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Learn more: PMC Disclaimer | PMC Copyright Notice Public Health Nutr . 2022 May 16;26(2):393–407. doi: 10.1017/S1368980022001148 Search in PMC Search in PubMed View in NLM Catalog Add to search Temporal patterns of sleep and eating among children during school closure in Japan due to COVID-19 pandemic: associations with lifestyle behaviours and dietary intake Minami Sugimoto Minami Sugimoto 1 Institute for Future Initiatives, The University of Tokyo, Tokyo, Japan Find articles by Minami Sugimoto 1 , Kentaro Murakami Kentaro Murakami 2 Department of Social and Preventive Epidemiology, School of Public Health, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan Find articles by Kentaro Murakami 2 , Satoshi Sasaki Satoshi Sasaki 2 Department of Social and Preventive Epidemiology, School of Public Health, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan 3 Department of Social and Preventive Epidemiology, Division of Health Sciences and Nursing, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan Find articles by Satoshi Sasaki 2, 3, * Author information Article notes Copyright and License information 1 Institute for Future Initiatives, The University of Tokyo, Tokyo, Japan 2 Department of Social and Preventive Epidemiology, School of Public Health, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan 3 Department of Social and Preventive Epidemiology, Division of Health Sciences and Nursing, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan * Corresponding author : Email [email protected] Received 2021 Dec 22; Revised 2022 Mar 18; Accepted 2022 Apr 19; Collection date 2023 Feb. © The Authors 2022 This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence ( https://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. PMC Copyright notice PMCID: PMC13076087 PMID: 35570702 Abstract Objective: To identify temporal patterns of sleep and eating among school-age children during school closure due to the COVID-19 pandemic and to examine their associations with lifestyle behaviours and dietary intake. Design: In this cross-sectional study, questionnaires were used to assess sleep and eating times, lifestyle behaviours and dietary intake during school closure. Latent class analysis was performed to identify temporal patterns of sleep and eating based on self-reported clock times for wake-up, going to bed and eating meals. Lifestyle behaviours and dietary intake were compared between latent classes. Setting: Forty-eight primary and secondary schools in Japan. Participants: Totally, 6220 children (aged 8–15 years). Results: Four patterns, labelled ‘Very early (20 % of children)’, ‘Early (24 %)’, ‘Late (30 %)’ and ‘Very late (26 %),’ were identified and ordered according to the circadian timing. Latter patterns were characterised by later timings of sleep and eating, especially in clock times for wake-up, breakfast and lunch compared with earlier patterns. Children with latter patterns had a less physically active lifestyle, longer screen time (≥4 h/d), shorter study time (<2 h/d) and more frequent skipping of breakfast and lunch than those with earlier patterns. In addition, children with latter patterns had lower intakes of several vitamins, vegetables, fruits, fish and shellfish and dairy products and higher intakes of sugar and confectionaries and sweetened beverages. Conclusion: More than half of the participants had later wake-up, breakfast and lunch during school closure, which was associated with more unfavourable lifestyles and dietary intakes. Keywords: School closure, Temporal sleep and eating patterns, Lifestyle behaviours, Dietary intakes Under the global coronavirus disease 2019 (COVID-19) pandemic, school closure was conducted in many countries in 2020 ( 1 ) . For example, almost all primary and secondary schools in Japan were closed from early March to the end of May. The school schedule is one of the major factors associated with the sleep and dietary habits of school-age children. For example, studies have consistently shown that school starting time is associated with sleep patterns ( 2 , 3 ) . In addition, previous studies have reported differences in sleep patterns ( 4 ) , physical activity ( 5 ) , time of eating breakfast ( 6 ) and dietary intake ( 7 , 8 ) between weekdays with school and weekends without school among children. Thus, the long-term school closure due to the pandemic might have altered sleep and dietary habits, including the time of sleep and eating among school-age children. Recent studies concerning the influence of the COVID-19 pandemic have examined changes in lifestyle behaviours and dietary habits before and during school closure or lockdown among children. These studies mainly focused on screen time ( 9 , 10 ) , physical activity ( 9 – 11 ) , sleep habits ( 10 , 12 ) and dietary habits ( 11 – 13 ) . However, to our knowledge, no study has investigated circadian rhythms of sleep and eating as well as their associations with lifestyle behaviours and dietary intake among children during long no-school days. In the context of an emerging focus on the whole dietary pattern ( 14 , 15 ) and chrono-nutrition ( 16 ) , dietary intake should be examined at the level of specific eating occasions with the timing and distribution of daily eating ( 14 , 17 ) . Considering the circadian system of the human body ( 16 ) , the daily timing of eating should be captured in combination with sleep habits, which are associated with the time of eating meals ( 18 ) , breakfast skipping ( 19 , 20 ) , dietary intake ( 4 , 20 – 23 ) and longer screen time ( 23 ) . Moreover, previous studies primarily assessed dietary variables, such as dietary intake ( 12 ) , eating frequency ( 11 ) or diet quality scores ( 13 ) , with simple and/or non-validated questions. Quantitative assessments of dietary intake have rarely been conducted. In a usual school year, Japanese school-aged children wake up early ( 24 , 25 ) and eat school lunch ( 8 ) regularly on weekdays. The absence of a school schedule and school lunch during school closure might alter the patterns of sleep and eating among children on weekdays. Further, irregular sleep and eating habits possibly resulted in unfavourable lifestyle behaviours among school-age children during school closure. The aim of this cross-sectional study was (i) to identify daily temporal patterns of sleep and eating among Japanese school-age children during school closure and (ii) to examine the associations between temporal patterns of sleep and eating and lifestyle behaviours and dietary intake. Methods Study design and participants The target population of this cross-sectional survey was school-aged children (i.e. from third to sixth graders of elementary schools and from first to third graders of secondary schools; aged 8–15 years). First, we announced the conduct of the survey via a website, social media and direct e-mail to previous collaborators. As a result, forty-three elementary and secondary schools, one sports club and four research collaborators in fourteen out of forty-seven prefectures showed interest in the survey. Questionnaires were then distributed to school-age children by the schools, the sports club or research collaborators. The aim and procedure of the study were explained to children and their parents using a document attached to the questionnaires. The participants were considered to agree with participation in the survey when they answered and submitted the questionnaires. However, provided data from participants were excluded from the analysis when the participants indicated disagreement against using their data in the research even if they answered questionnaires. The present study was conducted according to the guidelines laid down in the Declaration of Helsinki. All procedures involving human subjects were approved by the Ethics Committee of the University of Tokyo, Faculty of Medicine (approval no. 2020056NI, 25 May 2020). The information on the timing of school closure in the schools attended by the study participants was collected through the schools, sports club and research collaborators participating in this study. Although the date of closure started and reopened slightly differ by school or region, all schools attended by the participants started school closure in the first week of March 2020 and ended in the last week of May or the first week of June 2020. According to the Ministry of Education, Culture, Sports, Science and Technology in Japan, 99·0 % of all primary and secondary schools in Japan closed as of 16 March 2020 ( 26 ) . The survey was conducted immediately after schools reopened in June 2020. Participants were instructed to answer two types of questionnaires: a questionnaire for lifestyle behaviours and a brief-type self-administered diet history questionnaire for children and adolescents (BDHQ15y) ( 27 , 28 ) . Participants were asked to recall and answer their lifestyle behaviour and dietary intake in the previous month during school closure. Among 11 958 participants invited to the survey, 8512 participants (71 %) answered both questionnaires distributed in June 2020. Measurements The questionnaire for lifestyle behaviours during school closure included questions on clock time for waking up and going to bed; frequency and clock time for eating breakfast, lunch, dinner and a late-night snack; the frequency of snacks; physical activity; screen time and study time. The participants were asked to recall and answer their lifestyle behaviour listed above in the previous month under school closure. In addition, parents were instructed to ask their children for their lifestyle if needed when answering the questionnaire. The questionnaire was answered by one of the family members of the participating children who mainly prepared meals at home. Table 1 summarises the analysed variables, the questions and/or methods used for measurement and generated categories for analysis. In brief, questions on clock times for waking up and going to bed were answered with units in h and min (hh:mm) and then categorised into <7:00, 7:00–7:59, 8:00–8:59 and ≥9:00 for wake-up time and <22:00, 22:00–22:59 and ≥23:00 for bedtime. Sleep duration was calculated using the wake-up time and bedtime. The midpoint of sleep was calculated as the midpoint between bedtime and wake-up time. Questions on the frequencies of eating breakfast, lunch, dinner and a late-night snack/week were answered in integer values from 0 to 7. Questions on clock times for eating breakfast, lunch, dinner and a late-night snack/week were answered in integer values from 0 to 24 h, as the clock time when the participants started to eat the concerned meal during school closure. Questions on snack frequency/d were answered in integer values, which were categorised into 0, 1, 2 and ≥3 times/d. Physical activity level (PAL) was calculated by dividing the metabolic equivalent-h score by 24 h. Metabolic equivalent-hour score was estimated by summing the self-reported time spent on each of a range of activities with various exercise intensities and metabolic equivalent value for each activity ( 29 , 30 ) . Participants with PAL < 1·40, 1·40–1·59, 1·60–1·89 and ≥1·9 ( 31 ) were categorised as inactive, low, middle and high, respectively. Questions on screen time and study time were answered using a nine-point scale; the answers of questions pertaining to both parameters were categorised into <2, 2–<4 and ≥4 h/d. Table 1. Summary of analysed variables, questions and/or methods for measurement and generated category for analysis Variables Questions and/or method for assessment Unit for answer (possible value range) or options Generated category for analysis Sleep habits Wake-up time ‘At what time did your child usually wake-up during school closure?’ h and min (0:01–24:00) <6:59, 7:00–7:59, 8:00–8:59, and ≥9:00 Bedtime ‘What was the usual bedtime of your child during school closure?’ h and min (0:01–24:00) <21:59, 22:00–22:59, and ≥23:00 Sleep duration Calculated using self-reported wake-up time and bedtime h and min (0:01–24:00) <8:00, 8:00–9:59, 10:00–11:59, and ≥12:00 Midpoint of sleep Calculated as the midpoint between the self-reported bedtime and wake-up time h and min (0:01–24:00) – Dietary habits Frequency and clock time of eating meals ‘How many times/week and at what time did your child usually eat breakfast, lunch, dinner and a late-night snack during school closure? Here, breakfast, lunch, dinner, and a late-night snack were defined as meals including some staple foods with a high content in carbohydrates such as rice, bread, and noodles. Do not count any eating occasion without staple foods.’ For frequency, times/week (0 to 7 (integer)) For clock time for eating, time (hour) at which the child starts eating (0 to 24 (integer)) For clock time for eating, (for breakfast) <7:00, 7:00–7:59, 8:00–8:59, and ≥9:00; (for lunch) <12:00, 12:00–12:59, and ≥13:00; (for dinner) <19:00, 19:00–19:59, and ≥20:00; (for a late-night snack) <21 h and ≥21 h Breakfast frequency Categorised according to the self-reported frequency of eating breakfast/week – <7 (i.e. skipping at least once a week) and 7 times/week Lunch frequency Categorised according to the self-reported frequency of eating lunch/week – <7 (i.e. skipping at least once a week) and 7 times/week A late-night snack frequency Categorised according to the self-reported frequency of eating/week – 0 and ≥ 1 time/week Snack frequency ‘How many times did your children eat snacks other than meals (i.e. breakfast, lunch, dinner, and a late-night snack)/d during school closure? Do not count occasions wherein only drinks were taken.’ Times/d (≥0 (integer)) <2 and ≥2 times/d (for logistic analysis) 0, 1, 2, and ≥3 times/d (for chi-square test in Supplemental Table 2 ) Lifestyle behaviour Physical activity level (PAL) ‘How long did your child do the following five activities (standing, walking, cycling, running, and other activities causing sweating) per day in the past month (during school closure)?’ PAL was calculated by dividing metabolic equivalent-hour score by 24 h. The metabolic equivalent-hour score was estimated by summing the self-reported time spent on each of a range of activities with various exercise intensities and metabolic equivalent value for each activity. * , † Close to 0, 5 min, 15 min, 30 min, 45 min, 1 h, 1·5 h, 2 h, 3 h, 4 h (ten-point scale) Inactive or low (PAL < 1·60), middle or high (PAL ≥ 1·60) (for logistic analysis) Inactive (PAL < 1·40), low (PAL = 1·40–1·59), middle (PAL = 1·60–1·89), high (PAL ≥ 1·9) ‡ (for chi-square test in Supplemental Table 2 ) Screen time ‘How long did your child spend a day watching a screen at home, such as playing computer games, watching television, or using a smartphone or tablet in an average week in the previous month (under school closure)?’ <30 min, 30 min–<1 h, 1 h–<2 h, 2 h–<3 h, 3 h–<4 h, 4 h–<5 h, 5 h–<6 h, 6 h–<7 h, ≥7h (nine-point scale) <4, ≥4 h/d (for logistic analysis) <2, 2–<4, ≥4 h/d (for chi-square test in Supplemental Table 2 ) Study time ‘How long did your child spend reading books and performing self-study at home/d during an average week in the previous month (under school closure)?’ <30 min, 30 min–<1 h, 1 h–<2 h, 2 h–<3 h, 3 h–<4 h, 4 h–<5 h, 5 h–<6 h, 6 h–<7 h, ≥7 h (nine-point scale) <2, ≥2 h/d (for logistic analysis) <2, 2–<4, ≥4 h/d (for chi-square test in Supplemental Table 2 ) Dietary intake Intakes of nutrients and foods Assessed with a brief-type self-administered diet history questionnaire for children and adolescents – – Binary variables for latent class analysis Wake-up time at 4–5, 6, 7, 8, 9, 10 and 11–15 h Assigned ‘1’ (= event) or ‘2’ (= no-event) to indicate whether the participant woke up within each hour according to the self-reported wake-up time. – 1 or 2 Bedtime at 19–20, 21, 22, 23, 24 and 1–5 h Assigned ‘1’ (= event) or ‘2’ (= no-event) to indicate whether the participant went to bed within each hour according to the self-reported bedtime. – 1 or 2 Eating meals at 5–6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 and 22–24 h Assigned ‘1’ (= event) or ‘2’ (= no-event) to indicate whether the participant ate meals (breakfast, lunch, dinner or a late-night snack) within each hour according to the self-reported eating time when eating frequency was 5 times or more/week for a meal. – 1 or 2 Open in a new tab * Ainsworth BE, Haskell WL, Herrmann SD et al. (2011) Compendium of physical activities: a second update of codes and MET values. Med Sci Sports Exerc 43 , 1575–1581. † Murakami K, Sasaki S, Okubo H et al. (2007) Association between dietary fiber, water and magnesium intake and functional constipation among young Japanese women. Eur J Clin Nutr 61 , 616–622. ‡ Ministry of Health Labour and Welfare (2020) Dietary reference intakes for Japanese. (in Japanese). Dietary intake during school closure was assessed using the BDHQ15y, which was filled by participating children themselves, one of the children’s family members (who was mainly responsible for meal preparation), or children and their family members together. The participants were asked to answer their dietary habits in the previous month under school closure. The BDHQ15y is a four-page fixed-portion questionnaire concerning the consumption frequency of selected foods commonly consumed in Japan, general dietary behaviours and usual cooking methods during the previous month. The BDHQ15y was developed based on the sixteen-page comprehensive ( 32 ) and four-page brief versions ( 33 , 34 ) of a validated self-administered diet history questionnaire for Japanese adults. Estimates of daily intakes of foods (ninety food items in total), energy and selected nutrients were calculated using an ad hoc computer algorithm for the BDHQ15y based on the Standard Tables of Food Composition in Japan ( 35 ) and sex-specific fixed portion size. The BDHQ15y was validated for selected nutrients, including protein, fatty acids and carotenoids, using biomarkers (erythrocyte fatty acid and serum carotenoid levels) as a gold standard ( 27 , 28 ) . Date of birth, sex, body weight and height were self-reported as part of the BDHQ15y. Living status and sibling status were determined based on the relationship of family members living with the participating children asked with the questionnaire for lifestyle behaviours. Living status and sibling status were determined based on the relationship of family members living with the participating children for whom the questionnaire for lifestyle behaviours was filled. Living status was categorised into two groups: (i) living with both parents or (ii) living with a single parent and/or other relatives (including those living with a mother or father, a mother or father and/or other relatives and other relatives without both parents). Sibling status was categorised into two groups: (i) having at least one sibling at or under primary school age or (ii) not having any siblings at or under primary school age (i.e. not having any siblings or having an only sibling(s) over primary-school-age). Analysed participants Participants who indicated disagreement in using their data ( n 402) were excluded from the analysis. Furthermore, we excluded participants who were not in the targeted grades ( n 88) and had missing information on the variables used ( n 1763; n 1 for sex; n 3 for identification number; n 186 for frequency and eating time of breakfast, lunch or dinner; n 528 for snack frequency; n 773 for wake-up time or bedtime; n 378 for anthropometric data; n 10 for family structure, n 863 for physical activity; n 162 for screen time; n 78 for study time and n 3 for dietary data; some participants had more than one missing value). To identify temporal patterns of sleep and eating with regard to the usual time of eating meals among participants, we excluded participants whose eating frequencies/week were less than five times for all eating occasions, or in whom the sum of the eating frequencies of all eating occasions was less than 7 times/week ( n 39). For example, participants were excluded when they ate breakfast, lunch, dinner and a late-night snack 3, 4, 3 and 1 time(s)/week, respectively, or 0, 1, 5 and 0 time(s)/week, respectively. Finally, the analysed participants were 6220 children of 8512 potentially eligible participants who replied to the surveys in June 2020 (Fig. 1 ). Fig. 1. Open in a new tab Flow chart of the participant for analysis. EER, estimated energy requirement, PAL, physical activity level. The thick line box shows the participants for the main analysis Statistical analyses Latent classes of temporal patterns of sleep and eating Summary of the analytical method was shown in Fig. 2 . Based on the clock time for wake-up; going to bed and eating breakfast, lunch, dinner and a late-night snack; binary variables (1 = event, 2 = no-event) were generated as input variables for latent class analysis (LCA). For clock time for wake-up and going to bed, binary variables indicated whether or not wake-up and going to bed had occurred within each hour of the day. For example, for the participants having clock time for wake-up at 7:45 am, a value of ‘1’ was assigned to the generated binary variable ‘wake-up time at 7 h’. A value of ‘2’ was assigned for the other binary variables for wake-up time. For the clock time of eating meals, binary variables indicated whether or not meal consumption had occurred within each hour of the day. To identify temporal eating patterns according to the usual clock time of eating meals, a value of ‘1’ was assigned when the eating frequency was 5 or more times/week for a particular meal; otherwise, a value of ‘2’ was assigned. Examples of variables generating eating time are shown in Supplemental Fig. 1 . During the variables generating process, the self-classification of meals was not distinguished. For example, a value of ‘1’ was assigned to the binary variable ‘eating time at 11 h’ for participants who reported a clock time for eating breakfast at 11 am and those who reported a clock time for eating lunch at 11 am. Subsequently, generated binary variables with a few events were integrated to establish the number of input variables that gave the feasible solution in LCA. For example, going to bed from 1:00 am to 5:00 am was expressed in one binary variable, ‘bedtime at 1–5 h’. Binary variables with no events were excluded from the input variables for the LCA. Finally, thirty binary variables were generated: seven, six, and seventeen variables for wake-up time, bedtime and eating meals, respectively. Fig. 2. Open in a new tab Summary of the analytical method. LCA, latent class analysis LCA was performed using SAS statistical software (version 9.4; SAS Institute Inc.) with the PLOC LCA procedure (version 1.3.2) ( 36 , 37 ) to identify temporal patterns of sleep and eating. Models with two to six latent classes were tested. The Bayesian information criterion, Akaike information criterion (AIC), adjusted AIC and entropy were computed for each LCA. Models with lower Bayesian information criterion and AIC values suggested better goodness of fit. The four-class solution was chosen considering a combination of model fit and the interpretability of the classes ( 37 ) . The values of Bayesian information criterion, AIC and adjusted AIC decreased as the number of classes increased, but the decrease of the values levelled off among four, five and six classes (Table 2 ). Some patterns of five or six classes were less interpretable and resulted in classes representing <10 % of the sample (see online Supplemental Fig. 2 ). Thus, the four-class solution was better than the other solutions for further analysis. Table 2. Model-fit indices for the latent class analysis model Number of classes Log likelihood df G2 AIC BIC Adjusted BIC Entropy 2 −57 622·03 1073741762 48 400·84 48 522·84 48 933·70 48 739·86 0·90 3 −54 944·33 1073741731 43 045·44 43 229·44 43 849·10 43 556·75 0·97 4 −52 432·79 1073741700 38 022·35 38 268·35 39 096·82 38 705·96 0·98 5 −51 819·20 1073741669 36 795·18 37 103·18 38 140·45 37 651·08 0·98 6 −51 461·63 1073741638 36 080·03 36 450·03 37 696·11 37 108·23 0·99 Open in a new tab AIC, the Akaike information criterion; BIC, Bayesian information criterion. To interpret the identified patterns of sleep and eating, three or four peaks in the conditional probability of eating events were labelled as breakfast, lunch, dinner and a late-night snack, according to the chronological order in each latent class. Associations between latent classes and socio-demographic and eating pattern indicators All statistical analyses were performed using SAS 9.4 statistical software. All reported P -values were two-tailed, and a P -value < 0·05 was considered statistically significant. Data are presented as means and sd for continuous variables and as numbers and percentages for categorical variables. The mean ( sd ) clock times of eating meals were calculated for participants who ate each type of meal ≥5 times/week. Mean differences in continuous variables (i.e. clock times for wake-up, going to bed and eating, as well as basic characteristics) among LCA-derived classes were tested with linear regression models using the PROC GLM procedure. The ordinal scale (i.e. 1 to 4) according to the circadian timing was assigned for each class and used as a continuous variable in the linear regression models. Differences in categorical variables were tested using the χ 2 test. The risks of classifying participants as having unfavourable lifestyles were tested using logistic regression. Examined unfavourable lifestyles were as follows: physical inactivity or low PAL; longer screen time (4 h or more/d); shorter study time (less than 2 h/d); skipping breakfast at least once/week; skipping lunch at least once/week; having a late-night snack at least once/week and having a snack at least twice/d. First, crude odds ratios (OR) and 95 % CI for the risk of having unfavourable lifestyles were calculated for each LCA-derived class; the LCA-derived class having the earliest circadian timing was used as the reference. Thereafter, multivariate-adjusted OR and 95 % CI were calculated by entering the following confounding factors into the regression model: age, sex, living status and sibling status. Linear regression models were constructed to examine the association between LCA-derived classes and intakes of nutrient and food using the ordinal scale of LCA-derived classes as a continuous variable. A multivariate-adjusted model analysis was also performed with age, sex, living status and sibling status as confounding variables. Sensitivity analysis Sensitivity analysis was performed only on participants with more plausible reported energy intake ( n 5978) (Fig. 1 ). Participants were excluded if their energy intake estimated using the BDHQ15y was <0·5 times the estimated energy requirement for children with the lowest PAL or ≥1·5 times the estimated energy requirement for those with the highest PAL. Further sensitivity analysis was performed with adjustments for household income and maternal education level ( n 4243) (Fig. 1 ). Information regarding household income and maternal education level was collected with an additional questionnaire distributed to the participants several months after the first survey, from July 2020 to February 2021. Participants who did not answer the additional questionnaire were excluded from the analysis. Results The mean ( sd ) age, height and weight of participants were 11·0 (1·9) years, 144·8 (12·5) cm and 38·4 (10·7) kg, respectively. Fifty-one percent of the participants were boys and 67 % were primary school-aged. Using LCA, four temporal patterns of sleep and eating were derived. Each class was labelled based on distinguishing features, as shown by high or low conditional probability for wake-up, going to bed and consuming a meal at each time frame of a usual day during school closure. Figure 3 shows the confidence probabilities for each pattern. The first pattern was labelled ‘Very early’, as participants in this class (20 % of participants) woke up and ate breakfast earlier than those in the other classes. The second pattern was labelled ‘Early’, as participants in this class (24 % of participants) woke up and ate breakfast 1 h later than those in the first pattern but earlier than those in the other two patterns. The third pattern was labelled ‘Late’, as participants in this pattern (30 % of participants) woke up and ate breakfast 1–2 h later than those in the first pattern. Peaks in the conditional probability of eating lunch at 12 pm were similar among the ‘Very early,’ ‘Early’ and ‘Late’ patterns. The fourth pattern labelled ‘Very late’ (26 % of participants) was characterised by participants with the latest timings of wake-up and eating breakfast. Latter patterns were also characterised by participants with later timings of eating dinner or a late-night snack and bedtime. However, differences in the timings of eating dinner or a late-night snack and bedtime were less distinctive among classes compared with those of wake-up and eating breakfast. Fig. 3. Open in a new tab Conditional probabilities of: (a) wake-up time; (b) bedtime and (c) eating time across the day according to latent class analysis-derived temporal patterns of sleeping and eating among 6220 school-aged children. Dashed lines with a white circle represent the ‘Very early’ pattern, solid lines with a black circle represent the ‘Early’ pattern, dashed lines with a white triangle represent the ‘Late’ pattern, and dashed lines with a black triangle represent the ‘Very late’ pattern Among all participants, the mean ( sd ) clock times were 7:39 (1:10) for wake-up and 22:18 (1:01) for going to bed (Table 3 ). The mean ( sd ) sleep duration was 9:21 (0:58), and the midpoint of sleep was 2:58 (0:58). The mean ( sd ) clock times for each eating occasion were 8 (1) h ( n 5650) for breakfast, 12 (1) ( n 6108) h for lunch, 19 (1) h ( n 6180) for dinner and 20 (1) h ( n 69) for a late-night snack, among participants eating the concerned meals ≥5 times/week. The clock times for wake-up, going to bed and eating meals, as well as the midpoint of sleep were later for children with latter patterns than for those in earlier patterns ( P < 0·0001). Sleep duration was longer in participants with the latter patterns than those with earlier patterns ( P < 0·0001). Table 3. Sleep habits and clock time for eating in 6220 school-age children (third to sixth grade of primary school and first to third grade of secondary school) according to latent class analysis-derived temporal patterns of sleep and eating All ( n 6220) ‘Very early’ pattern ( n 1219) ‘Early’ pattern ( n 1503) “Late” pattern ( n 1868) ‘Very late’ pattern ( n 1630) P * n % Mean SD n % Mean SD n % Mean SD n % Mean SD n % Mean SD Sleep habits Clock time for wake-up (hh:mm) 7:39 1:10 6:19 0:29 7:09 0:13 7:44 0:34 9:00 1:04 <0·0001 <7:00 1280 21 1197 98 0 0 65 3 18 1 <0·0001 7:00–7:59 2297 37 0 0 1503 100 713 38 81 5 8:00–8:59 1562 25 11 1 0 0 1060 57 491 30 ≥9:00 1081 17 11 1 0 0 30 2 1040 64 Clock time for going to bed (hh:mm) 22:18 1:01 21:35 0:44 22:00 0:45 22:17 0:46 23:09 1:04 <0·0001 <21:59 1738 28 754 62 543 36 373 20 68 4 <0·0001 22:00–22:59 2543 41 384 32 722 48 980 52 457 28 ≥23:00 1939 31 81 7 238 16 515 28 1105 68 Sleep duration (hh:mm) 9:21 0:58 8:44 0:51 9:09 0:45 9:27 0:50 9:51 1:05 <0·0001 <8:00 271 4 139 11 44 3 40 2 48 3 <0·0001 8:00–9:59 3856 62 1002 82 1132 75 1130 60 592 36 10:00–11:59 1996 32 65 5 326 22 682 37 923 57 ≥12:00 97 2 13 1 1 0 16 1 67 4 Midpoint of sleep (hh:mm) 2:58 0:58 1:57 0:27 2:34 0:24 3:01 0:32 4:04 0:55 <0·0001 Clock time for eating † , ‡ Breakfast (clock time of day, hour) 5650 100 8 1 1204 100 7 1 1451 100 7 0 1866 100 8 0 1129 100 9 1 <0·0001 <7:00 477 8 464 39 12 1 0 0 1 0 <0·0001 7:00–7:59 2170 38 699 58 1439 99 0 0 32 3 8:00–8:59 1904 34 38 3 0 0 1866 100 0 0 ≥9:00 1099 19 3 0 0 0 0 0 1096 97 Lunch (clock time of day, hour) 6108 100 12 1 1209 100 12 0 1492 100 12 0 1856 100 12 1 1551 100 13 1 <0·0001 <12:00 194 3 76 6 52 3 22 1 44 3 <0·0001 12:00–12:59 4239 68 1018 84 1266 85 1294 70 658 42 ≥13:00 1675 27 115 10 174 12 537 29 849 55 Dinner (clock time of day, hour) 6180 100 19 1 1213 100 19 1 1496 100 19 1 1860 100 19 1 1611 100 19 1 <0·0001 <19:00 2180 35 607 50 590 39 586 32 397 25 <0·0001 19:00–19:59 3220 52 513 42 762 51 1057 57 888 55 ≥20:00 780 13 93 8 144 10 217 12 326 20 Late-night snack (clock time of day, hour) 69 100 20 1 9 100 20 1 9 100 19 1 17 100 20 2 34 100 21 1 0·0001 <21:00 37 54 7 78 8 89 11 65 11 32 0·003 ≥21:00 32 46 2 22 1 11 6 35 23 68 Open in a new tab * For continuous variables, P values represent P for trend in temporal patterns of sleep and eating. For categorical variables, P values were tested using the χ 2 test. The trend of association was examined using a linear regression model with the ordinal scale of temporal patterns of sleep and eating (1 = ‘Very early’ pattern, 2 = ‘Early’ pattern, 3 = ‘Late’ pattern and 4 = ‘Very late’ pattern) as a continuous variable. † In the questionnaire, the clock time for eating was answered with an integer value of hour by participants (e.g. breakfast consumed between 7:00 and 7:59 was answered as ‘7’). ‡ Participants who ate breakfast, lunch, dinner or late-night snacks less than 5 times/week were excluded from the analysis. Participants with latter patterns tended to be older and were predominantly girls (Table 4 ). The proportion of participants living with a single parent and/or other relatives and having siblings aged at or under primary school age was higher in the latter patterns ( P < 0·0001). Latter patterns had higher proportions of participants who were inactive or had a low PAL ( P < 0·0001), had a screen time of 4 h or more ( P < 0·001), had a study time of less than 2 h ( P < 0·0001), skipped breakfast ( P < 0·001) and lunch ( P < 0·0001), had a late-night snack at least once/week ( P < 0·001) and had snacks at least twice/week ( P = 0·001) (Table 4 and see online Supplemental Table 2 ). Table 4. Characteristics of 6220 school-aged children (third to sixth grade of primary school and first to third grade of secondary school) according to latent class analysis-derived temporal patterns of sleeping and eating All ( n 6220) ‘Very early’ pattern ( n 1219) ‘Early’ pattern ( n 1503) ‘Late’ pattern ( n 1868) ‘Very late’ pattern ( n 1630) P * Mean sd Mean sd Mean sd Mean sd Mean sd Age (years) 11·0 1·9 10·6 1·8 10·8 1·8 10·9 1·8 11·6 1·9 <0·0001 Height (cm) 144·8 12·5 142·5 12·0 143·5 12·1 144·4 12·4 147·9 12·6 <0·0001 Weight (kg) 38·4 10·7 36·7 10·0 37·3 10·1 38·1 11·0 41·1 11·0 <0·0001 n % n % n % n % n % Sex (boys) 3158 51 692 57 767 51 930 50 769 47 <0·0001 Grade Primary school 4137 67 896 74 1086 72 1298 69 857 53 <0·0001 Secondary school 2083 33 323 26 417 28 570 31 773 47 Family structure Living with both parents 5459 88 1082 89 1341 89·2 1686 90·3 1350 82·8 <0·0001 Living with single parent and/or other relatives † 761 12 137 11 162 10·8 182 9·7 280 17·2 Sibling status: having sibling(s) aged at or under primary-school-age <0·0001 Yes 3048 49 501 41·1 716 47·6 882 47·2 949 58·2 No 3172 51 718 58·9 787 52·4 986 52·8 681 41·8 Physical activity level ‡ <0·0001 Inactive or low 3865 62 674 55 908 60 1158 62 1125 69 Middle or high 2355 38 545 45 595 40 710 38 505 31 Screen time/d § <0·0001 <4 h 3604 58 840 69 1011 67 1119 60 634 39 ≥4 h 2616 42 379 31 492 33 749 40 996 61 Study time/d ║ <0·0001 <2 h 3374 54 585 48 724 48 970 52 1095 67 ≥2 h 2846 46 634 52 779 52 898 48 535 33 Breakfast frequency <0·0001 Skipping ≥1 times/week 1013 16 60 5 112 7 151 8 690 42 Daily 5207 84 1159 95 1391 93 1717 92 940 58 Lunch frequency (times/week) <0·0001 Skipping ≥1 times/week 381 6 43 4 52 3 73 4 213 13 Daily 5839 94 1176 96 1451 97 1795 96 1417 87 A late-night snack frequency (times/week) ¶ <0·0001 0 5847 94 1186 97 1447 96 1759 94 1455 89 ≥ 1 373 6 33 3 56 4 109 6 175 11 Snack frequency/d 0·001 0–1 3728 60 742 61 918 61 1158 62 910 56 ≥ 2 2492 40 477 39 585 39 710 38 720 44 Open in a new tab * For continuous variables, P values represent P for trend in temporal patterns of sleep and eating. For categorical variables, P values were tested using the χ 2 test. The trend of association was examined using a linear regression model with the ordinal scale of temporal patterns of sleeping and eating (1 = ‘Very early’ pattern, 2 = ‘Early’ pattern, 3 = ‘Late’ pattern and 4 = ‘Very late’ pattern) as a continuous variable. † Including participants living with mother (or father), living with mother (or father) and/or other relatives (e.g. living with mother and grandparents) and living with other relatives without both parents (e.g. living with grandparents). ‡ Participants with physical activity level (PAL) <1·60 ( 31 ) were categorised as ‘inactive or low PAL.’ PAL was calculated by dividing the metabolic equivalent-hour score by 24 h. The metabolic equivalent-hour score was estimated by summing the product of the time spent on each of a range of activities (sleeping, standing, walking, cycling, running and other activities causing sweating) with various exercise intensities and metabolic equivalent values for each activity ( 29 , 30 ) . § Screen time included the time spent in watching television; using a computer, smartphone or tablet and playing video games. ║ Study time included time spent reading books and self-studying. ¶ A late-night snack was defined as a meal including staple foods (i.e. rice, bread or noodles). The risk of having unfavourable lifestyle behaviours was high in participants with latter patterns compared with those with the ‘Very early’ pattern (all P for trend <0·0001) (Table 5 ). The results were similar after adjustment for confounding factors (all P for trend <0·0001, except for having snacks ≥2 times/d ( P for trend was 0·003)). Compared with the ‘Early’ and ‘Late’ patterns, the ‘Very late’ pattern had much higher OR for longer screen time (adjusted OR = 3·13, 95 % CI (2·66, 3·68)), skipping breakfast (adjusted OR = 12·28, 95 % CI (9·28, 16·26)), skipping lunch (adjusted OR = 3·71, 95 % CI (2·63, 5·12)) and having a late-night snack (adjusted OR = 3·86, 95 % CI (2·62, 5·69)). Table 5. Odds ratios for unfavourable lifestyle and dietary habits according to latent class analysis-derived temporal patterns of sleeping and eating among 6220 school-age children (third to sixth grade of primary school and first to third grade of secondary school) ‘Very early’ pattern ( n 1219) ‘Early’ pattern ( n 1503) ‘Late’ pattern ( n 1868) ‘Very late’ pattern ( n 1630) P for trend * n % n % OR 95 % CI n % OR 95 % CI n % OR 95 % CI Inactive or low physical activity level † Crude 674 55 Ref 908 60 1·23 1·06, 1·44 1158 62 1·32 1·14, 1·53 1125 69 1·80 1·54, 2·10 <0·0001 Adjusted ‡ Ref 1·19 1·02, 1·39 1·27 1·09, 1·47 1·63 1·39, 1·91 <0·0001 Longer screen time (≥4 h/d) Crude 379 31 Ref 492 33 1·08 0·92, 1·27 749 40 1·48 1·27, 1·73 996 61 3·48 2·98, 4·07 <0·0001 Adjusted ‡ Ref 1·07 0·91, 1·27 1·47 1·26, 1·72 3·13 2·66, 3·68 <0·0001 Short study time (<2 h/d) Crude 585 48 Ref 724 48 1·01 0·87, 1·17 970 52 1·17 1·01, 1·35 1095 67 2·22 1·90, 2·58 <0·0001 Adjusted ‡ Ref 1·04 0·89, 1·21 1·23 1·06, 1·43 2·47 2·11, 2·89 <0·0001 Skipping breakfast ≥1 times/week Crude 60 5 Ref 112 7 1·56 1·13, 2·15 151 8 1·70 1·25, 2·31 690 42 14·18 10·74, 18·71 <0·0001 Adjusted ‡ Ref 1·52 1·10, 2·10 1·64 1·20, 2·23 12·28 9·28, 16·26 <0·0001 Skipping lunch ≥1 times/week Crude 43 4 Ref 52 3 0·98 0·65, 1·48 73 4 1·11 0·76, 1·63 213 13 4·11 2·94, 5·76 <0·0001 Adjusted ‡ Ref 0·96 0·63, 1·44 1·08 0·74, 1·59 3·71 2·63, 5·23 <0·0001 Having late-night snack ≥1 times/week Crude 33 3 Ref 56 4 1·39 0·90, 2·15 109 6 2·23 1·50, 3·31 175 11 4·32 2·96, 6·32 <0·0001 Adjusted ‡ Ref 1·42 0·91, 2·20 2·25 1·51, 3·35 3·86 2·62, 5·69 <0·0001 Having snack ≥2 times/d Crude 477 39 Ref 575 38 0·99 0·85, 1·16 710 38 0·95 0·82, 1·11 720 44 1·23 1·06, 1·43 <0·0001 Adjusted ‡ Ref 1·00 0·86, 1·17 0·96 0·83, 1·12 1·28 1·10, 1·50 0·003 Open in a new tab * Logistic regression models were used with the ordinal scale of temporal patterns of sleeping and eating (1 = ‘Very early’ pattern, 2 = ‘Early’ pattern, 3 = ‘Late’ pattern and 4 = ‘Very late’ pattern) as a continuous variable. † Participants with physical activity level (PAL) <1·60 ( 31 ) were categorised as ‘Inactive or low PAL.’ PAL was calculated by dividing the metabolic equivalent-hour score by 24 h. The metabolic equivalent-hour score was estimated by summing the product of the time spent on each of a range of activities (sleeping, standing, walking, cycling, running and other activities causing sweating) with various exercise intensities and metabolic equivalent values for each activity ( 29 , 30 ) . ‡ In the adjusted model, sex, age, living status and sibling status were adjusted. Participants with latter patterns had lower intakes for protein; dietary fibre; vitamins A, C, B 6 and B 12 ; thiamine; riboflavin; niacin; folate; potassium; Ca; Mg; Fe; pulses; vegetables; fruits; fish and shellfish and dairy products and higher intakes of carbohydrate, sugars and confectionaries and sweetened beverages compared with those with earlier patterns (all P for trend < 0·0001 except for carbohydrates) (Table 6 ). Dietary intakes did not significantly differ among participants with different patterns for total fat, saturated fat, Na and meat. After adjusting for confounding factors, the direction of association between LCA-derived classes and dietary intake did not change (see online Supplemental Table 2 ). The statistical significance of P for trend for cereal intake disappeared after the adjustment, but the results of other nutrients and foods did not change before and after the adjustment. Table 6. Dietary intakes according to latent class analysis-derived temporal patterns of sleep and eating among 6220 school-age children (3rd to 6th grade of primary school and 1st to 3rd grade of secondary school) All ( n 6220) “Very early” pattern ( n 1219) “Early” pattern ( n 1503) “Late” pattern ( n 1868) “Very late” pattern ( n 1630) P for trend * Mean sd Mean sd Mean sd Mean sd Mean sd Nutrient intake Protein (% of energy) 13·9 2·2 14·4 2·3 14·1 2·2 13·9 2·1 13·5 2·3 <0·0001 Total fat (% of energy) 30·8 5·5 30·8 5·4 31·1 5·6 30·7 5·4 30·8 5·4 0·23 SFA (% of energy) 10·1 2·4 10·1 2·4 10·2 2·5 10·1 2·4 10·1 2·5 0·29 Carbohydrate (% of energy) 53·7 6·4 53·4 6·3 53·3 6·6 53·9 6·3 54·2 6·5 0·0007 Dietary fibre (g/4184 kJ) 5·7 1·6 5·9 1·7 5·9 1·6 5·8 1·5 5·5 1·5 <0·0001 Vitamin A (μg retinol equivalents/4184 kJ) 313 175 333 212 320 137 315 186 288 157 <0·0001 Thiamine (mg/4184 kJ) 0·4 0·1 0·41 0·08 0·40 0·07 0·40 0·07 0·39 0·07 <0·0001 Riboflavin (mg/4184 kJ) 0·7 0·2 0·75 0·18 0·73 0·17 0·71 0·17 0·68 0·18 <0·0001 Niacin (mg/4184 kJ) 7·0 1·7 7·2 1·8 7·1 1·7 7·1 1·7 6·8 1·8 <0·0001 Vitamin B 6 (mg/4184 kJ) 0·6 0·1 0·58 0·14 0·57 0·13 0·56 0·13 0·54 0·13 <0·0001 Vitamin B 12 (μg/4184 kJ) 3·4 1·7 3·5 1·8 3·4 1·5 3·4 1·6 3·2 1·7 <0·0001 Folate (μg/4184 kJ) 160 53 168 55 165 53 161 52 149 52 <0·0001 Vitamin C (mg/4184 kJ) 54·1 22·9 55·7 22·6 55·5 22·7 54·9 22·6 50·5 23·4 <0·0001 Na (mg/4184 kJ) 1901 404 1906 402 1910 405 1905 395 1883 415 0·22 Potassium (mg/4184 kJ) 1168 274 1218 287 1191 270 1167 264 1112 272 <0·0001 Ca (mg/4184 kJ) 322 108 344 114 330 104 318 104 304 108 <0·0001 Mg (mg/4184 kJ) 119 23 123 25 121 23 119 22 114 22 <0·0001 Fe (mg/4184 kJ) 3·8 0·9 4·0 0·9 3·9 0·8 3·8 0·8 3·7 0·8 <0·0001 Food intake (g/4184 kJ) Cereal 220 64 220 63 216 62 222 64 220 65 0·048 Sugars and confectioneries 52·3 28·2 49·7 28·3 52·4 28·1 51·9 27·0 54·5 29·4 0·0001 Pulses 25·3 17·4 26·9 18·1 26·6 17·5 25·3 17·2 22·8 16·5 <0·0001 Vegetables 108 58 117 63 113 58 109 56 97 57 <0·0001 Fruits 28·2 28·4 29·8 29·2 30·0 28·3 28·7 28·1 24·7 27·9 <0·0001 Fish and shellfish 27·2 15·6 28·6 15·7 27·9 15·4 27·3 15·4 25·4 16·0 <0·0001 Meat 39·5 16·9 39·8 17·5 39·9 16·9 39·3 16·0 39·1 17·3 0·52 Dairy products 104 88 118 92 106 84 100 85 94 90 <0·0001 Sweetened beverages 42·5 68·8 30·3 50·7 38·2 64·3 39·2 60·3 59·1 87·9 <0·0001 Open in a new tab * Trend of association was examined using a linear regression model with the ordinal scale of temporal patterns of sleeping and eating (1 = ‘Very early’ pattern, 2 = ‘Early’ pattern, 3 = ‘Late’ pattern and 4 = ‘Very late’ pattern) as a continuous variable. Sensitivity analyses limiting analysed participants with reported energy intake and a further adjustment of household income and maternal education level did not considerably alter the abovementioned findings. Discussion To our knowledge, this is the first study that explored temporal patterns of sleep and eating and examined their associations with lifestyle behaviours and dietary intake. In addition, no study has identified temporal eating patterns among school-age children during school closure in any country including Japan. Inconsistent with the findings of a previous study wherein three eating patterns (including irregular eating) were identified among Australian adults using LCA ( 17 ) participants with all four identified patterns in our study had three main meals/d at a regular time. This inconsistency with the previous study might be because we did not include the time of eating snacks in the LCA in this study. Considering the higher snack frequency among participants with the ‘Very late’ pattern than among participants with the other three patterns, different temporal eating patterns might be identified when the time of eating snacks is considered. Among participants with the four identified patterns, those classified as having ‘Late’ and ‘Very late’ patterns waked up on average at 7:44 am and 9:00 am, respectively. On weekdays during usual school days, Japanese school-age children wake up between 6 am and 7 am on average ( 24 , 25 ) . Thus, children with ‘Late’ and ‘Very late’ patterns woke up and ate breakfast much later than they did during usual school days on average, whereas children with ‘Very early’ and ‘Early’ patterns kept their time schedule as they did during usual school days. However, dinner time and bedtime were not as distinctively different as wake-up and breakfast times between participants with different patterns. A possible reason for the less distinctive difference in dinner time and bedtime between participants with different patterns is the frequency of family eating. A previous study reported a higher frequency of family eating at dinner compared with that at breakfast ( 38 ) . Having family meals at breakfast or dinner might play a role in preventing a delay in eating time, which in turn prevents delays in wake-up time or bedtime. Although the frequency of family eating at each meal was not evaluated among participants in this study, it is possible that participants with earlier patterns frequently had family eating at both breakfast and dinner, whereas those with latter patterns frequently had family meals at dinner but not at breakfast. Consequently, the time of dinner and bedtime would not be delayed and would less distinctively differ between participants with different patterns. In contrast, a lower frequency of family eating at breakfast could delay wake-up and breakfast times in participants with latter patterns, possibly because the management of wake-up and breakfast times highly depends on children themselves. Regarding sleep habits among participants, children with ‘Late’ and ‘Very late’ patterns might have later chronotypes because they had a later time of midpoint sleep. Previous studies reported associations between later chronotype or later midpoint sleep and longer screen time ( 23 ) , a higher frequency of skipping meals ( 19 , 20 ) , consuming food at late hours ( 18 ) , a lower intake of fruits and/or vegetables ( 4 , 21 , 23 ) and a higher intake of soft drinks ( 21 , 23 ) or lower diet quality ( 22 ) . Thus, our study findings are consistent with those of previous studies. A higher frequency of skipping meals and consuming snacks might have resulted in poor dietary intake (such as lower intakes of vegetables and fruits, as well as higher intakes of sugar, confectionaries and sweetened beverages) among participants with latter patterns. Considering the larger social jet lag among children with later chronotypes ( 39 ) and the associations between school time and time for sleep ( 2 , 3 ) and breakfast ( 6 ) , children with later chronotypes could be more vulnerable to changes in their circadian timing during no-school days. A possible reason for the association of latter temporal patterns of sleep and eating with unfavourable lifestyles in the present study was the family environment, including parenting practice. Previous studies have shown an association between parenting practice and screen time ( 40 ) , PAL ( 40 ) and sleep habits ( 41 ) among children. Children with latter patterns were possibly less likely to have family rules regarding screen time and sleep habits and less likely to be encouraged to engage in physical activity. During school closure, managing children’s lifestyles might depend highly on their family environment and caregivers. Mothers reportedly have more childcare and household work during lockdown periods than fathers ( 42 , 43 ) . In Japan, working mothers with primary school-age children are more likely to work from home, unlike fathers with primary school-age children and parents with secondary school-age children ( 44 ) . These results suggest a higher burden and gender inequality in childcare in different households during school closure. Hence, social support for parents of school-age children is needed. In addition, some strategies to encourage children to manage circadian rhythms would be needed during school closure, such as having regular online meetings in the morning, although there is no established causal relationship between the temporal pattern of sleep and eating, lifestyle behaviours and dietary intake. There are several limitations to the present study. First, our study sample was a convenient, not a nationally representative, sample. Possibility of selection bias needs to be recognised in this study. The study area was limited to only fourteen of the forty-seven prefectures in Japan. In addition, participants included in the analysis possibly had different characteristics from non-participants, although our sensitivity analysis with adjustment for household income and maternal education level did not change the direction of associations between LCA-derived classes and lifestyle behaviours and dietary intake. Thus, the generalisability of our results may be low. Second, the survey period might have affected the answers of participants, as the questionnaires were filled after schools reopened. Thus, participant answers did not fully reflect their lifestyle during school closure. However, four distinctive temporal patterns of sleep and eating were identified among participants. ‘Late’ and ‘Very late’ patterns might be rarely identified in a usual school year. This suggests that the effect of recall bias on the identification of patterns and the association between patterns and lifestyle variables might be low. Third, the variables used for analysis were all self-reported, and the validity of the questionnaires was insufficient or unknown. Previous studies have investigated the validity of the BDHQ15y against several biomarkers; the correlation coefficients were found to be low for all the examined nutrients ( 27 , 28 ) . The nonvalidated questions were used to assess sleep habits, physical activity, screen time and study time estimates. A previous systematic review found a high correlation between self-reported sleep time and those assessed with accelerometer among children ( 45 ) . Thus, analysing patterns of temporal sleep and eating time based on self-reported sleep habits could be acceptable. However, the questionnaire had the potential of measurement error resulting from recall bias and social desirability bias ( 46 , 47 ) . The parents of participating children possibly reported later wake-up time and earlier bedtime than the time when their children actually went to bed and woke up ( 48 , 49 ) . Thus, some participants were possibly misclassified. Regarding physical activity and sedentary behaviours, parents could overestimate their children’s physical activity and study time, while they underestimated screen time due to social desirability bias ( 47 ) . However, it was also possible that physical activity was possibly underestimated and sedentary behaviour was overestimated under the situation of the pandemic. Thus, it is unknown whether a possible measurement error overestimated or attenuated the associations between later timing of sleep and eating patterns and unfavourable lifestyle behaviour. Finally, we could not determine a causal relationship between temporal patterns of sleep and eating and lifestyle behaviours due to the cross-sectional study design. A sedentary lifestyle or longer screen time could have potentially adversely affected sleep habits. In conclusion, more than half of the children in the present study had later circadian timings, especially for wake-up and eating breakfast and lunch. Later timings for sleeping and eating meals were associated with unfavourable lifestyle behaviours and dietary intake, including longer screen time and short study time, skipping meals, a lower consumption of vegetables and fruits and a higher consumption of sweetened beverages. Further studies are needed to investigate the environmental and social factors that determine differences in the temporal patterns of sleep and eating among children during school closure. Acknowledgements Acknowledgements: The authors and their colleagues thank the all participating schools and organizations, the dietitians, school nurses, teachers, research collaborators and stuff who supported the research in each school or organisation and research centre, namely, Elementary School attached to Miyagi University of Education, Fukaya Municipal Fukaya Elementary School, Fukaya Municipal Sakuragaoka Elementary School, Fukui Prefectural Koshi Junior High School, Hikari Elementary School attached to Faculty of Education, Yamaguchi University, Hikari Junior high School attached to Faculty of Education,Yamaguchi University, Komae Municipal Izumi Elementary School, Kurashiki Municipal Kojima Elementary School, Mito Municipal Kasahara Elementary School, Mito Municipal Midorioka Elementary School, Mito Municipal Migawa Elementary School, Mito Municipal Sakado Elementary School, Mito Municipal Sannomaru Elementary School, Mito Municipal Yoshizawa Elementary School, Nara Prefectural School for the Deaf, Nara Prefectural School for the Blind, Non-profit organization ATTAKA, Office Noriko Higuchi (NGO Equalnet Sendai and Sendai Teacher’s Union), Okayama Municipal Oomoto Elementary School, Okegawa Municipal Asahi Elementary School, Ryugasaki Municipal Ryugasaki Elementary School, Saitama Municipal Sashiogi Junior High School, Saitama Municipal Takasago Elementary School, Sakai Municipal Sanbo Elementary School, Setagaya Ward Kitami Junior High School, Shinjuku Ward Kashiwagi Elementary School, Soja Municipal Soja Elementary School, Soja Municipal Soja Higashi Junior High School, Tokashiki Municipal Aharen Elementary School, Tokashiki Municipal Tokashiki Elementary School, and Tokashiki Junior High School, Tsumagoi MunicipalTsumagoi Junior High School, Yamaguchi Elementary School attached to the Faculty of Education, Yamaguchi University, Mieko Aoki, Sachiyo Otani, Fumi Ono, and Shoma Kunisho and other 14 primary and secondary schools. The authors and their colleagues thank Editage ( www.editage.jp ) for English language editing. Financial support: The present study was supported by the Foundation for Dietary Scientific Research, Institute for Food and Health Science, Yazuya Co., Ltd., Japan Crop Protection Association, Bourbon Corporation, Kao Corporation, ILSI, Japan and Mr. Tatsuya Yoshida. Authorship: M.S. involved in the recruitment of study participants, directed the dietary survey, designed the research, analysed and interpreted the data for the work and prepared the first draft of the manuscript; K.M. designed the research, interpretated of data for the work, provided critical oversight of the project, including critical input into the final draft of the manuscript and S.S. designed and directed the dietary survey, involved in the recruitment of study participants and assisted in the writing. All authors contributed to the development of the manuscript. 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