control [5]. On the contrary, health-conscious individuals are likely to have greater nutrition knowledge that enable them to override the desire for energy-dense nutrient-poor foods and make more conscious food choices at night [63, 64]. Previous studies have shown that night-time food choices made by health-conscious individuals include nutrient-dense foods such as protein-rich beverages [65] or fruit [44]. This is consistent with the findings in our university sample. Sebastian et al. discovered six main late evening (≥20:00 h) eating patterns in US adults using cluster analysis – (1) baked goods, sandwiches, and other desserts, (2) mixed dishes and meats, (3) alcoholic beverages and savoury snacks, (4) nuts and candy, (5) fruit, and (6) milk and baked goods [44]. These eating patterns represented the food/beverage groups that contributed the most energy during late evening eating. They found that individuals following the fruit pattern had higher diet quality scores than individuals who did not engage in late evening eating as well as individuals with any of the other late evening eating patterns as it increased scores for total and whole fruit and moderated the scores for sodium, refined grains, and saturated fats [44]. Taken together, the literature confirms our findings and indicates a congruency between nutrition knowledge or interest level and healthy eating habits. Interventions geared towards modifying dietary behaviours later in the day such as evening food choices may be more successful at improving diet quality [4].
Whilst our study did not find any associations between the stability of meal timing and overall diet quality, the current literature around eating patterns and meal timing variability versus diet quality is inconclusive. Unstable meal times, defined as frequent eating occasions occurring at unconventional times throughout the day, was associated with lower diet quality scores in some studies [9, 19] whereas grazing, defined by the frequency of snack consumption, was associated with higher diet quality scores in other studies [27,28,29]. Some studies have suggested that the timing of grazing may play a role in diet quality – snacking in the morning increased diet quality whilst snacking in the evening decreased diet quality [26,27,28, 61, 66] due to the types of foods selected. The link between chronotype and meal regularity and timing, caloric midpoint, meal frequency, eating window, and timing of meals relative to sleep and wake times has also not yet been established due to the small number of studies that have explored this relationship [62].
A key strength of our study was the use of a combination of food images and food diaries to collect dietary data as this may help minimise inaccurate recalls and missing data [67]. Whilst the food images were able to provide objective data around the time of food consumption and enhance self-reported dietary intake by revealing unreported foods and misreporting errors [68,69,70], the food diaries were able to provide data that could not be determined from image analysis alone such as hidden ingredients [71,72,73,74]. Our paper compared meal timing variability over multiple days with diet quality. Most studies examining this relationship used the distribution of meals and snacks over one day [9, 19].
Our study had several limitations, the most notable being the small sample size which may have been insufficiently powered to detect differences between groups. Although the community sample was more diverse in gender, BMI, and socio-economic status than the university sample, their results are likely not generalisable to the Australian young adult population as they had a higher HEIFA score [52] and a lower proportion of individuals in the overweight and obese BMI categories [1] than the general young adult population in Australia. This is based on data from the 2011–2012 National Nutrition and Physical Activity Survey [52] and the Australian Bureau of Statistics [1]. They showed that the average HEIFA score for young Australian adults (18–24 years) was 41.6, 4.7 points lower than our community sample [52], and that the proportion of young people (18–24 years) whose weights were in the overweight and obese BMI categories in 2017-18 was 46%, 14% more than our community sample [1]. As with most dietary assessment methods, participants may have altered their eating behaviour on the days of data collection or chosen days that were more convenient to record such as at home or away from social settings. Therefore, collected dietary data may not be representative of participants’ usual intake and week-long cross-sectional studies are likely needed to capture habitual eating behaviours [43]. The three days of data collection was also different for the university and community samples as one was over consecutive days and the other was over non-consecutive days and so they may not be directly comparable. Dietary data collected over non-consecutive or random days have been shown to be more representative of usual intake [75, 76] likely because foods and amounts consumed on consecutive days are related, e.g., the consumption of leftovers or eating less as a result of excessive intake the previous day [77]. For future studies, larger sample sizes are needed to verify our findings and data on individuals’ chronotypes should be collected given its influence on eating patterns.
Conclusion
Our study explored the relationship between chrononutritional patterns and diet quality and compared this relationship across two groups of young adults – a university sample with better diet quality and a community sample with eating behaviours more in line with the general Australian young adult population. Within the university group, participants who engaged in evening eating had improved diet quality, suggesting that late eating did not impact their diet quality and that meal timing may not be as relevant for young adults who already engage in healthy dietary patterns. However, for the average young adult, interventions geared towards modifying evening food choices may be more effective at improving diet quality.
Data availability
The data presented in this study are available on request from the corresponding author subject to ethical approval.
References
Australian Bureau of Statistics. National Health Survey: First results (2017) -18 [cited 2023 10 January]; Available from: https://www.abs.gov.au/statistics/health/health-conditions-and-risks/national-health-survey-first-results/latest-release
National Institutes of Health. What causes obesity & overweight? (2021) [cited 2023 10 January]; Available from: https://www.nichd.nih.gov/health/topics/obesity/conditioninfo/cause
Teixeira GP et al (2022) Role of chronotype in dietary intake, meal timing, and obesity: a systematic review. Nutr Rev 81(1):75–90
Lopez-Minguez J et al (2019) Heritability of the timing of food intake. Clin Nutr 38(2):767–773
Beccuti G et al (2017) Timing of food intake: sounding the alarm about metabolic impairments? A systematic review. Pharmacol Res 125:132–141
Wang L et al (2022) Wearable Cameras reveal large intra-individual variability in timing of eating among young adults. Nutrients 14(20):4349
Leech RM et al (2017) Temporal eating patterns: a latent class analysis approach. Int J Behav Nutr Phys Activity 14(1):3
Gill S, Panda S (2015) A smartphone app reveals erratic diurnal eating patterns in humans that can be modulated for Health benefits. Cell Metabol 22(5):789–798
Leech RM et al (2017) Temporal eating patterns: associations with nutrient intakes, diet quality, and measures of adiposity. Am J Clin Nutr 106(4):1121–1130
Winkler G, Döring A, Keil U (1999) Meal patterns in middle-aged men in Southern Germany: results from the MONICA Augsburg dietary survey 1984/85. Appetite 32(1):33–37
Kant AK, Graubard BI (2015) Within-person comparison of eating behaviors, time of eating, and dietary intake on days with and without breakfast: NHANES 2005–2010. Am J Clin Nutr 102(3):661–670
Zhao L et al (2022) Eating architecture in adults at increased risk of type 2 diabetes: associations with body fat and glycaemic control. Br J Nutr 128(2):324–333
Zerón-Rugerio MF et al (2019) Eating Jet lag: a marker of the variability in meal timing and its association with body Mass Index. Nutrients 11(12):2980
Wittig F et al (2017) Energy and macronutrient intake over the course of the day of German adults: a DEDIPAC-study. Appetite 114:125–136
Popp CJ et al (2021) Temporal eating patterns and eating Windows among adults with overweight or obesity. Nutrients, 13(12)
Makarem N et al (2021) Variability in daily eating patterns and eating Jetlag are Associated with worsened cardiometabolic risk profiles in the American Heart Association Go Red for Women Strategically Focused Research Network. J Am Heart Assoc 10(18):e022024
Makarem N et al (2022) Variable eating patterns: a potential novel risk factor for systemic inflammation in women. Ann Behav Med 57(1):93–97
Fleischer JG et al (2022) Associations between the timing of eating and weight-loss in calorically restricted healthy adults: findings from the CALERIE study. Exp Gerontol 165:111837
Eicher-Miller HA et al (2016) Temporal dietary patterns derived among the Adult Participants of the National Health and Nutrition Examination Survey 1999–2004 are Associated with Diet Quality. J Acad Nutr Diet 116(2):283–291
Sierra-Johnson J et al (2008) Eating meals irregularly: a Novel Environmental Risk factor for the metabolic syndrome. Obesity 16(6):1302–1307
Wennberg M et al (2016) Irregular eating of meals in adolescence and the metabolic syndrome in adulthood: results from a 27-year prospective cohort. Public Health Nutr 19(4):667–673
Farshchi HR, Taylor MA, Macdonald IA (2004) Regular meal frequency creates more appropriate insulin sensitivity and lipid profiles compared with irregular meal frequency in healthy lean women. Eur J Clin Nutr 58(7):1071–1077
Meth EMS et al (2022) Association of Daily Eating Duration and Day-To-Day variability in the timing of eating with fatal Cancer risk in older men. Front Nutr 9:889926
Farshchi HR, Taylor MA, Macdonald IA (2005) Beneficial metabolic effects of regular meal frequency on dietary thermogenesis, insulin sensitivity, and fasting lipid profiles in healthy obese women. Am J Clin Nutr 81(1):16–24
Alkhulaifi F, Darkoh C (2022) Meal timing, Meal frequency and metabolic syndrome. Nutrients 14(9):1719
Djuric Z et al (2020) Association of meal timing with dietary quality in a Serbian population sample. BMC Nutr 6:45
Aljuraiban GS et al (2015) The impact of eating frequency and time of intake on nutrient quality and body Mass Index: the INTERMAP Study, a Population-based study. J Acad Nutr Diet, 115(4): p. 528 – 36.e1.
Zeballos E, Chelius C (2021) The effects of grazing on daily caloric intake and dietary quality. Int J Behav Nutr Phys Activity 18(1):163
Zizza CA, Xu B (2012) Snacking is Associated with overall Diet Quality among adults. J Acad Nutr Dietetics 112(2):291–296
Nour M et al (2017) The fruit and vegetable intake of young Australian adults: a population perspective. Public Health Nutr 20(14):2499–2512
Sui Z et al (2017) Discretionary food and beverage consumption and its association with demographic characteristics, weight status, and fruit and vegetable intakes in Australian adults. Public Health Nutr 20(2):274–281
Davies A et al (2020) Validity of self-reported weight and height for BMI classification: a cross-sectional study among young adults. Nutrition 71:110622
Australian Bureau of Statistics. Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA) (2016) [cited 2023 11 January]; Available from: https://www.abs.gov.au/ausstats/abs@.nsf/mf/2033.0.55.001
Xyris Pty Ltd (2019) Research Food Diary
Xyris Pty, Ltd (2019) FoodWorks 10. Brisbane
Food Standards Australia New Zealand (FSANZ). AUSNUT 2011–13 - Australian Food, Supplement and Nutritient Database for Estimation of Population Nutrient Intakes (2020) [cited 2023 11 January]; Available from: http://www.foodstandards.gov.au/science/monitoringnutrients/ausnut/pages/default.aspx
Food Standards Australia New Zealand (FSANZ). Australian Food Composition Database (2022) [cited 2023 11 January]; Available from: https://www.foodstandards.gov.au/science/monitoringnutrients/afcd/pages/default.aspx
Chan V et al (2022) The association of social and food preparation location context with the quality of meals and snacks consumed by young adults: findings from the MYMeals wearable camera study. Eur J Nutr 61(7):3407–3422
Wellard-Cole L et al (2021) The Contribution of foods prepared outside the home to the diets of 18- to 30-Year-old australians: the MYMeals Study. Nutrients, 13(6)
Wellard-Cole L et al (2018) Examining the frequency and Contribution of Foods Eaten Away from Home in the diets of 18- to 30-Year-old australians using Smartphone Dietary Assessment (MYMeals): protocol for a cross-sectional study. JMIR Res Protoc 7(1):e24
Park Y et al (2018) Comparison of self-reported dietary intakes from the automated self-administered 24-h recall, 4-d food records, and food-frequency questionnaires against recovery biomarkers. Am J Clin Nutr 107(1):80–93
Chan V et al (2021) Using Wearable Cameras to Assess Foods and beverages omitted in 24 Hour Dietary recalls and a text Entry Food Record App. Nutrients 13(6):1806
McHill AW et al (2020) Stability of the timing of food intake at daily and monthly timescales in young adults. Sci Rep 10(1):20849
Sebastian RS et al (2022) Late Evening eating patterns among US adults vary in their associations with, and impact on, Energy Intake and Diet Quality: evidence from what we eat in America, National Health and Nutrition Examination Survey 2013–2016. J Acad Nutr Dietetics 122(5):932–948e3
Baron KG et al (2013) Contribution of evening macronutrient intake to total caloric intake and body mass index. Appetite 60(1):246–251
National Health and Medical Research Council (NHMRC). [cited 2023 11 January]; Available from: https://www.eatforhealth.gov.au/food-essentials/discretionary-food-and-drink-choices
National Health and Medical Research Council (NHMRC) (2023) 11 January]; Available from: https://www.eatforhealth.gov.au/guidelines/about-australian-dietary-guidelines
Hlaing-Hlaing H et al (2020) Diet Quality indices used in Australian and New Zealand adults: a systematic review and critical Appraisal. Nutrients 12(12):3777
Trijsburg L et al (2019) Diet quality indices for research in low- and middle-income countries: a systematic review. Nutr Rev 77(8):515–540
Burggraf C et al (2018) Review of a priori dietary quality indices in relation to their construction criteria. Nutr Rev 76(10):747–764
Roy R et al (2016) The development, application, and validation of a healthy eating index for Australian adults (HEIFA-2013). Nutrition 32(4):432–440
Grech A et al (2017) Socio-demographic determinants of Diet Quality in Australian adults using the validated healthy eating index for Australian adults (HEIFA-2013). Healthc (Basel), 5(1)
Corp IBM (2020) IBM SPSS Statistics for Windows
Sanchez C et al (2018) 0127 Nighttime Snacking: Prevalence and associations with Poor Sleep, Health, obesity, and diabetes. Sleep 41(suppl1):A49–A50
Reichenberger J et al (2018) It’s craving time: time of day effects on momentary hunger and food craving in daily life. Nutrition, 55–56: p. 15–20
Gallant A, Lundgren J, Drapeau V (2014) Nutritional aspects of late eating and night eating. Curr Obes Rep 3(1):101–107
Nowakowska-Domagała K et al (2022) Chronotype and poor sleep quality in young adults – a pilot study on the role of rumination. Sleep Med 100:206–211
Hasler BP et al (2013) An altered neural response to reward may contribute to alcohol problems among late adolescents with an evening chronotype. Psychiatry Research: Neuroimaging 214(3):357–364
Taheri S et al (2004) Short sleep duration is associated with reduced leptin, elevated ghrelin, and increased body mass index. PLoS Med 1(3):e62
Laska MN et al (2011) Situational characteristics of young adults’ eating occasions: a real-time data collection using Personal Digital assistants. Public Health Nutr 14(3):472–479
Barrington WE, Beresford SAA (2019) Eating occasions, obesity and related behaviors in working adults: does it Matter when you snack? Nutrients 11(10):2320
Phoi YY et al (2022) A scoping review of chronotype and temporal patterns of eating of adults: tools used, findings, and future directions. Nutr Res Rev 35(1):112–135
Sogari G et al (2018) College Students and Eating habits: a study using an ecological model for Healthy Behavior. Nutrients, 10(12)
Scalvedi ML et al (2021) Relationship between Nutrition Knowledge and Dietary Intake: an Assessment among a sample of Italian adults. Frontiers in Nutrition, p 8
Kinsey AW, Ormsbee MJ (2015) The health impact of nighttime eating: old and new perspectives. Nutrients 7(4):2648–2662
Si Hassen W et al (2018) Energy, nutrient and food content of snacks in French adults. Nutr J 17(1):33
Höchsmann C, Martin CK (2020) Review of the validity and feasibility of image-assisted methods for dietary assessment. Int J Obes (Lond) 44(12):2358–2371
Gregory R et al (2006) A feasibility study of the use of photographic food diaries in the management of obesity. Practical Diabetes Int 23(2):66–68
Gemming L et al (2013) Feasibility of a SenseCam-assisted 24-h recall to reduce under-reporting of energy intake. Eur J Clin Nutr 67(10):1095–1099
Weiss R, Stumbo PJ, Divakaran A (2010) Automatic food documentation and Volume Computation Using Digital Imaging and Electronic Transmission. J Am Diet Assoc 110(1):42–44
Gemming L, Utter J, Ni C, Mhurchu (2015) Image-assisted dietary assessment: a systematic review of the evidence. J Acad Nutr Diet 115(1):64–77
Kikunaga S et al (2007) The application of a Handheld Personal Digital Assistant with Camera and Mobile phone card (Wellnavi) to the General Population in a Dietary Survey. J Nutri Sci Vitaminol 53(2):109–116
Martin CK et al (2008) A novel method to remotely measure food intake of free-living individuals in real time: the remote food photography method. Br J Nutr 101(3):446–456
Rollo ME et al (2011) Trial of a mobile phone method for recording dietary intake in adults with type 2 diabetes: evaluation and implications for future applications. J Telemed Telecare 17(6):318–323
Huang K et al (2022) Comparison of the 24 h Dietary Recall of Two Consecutive Days, Two Non-Consecutive Days, Three Consecutive Days, and Three Non-Consecutive Days for Estimating Dietary Intake of Chinese Adult. Nutrients, 14(9)
Larkin FA, Metzner HL, Guire KE (1991) Comparison of three consecutive-day and three random-day records of dietary intake. J Am Diet Assoc 91(12):1538–1542
Thompson FE, Subar AF et al (2017) Chap. 1 - Dietary Assessment Methodology, in Nutrition in the Prevention and Treatment of Disease (Fourth Edition), A.M. Coulston, Editors. Academic Press. p. 5–48
Acknowledgements
The authors thank the investigators of the original MYMeals study for allowing secondary analyses of their data as well as the Australian Research Council and Cancer Council NSW for funding the original study. The authors also acknowledge the technical assistance of Alex Shaw of the Sydney Informatics Hub, a Core Research Facility of the University of Sydney.
Funding
This research analysis received no external funding. However, the MYMeals project was funded by an Australian Research Council Linkage grant LP150100831 and Cancer Council NSW. Lyndal Wellard-Cole is employed by Cancer Council NSW.
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Conceptualization: Anna Rangan and Margaret Allman-Farinelli; Methodology: Anna Rangan, Leanne Wang, Virginia Chan, Alyse Davies, Margaret Allman-Farinelli and Lyndal Wellard-Cole; Formal analysis and investigation: Leanne Wang, Virginia Chan, and Alyse Davies; Writing – original draft preparation: Leanne Wang; Writing – review and editing, Anna Rangan; Resources: Anna Rangan, Lyndal Wellard-Cole, and Margaret Allman-Farinelli; Supervision: Anna Rangan and Margaret Allman-Farinelli.
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Wang, L., Chan, V., Allman-Farinelli, M. et al. The association between diet quality and chrononutritional patterns in young adults. Eur J Nutr 63, 1271–1281 (2024). https://doi.org/10.1007/s00394-024-03353-7
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DOI: https://doi.org/10.1007/s00394-024-03353-7